projection_layer",
+ r"(?P\b(vision_model|model\.vision_model)\b.*\.)norm(?=\.|\s|$)": r"\glayer_norm",
+ r"(?P\b(vision_model|model\.vision_model)\b.*\.)norm1(?=\.|\s|$)": r"\glayer_norm1",
+ r"(?P\b(vision_model|model\.vision_model)\b.*\.)norm2(?=\.|\s|$)": r"\glayer_norm2",
+ r"\bvision_model\.vision_tower\.attn_pool\.[^\s$]*": None,
+ # VQ Model
+ r"gen_vision_model": "model.vqmodel",
+ r"(?P\b(gen_vision_model|model\.vqmodel)\b.*\.)decoder\.conv_blocks(?=\.|\s|$)": r"\gdecoder.up",
+ r"(?P\b(gen_vision_model|model\.vqmodel)\b.*\.)encoder\.conv_blocks(?=\.|\s|$)": r"\gencoder.down",
+ r"(?P\b(gen_vision_model|model\.vqmodel)\b.*\.)res(?=\.|\s|$)": r"\gblock",
+ r"(?P\b(gen_vision_model|model\.vqmodel)\b.*\.)mid\.0(?=\.|\s|$)": r"\gmid.block_1",
+ r"(?P\b(gen_vision_model|model\.vqmodel)\b.*\.)mid\.1(?=\.|\s|$)": r"\gmid.attn_1",
+ r"(?P\b(gen_vision_model|model\.vqmodel)\b.*\.)mid\.2(?=\.|\s|$)": r"\gmid.block_2",
+ # Aligner Modules
+ r"(gen_aligner)\.layers\.0": r"model.generation_aligner.fc1",
+ r"(gen_aligner)\.layers\.2": r"model.generation_aligner.hidden_layers.0",
+ r"(?']%}"
+ "{%set i=0%}"
+ "{%for message in messages%}"
+ "{%if message['role']|lower=='user'%}"
+ "<|User|>: "
+ "{%elif message['role']|lower=='assistant'%}"
+ "<|Assistant|>:{%if not (loop.last and not add_generation_prompt and message['content'][0]['type']=='text' and message['content'][0]['text']=='')%} {%endif%}"
+ "{%else%}"
+ "{{message['role'].capitalize()}}: "
+ "{%endif%}"
+ "{%for content in message['content']%}"
+ "{%if content['type']=='image'%}"
+ "{%if not loop.first%}{{'\n'}}{%endif%}"
+ ""
+ "{%if not loop.last%}{{'\n'}}{%endif%}"
+ "{%elif content['type']=='text'%}"
+ "{%set text=content['text']%}"
+ "{%if loop.first%}{%set text=text.lstrip()%}{%endif%}"
+ "{%if loop.last%}{%set text=text.rstrip()%}{%endif%}"
+ "{%if not loop.first and message['content'][loop.index0-1]['type']=='text'%}"
+ "{{' '+text}}"
+ "{%else%}"
+ "{{text}}"
+ "{%endif%}"
+ "{%endif%}"
+ "{%endfor%}"
+ "{%if not loop.last or add_generation_prompt%}"
+ "{%if message['role']|lower=='user'%}"
+ "{{seps[0]}}"
+ "{%else%}"
+ "{{seps[1]}}"
+ "{%endif%}"
+ "{%endif%}"
+ "{%endfor%}"
+ "{%if add_generation_prompt%}<|Assistant|>:{%endif%}"
+)
+
+
+def convert_old_keys_to_new_keys(state_dict):
+ keys_as_text = "\n".join(state_dict.keys())
+ new_keys_as_text = keys_as_text
+ for old, repl in MAPPINGS.items():
+ if repl is None:
+ new_keys_as_text = re.sub(old, "", new_keys_as_text)
+ else:
+ new_keys_as_text = re.sub(old, repl, new_keys_as_text)
+ output_dict = dict(zip(keys_as_text.split("\n"), new_keys_as_text.split("\n")))
+ return output_dict
+
+
+def split_tensor(tensor, key):
+ """Splits a merged tensor (qkv or kv) into separate tensors and creates keys for each part."""
+
+ if "qkv" in key:
+ prefix_to_replace = "qkv"
+ num_splits = 3
+ new_keys = ["q_proj", "k_proj", "v_proj"]
+ elif "kv" in key:
+ prefix_to_replace = "kv"
+ num_splits = 2
+ new_keys = ["k_proj", "v_proj"]
+ else:
+ raise ValueError(f"Unrecognized tensor type in key: {key}")
+
+ split_size = tensor.shape[0] // num_splits
+ tensors = torch.split(tensor, split_size, dim=0)
+ return {key.replace(prefix_to_replace, new_keys[i]): tensors[i] for i in range(num_splits)}
+
+
+def convert_state_dict_to_hf(state_dict):
+ """Convert state dict keys to HF format."""
+ conversion_dict = convert_old_keys_to_new_keys(state_dict)
+ converted_state_dict = {}
+
+ for old_key, new_key in conversion_dict.items():
+ if new_key:
+ if "qkv" in new_key or "kv" in new_key: # Detect merged attention keys and split them.
+ qkv_split_dict = split_tensor(state_dict[old_key], new_key)
+ converted_state_dict.update(qkv_split_dict)
+ else:
+ converted_state_dict[new_key] = state_dict[old_key]
+
+ # Embeddings will not have initial dimension
+ pos_embed_key = "model.vision_model.embeddings.position_embedding.weight"
+ converted_state_dict[pos_embed_key] = converted_state_dict[pos_embed_key].squeeze(0)
+
+ return converted_state_dict
+
+
+def ensure_model_downloaded(
+ repo_id: Optional[str] = None, revision: Optional[str] = None, local_dir: Optional[str] = None
+) -> str:
+ """
+ Ensures model files are downloaded locally, downloads them if not.
+ Returns path to local files.
+
+ Args:
+ repo_id: The Hugging Face model repo ID (required if local_dir not provided)
+ revision: Optional git revision to use
+ local_dir: Optional local directory path where model files should be stored/found
+ """
+ if local_dir is not None:
+ if os.path.exists(local_dir):
+ print(f"Using provided local directory: {local_dir}")
+ else:
+ # Create the local directory if it doesn't exist
+ os.makedirs(local_dir, exist_ok=True)
+ print(f"Created local directory: {local_dir}")
+
+ if repo_id is None:
+ raise ValueError("Either repo_id or local_dir must be provided")
+
+ print(f"Ensuring {repo_id} (revision: {revision or 'latest'}) is downloaded...")
+
+ try:
+ # First try to find files locally
+ download_dir = snapshot_download(repo_id, revision=revision, local_files_only=True, local_dir=local_dir)
+ print(f"Found model files locally at {download_dir}")
+ return download_dir
+ except Exception:
+ # If files not found locally, download them
+ print(f"Downloading model files for {repo_id}...")
+ download_dir = snapshot_download(repo_id, revision=revision, local_files_only=False, local_dir=local_dir)
+ print(f"Downloaded model files to {download_dir}")
+ return download_dir
+
+
+def load_model_state_dict(input_path: str) -> dict:
+ """
+ Load model state dict, handling both single and sharded files.
+ """
+ index_path = os.path.join(input_path, "pytorch_model.bin.index.json")
+ single_file_path = os.path.join(input_path, "pytorch_model.bin")
+
+ # Check if we have a sharded model
+ if os.path.exists(index_path):
+ print("Loading sharded model...")
+ state_dict = {}
+ with open(index_path, "r") as f:
+ index = json.load(f)
+
+ # Get unique shard files and load each one only once
+ unique_shard_files = sorted(set(index["weight_map"].values()))
+ for shard_file in unique_shard_files:
+ print(f"Loading shard {shard_file}...")
+ shard_path = os.path.join(input_path, shard_file)
+ shard_dict = torch.load(shard_path, map_location="cpu")
+ state_dict.update(shard_dict)
+
+ return state_dict
+
+ # Single file model
+ elif os.path.exists(single_file_path):
+ print("Loading single file model...")
+ return torch.load(single_file_path, map_location="cpu")
+
+ else:
+ raise ValueError(f"No model files found in {input_path}")
+
+
+def convert_model(
+ repo_id=None,
+ local_dir=None,
+ text_model_id=None,
+ output_dir=None,
+ output_hub_path=None,
+ safe_serialization=True,
+ revision=None,
+):
+ """Convert and save the model weights, processor, and configuration."""
+ if output_dir is None and output_hub_path is None:
+ raise ValueError("At least one of output_dir or output_hub_path must be specified")
+
+ if repo_id is None and local_dir is None:
+ raise ValueError("Either repo_id or local_dir must be specified")
+
+ # Create output directory if specified
+ if output_dir:
+ os.makedirs(output_dir, exist_ok=True)
+ print(f"Created/verified output directory: {output_dir}")
+
+ torch.set_default_dtype(torch.float16)
+
+ # Download or locate model files
+ input_path = ensure_model_downloaded(repo_id=repo_id, revision=revision, local_dir=local_dir)
+
+ # Load configuration files
+ required_files = ["config.json", "preprocessor_config.json", "special_tokens_map.json", "tokenizer_config.json"]
+
+ missing_files = [f for f in required_files if not os.path.exists(os.path.join(input_path, f))]
+ if missing_files:
+ raise ValueError(
+ f"The following required configuration files are missing from {input_path}: {', '.join(missing_files)}. "
+ "Please ensure you have downloaded all necessary model files."
+ )
+
+ with open(os.path.join(input_path, "config.json"), "r") as f:
+ config_data = json.load(f)
+ with open(os.path.join(input_path, "preprocessor_config.json"), "r") as f:
+ preprocessor_config = json.load(f)
+ with open(os.path.join(input_path, "special_tokens_map.json"), "r") as f:
+ special_tokens_map = json.load(f)
+ with open(os.path.join(input_path, "tokenizer_config.json"), "r") as f:
+ tokenizer_config = json.load(f)
+
+ # Create tokenizer directly from tokenizer.json if it exists
+ tokenizer_json_path = os.path.join(input_path, "tokenizer.json")
+ special_image_tokens = {
+ "image_token": "",
+ "boi_token": "",
+ "eoi_token": "",
+ }
+
+ if os.path.exists(tokenizer_json_path) and not text_model_id:
+ tokenizer = AutoTokenizer.from_pretrained(
+ input_path, # This will load tokenizer.json directly
+ model_max_length=tokenizer_config["model_max_length"],
+ extra_special_tokens=special_image_tokens,
+ )
+ else:
+ # Fallback to creating from text_model_id with special tokens
+ tokenizer = AutoTokenizer.from_pretrained(
+ text_model_id,
+ bos_token=special_tokens_map["bos_token"],
+ eos_token=special_tokens_map["eos_token"],
+ pad_token=special_tokens_map["pad_token"],
+ additional_special_tokens=special_tokens_map["additional_special_tokens"],
+ model_max_length=tokenizer_config["model_max_length"],
+ extra_special_tokens=special_image_tokens,
+ )
+
+ # Create image processor from config
+ image_processor_kwargs = {}
+ for key in ["do_normalize", "image_mean", "image_std", "min_size", "rescale_factor"]:
+ if key in preprocessor_config:
+ image_processor_kwargs[key] = preprocessor_config[key]
+
+ if "image_size" in preprocessor_config:
+ image_processor_kwargs["size"] = {
+ "height": preprocessor_config["image_size"],
+ "width": preprocessor_config["image_size"],
+ }
+
+ image_processor = JanusImageProcessor(**image_processor_kwargs)
+
+ # Create processor with chat template
+ processor = JanusProcessor(
+ image_processor=image_processor,
+ tokenizer=tokenizer,
+ chat_template=CHAT_TEMPLATE,
+ use_default_system_prompt=True,
+ )
+
+ if output_dir:
+ print(f"Saving processor to {output_dir}...")
+ processor.save_pretrained(output_dir)
+ if output_hub_path:
+ print(f"Pushing processor to hub at {output_hub_path}...")
+ processor.push_to_hub(output_hub_path)
+
+ # Create model configurations
+ text_config_kwargs = {}
+ for key in [
+ "vocab_size",
+ "hidden_size",
+ "intermediate_size",
+ "num_hidden_layers",
+ "num_attention_heads",
+ "num_key_value_heads",
+ "hidden_act",
+ "max_position_embeddings",
+ "torch_dtype",
+ ]:
+ if key in config_data["language_config"]:
+ text_config_kwargs[key] = config_data["language_config"][key]
+
+ # Add token IDs from tokenizer
+ text_config_kwargs.update(
+ {
+ "pad_token_id": tokenizer.pad_token_id,
+ "bos_token_id": tokenizer.bos_token_id,
+ "eos_token_id": tokenizer.eos_token_id,
+ }
+ )
+
+ text_config = LlamaConfig(**text_config_kwargs)
+
+ # Create vision config
+ vision_config_kwargs = {}
+ if "image_size" in config_data["vision_config"]["params"]:
+ vision_config_kwargs["image_size"] = config_data["vision_config"]["params"]["image_size"]
+
+ # Add aligner params if present
+ if "aligner_config" in config_data and "params" in config_data["aligner_config"]:
+ if "n_embed" in config_data["aligner_config"]["params"]:
+ vision_config_kwargs["projection_dim"] = config_data["aligner_config"]["params"]["n_embed"]
+ if "depth" in config_data["aligner_config"]["params"]:
+ vision_config_kwargs["depth"] = config_data["aligner_config"]["params"]["depth"]
+
+ vision_config = JanusVisionConfig(**vision_config_kwargs)
+
+ vq_config = JanusVQVAEConfig(
+ embed_dim=config_data["gen_vision_config"]["params"]["n_embed"],
+ num_embeddings=config_data["gen_vision_config"]["params"]["image_token_size"],
+ projection_dim=config_data["gen_aligner_config"]["params"]["n_embed"],
+ depth=config_data["gen_aligner_config"]["params"]["depth"],
+ image_token_embed_dim=config_data["gen_head_config"]["params"]["image_token_embed"],
+ )
+
+ # Create the main config
+ config = JanusConfig(
+ text_config=text_config,
+ vision_config=vision_config,
+ vq_config=vq_config,
+ image_token_id=tokenizer.vocab.get(""),
+ )
+
+ # Save the config
+ if output_dir:
+ config.save_pretrained(output_dir)
+ if output_hub_path:
+ config.push_to_hub(output_hub_path)
+
+ # Initialize model with empty weights
+ print("Creating empty model...")
+ with init_empty_weights():
+ model = JanusForConditionalGeneration(config)
+
+ model.generation_config._from_model_config = False
+ model.generation_config.temperature = 1
+ model.generation_config.guidance_scale = 5
+ model.generation_config.pad_token_id = tokenizer.vocab.get("<\uff5c\u2581pad\u2581\uff5c>")
+ if not hasattr(model.generation_config, "generation_kwargs"):
+ model.generation_config.generation_kwargs = {}
+ model.generation_config.generation_kwargs["boi_token_id"] = tokenizer.vocab.get("")
+
+ # Load and convert state dict
+ print("Loading state dict...")
+ state_dict = load_model_state_dict(input_path)
+ state_dict = convert_state_dict_to_hf(state_dict)
+
+ # Load converted state dict
+ print("Loading converted weights into model...")
+ model.load_state_dict(state_dict, strict=True, assign=True)
+
+ # Tie weights before any device mapping
+ print("Tying weights...")
+ model.tie_weights()
+
+ # Save the model
+ if output_dir:
+ print(f"Saving model to {output_dir}...")
+ model.save_pretrained(output_dir, safe_serialization=safe_serialization)
+ if output_hub_path:
+ print(f"Pushing model to hub at {output_hub_path}...")
+ model.push_to_hub(output_hub_path, safe_serialization=safe_serialization)
+
+ del state_dict, model
+ gc.collect()
+
+ # Validate the saved model if saved locally
+ if output_dir:
+ print("Reloading the local model to check if it's saved correctly...")
+ # TODO: warning about weights not being tied is raised here regardless of model.tie_weights() above
+ JanusForConditionalGeneration.from_pretrained(output_dir, device_map="auto")
+ print("Local model reloaded successfully.")
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--repo_id",
+ help="HuggingFace Hub repo ID for the model",
+ default=None,
+ )
+ parser.add_argument(
+ "--local_dir",
+ help="Local directory containing the model files",
+ default=None,
+ )
+ parser.add_argument(
+ "--revision",
+ help="Specific revision to download from the Hub",
+ default=None,
+ )
+ parser.add_argument(
+ "--output_dir",
+ help="Location to write HF model locally",
+ default=None,
+ )
+ parser.add_argument(
+ "--output_hub_path",
+ help="Repository ID to push model to hub (e.g. 'username/model-name')",
+ default=None,
+ )
+ parser.add_argument(
+ "--text_model_id",
+ help="Hub ID of the text model to get tokenizer from. Optional if tokenizer.json exists in the model directory.",
+ required=False,
+ )
+ parser.add_argument(
+ "--safe_serialization",
+ action="store_true",
+ help="Whether to save using safetensors",
+ )
+ args = parser.parse_args()
+
+ if args.output_dir is None and args.output_hub_path is None:
+ raise ValueError("At least one of --output_dir or --output_hub_path must be specified")
+
+ if args.repo_id is None and args.local_dir is None:
+ raise ValueError("Either --repo_id or --local_dir must be specified")
+
+ convert_model(
+ repo_id=args.repo_id,
+ local_dir=args.local_dir,
+ text_model_id=args.text_model_id,
+ output_dir=args.output_dir,
+ output_hub_path=args.output_hub_path,
+ safe_serialization=args.safe_serialization,
+ revision=args.revision,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/image_processing_janus.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/image_processing_janus.py
new file mode 100644
index 0000000000000000000000000000000000000000..0535f38a33c5dccf592a366c010c1fcae897e85c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/image_processing_janus.py
@@ -0,0 +1,515 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/janus/modular_janus.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_janus.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import convert_to_rgb, resize, to_channel_dimension_format
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ get_image_size,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_flat_list_of_images,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import (
+ TensorType,
+ filter_out_non_signature_kwargs,
+ is_vision_available,
+ logging,
+)
+
+
+if is_vision_available():
+ import PIL
+
+
+logger = logging.get_logger(__name__)
+
+
+class JanusImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a JANUS image processor.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
+ `do_resize` parameter in the `preprocess` method.
+ size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`):
+ Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
+ method.
+ min_size (`int`, *optional*, defaults to 14):
+ The minimum allowed size for the resized image. Ensures that neither the height nor width
+ falls below this value after resizing.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+ Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
+ overridden by the `resample` parameter in the `preprocess` method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
+ `do_rescale` parameter in the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
+ overridden by the `rescale_factor` parameter in the `preprocess` method.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+ method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
+ overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
+ Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+ number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+ Can be overridden by the `image_std` parameter in the `preprocess` method.
+ do_convert_rgb (`bool`, *optional*, defaults to `True`):
+ Whether to convert the image to RGB.
+ """
+
+ model_input_names = ["pixel_values"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ min_size: int = 14,
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_convert_rgb: Optional[bool] = None,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"height": 384, "width": 384}
+ size = get_size_dict(size, default_to_square=True)
+
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
+ self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+ self.do_convert_rgb = do_convert_rgb
+
+ self.min_size = min_size
+ if image_mean is None:
+ self.background_color = (127, 127, 127)
+ else:
+ self.background_color = tuple(int(x * 255) for x in image_mean)
+
+ def resize(
+ self,
+ image: np.ndarray,
+ size: Union[dict[str, int], int],
+ background_color: Optional[tuple[int, int, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image to dynamically calculated size.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]` or `int`):
+ The size to resize the image to. If a dictionary, it should have the keys `"height"` and `"width"`.
+ background_color (`tuple[int, int, int]`):
+ The background color to use for the padding.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
+ data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the output image. If unset, the channel dimension format of the input
+ image is used. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `None`: will be inferred from input
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+
+ Returns:
+ `np.ndarray`: The resized image.
+ """
+ background_color = background_color if background_color is not None else self.background_color
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(image)
+
+ height, width = get_image_size(image, input_data_format)
+ max_size = max(height, width)
+
+ size = get_size_dict(size, default_to_square=True)
+ if size["height"] != size["width"]:
+ raise ValueError(
+ f"Output height and width must be the same. Got height={size['height']} and width={size['width']}"
+ )
+ size = size["height"]
+
+ delta = size / max_size
+ # Largest side becomes `size` and the other side is scaled according to the aspect ratio.
+ output_size_nonpadded = [
+ max(int(height * delta), self.min_size),
+ max(int(width * delta), self.min_size),
+ ]
+
+ image = resize(
+ image,
+ size=output_size_nonpadded,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+ # Expand and pad the images to obtain a square image of dimensions `size x size`
+ image = self.pad_to_square(
+ image=image,
+ background_color=background_color,
+ input_data_format=input_data_format,
+ )
+ return image
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ do_convert_rgb: Optional[bool] = None,
+ data_format: ChannelDimension = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> PIL.Image.Image:
+ """
+ Preprocess an image or batch of images.
+
+ Args:
+ images (`ImageInput`):
+ Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Controls the size of the image after `resize`. The shortest edge of the image is resized to
+ `size["shortest_edge"]` whilst preserving the aspect ratio. If the longest edge of this resized image
+ is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest
+ edge equal to `int(size["shortest_edge"] * (1333 / 800))`.
+ resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image values between [0 - 1].
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to normalize the image by if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to normalize the image by if `do_normalize` is set to `True`.
+ do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+ Whether to convert the image to RGB.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ resample = resample if resample is not None else self.resample
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+ size = size if size is not None else self.size
+ size = get_size_dict(size, default_to_square=False)
+ images = make_flat_list_of_images(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+ # PIL RGBA images are converted to RGB
+ if do_convert_rgb:
+ images = [convert_to_rgb(image) for image in images]
+
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if do_rescale and is_scaled_image(images[0]):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ if do_resize:
+ images = [
+ self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ if do_rescale:
+ images = [
+ self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ if do_normalize:
+ images = [
+ self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ images = [
+ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
+ ]
+
+ encoded_outputs = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)
+
+ return encoded_outputs
+
+ def pad_to_square(
+ self,
+ image: np.ndarray,
+ background_color: Union[int, tuple[int, int, int]] = 0,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.array:
+ """
+ Pads an image to a square based on the longest edge.
+
+ Args:
+ image (`np.ndarray`):
+ The image to pad.
+ background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+ The color to use for the padding. Can be an integer for single channel or a
+ tuple of integers representing for multi-channel images. If passed as integer
+ in mutli-channel mode, it will default to `0` in subsequent channels.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use same as the input image.
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+
+ Returns:
+ `np.ndarray`: The padded image.
+ """
+ height, width = get_image_size(image, input_data_format)
+ num_channels = image.shape[0] if input_data_format == ChannelDimension.FIRST else image.shape[-1]
+
+ if height == width:
+ image = (
+ to_channel_dimension_format(image, data_format, input_data_format)
+ if data_format is not None
+ else image
+ )
+ return image
+
+ max_dim = max(height, width)
+
+ # Ensure background_color is the correct shape
+ if isinstance(background_color, int):
+ background_color = [background_color]
+ elif len(background_color) != num_channels:
+ raise ValueError(
+ f"background_color must have no more than {num_channels} elements to match the number of channels"
+ )
+
+ if input_data_format == ChannelDimension.FIRST:
+ result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype)
+ for i, color in enumerate(background_color):
+ result[i, :, :] = color
+ if width > height:
+ start = (max_dim - height) // 2
+ result[:, start : start + height, :] = image
+ else:
+ start = (max_dim - width) // 2
+ result[:, :, start : start + width] = image
+ else:
+ result = np.zeros((max_dim, max_dim, num_channels), dtype=image.dtype)
+ for i, color in enumerate(background_color):
+ result[:, :, i] = color
+ if width > height:
+ start = (max_dim - height) // 2
+ result[start : start + height, :, :] = image
+ else:
+ start = (max_dim - width) // 2
+ result[:, start : start + width, :] = image
+
+ return result
+
+ def postprocess(
+ self,
+ images: ImageInput,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[list[float]] = None,
+ image_std: Optional[list[float]] = None,
+ input_data_format: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ ):
+ """Applies post-processing to the decoded image tokens by reversing transformations applied during preprocessing."""
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = 1.0 / self.rescale_factor if rescale_factor is None else rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+
+ images = make_list_of_images(images) # Ensures input is a list
+
+ if isinstance(images[0], PIL.Image.Image):
+ return images if len(images) > 1 else images[0]
+
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(images[0]) # Determine format dynamically
+
+ pixel_values = []
+
+ for image in images:
+ image = to_numpy_array(image) # Ensure NumPy format
+
+ if do_normalize:
+ image = self.unnormalize(
+ image=image, image_mean=image_mean, image_std=image_std, input_data_format=input_data_format
+ )
+
+ if do_rescale:
+ image = self.rescale(image, scale=rescale_factor, input_data_format=input_data_format)
+ image = image.clip(0, 255).astype(np.uint8)
+
+ if do_normalize and do_rescale and return_tensors == "PIL.Image.Image":
+ image = to_channel_dimension_format(image, ChannelDimension.LAST, input_channel_dim=input_data_format)
+ image = PIL.Image.fromarray(image)
+
+ pixel_values.append(image)
+
+ data = {"pixel_values": pixel_values}
+ return_tensors = return_tensors if return_tensors != "PIL.Image.Image" else None
+
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+ def unnormalize(
+ self,
+ image: np.array,
+ image_mean: Union[float, Iterable[float]],
+ image_std: Union[float, Iterable[float]],
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.array:
+ """
+ Unnormalizes `image` using the mean and standard deviation specified by `mean` and `std`.
+ image = (image * image_std) + image_mean
+ Args:
+ image (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)` or `(num_channels, image_size, image_size)`):
+ Batch of pixel values to postprocess.
+ image_mean (`float` or `Iterable[float]`):
+ The mean to use for unnormalization.
+ image_std (`float` or `Iterable[float]`):
+ The standard deviation to use for unnormalization.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ num_channels = 3
+
+ if isinstance(image_mean, Iterable):
+ if len(image_mean) != num_channels:
+ raise ValueError(f"mean must have {num_channels} elements if it is an iterable, got {len(image_mean)}")
+ else:
+ image_mean = [image_mean] * num_channels
+
+ if isinstance(image_std, Iterable):
+ if len(image_std) != num_channels:
+ raise ValueError(f"std must have {num_channels} elements if it is an iterable, got {len(image_std)}")
+ else:
+ image_std = [image_std] * num_channels
+
+ rev_image_mean = tuple(-mean / std for mean, std in zip(image_mean, image_std))
+ rev_image_std = tuple(1 / std for std in image_std)
+ image = self.normalize(
+ image=image, mean=rev_image_mean, std=rev_image_std, input_data_format=input_data_format
+ )
+ return image
+
+
+__all__ = ["JanusImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/image_processing_janus_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/image_processing_janus_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..12f0d0f394fe4ce4251c7cfdb215a50a187e8867
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/image_processing_janus_fast.py
@@ -0,0 +1,245 @@
+# coding=utf-8
+# Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from typing import Optional, Union
+
+from ...image_processing_utils import BatchFeature
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ImageInput,
+ PILImageResampling,
+ SizeDict,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+)
+
+
+if is_torch_available():
+ import torch
+if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+elif is_torchvision_available():
+ from torchvision.transforms import functional as F
+
+
+class JanusFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ r"""
+ min_size (`int`, *optional*, defaults to 14):
+ The minimum allowed size for the resized image. Ensures that neither the height nor width
+ falls below this value after resizing.
+ """
+
+ min_size: int
+
+
+@auto_docstring
+class JanusImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BICUBIC
+ image_mean = OPENAI_CLIP_MEAN
+ image_std = OPENAI_CLIP_STD
+ size = {"height": 384, "width": 384}
+ min_size = 14
+ do_resize = True
+ do_rescale = True
+ do_normalize = True
+ valid_kwargs = JanusFastImageProcessorKwargs
+
+ def __init__(self, **kwargs: Unpack[JanusFastImageProcessorKwargs]):
+ if kwargs.get("image_mean") is None:
+ background_color = (127, 127, 127)
+ else:
+ background_color = tuple(int(x * 255) for x in kwargs.get("image_mean"))
+ super().__init__(**kwargs)
+ self.background_color = tuple(background_color)
+
+ def resize(
+ self,
+ image: "torch.Tensor",
+ size: SizeDict,
+ min_size: int,
+ interpolation: "F.InterpolationMode" = None,
+ antialias: bool = True,
+ **kwargs,
+ ) -> "torch.Tensor":
+ if size.height is None or size.width is None or size.height != size.width:
+ raise ValueError(
+ f"Output height and width must be the same. Got height={size['height']} and width={size['width']}"
+ )
+ size = size.height
+
+ height, width = image.shape[-2:]
+ max_size = max(height, width)
+
+ delta = size / max_size
+ # Largest side becomes `size` and the other side is scaled according to the aspect ratio.
+ output_size_nonpadded = SizeDict(
+ height=max(int(height * delta), min_size),
+ width=max(int(width * delta), min_size),
+ )
+
+ return super().resize(image, size=output_size_nonpadded, interpolation=interpolation, antialias=antialias)
+
+ def pad_to_square(
+ self,
+ images: "torch.Tensor",
+ background_color: Union[int, tuple[int, int, int]] = 0,
+ ) -> "torch.Tensor":
+ """
+ Pads an image to a square based on the longest edge.
+
+ Args:
+ images (`torch.Tensor`):
+ The images to pad.
+ background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+ The color to use for the padding. Can be an integer for single channel or a
+ tuple of integers representing for multi-channel images. If passed as integer
+ in mutli-channel mode, it will default to `0` in subsequent channels.
+
+ Returns:
+ `torch.Tensor`: The padded images.
+ """
+ height, width = images.shape[-2:]
+ num_channels = images.shape[1]
+ batch_size = images.shape[0]
+
+ if height == width:
+ return images
+
+ max_dim = max(height, width)
+
+ # Ensure background_color is the correct shape
+ if isinstance(background_color, int):
+ background_color = [background_color]
+ elif len(background_color) != num_channels:
+ raise ValueError(
+ f"background_color must have no more than {num_channels} elements to match the number of channels"
+ )
+
+ padded_images = torch.zeros(
+ (batch_size, num_channels, max_dim, max_dim), dtype=images.dtype, device=images.device
+ )
+ for i, color in enumerate(background_color):
+ padded_images[:, i, :, :] = color
+ if width > height:
+ start = (max_dim - height) // 2
+ padded_images[:, :, start : start + height, :] = images
+ else:
+ start = (max_dim - width) // 2
+ padded_images[:, :, :, start : start + width] = images
+
+ return padded_images
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ do_resize: bool,
+ size: SizeDict,
+ min_size: int,
+ interpolation: Optional["F.InterpolationMode"],
+ do_rescale: bool,
+ rescale_factor: float,
+ do_normalize: bool,
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ disable_grouping: Optional[bool],
+ return_tensors: Optional[Union[str, TensorType]],
+ do_pad: bool = True,
+ **kwargs,
+ ) -> BatchFeature:
+ # Group images by size for batched resizing
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ resized_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_resize:
+ stacked_images = self.resize(
+ image=stacked_images, size=size, min_size=min_size, interpolation=interpolation
+ )
+ resized_images_grouped[shape] = stacked_images
+ resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+ # Group images by size for further processing
+ # Needed in case do_resize is False, or resize returns images with different sizes
+ grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+ processed_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_pad:
+ stacked_images = self.pad_to_square(stacked_images, background_color=self.background_color)
+ # Fused rescale and normalize
+ stacked_images = self.rescale_and_normalize(
+ stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ processed_images_grouped[shape] = stacked_images
+
+ processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+ processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+
+ return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
+
+ def postprocess(
+ self,
+ images: ImageInput,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[list[float]] = None,
+ image_std: Optional[list[float]] = None,
+ return_tensors: Optional[str] = None,
+ ) -> "torch.Tensor":
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = 1.0 / self.rescale_factor if rescale_factor is None else rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ image_mean = tuple(-rescale_factor * mean / std for mean, std in zip(image_mean, image_std))
+ image_std = tuple(1 / std for std in image_std)
+
+ images = self.preprocess(
+ images,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_resize=False,
+ do_pad=False,
+ return_tensors=return_tensors,
+ ).pixel_values
+ if do_rescale:
+ images = [image.clip(0, 255).to(torch.uint8) for image in images]
+
+ if do_normalize and do_rescale and return_tensors == "PIL.Image.Image":
+ images = [F.to_pil_image(image) for image in images]
+
+ data = {"pixel_values": images}
+ return_tensors = return_tensors if return_tensors != "PIL.Image.Image" else None
+
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+
+__all__ = ["JanusImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/modeling_janus.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/modeling_janus.py
new file mode 100644
index 0000000000000000000000000000000000000000..4729a3677b4e2c9950843e40e44d3bf31c475dff
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/modeling_janus.py
@@ -0,0 +1,1441 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/janus/modular_janus.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_janus.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+from dataclasses import dataclass
+from typing import Callable, Optional, Union
+
+import torch
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache
+from ...generation import ClassifierFreeGuidanceLogitsProcessor, GenerationMixin, GenerationMode, LogitsProcessorList
+from ...generation.utils import GenerateDecoderOnlyOutput
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ModelOutput
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import (
+ TransformersKwargs,
+ auto_docstring,
+ can_return_tuple,
+ is_torch_available,
+ logging,
+ torch_int,
+)
+from ..auto import AutoModel
+from .configuration_janus import JanusConfig, JanusVisionConfig, JanusVQVAEConfig
+
+
+if is_torch_available():
+ import torch.nn.functional as F
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring
+class JanusPreTrainedModel(PreTrainedModel):
+ config: JanusConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["LlamaDecoderLayer", "JanusVisionEncoderLayer"]
+ _skip_keys_device_placement = ["past_key_values", "causal_mask"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+ _supports_param_buffer_assignment = False
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Janus VQ-VAE mode model outputs.
+ """
+)
+class JanusVQVAEOutput(ModelOutput):
+ r"""
+ decoded_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+ Reconstructed pixel values after encoding and decoding the input.
+ embedding_loss (`torch.FloatTensor`):
+ Embedding loss.
+ """
+
+ decoded_pixel_values: Optional[torch.FloatTensor] = None
+ embedding_loss: torch.FloatTensor = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Janus model's outputs that may also contain a past key/values (to speed up sequential decoding).
+ """
+)
+class JanusBaseModelOutputWithPast(ModelOutput):
+ r"""
+ last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the model.
+
+ If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
+ hidden_size)` is output.
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+ `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+ encoder_sequence_length, embed_size_per_head)`.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
+ `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
+ input) to speed up sequential decoding.
+ image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+ Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
+ sequence_length, hidden_size)`.
+
+ image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ image_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Janus causal language model (or autoregressive) outputs.
+ """
+)
+class JanusCausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+ Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
+ sequence_length, hidden_size)`.
+
+ image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ image_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+
+
+class JanusVisionEmbeddings(nn.Module):
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.image_size = config.image_size
+ self.patch_size = config.patch_size
+
+ self.patch_embedding = nn.Conv2d(
+ in_channels=config.num_channels,
+ out_channels=self.embed_dim,
+ kernel_size=self.patch_size,
+ stride=self.patch_size,
+ padding="valid",
+ )
+
+ self.num_patches = (self.image_size // self.patch_size) ** 2
+ self.num_positions = self.num_patches
+ self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+ self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
+
+ def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
+ """
+ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
+ images. This method is also adapted to support torch.jit tracing and no class embeddings.
+
+ Adapted from:
+ - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
+ - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
+ """
+
+ num_patches = embeddings.shape[1]
+ num_positions = self.position_embedding.weight.shape[0]
+
+ # always interpolate when tracing to ensure the exported model works for dynamic input shapes
+ if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
+ return self.position_embedding(self.position_ids)
+
+ patch_pos_embed = self.position_embedding.weight.unsqueeze(0)
+
+ dim = embeddings.shape[-1]
+
+ new_height = height // self.patch_size
+ new_width = width // self.patch_size
+
+ sqrt_num_positions = torch_int(num_positions**0.5)
+ patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
+ patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+ patch_pos_embed = nn.functional.interpolate(
+ patch_pos_embed,
+ size=(new_height, new_width),
+ mode="bicubic",
+ align_corners=False,
+ )
+
+ patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+ return patch_pos_embed
+
+ def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
+ _, _, height, width = pixel_values.shape
+ target_dtype = self.patch_embedding.weight.dtype
+ patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
+ embeddings = patch_embeds.flatten(2).transpose(1, 2)
+
+ if interpolate_pos_encoding:
+ pos_embeds = self.interpolate_pos_encoding(embeddings, height, width)
+ else:
+ pos_embeds = self.position_embedding(self.position_ids)
+
+ embeddings = embeddings + pos_embeds
+
+ return embeddings
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class JanusVisionAttention(nn.Module):
+ """Attention Class for Janus Vision Encoder"""
+
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.embed_dim // self.num_heads
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+ f" {self.num_heads})."
+ )
+ self.scale = self.head_dim**-0.5
+ self.attention_dropout = config.attention_dropout
+ proj_dropout = config.projection_dropout
+ qk_norm = config.use_qk_norm
+ self.is_causal = False
+
+ # Janus has no MHA, hence for `eager_attention_forward` call setting `num_key_value_groups` to 1.
+ self.num_key_value_groups = 1
+
+ self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias)
+ self.k_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias)
+ self.v_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias)
+ self.projection_layer = nn.Linear(self.embed_dim, self.embed_dim)
+ self.projection_dropout = nn.Dropout(proj_dropout) if proj_dropout > 0 else nn.Identity()
+
+ self.q_norm = nn.LayerNorm(self.embed_dim) if qk_norm else nn.Identity()
+ self.k_norm = nn.LayerNorm(self.embed_dim) if qk_norm else nn.Identity()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ):
+ batch_size, seq_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.reshape(-1, self.num_heads, self.head_dim)
+ query_states = self.q_norm(query_states)
+
+ key_states = key_states.reshape(-1, self.num_heads, self.head_dim)
+ key_states = self.k_norm(key_states)
+
+ query_states = query_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scale,
+ is_causal=self.is_causal,
+ **kwargs,
+ )
+ attn_output = attn_output.reshape(batch_size, seq_len, self.embed_dim)
+
+ output = self.projection_layer(attn_output)
+ output = self.projection_dropout(output)
+ return output, attn_weights
+
+
+class JanusVisionMLP(nn.Module):
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__()
+ self.config = config
+ self.intermediate_size = int(config.hidden_size * config.mlp_ratio)
+ self.activation_fn = ACT2FN[config.hidden_act] # Gelu act
+ self.fc1 = nn.Linear(config.hidden_size, self.intermediate_size)
+ self.fc2 = nn.Linear(self.intermediate_size, config.hidden_size)
+ self.dropout1 = nn.Dropout(config.hidden_dropout_rate)
+ self.dropout2 = nn.Dropout(config.hidden_dropout_rate)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.dropout1(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout2(hidden_states)
+ return hidden_states
+
+
+class JanusVisionEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__()
+ self.embed_dim = config.hidden_size
+ self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+ self.self_attn = JanusVisionAttention(config)
+ self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+ self.mlp = JanusVisionMLP(config)
+ self.config = config
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.FloatTensor]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`):
+ Input to the layer of shape `(batch, seq_len, embed_dim)`.
+ attention_mask (`torch.FloatTensor`):
+ Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
+ output_attentions (`bool`, *optional*, defaults to `False`):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+
+ hidden_states = self.layer_norm1(hidden_states)
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+ hidden_states = self.layer_norm2(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs
+
+
+class JanusVisionEncoder(nn.Module):
+ """
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+ [`JanusVisionEncoderLayer`].
+
+ Args:
+ config: JanusVisionConfig
+ """
+
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__()
+ self.config = config
+ self.layers = nn.ModuleList([JanusVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ # Ignore copy
+ @can_return_tuple
+ def forward(
+ self,
+ inputs_embeds,
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ ) -> BaseModelOutput:
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ hidden_states = inputs_embeds
+ for encoder_layer in self.layers:
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=encoder_states,
+ attentions=all_attentions,
+ )
+
+
+@auto_docstring
+class JanusVisionModel(JanusPreTrainedModel):
+ main_input_name = "pixel_values"
+ config: JanusVisionConfig
+
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__(config)
+ self.config = config
+ embed_dim = config.hidden_size
+
+ self.embeddings = JanusVisionEmbeddings(config)
+ self.encoder = JanusVisionEncoder(config)
+ self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ interpolate_pos_encoding: bool = False,
+ ) -> Union[tuple, BaseModelOutputWithPooling]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
+
+ encoder_outputs = self.encoder(
+ inputs_embeds=hidden_states,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ last_hidden_state = encoder_outputs[0]
+ last_hidden_state = self.post_layernorm(last_hidden_state)
+
+ pooled_output = last_hidden_state[:, 0, :]
+ pooled_output = self.post_layernorm(pooled_output)
+
+ if not return_dict:
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+ def get_input_embeddings(self):
+ return self.embeddings
+
+
+class JanusVisionAlignerMLP(nn.Module):
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__()
+
+ self.fc1 = nn.Linear(config.hidden_size, config.projection_dim)
+ self.hidden_layers = nn.ModuleList(
+ [nn.Linear(config.projection_dim, config.projection_dim) for _ in range(1, config.depth)]
+ )
+ self.activation_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_states):
+ hidden_states = self.fc1(hidden_states)
+ for layer in self.hidden_layers:
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = layer(hidden_states)
+ return hidden_states
+
+
+class JanusVQVAEVectorQuantizer(nn.Module):
+ """
+ A module for vector quantization using learned embedding vectors.
+
+ This module implements the quantization process similar to te one described in
+ the VQ-VAE (Vector Quantized Variational AutoEncoder) paper. It quantizes continuous
+ input vectors into discrete codebook vectors, which are learned during training.
+ Current implementation improves over previous ones by avoiding costly matrix multiplications
+ and allowing for post-hoc remapping of indices.
+ """
+
+ def __init__(self, config: JanusVQVAEConfig):
+ super().__init__()
+ self.num_embeddings = config.num_embeddings
+ self.embedding_dim = config.embed_dim
+ self.beta = getattr(config, "beta", 0.25)
+
+ self.embedding = nn.Embedding(self.num_embeddings, self.embedding_dim)
+ self.quant_state_dims = [config.num_patches] * 2
+
+ def forward(self, hidden_state: torch.Tensor):
+ hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous()
+ hidden_state_flattened = hidden_state.view(-1, self.embedding_dim)
+
+ # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+ distances = (
+ torch.sum(hidden_state_flattened**2, dim=1, keepdim=True)
+ + torch.sum(self.embedding.weight**2, dim=1)
+ - 2 * torch.einsum("bd,dn->bn", hidden_state_flattened, self.embedding.weight.transpose(0, 1))
+ )
+
+ min_encoding_indices = torch.argmin(distances, dim=1)
+ hidden_state_quant = self.embedding(min_encoding_indices).view(hidden_state.shape)
+
+ # compute loss for embedding
+ loss = torch.mean((hidden_state_quant.detach() - hidden_state) ** 2) + self.beta * torch.mean(
+ (hidden_state_quant - hidden_state.detach()) ** 2
+ )
+
+ # preserve gradients
+ hidden_state_quant = hidden_state + (hidden_state_quant - hidden_state).detach()
+
+ # reshape back to match original input shape
+ hidden_state_quant = hidden_state_quant.permute(0, 3, 1, 2).contiguous()
+
+ return hidden_state_quant, loss, min_encoding_indices
+
+ def get_codebook_entry(self, image_tokens: torch.LongTensor) -> torch.FloatTensor:
+ batch_size = image_tokens.shape[0]
+ emb_dim: int = self.embedding.weight.shape[-1]
+
+ # get quantized latent vectors
+ hidden_state_quant = self.embedding(image_tokens)
+ # l2 normalization on the last dimension
+ hidden_state_quant = F.normalize(hidden_state_quant, p=2, dim=-1)
+
+ # reshape back to match original input shape
+ hidden_state_quant = hidden_state_quant.view((batch_size, *self.quant_state_dims, emb_dim))
+ hidden_state_quant = hidden_state_quant.permute(0, 3, 1, 2).contiguous()
+
+ return hidden_state_quant
+
+
+class JanusVQVAEResnetBlock(nn.Module):
+ def __init__(
+ self,
+ config,
+ in_channels,
+ out_channels=None,
+ conv_shortcut=False,
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.out_channels = in_channels if out_channels is None else out_channels
+ self.use_conv_shortcut = conv_shortcut
+
+ self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+ self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+ self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
+ self.dropout = torch.nn.Dropout(config.dropout)
+ self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+ else:
+ self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+ def forward(self, hidden_states):
+ residual = hidden_states
+ hidden_states = self.norm1(hidden_states)
+ hidden_states *= torch.sigmoid(hidden_states)
+ hidden_states = self.conv1(hidden_states)
+
+ hidden_states = self.norm2(hidden_states)
+ hidden_states *= torch.sigmoid(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.conv2(hidden_states)
+
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ residual = self.conv_shortcut(residual)
+ else:
+ residual = self.nin_shortcut(residual)
+
+ return residual + hidden_states
+
+
+class JanusVQVAEAttnBlock(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+ self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+ self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+ self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+
+ def forward(self, hidden_states):
+ residual = hidden_states
+ hidden_states = self.norm(hidden_states)
+ query_states = self.q(hidden_states)
+ key_states = self.k(hidden_states)
+ value_states = self.v(hidden_states)
+
+ # compute attention
+ batch_size, channels, height, width = query_states.shape
+ query_states = query_states.reshape(batch_size, channels, height * width).permute(0, 2, 1)
+ key_states = key_states.reshape(batch_size, channels, height * width)
+ attn_weights = torch.bmm(query_states, key_states)
+ attn_weights = attn_weights * (int(channels) ** (-0.5))
+ attn_weights = F.softmax(attn_weights, dim=2)
+
+ # attend to values
+ value_states = value_states.reshape(batch_size, channels, height * width)
+ attn_weights = attn_weights.permute(0, 2, 1)
+ attn_output = torch.bmm(value_states, attn_weights).reshape(batch_size, channels, height, width)
+
+ attn_output = self.proj_out(attn_output)
+ return residual + attn_output
+
+
+class JanusVQVAEConvDownsample(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+
+ def forward(self, hidden_states):
+ # no asymmetric padding in torch conv, must do it ourselves
+ hidden_states = F.pad(hidden_states, pad=(0, 1, 0, 1), mode="constant", value=0)
+ hidden_states = self.conv(hidden_states)
+ return hidden_states
+
+
+class JanusVQVAEConvUpsample(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+ def forward(self, hidden_states):
+ hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
+ hidden_states = self.conv(hidden_states)
+ return hidden_states
+
+
+class JanusVQVAEMidBlock(nn.Module):
+ def __init__(self, config: JanusVQVAEConfig, channels: int):
+ super().__init__()
+ self.block_1 = JanusVQVAEResnetBlock(
+ config=config,
+ in_channels=channels,
+ out_channels=channels,
+ )
+ self.attn_1 = JanusVQVAEAttnBlock(channels)
+ self.block_2 = JanusVQVAEResnetBlock(
+ config=config,
+ in_channels=channels,
+ out_channels=channels,
+ )
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.block_1(hidden_states)
+ hidden_states = self.attn_1(hidden_states)
+ hidden_states = self.block_2(hidden_states)
+ return hidden_states
+
+
+class JanusVQVAEEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ self.num_resolutions = len(config.channel_multiplier)
+ self.num_res_blocks = config.num_res_blocks
+ base_channels = config.base_channels
+ in_channels = config.in_channels
+ double_latent = config.double_latent
+ latent_channels = config.latent_channels
+ channel_multiplier = config.channel_multiplier
+
+ self.conv_in = torch.nn.Conv2d(in_channels, base_channels, kernel_size=3, stride=1, padding=1)
+
+ in_channel_multiplier = (1,) + tuple(channel_multiplier)
+ self.in_channel_multiplier = in_channel_multiplier
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = base_channels * in_channel_multiplier[i_level]
+ block_out = base_channels * channel_multiplier[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(
+ JanusVQVAEResnetBlock(
+ config=config,
+ in_channels=block_in,
+ out_channels=block_out,
+ )
+ )
+ block_in = block_out
+ if i_level == self.num_resolutions - 1:
+ attn.append(JanusVQVAEAttnBlock(block_in))
+
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions - 1:
+ down.downsample = JanusVQVAEConvDownsample(block_in)
+ self.down.append(down)
+
+ self.mid = JanusVQVAEMidBlock(config, block_in)
+
+ self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+ self.conv_out = torch.nn.Conv2d(
+ block_in,
+ 2 * latent_channels if double_latent else latent_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ )
+
+ def forward(self, pixel_values: torch.LongTensor):
+ # downsampling
+ hidden_states = [self.conv_in(pixel_values)]
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ hidden_state = self.down[i_level].block[i_block](
+ hidden_states[-1],
+ )
+ if len(self.down[i_level].attn) > 0:
+ hidden_state = self.down[i_level].attn[i_block](hidden_state)
+ hidden_states.append(hidden_state)
+ if i_level != self.num_resolutions - 1:
+ hidden_states.append(self.down[i_level].downsample(hidden_states[-1]))
+
+ # middle
+ last_hidden_state = hidden_states[-1]
+ last_hidden_state = self.mid(last_hidden_state)
+
+ # end
+ last_hidden_state = self.norm_out(last_hidden_state)
+ last_hidden_state *= torch.sigmoid(last_hidden_state)
+ last_hidden_state = self.conv_out(last_hidden_state)
+ return last_hidden_state
+
+
+class JanusVQVAEDecoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ self.num_resolutions = len(config.channel_multiplier)
+ self.num_res_blocks = config.num_res_blocks
+ base_channels = config.base_channels
+ latent_channels = config.latent_channels
+ out_channels = config.out_channels
+
+ # compute in_ch_mult, block_in and curr_res at lowest res
+ block_in = base_channels * config.channel_multiplier[self.num_resolutions - 1]
+
+ # z to block_in
+ self.conv_in = torch.nn.Conv2d(latent_channels, block_in, kernel_size=3, stride=1, padding=1)
+
+ # middle
+ self.mid = JanusVQVAEMidBlock(config, block_in)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = base_channels * config.channel_multiplier[i_level]
+ for i_block in range(self.num_res_blocks + 1):
+ block.append(
+ JanusVQVAEResnetBlock(
+ config=config,
+ in_channels=block_in,
+ out_channels=block_out,
+ )
+ )
+ block_in = block_out
+ if i_level == self.num_resolutions - 1:
+ attn.append(JanusVQVAEAttnBlock(block_in))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = JanusVQVAEConvUpsample(block_in)
+ self.up.append(up)
+
+ # end
+ self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+ self.conv_out = torch.nn.Conv2d(block_in, out_channels, kernel_size=3, stride=1, padding=1)
+
+ def forward(self, hidden_state: torch.FloatTensor) -> torch.FloatTensor:
+ hidden_state = self.conv_in(hidden_state)
+
+ # middle
+ hidden_state = self.mid(hidden_state)
+
+ # upsampling
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks + 1):
+ hidden_state = self.up[i_level].block[i_block](hidden_state)
+ if len(self.up[i_level].attn) > 0:
+ hidden_state = self.up[i_level].attn[i_block](hidden_state)
+ if i_level != self.num_resolutions - 1:
+ hidden_state = self.up[i_level].upsample(hidden_state)
+
+ hidden_state = self.norm_out(hidden_state)
+ hidden_state *= torch.sigmoid(hidden_state)
+ hidden_state = self.conv_out(hidden_state)
+ return hidden_state
+
+
+@auto_docstring(
+ custom_intro="""
+ The VQ-VAE model used in Janus for encoding/decoding images into discrete tokens.
+ This model follows the "Make-a-scene: Scene-based text-to-image generation with human priors" paper from
+ [ Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv
+ Taigman](https://huggingface.co/papers/2203.13131).
+ """
+)
+class JanusVQVAE(JanusPreTrainedModel):
+ config: JanusVQVAEConfig
+ _no_split_modules = [
+ "JanusVQVAEAttnBlock",
+ "JanusVQVAEResnetBlock",
+ "JanusVQVAEVectorQuantizer",
+ ]
+ main_input_name = "pixel_values"
+
+ def __init__(self, config: JanusVQVAEConfig):
+ super().__init__(config)
+
+ self.encoder = JanusVQVAEEncoder(config)
+ self.quantize = JanusVQVAEVectorQuantizer(config)
+ self.quant_conv = torch.nn.Conv2d(config.latent_channels, config.embed_dim, 1)
+ self.post_quant_conv = torch.nn.Conv2d(config.embed_dim, config.latent_channels, 1)
+ self.eval() # Janus's VQ model is frozen
+ self.decoder = JanusVQVAEDecoder(config)
+ self.gradient_checkpointing = False
+
+ # Initialize the VQVAE model.
+ self.post_init()
+
+ def encode(self, pixel_values: torch.LongTensor):
+ hidden_states = self.encoder(pixel_values)
+ hidden_states = self.quant_conv(hidden_states)
+ quant, emb_loss, indices = self.quantize(hidden_states)
+ return quant, emb_loss, indices
+
+ def decode(self, image_tokens: torch.LongTensor) -> torch.FloatTensor:
+ """
+ Decodes quantized token IDs into pixel values.
+ Args:
+ image_tokens (torch.LongTensor): Batch of token IDs.
+ Returns:
+ pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+ Pixel values decoded from the token IDs.
+ """
+ if image_tokens.shape[1] != self.quantize.quant_state_dims[0] * self.quantize.quant_state_dims[1]:
+ raise ValueError(
+ f"Expected `image_tokens` to have shape `(batch_size, {self.quantize.quant_state_dims[0] * self.quantize.quant_state_dims[1]})`, "
+ f"but got shape `{image_tokens.shape}`."
+ )
+ codebook_entry = self.quantize.get_codebook_entry(image_tokens)
+ hidden_states = self.post_quant_conv(codebook_entry)
+ pixel_values = self.decoder(hidden_states)
+ return pixel_values
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: torch.FloatTensor,
+ ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
+ batch_size = pixel_values.shape[0]
+ quant, embedding_loss, indices = self.encode(pixel_values)
+ decoded_pixel_values = self.decode(indices.view(batch_size, -1))
+
+ return JanusVQVAEOutput(decoded_pixel_values, embedding_loss)
+
+
+class JanusVQVAEAlignerMLP(nn.Module):
+ def __init__(self, config: JanusVQVAEConfig):
+ super().__init__()
+
+ self.fc1 = nn.Linear(config.embed_dim, config.projection_dim)
+ self.hidden_layers = nn.ModuleList(
+ [nn.Linear(config.projection_dim, config.projection_dim) for _ in range(1, config.num_hidden_layers)]
+ )
+ self.activation_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_states):
+ hidden_states = self.fc1(hidden_states)
+ for layer in self.hidden_layers:
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = layer(hidden_states)
+ return hidden_states
+
+
+class JanusVQVAEHead(nn.Module):
+ """Head used for sampling tokens in image generation, replacing the usual lm head."""
+
+ def __init__(self, config: JanusVQVAEConfig):
+ super().__init__()
+ self.proj_out = nn.Linear(config.image_token_embed_dim, config.projection_dim)
+ self.activation_fn = ACT2FN[config.hidden_act]
+ self.vision_head = nn.Linear(config.projection_dim, config.num_embeddings)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.tensor:
+ hidden_states = self.proj_out(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.vision_head(hidden_states)
+ return hidden_states
+
+
+@auto_docstring(
+ custom_intro="""
+ The Janus model which consists of a siglip vision backbone, a Llama language model and a VQ model.
+ """
+)
+class JanusModel(JanusPreTrainedModel):
+ def __init__(self, config: JanusConfig):
+ super().__init__(config)
+ self.config = config
+ # This is necessary for backward compatibility, see SiglipModel initialization
+ self.vision_model = JanusVisionModel._from_config(config.vision_config)
+ self.aligner = JanusVisionAlignerMLP(self.vision_model.config)
+
+ self.vqmodel = JanusVQVAE._from_config(config.vq_config)
+
+ # Below generation_* modules are used for Image generation.
+ # Embeddings used for image generation, instead of Janus vision embeddings.
+ self.generation_embeddings = nn.Embedding(self.vqmodel.config.num_embeddings, self.vqmodel.config.embed_dim)
+ self.generation_aligner = JanusVQVAEAlignerMLP(self.vqmodel.config)
+ self.generation_head = JanusVQVAEHead(self.vqmodel.config)
+
+ self.language_model = AutoModel.from_config(config=config.text_config)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing.
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def get_image_features(self, pixel_values):
+ image_embeds = self.vision_model(pixel_values)
+ image_embeds = self.aligner(image_embeds.last_hidden_state)
+ return image_embeds
+
+ def get_placeholder_mask(
+ self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
+ ):
+ """
+ Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if inputs_embeds[special_image_mask].numel() != image_features.numel():
+ n_image_features = image_features.shape[0] * image_features.shape[1]
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+ )
+ return special_image_mask
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs,
+ ):
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError(
+ "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+ )
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ if pixel_values is not None:
+ image_embeds = self.get_image_features(pixel_values)
+ image_features = image_embeds.reshape(-1, inputs_embeds.shape[-1])
+ image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+ image_attention_mask = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(image_attention_mask, image_features)
+
+ lm_output = self.language_model(
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ return JanusBaseModelOutputWithPast(
+ last_hidden_state=lm_output.last_hidden_state,
+ past_key_values=lm_output.past_key_values,
+ hidden_states=lm_output.hidden_states,
+ attentions=lm_output.attentions,
+ image_hidden_states=image_embeds if pixel_values is not None else None,
+ )
+
+
+class JanusForConditionalGeneration(JanusPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["model.language_model.embed_tokens.weight", "lm_head.weight"]
+ _can_compile_fullgraph = True
+
+ def __init__(self, config: JanusConfig):
+ super().__init__(config)
+ self.config = config
+ self.model = JanusModel(config)
+ self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing.
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.language_model.set_input_embeddings(value)
+
+ def prepare_embeddings_for_image_generation(self, inputs: torch.Tensor) -> torch.Tensor:
+ hidden_state = self.model.generation_embeddings(inputs)
+ hidden_state = self.model.generation_aligner(hidden_state)
+ return hidden_state
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ):
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ """
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return JanusCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ pixel_values=None,
+ past_key_values=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- extra custom processing
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ if cache_position[0] == 0:
+ model_inputs["pixel_values"] = pixel_values
+
+ return model_inputs
+
+ def decode_image_tokens(self, image_tokens: torch.Tensor):
+ """
+ Decodes generated image tokens from language model to continuous pixel values
+ with VQGAN module via upsampling.
+ Args:
+ image_tokens (`torch.LongTensor` of shape `(batch_size, num_of_tokens)`):
+ The tensors corresponding to the input images.
+ """
+ decoded_image = self.model.vqmodel.decode(image_tokens)
+ decoded_image = decoded_image.permute(0, 2, 3, 1)
+ return decoded_image
+
+ @torch.no_grad
+ def generate(
+ self,
+ inputs: torch.Tensor = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ logits_processor: Optional[LogitsProcessorList] = None,
+ **kwargs,
+ ):
+ # 1. Handle generation config and model kwargs
+ generation_config = kwargs.pop("generation_config", self.generation_config)
+ generation_config = copy.deepcopy(generation_config)
+
+ # Default to "text" generation if mode isn't provided
+ generation_mode = kwargs.pop("generation_mode", "text")
+ if generation_mode == "text":
+ # Set guidance_scale=None to prevent running UnbatchedCFG processor.
+ return super().generate(
+ inputs=inputs,
+ attention_mask=attention_mask,
+ generation_config=generation_config,
+ guidance_scale=None,
+ **kwargs,
+ )
+
+ model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
+
+ # Validate generation mode
+ if generation_config.get_generation_mode() not in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH):
+ raise ValueError(
+ "Got incompatible mode for Image Generation, should be one of greedy or sampling. "
+ "Ensure that beam search is de-activated by setting `num_beams=1` and `num_beam_groups=1`."
+ )
+
+ # Validate the configuration and model kwargs
+ generation_config.validate()
+ self._validate_model_kwargs(model_kwargs.copy())
+
+ # 2. Initialize logit processors
+ logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+
+ # Set `use_cache=True` as we will be using input embeds for generation.
+ model_kwargs["use_cache"] = True
+
+ if generation_config.guidance_scale is None:
+ logger.warning("`guidance_scale` is required for CFG but not provided. Setting to default value of 5.")
+ generation_config.guidance_scale = 5
+ model_kwargs["guidance_scale"] = generation_config.guidance_scale
+
+ # 3. Prepare model inputs
+ input_ids, model_input_name, model_kwargs = self._prepare_model_inputs(
+ inputs, generation_config.bos_token_id, model_kwargs
+ )
+ dtype, device = input_ids.dtype, input_ids.device
+
+ if len(input_ids.shape) != 2:
+ raise ValueError(
+ f"Expected input ids of shape (batch_size, seq_len), but got {input_ids.shape}"
+ "Passing `inputs embeds` is not supported currently."
+ )
+
+ # Prepare special tokens which will be used generate internally.
+ kwargs_has_attention_mask = attention_mask is not None
+ self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=input_ids.device)
+
+ # 4. Add CFG processor along with user passed logit processor.
+ if generation_config.guidance_scale and generation_config.guidance_scale > 1:
+ logits_processor.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale))
+ generation_config.guidance_scale = None # Reset to prevent processor duplication.
+
+ # 5. Prepare logits processor
+ logits_processor = self._get_logits_processor(
+ generation_config=generation_config,
+ input_ids_seq_length=input_ids.shape[1],
+ encoder_input_ids=input_ids,
+ prefix_allowed_tokens_fn=None,
+ logits_processor=logits_processor,
+ device=device,
+ )
+
+ # 6. Expand inputs for multiple image generations per prompt.
+ input_ids, model_kwargs = self._expand_inputs_for_generation(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ expand_size=generation_config.num_return_sequences,
+ **model_kwargs,
+ )
+
+ # 7. Prepare input and model caches
+ num_image_tokens = self.model.vision_model.config.num_image_tokens
+ batch_size, seq_len = input_ids.shape
+
+ input_tokens = input_ids.repeat(2, 1) # Double batch size for conditional/unconditional logits
+ attention_mask = model_kwargs.pop("attention_mask", None)
+ attention_mask = attention_mask.repeat(2, 1)
+ model_kwargs["attention_mask"] = attention_mask
+
+ # Mask all the tokens that are neither BOS nor BOI with pad token in the unconditional logits.
+ mask = (input_tokens[batch_size:, :] != generation_config.bos_token_id) & (
+ input_tokens[batch_size:, :] != generation_config.generation_kwargs["boi_token_id"]
+ )
+ input_tokens[batch_size:, :].masked_fill_(mask, generation_config.pad_token_id)
+
+ inputs_embeds = self.get_input_embeddings()(input_tokens)
+
+ model_kwargs = self._get_initial_cache_position(seq_len, device, model_kwargs)
+
+ if model_kwargs.get("past_key_values", None) is None:
+ # Prepare cache if not provided.
+ model_kwargs["past_key_values"] = self._get_cache(
+ cache_implementation=generation_config.cache_implementation or "static",
+ # batch_size should account for both conditional/unconditional input; hence multiplied by 2.
+ batch_size=batch_size * 2,
+ # we should have at least a cache len of seq_len + num_image_tokens.
+ max_cache_len=max(generation_config.max_length, num_image_tokens + seq_len),
+ model_kwargs=model_kwargs,
+ )
+
+ # Placeholder for generated tokens.
+ generated_tokens = torch.zeros((batch_size, num_image_tokens), dtype=dtype, device=device)
+
+ # 8. init attention / hidden states / scores tuples
+ output_attentions = generation_config.output_attentions
+ output_hidden_states = generation_config.output_hidden_states
+ output_scores = generation_config.output_scores
+ output_logits = generation_config.output_logits
+ return_dict_in_generate = generation_config.return_dict_in_generate
+
+ raw_scores = () if (return_dict_in_generate and output_scores) else None
+ raw_logits = () if (return_dict_in_generate and output_logits) else None
+ decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+ decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+
+ for i in range(num_image_tokens):
+ model_inputs = self.prepare_inputs_for_generation(
+ inputs_embeds=inputs_embeds, input_ids=input_tokens, **model_kwargs
+ )
+
+ model_inputs["attention_mask"] = model_inputs["attention_mask"].to(inputs_embeds.device)
+ model_inputs["cache_position"] = model_inputs["cache_position"].to(inputs_embeds.device)
+
+ outputs = self.model.language_model(
+ **model_inputs,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ )
+
+ # Update model_kwargs like cache_position for next generation.
+ model_kwargs = self._update_model_kwargs_for_generation(outputs, model_kwargs)
+ hidden_state = outputs.last_hidden_state[:, -1, :].clone()
+
+ # Generate scores using the generation head (Not using above defined LM Head)
+ scores = self.model.generation_head(hidden_state)
+ next_token_scores = logits_processor(input_ids, scores)
+
+ # Sample next token.
+ if generation_config.do_sample:
+ probs = torch.softmax(next_token_scores, dim=-1)
+ next_token = torch.multinomial(probs, num_samples=1).squeeze(-1)
+ else:
+ next_token = torch.argmax(next_token_scores, dim=-1)
+
+ generated_tokens[:, i] = next_token
+
+ # Prepare embeddings for the next step.
+ next_token = torch.cat([next_token, next_token])
+ next_token = next_token.unsqueeze(-1)
+
+ inputs_embeds = self.prepare_embeddings_for_image_generation(next_token)
+
+ if return_dict_in_generate:
+ if output_scores:
+ raw_scores += (scores,)
+ if output_logits:
+ raw_logits += (hidden_state.float(),)
+ if output_attentions:
+ decoder_attentions += outputs.attentions
+ if output_hidden_states:
+ decoder_hidden_states += outputs.hidden_states
+
+ if return_dict_in_generate:
+ return GenerateDecoderOnlyOutput(
+ sequences=generated_tokens,
+ scores=scores,
+ logits=raw_logits,
+ attentions=decoder_attentions,
+ hidden_states=decoder_hidden_states,
+ past_key_values=outputs.past_key_values,
+ )
+ else:
+ return generated_tokens
+
+
+__all__ = ["JanusPreTrainedModel", "JanusForConditionalGeneration", "JanusModel", "JanusVQVAE", "JanusVisionModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/modular_janus.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/modular_janus.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5341ac33b4a79be8b60f5a72adaad5fa8ad5154
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/modular_janus.py
@@ -0,0 +1,1617 @@
+# coding=utf-8
+# Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+from collections.abc import Iterable
+from dataclasses import dataclass
+from typing import Callable, Optional, Union
+
+import numpy as np
+import torch
+from torch import nn
+
+from transformers.models.blip.image_processing_blip import BlipImageProcessor
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache
+from ...generation import ClassifierFreeGuidanceLogitsProcessor, GenerationMixin, GenerationMode, LogitsProcessorList
+from ...generation.utils import GenerateDecoderOnlyOutput
+from ...image_processing_utils import BatchFeature, get_size_dict
+from ...image_transforms import resize, to_channel_dimension_format
+from ...image_utils import (
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ get_image_size,
+ infer_channel_dimension_format,
+ make_list_of_images,
+ to_numpy_array,
+)
+from ...modeling_outputs import ModelOutput
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import (
+ TransformersKwargs,
+ auto_docstring,
+ can_return_tuple,
+ is_torch_available,
+ is_vision_available,
+ logging,
+)
+from ..auto import AutoModel
+from ..blip_2.modeling_blip_2 import Blip2VisionModel
+from ..chameleon.configuration_chameleon import ChameleonVQVAEConfig
+from ..chameleon.modeling_chameleon import (
+ ChameleonVQVAE,
+ ChameleonVQVAEEncoderAttnBlock,
+ ChameleonVQVAEEncoderConvDownsample,
+ ChameleonVQVAEEncoderResnetBlock,
+ ChameleonVQVAEVectorQuantizer,
+)
+from ..idefics.modeling_idefics import IdeficsBaseModelOutputWithPast, IdeficsCausalLMOutputWithPast
+from ..llama.modeling_llama import eager_attention_forward
+from ..siglip.configuration_siglip import SiglipVisionConfig
+from ..siglip.modeling_siglip import SiglipEncoder, SiglipEncoderLayer, SiglipVisionEmbeddings
+
+
+if is_torch_available():
+ import torch
+ import torch.nn as nn
+ import torch.nn.functional as F
+ import torch.utils.checkpoint
+
+if is_vision_available():
+ import PIL
+
+from ...configuration_utils import PretrainedConfig
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+logger = logging.get_logger(__name__)
+
+# General docstring
+
+
+class JanusVisionConfig(SiglipVisionConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`JanusVisionModel`]. It is used to instantiate a
+ `JanusVisionModel` according to the specified arguments, defining the model architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+ Args:
+ hidden_size (`int`, *optional*, defaults to 1024):
+ Dimensionality of the encoder layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 24):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ patch_size (`int`, *optional*, defaults to 16):
+ The size (resolution) of each patch.
+ image_size (`int`, *optional*, defaults to 384):
+ The size (resolution) of each image.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ Dropout probability for attention weights.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the layer normalization layers.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"`, and `"gelu_new"` are supported.
+ mlp_ratio (`float`, *optional*, defaults to 4.0):
+ Ratio of MLP hidden dimensionality to embedding dimensionality.
+ attention_bias (`bool`, *optional*, defaults to `True`):
+ Whether to add a bias to the queries, keys, and values in the attention layers.
+ hidden_dropout_rate (`float`, *optional*, defaults to 0.0):
+ The dropout probability for fully connected layers in the encoder.
+ projection_dim (`int`, *optional*, defaults to 2048):
+ Dimensionality of the MLP projection head.
+ projection_dropout (`float`, *optional*, defaults to 0.0):
+ Dropout probability for the projection layer.
+ use_qk_norm (`bool`, *optional*, defaults to `False`):
+ Whether to normalize the query and key matrices.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated normal initializer for initializing all weight matrices.
+ depth (`int`, *optional*, defaults to 2):
+ Number of hidden layers in the aligner module.
+ num_image_tokens (`int`, *optional*, defaults to 576):
+ Number of image tokens.
+ """
+
+ model_type = "janus_vision_model"
+ base_config_key = "vision_config"
+
+ def __init__(
+ self,
+ hidden_size=1024,
+ num_hidden_layers=24,
+ num_attention_heads=16,
+ num_channels=3,
+ patch_size=16,
+ image_size=384,
+ attention_dropout=0.0,
+ layer_norm_eps=1e-6,
+ hidden_act="gelu",
+ mlp_ratio=4.0,
+ attention_bias=True,
+ hidden_dropout_rate=0.0,
+ projection_dim=2048,
+ projection_dropout=0.0,
+ use_qk_norm=False,
+ initializer_range=0.02,
+ depth=2,
+ num_image_tokens=576,
+ **kwargs,
+ ):
+ super().__init__(
+ hidden_size=hidden_size,
+ num_hidden_layers=num_hidden_layers,
+ num_attention_heads=num_attention_heads,
+ num_channels=num_channels,
+ patch_size=patch_size,
+ image_size=image_size,
+ attention_dropout=attention_dropout,
+ layer_norm_eps=layer_norm_eps,
+ hidden_act=hidden_act,
+ **kwargs,
+ )
+ del self.intermediate_size
+
+ self.mlp_ratio = mlp_ratio
+ self.attention_bias = attention_bias
+ self.hidden_dropout_rate = hidden_dropout_rate
+ self.projection_dim = projection_dim
+ self.projection_dropout = projection_dropout
+ self.use_qk_norm = use_qk_norm
+ self.initializer_range = initializer_range
+ self.depth = depth
+ self.num_image_tokens = num_image_tokens
+
+
+class JanusVQVAEConfig(ChameleonVQVAEConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`JanusVQVAEModel`]. It is used to instantiate a
+ `JanusVQVAEModel` according to the specified arguments, defining the model architecture.
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information. Instantiating a
+ configuration with the defaults will yield a similar configuration to the VQModel of the
+ [deepseek-community/Janus-Pro-1B](https://huggingface.co/deepseek-community/Janus-Pro-1B).
+
+ Args:
+ embed_dim (`int`, *optional*, defaults to 8):
+ Dimensionality of each embedding vector.
+ num_embeddings (`int`, *optional*, defaults to 16384):
+ Number of codebook embeddings.
+ double_latent (`bool`, *optional*, defaults to `False`):
+ Whether to use double z channels.
+ latent_channels (`int`, *optional*, defaults to 256):
+ Number of channels for the latent space.
+ num_patches (`int`, *optional*, defaults to 32):
+ Num of patches the input images can be divided into.
+ in_channels (`int`, *optional*, defaults to 3):
+ Number of input channels.
+ out_channels (`int`, *optional*, defaults to 3):
+ Number of out channels.
+ base_channels (`int`, *optional*, defaults to 128):
+ Base channel count.
+ channel_multiplier (`list[int]`, *optional*, defaults to `[1, 1, 2, 2, 4]`):
+ Channel multipliers for each resolution.
+ num_res_blocks (`int`, *optional*, defaults to 2):
+ Number of residual blocks.
+ dropout (`float`, *optional*, defaults to 0.0):
+ Dropout rate.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ projection_dim (`int`, *optional*, defaults to 2048):
+ Dimensionality of the MLP projection head.
+ num_hidden_layers (`int`, *optional*, defaults to 2):
+ Number of hidden layers in VAVAE MLP Connecter module.
+ hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ image_token_embed_dim (`int`, *optional*, defaults to 2048):
+ Dimension of image embeddings. It should be same as the dimensionality of text embeddings.
+ """
+
+ def __init__(
+ self,
+ embed_dim: int = 8,
+ num_embeddings: int = 16384,
+ double_latent: bool = False,
+ latent_channels: int = 256,
+ num_patches: int = 32,
+ in_channels: int = 3,
+ out_channels: int = 3,
+ base_channels: int = 128,
+ channel_multiplier: list[int] = [1, 1, 2, 2, 4],
+ num_res_blocks: int = 2,
+ dropout: float = 0.0,
+ initializer_range=0.02,
+ projection_dim=2048,
+ num_hidden_layers=2,
+ hidden_act="gelu",
+ image_token_embed_dim=2048,
+ **kwargs,
+ ):
+ super().__init__(
+ embed_dim=embed_dim,
+ num_embeddings=num_embeddings,
+ double_latent=double_latent,
+ latent_channels=latent_channels,
+ in_channels=in_channels,
+ base_channels=base_channels,
+ channel_multiplier=channel_multiplier,
+ num_res_blocks=num_res_blocks,
+ dropout=dropout,
+ initializer_range=initializer_range,
+ **kwargs,
+ )
+ self.num_patches = num_patches
+ self.out_channels = out_channels
+ self.projection_dim = projection_dim
+ self.num_hidden_layers = num_hidden_layers
+ self.hidden_act = hidden_act
+ self.image_token_embed_dim = image_token_embed_dim
+
+ del self.resolution
+ del self.attn_resolutions
+ del self.attn_type
+
+
+class JanusConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`JanusModel`]. It is used to instantiate an
+ Janus model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Janus-1B or Janus-7B models.
+
+ e.g. [deepseek-community/Janus-Pro-1B](https://huggingface.co/deepseek-community/Janus-Pro-1B) or
+ [deepseek-community/Janus-Pro-7B](https://huggingface.co/deepseek-community/Janus-Pro-7B)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
+ The config object or dictionary of the text backbone.
+ vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `JanusVisionConfig`):
+ The config object or dictionary of the vision backbone.
+ vq_config (`Union[AutoConfig, dict]`, *optional*, defaults to `JanusVQVAEConfig`):
+ The config object or dictionary of the VQVAE backbone.
+ image_token_id (`int`, *optional*, defaults to 100581):
+ Token index of a placeholder image token.
+
+ Example:
+
+ ```python
+ >>> from transformers import JanusForConditionalGeneration, JanusConfig, JanusVisionConfig, JanusVQVAEConfig, LlamaConfig
+
+ >>> # Initializing a Janus vision config
+ >>> vision_config = JanusVisionConfig()
+
+ >>> # Initializing a Llama config
+ >>> text_config = LlamaConfig()
+
+ >>> # Initializing a VQ config
+ >>> vq_config = JanusVQVAEConfig()
+
+ >>> # Initializing a Janus Pro 1B style configuration
+ >>> configuration = JanusConfig(vision_config=vision_config, text_config=text_config, vq_config=vq_config)
+
+ >>> # Initializing a model from the Janus Pro 1B style configuration
+ >>> model = JanusForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "janus"
+ sub_configs = {
+ "text_config": AutoConfig,
+ "vision_config": JanusVisionConfig,
+ "vq_config": JanusVQVAEConfig,
+ }
+
+ def __init__(
+ self,
+ text_config=None,
+ vision_config=None,
+ vq_config=None,
+ image_token_id=100581,
+ **kwargs,
+ ):
+ if isinstance(text_config, dict):
+ text_config["model_type"] = text_config.get("model_type", "llama")
+ self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+
+ elif text_config is None:
+ logger.info("`text_config` is None. Initializing with default values")
+ self.text_config = CONFIG_MAPPING["llama"]()
+ elif isinstance(text_config, PretrainedConfig):
+ self.text_config = text_config
+ else:
+ raise ValueError(
+ f"Invalid type for `text_config`. Must be either `dict` or `LlamaConfig`."
+ f" Type found: {type(text_config)}"
+ )
+
+ if vision_config is None:
+ logger.info("`vision_config` is None. Initializing with default JanusVisionConfig values")
+ self.vision_config = JanusVisionConfig()
+ elif isinstance(vision_config, dict):
+ self.vision_config = JanusVisionConfig(**vision_config)
+ elif isinstance(vision_config, JanusVisionConfig):
+ self.vision_config = vision_config
+ else:
+ raise ValueError(
+ f"Invalid type for `vision_config`. Must be either `dict` or `JanusVisionConfig`."
+ f" Type found: {type(vision_config)}"
+ )
+
+ if vq_config is None:
+ logger.info("`vq_config` is None. Initializing with default JanusVQVAEConfig values")
+ self.vq_config = JanusVQVAEConfig()
+ elif isinstance(vq_config, dict):
+ self.vq_config = JanusVQVAEConfig(**vq_config)
+ elif isinstance(vq_config, JanusVQVAEConfig):
+ self.vq_config = vq_config
+ else:
+ raise ValueError(
+ f"Invalid type for `vq_config`. Must be either `dict` or `JanusVQVAEConfig`."
+ f" Type found: {type(vq_config)}"
+ )
+
+ self.initializer_range = self.vision_config.initializer_range
+ # This dimension is required when decoding discrete image tokens to continuous input.
+ self.vq_config.num_patches = self.vision_config.image_size // self.vision_config.patch_size
+ # The default is only the index for the 1B model, 7B uses a different one
+ self.image_token_id = image_token_id
+ super().__init__(**kwargs)
+
+
+@auto_docstring
+class JanusPreTrainedModel(PreTrainedModel):
+ config: JanusConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["LlamaDecoderLayer", "JanusVisionEncoderLayer"]
+ _skip_keys_device_placement = ["past_key_values", "causal_mask"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+ _supports_param_buffer_assignment = False
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Janus VQ-VAE mode model outputs.
+ """
+)
+class JanusVQVAEOutput(ModelOutput):
+ r"""
+ decoded_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+ Reconstructed pixel values after encoding and decoding the input.
+ embedding_loss (`torch.FloatTensor`):
+ Embedding loss.
+ """
+
+ decoded_pixel_values: Optional[torch.FloatTensor] = None
+ embedding_loss: torch.FloatTensor = None
+
+
+class JanusBaseModelOutputWithPast(IdeficsBaseModelOutputWithPast):
+ pass
+
+
+class JanusCausalLMOutputWithPast(IdeficsCausalLMOutputWithPast):
+ pass
+
+
+class JanusVisionEmbeddings(SiglipVisionEmbeddings):
+ def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
+ _, _, height, width = pixel_values.shape
+ target_dtype = self.patch_embedding.weight.dtype
+ patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
+ embeddings = patch_embeds.flatten(2).transpose(1, 2)
+
+ if interpolate_pos_encoding:
+ pos_embeds = self.interpolate_pos_encoding(embeddings, height, width)
+ else:
+ pos_embeds = self.position_embedding(self.position_ids)
+
+ embeddings = embeddings + pos_embeds
+
+ return embeddings
+
+
+class JanusVisionAttention(nn.Module):
+ """Attention Class for Janus Vision Encoder"""
+
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.embed_dim // self.num_heads
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+ f" {self.num_heads})."
+ )
+ self.scale = self.head_dim**-0.5
+ self.attention_dropout = config.attention_dropout
+ proj_dropout = config.projection_dropout
+ qk_norm = config.use_qk_norm
+ self.is_causal = False
+
+ # Janus has no MHA, hence for `eager_attention_forward` call setting `num_key_value_groups` to 1.
+ self.num_key_value_groups = 1
+
+ self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias)
+ self.k_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias)
+ self.v_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias)
+ self.projection_layer = nn.Linear(self.embed_dim, self.embed_dim)
+ self.projection_dropout = nn.Dropout(proj_dropout) if proj_dropout > 0 else nn.Identity()
+
+ self.q_norm = nn.LayerNorm(self.embed_dim) if qk_norm else nn.Identity()
+ self.k_norm = nn.LayerNorm(self.embed_dim) if qk_norm else nn.Identity()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ):
+ batch_size, seq_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.reshape(-1, self.num_heads, self.head_dim)
+ query_states = self.q_norm(query_states)
+
+ key_states = key_states.reshape(-1, self.num_heads, self.head_dim)
+ key_states = self.k_norm(key_states)
+
+ query_states = query_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scale,
+ is_causal=self.is_causal,
+ **kwargs,
+ )
+ attn_output = attn_output.reshape(batch_size, seq_len, self.embed_dim)
+
+ output = self.projection_layer(attn_output)
+ output = self.projection_dropout(output)
+ return output, attn_weights
+
+
+class JanusVisionMLP(nn.Module):
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__()
+ self.config = config
+ self.intermediate_size = int(config.hidden_size * config.mlp_ratio)
+ self.activation_fn = ACT2FN[config.hidden_act] # Gelu act
+ self.fc1 = nn.Linear(config.hidden_size, self.intermediate_size)
+ self.fc2 = nn.Linear(self.intermediate_size, config.hidden_size)
+ self.dropout1 = nn.Dropout(config.hidden_dropout_rate)
+ self.dropout2 = nn.Dropout(config.hidden_dropout_rate)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.dropout1(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout2(hidden_states)
+ return hidden_states
+
+
+class JanusVisionEncoderLayer(SiglipEncoderLayer):
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.self_attn = JanusVisionAttention(config)
+ self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+ self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+ self.mlp = JanusVisionMLP(config)
+
+
+class JanusVisionEncoder(SiglipEncoder):
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__(config)
+ self.layers = nn.ModuleList([JanusVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+
+
+class JanusVisionModel(Blip2VisionModel):
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__(config)
+ self.encoder = JanusVisionEncoder(config)
+
+
+class JanusVisionAlignerMLP(nn.Module):
+ def __init__(self, config: JanusVisionConfig):
+ super().__init__()
+
+ self.fc1 = nn.Linear(config.hidden_size, config.projection_dim)
+ self.hidden_layers = nn.ModuleList(
+ [nn.Linear(config.projection_dim, config.projection_dim) for _ in range(1, config.depth)]
+ )
+ self.activation_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_states):
+ hidden_states = self.fc1(hidden_states)
+ for layer in self.hidden_layers:
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = layer(hidden_states)
+ return hidden_states
+
+
+class JanusVQVAEVectorQuantizer(ChameleonVQVAEVectorQuantizer):
+ def __init__(self, config: JanusVQVAEConfig):
+ super().__init__(config)
+ self.quant_state_dims = [config.num_patches] * 2
+
+ def get_codebook_entry(self, image_tokens: torch.LongTensor) -> torch.FloatTensor:
+ batch_size = image_tokens.shape[0]
+ emb_dim: int = self.embedding.weight.shape[-1]
+
+ # get quantized latent vectors
+ hidden_state_quant = self.embedding(image_tokens)
+ # l2 normalization on the last dimension
+ hidden_state_quant = F.normalize(hidden_state_quant, p=2, dim=-1)
+
+ # reshape back to match original input shape
+ hidden_state_quant = hidden_state_quant.view((batch_size, *self.quant_state_dims, emb_dim))
+ hidden_state_quant = hidden_state_quant.permute(0, 3, 1, 2).contiguous()
+
+ return hidden_state_quant
+
+
+class JanusVQVAEResnetBlock(ChameleonVQVAEEncoderResnetBlock):
+ pass
+
+
+class JanusVQVAEAttnBlock(ChameleonVQVAEEncoderAttnBlock):
+ pass
+
+
+class JanusVQVAEConvDownsample(ChameleonVQVAEEncoderConvDownsample):
+ pass
+
+
+class JanusVQVAEConvUpsample(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+ def forward(self, hidden_states):
+ hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
+ hidden_states = self.conv(hidden_states)
+ return hidden_states
+
+
+class JanusVQVAEMidBlock(nn.Module):
+ def __init__(self, config: JanusVQVAEConfig, channels: int):
+ super().__init__()
+ self.block_1 = JanusVQVAEResnetBlock(
+ config=config,
+ in_channels=channels,
+ out_channels=channels,
+ )
+ self.attn_1 = JanusVQVAEAttnBlock(channels)
+ self.block_2 = JanusVQVAEResnetBlock(
+ config=config,
+ in_channels=channels,
+ out_channels=channels,
+ )
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.block_1(hidden_states)
+ hidden_states = self.attn_1(hidden_states)
+ hidden_states = self.block_2(hidden_states)
+ return hidden_states
+
+
+class JanusVQVAEEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ self.num_resolutions = len(config.channel_multiplier)
+ self.num_res_blocks = config.num_res_blocks
+ base_channels = config.base_channels
+ in_channels = config.in_channels
+ double_latent = config.double_latent
+ latent_channels = config.latent_channels
+ channel_multiplier = config.channel_multiplier
+
+ self.conv_in = torch.nn.Conv2d(in_channels, base_channels, kernel_size=3, stride=1, padding=1)
+
+ in_channel_multiplier = (1,) + tuple(channel_multiplier)
+ self.in_channel_multiplier = in_channel_multiplier
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = base_channels * in_channel_multiplier[i_level]
+ block_out = base_channels * channel_multiplier[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(
+ JanusVQVAEResnetBlock(
+ config=config,
+ in_channels=block_in,
+ out_channels=block_out,
+ )
+ )
+ block_in = block_out
+ if i_level == self.num_resolutions - 1:
+ attn.append(JanusVQVAEAttnBlock(block_in))
+
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions - 1:
+ down.downsample = JanusVQVAEConvDownsample(block_in)
+ self.down.append(down)
+
+ self.mid = JanusVQVAEMidBlock(config, block_in)
+
+ self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+ self.conv_out = torch.nn.Conv2d(
+ block_in,
+ 2 * latent_channels if double_latent else latent_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ )
+
+ def forward(self, pixel_values: torch.LongTensor):
+ # downsampling
+ hidden_states = [self.conv_in(pixel_values)]
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ hidden_state = self.down[i_level].block[i_block](
+ hidden_states[-1],
+ )
+ if len(self.down[i_level].attn) > 0:
+ hidden_state = self.down[i_level].attn[i_block](hidden_state)
+ hidden_states.append(hidden_state)
+ if i_level != self.num_resolutions - 1:
+ hidden_states.append(self.down[i_level].downsample(hidden_states[-1]))
+
+ # middle
+ last_hidden_state = hidden_states[-1]
+ last_hidden_state = self.mid(last_hidden_state)
+
+ # end
+ last_hidden_state = self.norm_out(last_hidden_state)
+ last_hidden_state *= torch.sigmoid(last_hidden_state)
+ last_hidden_state = self.conv_out(last_hidden_state)
+ return last_hidden_state
+
+
+class JanusVQVAEDecoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ self.num_resolutions = len(config.channel_multiplier)
+ self.num_res_blocks = config.num_res_blocks
+ base_channels = config.base_channels
+ latent_channels = config.latent_channels
+ out_channels = config.out_channels
+
+ # compute in_ch_mult, block_in and curr_res at lowest res
+ block_in = base_channels * config.channel_multiplier[self.num_resolutions - 1]
+
+ # z to block_in
+ self.conv_in = torch.nn.Conv2d(latent_channels, block_in, kernel_size=3, stride=1, padding=1)
+
+ # middle
+ self.mid = JanusVQVAEMidBlock(config, block_in)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = base_channels * config.channel_multiplier[i_level]
+ for i_block in range(self.num_res_blocks + 1):
+ block.append(
+ JanusVQVAEResnetBlock(
+ config=config,
+ in_channels=block_in,
+ out_channels=block_out,
+ )
+ )
+ block_in = block_out
+ if i_level == self.num_resolutions - 1:
+ attn.append(JanusVQVAEAttnBlock(block_in))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = JanusVQVAEConvUpsample(block_in)
+ self.up.append(up)
+
+ # end
+ self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+ self.conv_out = torch.nn.Conv2d(block_in, out_channels, kernel_size=3, stride=1, padding=1)
+
+ def forward(self, hidden_state: torch.FloatTensor) -> torch.FloatTensor:
+ hidden_state = self.conv_in(hidden_state)
+
+ # middle
+ hidden_state = self.mid(hidden_state)
+
+ # upsampling
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks + 1):
+ hidden_state = self.up[i_level].block[i_block](hidden_state)
+ if len(self.up[i_level].attn) > 0:
+ hidden_state = self.up[i_level].attn[i_block](hidden_state)
+ if i_level != self.num_resolutions - 1:
+ hidden_state = self.up[i_level].upsample(hidden_state)
+
+ hidden_state = self.norm_out(hidden_state)
+ hidden_state *= torch.sigmoid(hidden_state)
+ hidden_state = self.conv_out(hidden_state)
+ return hidden_state
+
+
+class JanusVQVAE(ChameleonVQVAE):
+ _no_split_modules = [
+ "JanusVQVAEAttnBlock",
+ "JanusVQVAEResnetBlock",
+ "JanusVQVAEVectorQuantizer",
+ ]
+ main_input_name = "pixel_values"
+
+ def __init__(self, config: JanusVQVAEConfig):
+ super().__init__(config)
+ self.decoder = JanusVQVAEDecoder(config)
+ self.gradient_checkpointing = False
+
+ # Initialize the VQVAE model.
+ self.post_init()
+
+ def decode(self, image_tokens: torch.LongTensor) -> torch.FloatTensor:
+ """
+ Decodes quantized token IDs into pixel values.
+ Args:
+ image_tokens (torch.LongTensor): Batch of token IDs.
+ Returns:
+ pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+ Pixel values decoded from the token IDs.
+ """
+ if image_tokens.shape[1] != self.quantize.quant_state_dims[0] * self.quantize.quant_state_dims[1]:
+ raise ValueError(
+ f"Expected `image_tokens` to have shape `(batch_size, {self.quantize.quant_state_dims[0] * self.quantize.quant_state_dims[1]})`, "
+ f"but got shape `{image_tokens.shape}`."
+ )
+ codebook_entry = self.quantize.get_codebook_entry(image_tokens)
+ hidden_states = self.post_quant_conv(codebook_entry)
+ pixel_values = self.decoder(hidden_states)
+ return pixel_values
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: torch.FloatTensor,
+ ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
+ batch_size = pixel_values.shape[0]
+ quant, embedding_loss, indices = self.encode(pixel_values)
+ decoded_pixel_values = self.decode(indices.view(batch_size, -1))
+
+ return JanusVQVAEOutput(decoded_pixel_values, embedding_loss)
+
+
+class JanusVQVAEAlignerMLP(nn.Module):
+ def __init__(self, config: JanusVQVAEConfig):
+ super().__init__()
+
+ self.fc1 = nn.Linear(config.embed_dim, config.projection_dim)
+ self.hidden_layers = nn.ModuleList(
+ [nn.Linear(config.projection_dim, config.projection_dim) for _ in range(1, config.num_hidden_layers)]
+ )
+ self.activation_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_states):
+ hidden_states = self.fc1(hidden_states)
+ for layer in self.hidden_layers:
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = layer(hidden_states)
+ return hidden_states
+
+
+class JanusVQVAEHead(nn.Module):
+ """Head used for sampling tokens in image generation, replacing the usual lm head."""
+
+ def __init__(self, config: JanusVQVAEConfig):
+ super().__init__()
+ self.proj_out = nn.Linear(config.image_token_embed_dim, config.projection_dim)
+ self.activation_fn = ACT2FN[config.hidden_act]
+ self.vision_head = nn.Linear(config.projection_dim, config.num_embeddings)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.tensor:
+ hidden_states = self.proj_out(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.vision_head(hidden_states)
+ return hidden_states
+
+
+@auto_docstring(
+ custom_intro="""
+ The Janus model which consists of a siglip vision backbone, a Llama language model and a VQ model.
+ """
+)
+class JanusModel(JanusPreTrainedModel):
+ def __init__(self, config: JanusConfig):
+ super().__init__(config)
+ self.config = config
+ # This is necessary for backward compatibility, see SiglipModel initialization
+ self.vision_model = JanusVisionModel._from_config(config.vision_config)
+ self.aligner = JanusVisionAlignerMLP(self.vision_model.config)
+
+ self.vqmodel = JanusVQVAE._from_config(config.vq_config)
+
+ # Below generation_* modules are used for Image generation.
+ # Embeddings used for image generation, instead of Janus vision embeddings.
+ self.generation_embeddings = nn.Embedding(self.vqmodel.config.num_embeddings, self.vqmodel.config.embed_dim)
+ self.generation_aligner = JanusVQVAEAlignerMLP(self.vqmodel.config)
+ self.generation_head = JanusVQVAEHead(self.vqmodel.config)
+
+ self.language_model = AutoModel.from_config(config=config.text_config)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing.
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def get_image_features(self, pixel_values):
+ image_embeds = self.vision_model(pixel_values)
+ image_embeds = self.aligner(image_embeds.last_hidden_state)
+ return image_embeds
+
+ def get_placeholder_mask(
+ self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
+ ):
+ """
+ Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if inputs_embeds[special_image_mask].numel() != image_features.numel():
+ n_image_features = image_features.shape[0] * image_features.shape[1]
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+ )
+ return special_image_mask
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs,
+ ):
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError(
+ "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+ )
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ if pixel_values is not None:
+ image_embeds = self.get_image_features(pixel_values)
+ image_features = image_embeds.reshape(-1, inputs_embeds.shape[-1])
+ image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+ image_attention_mask = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(image_attention_mask, image_features)
+
+ lm_output = self.language_model(
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ return JanusBaseModelOutputWithPast(
+ last_hidden_state=lm_output.last_hidden_state,
+ past_key_values=lm_output.past_key_values,
+ hidden_states=lm_output.hidden_states,
+ attentions=lm_output.attentions,
+ image_hidden_states=image_embeds if pixel_values is not None else None,
+ )
+
+
+class JanusForConditionalGeneration(JanusPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["model.language_model.embed_tokens.weight", "lm_head.weight"]
+ _can_compile_fullgraph = True
+
+ def __init__(self, config: JanusConfig):
+ super().__init__(config)
+ self.config = config
+ self.model = JanusModel(config)
+ self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing.
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.language_model.set_input_embeddings(value)
+
+ def prepare_embeddings_for_image_generation(self, inputs: torch.Tensor) -> torch.Tensor:
+ hidden_state = self.model.generation_embeddings(inputs)
+ hidden_state = self.model.generation_aligner(hidden_state)
+ return hidden_state
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ):
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ """
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return JanusCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ pixel_values=None,
+ past_key_values=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- extra custom processing
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ if cache_position[0] == 0:
+ model_inputs["pixel_values"] = pixel_values
+
+ return model_inputs
+
+ def decode_image_tokens(self, image_tokens: torch.Tensor):
+ """
+ Decodes generated image tokens from language model to continuous pixel values
+ with VQGAN module via upsampling.
+ Args:
+ image_tokens (`torch.LongTensor` of shape `(batch_size, num_of_tokens)`):
+ The tensors corresponding to the input images.
+ """
+ decoded_image = self.model.vqmodel.decode(image_tokens)
+ decoded_image = decoded_image.permute(0, 2, 3, 1)
+ return decoded_image
+
+ @torch.no_grad
+ def generate(
+ self,
+ inputs: torch.Tensor = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ logits_processor: Optional[LogitsProcessorList] = None,
+ **kwargs,
+ ):
+ # 1. Handle generation config and model kwargs
+ generation_config = kwargs.pop("generation_config", self.generation_config)
+ generation_config = copy.deepcopy(generation_config)
+
+ # Default to "text" generation if mode isn't provided
+ generation_mode = kwargs.pop("generation_mode", "text")
+ if generation_mode == "text":
+ # Set guidance_scale=None to prevent running UnbatchedCFG processor.
+ return super().generate(
+ inputs=inputs,
+ attention_mask=attention_mask,
+ generation_config=generation_config,
+ guidance_scale=None,
+ **kwargs,
+ )
+
+ model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
+
+ # Validate generation mode
+ if generation_config.get_generation_mode() not in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH):
+ raise ValueError(
+ "Got incompatible mode for Image Generation, should be one of greedy or sampling. "
+ "Ensure that beam search is de-activated by setting `num_beams=1` and `num_beam_groups=1`."
+ )
+
+ # Validate the configuration and model kwargs
+ generation_config.validate()
+ self._validate_model_kwargs(model_kwargs.copy())
+
+ # 2. Initialize logit processors
+ logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+
+ # Set `use_cache=True` as we will be using input embeds for generation.
+ model_kwargs["use_cache"] = True
+
+ if generation_config.guidance_scale is None:
+ logger.warning("`guidance_scale` is required for CFG but not provided. Setting to default value of 5.")
+ generation_config.guidance_scale = 5
+ model_kwargs["guidance_scale"] = generation_config.guidance_scale
+
+ # 3. Prepare model inputs
+ input_ids, model_input_name, model_kwargs = self._prepare_model_inputs(
+ inputs, generation_config.bos_token_id, model_kwargs
+ )
+ dtype, device = input_ids.dtype, input_ids.device
+
+ if len(input_ids.shape) != 2:
+ raise ValueError(
+ f"Expected input ids of shape (batch_size, seq_len), but got {input_ids.shape}"
+ "Passing `inputs embeds` is not supported currently."
+ )
+
+ # Prepare special tokens which will be used generate internally.
+ kwargs_has_attention_mask = attention_mask is not None
+ self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=input_ids.device)
+
+ # 4. Add CFG processor along with user passed logit processor.
+ if generation_config.guidance_scale and generation_config.guidance_scale > 1:
+ logits_processor.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale))
+ generation_config.guidance_scale = None # Reset to prevent processor duplication.
+
+ # 5. Prepare logits processor
+ logits_processor = self._get_logits_processor(
+ generation_config=generation_config,
+ input_ids_seq_length=input_ids.shape[1],
+ encoder_input_ids=input_ids,
+ prefix_allowed_tokens_fn=None,
+ logits_processor=logits_processor,
+ device=device,
+ )
+
+ # 6. Expand inputs for multiple image generations per prompt.
+ input_ids, model_kwargs = self._expand_inputs_for_generation(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ expand_size=generation_config.num_return_sequences,
+ **model_kwargs,
+ )
+
+ # 7. Prepare input and model caches
+ num_image_tokens = self.model.vision_model.config.num_image_tokens
+ batch_size, seq_len = input_ids.shape
+
+ input_tokens = input_ids.repeat(2, 1) # Double batch size for conditional/unconditional logits
+ attention_mask = model_kwargs.pop("attention_mask", None)
+ attention_mask = attention_mask.repeat(2, 1)
+ model_kwargs["attention_mask"] = attention_mask
+
+ # Mask all the tokens that are neither BOS nor BOI with pad token in the unconditional logits.
+ mask = (input_tokens[batch_size:, :] != generation_config.bos_token_id) & (
+ input_tokens[batch_size:, :] != generation_config.generation_kwargs["boi_token_id"]
+ )
+ input_tokens[batch_size:, :].masked_fill_(mask, generation_config.pad_token_id)
+
+ inputs_embeds = self.get_input_embeddings()(input_tokens)
+
+ model_kwargs = self._get_initial_cache_position(seq_len, device, model_kwargs)
+
+ if model_kwargs.get("past_key_values", None) is None:
+ # Prepare cache if not provided.
+ model_kwargs["past_key_values"] = self._get_cache(
+ cache_implementation=generation_config.cache_implementation or "static",
+ # batch_size should account for both conditional/unconditional input; hence multiplied by 2.
+ batch_size=batch_size * 2,
+ # we should have at least a cache len of seq_len + num_image_tokens.
+ max_cache_len=max(generation_config.max_length, num_image_tokens + seq_len),
+ model_kwargs=model_kwargs,
+ )
+
+ # Placeholder for generated tokens.
+ generated_tokens = torch.zeros((batch_size, num_image_tokens), dtype=dtype, device=device)
+
+ # 8. init attention / hidden states / scores tuples
+ output_attentions = generation_config.output_attentions
+ output_hidden_states = generation_config.output_hidden_states
+ output_scores = generation_config.output_scores
+ output_logits = generation_config.output_logits
+ return_dict_in_generate = generation_config.return_dict_in_generate
+
+ raw_scores = () if (return_dict_in_generate and output_scores) else None
+ raw_logits = () if (return_dict_in_generate and output_logits) else None
+ decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+ decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+
+ for i in range(num_image_tokens):
+ model_inputs = self.prepare_inputs_for_generation(
+ inputs_embeds=inputs_embeds, input_ids=input_tokens, **model_kwargs
+ )
+
+ model_inputs["attention_mask"] = model_inputs["attention_mask"].to(inputs_embeds.device)
+ model_inputs["cache_position"] = model_inputs["cache_position"].to(inputs_embeds.device)
+
+ outputs = self.model.language_model(
+ **model_inputs,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ )
+
+ # Update model_kwargs like cache_position for next generation.
+ model_kwargs = self._update_model_kwargs_for_generation(outputs, model_kwargs)
+ hidden_state = outputs.last_hidden_state[:, -1, :].clone()
+
+ # Generate scores using the generation head (Not using above defined LM Head)
+ scores = self.model.generation_head(hidden_state)
+ next_token_scores = logits_processor(input_ids, scores)
+
+ # Sample next token.
+ if generation_config.do_sample:
+ probs = torch.softmax(next_token_scores, dim=-1)
+ next_token = torch.multinomial(probs, num_samples=1).squeeze(-1)
+ else:
+ next_token = torch.argmax(next_token_scores, dim=-1)
+
+ generated_tokens[:, i] = next_token
+
+ # Prepare embeddings for the next step.
+ next_token = torch.cat([next_token, next_token])
+ next_token = next_token.unsqueeze(-1)
+
+ inputs_embeds = self.prepare_embeddings_for_image_generation(next_token)
+
+ if return_dict_in_generate:
+ if output_scores:
+ raw_scores += (scores,)
+ if output_logits:
+ raw_logits += (hidden_state.float(),)
+ if output_attentions:
+ decoder_attentions += outputs.attentions
+ if output_hidden_states:
+ decoder_hidden_states += outputs.hidden_states
+
+ if return_dict_in_generate:
+ return GenerateDecoderOnlyOutput(
+ sequences=generated_tokens,
+ scores=scores,
+ logits=raw_logits,
+ attentions=decoder_attentions,
+ hidden_states=decoder_hidden_states,
+ past_key_values=outputs.past_key_values,
+ )
+ else:
+ return generated_tokens
+
+
+class JanusImageProcessor(BlipImageProcessor):
+ r"""
+ Constructs a JANUS image processor.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
+ `do_resize` parameter in the `preprocess` method.
+ size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`):
+ Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
+ method.
+ min_size (`int`, *optional*, defaults to 14):
+ The minimum allowed size for the resized image. Ensures that neither the height nor width
+ falls below this value after resizing.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+ Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
+ overridden by the `resample` parameter in the `preprocess` method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
+ `do_rescale` parameter in the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
+ overridden by the `rescale_factor` parameter in the `preprocess` method.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+ method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
+ overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
+ Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+ number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+ Can be overridden by the `image_std` parameter in the `preprocess` method.
+ do_convert_rgb (`bool`, *optional*, defaults to `True`):
+ Whether to convert the image to RGB.
+ """
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ min_size: int = 14,
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_convert_rgb: Optional[bool] = None,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.min_size = min_size
+ if image_mean is None:
+ self.background_color = (127, 127, 127)
+ else:
+ self.background_color = tuple(int(x * 255) for x in image_mean)
+
+ def pad_to_square(
+ self,
+ image: np.ndarray,
+ background_color: Union[int, tuple[int, int, int]] = 0,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.array:
+ """
+ Pads an image to a square based on the longest edge.
+
+ Args:
+ image (`np.ndarray`):
+ The image to pad.
+ background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+ The color to use for the padding. Can be an integer for single channel or a
+ tuple of integers representing for multi-channel images. If passed as integer
+ in mutli-channel mode, it will default to `0` in subsequent channels.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use same as the input image.
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+
+ Returns:
+ `np.ndarray`: The padded image.
+ """
+ height, width = get_image_size(image, input_data_format)
+ num_channels = image.shape[0] if input_data_format == ChannelDimension.FIRST else image.shape[-1]
+
+ if height == width:
+ image = (
+ to_channel_dimension_format(image, data_format, input_data_format)
+ if data_format is not None
+ else image
+ )
+ return image
+
+ max_dim = max(height, width)
+
+ # Ensure background_color is the correct shape
+ if isinstance(background_color, int):
+ background_color = [background_color]
+ elif len(background_color) != num_channels:
+ raise ValueError(
+ f"background_color must have no more than {num_channels} elements to match the number of channels"
+ )
+
+ if input_data_format == ChannelDimension.FIRST:
+ result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype)
+ for i, color in enumerate(background_color):
+ result[i, :, :] = color
+ if width > height:
+ start = (max_dim - height) // 2
+ result[:, start : start + height, :] = image
+ else:
+ start = (max_dim - width) // 2
+ result[:, :, start : start + width] = image
+ else:
+ result = np.zeros((max_dim, max_dim, num_channels), dtype=image.dtype)
+ for i, color in enumerate(background_color):
+ result[:, :, i] = color
+ if width > height:
+ start = (max_dim - height) // 2
+ result[start : start + height, :, :] = image
+ else:
+ start = (max_dim - width) // 2
+ result[:, start : start + width, :] = image
+
+ return result
+
+ def resize(
+ self,
+ image: np.ndarray,
+ size: Union[dict[str, int], int],
+ background_color: Optional[tuple[int, int, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image to dynamically calculated size.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]` or `int`):
+ The size to resize the image to. If a dictionary, it should have the keys `"height"` and `"width"`.
+ background_color (`tuple[int, int, int]`):
+ The background color to use for the padding.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
+ data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the output image. If unset, the channel dimension format of the input
+ image is used. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `None`: will be inferred from input
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+
+ Returns:
+ `np.ndarray`: The resized image.
+ """
+ background_color = background_color if background_color is not None else self.background_color
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(image)
+
+ height, width = get_image_size(image, input_data_format)
+ max_size = max(height, width)
+
+ size = get_size_dict(size, default_to_square=True)
+ if size["height"] != size["width"]:
+ raise ValueError(
+ f"Output height and width must be the same. Got height={size['height']} and width={size['width']}"
+ )
+ size = size["height"]
+
+ delta = size / max_size
+ # Largest side becomes `size` and the other side is scaled according to the aspect ratio.
+ output_size_nonpadded = [
+ max(int(height * delta), self.min_size),
+ max(int(width * delta), self.min_size),
+ ]
+
+ image = resize(
+ image,
+ size=output_size_nonpadded,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+ # Expand and pad the images to obtain a square image of dimensions `size x size`
+ image = self.pad_to_square(
+ image=image,
+ background_color=background_color,
+ input_data_format=input_data_format,
+ )
+ return image
+
+ def postprocess(
+ self,
+ images: ImageInput,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[list[float]] = None,
+ image_std: Optional[list[float]] = None,
+ input_data_format: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ ):
+ """Applies post-processing to the decoded image tokens by reversing transformations applied during preprocessing."""
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = 1.0 / self.rescale_factor if rescale_factor is None else rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+
+ images = make_list_of_images(images) # Ensures input is a list
+
+ if isinstance(images[0], PIL.Image.Image):
+ return images if len(images) > 1 else images[0]
+
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(images[0]) # Determine format dynamically
+
+ pixel_values = []
+
+ for image in images:
+ image = to_numpy_array(image) # Ensure NumPy format
+
+ if do_normalize:
+ image = self.unnormalize(
+ image=image, image_mean=image_mean, image_std=image_std, input_data_format=input_data_format
+ )
+
+ if do_rescale:
+ image = self.rescale(image, scale=rescale_factor, input_data_format=input_data_format)
+ image = image.clip(0, 255).astype(np.uint8)
+
+ if do_normalize and do_rescale and return_tensors == "PIL.Image.Image":
+ image = to_channel_dimension_format(image, ChannelDimension.LAST, input_channel_dim=input_data_format)
+ image = PIL.Image.fromarray(image)
+
+ pixel_values.append(image)
+
+ data = {"pixel_values": pixel_values}
+ return_tensors = return_tensors if return_tensors != "PIL.Image.Image" else None
+
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+ def unnormalize(
+ self,
+ image: np.array,
+ image_mean: Union[float, Iterable[float]],
+ image_std: Union[float, Iterable[float]],
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.array:
+ """
+ Unnormalizes `image` using the mean and standard deviation specified by `mean` and `std`.
+ image = (image * image_std) + image_mean
+ Args:
+ image (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)` or `(num_channels, image_size, image_size)`):
+ Batch of pixel values to postprocess.
+ image_mean (`float` or `Iterable[float]`):
+ The mean to use for unnormalization.
+ image_std (`float` or `Iterable[float]`):
+ The standard deviation to use for unnormalization.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ num_channels = 3
+
+ if isinstance(image_mean, Iterable):
+ if len(image_mean) != num_channels:
+ raise ValueError(f"mean must have {num_channels} elements if it is an iterable, got {len(image_mean)}")
+ else:
+ image_mean = [image_mean] * num_channels
+
+ if isinstance(image_std, Iterable):
+ if len(image_std) != num_channels:
+ raise ValueError(f"std must have {num_channels} elements if it is an iterable, got {len(image_std)}")
+ else:
+ image_std = [image_std] * num_channels
+
+ rev_image_mean = tuple(-mean / std for mean, std in zip(image_mean, image_std))
+ rev_image_std = tuple(1 / std for std in image_std)
+ image = self.normalize(
+ image=image, mean=rev_image_mean, std=rev_image_std, input_data_format=input_data_format
+ )
+ return image
+
+
+__all__ = [
+ "JanusImageProcessor",
+ "JanusPreTrainedModel",
+ "JanusForConditionalGeneration",
+ "JanusModel",
+ "JanusVQVAE",
+ "JanusVisionModel",
+ "JanusVQVAEConfig",
+ "JanusVisionConfig",
+ "JanusConfig",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/processing_janus.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/processing_janus.py
new file mode 100644
index 0000000000000000000000000000000000000000..3db5637006e5764d1b16ed8cae61fe7fbaf1fb77
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/janus/processing_janus.py
@@ -0,0 +1,184 @@
+# coding=utf-8
+# Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for Janus.
+"""
+
+from typing import Union
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_utils import ImageInput
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+DEFAULT_SYSTEM_PROMPT = (
+ "You are a helpful language and vision assistant. "
+ "You are able to understand the visual content that the user provides, "
+ "and assist the user with a variety of tasks using natural language.\n\n"
+)
+
+
+class JanusTextKwargs(TextKwargs, total=False):
+ generation_mode: str
+
+
+class JanusProcessorKwargs(ProcessingKwargs, total=False):
+ text_kwargs: JanusTextKwargs
+ _defaults = {
+ "text_kwargs": {"padding": False, "generation_mode": "text"},
+ "common_kwargs": {"return_tensors": "pt"},
+ }
+
+
+class JanusProcessor(ProcessorMixin):
+ r"""
+ Constructs a Janus processor which wraps a Janus Image Processor and a Llama tokenizer into a single processor.
+
+ [`JanusProcessor`] offers all the functionalities of [`JanusImageProcessor`] and [`LlamaTokenizerFast`]. See the
+ [`~JanusProcessor.__call__`] and [`~JanusProcessor.decode`] for more information.
+
+ Args:
+ image_processor ([`JanusImageProcessor`]):
+ The image processor is a required input.
+ tokenizer ([`LlamaTokenizerFast`]):
+ The tokenizer is a required input.
+ chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+ in a chat into a tokenizable string.
+ use_default_system_prompt (`str`, *optional*, defaults to `False`):
+ Use default system prompt for Text Generation.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "JanusImageProcessor"
+ tokenizer_class = "LlamaTokenizerFast"
+
+ def __init__(self, image_processor, tokenizer, chat_template=None, use_default_system_prompt=False, **kwargs):
+ self.num_image_tokens = 576
+ self.image_token = tokenizer.image_token
+ self.image_start_token = tokenizer.boi_token
+ self.image_end_token = tokenizer.eoi_token
+ self.use_default_system_prompt = use_default_system_prompt
+
+ super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+ def __call__(
+ self,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+ images: ImageInput = None,
+ videos=None,
+ audio=None,
+ **kwargs: Unpack[JanusProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+ and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
+ the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+ JanusImageProcessor's [`~JanusImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
+ of the above two methods for more information.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors of a particular framework. Acceptable values are:
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return NumPy `np.ndarray` objects.
+ - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+ `None`).
+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+ """
+
+ output_kwargs = self._merge_kwargs(
+ JanusProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs
+ )
+
+ if text is None and images is None:
+ raise ValueError("You must specify either text or images.")
+
+ if text is not None:
+ if isinstance(text, str):
+ text = [text]
+ elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
+ raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+
+ generation_mode = output_kwargs["text_kwargs"].pop("generation_mode")
+
+ # Replace the image token with expanded image tokens.
+ prompt_strings = []
+ one_img_tokens = self.image_start_token + (self.image_token * self.num_image_tokens) + self.image_end_token
+ for prompt in text:
+ prompt = prompt.replace(self.image_token, one_img_tokens)
+ if self.use_default_system_prompt and generation_mode == "text":
+ prompt = DEFAULT_SYSTEM_PROMPT + prompt
+ if generation_mode == "image":
+ prompt += self.image_start_token
+ prompt_strings.append(prompt)
+
+ data = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
+
+ # Process images if pixel values are provided.
+ if images is not None and generation_mode != "image":
+ data["pixel_values"] = self.image_processor(images=images, **output_kwargs["images_kwargs"])[
+ "pixel_values"
+ ]
+
+ return BatchFeature(data=data)
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ def postprocess(self, images: ImageInput, **kwargs):
+ """
+ Forwards all arguments to the image processor's `postprocess` method.
+ Refer to the original method's docstring for more details.
+ """
+ return self.image_processor.postprocess(images, **kwargs)
+
+ @property
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+__all__ = ["JanusProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/jetmoe/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/jetmoe/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7058590acc8859f934330ced9556d6ca66b50a51
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/jetmoe/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_jetmoe import *
+ from .modeling_jetmoe import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/jetmoe/configuration_jetmoe.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/jetmoe/configuration_jetmoe.py
new file mode 100644
index 0000000000000000000000000000000000000000..5846ea369da7adf7f1a96410ca05808583cc478b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/jetmoe/configuration_jetmoe.py
@@ -0,0 +1,152 @@
+# coding=utf-8
+# Copyright 2024 JetMoe AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""JetMoe model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class JetMoeConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`JetMoeModel`]. It is used to instantiate a
+ JetMoe model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a configuration of the JetMoe-4B.
+
+ [jetmoe/jetmoe-8b](https://huggingface.co/jetmoe/jetmoe-8b)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the JetMoe model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`JetMoeModel`]
+ hidden_size (`int`, *optional*, defaults to 2048):
+ Dimension of the hidden representations.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_key_value_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each key and value in the Transformer encoder.
+ kv_channels (`int`, *optional*, defaults to 128):
+ Defines the number of channels for the key and value tensors.
+ intermediate_size (`int`, *optional*, defaults to 5632):
+ Dimension of the MLP representations.
+ max_position_embeddings (`int`, *optional*, defaults to 4096):
+ The maximum sequence length that this model might ever be used with. JetMoe's attention allows sequence of
+ up to 4096 tokens.
+ activation_function (`string`, *optional*, defaults to `"silu"`):
+ Defines the activation function for MLP experts.
+ num_local_experts (`int`, *optional*, defaults to 8):
+ Defines the number of experts in the MoE and MoA.
+ num_experts_per_tok (`int, *optional*, defaults to 2):
+ The number of experts to route per-token and for MoE and MoA.
+ output_router_logits (`bool`, *optional*, defaults to `False`):
+ Whether or not the router logits should be returned by the model. Enabling this will also
+ allow the model to output the auxiliary loss.
+ aux_loss_coef (`float`, *optional*, defaults to 0.01):
+ The coefficient for the auxiliary loss.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ The id of the "beginning-of-sequence" token.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ The id of the "end-of-sequence" token.
+ tie_word_embeddings (`bool`, *optional*, defaults to `True`):
+ Whether the model's input and output word embeddings should be tied.
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms normalization layers.
+ initializer_range (`float`, *optional*, defaults to 0.01):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+
+ ```python
+ >>> from transformers import JetMoeModel, JetMoeConfig
+
+ >>> # Initializing a JetMoe 4B style configuration
+ >>> configuration = JetMoeConfig()
+
+ >>> # Initializing a model from the JetMoe 4B style configuration
+ >>> model = JetMoeModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "jetmoe"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=32000,
+ hidden_size=2048,
+ num_hidden_layers=12,
+ num_key_value_heads=16,
+ kv_channels=128,
+ intermediate_size=5632,
+ max_position_embeddings=4096,
+ activation_function="silu",
+ num_local_experts=8,
+ num_experts_per_tok=2,
+ output_router_logits=False,
+ aux_loss_coef=0.01,
+ use_cache=True,
+ bos_token_id=1,
+ eos_token_id=2,
+ tie_word_embeddings=True,
+ rope_theta=10000.0,
+ rms_norm_eps=1e-6,
+ initializer_range=0.01,
+ attention_dropout=0.0,
+ **kwargs,
+ ):
+ if num_experts_per_tok > num_local_experts:
+ raise ValueError("`num_experts_per_tok` must be less than or equal to `num_local_experts`")
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_key_value_heads * num_experts_per_tok
+ self.num_key_value_heads = num_key_value_heads
+ self.kv_channels = kv_channels
+ self.intermediate_size = intermediate_size
+ self.max_position_embeddings = max_position_embeddings
+ self.activation_function = activation_function
+ self.num_local_experts = num_local_experts
+ self.num_experts_per_tok = num_experts_per_tok
+ self.output_router_logits = output_router_logits
+ self.aux_loss_coef = aux_loss_coef
+ self.use_cache = use_cache
+ self.initializer_range = initializer_range
+ self.attention_dropout = attention_dropout
+
+ self.bos_token_id = bos_token_id
+ self.eos_token_id = eos_token_id
+
+ self.rope_theta = rope_theta
+ self.rms_norm_eps = rms_norm_eps
+
+ super().__init__(
+ bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
+ )
+
+
+__all__ = ["JetMoeConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/jetmoe/modeling_jetmoe.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/jetmoe/modeling_jetmoe.py
new file mode 100644
index 0000000000000000000000000000000000000000..615aacdf873718cfcb0cb77699924bd2f799cc9f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/jetmoe/modeling_jetmoe.py
@@ -0,0 +1,1225 @@
+# coding=utf-8
+# Copyright 2024 JetMoe AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch JetMoe model."""
+
+import math
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import functional as F
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import AttentionMaskConverter
+from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
+from ...modeling_layers import (
+ GenericForSequenceClassification,
+ GradientCheckpointingLayer,
+)
+from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_jetmoe import JetMoeConfig
+
+
+if is_torch_flex_attn_available():
+ from torch.nn.attention.flex_attention import BlockMask
+
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+
+if is_flash_attn_available():
+ from ...modeling_flash_attention_utils import _flash_attention_forward
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.qwen2_moe.modeling_qwen2_moe.load_balancing_loss_func
+def load_balancing_loss_func(
+ gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
+ num_experts: Optional[int] = None,
+ top_k=2,
+ attention_mask: Optional[torch.Tensor] = None,
+) -> Union[torch.Tensor, int]:
+ r"""
+ Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+ See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
+ function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+ experts is too unbalanced.
+
+ Args:
+ gate_logits:
+ Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+ shape [batch_size X sequence_length, num_experts].
+ num_experts:
+ Number of experts
+ top_k:
+ The number of experts to route per-token, can be also interpreted as the `top-k` routing
+ parameter.
+ attention_mask (`torch.Tensor`, *optional*):
+ The attention_mask used in forward function
+ shape [batch_size X sequence_length] if not None.
+
+ Returns:
+ The auxiliary loss.
+ """
+ if gate_logits is None or not isinstance(gate_logits, tuple):
+ return 0
+
+ if isinstance(gate_logits, tuple):
+ compute_device = gate_logits[0].device
+ concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+ routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+ _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+ if attention_mask is None:
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.mean(routing_weights, dim=0)
+ else:
+ batch_size, sequence_length = attention_mask.shape
+ num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+ expert_attention_mask = (
+ attention_mask[None, :, :, None, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+ .reshape(-1, top_k, num_experts)
+ .to(compute_device)
+ )
+
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+ expert_attention_mask, dim=0
+ )
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+ router_per_expert_attention_mask = (
+ attention_mask[None, :, :, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, routing_weights.shape[1]))
+ .reshape(-1, routing_weights.shape[1])
+ .to(compute_device)
+ )
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+ router_per_expert_attention_mask, dim=0
+ )
+
+ device_index = routing_weights.device.index if routing_weights.device.index is not None else 0
+ rank = routing_weights.shape[1] * int(device_index)
+ overall_loss = torch.sum(
+ tokens_per_expert[:, rank : rank + routing_weights.shape[1]] * router_prob_per_expert.unsqueeze(0)
+ )
+ return overall_loss * num_experts
+
+
+class JetMoeParallelExperts(nn.Module):
+ def __init__(self, num_experts: int, input_size: int, output_size: int) -> None:
+ """
+ Initialize the JetMoeParallelExperts module.
+ The experts weights are stored in [num_experts, output_size, input_size] format. Such that it's compatible with
+ many MoE libraries, such as [Megablock](https://github.com/databricks/megablocks) and
+ [ScatterMoE](https://github.com/shawntan/scattermoe), as well as the
+ [MoE kernel](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/fused_moe.py)
+ used in vllm.
+
+ Args:
+ num_experts (int):
+ Number of experts.
+ input_size (int):
+ Size of the input.
+ output_size (int):
+ Size of the output.
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.empty(num_experts, output_size, input_size))
+ self.num_experts = num_experts
+ self.input_size = input_size
+ self.output_size = output_size
+
+ def forward(self, inputs, expert_size):
+ """
+ Forward pass of the JetMoeParallelExperts module.
+
+ Args:
+ inputs (Tensor):
+ Input tensor.
+ expert_size:
+ Expert size information.
+
+ Returns:
+ Tensor: Output tensor.
+ """
+ input_list = inputs.split(expert_size, dim=0)
+ output_list = []
+ for i in range(self.num_experts):
+ output_list.append(F.linear(input_list[i], self.weight[i]))
+ results = torch.cat(output_list, dim=0)
+ return results
+
+
+class JetMoeTopKGating(nn.Module):
+ def __init__(self, input_size: int, num_experts: int, top_k: int):
+ """
+ Initialize the top-k gating mechanism.
+
+ Args:
+ input_size (`int`):
+ Size of the input.
+ num_experts (`int`):
+ Number of experts.
+ top_k (`int`):
+ Number of top experts to select.
+ """
+ super().__init__()
+
+ self.num_experts = num_experts
+ self.input_size = input_size
+ self.top_k = top_k
+
+ self.layer = nn.Linear(input_size, num_experts, bias=False)
+
+ def forward(self, hidden_states):
+ # compute the top_k routing decision
+ logits = self.layer(hidden_states).float() # [batch_size x seq_len, num_experts]
+ top_k_logits, top_k_indices = logits.topk(self.top_k, dim=1) # [num_tokens, top_k]
+ top_k_gates = torch.softmax(top_k_logits, dim=1).type_as(hidden_states) # [num_tokens, top_k]
+
+ # compute number of input given to each expert
+ zeros = torch.zeros(
+ [top_k_gates.size(0), self.num_experts], dtype=top_k_gates.dtype, device=top_k_gates.device
+ ) # [num_tokens, num_experts]
+ gates = zeros.scatter(1, top_k_indices, 1) # [num_tokens, num_experts]
+ expert_size = gates.long().sum(0) # [num_experts,]
+ # (This cause torch.compile to fail with `torch._dynamo.exc.Unsupported: Backend compiler failed with a fake tensor exception at`)
+ # (and `DataDependentOutputException`)
+ expert_size = expert_size.tolist()
+
+ # sort and group input tokens according to expert assignment
+ top_k_experts = top_k_indices.flatten() # [num_tokens * top_k]
+ _, index_sorted_experts = top_k_experts.sort(0) # [num_tokens * top_k]
+ batch_index = index_sorted_experts.div(self.top_k, rounding_mode="trunc") # [num_tokens * top_k]
+
+ # gather the gate values for grouped input tokens
+ top_k_gates = top_k_gates.flatten() # [num_tokens * top_k]
+ batch_gates = top_k_gates[index_sorted_experts] # [num_tokens * top_k]
+
+ return index_sorted_experts, batch_index, batch_gates, expert_size, logits
+
+
+class JetMoeMoE(nn.Module):
+ """
+ A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
+
+ Args:
+ config:
+ Configuration object with model hyperparameters.
+ """
+
+ def __init__(self, config: JetMoeConfig):
+ super().__init__()
+
+ self.input_size = config.hidden_size
+ self.hidden_size = config.intermediate_size
+ self.activation = ACT2FN[config.activation_function]
+ self.bias = torch.nn.Parameter(torch.empty(self.input_size))
+ self.input_linear = JetMoeParallelExperts(config.num_local_experts, self.input_size, self.hidden_size * 2)
+ self.output_linear = JetMoeParallelExperts(config.num_local_experts, self.hidden_size, self.input_size)
+
+ self.router = JetMoeTopKGating(
+ input_size=self.input_size,
+ num_experts=config.num_local_experts,
+ top_k=config.num_experts_per_tok,
+ )
+
+ def forward(self, layer_input):
+ """
+ Forward pass of the mixture of experts layer.
+
+ Args:
+ layer_input (Tensor):
+ Input tensor.
+
+ Returns:
+ Tensor:
+ Output tensor.
+ Tensor:
+ Router logits.
+ """
+ bsz, length, emb_size = layer_input.size()
+ layer_input = layer_input.reshape(-1, emb_size)
+ _, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
+
+ expert_inputs = layer_input[batch_index]
+ hidden_states = self.input_linear(expert_inputs, expert_size)
+ chunked_hidden_states = hidden_states.chunk(2, dim=-1)
+ hidden_states = self.activation(chunked_hidden_states[0]) * chunked_hidden_states[1]
+ expert_outputs = self.output_linear(hidden_states, expert_size)
+
+ expert_outputs = expert_outputs * batch_gates[:, None]
+
+ zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
+ layer_output = zeros.index_add(0, batch_index, expert_outputs)
+ layer_output = layer_output.view(bsz, length, self.input_size)
+ layer_output = layer_output + self.bias
+ return layer_output, router_logits
+
+
+class JetMoeMoA(nn.Module):
+ """
+ A Sparsely gated mixture of attention layer with pairs of query- and output-projections as experts.
+
+ Args:
+ config:
+ Configuration object with model hyperparameters.
+ """
+
+ def __init__(self, config: JetMoeConfig):
+ super().__init__()
+
+ self.num_experts = config.num_local_experts
+ self.input_size = config.hidden_size
+ self.hidden_size = config.kv_channels * config.num_key_value_heads
+ self.top_k = config.num_experts_per_tok
+ self.bias = torch.nn.Parameter(torch.empty(self.input_size))
+
+ self.input_linear = JetMoeParallelExperts(self.num_experts, self.input_size, self.hidden_size)
+ self.output_linear = JetMoeParallelExperts(self.num_experts, self.hidden_size, self.input_size)
+
+ self.router = JetMoeTopKGating(
+ input_size=self.input_size,
+ num_experts=self.num_experts,
+ top_k=self.top_k,
+ )
+
+ def map(self, layer_input):
+ """
+ Map inputs to attention experts according to routing decision and compute query projection inside each experts.
+ """
+
+ # Compute gating topology
+ bsz, length, emb_size = layer_input.size()
+ layer_input = layer_input.reshape(-1, emb_size) # [bsz * length, emb_size]
+ index_sorted_experts, batch_index, batch_gates, expert_size, router_logits = self.router(layer_input)
+ topo_info = (index_sorted_experts, batch_index, batch_gates, expert_size)
+
+ # Group inputs according to topology and compute query projection
+ expert_inputs = layer_input[batch_index] # [bsz * length * top_k, emb_size]
+ expert_outputs = self.input_linear(expert_inputs, expert_size) # [bsz * length * top_k, hidden_size]
+
+ # Ungroup queries back to original order
+ zeros = torch.zeros(
+ (bsz * length * self.top_k, self.hidden_size), dtype=expert_outputs.dtype, device=expert_outputs.device
+ )
+ layer_output = zeros.index_add(0, index_sorted_experts, expert_outputs)
+ layer_output = layer_output.view(bsz, length, self.top_k, -1) # [bsz, length, top_k, hidden_size]
+ return layer_output, router_logits, topo_info
+
+ def reduce(self, layer_input, topo_info):
+ """
+ Compute output projection inside each attention experts and merge the outputs of different experts.
+ """
+ bsz, length, k, hidden_size = layer_input.size()
+ layer_input = layer_input.reshape(-1, hidden_size) # [bsz * length * k, hidden_size]
+ index_sorted_experts, batch_index, batch_gates, expert_size = topo_info
+
+ # Group inputs according to topology and compute output projection
+ expert_inputs = layer_input[index_sorted_experts] # [bsz * length * top_k, hidden_size]
+ expert_outputs = self.output_linear(expert_inputs, expert_size) # [bsz * length * top_k, emb_size]
+
+ # Apply gates to attention expert outputs
+ expert_outputs = expert_outputs * batch_gates[:, None]
+
+ # Ungroup and merge outputs to original order
+ zeros = torch.zeros((bsz * length, self.input_size), dtype=expert_outputs.dtype, device=expert_outputs.device)
+ layer_output = zeros.index_add(0, batch_index, expert_outputs)
+ layer_output = layer_output.view(bsz, length, self.input_size)
+ layer_output = layer_output + self.bias
+ return layer_output
+
+ def forward(self, layer_input):
+ raise NotImplementedError("This module doesn't support call and forward.")
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->JetMoe
+class JetMoeRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ JetMoeRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+# Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with Gemma->JetMoe
+class JetMoeRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: JetMoeConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class JetMoeAttention(nn.Module):
+ """
+ Multi-headed attention from 'Attention Is All You Need' paper.
+ """
+
+ def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
+ """
+ Initialize the JetMoeAttention module.
+
+ Args:
+ config:
+ Configuration object with model hyperparameters.
+ layer_idx:
+ Index of the layer in the model.
+ """
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.is_causal = True
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.top_k = config.num_experts_per_tok
+ self.attention_dropout = config.attention_dropout
+ self.kv_projection_size = config.kv_channels * config.num_key_value_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_heads = config.num_attention_heads
+ self.head_dim = config.kv_channels
+
+ self.experts = JetMoeMoA(config)
+
+ self.kv_proj = torch.nn.Linear(config.hidden_size, self.kv_projection_size * 2, bias=False)
+
+ self.rotary_emb = JetMoeRotaryEmbedding(config)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states, router_logits, topo_info = self.experts.map(hidden_states)
+ key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ cos, sin = self.rotary_emb(value_states, position_ids)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # repeat k/v heads for top-k attention experts
+ key_states = key_states.repeat(1, self.top_k, 1, 1)
+ value_states = value_states.repeat(1, self.top_k, 1, 1)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+ if attention_mask is not None: # no matter the length, we just slice it
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
+
+ attn_output = self.experts.reduce(attn_output, topo_info)
+ attn_output = attn_output.view(bsz, q_len, -1)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, router_logits
+
+
+class JetMoeSdpaAttention(JetMoeAttention):
+ """
+ JetMoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `JetMoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+ SDPA API.
+ """
+
+ # Adapted from JetMoeAttention.forward
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]], Optional[torch.Tensor]]:
+ if output_attentions:
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+ logger.warning_once(
+ "JetMoeModel is using JetMoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ return super().forward(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states, router_logits, topo_info = self.experts.map(hidden_states)
+ key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ cos, sin = self.rotary_emb(value_states, position_ids)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # repeat k/v heads for top-k attention experts
+ key_states = key_states.repeat(1, self.top_k, 1, 1)
+ value_states = value_states.repeat(1, self.top_k, 1, 1)
+
+ causal_mask = attention_mask
+ if attention_mask is not None:
+ causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ if query_states.device.type == "cuda" and causal_mask is not None:
+ query_states = query_states.contiguous()
+ key_states = key_states.contiguous()
+ value_states = value_states.contiguous()
+
+ # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+ # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+ is_causal = causal_mask is None and q_len > 1
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=causal_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ is_causal=is_causal,
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
+
+ attn_output = self.experts.reduce(attn_output, topo_info)
+ attn_output = attn_output.view(bsz, q_len, -1)
+
+ return attn_output, None, router_logits
+
+
+class JetMoeFlashAttention2(JetMoeAttention):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+ self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: Optional[torch.FloatTensor],
+ attention_mask: Optional[torch.FloatTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ output_attentions: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[
+ tuple[torch.Tensor, tuple[torch.Tensor]],
+ Optional[tuple[torch.Tensor, tuple[torch.Tensor], tuple[torch.Tensor, ...]]],
+ ]:
+ """
+ Forward pass of the JetMoeAttention module.
+
+ Args:
+ hidden_states (Optional[torch.FloatTensor]): Input hidden states.
+ attention_mask (Optional[torch.FloatTensor]): Attention mask.
+ layer_past (Optional[tuple[torch.Tensor]]): Past layer state.
+ use_cache (Optional[bool]): Whether to use cached states.
+ output_attentions (Optional[bool]): Whether to output attention weights.
+ cache_position (Optional[torch.LongTensor]): Position of the cache.
+
+ Returns:
+ Union[tuple[torch.Tensor, tuple[torch.Tensor]], Optional[tuple[...]]]: Tuple containing outputs.
+ """
+ output_attentions = False
+ bsz, q_len, hidden_size = hidden_states.size()
+
+ # calculate query, key, values
+ query_states, router_logits, topo_info = self.experts.map(hidden_states)
+ key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ cos, sin = self.rotary_emb(value_states, position_ids)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # repeat k/v heads for top-k attention experts
+ key_states = key_states.repeat(1, self.top_k, 1, 1)
+ value_states = value_states.repeat(1, self.top_k, 1, 1)
+
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+ # to be able to avoid many of these transpose/reshape/view.
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ dropout_rate = self.attention_dropout if self.training else 0.0
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in the correct dtype just to be sure everything works as expected.
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+ # in fp32. (LlamaRMSNorm handles it correctly)
+
+ input_dtype = query_states.dtype
+ device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
+ if input_dtype == torch.float32:
+ if torch.is_autocast_enabled():
+ target_dtype = (
+ torch.get_autocast_dtype(device_type)
+ if hasattr(torch, "get_autocast_dtype")
+ else torch.get_autocast_gpu_dtype()
+ )
+ # Handle the case where the model is quantized
+ elif hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ else:
+ target_dtype = self.kv_proj.weight.dtype
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ attn_output = _flash_attention_forward(
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ q_len,
+ dropout=dropout_rate,
+ use_top_left_mask=self._flash_attn_uses_top_left_mask,
+ is_causal=self.is_causal,
+ ).to(input_dtype)
+
+ # output projection
+ attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)
+ attn_output = self.experts.reduce(attn_output, topo_info)
+ attn_output = attn_output.view(bsz, q_len, hidden_size) # re-assemble all head outputs side by side
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, router_logits
+
+
+JETMOE_ATTENTION_CLASSES = {
+ "eager": JetMoeAttention,
+ "flash_attention_2": JetMoeFlashAttention2,
+ "sdpa": JetMoeSdpaAttention,
+}
+
+
+class JetMoeBlock(GradientCheckpointingLayer):
+ def __init__(self, config: JetMoeConfig, layer_idx: Optional[int] = None):
+ """
+ Initialize the JetMoeBlock module.
+
+ Args:
+ config:
+ Configuration object with model hyperparameters.
+ """
+ super().__init__()
+ self.input_layernorm = JetMoeRMSNorm(config.hidden_size)
+ self.self_attention = JETMOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+ self.post_attention_layernorm = JetMoeRMSNorm(config.hidden_size)
+
+ self.mlp = JetMoeMoE(config)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: Optional[torch.FloatTensor],
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[torch.Tensor]] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ output_router_logits: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple[torch.Tensor], Optional[tuple[torch.Tensor, tuple[torch.FloatTensor, ...]]]]:
+ # Self Attention
+ attn_output, self_attn_weights, attn_router_logits = self.self_attention(
+ hidden_states=self.input_layernorm(hidden_states),
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+
+ hidden_states = hidden_states + attn_output
+ x_mlp, mlp_router_logits = self.mlp(self.post_attention_layernorm(hidden_states))
+ hidden_states = hidden_states + x_mlp
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if output_router_logits:
+ outputs += attn_router_logits, mlp_router_logits
+
+ return outputs
+
+
+@auto_docstring
+class JetMoePreTrainedModel(PreTrainedModel):
+ config: JetMoeConfig
+ base_model_prefix = "transformer"
+ supports_gradient_checkpointing = False
+ _no_split_modules = ["JetMoeBlock"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ def _init_weights(self, module):
+ """Initialize the weights."""
+ if isinstance(module, (nn.Linear,)):
+ # Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, JetMoeRMSNorm):
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, JetMoeParallelExperts):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ elif isinstance(module, (JetMoeMoA, JetMoeMoE)):
+ module.bias.data.zero_()
+
+
+@auto_docstring
+class JetMoeModel(JetMoePreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`JetMoeBlock`]
+
+ Args:
+ config:
+ JetMoeConfig
+ """
+
+ def __init__(self, config: JetMoeConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList([JetMoeBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
+ self._attn_implementation = config._attn_implementation
+ self.norm = JetMoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> MoeModelOutputWithPast:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ output_router_logits = (
+ output_router_logits if output_router_logits is not None else self.config.output_router_logits
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+ )
+ use_cache = False
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
+ if not isinstance(past_key_values, (type(None), Cache)):
+ raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+ batch_size = inputs_embeds.shape[0]
+ is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+ if is_padding_right:
+ raise ValueError(
+ "You are attempting to perform batched generation with padding_side='right'"
+ " this may lead to unexpected behaviour for Flash Attention version of JetMoe. Make sure to "
+ " call `tokenizer.padding_side = 'left'` before tokenizing the input. "
+ )
+ causal_mask = self._update_causal_mask(
+ attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+ )
+
+ hidden_states = inputs_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_router_logits = () if output_router_logits else None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ output_router_logits=output_router_logits,
+ use_cache=use_cache,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if output_router_logits:
+ all_router_logits += (layer_outputs[-2], layer_outputs[-1])
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ return MoeModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ router_logits=all_router_logits,
+ )
+
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, "BlockMask"],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ output_attentions: bool = False,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and (attention_mask == 0.0).any():
+ return attention_mask
+ return None
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ return attention_mask
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ sequence_length = input_tensor.shape[1]
+ if using_compilable_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ and not output_attentions
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+
+class JetMoeForCausalLM(JetMoePreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = JetMoeModel(config)
+ self.vocab_size = config.vocab_size
+ self.aux_loss_coef = config.aux_loss_coef
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ self.tie_word_embeddings = config.tie_word_embeddings
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs,
+ ) -> MoeCausalLMOutputWithPast:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ """
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs: MoeModelOutputWithPast = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ cache_position=cache_position,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits,
+ labels,
+ vocab_size=self.config.vocab_size,
+ **kwargs,
+ )
+
+ aux_loss = None
+ if output_router_logits:
+ aux_loss = load_balancing_loss_func(
+ outputs.router_logits,
+ self.num_experts,
+ self.num_experts_per_tok,
+ attention_mask,
+ )
+ if labels is not None:
+ loss += self.aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
+
+ return MoeCausalLMOutputWithPast(
+ loss=loss,
+ aux_loss=aux_loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ router_logits=outputs.router_logits,
+ )
+
+
+class JetMoeForSequenceClassification(GenericForSequenceClassification, JetMoePreTrainedModel): ...
+
+
+__all__ = ["JetMoeForCausalLM", "JetMoeModel", "JetMoePreTrainedModel", "JetMoeForSequenceClassification"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..51dbdaf40a1050b8ff624d86ed58cd07fcd9174f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_kosmos2 import *
+ from .modeling_kosmos2 import *
+ from .processing_kosmos2 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/configuration_kosmos2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/configuration_kosmos2.py
new file mode 100644
index 0000000000000000000000000000000000000000..56b26eb171780f5a2f1a022ad991f726773e8bb7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/configuration_kosmos2.py
@@ -0,0 +1,264 @@
+# coding=utf-8
+# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""KOSMOS-2 model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Kosmos2TextConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Kosmos2TextModel`]. It is used to instantiate a
+ KOSMOS-2 text decoder according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the text decoder of the KOSMOS-2
+ [microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 65037):
+ Vocabulary size of the Kosmos2 model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`Kosmos2Model`].
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ embed_dim (`int`, *optional*, defaults to 2048):
+ Dimensionality of the layers and the pooler layer.
+ layers (`int`, *optional*, defaults to 24):
+ Number of hidden layers in the Transformer encoder.
+ ffn_dim (`int`, *optional*, defaults to 8192):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the layer normalization layers.
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ scale_embedding (`bool`, *optional*, defaults to `True`):
+ Scale embeddings by diving by sqrt(embed_dim).
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models).
+ pad_token_id (`int`, *optional*, defaults to 1):
+ Token id used for padding.
+ bos_token_id (`int`, *optional*, defaults to 0):
+ Token id used for beginning of string.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ Token id used for end of string.
+ ```"""
+
+ model_type = "kosmos_2_text_model"
+ base_config_key = "text_config"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {
+ "num_attention_heads": "attention_heads",
+ "hidden_size": "embed_dim",
+ "num_hidden_layers": "layers",
+ }
+
+ def __init__(
+ self,
+ vocab_size=65037,
+ max_position_embeddings=2048,
+ embed_dim=2048,
+ layers=24,
+ ffn_dim=8192,
+ attention_heads=32,
+ activation_function="gelu",
+ dropout=0.1,
+ attention_dropout=0.1,
+ activation_dropout=0.0,
+ layerdrop=0.0,
+ layer_norm_eps=1e-5,
+ init_std=0.02,
+ scale_embedding=True,
+ use_cache=True,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ **kwargs,
+ ):
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ **kwargs,
+ )
+
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.embed_dim = embed_dim
+ self.layers = layers
+ self.ffn_dim = ffn_dim
+ self.attention_heads = attention_heads
+ self.activation_function = activation_function
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.layerdrop = layerdrop
+ self.layer_norm_eps = layer_norm_eps
+ self.init_std = init_std
+ self.scale_embedding = scale_embedding
+ self.use_cache = use_cache
+
+
+class Kosmos2VisionConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Kosmos2VisionModel`]. It is used to instantiate a
+ KOSMOS-2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the vision encoder of the KOSMOS-2
+ [microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ hidden_size (`int`, *optional*, defaults to 1024):
+ Dimensionality of the encoder layers and the pooler layer.
+ intermediate_size (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ num_hidden_layers (`int`, *optional*, defaults to 24):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ image_size (`int`, *optional*, defaults to 224):
+ The size (resolution) of each image.
+ patch_size (`int`, *optional*, defaults to 14):
+ The size (resolution) of each patch.
+ hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the layer normalization layers.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ initializer_factor (`float`, *optional*, defaults to 1.0):
+ A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+ testing).
+ ```"""
+
+ model_type = "kosmos_2_vision_model"
+ base_config_key = "vision_config"
+
+ def __init__(
+ self,
+ hidden_size=1024,
+ intermediate_size=4096,
+ num_hidden_layers=24,
+ num_attention_heads=16,
+ num_channels=3,
+ image_size=224,
+ patch_size=14,
+ hidden_act="quick_gelu",
+ layer_norm_eps=1e-5,
+ attention_dropout=0.0,
+ initializer_range=0.02,
+ initializer_factor=1.0,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.num_channels = num_channels
+ self.patch_size = patch_size
+ self.image_size = image_size
+ self.initializer_range = initializer_range
+ self.initializer_factor = initializer_factor
+ self.attention_dropout = attention_dropout
+ self.layer_norm_eps = layer_norm_eps
+ self.hidden_act = hidden_act
+
+
+class Kosmos2Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Kosmos2Model`]. It is used to instantiate a
+ KOSMOS-2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the KOSMOS-2
+ [microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.
+
+ Args:
+ text_config (`dict`, *optional*):
+ Dictionary of configuration options used to initialize [`Kosmos2TextConfig`].
+ vision_config (`dict`, *optional*):
+ Dictionary of configuration options used to initialize [`Kosmos2VisionConfig`].
+ latent_query_num (`int`, *optional*, defaults to 64):
+ The number of latent query tokens that represent the image features used in the text decoder component.
+ kwargs (*optional*):
+ Dictionary of keyword arguments.
+
+ Example:
+
+ ```python
+ >>> from transformers import Kosmos2Config, Kosmos2Model
+
+ >>> # Initializing a Kosmos-2 kosmos-2-patch14-224 style configuration
+ >>> configuration = Kosmos2Config()
+
+ >>> # Initializing a model (with random weights) from the kosmos-2-patch14-224 style configuration
+ >>> model = Kosmos2Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "kosmos-2"
+ sub_configs = {"text_config": Kosmos2TextConfig, "vision_config": Kosmos2VisionConfig}
+
+ def __init__(
+ self,
+ text_config=None,
+ vision_config=None,
+ latent_query_num=64,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ if text_config is None:
+ text_config = {}
+ logger.info("`text_config` is `None`. Initializing the `Kosmos2TextConfig` with default values.")
+
+ if vision_config is None:
+ vision_config = {}
+ logger.info("`vision_config` is `None`. Initializing the `Kosmos2VisionConfig` with default values.")
+
+ self.text_config = Kosmos2TextConfig(**text_config)
+ self.vision_config = Kosmos2VisionConfig(**vision_config)
+
+ self.latent_query_num = latent_query_num
+
+
+__all__ = ["Kosmos2Config"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/convert_kosmos2_original_pytorch_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/convert_kosmos2_original_pytorch_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..04c7712aa846a72726f0c3a78b8b9e2543ff9be6
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/convert_kosmos2_original_pytorch_checkpoint_to_pytorch.py
@@ -0,0 +1,77 @@
+import argparse
+
+from fairseq.checkpoint_utils import load_checkpoint_to_cpu
+
+from transformers import Kosmos2Config, Kosmos2ForConditionalGeneration
+
+
+KEYS_TO_MODIFY_MAPPING = {
+ "gpt_model.decoder.output_projection": "text_model.lm_head",
+ "gpt_model.decoder": "text_model.model",
+ "img_connector": "image_to_text_projection",
+ "img_model.visual.class_embedding": "vision_model.model.embeddings.class_embedding",
+ "img_model.visual.positional_embedding": "vision_model.model.embeddings.position_embedding.weight",
+ "img_model.visual.conv1": "vision_model.model.embeddings.patch_embedding",
+ "img_model.visual": "vision_model.model",
+ "ln_pre": "pre_layrnorm",
+ "ln_post": "post_layernorm",
+ "transformer.resblocks": "encoder.layers",
+ "ts_attn": "self_attn",
+ "ln_1": "layer_norm1",
+ "ln_2": "layer_norm2",
+ "c_fc": "fc1",
+ "c_proj": "fc2",
+}
+
+
+KEYS_TO_IGNORE = [
+ # this buffer in the original code is only used to send weights to the desired device
+ "gpt_model.decoder.embed_positions._float_tensor",
+ # this weight is never used in the forward in the original KOSMOS-2)
+ "gpt_model.decoder.self_attn_sope.scale",
+]
+
+
+def rename_key(key):
+ for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
+ if key_to_modify in key:
+ key = key.replace(key_to_modify, new_key)
+
+ return key
+
+
+def convert_kosmos2_checkpoint_to_pytorch(checkpoint_path, pytorch_dump_folder_path):
+ state = load_checkpoint_to_cpu(checkpoint_path)
+ state_dict = state["model"]
+ state_dict_keys = list(state_dict.keys())
+
+ config = Kosmos2Config()
+ # This is necessary to match the results given by the original demo
+ config.text_config.no_repeat_ngram_size = 3
+ model = Kosmos2ForConditionalGeneration(config)
+
+ # convert (by renaming keys)
+ converted_state_dict = {}
+ for key in state_dict_keys:
+ if key in KEYS_TO_IGNORE:
+ continue
+ renamed_key = rename_key(key)
+ converted_state_dict[renamed_key] = state_dict[key]
+
+ # check weight loading
+ model.load_state_dict(converted_state_dict, strict=True)
+ # save the result
+ model.save_pretrained(pytorch_dump_folder_path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--kosmos2_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
+ )
+ args = parser.parse_args()
+ convert_kosmos2_checkpoint_to_pytorch(args.kosmos2_checkpoint_path, args.pytorch_dump_folder_path)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/modeling_kosmos2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/modeling_kosmos2.py
new file mode 100644
index 0000000000000000000000000000000000000000..2aeaa21dff36ce018ca6b2479fe0f370fd1fa1f9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kosmos2/modeling_kosmos2.py
@@ -0,0 +1,1864 @@
+# coding=utf-8
+# Copyright 2023 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch KOSMOS-2 model."""
+
+import math
+from dataclasses import dataclass
+from typing import Any, Callable, Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPastAndCrossAttentions,
+ BaseModelOutputWithPooling,
+ CausalLMOutputWithCrossAttentions,
+)
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_int
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_kosmos2 import Kosmos2Config, Kosmos2TextConfig, Kosmos2VisionConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+ """
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+ """
+ bsz, src_len = mask.size()
+ tgt_len = tgt_len if tgt_len is not None else src_len
+
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+ inverted_mask = 1.0 - expanded_mask
+
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+def _make_causal_mask(
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+ """
+ Make causal mask used for bi-directional self-attention.
+ """
+ bsz, tgt_len = input_ids_shape
+ mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+ mask_cond = torch.arange(mask.size(-1), device=device)
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+ mask = mask.to(dtype)
+
+ if past_key_values_length > 0:
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
+def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+ are ignored. This is modified from fairseq's `utils.make_positions`.
+
+ Args:
+ x: torch.Tensor x:
+
+ Returns: torch.Tensor
+ """
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+ mask = input_ids.ne(padding_idx).int()
+ incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
+ return incremental_indices.long() + padding_idx
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for text model's outputs that also contains a pooling of the last hidden states.
+ """
+)
+class Kosmos2ModelOutput(ModelOutput):
+ r"""
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+ `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+ encoder_sequence_length, embed_size_per_head)`.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
+ `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
+ input) to speed up sequential decoding.
+ image_embeds (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
+ projection_attentions (`tuple(torch.FloatTensor)`, *optional*):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`.
+
+ Attentions weights given by `Kosmos2ImageToTextProjection`, after the attention softmax, used to compute
+ the weighted average in the self-attention heads.
+ vision_model_output (`BaseModelOutputWithPooling`, *optional*):
+ The output of the [`Kosmos2VisionModel`].
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ image_embeds: Optional[torch.FloatTensor] = None
+ projection_attentions: Optional[tuple[torch.FloatTensor]] = None
+ vision_model_output: BaseModelOutputWithPooling = None
+
+ def to_tuple(self) -> tuple[Any]:
+ return tuple(
+ self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
+ for k in self.keys()
+ )
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Model output class for `Kosmos2ForConditionalGeneration`.
+ """
+)
+class Kosmos2ForConditionalGenerationModelOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+ `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+ encoder_sequence_length, embed_size_per_head)`.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
+ `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
+ input) to speed up sequential decoding.
+ image_embeds (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
+ projection_attentions (`tuple(torch.FloatTensor)`, *optional*):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`.
+
+ Attentions weights given by `Kosmos2ImageToTextProjection`, after the attention softmax, used to compute
+ the weighted average in the self-attention heads.
+ vision_model_output (`BaseModelOutputWithPooling`, *optional*):
+ The output of the [`Kosmos2VisionModel`].
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ image_embeds: Optional[torch.FloatTensor] = None
+ projection_attentions: Optional[tuple[torch.FloatTensor]] = None
+ vision_model_output: BaseModelOutputWithPooling = None
+
+ def to_tuple(self) -> tuple[Any]:
+ return tuple(
+ self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
+ for k in self.keys()
+ )
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->Kosmos2
+class Kosmos2VisionEmbeddings(nn.Module):
+ def __init__(self, config: Kosmos2VisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.image_size = config.image_size
+ self.patch_size = config.patch_size
+
+ self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
+
+ self.patch_embedding = nn.Conv2d(
+ in_channels=config.num_channels,
+ out_channels=self.embed_dim,
+ kernel_size=self.patch_size,
+ stride=self.patch_size,
+ bias=False,
+ )
+
+ self.num_patches = (self.image_size // self.patch_size) ** 2
+ self.num_positions = self.num_patches + 1
+ self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+ self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
+
+ def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
+ """
+ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
+ images. This method is also adapted to support torch.jit tracing.
+
+ Adapted from:
+ - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
+ - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
+ """
+
+ num_patches = embeddings.shape[1] - 1
+ position_embedding = self.position_embedding.weight.unsqueeze(0)
+ num_positions = position_embedding.shape[1] - 1
+
+ # always interpolate when tracing to ensure the exported model works for dynamic input shapes
+ if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
+ return self.position_embedding(self.position_ids)
+
+ class_pos_embed = position_embedding[:, :1]
+ patch_pos_embed = position_embedding[:, 1:]
+
+ dim = embeddings.shape[-1]
+
+ new_height = height // self.patch_size
+ new_width = width // self.patch_size
+
+ sqrt_num_positions = torch_int(num_positions**0.5)
+ patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
+ patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+ patch_pos_embed = nn.functional.interpolate(
+ patch_pos_embed,
+ size=(new_height, new_width),
+ mode="bicubic",
+ align_corners=False,
+ )
+
+ patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+
+ return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
+
+ def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor:
+ batch_size, _, height, width = pixel_values.shape
+ if not interpolate_pos_encoding and (height != self.image_size or width != self.image_size):
+ raise ValueError(
+ f"Input image size ({height}*{width}) doesn't match model ({self.image_size}*{self.image_size})."
+ )
+ target_dtype = self.patch_embedding.weight.dtype
+ patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
+ patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+
+ class_embeds = self.class_embedding.expand(batch_size, 1, -1)
+ embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+ if interpolate_pos_encoding:
+ embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+ else:
+ embeddings = embeddings + self.position_embedding(self.position_ids)
+ return embeddings
+
+
+# Adapted from transformers.models.siglip.modeling_siglip.eager_attention_forward -> Kosmos2 doesn't cast attn weights to fp32
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs,
+):
+ attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
+ if attention_mask is not None:
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ return attn_output, attn_weights
+
+
+class Kosmos2VisionAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.embed_dim // self.num_heads
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+ f" {self.num_heads})."
+ )
+ self.scale = self.head_dim**-0.5
+ self.dropout = config.attention_dropout
+ self.is_causal = False
+
+ self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
+ self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
+ self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
+ self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ causal_attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ """Input shape: Batch x Time x Channel"""
+
+ batch_size, seq_length, embed_dim = hidden_states.shape
+
+ queries = self.q_proj(hidden_states)
+ keys = self.k_proj(hidden_states)
+ values = self.v_proj(hidden_states)
+
+ queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+ keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+ values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+ # CLIP text model uses both `causal_attention_mask` and `attention_mask`
+ # in case FA2 kernel is called, `is_causal` should be inferred from `causal_attention_mask`
+ if self.config._attn_implementation != "flash_attention_2":
+ if attention_mask is not None and causal_attention_mask is not None:
+ attention_mask = attention_mask + causal_attention_mask
+ elif causal_attention_mask is not None:
+ attention_mask = causal_attention_mask
+ else:
+ self.is_causal = causal_attention_mask is not None
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ if self.config._attn_implementation == "sdpa" and output_attentions:
+ logger.warning_once(
+ "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
+ 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ else:
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ queries,
+ keys,
+ values,
+ attention_mask,
+ is_causal=self.is_causal,
+ scaling=self.scale,
+ dropout=0.0 if not self.training else self.dropout,
+ )
+
+ attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous()
+ attn_output = self.out_proj(attn_output)
+ if not output_attentions:
+ attn_weights = None
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Kosmos2Vision
+class Kosmos2VisionMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.activation_fn = ACT2FN[config.hidden_act]
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoderLayer with AltCLIP->Kosmos2Vision
+class Kosmos2VisionEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: Kosmos2VisionConfig):
+ super().__init__()
+ self.embed_dim = config.hidden_size
+ self.self_attn = Kosmos2VisionAttention(config)
+ self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+ self.mlp = Kosmos2VisionMLP(config)
+ self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ causal_attention_mask: torch.Tensor,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.FloatTensor]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ `(config.encoder_attention_heads,)`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+
+ hidden_states = self.layer_norm1(hidden_states)
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ causal_attention_mask=causal_attention_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+ hidden_states = self.layer_norm2(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs
+
+
+# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoder with AltCLIP->Kosmos2Vision
+class Kosmos2VisionEncoder(nn.Module):
+ """
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+ [`Kosmos2VisionEncoderLayer`].
+
+ Args:
+ config: Kosmos2VisionConfig
+ """
+
+ def __init__(self, config: Kosmos2VisionConfig):
+ super().__init__()
+ self.config = config
+ self.layers = nn.ModuleList([Kosmos2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ @can_return_tuple
+ def forward(
+ self,
+ inputs_embeds,
+ attention_mask: Optional[torch.Tensor] = None,
+ causal_attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutput]:
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Causal mask for the text model. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ hidden_states = inputs_embeds
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ causal_attention_mask,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ return BaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+ )
+
+
+# Similar to `transformers.models.clip.modeling_clip.CLIPVisionTransformer` but without docstring for `forward`
+class Kosmos2VisionTransformer(nn.Module):
+ # Copied from transformers.models.altclip.modeling_altclip.AltCLIPVisionTransformer.__init__ with AltCLIPVision->Kosmos2Vision,ALTCLIP_VISION->KOSMOS2_VISION,AltCLIP->Kosmos2Vision
+ def __init__(self, config: Kosmos2VisionConfig):
+ super().__init__()
+ self.config = config
+ embed_dim = config.hidden_size
+
+ self.embeddings = Kosmos2VisionEmbeddings(config)
+ self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+ self.encoder = Kosmos2VisionEncoder(config)
+ self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ interpolate_pos_encoding: bool = False,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPooling]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
+ hidden_states = self.pre_layrnorm(hidden_states)
+
+ encoder_outputs = self.encoder(
+ inputs_embeds=hidden_states,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ last_hidden_state = encoder_outputs[0]
+ pooled_output = last_hidden_state[:, 0, :]
+ pooled_output = self.post_layernorm(pooled_output)
+
+ if not return_dict:
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+# Similar to `transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding` but allowing to pass `position_ids`
+class Kosmos2TextSinusoidalPositionalEmbedding(nn.Module):
+ """This module produces sinusoidal positional embeddings of any length."""
+
+ # Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.__init__
+ def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
+ super().__init__()
+ self.offset = 2
+ self.embedding_dim = embedding_dim
+ self.padding_idx = padding_idx
+ self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
+
+ # Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.make_weights
+ def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
+ emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
+ if hasattr(self, "weights"):
+ # in forward put the weights on the correct dtype and device of the param
+ emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
+
+ self.register_buffer("weights", emb_weights, persistent=False)
+
+ @staticmethod
+ # Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.get_embedding
+ def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
+ """
+ Build sinusoidal embeddings.
+
+ This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
+ "Attention Is All You Need".
+ """
+ half_dim = embedding_dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
+ emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
+ if embedding_dim % 2 == 1:
+ # zero pad
+ emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
+ if padding_idx is not None:
+ emb[padding_idx, :] = 0
+
+ return emb.to(torch.get_default_dtype())
+
+ @torch.no_grad()
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ past_key_values_length: int = 0,
+ position_ids: Optional[torch.Tensor] = None,
+ ):
+ if input_ids is not None:
+ bsz, seq_len = input_ids.size()
+ if position_ids is None:
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
+ position_ids = create_position_ids_from_input_ids(
+ input_ids, self.padding_idx, past_key_values_length
+ ).to(input_ids.device)
+ else:
+ bsz, seq_len = inputs_embeds.size()[:-1]
+ if position_ids is None:
+ position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length)
+
+ # expand embeddings if needed
+ max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
+ if max_pos > self.weights.size(0):
+ self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
+
+ return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()
+
+ # Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding.create_position_ids_from_inputs_embeds
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds, past_key_values_length):
+ """
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+
+ Args:
+ inputs_embeds: torch.Tensor
+
+ Returns: torch.Tensor
+ """
+ input_shape = inputs_embeds.size()[:-1]
+ sequence_length = input_shape[1]
+
+ position_ids = torch.arange(
+ self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+ )
+ return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length
+
+
+class KosmosTextAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ # Similar to transformers.models.bart.modeling_bart.BartAttention.__init__ except an additional `inner_attn_ln`.
+ def __init__(
+ self,
+ config,
+ embed_dim: int,
+ num_heads: int,
+ dropout: float = 0.0,
+ is_decoder: Optional[bool] = False,
+ add_inner_attn_layernorm: Optional[bool] = False,
+ bias: Optional[bool] = True,
+ layer_idx: Optional[bool] = None,
+ ):
+ super().__init__()
+ self.config = config
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+
+ if (self.head_dim * num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+ self.layer_idx = layer_idx
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ # End opy
+ self.inner_attn_ln = None
+ if add_inner_attn_layernorm:
+ self.inner_attn_ln = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = encoder_hidden_states is not None
+ batch_size, seq_length = hidden_states.shape[:2]
+
+ query_states = self.q_proj(hidden_states)
+ query_states = query_states.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+
+ if past_key_values is not None:
+ if isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_states from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = encoder_hidden_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_states = curr_past_key_value.layers[self.layer_idx].keys
+ value_states = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_states = self.k_proj(current_states)
+ value_states = self.v_proj(current_states)
+ key_states = key_states.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_states, value_states = curr_past_key_value.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ attention_interface: Callable = eager_attention_forward
+
+ if self.config._attn_implementation != "eager":
+ if self.config._attn_implementation == "sdpa" and output_attentions:
+ logger.warning_once(
+ "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
+ 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ else:
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
+ if self.inner_attn_ln is not None:
+ attn_output = self.inner_attn_ln(attn_output)
+
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+class Kosmos2TextFFN(nn.Module):
+ def __init__(self, config: Kosmos2TextConfig):
+ super().__init__()
+
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+
+ self.fc1 = nn.Linear(config.embed_dim, config.ffn_dim)
+ self.fc2 = nn.Linear(config.ffn_dim, config.embed_dim)
+
+ self.ffn_layernorm = nn.LayerNorm(config.ffn_dim, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states):
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.ffn_layernorm(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ return hidden_states
+
+
+class Kosmos2TextBlock(GradientCheckpointingLayer):
+ def __init__(self, config: Kosmos2TextConfig, layer_idx=None):
+ super().__init__()
+ self.embed_dim = config.embed_dim
+
+ self.self_attn = KosmosTextAttention(
+ config,
+ embed_dim=self.embed_dim,
+ num_heads=config.attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ add_inner_attn_layernorm=True,
+ layer_idx=layer_idx,
+ )
+ self.dropout = config.dropout
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+ if config.add_cross_attention:
+ self.encoder_attn = KosmosTextAttention(
+ config,
+ embed_dim=self.embed_dim,
+ num_heads=config.attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ add_inner_attn_layernorm=False,
+ layer_idx=layer_idx,
+ )
+ self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+ self.ffn = Kosmos2TextFFN(config)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = True,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # Cross-Attention Block
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ if not hasattr(self, "encoder_attn"):
+ raise ValueError(
+ f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
+ " by setting `config.add_cross_attention=True`"
+ )
+
+ residual = hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ hidden_states, cross_attn_weights = self.encoder_attn(
+ hidden_states=hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ layer_head_mask=cross_attn_layer_head_mask,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ # FFN
+ hidden_states = self.ffn(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+ return outputs
+
+
+class Kosmos2TextTransformer(nn.Module):
+ """
+ Transformer decoder consisting of `config.layers` layers. Each layer is a [`Kosmos2TextBlock`].
+
+ Args:
+ config: Kosmos2TextConfig
+ """
+
+ def __init__(self, config: Kosmos2TextConfig):
+ super().__init__()
+ self.config = config
+ self.dropout = config.dropout
+ self.layerdrop = config.layerdrop
+
+ self.embed_scale = math.sqrt(config.embed_dim) if config.scale_embedding else 1.0
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.embed_dim, padding_idx=config.pad_token_id)
+
+ self.embed_positions = Kosmos2TextSinusoidalPositionalEmbedding(
+ num_positions=config.max_position_embeddings,
+ embedding_dim=config.embed_dim,
+ padding_idx=config.pad_token_id,
+ )
+
+ self.layers = nn.ModuleList([Kosmos2TextBlock(config, layer_idx=i) for i in range(config.layers)])
+ self.layer_norm = nn.LayerNorm(config.embed_dim, config.layer_norm_eps)
+
+ self.gradient_checkpointing = False
+
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+ # create causal mask
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ combined_attention_mask = None
+ if input_shape[-1] > 1:
+ combined_attention_mask = _make_causal_mask(
+ input_shape,
+ inputs_embeds.dtype,
+ device=inputs_embeds.device,
+ past_key_values_length=past_key_values_length,
+ )
+
+ if attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+ inputs_embeds.device
+ )
+ combined_attention_mask = (
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+ )
+
+ return combined_attention_mask
+
+ def forward_embedding(
+ self,
+ input_ids,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ image_embeds: Optional[torch.Tensor] = None,
+ img_input_mask: Optional[torch.Tensor] = None,
+ past_key_values_length: int = 0,
+ position_ids: Optional[torch.Tensor] = None,
+ ):
+ # The argument `inputs_embeds` should be the one without being multiplied by `self.embed_scale`.
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if image_embeds is not None:
+ inputs_embeds[img_input_mask.to(dtype=torch.bool)] = image_embeds.to(inputs_embeds.device).view(
+ -1, image_embeds.size(-1)
+ )
+
+ inputs_embeds = inputs_embeds * self.embed_scale
+
+ # embed positions
+ positions = self.embed_positions(
+ input_ids=input_ids,
+ inputs_embeds=inputs_embeds,
+ past_key_values_length=past_key_values_length,
+ position_ids=position_ids,
+ )
+ positions = positions.to(inputs_embeds.device)
+
+ hidden_states = inputs_embeds + positions
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ return hidden_states
+
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ image_embeds: Optional[torch.Tensor] = None,
+ image_embeds_position_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, BaseModelOutputWithPastAndCrossAttentions]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = input_ids.shape
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ if use_cache and past_key_values is None:
+ past_key_values = (
+ EncoderDecoderCache(DynamicCache(), DynamicCache())
+ if encoder_hidden_states is not None
+ else DynamicCache()
+ )
+ if use_cache and isinstance(past_key_values, tuple):
+ logger.warning_once(
+ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
+ "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
+ "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
+ )
+ past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
+
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+
+ # We don't need img info. when `past_key_values_length` > 0
+ if past_key_values_length > 0:
+ image_embeds = None
+ image_embeds_position_mask = None
+
+ hidden_states = self.forward_embedding(
+ input_ids=input_ids,
+ inputs_embeds=inputs_embeds,
+ image_embeds=image_embeds,
+ img_input_mask=image_embeds_position_mask,
+ past_key_values_length=past_key_values_length,
+ position_ids=position_ids,
+ )
+
+ attention_mask = self._prepare_decoder_attention_mask(
+ attention_mask, input_shape, hidden_states, past_key_values_length
+ )
+
+ # expand encoder attention mask
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
+
+ # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
+ for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
+ if attn_mask is not None:
+ if attn_mask.size()[0] != (len(self.layers)):
+ raise ValueError(
+ f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ if self.training:
+ dropout_probability = torch.rand([])
+ if dropout_probability < self.layerdrop:
+ continue
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask,
+ encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if encoder_hidden_states is not None:
+ all_cross_attentions += (layer_outputs[2],)
+
+ # add final layer norm
+ hidden_states = self.layer_norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+@auto_docstring
+class Kosmos2PreTrainedModel(PreTrainedModel):
+ config: Kosmos2Config
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["Kosmos2VisionEncoderLayer", "Kosmos2TextBlock"]
+ _supports_attention_backend = True
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ def _init_weights(self, module: nn.Module):
+ """Initialize the weights"""
+ if isinstance(self, Kosmos2VisionModel):
+ factor = self.config.initializer_factor
+ elif isinstance(self, (Kosmos2Model, Kosmos2ForConditionalGeneration)):
+ factor = self.config.vision_config.initializer_factor
+
+ if isinstance(self, (Kosmos2TextModel, Kosmos2TextForCausalLM)):
+ std = self.config.init_std
+ elif isinstance(self, (Kosmos2Model, Kosmos2ForConditionalGeneration)):
+ std = self.config.text_config.init_std
+
+ if isinstance(module, Kosmos2VisionEmbeddings):
+ nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
+ nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
+ nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
+ elif isinstance(module, Kosmos2VisionAttention):
+ in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
+ out_proj_std = (module.embed_dim**-0.5) * factor
+ nn.init.normal_(module.q_proj.weight, std=in_proj_std)
+ nn.init.normal_(module.k_proj.weight, std=in_proj_std)
+ nn.init.normal_(module.v_proj.weight, std=in_proj_std)
+ nn.init.normal_(module.out_proj.weight, std=out_proj_std)
+ elif isinstance(module, Kosmos2VisionMLP):
+ in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
+ fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
+ nn.init.normal_(module.fc1.weight, std=fc_std)
+ nn.init.normal_(module.fc2.weight, std=in_proj_std)
+ elif isinstance(module, KosmosTextAttention):
+ nn.init.normal_(module.q_proj.weight, std=std)
+ nn.init.normal_(module.k_proj.weight, std=std)
+ nn.init.normal_(module.v_proj.weight, std=std)
+ nn.init.normal_(module.out_proj.weight, std=std)
+ elif isinstance(module, Kosmos2TextFFN):
+ nn.init.normal_(module.fc1.weight, std=std)
+ nn.init.normal_(module.fc2.weight, std=std)
+ elif isinstance(module, Kosmos2TextForCausalLM):
+ nn.init.normal_(module.lm_head.weight, std=std)
+ elif isinstance(module, Kosmos2ImageToTextProjection):
+ nn.init.normal_(module.dense.weight, std=std)
+ nn.init.normal_(module.latent_query)
+ elif isinstance(module, Kosmos2TextTransformer):
+ module.embed_tokens.weight.data.normal_(mean=0.0, std=std)
+ if module.embed_tokens.padding_idx is not None:
+ module.embed_tokens.weight.data[module.embed_tokens.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+
+ if isinstance(module, nn.Linear) and module.bias is not None:
+ module.bias.data.zero_()
+
+
+class Kosmos2VisionModel(Kosmos2PreTrainedModel):
+ config: Kosmos2VisionConfig
+ main_input_name = "pixel_values"
+
+ # Copied from transformers.models.clip.modeling_clip.CLIPVisionModel.__init__ with CLIP_VISION->KOSMOS2_VISION,CLIP->Kosmos2,self.vision_model->self.model
+ def __init__(self, config: Kosmos2VisionConfig):
+ super().__init__(config)
+ self.model = Kosmos2VisionTransformer(config)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ # Copied from transformers.models.clip.modeling_clip.CLIPVisionModel.get_input_embeddings with CLIP_VISION->KOSMOS2_VISION,CLIP->Kosmos2,self.vision_model->self.model
+ def get_input_embeddings(self) -> nn.Module:
+ return self.model.embeddings.patch_embedding
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ interpolate_pos_encoding: bool = False,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPooling]:
+ return self.model(
+ pixel_values=pixel_values,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ interpolate_pos_encoding=interpolate_pos_encoding,
+ return_dict=return_dict,
+ )
+
+
+class Kosmos2TextModel(Kosmos2PreTrainedModel):
+ config: Kosmos2TextConfig
+
+ def __init__(self, config: Kosmos2TextConfig):
+ super().__init__(config)
+ self.model = Kosmos2TextTransformer(config)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self) -> nn.Module:
+ return self.model.embed_tokens
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ image_embeds: Optional[torch.Tensor] = None,
+ image_embeds_position_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, BaseModelOutputWithPastAndCrossAttentions]:
+ r"""
+ image_embeds (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
+ image_embeds_position_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to indicate the location in a sequence to insert the image features . Mask values selected in `[0,
+ 1]`:
+
+ - 1 for places where to put the image features,
+ - 0 for places that are not for image features (i.e. for text tokens).
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ """
+ return self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ image_embeds=image_embeds,
+ image_embeds_position_mask=image_embeds_position_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ head_mask=head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ position_ids=position_ids,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The text model from KOSMOS-2 with a language modeling head on top (linear layer with weights tied to the input
+ embeddings).
+ """
+)
+class Kosmos2TextForCausalLM(Kosmos2PreTrainedModel, GenerationMixin):
+ config: Kosmos2TextConfig
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: Kosmos2TextConfig):
+ super().__init__(config)
+
+ self.model = Kosmos2TextTransformer(config)
+ self.lm_head = nn.Linear(in_features=config.embed_dim, out_features=config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self) -> nn.Module:
+ return self.model.embed_tokens
+
+ def get_output_embeddings(self) -> nn.Module:
+ return self.lm_head
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ image_embeds: Optional[torch.Tensor] = None,
+ image_embeds_position_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
+ r"""
+ image_embeds (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
+ image_embeds_position_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to indicate the location in a sequence to insert the image features . Mask values selected in `[0,
+ 1]`:
+
+ - 1 for places where to put the image features,
+ - 0 for places that are not for image features (i.e. for text tokens).
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
+ `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
+ ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if labels is not None:
+ if use_cache:
+ logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+ use_cache = False
+
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ image_embeds=image_embeds,
+ image_embeds_position_mask=image_embeds_position_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ head_mask=head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ position_ids=position_ids,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ lm_logits = self.lm_head(outputs[0])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=lm_logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+ return CausalLMOutputWithCrossAttentions(
+ loss=loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ cross_attentions=outputs.cross_attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ image_embeds=None,
+ image_embeds_position_mask=None,
+ past_key_values=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ use_cache=None,
+ cache_position=None,
+ **model_kwargs,
+ ):
+ # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ if cache_position[0] != 0:
+ image_embeds = None
+ image_embeds_position_mask = None
+
+ # appending `False` to `image_embeds_position_mask` (because `input_ids` grows during generation)
+ elif image_embeds_position_mask is not None:
+ batch_size, seq_len = inputs_embeds.size()[:-1] if inputs_embeds is not None else input_ids.size()
+ mask_len = image_embeds_position_mask.size()[-1]
+ image_embeds_position_mask = torch.cat(
+ (
+ image_embeds_position_mask,
+ torch.zeros(size=(batch_size, seq_len - mask_len), dtype=torch.bool, device=input_ids.device),
+ ),
+ dim=1,
+ )
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ image_embeds=image_embeds,
+ image_embeds_position_mask=image_embeds_position_mask,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **model_kwargs,
+ )
+ # Kosmos2 has offset for position ids, so we need to create them correctly in PositionEmbedding layer
+ model_inputs.pop("position_ids", None)
+
+ return model_inputs
+
+
+class Kosmos2ImageToTextProjection(nn.Module):
+ """The layer that transforms the image model's output to part of the text model's input (namely, image features)"""
+
+ def __init__(self, config: Kosmos2Config):
+ super().__init__()
+ self.dense = nn.Linear(config.vision_config.hidden_size, config.text_config.embed_dim)
+ self.latent_query = nn.Parameter(torch.randn(config.latent_query_num, config.text_config.embed_dim))
+
+ self.x_attn = KosmosTextAttention(
+ config.text_config,
+ config.text_config.embed_dim,
+ config.text_config.attention_heads,
+ dropout=config.text_config.attention_dropout,
+ is_decoder=False,
+ add_inner_attn_layernorm=False,
+ )
+
+ def forward(self, features):
+ hidden_states = self.dense(features)
+
+ # shape = [batch, latent_query_num, h_dim]
+ latent_query = self.latent_query.unsqueeze(0).expand(hidden_states.size(0), -1, -1)
+ key_value_states = torch.cat([hidden_states, latent_query], dim=1)
+
+ hidden_states, attn_weights = self.x_attn(
+ hidden_states=latent_query,
+ encoder_hidden_states=key_value_states,
+ past_key_values=None,
+ attention_mask=None,
+ output_attentions=None,
+ )
+
+ return hidden_states, attn_weights
+
+
+@auto_docstring(
+ custom_intro="""
+ KOSMOS-2 Model for generating text and image features. The model consists of a vision encoder and a language model.
+ """
+)
+class Kosmos2Model(Kosmos2PreTrainedModel):
+ config: Kosmos2Config
+ main_input_name = "pixel_values"
+
+ def __init__(self, config: Kosmos2Config):
+ super().__init__(config)
+
+ self.text_model = Kosmos2TextModel(config.text_config)
+ self.vision_model = Kosmos2VisionModel(config.vision_config)
+ self.image_to_text_projection = Kosmos2ImageToTextProjection(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self) -> nn.Module:
+ return self.text_model.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.text_model.model.embed_tokens = value
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ return_attentions: Optional[bool] = False,
+ interpolate_pos_encoding: Optional[bool] = False,
+ ):
+ """
+ Encodes images into continuous embeddings that can be forwarded to the language model.
+
+ Args:
+ pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+ The tensors corresponding to the input images.
+ return_attentions (`bool`, *optional*, defaults to `False`):
+ Whether to return `projection_attentions` or not.
+ interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
+ Whether to interpolate positional embeddings or not.
+ """
+ vision_model_output = self.vision_model(
+ pixel_values=pixel_values,
+ interpolate_pos_encoding=interpolate_pos_encoding,
+ )
+ # The whole `last_hidden_state` through `post_layernorm` instead of just `pooled_output`.
+ image_embeds = self.vision_model.model.post_layernorm(vision_model_output[0])
+ # normalized features
+ image_embeds = nn.functional.normalize(image_embeds, dim=-1)
+ image_embeds, projection_attentions = self.image_to_text_projection(image_embeds)
+
+ if return_attentions:
+ return image_embeds, projection_attentions
+ return image_embeds
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ input_ids: Optional[torch.Tensor] = None,
+ image_embeds_position_mask: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ image_embeds: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ interpolate_pos_encoding: bool = False,
+ return_dict: Optional[bool] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, Kosmos2ModelOutput]:
+ r"""
+ image_embeds_position_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to indicate the location in a sequence to insert the image features . Mask values selected in `[0,
+ 1]`:
+
+ - 1 for places where to put the image features,
+ - 0 for places that are not for image features (i.e. for text tokens).
+ image_embeds (`torch.FloatTensor` of shape `(batch_size, latent_query_num, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of `Kosmos2ImageToTextProjection`.
+
+ Examples:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import AutoProcessor, Kosmos2Model
+
+ >>> model = Kosmos2Model.from_pretrained("microsoft/kosmos-2-patch14-224")
+ >>> processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
+
+ >>> url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> text = (
+ ... " An image of a snowman "
+ ... " a fire "
+ ... " An image of"
+
+ >>> inputs = processor(text=prompt, images=image, return_tensors="pt")
+
+ >>> generated_ids = model.generate(
+ ... pixel_values=inputs["pixel_values"],
+ ... input_ids=inputs["input_ids"],
+ ... attention_mask=inputs["attention_mask"],
+ ... image_embeds=None,
+ ... image_embeds_position_mask=inputs["image_embeds_position_mask"],
+ ... use_cache=True,
+ ... max_new_tokens=64,
+ ... )
+ >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+ >>> processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
+ >>> processed_text
+ ' An image of a snowman a fire "
+ self.eoi_token = " "
+
+ self.eoc_token = ""
+ self.eol_token = ""
+
+ self.bop_token = ""
+ self.eop_token = " "
+
+ self.boo_token = ""
+ self.eoo_token = " "
+
+ self.dom_token = ""
+
+ self.grd_token = ""
+
+ self.tag_tokens = [
+ self.eod_token,
+ self.boi_token,
+ self.eoi_token,
+ self.eoc_token,
+ self.eol_token,
+ self.bop_token,
+ self.eop_token,
+ self.boo_token,
+ self.eoo_token,
+ self.dom_token,
+ self.grd_token,
+ ]
+
+ self.num_patch_index_tokens = num_patch_index_tokens
+ patch_index_tokens = [f"" for x in range(self.num_patch_index_tokens)]
+
+ tokens_to_add = []
+ for token in self.tag_tokens + patch_index_tokens:
+ tokens_to_add.append(AddedToken(token, lstrip=True, rstrip=False, normalized=False))
+ tokenizer.add_tokens(tokens_to_add)
+
+ super().__init__(image_processor, tokenizer)
+
+ def __call__(
+ self,
+ images: ImageInput = None,
+ text: Union[TextInput, list[TextInput]] = None,
+ audio=None,
+ videos=None,
+ **kwargs: Unpack[Kosmos2ProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ This method uses [`CLIPImageProcessor.__call__`] method to prepare image(s) for the model, and
+ [`XLMRobertaTokenizerFast.__call__`] to prepare text for the model.
+
+ Please refer to the docstring of the above two methods for more information.
+
+ The rest of this documentation shows the arguments specific to `Kosmos2Processor`.
+
+ Args:
+ bboxes (`Union[list[tuple[int]], list[tuple[float]], list[list[tuple[int]]], list[list[tuple[float]]]]`, *optional*):
+ The bounding bboxes associated to `texts`.
+ num_image_tokens (`int`, *optional* defaults to 64):
+ The number of (consecutive) places that are used to mark the placeholders to store image information.
+ This should be the same as `latent_query_num` in the instance of `Kosmos2Config` you are using.
+ first_image_token_id (`int`, *optional*):
+ The token id that will be used for the first place of the subsequence that is reserved to store image
+ information. If unset, will default to `self.tokenizer.unk_token_id + 1`.
+ add_eos_token (`bool`, defaults to `False`):
+ Whether or not to include `EOS` token id in the encoding when `add_special_tokens=True`.
+ """
+ if images is None and text is None:
+ raise ValueError("You have to specify either images or text.")
+
+ output_kwargs = self._merge_kwargs(
+ Kosmos2ProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+
+ bboxes = output_kwargs["images_kwargs"].pop("bboxes", None)
+ num_image_tokens = output_kwargs["images_kwargs"].pop("num_image_tokens", 64)
+ first_image_token_id = output_kwargs["images_kwargs"].pop("first_image_token_id", None)
+ add_eos_token = output_kwargs["text_kwargs"].pop("add_eos_token", False)
+
+ add_special_tokens = output_kwargs["text_kwargs"]["add_special_tokens"]
+ padding = output_kwargs["text_kwargs"]["padding"]
+ return_tensors = output_kwargs["text_kwargs"].setdefault("return_tensors", None)
+
+ encoding = BatchFeature()
+
+ if images is not None:
+ image_encoding = self.image_processor(images, **output_kwargs["images_kwargs"])
+ encoding.update(image_encoding)
+
+ if text is not None:
+ text = self.preprocess_examples(text, images, bboxes, num_image_tokens=num_image_tokens)
+
+ if add_special_tokens and not add_eos_token:
+ if isinstance(text, str):
+ text = f"{self.tokenizer.bos_token}{text}"
+ elif isinstance(text, list):
+ text = [f"{self.tokenizer.bos_token}{s}" for s in text]
+ output_kwargs["text_kwargs"]["add_special_tokens"] = (
+ output_kwargs["text_kwargs"]["add_special_tokens"] and add_eos_token
+ )
+ output_kwargs["text_kwargs"]["padding"] = padding if images is None else False
+ output_kwargs["text_kwargs"]["return_tensors"] = return_tensors if images is None else None
+ text_encoding = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
+ encoding.update(text_encoding)
+
+ output_kwargs["text_kwargs"]["add_special_tokens"] = add_special_tokens
+ output_kwargs["text_kwargs"]["padding"] = padding
+ output_kwargs["text_kwargs"]["return_tensors"] = return_tensors
+
+ if text is not None and images is not None:
+ # Use the id of the first token after
+ if first_image_token_id is None:
+ first_image_token_id = self.tokenizer.unk_token_id + 1
+
+ # To see if we need one more `0` (for ``) at the beginning of `image_embeds_position_mask`.
+ with_bos = add_special_tokens
+
+ # The first (actual) `` token is always at the 1st or 2nd place (after `` if any). Here we look
+ # for the second `` token (which indicate the first image token).
+ start_index = int(with_bos) + 1
+
+ # Add `image_embeds_position_mask`: the leading and trailing `0` are for `boi` and `eoi` tokens. The `1` indicates
+ # the places of image tokens.
+ image_token_ids = list(range(first_image_token_id, first_image_token_id + num_image_tokens))
+ base_image_embeds_position_mask = [0] + [1] * num_image_tokens + [0]
+
+ # loop over `encoding["input_ids"]`
+ input_ids = []
+ image_embeds_position_mask = []
+ all_input_ids = encoding["input_ids"]
+ # not batched -> (changed to) batch of size 1
+ if isinstance(text, str):
+ all_input_ids = [all_input_ids]
+ encoding["attention_mask"] = [encoding["attention_mask"]]
+ for text_ids in all_input_ids:
+ # change the ids for the fake `` tokens in `input_ids`
+ text_ids = text_ids[:start_index] + image_token_ids + text_ids[start_index + num_image_tokens :]
+ input_ids.append(text_ids)
+
+ mask = copy.copy(base_image_embeds_position_mask)
+ if with_bos:
+ # for ``
+ mask = [0] + mask
+ # trailing part (which are not related to the image)
+ mask += [0] * (len(text_ids) - len(mask))
+ image_embeds_position_mask.append(mask)
+
+ if isinstance(text, list):
+ sorted_length = sorted(
+ [(idx, len(x)) for idx, x in enumerate(text_encoding.input_ids)], key=lambda x: x[-1]
+ )
+ _, min_len_not_padded = sorted_length[0]
+ idx, _ = sorted_length[-1]
+ output_kwargs["text_kwargs"]["add_special_tokens"] = (
+ output_kwargs["text_kwargs"]["add_special_tokens"] and add_eos_token
+ )
+ output_kwargs["text_kwargs"]["return_tensors"] = None
+
+ text_encoding = self.tokenizer(text=[text[idx]], **output_kwargs["text_kwargs"])
+ max_len_padded = len(text_encoding.input_ids[0])
+
+ if min_len_not_padded != max_len_padded:
+ if self.tokenizer.padding_side == "right":
+ input_ids = [x + [self.tokenizer.pad_token_id] * (max_len_padded - len(x)) for x in input_ids]
+ image_embeds_position_mask = [
+ x + [0] * (max_len_padded - len(x)) for x in image_embeds_position_mask
+ ]
+ encoding["attention_mask"] = [
+ x + [0] * (max_len_padded - len(x)) for x in encoding["attention_mask"]
+ ]
+ elif self.tokenizer.padding_side == "left":
+ input_ids = [[self.tokenizer.pad_token_id] * (max_len_padded - len(x)) + x for x in input_ids]
+ image_embeds_position_mask = [
+ [0] * (max_len_padded - len(x)) + x for x in image_embeds_position_mask
+ ]
+ encoding["attention_mask"] = [
+ [0] * (max_len_padded - len(x)) + x for x in encoding["attention_mask"]
+ ]
+
+ # un-batch if necessary
+ if isinstance(text, str) and return_tensors is None:
+ input_ids = input_ids[0]
+ encoding["attention_mask"] = encoding["attention_mask"][0]
+ image_embeds_position_mask = image_embeds_position_mask[0]
+
+ # update (with the target tensor type if specified)
+ encoding.update(
+ BatchEncoding(
+ data={
+ "input_ids": input_ids,
+ "attention_mask": encoding["attention_mask"],
+ "image_embeds_position_mask": image_embeds_position_mask,
+ },
+ tensor_type=return_tensors,
+ )
+ )
+
+ return encoding
+
+ def _check_bboxes_for_single_text(self, bboxes):
+ """
+ Check `bboxes` for a single text example. It could be
+ - `None`: no bounding box associated to a text.
+ - A list with each element being the bounding boxes associated to one ` ... ` pair found
+ in a text. This could be:
+ - `None`: no bounding box associated to a ` ... ` pair.
+ - A tuple of 2 integers: A single bounding box specified by patch indices.
+ - A tuple of 4 float point number: A single bounding box specified by (normalized) coordinates.
+ - A list containing the above 2 tuple types: Multiple bounding boxes for a
+ ` ... ` pair.
+ """
+ if bboxes is None:
+ return
+ elif not isinstance(bboxes, list):
+ raise ValueError("`bboxes` (for a single text example) should be `None` or a list.")
+
+ # `bbox` is the bounding boxes for a single pair
+ for bbox in bboxes:
+ if bbox is None:
+ continue
+ elif not isinstance(bbox, list):
+ bbox = [bbox]
+ for element in bbox:
+ if not isinstance(element, tuple) or not (
+ (len(element) == 2 and all(isinstance(x, int) for x in element))
+ or (len(element) == 4 and all(isinstance(x, float) for x in element))
+ ):
+ raise ValueError(
+ "Each element in `bboxes` (for a single text example) should be either `None`, a tuple containing "
+ "2 integers or 4 float point numbers, or a list containing such tuples. Also "
+ "make sure the arguments `texts` and `bboxes` passed to `preprocess_text` are both in "
+ "batches or both for a single example."
+ )
+
+ def _preprocess_single_example(self, text, image, bboxes, img_info_tokens):
+ text = text.strip()
+ if image is not None:
+ # Add ` ... (fake) image tokens ... `
+ text = f"{img_info_tokens} {text}"
+
+ # Add ` ` after ` phrase text `
+ text = self._insert_patch_index_tokens(text, bboxes)
+ return text
+
+ def preprocess_examples(
+ self,
+ texts: Union[TextInput, list[TextInput]],
+ images: ImageInput = None,
+ bboxes: BboxInput = None,
+ num_image_tokens: Optional[int] = 64,
+ ) -> Union[str, list[str]]:
+ """Add image and bounding box information to `texts` as image and patch index tokens.
+
+ Args:
+ texts (`Union[TextInput, list[TextInput]]`): The texts to be processed.
+ images (`ImageInput`, *optional*): The images associated to `texts`.
+ bboxes (`Union[list[tuple[int]], list[tuple[float]], list[list[tuple[int]]], list[list[tuple[float]]]]`, *optional*):
+ The bounding bboxes associated to `texts`.
+ num_image_tokens (`int`, *optional*, defaults to 64):
+ The number of image tokens (used as latent queries). This should corresponds to the `latent_query_num`
+ attribute in `Kosmos2Config`.
+
+ Returns:
+ `Union[TextInput, list[TextInput]]`: The processed texts with image and patch index tokens.
+ """
+ # These are fake `` tokens enclosed between (the actual) `` token and ` `.
+ img_tokens = [self.boi_token] * num_image_tokens
+ img_info_tokens = " ".join([self.boi_token] + img_tokens + [self.eoi_token])
+
+ # make batch to simplify processing logic
+ batched = True
+ if isinstance(texts, str):
+ batched = False
+ texts = [texts]
+
+ if images is None:
+ images = [None] * len(texts)
+ elif not is_batched(images):
+ images = [images]
+ if len(texts) != len(images):
+ raise ValueError(
+ f"The number of examples in `texts` and `images` should be the same. Got {len(texts)} v.s. {len(images)} instead."
+ )
+
+ if not batched:
+ self._check_bboxes_for_single_text(bboxes)
+ bboxes = [bboxes]
+ elif bboxes is not None:
+ if not isinstance(bboxes, list):
+ raise ValueError("`bboxes` should be `None` or a list (as a batch) when `texts` is passed as a batch.")
+ for x in bboxes:
+ self._check_bboxes_for_single_text(x)
+ else:
+ bboxes = [None] * len(texts)
+
+ if len(bboxes) != len(texts):
+ raise ValueError(
+ f"The number of examples in `texts` and `bboxes` should be the same. Got {len(texts)} v.s. {len(bboxes)} instead."
+ )
+
+ result = [
+ self._preprocess_single_example(text, image, bbox, img_info_tokens)
+ for text, image, bbox in zip(texts, images, bboxes)
+ ]
+ # un-batch if necessary
+ if not batched:
+ result = result[0]
+
+ return result
+
+ # Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ # Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ def post_process_generation(self, text, cleanup_and_extract=True):
+ caption = text.split(self.eoi_token)[-1]
+ if cleanup_and_extract:
+ return clean_text_and_extract_entities_with_bboxes(caption)
+ return caption
+
+ def post_process_image_text_to_text(self, generated_outputs, skip_special_tokens=True, **kwargs):
+ """
+ Post-process the output of the model to decode the text.
+
+ Args:
+ generated_outputs (`torch.Tensor` or `np.ndarray`):
+ The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+ or `(sequence_length,)`.
+ skip_special_tokens (`bool`, *optional*, defaults to `True`):
+ Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+ **kwargs:
+ Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+ Returns:
+ `list[str]`: The decoded text.
+ """
+ generated_texts = self.batch_decode(generated_outputs, skip_special_tokens=skip_special_tokens, **kwargs)
+ return [self.post_process_generation(text, cleanup_and_extract=False) for text in generated_texts]
+
+ @property
+ # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+ def _insert_patch_index_tokens(self, text: str, bboxes: Union[list[tuple[int]], list[tuple[float]]]) -> str:
+ if bboxes is None or len(bboxes) == 0:
+ return text
+
+ matched_phrases = list(re.finditer(r".+? ", string=text))
+ if len(matched_phrases) != len(bboxes):
+ raise ValueError(
+ f"The number of elements in `bboxes` should be the same as the number of ` ... ` pairs in `text`. Got {len(matched_phrases)} v.s. {len(bboxes)} instead."
+ )
+
+ # insert object's patch index tokens
+ # the found ` ... ` pairs.
+ curr_pos = 0
+ buffer = []
+ for matched, bbox in zip(matched_phrases, bboxes):
+ _, end = matched.span()
+ buffer.append(text[curr_pos:end])
+ curr_pos = end
+ # A phrase without bbox
+ if bbox is None:
+ continue
+ # A phrase with a single bbox
+ if isinstance(bbox, tuple):
+ bbox = [bbox]
+ patch_index_strings = []
+ # A phrase could have multiple bboxes
+ if not all(box is not None for box in bbox):
+ raise ValueError(
+ "The multiple bounding boxes for a single phrase should not contain any `None` value."
+ )
+ for box in bbox:
+ patch_index_1, patch_index_2 = self._convert_bbox_to_patch_index_tokens(box)
+ patch_index_strings.append(f"{patch_index_1} {patch_index_2}")
+ # `bbox` being an empty list
+ if len(patch_index_strings) == 0:
+ continue
+ position_str = " ".join(patch_index_strings)
+ buffer.append(f" {position_str} ")
+ # remaining
+ if curr_pos < len(text):
+ buffer.append(text[curr_pos:])
+
+ text = "".join(buffer)
+ return text
+
+ def _convert_bbox_to_patch_index_tokens(
+ self, bbox: Union[tuple[int, int], tuple[float, float, float, float]]
+ ) -> tuple[str, str]:
+ # already computed patch indices
+ if len(bbox) == 2:
+ idx_1, idx_2 = bbox
+ # bbox specified with (normalized) coordinates
+ else:
+ # use `self.tokenizer` to get `num_patches_per_side`
+ num_patches_per_side = int(math.sqrt(self.num_patch_index_tokens))
+ idx_1, idx_2 = coordinate_to_patch_index(bbox, num_patches_per_side)
+
+ token_1 = f""
+ token_2 = f""
+
+ return token_1, token_2
+
+
+def coordinate_to_patch_index(bbox: tuple[float, float, float, float], num_patches_per_side: int) -> tuple[int, int]:
+ """Convert a bounding box to a pair of patch indices.
+
+ Args:
+ bbox (`tuple[float, float, float, float]`):
+ The 4 coordinates of the bounding box, with the format being (x1, y1, x2, y2) specifying the upper-left and
+ lower-right corners of the box. It should have x2 > x1 and y2 > y1.
+ num_patches_per_side (`int`): the number of patches along each side.
+
+ Returns:
+ `tuple[int, int]`: A pair of patch indices representing the upper-left patch and lower-right patch.
+ """
+ (x1, y1, x2, y2) = bbox
+
+ if not (x2 > x1 and y2 > y1):
+ raise ValueError("The coordinates in `bbox` should be `(x1, y1, x2, y2)` with `x2 > x1` and `y2 > y1`.")
+
+ ul_x = math.floor(x1 * num_patches_per_side)
+ ul_y = math.floor(y1 * num_patches_per_side)
+
+ lr_x = math.ceil(x2 * num_patches_per_side - 1)
+ lr_y = math.ceil(y2 * num_patches_per_side - 1)
+
+ ul_idx = ul_y * num_patches_per_side + ul_x
+ lr_idx = lr_y * num_patches_per_side + lr_x
+
+ return ul_idx, lr_idx
+
+
+# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L35C1-L75C38
+# (with format modifications)
+def patch_index_to_coordinate(ul_idx: int, lr_idx: int, num_patches_per_side: int):
+ """
+ Given a grid of length `num_patches_per_side` and the indices of the upper-left and lower-right corners of a
+ bounding box, returns the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2).
+
+ Args:
+ ul_idx (`int`): the index of the grid cell that corresponds to the upper-left corner of the bounding box.
+ lr_idx (`int`): the index of the grid cell that corresponds to the lower-right corner of the bounding box.
+ num_patches_per_side (`int`): the number of patches along each side.
+
+ Returns:
+ `tuple[float]`: the normalized coordinates of the bounding box, in the form (x1, y1, x2, y2).
+ """
+ # Compute the size of each cell in the grid
+ cell_size = 1.0 / num_patches_per_side
+
+ # Compute the x and y indices of the upper-left and lower-right corners of the bounding box
+ ul_x = ul_idx % num_patches_per_side
+ ul_y = ul_idx // num_patches_per_side
+
+ lr_x = lr_idx % num_patches_per_side
+ lr_y = lr_idx // num_patches_per_side
+
+ # Compute the normalized coordinates of the bounding box
+ if ul_idx == lr_idx:
+ x1 = ul_x * cell_size
+ y1 = ul_y * cell_size
+ x2 = lr_x * cell_size + cell_size
+ y2 = lr_y * cell_size + cell_size
+ elif ul_x == lr_x or ul_y == lr_y:
+ x1 = ul_x * cell_size
+ y1 = ul_y * cell_size
+ x2 = lr_x * cell_size + cell_size
+ y2 = lr_y * cell_size + cell_size
+ else:
+ x1 = ul_x * cell_size + cell_size / 2
+ y1 = ul_y * cell_size + cell_size / 2
+ x2 = lr_x * cell_size + cell_size / 2
+ y2 = lr_y * cell_size + cell_size / 2
+
+ return x1, y1, x2, y2
+
+
+# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L4-L33
+# (with format modifications)
+def extract_entities_with_patch_indices(text):
+ """Extract entities contained in `text`. The bounding bboxes is given in the form of patch indices.
+
+ This functioin is only intended to be used within `clean_text_and_extract_entities_with_bboxes` where further
+ processing happens, including converting to normalized coordinates and whitespace character cleaning up.
+
+ Examples:
+
+ ```python
+ >>> text = " An image of a snowman a fire ([^<]+) ))?((?:)*) "
+
+ # Find all matches in the given string
+ matches = re.finditer(pattern, text)
+
+ # Initialize an empty list to store the valid patch_index combinations
+ entities_with_patch_indices = []
+
+ for match in matches:
+ # span of a `phrase` that is between and
+ span = match.span(2)
+ phrase_tag, phrase, match_content = match.groups()
+ if not phrase_tag:
+ phrase = None
+ # We take the starting position of ``
+ span = (match.span(0)[0], match.span(0)[0])
+
+ # Split the match_content by the delimiter to get individual patch_index pairs
+ patch_index_pairs = match_content.split("")
+
+ entity_bboxes = []
+ for pair in patch_index_pairs:
+ # Extract the xxxx and yyyy values from the patch_index pair
+ x = re.search(r"", pair)
+ y = re.search(r"", pair[1:])
+
+ if x and y:
+ if phrase:
+ entity_bboxes.append((int(x.group(1)), int(y.group(1))))
+ else:
+ entity_bboxes.append((int(x.group(1)), int(y.group(1))))
+
+ if phrase:
+ entities_with_patch_indices.append((phrase, span, entity_bboxes))
+ else:
+ for bbox in entity_bboxes:
+ # fake entity name
+ entity = f""
+ entities_with_patch_indices.append((entity, span, [bbox]))
+
+ return entities_with_patch_indices
+
+
+def adjust_entity_positions(entity, text):
+ """Adjust the positions of the entities in `text` to be relative to the text with special fields removed."""
+ entity_name, (start, end) = entity
+ # computed the length of strings with special fields (tag tokens, patch index tokens, etc.) removed
+ adjusted_start = len(re.sub("<.*?>", "", text[:start]))
+ adjusted_end = len(re.sub("<.*?>", "", text[:end]))
+ adjusted_entity = (entity_name, (adjusted_start, adjusted_end))
+ return adjusted_entity
+
+
+def _cleanup_spaces(text, entities):
+ """Remove the spaces around the text and the entities in it."""
+ new_text = text.strip()
+ leading_spaces = len(text) - len(text.lstrip())
+
+ new_entities = []
+ for entity_name, (start, end), bboxes in entities:
+ entity_name_leading_spaces = len(entity_name) - len(entity_name.lstrip())
+ entity_name_trailing_spaces = len(entity_name) - len(entity_name.rstrip())
+
+ start = start - leading_spaces + entity_name_leading_spaces
+ end = end - leading_spaces - entity_name_trailing_spaces
+ entity_name = entity_name.strip()
+
+ new_entities.append((entity_name, (start, end), bboxes))
+
+ return new_text, new_entities
+
+
+# copied from https://github.com/microsoft/unilm/blob/97e4923e97d3ee10b57e97013556e3fd0d207a9b/kosmos-2/demo/decode_string.py#L77-L87
+# (with format modifications)
+def clean_text_and_extract_entities_with_bboxes(text, num_patches_per_side=32):
+ """Remove the tag tokens from `text`, extract entities in it with some cleaning up of white characters.
+
+ Examples:
+
+ ```python
+ >>> text = " An image of a snowman a fire An image of a snowman "
+ ... " a fire "
+ ... " " #
+
+ >>> inputs = processor(text=prompt, images=image, return_tensors="pt")
+ >>> height, width = inputs.pop("height"), inputs.pop("width")
+ >>> inputs = {k: v.to(device) if v is not None else None for k, v in inputs.items()}
+ >>> inputs["flattened_patches"] = inputs["flattened_patches"].to(dtype)
+
+ >>> generated_ids = model.generate(**inputs,max_new_tokens=1024)
+ >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+ >>> generated_text
+ '" : fixed token for the start of image
+ self.image_end_token = tokenizer.eoi_token # " " : fixed token for the end of image
+ self.image_token = tokenizer.image_token # "" : within a ... pair, these tokens indicate they are positions reserved for an image
+ super().__init__(image_processor, tokenizer)
+
+ def __call__(
+ self,
+ images: ImageInput = None,
+ text: Union[TextInput, list[TextInput]] = None,
+ audio=None,
+ videos=None,
+ **kwargs: Unpack[Kosmos2_5ProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ This method uses [`Kosmos2_5ImageProcessor.preprocess`] method to prepare image(s) for the model, and
+ [`PreTrainedTokenizerFast.__call__`] to prepare text for the model.
+
+ Please refer to the docstring of the above two methods for more information.
+
+ The rest of this documentation shows the arguments specific to `Kosmos2_5Processor`.
+ """
+ if images is None and text is None:
+ raise ValueError("You have to specify either images or text.")
+
+ if images is None:
+ raise ValueError("Kosmos2_5Processor requires images to be passed.")
+
+ output_kwargs = self._merge_kwargs(
+ Kosmos2_5ProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+ num_image_tokens = output_kwargs["images_kwargs"].setdefault("num_image_tokens", None)
+
+ encoding = BatchFeature()
+
+ if images is not None:
+ image_encoding = self.image_processor(images, **output_kwargs["images_kwargs"])
+ image_encoding.pop("rows")
+ image_encoding.pop("cols")
+ encoding.update(image_encoding)
+
+ prompt = f"{self.tokenizer.bos_token}{self.image_start_token}{self.image_token * num_image_tokens}{self.image_end_token}"
+
+ if text is not None:
+ if isinstance(text, str):
+ text = [prompt + text]
+ else:
+ text = [prompt + t for t in text]
+ input = self.tokenizer(text, **output_kwargs["text_kwargs"])
+
+ batch_size, seq_len = input.input_ids.shape
+ image_embeds_position_mask = [0, -1] + [1] * num_image_tokens + [-1]
+ image_embeds_position_mask += [0] * (seq_len - len(image_embeds_position_mask))
+ image_embeds_position_mask = (
+ torch.LongTensor(image_embeds_position_mask).unsqueeze(0).repeat(batch_size, 1)
+ )
+
+ encoding.update(
+ {
+ "input_ids": input.input_ids,
+ "attention_mask": input.attention_mask,
+ "image_embeds_position_mask": image_embeds_position_mask,
+ }
+ )
+
+ return encoding
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to Kosmos2_5TokenizerFast's [`~PreTrainedTokenizer.batch_decode`].
+ Please refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to Kosmos2_5TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+__all__ = ["Kosmos2_5Processor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5823883c6cb8ba35c0964d62d4f32e5ca3d24d64
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_kyutai_speech_to_text import *
+ from .feature_extraction_kyutai_speech_to_text import *
+ from .modeling_kyutai_speech_to_text import *
+ from .processing_kyutai_speech_to_text import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/configuration_kyutai_speech_to_text.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/configuration_kyutai_speech_to_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..40bfcf09374be8f1a210e7a87496fc151b185a4a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/configuration_kyutai_speech_to_text.py
@@ -0,0 +1,187 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.s
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ..auto.configuration_auto import AutoConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class KyutaiSpeechToTextConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`KyutaiSpeechToTextForConditionalGeneration`].
+ It is used to instantiate a Kyutai Speech-to-Text model according to the specified arguments, defining the model
+ architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the
+ 2.6b-en model.
+
+ e.g. [kyutai/stt-2.6b-en-trfs](https://huggingface.co/kyutai/stt-2.6b-en-trfs)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ codebook_vocab_size (`int`, *optional*, defaults to 2049):
+ Vocabulary size of the codebook. Defines the number of different audio tokens that can be represented by each codebook.
+ vocab_size (`int`, *optional*, defaults to 4001):
+ Vocabulary size of the model. Defines the number of different tokens that can be represented by the
+ `input_ids` passed when calling the model.
+ hidden_size (`int`, *optional*, defaults to 2048):
+ Dimensionality of the layers and the pooler layer of the main decoder.
+ num_hidden_layers (`int`, *optional*, defaults to 48):
+ Number of decoder layers.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the main decoder block.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details checkout [this
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`.
+ max_position_embeddings (`int`, *optional*, defaults to 750):
+ The maximum sequence length that this model might ever be used with. Typically, set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ rope_theta (`float`, *optional*, defaults to 100000.0):
+ The base period of the RoPE embeddings.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
+ The attention head dimension.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ sliding_window (`int`, *optional*, defaults to 375):
+ Sliding window attention window size. If not specified, will default to `3000`.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ ffn_dim (`int`, *optional*, defaults to 11264):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the main decoder block. Must be even.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-08):
+ The epsilon used by the rms normalization layers.
+ num_codebooks (`int`, *optional*, defaults to 32):
+ The number of audio codebooks for each audio channels.
+ audio_bos_token_id (`int`, *optional*, defaults to 2048):
+ Beginning of stream token id for codebook tokens.
+ audio_pad_token_id (`int`, *optional*, defaults to 69569):
+ Padding token id for codebook tokens.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings.
+ pad_token_id (`int`, *optional*, defaults to 3):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 48000):
+ Beginning of stream token id for text tokens.
+ codec_config (`PretrainedConfig`, *optional*):
+ Configuration for the codec.
+ kwargs (*optional*):
+ Dictionary of keyword arguments. Notably:
+ - **audio_encoder_config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
+ defines the audio encoder config.
+ - **depth__config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
+ defines the depth decoder config.
+
+
+ Example:
+ ```python
+ >>> from transformers import KyutaiSpeechToTextConfig, KyutaiSpeechToTextForConditionalGeneration
+
+ >>> # Initializing a KyutaiSpeechToTextConfig
+ >>> configuration = KyutaiSpeechToTextConfig()
+
+ >>> # Initializing a model
+ >>> model = KyutaiSpeechToTextForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "kyutai_speech_to_text"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ sub_configs = {"codec_config": AutoConfig}
+
+ def __init__(
+ self,
+ codebook_vocab_size=2049,
+ vocab_size=4001,
+ hidden_size=2048,
+ num_hidden_layers=48,
+ num_attention_heads=32,
+ num_key_value_heads=None,
+ max_position_embeddings=750,
+ rope_theta=100000.0,
+ hidden_act="silu",
+ head_dim=None,
+ initializer_range=0.02,
+ use_cache=True,
+ sliding_window=375,
+ attention_dropout=0.0,
+ ffn_dim=11264,
+ rms_norm_eps=1e-8,
+ num_codebooks=32,
+ audio_bos_token_id=2048,
+ audio_pad_token_id=69569,
+ tie_word_embeddings=False,
+ pad_token_id=3,
+ bos_token_id=48000,
+ codec_config=None,
+ **kwargs,
+ ):
+ super().__init__(
+ pad_token_id=pad_token_id, bos_token_id=bos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
+ )
+
+ if codec_config is None:
+ self.codec_config = AutoConfig.for_model("mimi")
+ logger.info("codec_config is None, using default audio encoder config.")
+ elif isinstance(codec_config, dict):
+ self.codec_config = AutoConfig.for_model(**codec_config)
+ elif isinstance(codec_config, PretrainedConfig):
+ self.codec_config = codec_config
+
+ self.num_codebooks = num_codebooks
+ self.frame_size = self.codec_config.frame_size
+
+ self.audio_bos_token_id = audio_bos_token_id
+ self.audio_pad_token_id = audio_pad_token_id
+ self.codebook_vocab_size = codebook_vocab_size
+
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ if ffn_dim % 2 == 1:
+ raise ValueError(f"`ffn_dim={ffn_dim}` must be even.")
+ self.ffn_dim = ffn_dim
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.attention_dropout = attention_dropout
+ self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
+ self.sliding_window = sliding_window
+
+
+__all__ = ["KyutaiSpeechToTextConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/convert_kyutai_speech_to_text_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/convert_kyutai_speech_to_text_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..d08550fa94415e8befb9afc1ee446730a2dec4a4
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/convert_kyutai_speech_to_text_to_hf.py
@@ -0,0 +1,384 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import gc
+import os
+import re
+
+import safetensors.torch
+import sentencepiece
+import torch
+
+from transformers import (
+ KyutaiSpeechToTextConfig,
+ KyutaiSpeechToTextFeatureExtractor,
+ KyutaiSpeechToTextForConditionalGeneration,
+ KyutaiSpeechToTextProcessor,
+ PreTrainedTokenizerFast,
+)
+from transformers.convert_slow_tokenizer import MoshiConverter
+from transformers.utils.hub import cached_file
+
+
+# fmt: off
+MOSHI_ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
+ r"out_norm": r"norm",
+ r"gating\.linear_in": r"mlp.fc1",
+ r"gating\.linear_out": r"mlp.fc2",
+ r"self_attn\.out_proj": r"self_attn.o_proj.linear",
+ r"norm1": r"input_layernorm",
+ r"norm2": r"post_attention_layernorm",
+ r"layer_scale_1": r"self_attn_layer_scale",
+ r"layer_scale_2": r"mlp_layer_scale",
+ r"alpha": r"weight",
+}
+# fmt: on
+
+
+# fmt: off
+MIMI_ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
+ r"conv\.conv\.conv": "conv",
+ r"convtr\.convtr\.convtr": "conv",
+ r"conv\.conv": "conv",
+ r"convtr\.convtr": "conv",
+ r"quantizer\.rvq_first\.vq": "quantizer.semantic_residual_vector_quantizer",
+ r"quantizer\.rvq_first": "quantizer.semantic_residual_vector_quantizer",
+ r"quantizer\.rvq_rest\.vq": "quantizer.acoustic_residual_vector_quantizer",
+ r"quantizer\.rvq_rest": "quantizer.acoustic_residual_vector_quantizer",
+ r"_codebook": "codebook",
+ r"_initialized": "initialized",
+ r"embedding_sum": "embed_sum",
+ r"encoder\.model": "encoder.layers",
+ r"decoder\.model": "decoder.layers",
+ r"encoder_transformer\.transformer": "encoder_transformer",
+ r"decoder_transformer\.transformer": "decoder_transformer",
+ r"linear1": "mlp.fc1",
+ r"linear2": "mlp.fc2",
+ r"self_attn\.out_proj": "self_attn.o_proj",
+ r"norm1": "input_layernorm",
+ r"norm2": "post_attention_layernorm",
+ r"layer_scale_1": "self_attn_layer_scale",
+ r"layer_scale_2": "mlp_layer_scale",
+}
+# fmt: on
+
+
+def permute_for_rope(input_tensor, n_heads, dim1, dim2):
+ """
+ When you go from the complex ROPE formulation to sin and cos one, you need
+ to permute the query and key weights (to avoid doing it on the fly)
+ """
+ return input_tensor.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
+
+
+def convert_key(key, mapping):
+ for pattern, replacement in mapping.items():
+ key = re.sub(pattern, replacement, key)
+ return key
+
+
+def convert_kyutai_speech_to_text_state_dict(state_dict, config, unwanted_prefix="transformer."):
+ hidden_size = config.hidden_size
+ head_dim = config.head_dim
+ num_heads = int(config.hidden_size // config.head_dim)
+ num_key_value_heads = config.num_key_value_heads
+ key_value_head_dim = config.num_key_value_heads * head_dim
+
+ # concat embeddings
+ embed_tokens_weight = []
+ for i in range(32):
+ embed_tokens_weight.append(state_dict.pop(f"emb.{i}.weight"))
+
+ embed_tokens_weight = torch.cat(embed_tokens_weight, dim=0)
+ embed_tokens_weight = torch.cat([state_dict.pop("text_emb.weight"), embed_tokens_weight])
+ embed_tokens_weight = torch.cat([embed_tokens_weight, torch.zeros(1, config.hidden_size)], dim=0)
+ state_dict["embed_tokens.embed_tokens.weight"] = embed_tokens_weight
+
+ for key, value in list(state_dict.items()):
+ if unwanted_prefix is not None and unwanted_prefix in key:
+ new_key = key[len(unwanted_prefix) :]
+ else:
+ new_key = key
+
+ new_key = convert_key(new_key, MOSHI_ORIGINAL_TO_CONVERTED_KEY_MAPPING)
+
+ # Post-process the current_parameter.
+ if "alpha" in key:
+ state_dict[key] = state_dict[key].squeeze()
+
+ if "in_proj_weight" in new_key:
+ # split qkv into query key and value
+ mixed_qkv = state_dict.pop(key)
+ qkv_dim = mixed_qkv.size(0) // 3
+
+ query_layer = mixed_qkv[:qkv_dim]
+ key_layer = mixed_qkv[qkv_dim : qkv_dim * 2]
+ value_layer = mixed_qkv[qkv_dim * 2 :]
+ state_dict[new_key.replace("in_proj_weight", "q_proj.linear.weight")] = permute_for_rope(
+ query_layer, num_heads, hidden_size, hidden_size
+ )
+ state_dict[new_key.replace("in_proj_weight", "k_proj.linear.weight")] = permute_for_rope(
+ key_layer, num_key_value_heads, key_value_head_dim, hidden_size
+ )
+
+ state_dict[new_key.replace("in_proj_weight", "v_proj.linear.weight")] = value_layer
+ else:
+ state_dict[new_key] = state_dict.pop(key)
+
+ return state_dict
+
+
+def convert_mimi_state_dict(state_dict, config, unwanted_prefix=None):
+ hidden_size = config.hidden_size
+ head_dim = config.head_dim
+ num_heads = int(config.hidden_size // config.head_dim)
+ num_key_value_heads = config.num_key_value_heads
+ key_value_head_dim = config.num_key_value_heads * head_dim
+
+ for key, value in list(state_dict.items()):
+ if unwanted_prefix is not None and unwanted_prefix in key:
+ new_key = key[len(unwanted_prefix) :]
+ else:
+ new_key = key
+
+ new_key = convert_key(new_key, MIMI_ORIGINAL_TO_CONVERTED_KEY_MAPPING)
+
+ if "in_proj_weight" in new_key:
+ # split qkv into query key and value
+ mixed_qkv = state_dict.pop(key)
+ qkv_dim = mixed_qkv.size(0) // 3
+
+ query_layer = mixed_qkv[:qkv_dim]
+ key_layer = mixed_qkv[qkv_dim : qkv_dim * 2]
+ value_layer = mixed_qkv[qkv_dim * 2 :]
+
+ state_dict[new_key.replace("in_proj_weight", "q_proj.weight")] = permute_for_rope(
+ query_layer, num_heads, hidden_size, hidden_size
+ )
+ state_dict[new_key.replace("in_proj_weight", "k_proj.weight")] = permute_for_rope(
+ key_layer, num_key_value_heads, key_value_head_dim, hidden_size
+ )
+ state_dict[new_key.replace("in_proj_weight", "v_proj.weight")] = value_layer
+ else:
+ state_dict[new_key] = state_dict.pop(key)
+
+ return state_dict
+
+
+def write_model(
+ input_path_or_repo,
+ model_name,
+ codec_model_path_or_repo,
+ codec_model_name,
+ output_dir,
+ safe_serialization=True,
+ unwanted_prefix="transformer.",
+):
+ print("Converting the model.")
+ os.makedirs(output_dir, exist_ok=True)
+
+ config = KyutaiSpeechToTextConfig(
+ vocab_size=8001,
+ max_position_embeddings=375,
+ num_hidden_layers=16,
+ num_attention_heads=16,
+ num_key_value_heads=16,
+ head_dim=128,
+ )
+ config.use_cache = True
+ config.codec_config.sliding_window = 250
+
+ model_path = cached_file(
+ input_path_or_repo,
+ model_name,
+ )
+
+ codec_path = cached_file(
+ codec_model_path_or_repo,
+ codec_model_name,
+ )
+
+ print(f"Fetching all parameters from the checkpoint at {model_path}...")
+ state_dict = safetensors.torch.load_file(model_path)
+
+ print(f"Fetching all parameters from the checkpoint at {codec_path}...")
+ codec_state_dict = safetensors.torch.load_file(codec_path)
+
+ print("Converting model...")
+ # -----------------------
+ # convert parameter names
+ # -----------------------
+ state_dict = convert_kyutai_speech_to_text_state_dict(state_dict, config, unwanted_prefix=unwanted_prefix)
+ codec_state_dict = convert_mimi_state_dict(codec_state_dict, config.codec_config, unwanted_prefix=None)
+
+ # -------------------------
+ # load the weights and save
+ # -------------------------
+ print("Loading the checkpoint in a Moshi ASR model.")
+ with torch.device("meta"):
+ model = KyutaiSpeechToTextForConditionalGeneration(config)
+
+ linear_weight = state_dict.pop("text_linear.weight")
+ model.model.load_state_dict(state_dict, strict=True, assign=True)
+
+ linear_weight = torch.cat([linear_weight, torch.zeros(1, config.hidden_size)])
+ model.lm_head.load_state_dict({"weight": linear_weight}, strict=True, assign=True)
+
+ model.codec_model.load_state_dict(codec_state_dict, strict=True, assign=True)
+
+ print("Checkpoint loaded successfully.")
+ del model.config._name_or_path
+ del model.config.codec_config._name_or_path
+
+ # default generation config
+ model.generation_config._from_model_config = False
+ model.generation_config.audio_window_size = 1
+ model.generation_config.cache_implementation = "sliding_window"
+
+ model.codec_model.generation_config._from_model_config = False
+ model.codec_model.generation_config.cache_implementation = "sliding_window"
+ model.codec_model.generation_config.use_cache = True
+
+ print("Saving the model.")
+ model.save_pretrained(output_dir, safe_serialization=safe_serialization)
+ del state_dict, model
+
+ # Safety check: reload the converted model
+ gc.collect()
+ print("Reloading the model to check if it's saved correctly.")
+ KyutaiSpeechToTextForConditionalGeneration.from_pretrained(
+ output_dir, torch_dtype=torch.bfloat16, device_map="auto"
+ )
+ print("Model reloaded successfully.")
+
+
+def write_processor(
+ input_path_or_repo,
+ tokenizer_model_name,
+ codec_model_path_or_repo,
+ output_dir,
+ audio_delay_seconds,
+ audio_silence_prefix_seconds,
+):
+ tokenizer_path = cached_file(
+ input_path_or_repo,
+ tokenizer_model_name,
+ )
+
+ tokenizer = MoshiConverter(tokenizer_path).converted()
+ original_tokenizer = sentencepiece.SentencePieceProcessor(tokenizer_path)
+
+ tokenizer = PreTrainedTokenizerFast(
+ tokenizer_object=tokenizer,
+ chat_template=None,
+ unk_token="",
+ model_input_names=["input_ids", "attention_mask"],
+ clean_up_tokenization_spaces=False,
+ bos_token_id=original_tokenizer.bos_id(),
+ eos_token_id=original_tokenizer.eos_id(),
+ pad_token_id=original_tokenizer.pad_id(),
+ )
+
+ feature_extractor = KyutaiSpeechToTextFeatureExtractor(
+ audio_delay_seconds=audio_delay_seconds,
+ audio_silence_prefix_seconds=audio_silence_prefix_seconds,
+ )
+
+ processor = KyutaiSpeechToTextProcessor(feature_extractor, tokenizer)
+ processor.save_pretrained(output_dir)
+ print(f"Processor saved successfully to {output_dir}")
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Convert Moshi ASR weights to HuggingFace format")
+ parser.add_argument(
+ "--input_path_or_repo",
+ type=str,
+ required=True,
+ help="Path or repo containing Moshi ASR weights",
+ )
+ parser.add_argument(
+ "--model_name",
+ type=str,
+ required=True,
+ help="Name of the model in input_path_or_repo",
+ )
+ parser.add_argument(
+ "--tokenizer_model_name",
+ type=str,
+ required=True,
+ help="Name of the tokenizer model in input_path_or_repo",
+ )
+ parser.add_argument(
+ "--codec_model_path_or_repo",
+ type=str,
+ required=True,
+ help="Path or repo containing the Mimi weights",
+ )
+ parser.add_argument(
+ "--mimi_name",
+ type=str,
+ required=True,
+ help="Name of the Mimi model in codec_model_path_or_repo",
+ )
+ parser.add_argument(
+ "--preprocessor_model_path_or_repo",
+ type=str,
+ required=True,
+ help="Path or repo containing the preprocessor config",
+ )
+ parser.add_argument(
+ "--output_dir",
+ help="Location to write HF model and tokenizer",
+ )
+ parser.add_argument(
+ "--safe_serialization", action="store_true", default=True, help="Whether or not to save using `safetensors`."
+ )
+ parser.add_argument(
+ "--audio_delay_seconds",
+ type=float,
+ required=True,
+ help="Audio delay in seconds to add to the right of the input",
+ )
+ parser.add_argument(
+ "--audio_silence_prefix_seconds",
+ type=float,
+ required=True,
+ help="Audio silence prefix in seconds to add to the left of the input",
+ )
+ args = parser.parse_args()
+
+ write_model(
+ args.input_path_or_repo,
+ args.model_name,
+ args.codec_model_path_or_repo,
+ args.mimi_name,
+ args.output_dir,
+ safe_serialization=args.safe_serialization,
+ )
+
+ write_processor(
+ args.input_path_or_repo,
+ args.tokenizer_model_name,
+ args.preprocessor_model_path_or_repo,
+ args.output_dir,
+ args.audio_delay_seconds,
+ args.audio_silence_prefix_seconds,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/feature_extraction_kyutai_speech_to_text.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/feature_extraction_kyutai_speech_to_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..bde1736f9da859ea1f40a5a2728399194542c023
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/feature_extraction_kyutai_speech_to_text.py
@@ -0,0 +1,237 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/kyutai_speech_to_text/modular_kyutai_speech_to_text.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_kyutai_speech_to_text.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 Kyutai and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Optional, Union
+
+import numpy as np
+
+from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
+from ...feature_extraction_utils import BatchFeature
+from ...utils import PaddingStrategy, TensorType, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class KyutaiSpeechToTextFeatureExtractor(SequenceFeatureExtractor):
+ r"""
+ Constructs an KyutaiSpeechToText feature extractor.
+
+ This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
+ most of the main methods. Users should refer to this superclass for more information regarding those methods.
+
+ Args:
+ feature_size (`int`, *optional*, defaults to 1):
+ The feature dimension of the extracted features. Use 1 for mono, 2 for stereo.
+ sampling_rate (`int`, *optional*, defaults to 24000):
+ The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz).
+ padding_value (`float`, *optional*, defaults to 0.0):
+ The value that is used to fill the padding values.
+ chunk_length_s (`float`, *optional*):
+ If defined the audio is pre-processed into chunks of lengths `chunk_length_s` and then encoded.
+ overlap (`float`, *optional*):
+ Defines the overlap between each chunk. It is used to compute the `chunk_stride` using the following
+ formulae : `int((1.0 - self.overlap) * self.chunk_length)`.
+ audio_delay_seconds (`float`, *optional*, defaults to 0.0):
+ The delay in seconds to add after the audio (right padding).
+ audio_silence_prefix_seconds (`float`, *optional*, defaults to 0.0):
+ The silence prefix in seconds to add before the audio (left padding).
+ """
+
+ model_input_names = ["input_values", "padding_mask"]
+
+ def __init__(
+ self,
+ feature_size: int = 1,
+ sampling_rate: int = 24000,
+ padding_value: float = 0.0,
+ chunk_length_s: Optional[float] = None,
+ overlap: Optional[float] = None,
+ audio_delay_seconds: Optional[float] = 0.0,
+ audio_silence_prefix_seconds: Optional[float] = 0.0,
+ **kwargs,
+ ):
+ super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
+ self.chunk_length_s = chunk_length_s
+ self.overlap = overlap
+ self.audio_delay_seconds = audio_delay_seconds
+ self.audio_silence_prefix_seconds = audio_silence_prefix_seconds
+
+ # This is a property because you might want to change the chunk_length_s on the fly
+ @property
+ def chunk_length(self) -> Optional[int]:
+ if self.chunk_length_s is None:
+ return None
+ else:
+ return int(self.chunk_length_s * self.sampling_rate)
+
+ # This is a property because you might want to change the chunk_length_s on the fly
+ @property
+ def chunk_stride(self) -> Optional[int]:
+ if self.chunk_length_s is None or self.overlap is None:
+ return None
+ else:
+ return max(1, int((1.0 - self.overlap) * self.chunk_length))
+
+ def __call__(
+ self,
+ raw_audio: Union[np.ndarray, list[float], list[np.ndarray], list[list[float]]],
+ padding: Optional[Union[bool, str, PaddingStrategy]] = None,
+ truncation: Optional[bool] = False,
+ max_length: Optional[int] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ sampling_rate: Optional[int] = None,
+ ) -> BatchFeature:
+ """
+ Main method to featurize and prepare for the model one or several sequence(s).
+
+ Args:
+ raw_audio (`np.ndarray`, `list[float]`, `list[np.ndarray]`, `list[list[float]]`):
+ The sequence or batch of sequences to be processed. Each sequence can be a numpy array, a list of float
+ values, a list of numpy arrays or a list of list of float values. The numpy array must be of shape
+ `(num_samples,)` for mono audio (`feature_size = 1`), or `(2, num_samples)` for stereo audio
+ (`feature_size = 2`).
+ padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
+ Select a strategy to pad the returned sequences (according to the model's padding side and padding
+ index) among:
+
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ truncation (`bool`, *optional*, defaults to `False`):
+ Activates truncation to cut input sequences longer than `max_length` to `max_length`.
+ max_length (`int`, *optional*):
+ Maximum length of the returned list and optionally padding length (see above).
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors instead of list of python integers. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return Numpy `np.ndarray` objects.
+ sampling_rate (`int`, *optional*):
+ The sampling rate at which the `audio` input was sampled. It is strongly recommended to pass
+ `sampling_rate` at the forward call to prevent silent errors.
+ """
+ if sampling_rate is not None:
+ if sampling_rate != self.sampling_rate:
+ raise ValueError(
+ f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
+ f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
+ f" {self.sampling_rate} and not {sampling_rate}."
+ )
+ else:
+ logger.warning(
+ f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. "
+ "Failing to do so can result in silent errors that might be hard to debug."
+ )
+
+ if padding and truncation:
+ raise ValueError("Both padding and truncation were set. Make sure you only set one.")
+ elif padding is None:
+ # by default let's pad the inputs
+ padding = True
+
+ is_batched = bool(
+ isinstance(raw_audio, (list, tuple)) and (isinstance(raw_audio[0], (np.ndarray, tuple, list)))
+ )
+
+ if is_batched:
+ raw_audio = [np.asarray(audio, dtype=np.float32).T for audio in raw_audio]
+ elif not is_batched and not isinstance(raw_audio, np.ndarray):
+ raw_audio = np.asarray(raw_audio, dtype=np.float32)
+ elif isinstance(raw_audio, np.ndarray) and raw_audio.dtype is np.dtype(np.float64):
+ raw_audio = raw_audio.astype(np.float32)
+
+ # always return batch
+ if not is_batched:
+ raw_audio = [np.asarray(raw_audio).T]
+
+ # verify inputs are valid
+ for idx, example in enumerate(raw_audio):
+ if example.ndim > 2:
+ raise ValueError(f"Expected input shape (channels, length) but got shape {example.shape}")
+ if self.feature_size == 1 and example.ndim != 1:
+ raise ValueError(f"Expected mono audio but example has {example.shape[-1]} channels")
+ if self.feature_size == 2 and example.shape[-1] != 2:
+ raise ValueError(f"Expected stereo audio but example has {example.shape[-1]} channels")
+
+ padded_inputs = None
+ input_values = BatchFeature({"input_values": raw_audio})
+ if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
+ if truncation:
+ max_length = min(array.shape[0] for array in raw_audio)
+ nb_step = int(np.floor(max_length / self.chunk_stride))
+ max_length = (nb_step - 1) * self.chunk_stride + self.chunk_length
+ elif padding:
+ max_length = max(array.shape[0] for array in raw_audio)
+ nb_step = int(np.ceil(max_length / self.chunk_stride))
+ max_length = (nb_step - 1) * self.chunk_stride + self.chunk_length
+ padding = "max_length"
+ else:
+ padded_inputs = input_values
+
+ # normal padding on batch
+ if padded_inputs is None:
+ padded_inputs = self.pad(
+ input_values,
+ max_length=max_length,
+ truncation=truncation,
+ padding=padding,
+ return_attention_mask=padding,
+ )
+
+ if padding:
+ padded_inputs["padding_mask"] = padded_inputs.pop("attention_mask")
+
+ # now let's padd left and right
+ pad_left = int(self.audio_silence_prefix_seconds * self.sampling_rate)
+ pad_right = int((self.audio_delay_seconds + 1.0) * self.sampling_rate)
+ padded_inputs["input_values"] = np.pad(
+ padded_inputs["input_values"],
+ ((0, 0), (pad_left, pad_right)),
+ mode="constant",
+ constant_values=0.0,
+ )
+ if padding:
+ padded_inputs["padding_mask"] = np.pad(
+ padded_inputs["padding_mask"],
+ ((0, 0), (pad_left, pad_right)),
+ mode="constant",
+ constant_values=0,
+ )
+
+ input_values = []
+ for example in padded_inputs.pop("input_values"):
+ if self.feature_size == 1:
+ example = example[..., None]
+ input_values.append(example.T)
+
+ padded_inputs["input_values"] = input_values
+ if return_tensors is not None:
+ padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
+
+ return padded_inputs
+
+
+__all__ = ["KyutaiSpeechToTextFeatureExtractor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/modeling_kyutai_speech_to_text.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/modeling_kyutai_speech_to_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2659b56935ad18299a6f326399ef12b93a1e30b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/modeling_kyutai_speech_to_text.py
@@ -0,0 +1,1387 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/kyutai_speech_to_text/modular_kyutai_speech_to_text.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_kyutai_speech_to_text.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 Kyutai and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+import types
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
+from ...generation import GenerationConfig, GenerationMixin
+from ...modeling_attn_mask_utils import AttentionMaskConverter
+from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
+from ...utils.deprecation import deprecate_kwarg
+from ..auto import AutoModel
+from .configuration_kyutai_speech_to_text import KyutaiSpeechToTextConfig
+
+
+if is_flash_attn_available():
+ from ...modeling_flash_attention_utils import _flash_attention_forward
+
+if is_torch_flex_attn_available():
+ from torch.nn.attention.flex_attention import BlockMask
+
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+
+logger = logging.get_logger(__name__)
+
+
+class KyutaiSpeechToTextRMSNorm(nn.Module):
+ def __init__(self, dim: int, eps: float = 1e-6):
+ super().__init__()
+ self.eps = eps
+ self.weight = nn.Parameter(torch.ones(dim)) # Ignore copy
+
+ def _norm(self, x):
+ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+ # Ignore copy
+ def forward(self, x):
+ output = self._norm(x.float())
+ output = output * self.weight.float()
+ return output.type_as(x)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.eps}"
+
+
+class KyutaiSpeechToTextFlexibleLinear(nn.Module):
+ def __init__(self, input_size, output_size, num_layers):
+ super().__init__()
+ # Stack the weights for N layers into a single tensor (num_layers, output_size, input_size)
+ self.weight = nn.Parameter(torch.randn(num_layers, output_size, input_size))
+
+ def forward(self, x, layer_idx=None):
+ """
+ `KyutaiSpeechToTextFlexibleLinear` creates one linear layer per codebook. There's multiple ways to use it.
+ In the default case, `sequence_length=num_layers`, so each element of the sequence will be matmul to the weights corresponding to its index on the sequence.
+
+ For more advanced cases, one can specify which codebook's layer(s) to use with `layer_idx`.
+ If `layer_idx` indicates a single integer, all of the element of the sequence will be matmul to this single codebook's layer.
+ But if `layer_idx` is a tensor of shape `(seq_length,)`, it will matmul each i-th element of the input sequence to the corresponding layer `weight[i]`.
+
+
+ Args:
+ x (`torch.FloatTensor): input to the layer of shape `(batch, num_layers, embed_dim)` or of shape `(batch, seq_length, embed_dim)`
+ layer_idx (`torch.Tensor`, *optional*):
+ Can be used to specify which codebook's layers(s) to use.
+ If it's a tensor of shape `(seq_length,)`, will matmul each element of the sequence to the corresponding weights.
+ But if `layer_idx` is a tensor of shape `(seq_length,)`, it will matmul each i-th element of the input sequence to the corresponding layer `weight[i]`.
+ """
+
+ # Use torch.gather to select the corresponding weights for each sample
+ # (codebooks, output_size, hidden_size)
+ selected_weights = torch.index_select(self.weight, 0, layer_idx) if layer_idx is not None else self.weight
+
+ # (1, codebooks, hidden_size, output_size)
+ selected_weights = selected_weights.transpose(1, 2)[None, :, :, :]
+
+ # (batch_size, codebooks, 1, hidden_size) x (1, codebooks, hidden_size, output_size)
+ # -> (batch_size, codebooks, 1, output_size)
+ x = torch.matmul(x[:, :, None, :], selected_weights)
+
+ # (batch_size, codebooks, output_size)
+ return x.squeeze(2)
+
+
+@auto_docstring
+class KyutaiSpeechToTextPreTrainedModel(PreTrainedModel):
+ config: KyutaiSpeechToTextConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["KyutaiSpeechToTextDecoderLayer", "MimiTransformerLayer"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ main_input_name = "input_ids"
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, KyutaiSpeechToTextFlexibleLinear):
+ module.weight.data.normal_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, KyutaiSpeechToTextRMSNorm):
+ module.weight.data.fill_(1.0)
+
+
+class KyutaiSpeechToTextConv1dPaddingCache:
+ """
+ Padding cache for KyutaiSpeechToTextConv1d causal convolutions in order to support streaming via cache padding.
+ See: https://arxiv.org/pdf/2005.06720 & https://arxiv.org/pdf/2204.07064
+
+ A padding cache is a list of cached partial hidden states for each convolution layer.
+ Hidden states are cached from the previous call to the KyutaiSpeechToTextConv1d forward pass, given the padding size.
+ """
+
+ def __init__(
+ self,
+ num_layers: int,
+ per_layer_padding: list[int],
+ per_layer_padding_mode: list[str],
+ per_layer_in_channels: list[int],
+ ):
+ # ensure correct number of layers for each arg
+ from_args_num_layers = {len(per_layer_padding), len(per_layer_padding_mode), len(per_layer_in_channels)}
+
+ if len(from_args_num_layers) != 1 or from_args_num_layers.pop() != num_layers:
+ raise ValueError(
+ f"Expected `num_layers` ({num_layers}) values in `per_layer_padding`, `per_layer_padding_mode` and `per_layer_in_channels`"
+ )
+ elif not all(mode in ["constant", "replicate"] for mode in per_layer_padding_mode):
+ raise NotImplementedError(
+ "`padding_cache` is not supported for convolutions using other than `constant` or `replicate` padding mode"
+ )
+
+ self.per_layer_padding = per_layer_padding
+ self.per_layer_padding_mode = per_layer_padding_mode
+ self.per_layer_in_channels = per_layer_in_channels
+ self.per_layer_is_init = [True] * num_layers
+
+ self.padding_cache = [None] * num_layers
+
+ def update(self, hidden_states: torch.Tensor, layer_idx: int):
+ """
+ Updates the padding cache with the new padding states for the layer `layer_idx` and returns the current cache.
+
+ Parameters:
+ hidden_states (`torch.Tensor`):
+ The hidden states to be partially cached.
+ layer_idx (`int`):
+ The index of the layer to cache the states for.
+ Returns:
+ `torch.Tensor` or `None`, the current padding cache.
+ """
+ batch_size, dtype, device = hidden_states.shape[0], hidden_states.dtype, hidden_states.device
+ padding = self.per_layer_padding[layer_idx]
+ padding_mode = self.per_layer_padding_mode[layer_idx]
+ in_channels = self.per_layer_in_channels[layer_idx]
+
+ if self.padding_cache[layer_idx] is None:
+ if padding_mode == "constant":
+ current_cache = torch.zeros(
+ batch_size,
+ in_channels,
+ padding,
+ device=device,
+ dtype=dtype,
+ )
+ elif padding_mode == "replicate":
+ current_cache = (
+ torch.ones(
+ batch_size,
+ in_channels,
+ padding,
+ device=device,
+ dtype=dtype,
+ )
+ * hidden_states[..., :1]
+ )
+ else:
+ current_cache = self.padding_cache[layer_idx]
+
+ # update the cache
+ if padding > 0:
+ padding_states = hidden_states[:, :, -padding:]
+ else:
+ padding_states = torch.empty(batch_size, in_channels, padding, dtype=dtype, device=device)
+ self.padding_cache[layer_idx] = padding_states
+
+ return current_cache
+
+
+class KyutaiSpeechToTextEmbeddings(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.embed_tokens = nn.Embedding(
+ config.vocab_size + (config.num_codebooks * config.codebook_vocab_size) + 1,
+ config.hidden_size,
+ padding_idx=config.audio_pad_token_id,
+ )
+ audio_tokens_offsets = torch.arange(config.num_codebooks) * config.codebook_vocab_size
+ audio_tokens_offsets += config.vocab_size
+ audio_tokens_offsets = nn.functional.pad(
+ audio_tokens_offsets, (1, 0)
+ ) # pad one 0 to the left for the text token
+ self.register_buffer("audio_tokens_offsets", audio_tokens_offsets, persistent=False)
+
+ def forward(self, input_ids):
+ input_ids = torch.where(
+ input_ids == self.embed_tokens.padding_idx, input_ids, input_ids + self.audio_tokens_offsets
+ )
+ inputs_embeds = self.embed_tokens(input_ids)
+ inputs_embeds = inputs_embeds.sum(dim=2)
+ return inputs_embeds
+
+
+class KyutaiSpeechToTextLinear(nn.Module):
+ def __init__(self, input_dim, output_dim, num_codebooks, use_flexible_linear=False):
+ super().__init__()
+
+ self.use_flexible_linear = use_flexible_linear
+
+ if not use_flexible_linear:
+ self.linear = nn.Linear(input_dim, output_dim, bias=False)
+ else:
+ self.linear = KyutaiSpeechToTextFlexibleLinear(input_dim, output_dim, num_layers=num_codebooks)
+
+ def forward(self, x, layer_idx=None):
+ if self.use_flexible_linear:
+ return self.linear(x, layer_idx)
+ else:
+ return self.linear(x)
+
+
+class KyutaiSpeechToTextRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: KyutaiSpeechToTextConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class KyutaiSpeechToTextGatingMLP(nn.Module):
+ def __init__(self, config, use_flexible_linear=False):
+ super().__init__()
+
+ self.activation_fn = ACT2FN[config.hidden_act]
+ ffn_dim = config.ffn_dim
+ hidden_size = config.hidden_size
+ num_layers = config.num_codebooks if use_flexible_linear else 1
+ if num_layers == 1:
+ self.fc1 = nn.Linear(hidden_size, ffn_dim, bias=False)
+ self.fc2 = nn.Linear(ffn_dim // 2, hidden_size, bias=False)
+ else:
+ self.fc1 = KyutaiSpeechToTextFlexibleLinear(hidden_size, ffn_dim, num_layers)
+ self.fc2 = KyutaiSpeechToTextFlexibleLinear(ffn_dim // 2, hidden_size, num_layers)
+
+ def forward(self, hidden_states: torch.Tensor, layer_idx: Optional[int] = None) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states) if layer_idx is None else self.fc1(hidden_states, layer_idx)
+
+ batch_size, sequence_length, _ = hidden_states.shape
+ hidden_states = hidden_states.view(batch_size, sequence_length, 2, -1)
+ hidden_states = self.activation_fn(hidden_states[..., 0, :]) * hidden_states[..., 1, :]
+ hidden_states = self.fc2(hidden_states) if layer_idx is None else self.fc2(hidden_states, layer_idx)
+ return hidden_states
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class KyutaiSpeechToTextAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ config: KyutaiSpeechToTextConfig,
+ layer_idx: Optional[int] = None,
+ use_flexible_linear=False,
+ use_rope=True,
+ ):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = config.head_dim
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.is_causal = True
+ self.scaling = 1 / math.sqrt(self.head_dim)
+
+ if self.hidden_size % self.num_heads != 0:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ self.q_proj = KyutaiSpeechToTextLinear(
+ self.hidden_size, self.num_heads * self.head_dim, config.num_codebooks, use_flexible_linear
+ )
+ self.k_proj = KyutaiSpeechToTextLinear(
+ self.hidden_size, self.num_key_value_heads * self.head_dim, config.num_codebooks, use_flexible_linear
+ )
+ self.v_proj = KyutaiSpeechToTextLinear(
+ self.hidden_size, self.num_key_value_heads * self.head_dim, config.num_codebooks, use_flexible_linear
+ )
+ self.o_proj = KyutaiSpeechToTextLinear(
+ self.num_heads * self.head_dim, self.hidden_size, config.num_codebooks, use_flexible_linear
+ )
+
+ # rotary embeddings are not used in the depth decoder
+ self.rotary_emb = None
+ if use_rope:
+ self.rope_theta = config.rope_theta
+ self.rotary_emb = KyutaiSpeechToTextRotaryEmbedding(config)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states, cache_position) # Ignore copy
+ key_states = self.k_proj(hidden_states, cache_position) # Ignore copy
+ value_states = self.v_proj(hidden_states, cache_position) # Ignore copy
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ if self.rotary_emb is not None: # Ignore copy
+ cos, sin = self.rotary_emb(value_states, position_ids) # Ignore copy
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) # Ignore copy
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = (
+ {"sin": sin, "cos": cos, "cache_position": cache_position}
+ if self.rotary_emb is not None
+ else {"cache_position": cache_position}
+ ) # Ignore copy
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
+
+ if attention_mask is not None: # no matter the length, we just slice it
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.view(bsz, q_len, -1)
+ attn_output = self.o_proj(attn_output, cache_position) # Ignore copy
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights
+
+
+# NO LONGER EXIST Copied from transformers.models.gemma.modeling_gemma.GemmaFlashAttention2 with Gemma->KyutaiSpeechToText
+# TODO cyril: modular
+class KyutaiSpeechToTextFlashAttention2(KyutaiSpeechToTextAttention):
+ """
+ KyutaiSpeechToText flash attention module. This module inherits from `KyutaiSpeechToTextAttention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+ self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ if isinstance(past_key_values, StaticCache):
+ raise ValueError(
+ "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
+ "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
+ )
+
+ output_attentions = False
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states, cache_position) # Ignore copy
+ key_states = self.k_proj(hidden_states, cache_position) # Ignore copy
+ value_states = self.v_proj(hidden_states, cache_position) # Ignore copy
+
+ # Flash attention requires the input to have the shape
+ # batch_size x seq_length x head_dim x hidden_dim
+ # therefore we just need to keep the original shape
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ if self.rotary_emb is not None: # Ignore copy
+ cos, sin = self.rotary_emb(value_states, position_ids) # Ignore copy
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) # Ignore copy
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = (
+ {"sin": sin, "cos": cos, "cache_position": cache_position}
+ if self.rotary_emb is not None
+ else {"cache_position": cache_position}
+ ) # Ignore copy
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+ # to be able to avoid many of these transpose/reshape/view.
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ dropout_rate = self.attention_dropout if self.training else 0.0
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in the correct dtype just to be sure everything works as expected.
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+ # in fp32. (KyutaiSpeechToTextRMSNorm handles it correctly)
+
+ input_dtype = query_states.dtype
+ device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
+ if input_dtype == torch.float32:
+ if torch.is_autocast_enabled():
+ target_dtype = (
+ torch.get_autocast_dtype(device_type)
+ if hasattr(torch, "get_autocast_dtype")
+ else torch.get_autocast_gpu_dtype()
+ )
+ # Handle the case where the model is quantized
+ elif hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ else:
+ target_dtype = self.q_proj.weight.dtype
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ attn_output = _flash_attention_forward(
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ q_len,
+ position_ids=position_ids,
+ dropout=dropout_rate,
+ sliding_window=getattr(self, "sliding_window", None),
+ is_causal=self.is_causal,
+ use_top_left_mask=self._flash_attn_uses_top_left_mask,
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+ attn_output = self.o_proj(attn_output, cache_position) # Ignore copy
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights
+
+
+# NO LONGER EXIST Copied from transformers.models.gemma.modeling_gemma.GemmaSdpaAttention with Gemma->KyutaiSpeechToText
+# TODO cyril: modular
+class KyutaiSpeechToTextSdpaAttention(KyutaiSpeechToTextAttention):
+ """
+ KyutaiSpeechToText attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `KyutaiSpeechToTextAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+ SDPA API.
+ """
+
+ # Adapted from KyutaiSpeechToTextAttention.forward
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ if output_attentions:
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+ logger.warning_once(
+ "KyutaiSpeechToTextModel is using KyutaiSpeechToTextSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ return super().forward(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states, cache_position) # Ignore copy
+ key_states = self.k_proj(hidden_states, cache_position) # Ignore copy
+ value_states = self.v_proj(hidden_states, cache_position) # Ignore copy
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ if self.rotary_emb is not None: # Ignore copy
+ cos, sin = self.rotary_emb(value_states, position_ids) # Ignore copy
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) # Ignore copy
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = (
+ {"sin": sin, "cos": cos, "cache_position": cache_position}
+ if self.rotary_emb is not None
+ else {"cache_position": cache_position}
+ ) # Ignore copy
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ causal_mask = attention_mask
+ if attention_mask is not None:
+ causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ if query_states.device.type == "cuda" and causal_mask is not None:
+ query_states = query_states.contiguous()
+ key_states = key_states.contiguous()
+ value_states = value_states.contiguous()
+
+ # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+ # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+ is_causal = causal_mask is None and q_len > 1
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=causal_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ is_causal=is_causal,
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.view(bsz, q_len, -1)
+
+ attn_output = self.o_proj(attn_output, cache_position) # Ignore copy
+
+ return attn_output, None
+
+
+KYUTAI_SPEECH_TO_TEXT_ATTENTION_CLASSES = {
+ "eager": KyutaiSpeechToTextAttention,
+ "flash_attention_2": KyutaiSpeechToTextFlashAttention2,
+ "sdpa": KyutaiSpeechToTextSdpaAttention,
+}
+
+
+class KyutaiSpeechToTextDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: KyutaiSpeechToTextConfig, layer_idx: int, use_flexible_linear: bool, use_rope=True):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.use_flexible_linear = use_flexible_linear
+
+ self.self_attn = KYUTAI_SPEECH_TO_TEXT_ATTENTION_CLASSES[config._attn_implementation](
+ config=config, layer_idx=layer_idx, use_flexible_linear=use_flexible_linear, use_rope=use_rope
+ )
+
+ self.mlp = KyutaiSpeechToTextGatingMLP(config, use_flexible_linear)
+ self.input_layernorm = KyutaiSpeechToTextRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = KyutaiSpeechToTextRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
+ self.sliding_window = config.sliding_window
+
+ self._attn_implementation = config._attn_implementation
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence
+ kwargs (`dict`, *optional*):
+ Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+ into the model
+ """
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = (
+ self.mlp(hidden_states) if not self.use_flexible_linear else self.mlp(hidden_states, cache_position)
+ )
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ return outputs
+
+
+@auto_docstring
+class KyutaiSpeechToTextModel(KyutaiSpeechToTextPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+ self.embed_tokens = KyutaiSpeechToTextEmbeddings(config)
+ self.layers = nn.ModuleList(
+ [
+ KyutaiSpeechToTextDecoderLayer(config, layer_idx, use_flexible_linear=False)
+ for layer_idx in range(config.num_hidden_layers)
+ ]
+ )
+ self.norm = KyutaiSpeechToTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+ )
+ use_cache = False
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = None
+ if attention_mask is not None:
+ causal_mask = self._update_causal_mask(
+ attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+ )
+
+ # embed positions
+ hidden_states = inputs_embeds
+
+ # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
+ if not isinstance(past_key_values, (type(None), Cache)):
+ raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None
+ )
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, "BlockMask"],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ output_attentions: bool = False,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and past_key_values is not None:
+ is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
+ if is_padding_right:
+ raise ValueError(
+ "You are attempting to perform batched generation with padding_side='right'"
+ " this may lead to unexpected behaviour for Flash Attention version of KyutaiSpeechToText. Make sure to "
+ " call `tokenizer.padding_side = 'left'` before tokenizing the input. "
+ )
+ if attention_mask is not None and 0.0 in attention_mask:
+ return attention_mask
+ return None
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ return attention_mask
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_static_cache = isinstance(past_key_values, StaticCache)
+ using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if (
+ self.config._attn_implementation == "sdpa"
+ and not (using_static_cache or using_sliding_window_cache)
+ and not output_attentions
+ ):
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ sliding_window=self.config.sliding_window,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ min_dtype = torch.finfo(dtype).min
+ sequence_length = input_tensor.shape[1]
+ # SlidingWindowCache or StaticCache
+ if using_sliding_window_cache or using_static_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ # DynamicCache or no cache
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ config=self.config,
+ past_key_values=past_key_values,
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ and not output_attentions
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ config: KyutaiSpeechToTextConfig,
+ past_key_values: Cache,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ config (`KyutaiSpeechToTextConfig`):
+ The model's configuration class
+ past_key_values (`Cache`):
+ The cache class that is being used currently to generate
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
+ -1, 1
+ )
+ text_config = config.get_text_config()
+ if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None:
+ # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
+ # the check is needed to verify is current checkpoint was trained with sliding window or not
+ if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
+ sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= (
+ cache_position.reshape(-1, 1) - text_config.sliding_window
+ )
+ diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
+ causal_mask *= diagonal_attend_mask
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ if attention_mask.shape[-1] > target_length:
+ attention_mask = attention_mask[:, :target_length]
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+ return causal_mask
+
+
+@auto_docstring
+class KyutaiSpeechToTextForConditionalGeneration(KyutaiSpeechToTextPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+ _tp_plan = {"lm_head": "colwise_rep"}
+ _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+ _keep_in_fp32_modules_strict = ["codec_model"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = KyutaiSpeechToTextModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ self.codec_model = AutoModel.from_config(config.codec_config)
+
+ # we are in an edge case where for the codec_model self.can_generate is False, setting self.codec_model.generation_config to None
+ # yet the codec_model needs a generation config to initalize it's cache for streaming inference
+ # we therefore initialize a generation config for the codec model
+ self.codec_model.generation_config = GenerationConfig.from_model_config(config.codec_config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> CausalLMOutputWithPast:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> import torch
+ >>> from datasets import load_dataset, Audio
+ >>> from transformers import KyutaiSpeechToTextProcessor, KyutaiSpeechToTextForConditionalGeneration
+
+ >>> torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+ >>> model_id = "kyutai/stt-2.6b-en-trfs"
+
+ >>> processor = KyutaiSpeechToTextProcessor.from_pretrained(model_id)
+ >>> model = KyutaiSpeechToTextForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
+
+ >>> ds = load_dataset(
+ ... "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation"
+ ... )
+
+ >>> ds = ds.cast_column("audio", Audio(sampling_rate=24000))
+ >>> inputs = processor(
+ ... ds[0]["audio"]["array"],
+ ... )
+ >>> inputs.to(torch_device)
+
+ >>> output_tokens = model.generate(**inputs)
+ >>> print(processor.batch_decode(output_tokens, skip_special_tokens=True))
+ ```"""
+ outputs: BaseModelOutputWithPast = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def _prepare_generation_config(self, *args, **kwargs):
+ generation_config, model_kwargs = super()._prepare_generation_config(*args, **kwargs)
+ # this should be passed to the model kwargs for the input preparation
+ model_kwargs["audio_window_size"] = (
+ generation_config.audio_window_size if hasattr(generation_config, "audio_window_size") else None
+ )
+ return generation_config, model_kwargs
+
+ def _prepare_model_inputs(
+ self,
+ inputs: Optional[torch.Tensor] = None,
+ bos_token_id: Optional[torch.Tensor] = None,
+ model_kwargs: Optional[dict[str, torch.Tensor]] = None,
+ ) -> tuple[torch.Tensor, Optional[str], dict[str, torch.Tensor]]:
+ inputs, input_name, model_kwargs = super()._prepare_model_inputs(
+ inputs=inputs,
+ bos_token_id=bos_token_id,
+ model_kwargs=model_kwargs,
+ )
+
+ audio_window_size = model_kwargs.get("audio_window_size", None)
+ if audio_window_size is None:
+ audio_window_size = self.codec_model.get_encoded_length(model_kwargs["input_values"].shape[-1]).item()
+ model_kwargs["audio_window_size"] = audio_window_size
+
+ batch_size = inputs.shape[0]
+ device = inputs.device
+
+ # initialize audio tokens
+ model_kwargs["audio_tokens"] = torch.zeros(
+ (batch_size, audio_window_size, self.config.num_codebooks),
+ device=device,
+ dtype=torch.long,
+ )
+ model_kwargs["current_window"] = (
+ torch.tensor([0, 0], device=device, dtype=torch.long).expand(batch_size, -1).contiguous()
+ )
+
+ # let's use generate's cache preparation to prepare the cache for the codec model
+ temporary_model_kwargs = {}
+
+ # monkey patching the codec model with cache preparation methods since we don't want it to inherit fully from GenerationMixin
+ # Add cache-related methods from GenerationMixin to codec model
+ cache_methods = [
+ "_prepare_cache_for_generation",
+ "_get_cache",
+ ]
+ for method in cache_methods:
+ setattr(self.codec_model, method, types.MethodType(getattr(self, method).__func__, self.codec_model))
+
+ setattr(
+ self.codec_model, "_supports_default_dynamic_cache", types.MethodType(lambda x: True, self.codec_model)
+ )
+
+ self.codec_model.generation_config.cache_implementation = "dynamic"
+ self.codec_model._prepare_cache_for_generation(
+ generation_config=self.codec_model.generation_config,
+ model_kwargs=temporary_model_kwargs,
+ assistant_model=None,
+ batch_size=batch_size,
+ max_cache_length=self.config.codec_config.sliding_window,
+ )
+
+ if "past_key_values" in temporary_model_kwargs:
+ model_kwargs["encoder_past_key_values"] = temporary_model_kwargs["past_key_values"]
+
+ # initialize the padding cache for the codec model
+ per_layer_padding, per_layer_padding_mode, per_layer_in_channels = [], [], []
+ for layer_name in self.codec_model.encoder._mimiconv1d_layer_names:
+ per_layer_padding.append(self.codec_model.encoder.get_submodule(layer_name).padding_total)
+ per_layer_padding_mode.append(self.codec_model.encoder.get_submodule(layer_name).pad_mode)
+ per_layer_in_channels.append(self.codec_model.encoder.get_submodule(layer_name).in_channels)
+
+ # downsample layer
+ per_layer_padding.append(self.codec_model.downsample.padding_total)
+ per_layer_padding_mode.append(self.codec_model.downsample.pad_mode)
+ per_layer_in_channels.append(self.codec_model.downsample.in_channels)
+
+ model_kwargs["padding_cache"] = KyutaiSpeechToTextConv1dPaddingCache(
+ num_layers=len(self.codec_model.encoder._mimiconv1d_layer_names) + 1,
+ per_layer_padding=per_layer_padding,
+ per_layer_padding_mode=per_layer_padding_mode,
+ per_layer_in_channels=per_layer_in_channels,
+ )
+
+ return inputs, input_name, model_kwargs
+
+ def prepare_inputs_for_generation(
+ self,
+ *args,
+ audio_tokens: Optional[torch.LongTensor] = None,
+ input_values: Optional[torch.FloatTensor] = None,
+ padding_mask: Optional[torch.Tensor] = None,
+ audio_window_size: Optional[int] = None,
+ current_window: Optional[tuple[int, int]] = None,
+ encoder_past_key_values: Optional[Cache] = None,
+ padding_cache: Optional[KyutaiSpeechToTextConv1dPaddingCache] = None,
+ **kwargs,
+ ):
+ model_inputs = super().prepare_inputs_for_generation(*args, **kwargs)
+
+ if input_values is not None:
+ cache_position = model_inputs["cache_position"]
+ start, end = current_window[0]
+
+ # first cache position is for bos token, so we need to offset by -1
+ if cache_position[-1] - 1 >= end:
+ # we need to encode the new audio tokens
+ with torch.no_grad():
+ input_values_start_idx = start * self.config.frame_size
+ input_values_end_idx = (start + audio_window_size) * self.config.frame_size
+ current_input_values = input_values[..., input_values_start_idx:input_values_end_idx]
+ codec_model_output = self.codec_model.encode(
+ current_input_values,
+ encoder_past_key_values=encoder_past_key_values,
+ padding_cache=padding_cache,
+ )
+ new_audio_tokens = codec_model_output.audio_codes.transpose(1, 2)
+
+ audio_tokens.copy_(new_audio_tokens)
+
+ start = end.clone()
+ end = end + audio_window_size
+ current_window.copy_(
+ torch.tensor([start, end], device=current_window.device).expand(current_window.shape[0], -1)
+ )
+
+ # first cache position is for bos token, so we need to offset by -1
+ current_audio_tokens_idxs = (cache_position - start - 1).clamp(min=0)
+ current_audio_tokens = audio_tokens[:, current_audio_tokens_idxs, :]
+
+ current_audio_tokens[:, cache_position == 0, :] = self.config.audio_bos_token_id
+
+ input_ids = model_inputs.pop("input_ids")
+ input_ids = torch.cat(
+ [input_ids.unsqueeze(2), current_audio_tokens],
+ dim=2,
+ )
+ model_inputs["input_ids"] = input_ids
+
+ return model_inputs
+
+ # TODO: @eustlb, this should be standardized
+ @classmethod
+ def from_pretrained(cls, *args, **kwargs):
+ if kwargs.get("output_loading_info", False):
+ model, loading_info = super().from_pretrained(*args, **kwargs)
+ else:
+ model = super().from_pretrained(*args, **kwargs)
+
+ # copy depth decoder generation conf attr to the depth decoder generation config
+ prefix = "codec_"
+ prefix_len = len(prefix)
+ codec_model_attrs = {
+ attr[prefix_len:]: value
+ for attr, value in vars(model.generation_config).items()
+ if attr.startswith(prefix)
+ }
+
+ vars(model.codec_model.generation_config).update({"_from_model_config": False, **codec_model_attrs})
+
+ # remove the depth decoder generation conf attr from the model generation config
+ for attr in codec_model_attrs:
+ delattr(model.generation_config, prefix + attr)
+
+ if "output_loading_info" in kwargs:
+ return model, loading_info
+ else:
+ return model
+
+ # TODO: @eustlb, this should be standardized
+ def save_pretrained(self, *args, **kwargs):
+ prefix = "codec_"
+ codec_model_attrs = self.codec_model.generation_config.to_diff_dict()
+ codec_model_attrs.pop("transformers_version", None)
+ for attr, value in codec_model_attrs.items():
+ setattr(self.generation_config, prefix + attr, value)
+
+ super().save_pretrained(*args, **kwargs)
+
+ def generate(self, *args, **kwargs):
+ r"""
+ This method forwards all its arguments to GenerationMixin's [`~GenerationMixin.generate`]. Please refer to the docstring of this method for more information.
+ """
+ max_new_tokens = kwargs.pop("max_new_tokens", None)
+ input_values = kwargs.get("input_values")
+
+ # TODO: @eustlb, we should have per-batch-idx values
+ # here we do not use padding_mask to be aligned to what's done in the original codebase
+ max_audio_frames = input_values.shape[-1] // self.config.codec_config.frame_size
+
+ if max_new_tokens is None or max_new_tokens > max_audio_frames:
+ if max_new_tokens is not None:
+ logger.warning(
+ f"`max_new_tokens` ({max_new_tokens}) is greater than the maximum number of audio frames ({max_audio_frames})."
+ f"Setting `max_new_tokens` to {max_audio_frames}."
+ )
+ max_new_tokens = max_audio_frames
+
+ return super().generate(
+ *args,
+ max_new_tokens=max_new_tokens,
+ **kwargs,
+ )
+
+
+__all__ = [
+ "KyutaiSpeechToTextPreTrainedModel",
+ "KyutaiSpeechToTextModel",
+ "KyutaiSpeechToTextForConditionalGeneration",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/modular_kyutai_speech_to_text.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/modular_kyutai_speech_to_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1612ba435da11b5b0d9acef75096804f5754126
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/modular_kyutai_speech_to_text.py
@@ -0,0 +1,512 @@
+# coding=utf-8
+# Copyright 2025 Kyutai and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import types
+from typing import Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from ...cache_utils import Cache
+from ...feature_extraction_utils import BatchFeature
+from ...generation import GenerationConfig, GenerationMixin
+from ...modeling_utils import PreTrainedModel
+from ...utils import PaddingStrategy, TensorType, logging
+from ..auto import AutoModel
+from ..encodec.feature_extraction_encodec import EncodecFeatureExtractor
+from ..llama.modeling_llama import LlamaForCausalLM
+from ..mimi.modeling_mimi import MimiConv1dPaddingCache
+from ..moshi.modeling_moshi import MoshiModel, MoshiPreTrainedModel
+
+
+logger = logging.get_logger(__name__)
+
+
+class KyutaiSpeechToTextFeatureExtractor(EncodecFeatureExtractor):
+ r"""
+ Constructs an KyutaiSpeechToText feature extractor.
+
+ This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
+ most of the main methods. Users should refer to this superclass for more information regarding those methods.
+
+ Args:
+ feature_size (`int`, *optional*, defaults to 1):
+ The feature dimension of the extracted features. Use 1 for mono, 2 for stereo.
+ sampling_rate (`int`, *optional*, defaults to 24000):
+ The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz).
+ padding_value (`float`, *optional*, defaults to 0.0):
+ The value that is used to fill the padding values.
+ chunk_length_s (`float`, *optional*):
+ If defined the audio is pre-processed into chunks of lengths `chunk_length_s` and then encoded.
+ overlap (`float`, *optional*):
+ Defines the overlap between each chunk. It is used to compute the `chunk_stride` using the following
+ formulae : `int((1.0 - self.overlap) * self.chunk_length)`.
+ audio_delay_seconds (`float`, *optional*, defaults to 0.0):
+ The delay in seconds to add after the audio (right padding).
+ audio_silence_prefix_seconds (`float`, *optional*, defaults to 0.0):
+ The silence prefix in seconds to add before the audio (left padding).
+ """
+
+ def __init__(
+ self,
+ audio_delay_seconds: Optional[float] = 0.0,
+ audio_silence_prefix_seconds: Optional[float] = 0.0,
+ **super_kwargs,
+ ):
+ super().__init__(**super_kwargs)
+ self.audio_delay_seconds = audio_delay_seconds
+ self.audio_silence_prefix_seconds = audio_silence_prefix_seconds
+
+ def __call__(
+ self,
+ raw_audio: Union[np.ndarray, list[float], list[np.ndarray], list[list[float]]],
+ padding: Optional[Union[bool, str, PaddingStrategy]] = None,
+ truncation: Optional[bool] = False,
+ max_length: Optional[int] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ sampling_rate: Optional[int] = None,
+ ) -> BatchFeature:
+ """
+ Main method to featurize and prepare for the model one or several sequence(s).
+
+ Args:
+ raw_audio (`np.ndarray`, `list[float]`, `list[np.ndarray]`, `list[list[float]]`):
+ The sequence or batch of sequences to be processed. Each sequence can be a numpy array, a list of float
+ values, a list of numpy arrays or a list of list of float values. The numpy array must be of shape
+ `(num_samples,)` for mono audio (`feature_size = 1`), or `(2, num_samples)` for stereo audio
+ (`feature_size = 2`).
+ padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
+ Select a strategy to pad the returned sequences (according to the model's padding side and padding
+ index) among:
+
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ truncation (`bool`, *optional*, defaults to `False`):
+ Activates truncation to cut input sequences longer than `max_length` to `max_length`.
+ max_length (`int`, *optional*):
+ Maximum length of the returned list and optionally padding length (see above).
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors instead of list of python integers. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return Numpy `np.ndarray` objects.
+ sampling_rate (`int`, *optional*):
+ The sampling rate at which the `audio` input was sampled. It is strongly recommended to pass
+ `sampling_rate` at the forward call to prevent silent errors.
+ """
+ if sampling_rate is not None:
+ if sampling_rate != self.sampling_rate:
+ raise ValueError(
+ f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
+ f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
+ f" {self.sampling_rate} and not {sampling_rate}."
+ )
+ else:
+ logger.warning(
+ f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. "
+ "Failing to do so can result in silent errors that might be hard to debug."
+ )
+
+ if padding and truncation:
+ raise ValueError("Both padding and truncation were set. Make sure you only set one.")
+ elif padding is None:
+ # by default let's pad the inputs
+ padding = True
+
+ is_batched = bool(
+ isinstance(raw_audio, (list, tuple)) and (isinstance(raw_audio[0], (np.ndarray, tuple, list)))
+ )
+
+ if is_batched:
+ raw_audio = [np.asarray(audio, dtype=np.float32).T for audio in raw_audio]
+ elif not is_batched and not isinstance(raw_audio, np.ndarray):
+ raw_audio = np.asarray(raw_audio, dtype=np.float32)
+ elif isinstance(raw_audio, np.ndarray) and raw_audio.dtype is np.dtype(np.float64):
+ raw_audio = raw_audio.astype(np.float32)
+
+ # always return batch
+ if not is_batched:
+ raw_audio = [np.asarray(raw_audio).T]
+
+ # verify inputs are valid
+ for idx, example in enumerate(raw_audio):
+ if example.ndim > 2:
+ raise ValueError(f"Expected input shape (channels, length) but got shape {example.shape}")
+ if self.feature_size == 1 and example.ndim != 1:
+ raise ValueError(f"Expected mono audio but example has {example.shape[-1]} channels")
+ if self.feature_size == 2 and example.shape[-1] != 2:
+ raise ValueError(f"Expected stereo audio but example has {example.shape[-1]} channels")
+
+ padded_inputs = None
+ input_values = BatchFeature({"input_values": raw_audio})
+ if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
+ if truncation:
+ max_length = min(array.shape[0] for array in raw_audio)
+ nb_step = int(np.floor(max_length / self.chunk_stride))
+ max_length = (nb_step - 1) * self.chunk_stride + self.chunk_length
+ elif padding:
+ max_length = max(array.shape[0] for array in raw_audio)
+ nb_step = int(np.ceil(max_length / self.chunk_stride))
+ max_length = (nb_step - 1) * self.chunk_stride + self.chunk_length
+ padding = "max_length"
+ else:
+ padded_inputs = input_values
+
+ # normal padding on batch
+ if padded_inputs is None:
+ padded_inputs = self.pad(
+ input_values,
+ max_length=max_length,
+ truncation=truncation,
+ padding=padding,
+ return_attention_mask=padding,
+ )
+
+ if padding:
+ padded_inputs["padding_mask"] = padded_inputs.pop("attention_mask")
+
+ # now let's padd left and right
+ pad_left = int(self.audio_silence_prefix_seconds * self.sampling_rate)
+ pad_right = int((self.audio_delay_seconds + 1.0) * self.sampling_rate)
+ padded_inputs["input_values"] = np.pad(
+ padded_inputs["input_values"],
+ ((0, 0), (pad_left, pad_right)),
+ mode="constant",
+ constant_values=0.0,
+ )
+ if padding:
+ padded_inputs["padding_mask"] = np.pad(
+ padded_inputs["padding_mask"],
+ ((0, 0), (pad_left, pad_right)),
+ mode="constant",
+ constant_values=0,
+ )
+
+ input_values = []
+ for example in padded_inputs.pop("input_values"):
+ if self.feature_size == 1:
+ example = example[..., None]
+ input_values.append(example.T)
+
+ padded_inputs["input_values"] = input_values
+ if return_tensors is not None:
+ padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
+
+ return padded_inputs
+
+
+class KyutaiSpeechToTextPreTrainedModel(MoshiPreTrainedModel):
+ pass
+
+
+class KyutaiSpeechToTextConv1dPaddingCache(MimiConv1dPaddingCache):
+ pass
+
+
+class KyutaiSpeechToTextEmbeddings(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.embed_tokens = nn.Embedding(
+ config.vocab_size + (config.num_codebooks * config.codebook_vocab_size) + 1,
+ config.hidden_size,
+ padding_idx=config.audio_pad_token_id,
+ )
+ audio_tokens_offsets = torch.arange(config.num_codebooks) * config.codebook_vocab_size
+ audio_tokens_offsets += config.vocab_size
+ audio_tokens_offsets = nn.functional.pad(
+ audio_tokens_offsets, (1, 0)
+ ) # pad one 0 to the left for the text token
+ self.register_buffer("audio_tokens_offsets", audio_tokens_offsets, persistent=False)
+
+ def forward(self, input_ids):
+ input_ids = torch.where(
+ input_ids == self.embed_tokens.padding_idx, input_ids, input_ids + self.audio_tokens_offsets
+ )
+ inputs_embeds = self.embed_tokens(input_ids)
+ inputs_embeds = inputs_embeds.sum(dim=2)
+ return inputs_embeds
+
+
+class KyutaiSpeechToTextModel(MoshiModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.embed_tokens = KyutaiSpeechToTextEmbeddings(config)
+
+
+class KyutaiSpeechToTextForConditionalGeneration(LlamaForCausalLM, GenerationMixin, PreTrainedModel):
+ _keep_in_fp32_modules_strict = ["codec_model"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.codec_model = AutoModel.from_config(config.codec_config)
+
+ # we are in an edge case where for the codec_model self.can_generate is False, setting self.codec_model.generation_config to None
+ # yet the codec_model needs a generation config to initalize it's cache for streaming inference
+ # we therefore initialize a generation config for the codec model
+ self.codec_model.generation_config = GenerationConfig.from_model_config(config.codec_config)
+
+ def forward(self, **super_kwargs):
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> import torch
+ >>> from datasets import load_dataset, Audio
+ >>> from transformers import KyutaiSpeechToTextProcessor, KyutaiSpeechToTextForConditionalGeneration
+
+ >>> torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+ >>> model_id = "kyutai/stt-2.6b-en-trfs"
+
+ >>> processor = KyutaiSpeechToTextProcessor.from_pretrained(model_id)
+ >>> model = KyutaiSpeechToTextForConditionalGeneration.from_pretrained(model_id, device_map=torch_device)
+
+ >>> ds = load_dataset(
+ ... "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation"
+ ... )
+
+ >>> ds = ds.cast_column("audio", Audio(sampling_rate=24000))
+ >>> inputs = processor(
+ ... ds[0]["audio"]["array"],
+ ... )
+ >>> inputs.to(torch_device)
+
+ >>> output_tokens = model.generate(**inputs)
+ >>> print(processor.batch_decode(output_tokens, skip_special_tokens=True))
+ ```"""
+ super().forward(**super_kwargs)
+
+ def _prepare_generation_config(self, *args, **kwargs):
+ generation_config, model_kwargs = GenerationMixin._prepare_generation_config(*args, **kwargs)
+ # this should be passed to the model kwargs for the input preparation
+ model_kwargs["audio_window_size"] = (
+ generation_config.audio_window_size if hasattr(generation_config, "audio_window_size") else None
+ )
+ return generation_config, model_kwargs
+
+ def _prepare_model_inputs(
+ self,
+ inputs: Optional[torch.Tensor] = None,
+ bos_token_id: Optional[torch.Tensor] = None,
+ model_kwargs: Optional[dict[str, torch.Tensor]] = None,
+ ) -> tuple[torch.Tensor, Optional[str], dict[str, torch.Tensor]]:
+ inputs, input_name, model_kwargs = GenerationMixin._prepare_model_inputs(
+ inputs=inputs,
+ bos_token_id=bos_token_id,
+ model_kwargs=model_kwargs,
+ )
+
+ audio_window_size = model_kwargs.get("audio_window_size", None)
+ if audio_window_size is None:
+ audio_window_size = self.codec_model.get_encoded_length(model_kwargs["input_values"].shape[-1]).item()
+ model_kwargs["audio_window_size"] = audio_window_size
+
+ batch_size = inputs.shape[0]
+ device = inputs.device
+
+ # initialize audio tokens
+ model_kwargs["audio_tokens"] = torch.zeros(
+ (batch_size, audio_window_size, self.config.num_codebooks),
+ device=device,
+ dtype=torch.long,
+ )
+ model_kwargs["current_window"] = (
+ torch.tensor([0, 0], device=device, dtype=torch.long).expand(batch_size, -1).contiguous()
+ )
+
+ # let's use generate's cache preparation to prepare the cache for the codec model
+ temporary_model_kwargs = {}
+
+ # monkey patching the codec model with cache preparation methods since we don't want it to inherit fully from GenerationMixin
+ # Add cache-related methods from GenerationMixin to codec model
+ cache_methods = [
+ "_prepare_cache_for_generation",
+ "_get_cache",
+ ]
+ for method in cache_methods:
+ setattr(self.codec_model, method, types.MethodType(getattr(self, method).__func__, self.codec_model))
+
+ setattr(
+ self.codec_model, "_supports_default_dynamic_cache", types.MethodType(lambda x: True, self.codec_model)
+ )
+
+ self.codec_model.generation_config.cache_implementation = "dynamic"
+ self.codec_model._prepare_cache_for_generation(
+ generation_config=self.codec_model.generation_config,
+ model_kwargs=temporary_model_kwargs,
+ assistant_model=None,
+ batch_size=batch_size,
+ max_cache_length=self.config.codec_config.sliding_window,
+ )
+
+ if "past_key_values" in temporary_model_kwargs:
+ model_kwargs["encoder_past_key_values"] = temporary_model_kwargs["past_key_values"]
+
+ # initialize the padding cache for the codec model
+ per_layer_padding, per_layer_padding_mode, per_layer_in_channels = [], [], []
+ for layer_name in self.codec_model.encoder._mimiconv1d_layer_names:
+ per_layer_padding.append(self.codec_model.encoder.get_submodule(layer_name).padding_total)
+ per_layer_padding_mode.append(self.codec_model.encoder.get_submodule(layer_name).pad_mode)
+ per_layer_in_channels.append(self.codec_model.encoder.get_submodule(layer_name).in_channels)
+
+ # downsample layer
+ per_layer_padding.append(self.codec_model.downsample.padding_total)
+ per_layer_padding_mode.append(self.codec_model.downsample.pad_mode)
+ per_layer_in_channels.append(self.codec_model.downsample.in_channels)
+
+ model_kwargs["padding_cache"] = KyutaiSpeechToTextConv1dPaddingCache(
+ num_layers=len(self.codec_model.encoder._mimiconv1d_layer_names) + 1,
+ per_layer_padding=per_layer_padding,
+ per_layer_padding_mode=per_layer_padding_mode,
+ per_layer_in_channels=per_layer_in_channels,
+ )
+
+ return inputs, input_name, model_kwargs
+
+ def prepare_inputs_for_generation(
+ self,
+ *args,
+ audio_tokens: Optional[torch.LongTensor] = None,
+ input_values: Optional[torch.FloatTensor] = None,
+ padding_mask: Optional[torch.Tensor] = None,
+ audio_window_size: Optional[int] = None,
+ current_window: Optional[tuple[int, int]] = None,
+ encoder_past_key_values: Optional[Cache] = None,
+ padding_cache: Optional[KyutaiSpeechToTextConv1dPaddingCache] = None,
+ **kwargs,
+ ):
+ model_inputs = GenerationMixin.prepare_inputs_for_generation(*args, **kwargs)
+
+ if input_values is not None:
+ cache_position = model_inputs["cache_position"]
+ start, end = current_window[0]
+
+ # first cache position is for bos token, so we need to offset by -1
+ if cache_position[-1] - 1 >= end:
+ # we need to encode the new audio tokens
+ with torch.no_grad():
+ input_values_start_idx = start * self.config.frame_size
+ input_values_end_idx = (start + audio_window_size) * self.config.frame_size
+ current_input_values = input_values[..., input_values_start_idx:input_values_end_idx]
+ codec_model_output = self.codec_model.encode(
+ current_input_values,
+ encoder_past_key_values=encoder_past_key_values,
+ padding_cache=padding_cache,
+ )
+ new_audio_tokens = codec_model_output.audio_codes.transpose(1, 2)
+
+ audio_tokens.copy_(new_audio_tokens)
+
+ start = end.clone()
+ end = end + audio_window_size
+ current_window.copy_(
+ torch.tensor([start, end], device=current_window.device).expand(current_window.shape[0], -1)
+ )
+
+ # first cache position is for bos token, so we need to offset by -1
+ current_audio_tokens_idxs = (cache_position - start - 1).clamp(min=0)
+ current_audio_tokens = audio_tokens[:, current_audio_tokens_idxs, :]
+
+ current_audio_tokens[:, cache_position == 0, :] = self.config.audio_bos_token_id
+
+ input_ids = model_inputs.pop("input_ids")
+ input_ids = torch.cat(
+ [input_ids.unsqueeze(2), current_audio_tokens],
+ dim=2,
+ )
+ model_inputs["input_ids"] = input_ids
+
+ return model_inputs
+
+ # TODO: @eustlb, this should be standardized
+ @classmethod
+ def from_pretrained(cls, *args, **kwargs):
+ if kwargs.get("output_loading_info", False):
+ model, loading_info = PreTrainedModel.from_pretrained(*args, **kwargs)
+ else:
+ model = PreTrainedModel.from_pretrained(*args, **kwargs)
+
+ # copy depth decoder generation conf attr to the depth decoder generation config
+ prefix = "codec_"
+ prefix_len = len(prefix)
+ codec_model_attrs = {
+ attr[prefix_len:]: value
+ for attr, value in vars(model.generation_config).items()
+ if attr.startswith(prefix)
+ }
+
+ vars(model.codec_model.generation_config).update({"_from_model_config": False, **codec_model_attrs})
+
+ # remove the depth decoder generation conf attr from the model generation config
+ for attr in codec_model_attrs:
+ delattr(model.generation_config, prefix + attr)
+
+ if "output_loading_info" in kwargs:
+ return model, loading_info
+ else:
+ return model
+
+ # TODO: @eustlb, this should be standardized
+ def save_pretrained(self, *args, **kwargs):
+ prefix = "codec_"
+ codec_model_attrs = self.codec_model.generation_config.to_diff_dict()
+ codec_model_attrs.pop("transformers_version", None)
+ for attr, value in codec_model_attrs.items():
+ setattr(self.generation_config, prefix + attr, value)
+
+ PreTrainedModel.save_pretrained(self, *args, **kwargs)
+
+ def generate(self, *args, **kwargs):
+ r"""
+ This method forwards all its arguments to GenerationMixin's [`~GenerationMixin.generate`]. Please refer to the docstring of this method for more information.
+ """
+ max_new_tokens = kwargs.pop("max_new_tokens", None)
+ input_values = kwargs.get("input_values")
+
+ # TODO: @eustlb, we should have per-batch-idx values
+ # here we do not use padding_mask to be aligned to what's done in the original codebase
+ max_audio_frames = input_values.shape[-1] // self.config.codec_config.frame_size
+
+ if max_new_tokens is None or max_new_tokens > max_audio_frames:
+ if max_new_tokens is not None:
+ logger.warning(
+ f"`max_new_tokens` ({max_new_tokens}) is greater than the maximum number of audio frames ({max_audio_frames})."
+ f"Setting `max_new_tokens` to {max_audio_frames}."
+ )
+ max_new_tokens = max_audio_frames
+
+ return GenerationMixin.generate(
+ *args,
+ max_new_tokens=max_new_tokens,
+ **kwargs,
+ )
+
+
+__all__ = [
+ "KyutaiSpeechToTextPreTrainedModel",
+ "KyutaiSpeechToTextModel",
+ "KyutaiSpeechToTextForConditionalGeneration",
+ "KyutaiSpeechToTextFeatureExtractor",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/processing_kyutai_speech_to_text.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/processing_kyutai_speech_to_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b3a021712362a25c231d2a22724cd9545f8196a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/kyutai_speech_to_text/processing_kyutai_speech_to_text.py
@@ -0,0 +1,104 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Optional
+
+from ...audio_utils import AudioInput, make_list_of_audio
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+
+
+class KyutaiSpeechToTextProcessorKwargs(ProcessingKwargs, total=False):
+ _defaults = {
+ "audio_kwargs": {
+ "sampling_rate": 24000,
+ },
+ "common_kwargs": {"return_tensors": "pt"},
+ }
+
+
+class KyutaiSpeechToTextProcessor(ProcessorMixin):
+ r"""
+ Constructs a Moshi ASR processor which wraps [`EncodecFeatureExtractor`] and
+ [`PreTrainedTokenizerFast`] into a single processor that inherits both the audio feature extraction and
+ tokenizer functionalities. See the [`~KyutaiSpeechToTextProcessor.__call__`] for more
+ information.
+ """
+
+ feature_extractor_class = "KyutaiSpeechToTextFeatureExtractor"
+ tokenizer_class = "PreTrainedTokenizerFast"
+
+ def __call__(
+ self,
+ audio: Optional[AudioInput] = None,
+ **kwargs: Unpack[KyutaiSpeechToTextProcessorKwargs],
+ ):
+ r"""
+ Main method to prepare audio to be fed as input to the model. This method forwards the `audio`
+ arguments to KyutaiSpeechToTextFeatureExtractor's [`~KyutaiSpeechToTextFeatureExtractor.__call__`]. Please refer
+ to the docstring of the above method for more information.
+
+ Args:
+ audio (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The audio or batch of audio to be prepared. Each audio can be a NumPy array or PyTorch
+ tensor.
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors of a particular framework. Acceptable values are:
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return NumPy `np.ndarray` objects.
+ - `'jax'`: Return JAX `jnp.ndarray` objects.
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **input_values** -- List of audio values to be fed to a model. Returned when `audio` is not `None`.
+ - **padding_mask** -- List of indices specifying which input values should be ignored by the model.
+ """
+
+ if audio is None:
+ raise ValueError("`audio` is required.")
+
+ output_kwargs = self._merge_kwargs(
+ KyutaiSpeechToTextProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+ audio_kwargs = output_kwargs["audio_kwargs"]
+
+ # ensure audio in correct format
+ audio = make_list_of_audio(audio)
+
+ inputs = self.feature_extractor(
+ audio,
+ **audio_kwargs,
+ )
+
+ return inputs
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to KyutaiSpeechToTextTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to KyutaiSpeechToTextTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+
+__all__ = ["KyutaiSpeechToTextProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f079c33c7157898807c5f405086804cc1533ff9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_layoutlm import *
+ from .modeling_layoutlm import *
+ from .modeling_tf_layoutlm import *
+ from .tokenization_layoutlm import *
+ from .tokenization_layoutlm_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/configuration_layoutlm.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/configuration_layoutlm.py
new file mode 100644
index 0000000000000000000000000000000000000000..95bc2eda6faba3d0f507344d53d553f818648f8b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/configuration_layoutlm.py
@@ -0,0 +1,213 @@
+# coding=utf-8
+# Copyright 2010, The Microsoft Research Asia LayoutLM Team authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LayoutLM model configuration"""
+
+import warnings
+from collections import OrderedDict
+from collections.abc import Mapping
+from typing import Any, Optional
+
+from ... import PretrainedConfig, PreTrainedTokenizer
+from ...onnx import OnnxConfig, PatchingSpec
+from ...utils import TensorType, is_torch_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class LayoutLMConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LayoutLMModel`]. It is used to instantiate a
+ LayoutLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the LayoutLM
+ [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) architecture.
+
+ Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
+ documentation from [`BertConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 30522):
+ Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the
+ *inputs_ids* passed to the forward method of [`LayoutLMModel`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimensionality of the encoder layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 2):
+ The vocabulary size of the `token_type_ids` passed into [`LayoutLMModel`].
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+ pad_token_id (`int`, *optional*, defaults to 0):
+ The value used to pad input_ids.
+ position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
+ Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
+ positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
+ [Self-Attention with Relative Position Representations (Shaw et al.)](https://huggingface.co/papers/1803.02155).
+ For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
+ with Better Relative Position Embeddings (Huang et al.)](https://huggingface.co/papers/2009.13658).
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
+ The maximum value that the 2D position embedding might ever used. Typically set this to something large
+ just in case (e.g., 1024).
+
+ Examples:
+
+ ```python
+ >>> from transformers import LayoutLMConfig, LayoutLMModel
+
+ >>> # Initializing a LayoutLM configuration
+ >>> configuration = LayoutLMConfig()
+
+ >>> # Initializing a model (with random weights) from the configuration
+ >>> model = LayoutLMModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "layoutlm"
+
+ def __init__(
+ self,
+ vocab_size=30522,
+ hidden_size=768,
+ num_hidden_layers=12,
+ num_attention_heads=12,
+ intermediate_size=3072,
+ hidden_act="gelu",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ type_vocab_size=2,
+ initializer_range=0.02,
+ layer_norm_eps=1e-12,
+ pad_token_id=0,
+ position_embedding_type="absolute",
+ use_cache=True,
+ max_2d_position_embeddings=1024,
+ **kwargs,
+ ):
+ super().__init__(pad_token_id=pad_token_id, **kwargs)
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.hidden_act = hidden_act
+ self.intermediate_size = intermediate_size
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.max_position_embeddings = max_position_embeddings
+ self.type_vocab_size = type_vocab_size
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self._position_embedding_type = position_embedding_type
+ self.use_cache = use_cache
+ self.max_2d_position_embeddings = max_2d_position_embeddings
+
+ @property
+ def position_embedding_type(self):
+ warnings.warn(
+ "The `position_embedding_type` attribute is deprecated and will be removed in v4.55.",
+ FutureWarning,
+ )
+ return self._position_embedding_type
+
+ @position_embedding_type.setter
+ def position_embedding_type(self, value):
+ self._position_embedding_type = value
+
+
+class LayoutLMOnnxConfig(OnnxConfig):
+ def __init__(
+ self,
+ config: PretrainedConfig,
+ task: str = "default",
+ patching_specs: Optional[list[PatchingSpec]] = None,
+ ):
+ super().__init__(config, task=task, patching_specs=patching_specs)
+ self.max_2d_positions = config.max_2d_position_embeddings - 1
+
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ return OrderedDict(
+ [
+ ("input_ids", {0: "batch", 1: "sequence"}),
+ ("bbox", {0: "batch", 1: "sequence"}),
+ ("attention_mask", {0: "batch", 1: "sequence"}),
+ ("token_type_ids", {0: "batch", 1: "sequence"}),
+ ]
+ )
+
+ def generate_dummy_inputs(
+ self,
+ tokenizer: PreTrainedTokenizer,
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ """
+ Generate inputs to provide to the ONNX exporter for the specific framework
+
+ Args:
+ tokenizer: The tokenizer associated with this model configuration
+ batch_size: The batch size (int) to export the model for (-1 means dynamic axis)
+ seq_length: The sequence length (int) to export the model for (-1 means dynamic axis)
+ is_pair: Indicate if the input is a pair (sentence 1, sentence 2)
+ framework: The framework (optional) the tokenizer will generate tensor for
+
+ Returns:
+ Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
+ """
+
+ input_dict = super().generate_dummy_inputs(
+ tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+ )
+
+ # Generate a dummy bbox
+ box = [48, 84, 73, 128]
+
+ if not framework == TensorType.PYTORCH:
+ raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch.")
+
+ if not is_torch_available():
+ raise ValueError("Cannot generate dummy inputs without PyTorch installed.")
+ import torch
+
+ batch_size, seq_length = input_dict["input_ids"].shape
+ input_dict["bbox"] = torch.tensor([*[box] * seq_length]).tile(batch_size, 1, 1)
+ return input_dict
+
+
+__all__ = ["LayoutLMConfig", "LayoutLMOnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/modeling_layoutlm.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/modeling_layoutlm.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3b79ef99d38cc1e2932e4d759f8719fd739cfc3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/modeling_layoutlm.py
@@ -0,0 +1,1145 @@
+# coding=utf-8
+# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch LayoutLM model."""
+
+from typing import Callable, Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPooling,
+ MaskedLMOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import auto_docstring, can_return_tuple, logging
+from .configuration_layoutlm import LayoutLMConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+LayoutLMLayerNorm = nn.LayerNorm
+
+
+class LayoutLMEmbeddings(nn.Module):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+ self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
+ self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
+ self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
+ self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ self.LayerNorm = LayoutLMLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ self.register_buffer(
+ "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+ )
+
+ def forward(
+ self,
+ input_ids=None,
+ bbox=None,
+ token_type_ids=None,
+ position_ids=None,
+ inputs_embeds=None,
+ ):
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if position_ids is None:
+ position_ids = self.position_ids[:, :seq_length]
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+
+ words_embeddings = inputs_embeds
+ position_embeddings = self.position_embeddings(position_ids)
+ try:
+ left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
+ upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
+ right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
+ lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
+ except IndexError as e:
+ raise IndexError("The `bbox`coordinate values should be within 0-1000 range.") from e
+
+ h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
+ w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = (
+ words_embeddings
+ + position_embeddings
+ + left_position_embeddings
+ + upper_position_embeddings
+ + right_position_embeddings
+ + lower_position_embeddings
+ + h_position_embeddings
+ + w_position_embeddings
+ + token_type_embeddings
+ )
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+# Copied from transformers.models.align.modeling_align.eager_attention_forward
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ head_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+):
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+
+ if head_mask is not None:
+ attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
+
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.align.modeling_align.AlignTextSelfAttention with AlignText->LayoutLM
+class LayoutLMSelfAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+
+ self.config = config
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+ self.attention_dropout = config.attention_probs_dropout_prob
+ self.scaling = self.attention_head_size**-0.5
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ **kwargs,
+ ) -> tuple[torch.Tensor]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.attention_head_size)
+
+ query_states = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
+ key_states = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.value(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ head_mask=head_mask,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->LayoutLM
+class LayoutLMSelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+# Copied from transformers.models.align.modeling_align.AlignTextAttention with AlignText->LayoutLM
+class LayoutLMAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.self = LayoutLMSelfAttention(config)
+ self.output = LayoutLMSelfOutput(config)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+ )
+
+ # Prune linear layers
+ self.self.query = prune_linear_layer(self.self.query, index)
+ self.self.key = prune_linear_layer(self.self.key, index)
+ self.self.value = prune_linear_layer(self.self.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+ self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ **kwargs,
+ ) -> tuple[torch.Tensor]:
+ self_outputs = self.self(
+ hidden_states,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ **kwargs,
+ )
+ attention_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate
+class LayoutLMIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->LayoutLM
+class LayoutLMOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+# Copied from transformers.models.align.modeling_align.AlignTextLayer with AlignText->LayoutLM
+class LayoutLMLayer(GradientCheckpointingLayer):
+ def __init__(self, config):
+ super().__init__()
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = LayoutLMAttention(config)
+ self.intermediate = LayoutLMIntermediate(config)
+ self.output = LayoutLMOutput(config)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ **kwargs,
+ ) -> tuple[torch.Tensor]:
+ self_attention_outputs = self.attention(
+ hidden_states,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ **kwargs,
+ )
+ attention_output = self_attention_outputs[0]
+
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+ layer_output = apply_chunking_to_forward(
+ self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+ )
+ outputs = (layer_output,) + outputs
+
+ return outputs
+
+ def feed_forward_chunk(self, attention_output):
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+
+# Copied from transformers.models.align.modeling_align.AlignTextEncoder with AlignText->LayoutLM
+class LayoutLMEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList([LayoutLMLayer(config) for i in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ @can_return_tuple
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ output_hidden_states: Optional[bool] = False,
+ return_dict: Optional[bool] = True,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], BaseModelOutput]:
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+
+ layer_outputs = layer_module(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ **kwargs,
+ )
+
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler
+class LayoutLMPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->LayoutLM
+class LayoutLMPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ if isinstance(config.hidden_act, str):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->LayoutLM
+class LayoutLMLMPredictionHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.transform = LayoutLMPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+ # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+ self.decoder.bias = self.bias
+
+ def _tie_weights(self):
+ self.decoder.bias = self.bias
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->LayoutLM
+class LayoutLMOnlyMLMHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.predictions = LayoutLMLMPredictionHead(config)
+
+ def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
+ prediction_scores = self.predictions(sequence_output)
+ return prediction_scores
+
+
+@auto_docstring
+class LayoutLMPreTrainedModel(PreTrainedModel):
+ config: LayoutLMConfig
+ base_model_prefix = "layoutlm"
+ supports_gradient_checkpointing = True
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, nn.Linear):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, LayoutLMLayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, LayoutLMLMPredictionHead):
+ module.bias.data.zero_()
+
+
+@auto_docstring
+class LayoutLMModel(LayoutLMPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.config = config
+
+ self.embeddings = LayoutLMEmbeddings(config)
+ self.encoder = LayoutLMEncoder(config)
+ self.pooler = LayoutLMPooler(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPooling]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LayoutLMModel
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+ >>> model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
+
+ >>> words = ["Hello", "world"]
+ >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+ >>> token_boxes = []
+ >>> for word, box in zip(words, normalized_word_boxes):
+ ... word_tokens = tokenizer.tokenize(word)
+ ... token_boxes.extend([box] * len(word_tokens))
+ >>> # add bounding boxes of cls + sep tokens
+ >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+ >>> encoding = tokenizer(" ".join(words), return_tensors="pt")
+ >>> input_ids = encoding["input_ids"]
+ >>> attention_mask = encoding["attention_mask"]
+ >>> token_type_ids = encoding["token_type_ids"]
+ >>> bbox = torch.tensor([token_boxes])
+
+ >>> outputs = model(
+ ... input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids
+ ... )
+
+ >>> last_hidden_states = outputs.last_hidden_state
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones(input_shape, device=device)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ if bbox is None:
+ bbox = torch.zeros(input_shape + (4,), dtype=torch.long, device=device)
+
+ extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+
+ extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
+ extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
+
+ if head_mask is not None:
+ if head_mask.dim() == 1:
+ head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+ head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
+ elif head_mask.dim() == 2:
+ head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
+ head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
+ else:
+ head_mask = [None] * self.config.num_hidden_layers
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ bbox=bbox,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ )
+ encoder_outputs = self.encoder(
+ embedding_output,
+ extended_attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output)
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring
+class LayoutLMForMaskedLM(LayoutLMPreTrainedModel):
+ _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.layoutlm = LayoutLMModel(config)
+ self.cls = LayoutLMOnlyMLMHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.layoutlm.embeddings.word_embeddings
+
+ def get_output_embeddings(self):
+ return self.cls.predictions.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.cls.predictions.decoder = new_embeddings
+ self.cls.predictions.bias = new_embeddings.bias
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, MaskedLMOutput]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LayoutLMForMaskedLM
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+ >>> model = LayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased")
+
+ >>> words = ["Hello", "[MASK]"]
+ >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+ >>> token_boxes = []
+ >>> for word, box in zip(words, normalized_word_boxes):
+ ... word_tokens = tokenizer.tokenize(word)
+ ... token_boxes.extend([box] * len(word_tokens))
+ >>> # add bounding boxes of cls + sep tokens
+ >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+ >>> encoding = tokenizer(" ".join(words), return_tensors="pt")
+ >>> input_ids = encoding["input_ids"]
+ >>> attention_mask = encoding["attention_mask"]
+ >>> token_type_ids = encoding["token_type_ids"]
+ >>> bbox = torch.tensor([token_boxes])
+
+ >>> labels = tokenizer("Hello world", return_tensors="pt")["input_ids"]
+
+ >>> outputs = model(
+ ... input_ids=input_ids,
+ ... bbox=bbox,
+ ... attention_mask=attention_mask,
+ ... token_type_ids=token_type_ids,
+ ... labels=labels,
+ ... )
+
+ >>> loss = outputs.loss
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlm(
+ input_ids,
+ bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ sequence_output = outputs[0]
+ prediction_scores = self.cls(sequence_output)
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ masked_lm_loss = loss_fct(
+ prediction_scores.view(-1, self.config.vocab_size),
+ labels.view(-1),
+ )
+
+ return MaskedLMOutput(
+ loss=masked_lm_loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ LayoutLM Model with a sequence classification head on top (a linear layer on top of the pooled output) e.g. for
+ document image classification tasks such as the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
+ """
+)
+class LayoutLMForSequenceClassification(LayoutLMPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.layoutlm = LayoutLMModel(config)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.layoutlm.embeddings.word_embeddings
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, SequenceClassifierOutput]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LayoutLMForSequenceClassification
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+ >>> model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")
+
+ >>> words = ["Hello", "world"]
+ >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+ >>> token_boxes = []
+ >>> for word, box in zip(words, normalized_word_boxes):
+ ... word_tokens = tokenizer.tokenize(word)
+ ... token_boxes.extend([box] * len(word_tokens))
+ >>> # add bounding boxes of cls + sep tokens
+ >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+ >>> encoding = tokenizer(" ".join(words), return_tensors="pt")
+ >>> input_ids = encoding["input_ids"]
+ >>> attention_mask = encoding["attention_mask"]
+ >>> token_type_ids = encoding["token_type_ids"]
+ >>> bbox = torch.tensor([token_boxes])
+ >>> sequence_label = torch.tensor([1])
+
+ >>> outputs = model(
+ ... input_ids=input_ids,
+ ... bbox=bbox,
+ ... attention_mask=attention_mask,
+ ... token_type_ids=token_type_ids,
+ ... labels=sequence_label,
+ ... )
+
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlm(
+ input_ids=input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ pooled_output = outputs[1]
+
+ pooled_output = self.dropout(pooled_output)
+ logits = self.classifier(pooled_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+ sequence labeling (information extraction) tasks such as the [FUNSD](https://guillaumejaume.github.io/FUNSD/)
+ dataset and the [SROIE](https://rrc.cvc.uab.es/?ch=13) dataset.
+ """
+)
+class LayoutLMForTokenClassification(LayoutLMPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.layoutlm = LayoutLMModel(config)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.layoutlm.embeddings.word_embeddings
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, TokenClassifierOutput]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LayoutLMForTokenClassification
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+ >>> model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased")
+
+ >>> words = ["Hello", "world"]
+ >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+ >>> token_boxes = []
+ >>> for word, box in zip(words, normalized_word_boxes):
+ ... word_tokens = tokenizer.tokenize(word)
+ ... token_boxes.extend([box] * len(word_tokens))
+ >>> # add bounding boxes of cls + sep tokens
+ >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+ >>> encoding = tokenizer(" ".join(words), return_tensors="pt")
+ >>> input_ids = encoding["input_ids"]
+ >>> attention_mask = encoding["attention_mask"]
+ >>> token_type_ids = encoding["token_type_ids"]
+ >>> bbox = torch.tensor([token_boxes])
+ >>> token_labels = torch.tensor([1, 1, 0, 0]).unsqueeze(0) # batch size of 1
+
+ >>> outputs = model(
+ ... input_ids=input_ids,
+ ... bbox=bbox,
+ ... attention_mask=attention_mask,
+ ... token_type_ids=token_type_ids,
+ ... labels=token_labels,
+ ... )
+
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlm(
+ input_ids=input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class LayoutLMForQuestionAnswering(LayoutLMPreTrainedModel):
+ def __init__(self, config, has_visual_segment_embedding=True):
+ r"""
+ has_visual_segment_embedding (`bool`, *optional*, defaults to `True`):
+ Whether or not to add visual segment embeddings.
+ """
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.layoutlm = LayoutLMModel(config)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.layoutlm.embeddings.word_embeddings
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, QuestionAnsweringModelOutput]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
+
+ Example:
+
+ In the example below, we prepare a question + context pair for the LayoutLM model. It will give us a prediction
+ of what it thinks the answer is (the span of the answer within the texts parsed from the image).
+
+ ```python
+ >>> from transformers import AutoTokenizer, LayoutLMForQuestionAnswering
+ >>> from datasets import load_dataset
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
+ >>> model = LayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac")
+
+ >>> dataset = load_dataset("nielsr/funsd", split="train")
+ >>> example = dataset[0]
+ >>> question = "what's his name?"
+ >>> words = example["words"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = tokenizer(
+ ... question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="pt"
+ ... )
+ >>> bbox = []
+ >>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
+ ... if s == 1:
+ ... bbox.append(boxes[w])
+ ... elif i == tokenizer.sep_token_id:
+ ... bbox.append([1000] * 4)
+ ... else:
+ ... bbox.append([0] * 4)
+ >>> encoding["bbox"] = torch.tensor([bbox])
+
+ >>> word_ids = encoding.word_ids(0)
+ >>> outputs = model(**encoding)
+ >>> loss = outputs.loss
+ >>> start_scores = outputs.start_logits
+ >>> end_scores = outputs.end_logits
+ >>> start, end = word_ids[start_scores.argmax(-1)], word_ids[end_scores.argmax(-1)]
+ >>> print(" ".join(words[start : end + 1]))
+ M. Hamann P. Harper, P. Martinez
+ ```"""
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlm(
+ input_ids=input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "LayoutLMForMaskedLM",
+ "LayoutLMForSequenceClassification",
+ "LayoutLMForTokenClassification",
+ "LayoutLMForQuestionAnswering",
+ "LayoutLMModel",
+ "LayoutLMPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/modeling_tf_layoutlm.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/modeling_tf_layoutlm.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6738693843be0fe9af32cfc4fe96f3e6fbceb59
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/modeling_tf_layoutlm.py
@@ -0,0 +1,1691 @@
+# coding=utf-8
+# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TF 2.0 LayoutLM model."""
+
+from __future__ import annotations
+
+import math
+import warnings
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_outputs import (
+ TFBaseModelOutputWithPastAndCrossAttentions,
+ TFBaseModelOutputWithPoolingAndCrossAttentions,
+ TFMaskedLMOutput,
+ TFQuestionAnsweringModelOutput,
+ TFSequenceClassifierOutput,
+ TFTokenClassifierOutput,
+)
+from ...modeling_tf_utils import (
+ TFMaskedLanguageModelingLoss,
+ TFModelInputType,
+ TFPreTrainedModel,
+ TFQuestionAnsweringLoss,
+ TFSequenceClassificationLoss,
+ TFTokenClassificationLoss,
+ get_initializer,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
+from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from .configuration_layoutlm import LayoutLMConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LayoutLMConfig"
+
+
+class TFLayoutLMEmbeddings(keras.layers.Layer):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config: LayoutLMConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.max_position_embeddings = config.max_position_embeddings
+ self.max_2d_position_embeddings = config.max_2d_position_embeddings
+ self.initializer_range = config.initializer_range
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+
+ def build(self, input_shape=None):
+ with tf.name_scope("word_embeddings"):
+ self.weight = self.add_weight(
+ name="weight",
+ shape=[self.config.vocab_size, self.hidden_size],
+ initializer=get_initializer(self.initializer_range),
+ )
+
+ with tf.name_scope("token_type_embeddings"):
+ self.token_type_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.config.type_vocab_size, self.hidden_size],
+ initializer=get_initializer(self.initializer_range),
+ )
+
+ with tf.name_scope("position_embeddings"):
+ self.position_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.max_position_embeddings, self.hidden_size],
+ initializer=get_initializer(self.initializer_range),
+ )
+
+ with tf.name_scope("x_position_embeddings"):
+ self.x_position_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.max_2d_position_embeddings, self.hidden_size],
+ initializer=get_initializer(self.initializer_range),
+ )
+
+ with tf.name_scope("y_position_embeddings"):
+ self.y_position_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.max_2d_position_embeddings, self.hidden_size],
+ initializer=get_initializer(self.initializer_range),
+ )
+
+ with tf.name_scope("h_position_embeddings"):
+ self.h_position_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.max_2d_position_embeddings, self.hidden_size],
+ initializer=get_initializer(self.initializer_range),
+ )
+
+ with tf.name_scope("w_position_embeddings"):
+ self.w_position_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.max_2d_position_embeddings, self.hidden_size],
+ initializer=get_initializer(self.initializer_range),
+ )
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ bbox: tf.Tensor | None = None,
+ position_ids: tf.Tensor | None = None,
+ token_type_ids: tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> tf.Tensor:
+ """
+ Applies embedding based on inputs tensor.
+
+ Returns:
+ final_embeddings (`tf.Tensor`): output embedding tensor.
+ """
+ assert not (input_ids is None and inputs_embeds is None)
+
+ if input_ids is not None:
+ check_embeddings_within_bounds(input_ids, self.config.vocab_size)
+ inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
+
+ input_shape = shape_list(inputs_embeds)[:-1]
+
+ if token_type_ids is None:
+ token_type_ids = tf.fill(dims=input_shape, value=0)
+
+ if position_ids is None:
+ position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
+
+ if position_ids is None:
+ position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
+
+ if bbox is None:
+ bbox = tf.fill(input_shape + [4], value=0)
+ try:
+ left_position_embeddings = tf.gather(self.x_position_embeddings, bbox[:, :, 0])
+ upper_position_embeddings = tf.gather(self.y_position_embeddings, bbox[:, :, 1])
+ right_position_embeddings = tf.gather(self.x_position_embeddings, bbox[:, :, 2])
+ lower_position_embeddings = tf.gather(self.y_position_embeddings, bbox[:, :, 3])
+ except IndexError as e:
+ raise IndexError("The `bbox`coordinate values should be within 0-1000 range.") from e
+ h_position_embeddings = tf.gather(self.h_position_embeddings, bbox[:, :, 3] - bbox[:, :, 1])
+ w_position_embeddings = tf.gather(self.w_position_embeddings, bbox[:, :, 2] - bbox[:, :, 0])
+
+ position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
+ token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
+ final_embeddings = (
+ inputs_embeds
+ + position_embeds
+ + token_type_embeds
+ + left_position_embeddings
+ + upper_position_embeddings
+ + right_position_embeddings
+ + lower_position_embeddings
+ + h_position_embeddings
+ + w_position_embeddings
+ )
+ final_embeddings = self.LayerNorm(inputs=final_embeddings)
+ final_embeddings = self.dropout(inputs=final_embeddings, training=training)
+
+ return final_embeddings
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->LayoutLM
+class TFLayoutLMSelfAttention(keras.layers.Layer):
+ def __init__(self, config: LayoutLMConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number "
+ f"of attention heads ({config.num_attention_heads})"
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+ self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
+
+ self.query = keras.layers.Dense(
+ units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
+ )
+ self.key = keras.layers.Dense(
+ units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
+ )
+ self.value = keras.layers.Dense(
+ units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
+ )
+ self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
+
+ self.is_decoder = config.is_decoder
+ self.config = config
+
+ def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
+ # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
+ tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
+
+ # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
+ return tf.transpose(tensor, perm=[0, 2, 1, 3])
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: tf.Tensor,
+ head_mask: tf.Tensor,
+ encoder_hidden_states: tf.Tensor,
+ encoder_attention_mask: tf.Tensor,
+ past_key_value: tuple[tf.Tensor],
+ output_attentions: bool,
+ training: bool = False,
+ ) -> tuple[tf.Tensor]:
+ batch_size = shape_list(hidden_states)[0]
+ mixed_query_layer = self.query(inputs=hidden_states)
+
+ # If this is instantiated as a cross-attention module, the keys
+ # and values come from an encoder; the attention mask needs to be
+ # such that the encoder's padding tokens are not attended to.
+ is_cross_attention = encoder_hidden_states is not None
+
+ if is_cross_attention and past_key_value is not None:
+ # reuse k,v, cross_attentions
+ key_layer = past_key_value[0]
+ value_layer = past_key_value[1]
+ attention_mask = encoder_attention_mask
+ elif is_cross_attention:
+ key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size)
+ value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size)
+ attention_mask = encoder_attention_mask
+ elif past_key_value is not None:
+ key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
+ value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
+ key_layer = tf.concat([past_key_value[0], key_layer], axis=2)
+ value_layer = tf.concat([past_key_value[1], value_layer], axis=2)
+ else:
+ key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
+ value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
+
+ query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
+
+ if self.is_decoder:
+ # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
+ # Further calls to cross_attention layer can then reuse all cross-attention
+ # key/value_states (first "if" case)
+ # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
+ # all previous decoder key/value_states. Further calls to uni-directional self-attention
+ # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+ # if encoder bi-directional self-attention `past_key_value` is always `None`
+ past_key_value = (key_layer, value_layer)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ # (batch size, num_heads, seq_len_q, seq_len_k)
+ attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
+ dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
+ attention_scores = tf.divide(attention_scores, dk)
+
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in TFLayoutLMModel call() function)
+ attention_scores = tf.add(attention_scores, attention_mask)
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = stable_softmax(logits=attention_scores, axis=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(inputs=attention_probs, training=training)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = tf.multiply(attention_probs, head_mask)
+
+ attention_output = tf.matmul(attention_probs, value_layer)
+ attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
+
+ # (batch_size, seq_len_q, all_head_size)
+ attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
+ outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
+
+ if self.is_decoder:
+ outputs = outputs + (past_key_value,)
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "query", None) is not None:
+ with tf.name_scope(self.query.name):
+ self.query.build([None, None, self.config.hidden_size])
+ if getattr(self, "key", None) is not None:
+ with tf.name_scope(self.key.name):
+ self.key.build([None, None, self.config.hidden_size])
+ if getattr(self, "value", None) is not None:
+ with tf.name_scope(self.value.name):
+ self.value.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->LayoutLM
+class TFLayoutLMSelfOutput(keras.layers.Layer):
+ def __init__(self, config: LayoutLMConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.dropout(inputs=hidden_states, training=training)
+ hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->LayoutLM
+class TFLayoutLMAttention(keras.layers.Layer):
+ def __init__(self, config: LayoutLMConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.self_attention = TFLayoutLMSelfAttention(config, name="self")
+ self.dense_output = TFLayoutLMSelfOutput(config, name="output")
+
+ def prune_heads(self, heads):
+ raise NotImplementedError
+
+ def call(
+ self,
+ input_tensor: tf.Tensor,
+ attention_mask: tf.Tensor,
+ head_mask: tf.Tensor,
+ encoder_hidden_states: tf.Tensor,
+ encoder_attention_mask: tf.Tensor,
+ past_key_value: tuple[tf.Tensor],
+ output_attentions: bool,
+ training: bool = False,
+ ) -> tuple[tf.Tensor]:
+ self_outputs = self.self_attention(
+ hidden_states=input_tensor,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ training=training,
+ )
+ attention_output = self.dense_output(
+ hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
+ )
+ # add attentions (possibly with past_key_value) if we output them
+ outputs = (attention_output,) + self_outputs[1:]
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self_attention", None) is not None:
+ with tf.name_scope(self.self_attention.name):
+ self.self_attention.build(None)
+ if getattr(self, "dense_output", None) is not None:
+ with tf.name_scope(self.dense_output.name):
+ self.dense_output.build(None)
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->LayoutLM
+class TFLayoutLMIntermediate(keras.layers.Layer):
+ def __init__(self, config: LayoutLMConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = get_tf_activation(config.hidden_act)
+ else:
+ self.intermediate_act_fn = config.hidden_act
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->LayoutLM
+class TFLayoutLMOutput(keras.layers.Layer):
+ def __init__(self, config: LayoutLMConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.dropout(inputs=hidden_states, training=training)
+ hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.intermediate_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->LayoutLM
+class TFLayoutLMLayer(keras.layers.Layer):
+ def __init__(self, config: LayoutLMConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.attention = TFLayoutLMAttention(config, name="attention")
+ self.is_decoder = config.is_decoder
+ self.add_cross_attention = config.add_cross_attention
+ if self.add_cross_attention:
+ if not self.is_decoder:
+ raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
+ self.crossattention = TFLayoutLMAttention(config, name="crossattention")
+ self.intermediate = TFLayoutLMIntermediate(config, name="intermediate")
+ self.bert_output = TFLayoutLMOutput(config, name="output")
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: tf.Tensor,
+ head_mask: tf.Tensor,
+ encoder_hidden_states: tf.Tensor | None,
+ encoder_attention_mask: tf.Tensor | None,
+ past_key_value: tuple[tf.Tensor] | None,
+ output_attentions: bool,
+ training: bool = False,
+ ) -> tuple[tf.Tensor]:
+ # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+ self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+ self_attention_outputs = self.attention(
+ input_tensor=hidden_states,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ past_key_value=self_attn_past_key_value,
+ output_attentions=output_attentions,
+ training=training,
+ )
+ attention_output = self_attention_outputs[0]
+
+ # if decoder, the last output is tuple of self-attn cache
+ if self.is_decoder:
+ outputs = self_attention_outputs[1:-1]
+ present_key_value = self_attention_outputs[-1]
+ else:
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ cross_attn_present_key_value = None
+ if self.is_decoder and encoder_hidden_states is not None:
+ if not hasattr(self, "crossattention"):
+ raise ValueError(
+ f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
+ " by setting `config.add_cross_attention=True`"
+ )
+
+ # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
+ cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
+ cross_attention_outputs = self.crossattention(
+ input_tensor=attention_output,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ past_key_value=cross_attn_past_key_value,
+ output_attentions=output_attentions,
+ training=training,
+ )
+ attention_output = cross_attention_outputs[0]
+ outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
+
+ # add cross-attn cache to positions 3,4 of present_key_value tuple
+ cross_attn_present_key_value = cross_attention_outputs[-1]
+ present_key_value = present_key_value + cross_attn_present_key_value
+
+ intermediate_output = self.intermediate(hidden_states=attention_output)
+ layer_output = self.bert_output(
+ hidden_states=intermediate_output, input_tensor=attention_output, training=training
+ )
+ outputs = (layer_output,) + outputs # add attentions if we output them
+
+ # if decoder, return the attn key/values as the last output
+ if self.is_decoder:
+ outputs = outputs + (present_key_value,)
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "attention", None) is not None:
+ with tf.name_scope(self.attention.name):
+ self.attention.build(None)
+ if getattr(self, "intermediate", None) is not None:
+ with tf.name_scope(self.intermediate.name):
+ self.intermediate.build(None)
+ if getattr(self, "bert_output", None) is not None:
+ with tf.name_scope(self.bert_output.name):
+ self.bert_output.build(None)
+ if getattr(self, "crossattention", None) is not None:
+ with tf.name_scope(self.crossattention.name):
+ self.crossattention.build(None)
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->LayoutLM
+class TFLayoutLMEncoder(keras.layers.Layer):
+ def __init__(self, config: LayoutLMConfig, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.layer = [TFLayoutLMLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: tf.Tensor,
+ head_mask: tf.Tensor,
+ encoder_hidden_states: tf.Tensor | None,
+ encoder_attention_mask: tf.Tensor | None,
+ past_key_values: tuple[tuple[tf.Tensor]] | None,
+ use_cache: bool | None,
+ output_attentions: bool,
+ output_hidden_states: bool,
+ return_dict: bool,
+ training: bool = False,
+ ) -> TFBaseModelOutputWithPastAndCrossAttentions | tuple[tf.Tensor]:
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+ all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+
+ next_decoder_cache = () if use_cache else None
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ past_key_value = past_key_values[i] if past_key_values is not None else None
+
+ layer_outputs = layer_module(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ head_mask=head_mask[i],
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ training=training,
+ )
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache += (layer_outputs[-1],)
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+ if self.config.add_cross_attention and encoder_hidden_states is not None:
+ all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
+
+ # Add last layer
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None
+ )
+
+ return TFBaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=next_decoder_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_attentions,
+ cross_attentions=all_cross_attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layer", None) is not None:
+ for layer in self.layer:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->LayoutLM
+class TFLayoutLMPooler(keras.layers.Layer):
+ def __init__(self, config: LayoutLMConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ activation="tanh",
+ name="dense",
+ )
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(inputs=first_token_tensor)
+
+ return pooled_output
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->LayoutLM
+class TFLayoutLMPredictionHeadTransform(keras.layers.Layer):
+ def __init__(self, config: LayoutLMConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="dense",
+ )
+
+ if isinstance(config.hidden_act, str):
+ self.transform_act_fn = get_tf_activation(config.hidden_act)
+ else:
+ self.transform_act_fn = config.hidden_act
+
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(inputs=hidden_states)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->LayoutLM
+class TFLayoutLMLMPredictionHead(keras.layers.Layer):
+ def __init__(self, config: LayoutLMConfig, input_embeddings: keras.layers.Layer, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.hidden_size = config.hidden_size
+
+ self.transform = TFLayoutLMPredictionHeadTransform(config, name="transform")
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.input_embeddings = input_embeddings
+
+ def build(self, input_shape=None):
+ self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "transform", None) is not None:
+ with tf.name_scope(self.transform.name):
+ self.transform.build(None)
+
+ def get_output_embeddings(self) -> keras.layers.Layer:
+ return self.input_embeddings
+
+ def set_output_embeddings(self, value: tf.Variable):
+ self.input_embeddings.weight = value
+ self.input_embeddings.vocab_size = shape_list(value)[0]
+
+ def get_bias(self) -> dict[str, tf.Variable]:
+ return {"bias": self.bias}
+
+ def set_bias(self, value: tf.Variable):
+ self.bias = value["bias"]
+ self.config.vocab_size = shape_list(value["bias"])[0]
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ hidden_states = self.transform(hidden_states=hidden_states)
+ seq_length = shape_list(hidden_states)[1]
+ hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
+ hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
+ hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size])
+ hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
+
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->LayoutLM
+class TFLayoutLMMLMHead(keras.layers.Layer):
+ def __init__(self, config: LayoutLMConfig, input_embeddings: keras.layers.Layer, **kwargs):
+ super().__init__(**kwargs)
+
+ self.predictions = TFLayoutLMLMPredictionHead(config, input_embeddings, name="predictions")
+
+ def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
+ prediction_scores = self.predictions(hidden_states=sequence_output)
+
+ return prediction_scores
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "predictions", None) is not None:
+ with tf.name_scope(self.predictions.name):
+ self.predictions.build(None)
+
+
+@keras_serializable
+class TFLayoutLMMainLayer(keras.layers.Layer):
+ config_class = LayoutLMConfig
+
+ def __init__(self, config: LayoutLMConfig, add_pooling_layer: bool = True, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+
+ self.embeddings = TFLayoutLMEmbeddings(config, name="embeddings")
+ self.encoder = TFLayoutLMEncoder(config, name="encoder")
+ self.pooler = TFLayoutLMPooler(config, name="pooler") if add_pooling_layer else None
+
+ def get_input_embeddings(self) -> keras.layers.Layer:
+ return self.embeddings
+
+ def set_input_embeddings(self, value: tf.Variable):
+ self.embeddings.weight = value
+ self.embeddings.vocab_size = shape_list(value)[0]
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ raise NotImplementedError
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ bbox: np.ndarray | tf.Tensor | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
+ encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ) -> TFBaseModelOutputWithPoolingAndCrossAttentions | tuple[tf.Tensor]:
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if attention_mask is None:
+ attention_mask = tf.fill(dims=input_shape, value=1)
+
+ if token_type_ids is None:
+ token_type_ids = tf.fill(dims=input_shape, value=0)
+ if bbox is None:
+ bbox = tf.fill(dims=input_shape + [4], value=0)
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ bbox=bbox,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ training=training,
+ )
+
+ # We create a 3D attention mask from a 2D tensor mask.
+ # Sizes are [batch_size, 1, 1, to_seq_length]
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+ # this attention mask is more simple than the triangular masking of causal attention
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+ extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1]))
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and -10000.0 for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+ extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
+ one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
+ ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
+ extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ if head_mask is not None:
+ raise NotImplementedError
+ else:
+ head_mask = [None] * self.config.num_hidden_layers
+
+ encoder_outputs = self.encoder(
+ hidden_states=embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ # Need to pass these required positional arguments to `Encoder`
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=None,
+ past_key_values=None,
+ use_cache=False,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None
+
+ if not return_dict:
+ return (
+ sequence_output,
+ pooled_output,
+ ) + encoder_outputs[1:]
+
+ return TFBaseModelOutputWithPoolingAndCrossAttentions(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ cross_attentions=encoder_outputs.cross_attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embeddings", None) is not None:
+ with tf.name_scope(self.embeddings.name):
+ self.embeddings.build(None)
+ if getattr(self, "encoder", None) is not None:
+ with tf.name_scope(self.encoder.name):
+ self.encoder.build(None)
+ if getattr(self, "pooler", None) is not None:
+ with tf.name_scope(self.pooler.name):
+ self.pooler.build(None)
+
+
+class TFLayoutLMPreTrainedModel(TFPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = LayoutLMConfig
+ base_model_prefix = "layoutlm"
+
+ @property
+ def input_signature(self):
+ signature = super().input_signature
+ signature["bbox"] = tf.TensorSpec(shape=(None, None, 4), dtype=tf.int32, name="bbox")
+ return signature
+
+
+LAYOUTLM_START_DOCSTRING = r"""
+
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Args:
+ config ([`LayoutLMConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+LAYOUTLM_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
+ [`PreTrainedTokenizer.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ bbox (`Numpy array` or `tf.Tensor` of shape `({0}, 4)`, *optional*):
+ Bounding Boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-
+ 1]`.
+ attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+"""
+
+
+@add_start_docstrings(
+ "The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.",
+ LAYOUTLM_START_DOCSTRING,
+)
+class TFLayoutLMModel(TFLayoutLMPreTrainedModel):
+ def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @replace_return_docstrings(
+ output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ bbox: np.ndarray | tf.Tensor | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
+ encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ ) -> TFBaseModelOutputWithPoolingAndCrossAttentions | tuple[tf.Tensor]:
+ r"""
+ Returns:
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, TFLayoutLMModel
+ >>> import tensorflow as tf
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+ >>> model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
+
+ >>> words = ["Hello", "world"]
+ >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+ >>> token_boxes = []
+ >>> for word, box in zip(words, normalized_word_boxes):
+ ... word_tokens = tokenizer.tokenize(word)
+ ... token_boxes.extend([box] * len(word_tokens))
+ >>> # add bounding boxes of cls + sep tokens
+ >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+ >>> encoding = tokenizer(" ".join(words), return_tensors="tf")
+ >>> input_ids = encoding["input_ids"]
+ >>> attention_mask = encoding["attention_mask"]
+ >>> token_type_ids = encoding["token_type_ids"]
+ >>> bbox = tf.convert_to_tensor([token_boxes])
+
+ >>> outputs = model(
+ ... input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids
+ ... )
+
+ >>> last_hidden_states = outputs.last_hidden_state
+ ```"""
+ outputs = self.layoutlm(
+ input_ids=input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layoutlm", None) is not None:
+ with tf.name_scope(self.layoutlm.name):
+ self.layoutlm.build(None)
+
+
+@add_start_docstrings("""LayoutLM Model with a `language modeling` head on top.""", LAYOUTLM_START_DOCSTRING)
+class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [
+ r"pooler",
+ r"cls.seq_relationship",
+ r"cls.predictions.decoder.weight",
+ r"nsp___cls",
+ ]
+
+ def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ if config.is_decoder:
+ logger.warning(
+ "If you want to use `TFLayoutLMForMaskedLM` make sure `config.is_decoder=False` for "
+ "bi-directional self-attention."
+ )
+
+ self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm")
+ self.mlm = TFLayoutLMMLMHead(config, input_embeddings=self.layoutlm.embeddings, name="mlm___cls")
+
+ def get_lm_head(self) -> keras.layers.Layer:
+ return self.mlm.predictions
+
+ def get_prefix_bias_name(self) -> str:
+ warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
+ return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ bbox: np.ndarray | tf.Tensor | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> TFMaskedLMOutput | tuple[tf.Tensor]:
+ r"""
+ labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+
+ Returns:
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, TFLayoutLMForMaskedLM
+ >>> import tensorflow as tf
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+ >>> model = TFLayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased")
+
+ >>> words = ["Hello", "[MASK]"]
+ >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+ >>> token_boxes = []
+ >>> for word, box in zip(words, normalized_word_boxes):
+ ... word_tokens = tokenizer.tokenize(word)
+ ... token_boxes.extend([box] * len(word_tokens))
+ >>> # add bounding boxes of cls + sep tokens
+ >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+ >>> encoding = tokenizer(" ".join(words), return_tensors="tf")
+ >>> input_ids = encoding["input_ids"]
+ >>> attention_mask = encoding["attention_mask"]
+ >>> token_type_ids = encoding["token_type_ids"]
+ >>> bbox = tf.convert_to_tensor([token_boxes])
+
+ >>> labels = tokenizer("Hello world", return_tensors="tf")["input_ids"]
+
+ >>> outputs = model(
+ ... input_ids=input_ids,
+ ... bbox=bbox,
+ ... attention_mask=attention_mask,
+ ... token_type_ids=token_type_ids,
+ ... labels=labels,
+ ... )
+
+ >>> loss = outputs.loss
+ ```"""
+ outputs = self.layoutlm(
+ input_ids=input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+ prediction_scores = self.mlm(sequence_output=sequence_output, training=training)
+ loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores)
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFMaskedLMOutput(
+ loss=loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layoutlm", None) is not None:
+ with tf.name_scope(self.layoutlm.name):
+ self.layoutlm.build(None)
+ if getattr(self, "mlm", None) is not None:
+ with tf.name_scope(self.mlm.name):
+ self.mlm.build(None)
+
+
+@add_start_docstrings(
+ """
+ LayoutLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+ pooled output) e.g. for GLUE tasks.
+ """,
+ LAYOUTLM_START_DOCSTRING,
+)
+class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, TFSequenceClassificationLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship"]
+ _keys_to_ignore_on_load_missing = [r"dropout"]
+
+ def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.num_labels = config.num_labels
+
+ self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+ self.classifier = keras.layers.Dense(
+ units=config.num_labels,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="classifier",
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ bbox: np.ndarray | tf.Tensor | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> TFSequenceClassifierOutput | tuple[tf.Tensor]:
+ r"""
+ labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+ Returns:
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, TFLayoutLMForSequenceClassification
+ >>> import tensorflow as tf
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+ >>> model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")
+
+ >>> words = ["Hello", "world"]
+ >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+ >>> token_boxes = []
+ >>> for word, box in zip(words, normalized_word_boxes):
+ ... word_tokens = tokenizer.tokenize(word)
+ ... token_boxes.extend([box] * len(word_tokens))
+ >>> # add bounding boxes of cls + sep tokens
+ >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+ >>> encoding = tokenizer(" ".join(words), return_tensors="tf")
+ >>> input_ids = encoding["input_ids"]
+ >>> attention_mask = encoding["attention_mask"]
+ >>> token_type_ids = encoding["token_type_ids"]
+ >>> bbox = tf.convert_to_tensor([token_boxes])
+ >>> sequence_label = tf.convert_to_tensor([1])
+
+ >>> outputs = model(
+ ... input_ids=input_ids,
+ ... bbox=bbox,
+ ... attention_mask=attention_mask,
+ ... token_type_ids=token_type_ids,
+ ... labels=sequence_label,
+ ... )
+
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+ ```"""
+ outputs = self.layoutlm(
+ input_ids=input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ pooled_output = outputs[1]
+ pooled_output = self.dropout(inputs=pooled_output, training=training)
+ logits = self.classifier(inputs=pooled_output)
+ loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFSequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layoutlm", None) is not None:
+ with tf.name_scope(self.layoutlm.name):
+ self.layoutlm.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+ Named-Entity-Recognition (NER) tasks.
+ """,
+ LAYOUTLM_START_DOCSTRING,
+)
+class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassificationLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [
+ r"pooler",
+ r"mlm___cls",
+ r"nsp___cls",
+ r"cls.predictions",
+ r"cls.seq_relationship",
+ ]
+ _keys_to_ignore_on_load_missing = [r"dropout"]
+
+ def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.num_labels = config.num_labels
+
+ self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+ self.classifier = keras.layers.Dense(
+ units=config.num_labels,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="classifier",
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @replace_return_docstrings(output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ bbox: np.ndarray | tf.Tensor | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> TFTokenClassifierOutput | tuple[tf.Tensor]:
+ r"""
+ labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+
+ Returns:
+
+ Examples:
+
+ ```python
+ >>> import tensorflow as tf
+ >>> from transformers import AutoTokenizer, TFLayoutLMForTokenClassification
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
+ >>> model = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased")
+
+ >>> words = ["Hello", "world"]
+ >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
+
+ >>> token_boxes = []
+ >>> for word, box in zip(words, normalized_word_boxes):
+ ... word_tokens = tokenizer.tokenize(word)
+ ... token_boxes.extend([box] * len(word_tokens))
+ >>> # add bounding boxes of cls + sep tokens
+ >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]
+
+ >>> encoding = tokenizer(" ".join(words), return_tensors="tf")
+ >>> input_ids = encoding["input_ids"]
+ >>> attention_mask = encoding["attention_mask"]
+ >>> token_type_ids = encoding["token_type_ids"]
+ >>> bbox = tf.convert_to_tensor([token_boxes])
+ >>> token_labels = tf.convert_to_tensor([1, 1, 0, 0])
+
+ >>> outputs = model(
+ ... input_ids=input_ids,
+ ... bbox=bbox,
+ ... attention_mask=attention_mask,
+ ... token_type_ids=token_type_ids,
+ ... labels=token_labels,
+ ... )
+
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+ ```"""
+ outputs = self.layoutlm(
+ input_ids=input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+ sequence_output = self.dropout(inputs=sequence_output, training=training)
+ logits = self.classifier(inputs=sequence_output)
+ loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFTokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layoutlm", None) is not None:
+ with tf.name_scope(self.layoutlm.name):
+ self.layoutlm.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ LayoutLM Model with a span classification head on top for extractive question-answering tasks such as
+ [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the final hidden-states output to compute `span
+ start logits` and `span end logits`).
+ """,
+ LAYOUTLM_START_DOCSTRING,
+)
+class TFLayoutLMForQuestionAnswering(TFLayoutLMPreTrainedModel, TFQuestionAnsweringLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [
+ r"pooler",
+ r"mlm___cls",
+ r"nsp___cls",
+ r"cls.predictions",
+ r"cls.seq_relationship",
+ ]
+
+ def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.num_labels = config.num_labels
+
+ self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm")
+ self.qa_outputs = keras.layers.Dense(
+ units=config.num_labels,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="qa_outputs",
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @replace_return_docstrings(output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ bbox: np.ndarray | tf.Tensor | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ start_positions: np.ndarray | tf.Tensor | None = None,
+ end_positions: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> TFQuestionAnsweringModelOutput | tuple[tf.Tensor]:
+ r"""
+ start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the start of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+ are not taken into account for computing the loss.
+ end_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the end of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+ are not taken into account for computing the loss.
+
+ Returns:
+
+ Examples:
+
+ ```python
+ >>> import tensorflow as tf
+ >>> from transformers import AutoTokenizer, TFLayoutLMForQuestionAnswering
+ >>> from datasets import load_dataset
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)
+ >>> model = TFLayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac")
+
+ >>> dataset = load_dataset("nielsr/funsd", split="train")
+ >>> example = dataset[0]
+ >>> question = "what's his name?"
+ >>> words = example["words"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = tokenizer(
+ ... question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="tf"
+ ... )
+ >>> bbox = []
+ >>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
+ ... if s == 1:
+ ... bbox.append(boxes[w])
+ ... elif i == tokenizer.sep_token_id:
+ ... bbox.append([1000] * 4)
+ ... else:
+ ... bbox.append([0] * 4)
+ >>> encoding["bbox"] = tf.convert_to_tensor([bbox])
+
+ >>> word_ids = encoding.word_ids(0)
+ >>> outputs = model(**encoding)
+ >>> loss = outputs.loss
+ >>> start_scores = outputs.start_logits
+ >>> end_scores = outputs.end_logits
+ >>> start, end = word_ids[tf.math.argmax(start_scores, -1)[0]], word_ids[tf.math.argmax(end_scores, -1)[0]]
+ >>> print(" ".join(words[start : end + 1]))
+ M. Hamann P. Harper, P. Martinez
+ ```"""
+
+ outputs = self.layoutlm(
+ input_ids=input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(inputs=sequence_output)
+ start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
+ start_logits = tf.squeeze(input=start_logits, axis=-1)
+ end_logits = tf.squeeze(input=end_logits, axis=-1)
+ loss = None
+
+ if start_positions is not None and end_positions is not None:
+ labels = {"start_position": start_positions}
+ labels["end_position"] = end_positions
+ loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFQuestionAnsweringModelOutput(
+ loss=loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layoutlm", None) is not None:
+ with tf.name_scope(self.layoutlm.name):
+ self.layoutlm.build(None)
+ if getattr(self, "qa_outputs", None) is not None:
+ with tf.name_scope(self.qa_outputs.name):
+ self.qa_outputs.build([None, None, self.config.hidden_size])
+
+
+__all__ = [
+ "TFLayoutLMForMaskedLM",
+ "TFLayoutLMForSequenceClassification",
+ "TFLayoutLMForTokenClassification",
+ "TFLayoutLMForQuestionAnswering",
+ "TFLayoutLMMainLayer",
+ "TFLayoutLMModel",
+ "TFLayoutLMPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/tokenization_layoutlm.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/tokenization_layoutlm.py
new file mode 100644
index 0000000000000000000000000000000000000000..4caccd691d0e5fcb64637f72d5c2860f6f096e9e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/tokenization_layoutlm.py
@@ -0,0 +1,483 @@
+# coding=utf-8
+# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization class for model LayoutLM."""
+
+import collections
+import os
+import unicodedata
+from typing import Optional
+
+from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+
+# Copied from transformers.models.bert.tokenization_bert.load_vocab
+def load_vocab(vocab_file):
+ """Loads a vocabulary file into a dictionary."""
+ vocab = collections.OrderedDict()
+ with open(vocab_file, "r", encoding="utf-8") as reader:
+ tokens = reader.readlines()
+ for index, token in enumerate(tokens):
+ token = token.rstrip("\n")
+ vocab[token] = index
+ return vocab
+
+
+# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
+def whitespace_tokenize(text):
+ """Runs basic whitespace cleaning and splitting on a piece of text."""
+ text = text.strip()
+ if not text:
+ return []
+ tokens = text.split()
+ return tokens
+
+
+# Copied from transformers.models.bert.tokenization_bert.BertTokenizer with Bert->LayoutLM,BERT->LayoutLM
+class LayoutLMTokenizer(PreTrainedTokenizer):
+ r"""
+ Construct a LayoutLM tokenizer. Based on WordPiece.
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ File containing the vocabulary.
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ do_basic_tokenize (`bool`, *optional*, defaults to `True`):
+ Whether or not to do basic tokenization before WordPiece.
+ never_split (`Iterable`, *optional*):
+ Collection of tokens which will never be split during tokenization. Only has an effect when
+ `do_basic_tokenize=True`
+ unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ sep_token (`str`, *optional*, defaults to `"[SEP]"`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ pad_token (`str`, *optional*, defaults to `"[PAD]"`):
+ The token used for padding, for example when batching sequences of different lengths.
+ cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ mask_token (`str`, *optional*, defaults to `"[MASK]"`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters.
+
+ This should likely be deactivated for Japanese (see this
+ [issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original LayoutLM).
+ clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
+ Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+ extra spaces.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+
+ def __init__(
+ self,
+ vocab_file,
+ do_lower_case=True,
+ do_basic_tokenize=True,
+ never_split=None,
+ unk_token="[UNK]",
+ sep_token="[SEP]",
+ pad_token="[PAD]",
+ cls_token="[CLS]",
+ mask_token="[MASK]",
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ clean_up_tokenization_spaces=True,
+ **kwargs,
+ ):
+ if not os.path.isfile(vocab_file):
+ raise ValueError(
+ f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
+ " model use `tokenizer = LayoutLMTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+ )
+ self.vocab = load_vocab(vocab_file)
+ self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
+ self.do_basic_tokenize = do_basic_tokenize
+ if do_basic_tokenize:
+ self.basic_tokenizer = BasicTokenizer(
+ do_lower_case=do_lower_case,
+ never_split=never_split,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ )
+
+ self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
+
+ super().__init__(
+ do_lower_case=do_lower_case,
+ do_basic_tokenize=do_basic_tokenize,
+ never_split=never_split,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ pad_token=pad_token,
+ cls_token=cls_token,
+ mask_token=mask_token,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+ **kwargs,
+ )
+
+ @property
+ def do_lower_case(self):
+ return self.basic_tokenizer.do_lower_case
+
+ @property
+ def vocab_size(self):
+ return len(self.vocab)
+
+ def get_vocab(self):
+ return dict(self.vocab, **self.added_tokens_encoder)
+
+ def _tokenize(self, text, split_special_tokens=False):
+ split_tokens = []
+ if self.do_basic_tokenize:
+ for token in self.basic_tokenizer.tokenize(
+ text, never_split=self.all_special_tokens if not split_special_tokens else None
+ ):
+ # If the token is part of the never_split set
+ if token in self.basic_tokenizer.never_split:
+ split_tokens.append(token)
+ else:
+ split_tokens += self.wordpiece_tokenizer.tokenize(token)
+ else:
+ split_tokens = self.wordpiece_tokenizer.tokenize(text)
+ return split_tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.ids_to_tokens.get(index, self.unk_token)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ out_string = " ".join(tokens).replace(" ##", "").strip()
+ return out_string
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A LayoutLM sequence has the following format:
+
+ - single sequence: `[CLS] X [SEP]`
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + token_ids_1 + sep
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is not None:
+ return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1]
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ index = 0
+ if os.path.isdir(save_directory):
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ else:
+ vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
+ with open(vocab_file, "w", encoding="utf-8") as writer:
+ for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+ " Please check that the vocabulary is not corrupted!"
+ )
+ index = token_index
+ writer.write(token + "\n")
+ index += 1
+ return (vocab_file,)
+
+
+# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
+class BasicTokenizer:
+ """
+ Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
+
+ Args:
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ never_split (`Iterable`, *optional*):
+ Collection of tokens which will never be split during tokenization. Only has an effect when
+ `do_basic_tokenize=True`
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters.
+
+ This should likely be deactivated for Japanese (see this
+ [issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original BERT).
+ do_split_on_punc (`bool`, *optional*, defaults to `True`):
+ In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
+ the full context of the words, such as contractions.
+ """
+
+ def __init__(
+ self,
+ do_lower_case=True,
+ never_split=None,
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ do_split_on_punc=True,
+ ):
+ if never_split is None:
+ never_split = []
+ self.do_lower_case = do_lower_case
+ self.never_split = set(never_split)
+ self.tokenize_chinese_chars = tokenize_chinese_chars
+ self.strip_accents = strip_accents
+ self.do_split_on_punc = do_split_on_punc
+
+ def tokenize(self, text, never_split=None):
+ """
+ Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
+
+ Args:
+ never_split (`List[str]`, *optional*)
+ Kept for backward compatibility purposes. Now implemented directly at the base class level (see
+ [`PreTrainedTokenizer.tokenize`]) List of token not to split.
+ """
+ # union() returns a new set by concatenating the two sets.
+ never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
+ text = self._clean_text(text)
+
+ # This was added on November 1st, 2018 for the multilingual and Chinese
+ # models. This is also applied to the English models now, but it doesn't
+ # matter since the English models were not trained on any Chinese data
+ # and generally don't have any Chinese data in them (there are Chinese
+ # characters in the vocabulary because Wikipedia does have some Chinese
+ # words in the English Wikipedia.).
+ if self.tokenize_chinese_chars:
+ text = self._tokenize_chinese_chars(text)
+ # prevents treating the same character with different unicode codepoints as different characters
+ unicode_normalized_text = unicodedata.normalize("NFC", text)
+ orig_tokens = whitespace_tokenize(unicode_normalized_text)
+ split_tokens = []
+ for token in orig_tokens:
+ if token not in never_split:
+ if self.do_lower_case:
+ token = token.lower()
+ if self.strip_accents is not False:
+ token = self._run_strip_accents(token)
+ elif self.strip_accents:
+ token = self._run_strip_accents(token)
+ split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+ output_tokens = whitespace_tokenize(" ".join(split_tokens))
+ return output_tokens
+
+ def _run_strip_accents(self, text):
+ """Strips accents from a piece of text."""
+ text = unicodedata.normalize("NFD", text)
+ output = []
+ for char in text:
+ cat = unicodedata.category(char)
+ if cat == "Mn":
+ continue
+ output.append(char)
+ return "".join(output)
+
+ def _run_split_on_punc(self, text, never_split=None):
+ """Splits punctuation on a piece of text."""
+ if not self.do_split_on_punc or (never_split is not None and text in never_split):
+ return [text]
+ chars = list(text)
+ i = 0
+ start_new_word = True
+ output = []
+ while i < len(chars):
+ char = chars[i]
+ if _is_punctuation(char):
+ output.append([char])
+ start_new_word = True
+ else:
+ if start_new_word:
+ output.append([])
+ start_new_word = False
+ output[-1].append(char)
+ i += 1
+
+ return ["".join(x) for x in output]
+
+ def _tokenize_chinese_chars(self, text):
+ """Adds whitespace around any CJK character."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if self._is_chinese_char(cp):
+ output.append(" ")
+ output.append(char)
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+ def _is_chinese_char(self, cp):
+ """Checks whether CP is the codepoint of a CJK character."""
+ # This defines a "chinese character" as anything in the CJK Unicode block:
+ # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+ #
+ # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+ # despite its name. The modern Korean Hangul alphabet is a different block,
+ # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+ # space-separated words, so they are not treated specially and handled
+ # like the all of the other languages.
+ if (
+ (cp >= 0x4E00 and cp <= 0x9FFF)
+ or (cp >= 0x3400 and cp <= 0x4DBF)
+ or (cp >= 0x20000 and cp <= 0x2A6DF)
+ or (cp >= 0x2A700 and cp <= 0x2B73F)
+ or (cp >= 0x2B740 and cp <= 0x2B81F)
+ or (cp >= 0x2B820 and cp <= 0x2CEAF)
+ or (cp >= 0xF900 and cp <= 0xFAFF)
+ or (cp >= 0x2F800 and cp <= 0x2FA1F)
+ ):
+ return True
+
+ return False
+
+ def _clean_text(self, text):
+ """Performs invalid character removal and whitespace cleanup on text."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if cp == 0 or cp == 0xFFFD or _is_control(char):
+ continue
+ if _is_whitespace(char):
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+
+# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
+class WordpieceTokenizer:
+ """Runs WordPiece tokenization."""
+
+ def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+ self.vocab = vocab
+ self.unk_token = unk_token
+ self.max_input_chars_per_word = max_input_chars_per_word
+
+ def tokenize(self, text):
+ """
+ Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
+ tokenization using the given vocabulary.
+
+ For example, `input = "unaffable"` will return as output `["un", "##aff", "##able"]`.
+
+ Args:
+ text: A single token or whitespace separated tokens. This should have
+ already been passed through *BasicTokenizer*.
+
+ Returns:
+ A list of wordpiece tokens.
+ """
+
+ output_tokens = []
+ for token in whitespace_tokenize(text):
+ chars = list(token)
+ if len(chars) > self.max_input_chars_per_word:
+ output_tokens.append(self.unk_token)
+ continue
+
+ is_bad = False
+ start = 0
+ sub_tokens = []
+ while start < len(chars):
+ end = len(chars)
+ cur_substr = None
+ while start < end:
+ substr = "".join(chars[start:end])
+ if start > 0:
+ substr = "##" + substr
+ if substr in self.vocab:
+ cur_substr = substr
+ break
+ end -= 1
+ if cur_substr is None:
+ is_bad = True
+ break
+ sub_tokens.append(cur_substr)
+ start = end
+
+ if is_bad:
+ output_tokens.append(self.unk_token)
+ else:
+ output_tokens.extend(sub_tokens)
+ return output_tokens
+
+
+__all__ = ["LayoutLMTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7ade6e0b8cdf04f2d4f06b6191b93a8ed7ee2a6
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py
@@ -0,0 +1,147 @@
+# coding=utf-8
+# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization class for model LayoutLM."""
+
+import json
+from typing import Optional
+
+from tokenizers import normalizers
+
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import logging
+from .tokenization_layoutlm import LayoutLMTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
+
+
+# Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast with Bert->LayoutLM,BERT->LayoutLM
+class LayoutLMTokenizerFast(PreTrainedTokenizerFast):
+ r"""
+ Construct a "fast" LayoutLM tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ File containing the vocabulary.
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ sep_token (`str`, *optional*, defaults to `"[SEP]"`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ pad_token (`str`, *optional*, defaults to `"[PAD]"`):
+ The token used for padding, for example when batching sequences of different lengths.
+ cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ mask_token (`str`, *optional*, defaults to `"[MASK]"`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ clean_text (`bool`, *optional*, defaults to `True`):
+ Whether or not to clean the text before tokenization by removing any control characters and replacing all
+ whitespaces by the classic one.
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
+ issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original LayoutLM).
+ wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
+ The prefix for subwords.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ slow_tokenizer_class = LayoutLMTokenizer
+
+ def __init__(
+ self,
+ vocab_file=None,
+ tokenizer_file=None,
+ do_lower_case=True,
+ unk_token="[UNK]",
+ sep_token="[SEP]",
+ pad_token="[PAD]",
+ cls_token="[CLS]",
+ mask_token="[MASK]",
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ **kwargs,
+ ):
+ super().__init__(
+ vocab_file,
+ tokenizer_file=tokenizer_file,
+ do_lower_case=do_lower_case,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ pad_token=pad_token,
+ cls_token=cls_token,
+ mask_token=mask_token,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ **kwargs,
+ )
+
+ normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
+ if (
+ normalizer_state.get("lowercase", do_lower_case) != do_lower_case
+ or normalizer_state.get("strip_accents", strip_accents) != strip_accents
+ or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars
+ ):
+ normalizer_class = getattr(normalizers, normalizer_state.pop("type"))
+ normalizer_state["lowercase"] = do_lower_case
+ normalizer_state["strip_accents"] = strip_accents
+ normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars
+ self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state)
+
+ self.do_lower_case = do_lower_case
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A LayoutLM sequence has the following format:
+
+ - single sequence: `[CLS] X [SEP]`
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+
+ if token_ids_1 is not None:
+ output += token_ids_1 + [self.sep_token_id]
+
+ return output
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+ return tuple(files)
+
+
+__all__ = ["LayoutLMTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b68a523c0b0c362d9930f5bee492cea73f3937f0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/__init__.py
@@ -0,0 +1,33 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_layoutlmv2 import *
+ from .feature_extraction_layoutlmv2 import *
+ from .image_processing_layoutlmv2 import *
+ from .image_processing_layoutlmv2_fast import *
+ from .modeling_layoutlmv2 import *
+ from .processing_layoutlmv2 import *
+ from .tokenization_layoutlmv2 import *
+ from .tokenization_layoutlmv2_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/configuration_layoutlmv2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/configuration_layoutlmv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..b729ddbb1d429f3cfe464f303ec55ae391d498c3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/configuration_layoutlmv2.py
@@ -0,0 +1,222 @@
+# coding=utf-8
+# Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LayoutLMv2 model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import is_detectron2_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+# soft dependency
+if is_detectron2_available():
+ import detectron2
+
+
+class LayoutLMv2Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LayoutLMv2Model`]. It is used to instantiate an
+ LayoutLMv2 model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the LayoutLMv2
+ [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 30522):
+ Vocabulary size of the LayoutLMv2 model. Defines the number of different tokens that can be represented by
+ the `inputs_ids` passed when calling [`LayoutLMv2Model`] or [`TFLayoutLMv2Model`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimension of the encoder layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 2):
+ The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv2Model`] or
+ [`TFLayoutLMv2Model`].
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+ max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
+ The maximum value that the 2D position embedding might ever be used with. Typically set this to something
+ large just in case (e.g., 1024).
+ max_rel_pos (`int`, *optional*, defaults to 128):
+ The maximum number of relative positions to be used in the self-attention mechanism.
+ rel_pos_bins (`int`, *optional*, defaults to 32):
+ The number of relative position bins to be used in the self-attention mechanism.
+ fast_qkv (`bool`, *optional*, defaults to `True`):
+ Whether or not to use a single matrix for the queries, keys, values in the self-attention layers.
+ max_rel_2d_pos (`int`, *optional*, defaults to 256):
+ The maximum number of relative 2D positions in the self-attention mechanism.
+ rel_2d_pos_bins (`int`, *optional*, defaults to 64):
+ The number of 2D relative position bins in the self-attention mechanism.
+ image_feature_pool_shape (`list[int]`, *optional*, defaults to [7, 7, 256]):
+ The shape of the average-pooled feature map.
+ coordinate_size (`int`, *optional*, defaults to 128):
+ Dimension of the coordinate embeddings.
+ shape_size (`int`, *optional*, defaults to 128):
+ Dimension of the width and height embeddings.
+ has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
+ Whether or not to use a relative attention bias in the self-attention mechanism.
+ has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
+ Whether or not to use a spatial attention bias in the self-attention mechanism.
+ has_visual_segment_embedding (`bool`, *optional*, defaults to `False`):
+ Whether or not to add visual segment embeddings.
+ detectron2_config_args (`dict`, *optional*):
+ Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to [this
+ file](https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/models/layoutlmv2/detectron2_config.py)
+ for details regarding default values.
+
+ Example:
+
+ ```python
+ >>> from transformers import LayoutLMv2Config, LayoutLMv2Model
+
+ >>> # Initializing a LayoutLMv2 microsoft/layoutlmv2-base-uncased style configuration
+ >>> configuration = LayoutLMv2Config()
+
+ >>> # Initializing a model (with random weights) from the microsoft/layoutlmv2-base-uncased style configuration
+ >>> model = LayoutLMv2Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "layoutlmv2"
+
+ def __init__(
+ self,
+ vocab_size=30522,
+ hidden_size=768,
+ num_hidden_layers=12,
+ num_attention_heads=12,
+ intermediate_size=3072,
+ hidden_act="gelu",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ type_vocab_size=2,
+ initializer_range=0.02,
+ layer_norm_eps=1e-12,
+ pad_token_id=0,
+ max_2d_position_embeddings=1024,
+ max_rel_pos=128,
+ rel_pos_bins=32,
+ fast_qkv=True,
+ max_rel_2d_pos=256,
+ rel_2d_pos_bins=64,
+ convert_sync_batchnorm=True,
+ image_feature_pool_shape=[7, 7, 256],
+ coordinate_size=128,
+ shape_size=128,
+ has_relative_attention_bias=True,
+ has_spatial_attention_bias=True,
+ has_visual_segment_embedding=False,
+ detectron2_config_args=None,
+ **kwargs,
+ ):
+ super().__init__(
+ vocab_size=vocab_size,
+ hidden_size=hidden_size,
+ num_hidden_layers=num_hidden_layers,
+ num_attention_heads=num_attention_heads,
+ intermediate_size=intermediate_size,
+ hidden_act=hidden_act,
+ hidden_dropout_prob=hidden_dropout_prob,
+ attention_probs_dropout_prob=attention_probs_dropout_prob,
+ max_position_embeddings=max_position_embeddings,
+ type_vocab_size=type_vocab_size,
+ initializer_range=initializer_range,
+ layer_norm_eps=layer_norm_eps,
+ pad_token_id=pad_token_id,
+ **kwargs,
+ )
+ self.max_2d_position_embeddings = max_2d_position_embeddings
+ self.max_rel_pos = max_rel_pos
+ self.rel_pos_bins = rel_pos_bins
+ self.fast_qkv = fast_qkv
+ self.max_rel_2d_pos = max_rel_2d_pos
+ self.rel_2d_pos_bins = rel_2d_pos_bins
+ self.convert_sync_batchnorm = convert_sync_batchnorm
+ self.image_feature_pool_shape = image_feature_pool_shape
+ self.coordinate_size = coordinate_size
+ self.shape_size = shape_size
+ self.has_relative_attention_bias = has_relative_attention_bias
+ self.has_spatial_attention_bias = has_spatial_attention_bias
+ self.has_visual_segment_embedding = has_visual_segment_embedding
+ self.detectron2_config_args = (
+ detectron2_config_args if detectron2_config_args is not None else self.get_default_detectron2_config()
+ )
+
+ @classmethod
+ def get_default_detectron2_config(cls):
+ return {
+ "MODEL.MASK_ON": True,
+ "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
+ "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
+ "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
+ "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
+ "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
+ "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
+ "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
+ "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
+ "MODEL.POST_NMS_TOPK_TEST": 1000,
+ "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
+ "MODEL.ROI_HEADS.NUM_CLASSES": 5,
+ "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
+ "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
+ "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
+ "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
+ "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
+ "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
+ "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
+ "MODEL.RESNETS.DEPTH": 101,
+ "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
+ "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
+ "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
+ "MODEL.RESNETS.NUM_GROUPS": 32,
+ "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
+ "MODEL.RESNETS.STRIDE_IN_1X1": False,
+ }
+
+ def get_detectron2_config(self):
+ detectron2_config = detectron2.config.get_cfg()
+ for k, v in self.detectron2_config_args.items():
+ attributes = k.split(".")
+ to_set = detectron2_config
+ for attribute in attributes[:-1]:
+ to_set = getattr(to_set, attribute)
+ setattr(to_set, attributes[-1], v)
+
+ return detectron2_config
+
+
+__all__ = ["LayoutLMv2Config"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c70e1ed643101401373ae29637d4d597873485e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py
@@ -0,0 +1,40 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Feature extractor class for LayoutLMv2.
+"""
+
+import warnings
+
+from ...utils import logging
+from ...utils.import_utils import requires
+from .image_processing_layoutlmv2 import LayoutLMv2ImageProcessor
+
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("vision",))
+class LayoutLMv2FeatureExtractor(LayoutLMv2ImageProcessor):
+ def __init__(self, *args, **kwargs) -> None:
+ warnings.warn(
+ "The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
+ " Please use LayoutLMv2ImageProcessor instead.",
+ FutureWarning,
+ )
+ super().__init__(*args, **kwargs)
+
+
+__all__ = ["LayoutLMv2FeatureExtractor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/image_processing_layoutlmv2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/image_processing_layoutlmv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfb61c352dc33ac8a5ef10270384bce5ece77386
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/image_processing_layoutlmv2.py
@@ -0,0 +1,303 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for LayoutLMv2."""
+
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
+from ...image_utils import (
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ infer_channel_dimension_format,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import (
+ TensorType,
+ filter_out_non_signature_kwargs,
+ is_pytesseract_available,
+ is_vision_available,
+ logging,
+ requires_backends,
+)
+from ...utils.import_utils import requires
+
+
+if is_vision_available():
+ import PIL
+
+# soft dependency
+if is_pytesseract_available():
+ import pytesseract
+
+logger = logging.get_logger(__name__)
+
+
+def normalize_box(box, width, height):
+ return [
+ int(1000 * (box[0] / width)),
+ int(1000 * (box[1] / height)),
+ int(1000 * (box[2] / width)),
+ int(1000 * (box[3] / height)),
+ ]
+
+
+def apply_tesseract(
+ image: np.ndarray,
+ lang: Optional[str],
+ tesseract_config: Optional[str] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+):
+ """Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes."""
+ tesseract_config = tesseract_config if tesseract_config is not None else ""
+
+ # apply OCR
+ pil_image = to_pil_image(image, input_data_format=input_data_format)
+ image_width, image_height = pil_image.size
+ data = pytesseract.image_to_data(pil_image, lang=lang, output_type="dict", config=tesseract_config)
+ words, left, top, width, height = data["text"], data["left"], data["top"], data["width"], data["height"]
+
+ # filter empty words and corresponding coordinates
+ irrelevant_indices = [idx for idx, word in enumerate(words) if not word.strip()]
+ words = [word for idx, word in enumerate(words) if idx not in irrelevant_indices]
+ left = [coord for idx, coord in enumerate(left) if idx not in irrelevant_indices]
+ top = [coord for idx, coord in enumerate(top) if idx not in irrelevant_indices]
+ width = [coord for idx, coord in enumerate(width) if idx not in irrelevant_indices]
+ height = [coord for idx, coord in enumerate(height) if idx not in irrelevant_indices]
+
+ # turn coordinates into (left, top, left+width, top+height) format
+ actual_boxes = []
+ for x, y, w, h in zip(left, top, width, height):
+ actual_box = [x, y, x + w, y + h]
+ actual_boxes.append(actual_box)
+
+ # finally, normalize the bounding boxes
+ normalized_boxes = []
+ for box in actual_boxes:
+ normalized_boxes.append(normalize_box(box, image_width, image_height))
+
+ assert len(words) == len(normalized_boxes), "Not as many words as there are bounding boxes"
+
+ return words, normalized_boxes
+
+
+@requires(backends=("vision",))
+class LayoutLMv2ImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a LayoutLMv2 image processor.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to `(size["height"], size["width"])`. Can be
+ overridden by `do_resize` in `preprocess`.
+ size (`dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
+ Size of the image after resizing. Can be overridden by `size` in `preprocess`.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
+ Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
+ `preprocess` method.
+ apply_ocr (`bool`, *optional*, defaults to `True`):
+ Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by
+ `apply_ocr` in `preprocess`.
+ ocr_lang (`str`, *optional*):
+ The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
+ used. Can be overridden by `ocr_lang` in `preprocess`.
+ tesseract_config (`str`, *optional*, defaults to `""`):
+ Any additional custom configuration flags that are forwarded to the `config` parameter when calling
+ Tesseract. For example: '--psm 6'. Can be overridden by `tesseract_config` in `preprocess`.
+ """
+
+ model_input_names = ["pixel_values"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ apply_ocr: bool = True,
+ ocr_lang: Optional[str] = None,
+ tesseract_config: Optional[str] = "",
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"height": 224, "width": 224}
+ size = get_size_dict(size)
+
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.apply_ocr = apply_ocr
+ self.ocr_lang = ocr_lang
+ self.tesseract_config = tesseract_config
+
+ # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image to `(size["height"], size["width"])`.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+ `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
+ data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the output image. If unset, the channel dimension format of the input
+ image is used. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+
+ Returns:
+ `np.ndarray`: The resized image.
+ """
+ size = get_size_dict(size)
+ if "height" not in size or "width" not in size:
+ raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
+ output_size = (size["height"], size["width"])
+ return resize(
+ image,
+ size=output_size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ apply_ocr: Optional[bool] = None,
+ ocr_lang: Optional[str] = None,
+ tesseract_config: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: ChannelDimension = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> PIL.Image.Image:
+ """
+ Preprocess an image or batch of images.
+
+ Args:
+ images (`ImageInput`):
+ Image to preprocess.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Desired size of the output image after resizing.
+ resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of the enum `PIL.Image` resampling
+ filter. Only has an effect if `do_resize` is set to `True`.
+ apply_ocr (`bool`, *optional*, defaults to `self.apply_ocr`):
+ Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes.
+ ocr_lang (`str`, *optional*, defaults to `self.ocr_lang`):
+ The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
+ used.
+ tesseract_config (`str`, *optional*, defaults to `self.tesseract_config`):
+ Any additional custom configuration flags that are forwarded to the `config` parameter when calling
+ Tesseract.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ """
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ size = get_size_dict(size)
+ resample = resample if resample is not None else self.resample
+ apply_ocr = apply_ocr if apply_ocr is not None else self.apply_ocr
+ ocr_lang = ocr_lang if ocr_lang is not None else self.ocr_lang
+ tesseract_config = tesseract_config if tesseract_config is not None else self.tesseract_config
+
+ images = make_list_of_images(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+ validate_preprocess_arguments(
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ if apply_ocr:
+ requires_backends(self, "pytesseract")
+ words_batch = []
+ boxes_batch = []
+ for image in images:
+ words, boxes = apply_tesseract(image, ocr_lang, tesseract_config, input_data_format=input_data_format)
+ words_batch.append(words)
+ boxes_batch.append(boxes)
+
+ if do_resize:
+ images = [
+ self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ # flip color channels from RGB to BGR (as Detectron2 requires this)
+ images = [flip_channel_order(image, input_data_format=input_data_format) for image in images]
+ images = [
+ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
+ ]
+
+ data = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)
+
+ if apply_ocr:
+ data["words"] = words_batch
+ data["boxes"] = boxes_batch
+ return data
+
+
+__all__ = ["LayoutLMv2ImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/image_processing_layoutlmv2_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/image_processing_layoutlmv2_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..c22612da5858bffc76e8d0bc2ea5b4aa3dd245fc
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/image_processing_layoutlmv2_fast.py
@@ -0,0 +1,144 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for LayoutLMv2."""
+
+from typing import Optional, Union
+
+from ...image_processing_utils_fast import BaseImageProcessorFast, BatchFeature, DefaultFastImageProcessorKwargs
+from ...image_transforms import ChannelDimension, group_images_by_shape, reorder_images
+from ...image_utils import ImageInput, PILImageResampling, SizeDict
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+ logging,
+ requires_backends,
+)
+from .image_processing_layoutlmv2 import apply_tesseract
+
+
+logger = logging.get_logger(__name__)
+
+if is_torch_available():
+ import torch
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+class LayoutLMv2FastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ """
+ Args:
+ apply_ocr (`bool`, *optional*, defaults to `True`):
+ Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by
+ the `apply_ocr` parameter in the `preprocess` method.
+ ocr_lang (`str`, *optional*):
+ The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
+ used. Can be overridden by the `ocr_lang` parameter in the `preprocess` method.
+ tesseract_config (`str`, *optional*):
+ Any additional custom configuration flags that are forwarded to the `config` parameter when calling
+ Tesseract. For example: '--psm 6'. Can be overridden by the `tesseract_config` parameter in the
+ `preprocess` method.
+ """
+
+ apply_ocr: Optional[bool]
+ ocr_lang: Optional[str]
+ tesseract_config: Optional[str]
+
+
+@auto_docstring
+class LayoutLMv2ImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BILINEAR
+ size = {"height": 224, "width": 224}
+ rescale_factor = None
+ do_resize = True
+ apply_ocr = True
+ ocr_lang = None
+ tesseract_config = ""
+ valid_kwargs = LayoutLMv2FastImageProcessorKwargs
+
+ def __init__(self, **kwargs: Unpack[LayoutLMv2FastImageProcessorKwargs]):
+ super().__init__(**kwargs)
+
+ @auto_docstring
+ def preprocess(self, images: ImageInput, **kwargs: Unpack[LayoutLMv2FastImageProcessorKwargs]) -> BatchFeature:
+ return super().preprocess(images, **kwargs)
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ do_resize: bool,
+ size: SizeDict,
+ interpolation: Optional["F.InterpolationMode"],
+ apply_ocr: bool,
+ ocr_lang: Optional[str],
+ tesseract_config: Optional[str],
+ disable_grouping: Optional[bool],
+ return_tensors: Optional[Union[str, TensorType]],
+ **kwargs,
+ ) -> BatchFeature:
+ # Tesseract OCR to get words + normalized bounding boxes
+ if apply_ocr:
+ requires_backends(self, "pytesseract")
+ words_batch = []
+ boxes_batch = []
+ for image in images:
+ if image.is_cuda:
+ logger.warning_once(
+ "apply_ocr can only be performed on cpu. Tensors will be transferred to cpu before processing."
+ )
+ words, boxes = apply_tesseract(
+ image.cpu(), ocr_lang, tesseract_config, input_data_format=ChannelDimension.FIRST
+ )
+ words_batch.append(words)
+ boxes_batch.append(boxes)
+
+ # Group images by size for batched resizing
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ resized_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_resize:
+ stacked_images = self.resize(image=stacked_images, size=size, interpolation=interpolation)
+ resized_images_grouped[shape] = stacked_images
+ resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+ # Group images by size for further processing
+ # Needed in case do_resize is False, or resize returns images with different sizes
+ grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+ processed_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ # flip color channels from RGB to BGR (as Detectron2 requires this)
+ stacked_images = stacked_images.flip(1)
+ processed_images_grouped[shape] = stacked_images
+
+ processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+ processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+
+ data = BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
+
+ if apply_ocr:
+ data["words"] = words_batch
+ data["boxes"] = boxes_batch
+
+ return data
+
+
+__all__ = ["LayoutLMv2ImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..11d8127ef6c2e15f54492659cb8b70f4360576b7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py
@@ -0,0 +1,1395 @@
+# coding=utf-8
+# Copyright 2021 Microsoft Research The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch LayoutLMv2 model."""
+
+import math
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPooling,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import apply_chunking_to_forward
+from ...utils import auto_docstring, is_detectron2_available, logging, requires_backends
+from .configuration_layoutlmv2 import LayoutLMv2Config
+
+
+# soft dependency
+if is_detectron2_available():
+ import detectron2
+ from detectron2.modeling import META_ARCH_REGISTRY
+
+ # This is needed as otherwise their overload will break sequential loading by overwriting buffer over and over. See
+ # https://github.com/facebookresearch/detectron2/blob/9604f5995cc628619f0e4fd913453b4d7d61db3f/detectron2/layers/batch_norm.py#L83-L86
+ detectron2.layers.batch_norm.FrozenBatchNorm2d._load_from_state_dict = torch.nn.Module._load_from_state_dict
+
+logger = logging.get_logger(__name__)
+
+
+class LayoutLMv2Embeddings(nn.Module):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+
+ self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
+ self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
+ self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
+ self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ self.register_buffer(
+ "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+ )
+
+ def _calc_spatial_position_embeddings(self, bbox):
+ try:
+ left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
+ upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
+ right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
+ lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
+ except IndexError as e:
+ raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e
+
+ h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
+ w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
+
+ spatial_position_embeddings = torch.cat(
+ [
+ left_position_embeddings,
+ upper_position_embeddings,
+ right_position_embeddings,
+ lower_position_embeddings,
+ h_position_embeddings,
+ w_position_embeddings,
+ ],
+ dim=-1,
+ )
+ return spatial_position_embeddings
+
+
+class LayoutLMv2SelfAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+ self.fast_qkv = config.fast_qkv
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.has_relative_attention_bias = config.has_relative_attention_bias
+ self.has_spatial_attention_bias = config.has_spatial_attention_bias
+
+ if config.fast_qkv:
+ self.qkv_linear = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=False)
+ self.q_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
+ self.v_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
+ else:
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+ def compute_qkv(self, hidden_states):
+ if self.fast_qkv:
+ qkv = self.qkv_linear(hidden_states)
+ q, k, v = torch.chunk(qkv, 3, dim=-1)
+ if q.ndimension() == self.q_bias.ndimension():
+ q = q + self.q_bias
+ v = v + self.v_bias
+ else:
+ _sz = (1,) * (q.ndimension() - 1) + (-1,)
+ q = q + self.q_bias.view(*_sz)
+ v = v + self.v_bias.view(*_sz)
+ else:
+ q = self.query(hidden_states)
+ k = self.key(hidden_states)
+ v = self.value(hidden_states)
+ return q, k, v
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ rel_pos=None,
+ rel_2d_pos=None,
+ ):
+ batch_size, seq_length, _ = hidden_states.shape
+ query, key, value = self.compute_qkv(hidden_states)
+
+ # (B, L, H*D) -> (B, H, L, D)
+ query_layer = query.view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
+ key_layer = key.view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
+ value_layer = value.view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
+
+ query_layer = query_layer / math.sqrt(self.attention_head_size)
+ # [BSZ, NAT, L, L]
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+ if self.has_relative_attention_bias:
+ attention_scores += rel_pos
+ if self.has_spatial_attention_bias:
+ attention_scores += rel_2d_pos
+ attention_scores = attention_scores.float().masked_fill_(
+ attention_mask.to(torch.bool), torch.finfo(attention_scores.dtype).min
+ )
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1, dtype=torch.float32).type_as(value_layer)
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+ return outputs
+
+
+class LayoutLMv2Attention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.self = LayoutLMv2SelfAttention(config)
+ self.output = LayoutLMv2SelfOutput(config)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ rel_pos=None,
+ rel_2d_pos=None,
+ ):
+ self_outputs = self.self(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions,
+ rel_pos=rel_pos,
+ rel_2d_pos=rel_2d_pos,
+ )
+ attention_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+class LayoutLMv2SelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->LayoutLMv2
+class LayoutLMv2Intermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->LayoutLM
+class LayoutLMv2Output(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class LayoutLMv2Layer(GradientCheckpointingLayer):
+ def __init__(self, config):
+ super().__init__()
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = LayoutLMv2Attention(config)
+ self.intermediate = LayoutLMv2Intermediate(config)
+ self.output = LayoutLMv2Output(config)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ rel_pos=None,
+ rel_2d_pos=None,
+ ):
+ self_attention_outputs = self.attention(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions=output_attentions,
+ rel_pos=rel_pos,
+ rel_2d_pos=rel_2d_pos,
+ )
+ attention_output = self_attention_outputs[0]
+
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ layer_output = apply_chunking_to_forward(
+ self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+ )
+ outputs = (layer_output,) + outputs
+
+ return outputs
+
+ def feed_forward_chunk(self, attention_output):
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+
+def relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+ """
+ Adapted from Mesh Tensorflow:
+ https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+ Translate relative position to a bucket number for relative attention. The relative position is defined as
+ memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+ position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for small
+ absolute relative_position and larger buckets for larger absolute relative_positions. All relative positions
+ >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. This should
+ allow for more graceful generalization to longer sequences than the model has been trained on.
+
+ Args:
+ relative_position: an int32 Tensor
+ bidirectional: a boolean - whether the attention is bidirectional
+ num_buckets: an integer
+ max_distance: an integer
+
+ Returns:
+ a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+ """
+
+ ret = 0
+ if bidirectional:
+ num_buckets //= 2
+ ret += (relative_position > 0).long() * num_buckets
+ n = torch.abs(relative_position)
+ else:
+ n = torch.max(-relative_position, torch.zeros_like(relative_position))
+ # now n is in the range [0, inf)
+
+ # half of the buckets are for exact increments in positions
+ max_exact = num_buckets // 2
+ is_small = n < max_exact
+
+ # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ val_if_large = max_exact + (
+ torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
+ ).to(torch.long)
+ val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
+
+ ret += torch.where(is_small, n, val_if_large)
+ return ret
+
+
+class LayoutLMv2Encoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList([LayoutLMv2Layer(config) for _ in range(config.num_hidden_layers)])
+
+ self.has_relative_attention_bias = config.has_relative_attention_bias
+ self.has_spatial_attention_bias = config.has_spatial_attention_bias
+
+ if self.has_relative_attention_bias:
+ self.rel_pos_bins = config.rel_pos_bins
+ self.max_rel_pos = config.max_rel_pos
+ self.rel_pos_bias = nn.Linear(self.rel_pos_bins, config.num_attention_heads, bias=False)
+
+ if self.has_spatial_attention_bias:
+ self.max_rel_2d_pos = config.max_rel_2d_pos
+ self.rel_2d_pos_bins = config.rel_2d_pos_bins
+ self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False)
+ self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False)
+
+ self.gradient_checkpointing = False
+
+ def _calculate_1d_position_embeddings(self, position_ids):
+ rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1)
+ rel_pos = relative_position_bucket(
+ rel_pos_mat,
+ num_buckets=self.rel_pos_bins,
+ max_distance=self.max_rel_pos,
+ )
+ # Since this is a simple indexing operation that is independent of the input,
+ # no need to track gradients for this operation
+ #
+ # Without this no_grad context, training speed slows down significantly
+ with torch.no_grad():
+ rel_pos = self.rel_pos_bias.weight.t()[rel_pos].permute(0, 3, 1, 2)
+ rel_pos = rel_pos.contiguous()
+ return rel_pos
+
+ def _calculate_2d_position_embeddings(self, bbox):
+ position_coord_x = bbox[:, :, 0]
+ position_coord_y = bbox[:, :, 3]
+ rel_pos_x_2d_mat = position_coord_x.unsqueeze(-2) - position_coord_x.unsqueeze(-1)
+ rel_pos_y_2d_mat = position_coord_y.unsqueeze(-2) - position_coord_y.unsqueeze(-1)
+ rel_pos_x = relative_position_bucket(
+ rel_pos_x_2d_mat,
+ num_buckets=self.rel_2d_pos_bins,
+ max_distance=self.max_rel_2d_pos,
+ )
+ rel_pos_y = relative_position_bucket(
+ rel_pos_y_2d_mat,
+ num_buckets=self.rel_2d_pos_bins,
+ max_distance=self.max_rel_2d_pos,
+ )
+ # Since this is a simple indexing operation that is independent of the input,
+ # no need to track gradients for this operation
+ #
+ # Without this no_grad context, training speed slows down significantly
+ with torch.no_grad():
+ rel_pos_x = self.rel_pos_x_bias.weight.t()[rel_pos_x].permute(0, 3, 1, 2)
+ rel_pos_y = self.rel_pos_y_bias.weight.t()[rel_pos_y].permute(0, 3, 1, 2)
+ rel_pos_x = rel_pos_x.contiguous()
+ rel_pos_y = rel_pos_y.contiguous()
+ rel_2d_pos = rel_pos_x + rel_pos_y
+ return rel_2d_pos
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ output_hidden_states=False,
+ return_dict=True,
+ bbox=None,
+ position_ids=None,
+ ):
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+
+ rel_pos = self._calculate_1d_position_embeddings(position_ids) if self.has_relative_attention_bias else None
+ rel_2d_pos = self._calculate_2d_position_embeddings(bbox) if self.has_spatial_attention_bias else None
+
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ output_attentions,
+ rel_pos=rel_pos,
+ rel_2d_pos=rel_2d_pos,
+ )
+
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ hidden_states,
+ all_hidden_states,
+ all_self_attentions,
+ ]
+ if v is not None
+ )
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+
+
+@auto_docstring
+class LayoutLMv2PreTrainedModel(PreTrainedModel):
+ config: LayoutLMv2Config
+ base_model_prefix = "layoutlmv2"
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, nn.Linear):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, LayoutLMv2SelfAttention):
+ if self.config.fast_qkv:
+ module.q_bias.data.zero_()
+ module.v_bias.data.zero_()
+ elif isinstance(module, LayoutLMv2Model):
+ if hasattr(module, "visual_segment_embedding"):
+ module.visual_segment_embedding.data.normal_(mean=0.0, std=self.config.initializer_range)
+
+
+def my_convert_sync_batchnorm(module, process_group=None):
+ # same as `nn.modules.SyncBatchNorm.convert_sync_batchnorm` but allowing converting from `detectron2.layers.FrozenBatchNorm2d`
+ if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
+ return nn.modules.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
+ module_output = module
+ if isinstance(module, detectron2.layers.FrozenBatchNorm2d):
+ module_output = torch.nn.SyncBatchNorm(
+ num_features=module.num_features,
+ eps=module.eps,
+ affine=True,
+ track_running_stats=True,
+ process_group=process_group,
+ )
+ module_output.weight = torch.nn.Parameter(module.weight)
+ module_output.bias = torch.nn.Parameter(module.bias)
+ module_output.running_mean = module.running_mean
+ module_output.running_var = module.running_var
+ module_output.num_batches_tracked = torch.tensor(0, dtype=torch.long, device=module.running_mean.device)
+ for name, child in module.named_children():
+ module_output.add_module(name, my_convert_sync_batchnorm(child, process_group))
+ del module
+ return module_output
+
+
+class LayoutLMv2VisualBackbone(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.cfg = config.get_detectron2_config()
+ meta_arch = self.cfg.MODEL.META_ARCHITECTURE
+ model = META_ARCH_REGISTRY.get(meta_arch)(self.cfg)
+ assert isinstance(model.backbone, detectron2.modeling.backbone.FPN)
+ self.backbone = model.backbone
+
+ assert len(self.cfg.MODEL.PIXEL_MEAN) == len(self.cfg.MODEL.PIXEL_STD)
+ num_channels = len(self.cfg.MODEL.PIXEL_MEAN)
+ self.register_buffer(
+ "pixel_mean",
+ torch.Tensor(self.cfg.MODEL.PIXEL_MEAN).view(num_channels, 1, 1),
+ persistent=False,
+ )
+ self.register_buffer(
+ "pixel_std", torch.Tensor(self.cfg.MODEL.PIXEL_STD).view(num_channels, 1, 1), persistent=False
+ )
+ self.out_feature_key = "p2"
+ if torch.are_deterministic_algorithms_enabled():
+ logger.warning("using `AvgPool2d` instead of `AdaptiveAvgPool2d`")
+ input_shape = (224, 224)
+ backbone_stride = self.backbone.output_shape()[self.out_feature_key].stride
+ self.pool = nn.AvgPool2d(
+ (
+ math.ceil(math.ceil(input_shape[0] / backbone_stride) / config.image_feature_pool_shape[0]),
+ math.ceil(math.ceil(input_shape[1] / backbone_stride) / config.image_feature_pool_shape[1]),
+ )
+ )
+ else:
+ self.pool = nn.AdaptiveAvgPool2d(config.image_feature_pool_shape[:2])
+ if len(config.image_feature_pool_shape) == 2:
+ config.image_feature_pool_shape.append(self.backbone.output_shape()[self.out_feature_key].channels)
+ assert self.backbone.output_shape()[self.out_feature_key].channels == config.image_feature_pool_shape[2]
+
+ def forward(self, images):
+ images_input = ((images if torch.is_tensor(images) else images.tensor) - self.pixel_mean) / self.pixel_std
+ features = self.backbone(images_input)
+ features = features[self.out_feature_key]
+ features = self.pool(features).flatten(start_dim=2).transpose(1, 2).contiguous()
+ return features
+
+ def synchronize_batch_norm(self):
+ if not (
+ torch.distributed.is_available()
+ and torch.distributed.is_initialized()
+ and torch.distributed.get_rank() > -1
+ ):
+ raise RuntimeError("Make sure torch.distributed is set up properly.")
+
+ self_rank = torch.distributed.get_rank()
+ node_size = torch.cuda.device_count()
+ world_size = torch.distributed.get_world_size()
+ if not (world_size % node_size == 0):
+ raise RuntimeError("Make sure the number of processes can be divided by the number of nodes")
+
+ node_global_ranks = [list(range(i * node_size, (i + 1) * node_size)) for i in range(world_size // node_size)]
+ sync_bn_groups = [
+ torch.distributed.new_group(ranks=node_global_ranks[i]) for i in range(world_size // node_size)
+ ]
+ node_rank = self_rank // node_size
+
+ self.backbone = my_convert_sync_batchnorm(self.backbone, process_group=sync_bn_groups[node_rank])
+
+
+class LayoutLMv2Pooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states):
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+@auto_docstring
+class LayoutLMv2Model(LayoutLMv2PreTrainedModel):
+ def __init__(self, config):
+ requires_backends(self, "detectron2")
+ super().__init__(config)
+ self.config = config
+ self.has_visual_segment_embedding = config.has_visual_segment_embedding
+ self.embeddings = LayoutLMv2Embeddings(config)
+
+ self.visual = LayoutLMv2VisualBackbone(config)
+ self.visual_proj = nn.Linear(config.image_feature_pool_shape[-1], config.hidden_size)
+ if self.has_visual_segment_embedding:
+ self.visual_segment_embedding = nn.Parameter(nn.Embedding(1, config.hidden_size).weight[0])
+ self.visual_LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.visual_dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ self.encoder = LayoutLMv2Encoder(config)
+ self.pooler = LayoutLMv2Pooler(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _calc_text_embeddings(self, input_ids, bbox, position_ids, token_type_ids, inputs_embeds=None):
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+
+ if position_ids is None:
+ position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+ position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros_like(input_ids)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embeddings.word_embeddings(input_ids)
+ position_embeddings = self.embeddings.position_embeddings(position_ids)
+ spatial_position_embeddings = self.embeddings._calc_spatial_position_embeddings(bbox)
+ token_type_embeddings = self.embeddings.token_type_embeddings(token_type_ids)
+
+ embeddings = inputs_embeds + position_embeddings + spatial_position_embeddings + token_type_embeddings
+ embeddings = self.embeddings.LayerNorm(embeddings)
+ embeddings = self.embeddings.dropout(embeddings)
+ return embeddings
+
+ def _calc_img_embeddings(self, image, bbox, position_ids):
+ visual_embeddings = self.visual_proj(self.visual(image))
+ position_embeddings = self.embeddings.position_embeddings(position_ids)
+ spatial_position_embeddings = self.embeddings._calc_spatial_position_embeddings(bbox)
+ embeddings = visual_embeddings + position_embeddings + spatial_position_embeddings
+ if self.has_visual_segment_embedding:
+ embeddings += self.visual_segment_embedding
+ embeddings = self.visual_LayerNorm(embeddings)
+ embeddings = self.visual_dropout(embeddings)
+ return embeddings
+
+ def _calc_visual_bbox(self, image_feature_pool_shape, bbox, device, final_shape):
+ visual_bbox_x = torch.div(
+ torch.arange(
+ 0,
+ 1000 * (image_feature_pool_shape[1] + 1),
+ 1000,
+ device=device,
+ dtype=bbox.dtype,
+ ),
+ self.config.image_feature_pool_shape[1],
+ rounding_mode="floor",
+ )
+ visual_bbox_y = torch.div(
+ torch.arange(
+ 0,
+ 1000 * (self.config.image_feature_pool_shape[0] + 1),
+ 1000,
+ device=device,
+ dtype=bbox.dtype,
+ ),
+ self.config.image_feature_pool_shape[0],
+ rounding_mode="floor",
+ )
+ visual_bbox = torch.stack(
+ [
+ visual_bbox_x[:-1].repeat(image_feature_pool_shape[0], 1),
+ visual_bbox_y[:-1].repeat(image_feature_pool_shape[1], 1).transpose(0, 1),
+ visual_bbox_x[1:].repeat(image_feature_pool_shape[0], 1),
+ visual_bbox_y[1:].repeat(image_feature_pool_shape[1], 1).transpose(0, 1),
+ ],
+ dim=-1,
+ ).view(-1, bbox.size(-1))
+
+ visual_bbox = visual_bbox.repeat(final_shape[0], 1, 1)
+
+ return visual_bbox
+
+ def _get_input_shape(self, input_ids=None, inputs_embeds=None):
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ return input_ids.size()
+ elif inputs_embeds is not None:
+ return inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ image: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPooling]:
+ r"""
+ bbox (`torch.LongTensor` of shape `((batch_size, sequence_length), 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner.
+ image (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `detectron.structures.ImageList` whose `tensors` is of shape `(batch_size, num_channels, height, width)`):
+ Batch of document images.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, LayoutLMv2Model, set_seed
+ >>> from PIL import Image
+ >>> import torch
+ >>> from datasets import load_dataset
+
+ >>> set_seed(0)
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
+ >>> model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased")
+
+
+ >>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa")
+ >>> image = dataset["test"][0]["image"]
+
+ >>> encoding = processor(image, return_tensors="pt")
+
+ >>> outputs = model(**encoding)
+ >>> last_hidden_states = outputs.last_hidden_state
+
+ >>> last_hidden_states.shape
+ torch.Size([1, 342, 768])
+ ```
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ input_shape = self._get_input_shape(input_ids, inputs_embeds)
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ visual_shape = list(input_shape)
+ visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1]
+ visual_shape = torch.Size(visual_shape)
+ # needs a new copy of input_shape for tracing. Otherwise wrong dimensions will occur
+ final_shape = list(self._get_input_shape(input_ids, inputs_embeds))
+ final_shape[1] += visual_shape[1]
+ final_shape = torch.Size(final_shape)
+
+ visual_bbox = self._calc_visual_bbox(self.config.image_feature_pool_shape, bbox, device, final_shape)
+ final_bbox = torch.cat([bbox, visual_bbox], dim=1)
+
+ if attention_mask is None:
+ attention_mask = torch.ones(input_shape, device=device)
+
+ visual_attention_mask = torch.ones(visual_shape, device=device)
+ final_attention_mask = torch.cat([attention_mask, visual_attention_mask], dim=1)
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ if position_ids is None:
+ seq_length = input_shape[1]
+ position_ids = self.embeddings.position_ids[:, :seq_length]
+ position_ids = position_ids.expand(input_shape)
+
+ visual_position_ids = torch.arange(0, visual_shape[1], dtype=torch.long, device=device).repeat(
+ input_shape[0], 1
+ )
+ final_position_ids = torch.cat([position_ids, visual_position_ids], dim=1)
+
+ if bbox is None:
+ bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device)
+
+ text_layout_emb = self._calc_text_embeddings(
+ input_ids=input_ids,
+ bbox=bbox,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ )
+
+ visual_emb = self._calc_img_embeddings(
+ image=image,
+ bbox=visual_bbox,
+ position_ids=visual_position_ids,
+ )
+ final_emb = torch.cat([text_layout_emb, visual_emb], dim=1)
+
+ extended_attention_mask = final_attention_mask.unsqueeze(1).unsqueeze(2)
+
+ extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
+ extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
+
+ if head_mask is not None:
+ if head_mask.dim() == 1:
+ head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+ head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
+ elif head_mask.dim() == 2:
+ head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
+ head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
+ else:
+ head_mask = [None] * self.config.num_hidden_layers
+
+ encoder_outputs = self.encoder(
+ final_emb,
+ extended_attention_mask,
+ bbox=final_bbox,
+ position_ids=final_position_ids,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output)
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ LayoutLMv2 Model with a sequence classification head on top (a linear layer on top of the concatenation of the
+ final hidden state of the [CLS] token, average-pooled initial visual embeddings and average-pooled final visual
+ embeddings, e.g. for document image classification tasks such as the
+ [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
+ """
+)
+class LayoutLMv2ForSequenceClassification(LayoutLMv2PreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.layoutlmv2 = LayoutLMv2Model(config)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size * 3, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.layoutlmv2.embeddings.word_embeddings
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ image: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, SequenceClassifierOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `batch_size, sequence_length`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner.
+ image (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `detectron.structures.ImageList` whose `tensors` is of shape `(batch_size, num_channels, height, width)`):
+ Batch of document images.
+ token_type_ids (`torch.LongTensor` of shape `batch_size, sequence_length`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`torch.LongTensor` of shape `batch_size, sequence_length`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoProcessor, LayoutLMv2ForSequenceClassification, set_seed
+ >>> from PIL import Image
+ >>> import torch
+ >>> from datasets import load_dataset
+
+ >>> set_seed(0)
+
+ >>> dataset = load_dataset("aharley/rvl_cdip", split="train", streaming=True)
+ >>> data = next(iter(dataset))
+ >>> image = data["image"].convert("RGB")
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
+ >>> model = LayoutLMv2ForSequenceClassification.from_pretrained(
+ ... "microsoft/layoutlmv2-base-uncased", num_labels=dataset.info.features["label"].num_classes
+ ... )
+
+ >>> encoding = processor(image, return_tensors="pt")
+ >>> sequence_label = torch.tensor([data["label"]])
+
+ >>> outputs = model(**encoding, labels=sequence_label)
+
+ >>> loss, logits = outputs.loss, outputs.logits
+ >>> predicted_idx = logits.argmax(dim=-1).item()
+ >>> predicted_answer = dataset.info.features["label"].names[4]
+ >>> predicted_idx, predicted_answer # results are not good without further fine-tuning
+ (7, 'advertisement')
+ ```
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ visual_shape = list(input_shape)
+ visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1]
+ visual_shape = torch.Size(visual_shape)
+ final_shape = list(input_shape)
+ final_shape[1] += visual_shape[1]
+ final_shape = torch.Size(final_shape)
+
+ visual_bbox = self.layoutlmv2._calc_visual_bbox(
+ self.config.image_feature_pool_shape, bbox, device, final_shape
+ )
+
+ visual_position_ids = torch.arange(0, visual_shape[1], dtype=torch.long, device=device).repeat(
+ input_shape[0], 1
+ )
+
+ initial_image_embeddings = self.layoutlmv2._calc_img_embeddings(
+ image=image,
+ bbox=visual_bbox,
+ position_ids=visual_position_ids,
+ )
+
+ outputs = self.layoutlmv2(
+ input_ids=input_ids,
+ bbox=bbox,
+ image=image,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+ sequence_output, final_image_embeddings = outputs[0][:, :seq_length], outputs[0][:, seq_length:]
+
+ cls_final_output = sequence_output[:, 0, :]
+
+ # average-pool the visual embeddings
+ pooled_initial_image_embeddings = initial_image_embeddings.mean(dim=1)
+ pooled_final_image_embeddings = final_image_embeddings.mean(dim=1)
+ # concatenate with cls_final_output
+ sequence_output = torch.cat(
+ [cls_final_output, pooled_initial_image_embeddings, pooled_final_image_embeddings], dim=1
+ )
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ LayoutLMv2 Model with a token classification head on top (a linear layer on top of the text part of the hidden
+ states) e.g. for sequence labeling (information extraction) tasks such as
+ [FUNSD](https://guillaumejaume.github.io/FUNSD/), [SROIE](https://rrc.cvc.uab.es/?ch=13),
+ [CORD](https://github.com/clovaai/cord) and [Kleister-NDA](https://github.com/applicaai/kleister-nda).
+ """
+)
+class LayoutLMv2ForTokenClassification(LayoutLMv2PreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.layoutlmv2 = LayoutLMv2Model(config)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.layoutlmv2.embeddings.word_embeddings
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ image: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, TokenClassifierOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `batch_size, sequence_length`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner.
+ image (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `detectron.structures.ImageList` whose `tensors` is of shape `(batch_size, num_channels, height, width)`):
+ Batch of document images.
+ token_type_ids (`torch.LongTensor` of shape `batch_size, sequence_length`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`torch.LongTensor` of shape `batch_size, sequence_length`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoProcessor, LayoutLMv2ForTokenClassification, set_seed
+ >>> from PIL import Image
+ >>> from datasets import load_dataset
+
+ >>> set_seed(0)
+
+ >>> datasets = load_dataset("nielsr/funsd", split="test")
+ >>> labels = datasets.features["ner_tags"].feature.names
+ >>> id2label = {v: k for v, k in enumerate(labels)}
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
+ >>> model = LayoutLMv2ForTokenClassification.from_pretrained(
+ ... "microsoft/layoutlmv2-base-uncased", num_labels=len(labels)
+ ... )
+
+ >>> data = datasets[0]
+ >>> image = Image.open(data["image_path"]).convert("RGB")
+ >>> words = data["words"]
+ >>> boxes = data["bboxes"] # make sure to normalize your bounding boxes
+ >>> word_labels = data["ner_tags"]
+ >>> encoding = processor(
+ ... image,
+ ... words,
+ ... boxes=boxes,
+ ... word_labels=word_labels,
+ ... padding="max_length",
+ ... truncation=True,
+ ... return_tensors="pt",
+ ... )
+
+ >>> outputs = model(**encoding)
+ >>> logits, loss = outputs.logits, outputs.loss
+
+ >>> predicted_token_class_ids = logits.argmax(-1)
+ >>> predicted_tokens_classes = [id2label[t.item()] for t in predicted_token_class_ids[0]]
+ >>> predicted_tokens_classes[:5] # results are not good without further fine-tuning
+ ['I-HEADER', 'I-HEADER', 'I-QUESTION', 'I-HEADER', 'I-QUESTION']
+ ```
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlmv2(
+ input_ids=input_ids,
+ bbox=bbox,
+ image=image,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+ # only take the text part of the output representations
+ sequence_output = outputs[0][:, :seq_length]
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class LayoutLMv2ForQuestionAnswering(LayoutLMv2PreTrainedModel):
+ def __init__(self, config, has_visual_segment_embedding=True):
+ r"""
+ has_visual_segment_embedding (`bool`, *optional*, defaults to `True`):
+ Whether or not to add visual segment embeddings.
+ """
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ config.has_visual_segment_embedding = has_visual_segment_embedding
+ self.layoutlmv2 = LayoutLMv2Model(config)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.layoutlmv2.embeddings.word_embeddings
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ image: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, QuestionAnsweringModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `batch_size, sequence_length`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner.
+ image (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `detectron.structures.ImageList` whose `tensors` is of shape `(batch_size, num_channels, height, width)`):
+ Batch of document images.
+ token_type_ids (`torch.LongTensor` of shape `batch_size, sequence_length`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`torch.LongTensor` of shape `batch_size, sequence_length`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+
+ Example:
+
+ In this example below, we give the LayoutLMv2 model an image (of texts) and ask it a question. It will give us
+ a prediction of what it thinks the answer is (the span of the answer within the texts parsed from the image).
+
+ ```python
+ >>> from transformers import AutoProcessor, LayoutLMv2ForQuestionAnswering, set_seed
+ >>> import torch
+ >>> from PIL import Image
+ >>> from datasets import load_dataset
+
+ >>> set_seed(0)
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
+ >>> model = LayoutLMv2ForQuestionAnswering.from_pretrained("microsoft/layoutlmv2-base-uncased")
+
+ >>> dataset = load_dataset("hf-internal-testing/fixtures_docvqa")
+ >>> image = dataset["test"][0]["image"]
+ >>> question = "When is coffee break?"
+ >>> encoding = processor(image, question, return_tensors="pt")
+
+ >>> outputs = model(**encoding)
+ >>> predicted_start_idx = outputs.start_logits.argmax(-1).item()
+ >>> predicted_end_idx = outputs.end_logits.argmax(-1).item()
+ >>> predicted_start_idx, predicted_end_idx
+ (30, 191)
+
+ >>> predicted_answer_tokens = encoding.input_ids.squeeze()[predicted_start_idx : predicted_end_idx + 1]
+ >>> predicted_answer = processor.tokenizer.decode(predicted_answer_tokens)
+ >>> predicted_answer # results are not good without further fine-tuning
+ '44 a. m. to 12 : 25 p. m. 12 : 25 to 12 : 58 p. m. 12 : 58 to 4 : 00 p. m. 2 : 00 to 5 : 00 p. m. coffee break coffee will be served for men and women in the lobby adjacent to exhibit area. please move into exhibit area. ( exhibits open ) trrf general session ( part | ) presiding : lee a. waller trrf vice president “ introductory remarks ” lee a. waller, trrf vice presi - dent individual interviews with trrf public board members and sci - entific advisory council mem - bers conducted by trrf treasurer philip g. kuehn to get answers which the public refrigerated warehousing industry is looking for. plus questions from'
+ ```
+
+ ```python
+ >>> target_start_index = torch.tensor([7])
+ >>> target_end_index = torch.tensor([14])
+ >>> outputs = model(**encoding, start_positions=target_start_index, end_positions=target_end_index)
+ >>> predicted_answer_span_start = outputs.start_logits.argmax(-1).item()
+ >>> predicted_answer_span_end = outputs.end_logits.argmax(-1).item()
+ >>> predicted_answer_span_start, predicted_answer_span_end
+ (30, 191)
+ ```
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlmv2(
+ input_ids=input_ids,
+ bbox=bbox,
+ image=image,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+ # only take the text part of the output representations
+ sequence_output = outputs[0][:, :seq_length]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "LayoutLMv2ForQuestionAnswering",
+ "LayoutLMv2ForSequenceClassification",
+ "LayoutLMv2ForTokenClassification",
+ "LayoutLMv2Layer",
+ "LayoutLMv2Model",
+ "LayoutLMv2PreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/processing_layoutlmv2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/processing_layoutlmv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f8453b492b550190f8f1f5d260b61de7ba58c27
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/processing_layoutlmv2.py
@@ -0,0 +1,204 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for LayoutLMv2.
+"""
+
+import warnings
+from typing import Optional, Union
+
+from ...processing_utils import ProcessorMixin
+from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from ...utils import TensorType
+
+
+class LayoutLMv2Processor(ProcessorMixin):
+ r"""
+ Constructs a LayoutLMv2 processor which combines a LayoutLMv2 image processor and a LayoutLMv2 tokenizer into a
+ single processor.
+
+ [`LayoutLMv2Processor`] offers all the functionalities you need to prepare data for the model.
+
+ It first uses [`LayoutLMv2ImageProcessor`] to resize document images to a fixed size, and optionally applies OCR to
+ get words and normalized bounding boxes. These are then provided to [`LayoutLMv2Tokenizer`] or
+ [`LayoutLMv2TokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`,
+ `attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned
+ into token-level `labels` for token classification tasks (such as FUNSD, CORD).
+
+ Args:
+ image_processor (`LayoutLMv2ImageProcessor`, *optional*):
+ An instance of [`LayoutLMv2ImageProcessor`]. The image processor is a required input.
+ tokenizer (`LayoutLMv2Tokenizer` or `LayoutLMv2TokenizerFast`, *optional*):
+ An instance of [`LayoutLMv2Tokenizer`] or [`LayoutLMv2TokenizerFast`]. The tokenizer is a required input.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "LayoutLMv2ImageProcessor"
+ tokenizer_class = ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")
+
+ def __init__(self, image_processor=None, tokenizer=None, **kwargs):
+ feature_extractor = None
+ if "feature_extractor" in kwargs:
+ warnings.warn(
+ "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
+ " instead.",
+ FutureWarning,
+ )
+ feature_extractor = kwargs.pop("feature_extractor")
+
+ image_processor = image_processor if image_processor is not None else feature_extractor
+ if image_processor is None:
+ raise ValueError("You need to specify an `image_processor`.")
+ if tokenizer is None:
+ raise ValueError("You need to specify a `tokenizer`.")
+
+ super().__init__(image_processor, tokenizer)
+
+ def __call__(
+ self,
+ images,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+ text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
+ boxes: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = False,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ This method first forwards the `images` argument to [`~LayoutLMv2ImageProcessor.__call__`]. In case
+ [`LayoutLMv2ImageProcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
+ bounding boxes along with the additional arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output,
+ together with resized `images`. In case [`LayoutLMv2ImageProcessor`] was initialized with `apply_ocr` set to
+ `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the additional
+ arguments to [`~LayoutLMv2Tokenizer.__call__`] and returns the output, together with resized `images``.
+
+ Please refer to the docstring of the above two methods for more information.
+ """
+ # verify input
+ if self.image_processor.apply_ocr and (boxes is not None):
+ raise ValueError(
+ "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."
+ )
+
+ if self.image_processor.apply_ocr and (word_labels is not None):
+ raise ValueError(
+ "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."
+ )
+
+ if return_overflowing_tokens is True and return_offsets_mapping is False:
+ raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")
+
+ # first, apply the image processor
+ features = self.image_processor(images=images, return_tensors=return_tensors)
+
+ # second, apply the tokenizer
+ if text is not None and self.image_processor.apply_ocr and text_pair is None:
+ if isinstance(text, str):
+ text = [text] # add batch dimension (as the image processor always adds a batch dimension)
+ text_pair = features["words"]
+
+ encoded_inputs = self.tokenizer(
+ text=text if text is not None else features["words"],
+ text_pair=text_pair if text_pair is not None else None,
+ boxes=boxes if boxes is not None else features["boxes"],
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ return_tensors=return_tensors,
+ **kwargs,
+ )
+
+ # add pixel values
+ images = features.pop("pixel_values")
+ if return_overflowing_tokens is True:
+ images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"])
+ encoded_inputs["image"] = images
+
+ return encoded_inputs
+
+ def get_overflowing_images(self, images, overflow_to_sample_mapping):
+ # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
+ images_with_overflow = []
+ for sample_idx in overflow_to_sample_mapping:
+ images_with_overflow.append(images[sample_idx])
+
+ if len(images_with_overflow) != len(overflow_to_sample_mapping):
+ raise ValueError(
+ "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
+ f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
+ )
+
+ return images_with_overflow
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
+ to the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ def model_input_names(self):
+ return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
+
+ @property
+ def feature_extractor_class(self):
+ warnings.warn(
+ "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
+ FutureWarning,
+ )
+ return self.image_processor_class
+
+ @property
+ def feature_extractor(self):
+ warnings.warn(
+ "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
+ FutureWarning,
+ )
+ return self.image_processor
+
+
+__all__ = ["LayoutLMv2Processor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d82b5cf41041b0024134c0f1a6294c0cace824c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py
@@ -0,0 +1,1545 @@
+# coding=utf-8
+# Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization class for LayoutLMv2."""
+
+import collections
+import os
+import sys
+import unicodedata
+from typing import Optional, Union
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
+from ...tokenization_utils_base import (
+ BatchEncoding,
+ EncodedInput,
+ PreTokenizedInput,
+ TextInput,
+ TextInputPair,
+ TruncationStrategy,
+)
+from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+
+LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING = r"""
+ add_special_tokens (`bool`, *optional*, defaults to `True`):
+ Whether or not to encode the sequences with the special tokens relative to their model.
+ padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
+ Activates and controls padding. Accepts the following values:
+
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+ Activates and controls truncation. Accepts the following values:
+
+ - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
+ to the maximum acceptable input length for the model if that argument is not provided. This will
+ truncate token by token, removing a token from the longest sequence in the pair if a pair of
+ sequences (or a batch of pairs) is provided.
+ - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
+ greater than the model maximum admissible input size).
+ max_length (`int`, *optional*):
+ Controls the maximum length to use by one of the truncation/padding parameters.
+
+ If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
+ is required by one of the truncation/padding parameters. If the model has no specific maximum input
+ length (like XLNet) truncation/padding to a maximum length will be deactivated.
+ stride (`int`, *optional*, defaults to 0):
+ If set to a number along with `max_length`, the overflowing tokens returned when
+ `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
+ returned to provide some overlap between truncated and overflowing sequences. The value of this
+ argument defines the number of overlapping tokens.
+ pad_to_multiple_of (`int`, *optional*):
+ If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
+ the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
+ return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+ If set, will return tensors instead of list of python integers. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return Numpy `np.ndarray` objects.
+"""
+
+LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
+ return_token_type_ids (`bool`, *optional*):
+ Whether to return token type IDs. If left to the default, will return the token type IDs according to
+ the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ return_attention_mask (`bool`, *optional*):
+ Whether to return the attention mask. If left to the default, will return the attention mask according
+ to the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+ [What are attention masks?](../glossary#attention-mask)
+ return_overflowing_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
+ of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
+ of returning overflowing tokens.
+ return_special_tokens_mask (`bool`, *optional*, defaults to `False`):
+ Whether or not to return special tokens mask information.
+ return_offsets_mapping (`bool`, *optional*, defaults to `False`):
+ Whether or not to return `(char_start, char_end)` for each token.
+
+ This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using
+ Python's tokenizer, this method will raise `NotImplementedError`.
+ return_length (`bool`, *optional*, defaults to `False`):
+ Whether or not to return the lengths of the encoded inputs.
+ verbose (`bool`, *optional*, defaults to `True`):
+ Whether or not to print more information and warnings.
+ **kwargs: passed to the `self.tokenize()` method
+
+ Return:
+ [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ - **bbox** -- List of bounding boxes to be fed to a model.
+
+ - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or
+ if *"token_type_ids"* is in `self.model_input_names`).
+
+ [What are token type IDs?](../glossary#token-type-ids)
+
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ - **labels** -- List of labels to be fed to a model. (when `word_labels` is specified).
+ - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
+ `return_overflowing_tokens=True`).
+ - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
+ `return_overflowing_tokens=True`).
+ - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
+ regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
+ - **length** -- The length of the inputs (when `return_length=True`).
+"""
+
+
+def load_vocab(vocab_file):
+ """Loads a vocabulary file into a dictionary."""
+ vocab = collections.OrderedDict()
+ with open(vocab_file, "r", encoding="utf-8") as reader:
+ tokens = reader.readlines()
+ for index, token in enumerate(tokens):
+ token = token.rstrip("\n")
+ vocab[token] = index
+ return vocab
+
+
+def whitespace_tokenize(text):
+ """Runs basic whitespace cleaning and splitting on a piece of text."""
+ text = text.strip()
+ if not text:
+ return []
+ tokens = text.split()
+ return tokens
+
+
+table = dict.fromkeys(i for i in range(sys.maxunicode) if unicodedata.category(chr(i)).startswith("P"))
+
+
+def subfinder(mylist, pattern):
+ matches = []
+ indices = []
+ for idx, i in enumerate(range(len(mylist))):
+ if mylist[i] == pattern[0] and mylist[i : i + len(pattern)] == pattern:
+ matches.append(pattern)
+ indices.append(idx)
+ if matches:
+ return matches[0], indices[0]
+ else:
+ return None, 0
+
+
+class LayoutLMv2Tokenizer(PreTrainedTokenizer):
+ r"""
+ Construct a LayoutLMv2 tokenizer. Based on WordPiece. [`LayoutLMv2Tokenizer`] can be used to turn words, word-level
+ bounding boxes and optional word labels to token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`, and
+ optional `labels` (for token classification).
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ [`LayoutLMv2Tokenizer`] runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the
+ word-level bounding boxes into token-level bounding boxes.
+
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+
+ def __init__(
+ self,
+ vocab_file,
+ do_lower_case=True,
+ do_basic_tokenize=True,
+ never_split=None,
+ unk_token="[UNK]",
+ sep_token="[SEP]",
+ pad_token="[PAD]",
+ cls_token="[CLS]",
+ mask_token="[MASK]",
+ cls_token_box=[0, 0, 0, 0],
+ sep_token_box=[1000, 1000, 1000, 1000],
+ pad_token_box=[0, 0, 0, 0],
+ pad_token_label=-100,
+ only_label_first_subword=True,
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ model_max_length: int = 512,
+ additional_special_tokens: Optional[list[str]] = None,
+ **kwargs,
+ ):
+ sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token
+ unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
+ cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token
+ mask_token = AddedToken(mask_token, special=True) if isinstance(mask_token, str) else mask_token
+
+ if not os.path.isfile(vocab_file):
+ raise ValueError(
+ f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
+ " model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+ )
+ self.vocab = load_vocab(vocab_file)
+ self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
+ self.do_basic_tokenize = do_basic_tokenize
+ if do_basic_tokenize:
+ self.basic_tokenizer = BasicTokenizer(
+ do_lower_case=do_lower_case,
+ never_split=never_split,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ )
+ self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
+
+ # additional properties
+ self.cls_token_box = cls_token_box
+ self.sep_token_box = sep_token_box
+ self.pad_token_box = pad_token_box
+ self.pad_token_label = pad_token_label
+ self.only_label_first_subword = only_label_first_subword
+ super().__init__(
+ do_lower_case=do_lower_case,
+ do_basic_tokenize=do_basic_tokenize,
+ never_split=never_split,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ pad_token=pad_token,
+ cls_token=cls_token,
+ mask_token=mask_token,
+ cls_token_box=cls_token_box,
+ sep_token_box=sep_token_box,
+ pad_token_box=pad_token_box,
+ pad_token_label=pad_token_label,
+ only_label_first_subword=only_label_first_subword,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ model_max_length=model_max_length,
+ additional_special_tokens=additional_special_tokens,
+ **kwargs,
+ )
+
+ @property
+ def do_lower_case(self):
+ return self.basic_tokenizer.do_lower_case
+
+ @property
+ def vocab_size(self):
+ return len(self.vocab)
+
+ def get_vocab(self):
+ return dict(self.vocab, **self.added_tokens_encoder)
+
+ def _tokenize(self, text):
+ split_tokens = []
+ if self.do_basic_tokenize:
+ for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
+ # If the token is part of the never_split set
+ if token in self.basic_tokenizer.never_split:
+ split_tokens.append(token)
+ else:
+ split_tokens += self.wordpiece_tokenizer.tokenize(token)
+ else:
+ split_tokens = self.wordpiece_tokenizer.tokenize(text)
+ return split_tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.ids_to_tokens.get(index, self.unk_token)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ out_string = " ".join(tokens).replace(" ##", "").strip()
+ return out_string
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A BERT sequence has the following format:
+
+ - single sequence: `[CLS] X [SEP]`
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + token_ids_1 + sep
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is not None:
+ return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1]
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ index = 0
+ if os.path.isdir(save_directory):
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ else:
+ vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
+ with open(vocab_file, "w", encoding="utf-8") as writer:
+ for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+ " Please check that the vocabulary is not corrupted!"
+ )
+ index = token_index
+ writer.write(token + "\n")
+ index += 1
+ return (vocab_file,)
+
+ @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def __call__(
+ self,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]],
+ text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
+ boxes: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
+ sequences with word-level normalized bounding boxes and optional labels.
+
+ Args:
+ text (`str`, `List[str]`, `List[List[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
+ (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
+ words).
+ text_pair (`List[str]`, `List[List[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
+ (pretokenized string).
+ boxes (`List[List[int]]`, `List[List[List[int]]]`):
+ Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
+ word_labels (`List[int]`, `List[List[int]]`, *optional*):
+ Word-level integer labels (for token classification tasks such as FUNSD, CORD).
+ """
+
+ # Input type checking for clearer error
+ def _is_valid_text_input(t):
+ if isinstance(t, str):
+ # Strings are fine
+ return True
+ elif isinstance(t, (list, tuple)):
+ # List are fine as long as they are...
+ if len(t) == 0:
+ # ... empty
+ return True
+ elif isinstance(t[0], str):
+ # ... list of strings
+ return True
+ elif isinstance(t[0], (list, tuple)):
+ # ... list with an empty list or with a list of strings
+ return len(t[0]) == 0 or isinstance(t[0][0], str)
+ else:
+ return False
+ else:
+ return False
+
+ if text_pair is not None:
+ # in case text + text_pair are provided, text = questions, text_pair = words
+ if not _is_valid_text_input(text):
+ raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
+ if not isinstance(text_pair, (list, tuple)):
+ raise ValueError(
+ "Words must be of type `List[str]` (single pretokenized example), "
+ "or `List[List[str]]` (batch of pretokenized examples)."
+ )
+ else:
+ # in case only text is provided => must be words
+ if not isinstance(text, (list, tuple)):
+ raise ValueError(
+ "Words must be of type `List[str]` (single pretokenized example), "
+ "or `List[List[str]]` (batch of pretokenized examples)."
+ )
+
+ if text_pair is not None:
+ is_batched = isinstance(text, (list, tuple))
+ else:
+ is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
+
+ words = text if text_pair is None else text_pair
+ if boxes is None:
+ raise ValueError("You must provide corresponding bounding boxes")
+ if is_batched:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide words and boxes for an equal amount of examples")
+ for words_example, boxes_example in zip(words, boxes):
+ if len(words_example) != len(boxes_example):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+ else:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+
+ if is_batched:
+ if text_pair is not None and len(text) != len(text_pair):
+ raise ValueError(
+ f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
+ f" {len(text_pair)}."
+ )
+ batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
+ is_pair = bool(text_pair is not None)
+ return self.batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+ else:
+ return self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[list[int]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def _batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[list[int]]]] = None,
+ word_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast."
+ )
+
+ batch_outputs = self._batch_prepare_for_model(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=return_tensors,
+ verbose=verbose,
+ )
+
+ return BatchEncoding(batch_outputs)
+
+ @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def _batch_prepare_for_model(
+ self,
+ batch_text_or_text_pairs,
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
+ adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
+ manages a moving window (with user defined stride) for overflowing tokens.
+
+ Args:
+ batch_ids_pairs: list of tokenized input ids or input ids pairs
+ """
+
+ batch_outputs = {}
+ for idx, example in enumerate(zip(batch_text_or_text_pairs, boxes)):
+ batch_text_or_text_pair, boxes_example = example
+ outputs = self.prepare_for_model(
+ batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair,
+ batch_text_or_text_pair[1] if is_pair else None,
+ boxes_example,
+ word_labels=word_labels[idx] if word_labels is not None else None,
+ add_special_tokens=add_special_tokens,
+ padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=None, # we pad in batch afterward
+ padding_side=None, # we pad in batch afterward
+ return_attention_mask=False, # we pad in batch afterward
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=None, # We convert the whole batch to tensors at the end
+ prepend_batch_axis=False,
+ verbose=verbose,
+ )
+
+ for key, value in outputs.items():
+ if key not in batch_outputs:
+ batch_outputs[key] = []
+ batch_outputs[key].append(value)
+
+ batch_outputs = self.pad(
+ batch_outputs,
+ padding=padding_strategy.value,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
+
+ return batch_outputs
+
+ @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING)
+ def encode(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> list[int]:
+ encoded_inputs = self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return encoded_inputs["input_ids"]
+
+ @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
+ `__call__` should be used instead.
+
+ Args:
+ text (`str`, `List[str]`, `List[List[str]]`):
+ The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
+ text_pair (`List[str]` or `List[int]`, *optional*):
+ Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
+ list of list of strings (words of a batch of examples).
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._encode_plus(
+ text=text,
+ boxes=boxes,
+ text_pair=text_pair,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def _encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast. "
+ "More information on available tokenizers at "
+ "https://github.com/huggingface/transformers/pull/2674"
+ )
+
+ return self.prepare_for_model(
+ text=text,
+ text_pair=text_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding_strategy.value,
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ prepend_batch_axis=True,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ verbose=verbose,
+ )
+
+ @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def prepare_for_model(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ prepend_batch_axis: bool = False,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
+ truncates sequences if overflowing while taking into account the special tokens and manages a moving window
+ (with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and
+ *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a
+ combination of arguments will raise an error.
+
+ Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into
+ token-level `labels`. The word label is used for the first token of the word, while remaining tokens are
+ labeled with -100, such that they will be ignored by the loss function.
+
+ Args:
+ text (`str`, `List[str]`, `List[List[str]]`):
+ The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
+ text_pair (`List[str]` or `List[int]`, *optional*):
+ Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
+ list of list of strings (words of a batch of examples).
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ tokens = []
+ pair_tokens = []
+ token_boxes = []
+ pair_token_boxes = []
+ labels = []
+
+ if text_pair is None:
+ if word_labels is None:
+ # CASE 1: document image classification (training + inference) + CASE 2: token classification (inference)
+ for word, box in zip(text, boxes):
+ if len(word) < 1: # skip empty words
+ continue
+ word_tokens = self.tokenize(word)
+ tokens.extend(word_tokens)
+ token_boxes.extend([box] * len(word_tokens))
+ else:
+ # CASE 2: token classification (training)
+ for word, box, label in zip(text, boxes, word_labels):
+ if len(word) < 1: # skip empty words
+ continue
+ word_tokens = self.tokenize(word)
+ tokens.extend(word_tokens)
+ token_boxes.extend([box] * len(word_tokens))
+ if self.only_label_first_subword:
+ # Use the real label id for the first token of the word, and padding ids for the remaining tokens
+ labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
+ else:
+ labels.extend([label] * len(word_tokens))
+ else:
+ # CASE 3: document visual question answering (inference)
+ # text = question
+ # text_pair = words
+ tokens = self.tokenize(text)
+ token_boxes = [self.pad_token_box for _ in range(len(tokens))]
+
+ for word, box in zip(text_pair, boxes):
+ if len(word) < 1: # skip empty words
+ continue
+ word_tokens = self.tokenize(word)
+ pair_tokens.extend(word_tokens)
+ pair_token_boxes.extend([box] * len(word_tokens))
+
+ # Create ids + pair_ids
+ ids = self.convert_tokens_to_ids(tokens)
+ pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None
+
+ if (
+ return_overflowing_tokens
+ and truncation_strategy == TruncationStrategy.LONGEST_FIRST
+ and pair_ids is not None
+ ):
+ raise ValueError(
+ "Not possible to return overflowing tokens for pair of sequences with the "
+ "`longest_first`. Please select another truncation strategy than `longest_first`, "
+ "for instance `only_second` or `only_first`."
+ )
+
+ # Compute the total size of the returned encodings
+ pair = bool(pair_ids is not None)
+ len_ids = len(ids)
+ len_pair_ids = len(pair_ids) if pair else 0
+ total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
+
+ # Truncation: Handle max sequence length
+ overflowing_tokens = []
+ overflowing_token_boxes = []
+ overflowing_labels = []
+ if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
+ (
+ ids,
+ token_boxes,
+ pair_ids,
+ pair_token_boxes,
+ labels,
+ overflowing_tokens,
+ overflowing_token_boxes,
+ overflowing_labels,
+ ) = self.truncate_sequences(
+ ids,
+ token_boxes,
+ pair_ids=pair_ids,
+ pair_token_boxes=pair_token_boxes,
+ labels=labels,
+ num_tokens_to_remove=total_len - max_length,
+ truncation_strategy=truncation_strategy,
+ stride=stride,
+ )
+
+ if return_token_type_ids and not add_special_tokens:
+ raise ValueError(
+ "Asking to return token_type_ids while setting add_special_tokens to False "
+ "results in an undefined behavior. Please set add_special_tokens to True or "
+ "set return_token_type_ids to None."
+ )
+
+ # Load from model defaults
+ if return_token_type_ids is None:
+ return_token_type_ids = "token_type_ids" in self.model_input_names
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ encoded_inputs = {}
+
+ if return_overflowing_tokens:
+ encoded_inputs["overflowing_tokens"] = overflowing_tokens
+ encoded_inputs["overflowing_token_boxes"] = overflowing_token_boxes
+ encoded_inputs["overflowing_labels"] = overflowing_labels
+ encoded_inputs["num_truncated_tokens"] = total_len - max_length
+
+ # Add special tokens
+ if add_special_tokens:
+ sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
+ token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
+ token_boxes = [self.cls_token_box] + token_boxes + [self.sep_token_box]
+ if pair_token_boxes:
+ pair_token_boxes = pair_token_boxes + [self.sep_token_box]
+ if labels:
+ labels = [self.pad_token_label] + labels + [self.pad_token_label]
+ else:
+ sequence = ids + pair_ids if pair else ids
+ token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
+
+ # Build output dictionary
+ encoded_inputs["input_ids"] = sequence
+ encoded_inputs["bbox"] = token_boxes + pair_token_boxes
+ if return_token_type_ids:
+ encoded_inputs["token_type_ids"] = token_type_ids
+ if return_special_tokens_mask:
+ if add_special_tokens:
+ encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
+ else:
+ encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
+
+ if labels:
+ encoded_inputs["labels"] = labels
+
+ # Check lengths
+ self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
+
+ # Padding
+ if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
+ encoded_inputs = self.pad(
+ encoded_inputs,
+ max_length=max_length,
+ padding=padding_strategy.value,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ if return_length:
+ encoded_inputs["length"] = len(encoded_inputs["input_ids"])
+
+ batch_outputs = BatchEncoding(
+ encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
+ )
+
+ return batch_outputs
+
+ def truncate_sequences(
+ self,
+ ids: list[int],
+ token_boxes: list[list[int]],
+ pair_ids: Optional[list[int]] = None,
+ pair_token_boxes: Optional[list[list[int]]] = None,
+ labels: Optional[list[int]] = None,
+ num_tokens_to_remove: int = 0,
+ truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
+ stride: int = 0,
+ ) -> tuple[list[int], list[int], list[int]]:
+ """
+ Truncates a sequence pair in-place following the strategy.
+
+ Args:
+ ids (`List[int]`):
+ Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
+ `convert_tokens_to_ids` methods.
+ token_boxes (`List[List[int]]`):
+ Bounding boxes of the first sequence.
+ pair_ids (`List[int]`, *optional*):
+ Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
+ and `convert_tokens_to_ids` methods.
+ pair_token_boxes (`List[List[int]]`, *optional*):
+ Bounding boxes of the second sequence.
+ labels (`List[int]`, *optional*):
+ Labels of the first sequence (for token classification tasks).
+ num_tokens_to_remove (`int`, *optional*, defaults to 0):
+ Number of tokens to remove using the truncation strategy.
+ truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+ The strategy to follow for truncation. Can be:
+
+ - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will truncate
+ token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
+ batch of pairs) is provided.
+ - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
+ than the model maximum admissible input size).
+ stride (`int`, *optional*, defaults to 0):
+ If set to a positive number, the overflowing tokens returned will contain some tokens from the main
+ sequence returned. The value of this argument defines the number of additional tokens.
+
+ Returns:
+ `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
+ overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair
+ of sequences (or a batch of pairs) is provided.
+ """
+ if num_tokens_to_remove <= 0:
+ return ids, token_boxes, pair_ids, pair_token_boxes, labels, [], [], []
+
+ if not isinstance(truncation_strategy, TruncationStrategy):
+ truncation_strategy = TruncationStrategy(truncation_strategy)
+
+ overflowing_tokens = []
+ overflowing_token_boxes = []
+ overflowing_labels = []
+ if truncation_strategy == TruncationStrategy.ONLY_FIRST or (
+ truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None
+ ):
+ if len(ids) > num_tokens_to_remove:
+ window_len = min(len(ids), stride + num_tokens_to_remove)
+ overflowing_tokens = ids[-window_len:]
+ overflowing_token_boxes = token_boxes[-window_len:]
+ overflowing_labels = labels[-window_len:]
+ ids = ids[:-num_tokens_to_remove]
+ token_boxes = token_boxes[:-num_tokens_to_remove]
+ labels = labels[:-num_tokens_to_remove]
+ else:
+ error_msg = (
+ f"We need to remove {num_tokens_to_remove} to truncate the input "
+ f"but the first sequence has a length {len(ids)}. "
+ )
+ if truncation_strategy == TruncationStrategy.ONLY_FIRST:
+ error_msg = (
+ error_msg + "Please select another truncation strategy than "
+ f"{truncation_strategy}, for instance 'longest_first' or 'only_second'."
+ )
+ logger.error(error_msg)
+ elif truncation_strategy == TruncationStrategy.LONGEST_FIRST:
+ logger.warning(
+ "Be aware, overflowing tokens are not returned for the setting you have chosen,"
+ f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
+ "truncation strategy. So the returned list will always be empty even if some "
+ "tokens have been removed."
+ )
+ for _ in range(num_tokens_to_remove):
+ if pair_ids is None or len(ids) > len(pair_ids):
+ ids = ids[:-1]
+ token_boxes = token_boxes[:-1]
+ labels = labels[:-1]
+ else:
+ pair_ids = pair_ids[:-1]
+ pair_token_boxes = pair_token_boxes[:-1]
+ elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
+ if len(pair_ids) > num_tokens_to_remove:
+ window_len = min(len(pair_ids), stride + num_tokens_to_remove)
+ overflowing_tokens = pair_ids[-window_len:]
+ overflowing_token_boxes = pair_token_boxes[-window_len:]
+ pair_ids = pair_ids[:-num_tokens_to_remove]
+ pair_token_boxes = pair_token_boxes[:-num_tokens_to_remove]
+ else:
+ logger.error(
+ f"We need to remove {num_tokens_to_remove} to truncate the input "
+ f"but the second sequence has a length {len(pair_ids)}. "
+ f"Please select another truncation strategy than {truncation_strategy}, "
+ "for instance 'longest_first' or 'only_first'."
+ )
+
+ return (
+ ids,
+ token_boxes,
+ pair_ids,
+ pair_token_boxes,
+ labels,
+ overflowing_tokens,
+ overflowing_token_boxes,
+ overflowing_labels,
+ )
+
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ """
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+ Args:
+ encoded_inputs:
+ Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+ max_length: maximum length of the returned list and optionally padding length (see below).
+ Will truncate by taking into account the special tokens.
+ padding_strategy: PaddingStrategy to use for padding.
+
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
+ The tokenizer padding sides are defined in self.padding_side:
+
+ - 'left': pads on the left of the sequences
+ - 'right': pads on the right of the sequences
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta).
+ padding_side:
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask:
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+ """
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = len(required_input)
+
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+ # Initialize attention mask if not present.
+ if return_attention_mask and "attention_mask" not in encoded_inputs:
+ encoded_inputs["attention_mask"] = [1] * len(required_input)
+
+ if needs_to_be_padded:
+ difference = max_length - len(required_input)
+ padding_side = padding_side if padding_side is not None else self.padding_side
+ if padding_side == "right":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = (
+ encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
+ )
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
+ encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
+ elif padding_side == "left":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
+ "token_type_ids"
+ ]
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+ else:
+ raise ValueError("Invalid padding strategy:" + str(padding_side))
+
+ return encoded_inputs
+
+
+# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
+class BasicTokenizer:
+ """
+ Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
+
+ Args:
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ never_split (`Iterable`, *optional*):
+ Collection of tokens which will never be split during tokenization. Only has an effect when
+ `do_basic_tokenize=True`
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters.
+
+ This should likely be deactivated for Japanese (see this
+ [issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original BERT).
+ do_split_on_punc (`bool`, *optional*, defaults to `True`):
+ In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
+ the full context of the words, such as contractions.
+ """
+
+ def __init__(
+ self,
+ do_lower_case=True,
+ never_split=None,
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ do_split_on_punc=True,
+ ):
+ if never_split is None:
+ never_split = []
+ self.do_lower_case = do_lower_case
+ self.never_split = set(never_split)
+ self.tokenize_chinese_chars = tokenize_chinese_chars
+ self.strip_accents = strip_accents
+ self.do_split_on_punc = do_split_on_punc
+
+ def tokenize(self, text, never_split=None):
+ """
+ Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
+
+ Args:
+ never_split (`List[str]`, *optional*)
+ Kept for backward compatibility purposes. Now implemented directly at the base class level (see
+ [`PreTrainedTokenizer.tokenize`]) List of token not to split.
+ """
+ # union() returns a new set by concatenating the two sets.
+ never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
+ text = self._clean_text(text)
+
+ # This was added on November 1st, 2018 for the multilingual and Chinese
+ # models. This is also applied to the English models now, but it doesn't
+ # matter since the English models were not trained on any Chinese data
+ # and generally don't have any Chinese data in them (there are Chinese
+ # characters in the vocabulary because Wikipedia does have some Chinese
+ # words in the English Wikipedia.).
+ if self.tokenize_chinese_chars:
+ text = self._tokenize_chinese_chars(text)
+ # prevents treating the same character with different unicode codepoints as different characters
+ unicode_normalized_text = unicodedata.normalize("NFC", text)
+ orig_tokens = whitespace_tokenize(unicode_normalized_text)
+ split_tokens = []
+ for token in orig_tokens:
+ if token not in never_split:
+ if self.do_lower_case:
+ token = token.lower()
+ if self.strip_accents is not False:
+ token = self._run_strip_accents(token)
+ elif self.strip_accents:
+ token = self._run_strip_accents(token)
+ split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+ output_tokens = whitespace_tokenize(" ".join(split_tokens))
+ return output_tokens
+
+ def _run_strip_accents(self, text):
+ """Strips accents from a piece of text."""
+ text = unicodedata.normalize("NFD", text)
+ output = []
+ for char in text:
+ cat = unicodedata.category(char)
+ if cat == "Mn":
+ continue
+ output.append(char)
+ return "".join(output)
+
+ def _run_split_on_punc(self, text, never_split=None):
+ """Splits punctuation on a piece of text."""
+ if not self.do_split_on_punc or (never_split is not None and text in never_split):
+ return [text]
+ chars = list(text)
+ i = 0
+ start_new_word = True
+ output = []
+ while i < len(chars):
+ char = chars[i]
+ if _is_punctuation(char):
+ output.append([char])
+ start_new_word = True
+ else:
+ if start_new_word:
+ output.append([])
+ start_new_word = False
+ output[-1].append(char)
+ i += 1
+
+ return ["".join(x) for x in output]
+
+ def _tokenize_chinese_chars(self, text):
+ """Adds whitespace around any CJK character."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if self._is_chinese_char(cp):
+ output.append(" ")
+ output.append(char)
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+ def _is_chinese_char(self, cp):
+ """Checks whether CP is the codepoint of a CJK character."""
+ # This defines a "chinese character" as anything in the CJK Unicode block:
+ # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+ #
+ # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+ # despite its name. The modern Korean Hangul alphabet is a different block,
+ # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+ # space-separated words, so they are not treated specially and handled
+ # like the all of the other languages.
+ if (
+ (cp >= 0x4E00 and cp <= 0x9FFF)
+ or (cp >= 0x3400 and cp <= 0x4DBF)
+ or (cp >= 0x20000 and cp <= 0x2A6DF)
+ or (cp >= 0x2A700 and cp <= 0x2B73F)
+ or (cp >= 0x2B740 and cp <= 0x2B81F)
+ or (cp >= 0x2B820 and cp <= 0x2CEAF)
+ or (cp >= 0xF900 and cp <= 0xFAFF)
+ or (cp >= 0x2F800 and cp <= 0x2FA1F)
+ ):
+ return True
+
+ return False
+
+ def _clean_text(self, text):
+ """Performs invalid character removal and whitespace cleanup on text."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if cp == 0 or cp == 0xFFFD or _is_control(char):
+ continue
+ if _is_whitespace(char):
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+
+# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
+class WordpieceTokenizer:
+ """Runs WordPiece tokenization."""
+
+ def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+ self.vocab = vocab
+ self.unk_token = unk_token
+ self.max_input_chars_per_word = max_input_chars_per_word
+
+ def tokenize(self, text):
+ """
+ Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
+ tokenization using the given vocabulary.
+
+ For example, `input = "unaffable"` will return as output `["un", "##aff", "##able"]`.
+
+ Args:
+ text: A single token or whitespace separated tokens. This should have
+ already been passed through *BasicTokenizer*.
+
+ Returns:
+ A list of wordpiece tokens.
+ """
+
+ output_tokens = []
+ for token in whitespace_tokenize(text):
+ chars = list(token)
+ if len(chars) > self.max_input_chars_per_word:
+ output_tokens.append(self.unk_token)
+ continue
+
+ is_bad = False
+ start = 0
+ sub_tokens = []
+ while start < len(chars):
+ end = len(chars)
+ cur_substr = None
+ while start < end:
+ substr = "".join(chars[start:end])
+ if start > 0:
+ substr = "##" + substr
+ if substr in self.vocab:
+ cur_substr = substr
+ break
+ end -= 1
+ if cur_substr is None:
+ is_bad = True
+ break
+ sub_tokens.append(cur_substr)
+ start = end
+
+ if is_bad:
+ output_tokens.append(self.unk_token)
+ else:
+ output_tokens.extend(sub_tokens)
+ return output_tokens
+
+
+__all__ = ["LayoutLMv2Tokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/tokenization_layoutlmv2_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/tokenization_layoutlmv2_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e324ee0b8fe971b06cd102eaa9930990a1f5b99
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv2/tokenization_layoutlmv2_fast.py
@@ -0,0 +1,789 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fast tokenization class for LayoutLMv2. It overwrites 2 methods of the slow tokenizer class, namely _batch_encode_plus
+and _encode_plus, in which the Rust tokenizer is used.
+"""
+
+import json
+from typing import Optional, Union
+
+from tokenizers import normalizers
+
+from ...tokenization_utils_base import (
+ BatchEncoding,
+ EncodedInput,
+ PaddingStrategy,
+ PreTokenizedInput,
+ TensorType,
+ TextInput,
+ TextInputPair,
+ TruncationStrategy,
+)
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import add_end_docstrings, logging
+from .tokenization_layoutlmv2 import (
+ LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING,
+ LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING,
+ LayoutLMv2Tokenizer,
+)
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
+
+
+class LayoutLMv2TokenizerFast(PreTrainedTokenizerFast):
+ r"""
+ Construct a "fast" LayoutLMv2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ File containing the vocabulary.
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ sep_token (`str`, *optional*, defaults to `"[SEP]"`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ pad_token (`str`, *optional*, defaults to `"[PAD]"`):
+ The token used for padding, for example when batching sequences of different lengths.
+ cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ mask_token (`str`, *optional*, defaults to `"[MASK]"`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ cls_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [CLS] token.
+ sep_token_box (`List[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`):
+ The bounding box to use for the special [SEP] token.
+ pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [PAD] token.
+ pad_token_label (`int`, *optional*, defaults to -100):
+ The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
+ CrossEntropyLoss.
+ only_label_first_subword (`bool`, *optional*, defaults to `True`):
+ Whether or not to only label the first subword, in case word labels are provided.
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
+ issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original LayoutLMv2).
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ slow_tokenizer_class = LayoutLMv2Tokenizer
+
+ def __init__(
+ self,
+ vocab_file=None,
+ tokenizer_file=None,
+ do_lower_case=True,
+ unk_token="[UNK]",
+ sep_token="[SEP]",
+ pad_token="[PAD]",
+ cls_token="[CLS]",
+ mask_token="[MASK]",
+ cls_token_box=[0, 0, 0, 0],
+ sep_token_box=[1000, 1000, 1000, 1000],
+ pad_token_box=[0, 0, 0, 0],
+ pad_token_label=-100,
+ only_label_first_subword=True,
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ **kwargs,
+ ):
+ super().__init__(
+ vocab_file,
+ tokenizer_file=tokenizer_file,
+ do_lower_case=do_lower_case,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ pad_token=pad_token,
+ cls_token=cls_token,
+ mask_token=mask_token,
+ cls_token_box=cls_token_box,
+ sep_token_box=sep_token_box,
+ pad_token_box=pad_token_box,
+ pad_token_label=pad_token_label,
+ only_label_first_subword=only_label_first_subword,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ **kwargs,
+ )
+
+ pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
+ if (
+ pre_tok_state.get("lowercase", do_lower_case) != do_lower_case
+ or pre_tok_state.get("strip_accents", strip_accents) != strip_accents
+ ):
+ pre_tok_class = getattr(normalizers, pre_tok_state.pop("type"))
+ pre_tok_state["lowercase"] = do_lower_case
+ pre_tok_state["strip_accents"] = strip_accents
+ self.backend_tokenizer.normalizer = pre_tok_class(**pre_tok_state)
+
+ self.do_lower_case = do_lower_case
+
+ # additional properties
+ self.cls_token_box = cls_token_box
+ self.sep_token_box = sep_token_box
+ self.pad_token_box = pad_token_box
+ self.pad_token_label = pad_token_label
+ self.only_label_first_subword = only_label_first_subword
+
+ @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def __call__(
+ self,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]],
+ text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
+ boxes: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
+ sequences with word-level normalized bounding boxes and optional labels.
+
+ Args:
+ text (`str`, `List[str]`, `List[List[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
+ (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
+ words).
+ text_pair (`List[str]`, `List[List[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
+ (pretokenized string).
+ boxes (`List[List[int]]`, `List[List[List[int]]]`):
+ Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
+ word_labels (`List[int]`, `List[List[int]]`, *optional*):
+ Word-level integer labels (for token classification tasks such as FUNSD, CORD).
+ """
+
+ # Input type checking for clearer error
+ def _is_valid_text_input(t):
+ if isinstance(t, str):
+ # Strings are fine
+ return True
+ elif isinstance(t, (list, tuple)):
+ # List are fine as long as they are...
+ if len(t) == 0:
+ # ... empty
+ return True
+ elif isinstance(t[0], str):
+ # ... list of strings
+ return True
+ elif isinstance(t[0], (list, tuple)):
+ # ... list with an empty list or with a list of strings
+ return len(t[0]) == 0 or isinstance(t[0][0], str)
+ else:
+ return False
+ else:
+ return False
+
+ if text_pair is not None:
+ # in case text + text_pair are provided, text = questions, text_pair = words
+ if not _is_valid_text_input(text):
+ raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
+ if not isinstance(text_pair, (list, tuple)):
+ raise ValueError(
+ "Words must be of type `List[str]` (single pretokenized example), "
+ "or `List[List[str]]` (batch of pretokenized examples)."
+ )
+ else:
+ # in case only text is provided => must be words
+ if not isinstance(text, (list, tuple)):
+ raise ValueError(
+ "Words must be of type `List[str]` (single pretokenized example), "
+ "or `List[List[str]]` (batch of pretokenized examples)."
+ )
+
+ if text_pair is not None:
+ is_batched = isinstance(text, (list, tuple))
+ else:
+ is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
+
+ words = text if text_pair is None else text_pair
+ if boxes is None:
+ raise ValueError("You must provide corresponding bounding boxes")
+ if is_batched:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide words and boxes for an equal amount of examples")
+ for words_example, boxes_example in zip(words, boxes):
+ if len(words_example) != len(boxes_example):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+ else:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+
+ if is_batched:
+ if text_pair is not None and len(text) != len(text_pair):
+ raise ValueError(
+ f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
+ f" {len(text_pair)}."
+ )
+ batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
+ is_pair = bool(text_pair is not None)
+ return self.batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+ else:
+ return self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[list[int]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> list[str]:
+ batched_input = [(text, pair)] if pair else [text]
+ encodings = self._tokenizer.encode_batch(
+ batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
+ )
+
+ return encodings[0].tokens
+
+ @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
+ `__call__` should be used instead.
+
+ Args:
+ text (`str`, `List[str]`, `List[List[str]]`):
+ The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
+ text_pair (`List[str]` or `List[int]`, *optional*):
+ Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
+ list of list of strings (words of a batch of examples).
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._encode_plus(
+ text=text,
+ boxes=boxes,
+ text_pair=text_pair,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def _batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[list[int]]]] = None,
+ word_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ ) -> BatchEncoding:
+ if not isinstance(batch_text_or_text_pairs, list):
+ raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})")
+
+ # Set the truncation and padding strategy and restore the initial configuration
+ self.set_truncation_and_padding(
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ )
+
+ if is_pair:
+ batch_text_or_text_pairs = [(text.split(), text_pair) for text, text_pair in batch_text_or_text_pairs]
+
+ encodings = self._tokenizer.encode_batch(
+ batch_text_or_text_pairs,
+ add_special_tokens=add_special_tokens,
+ is_pretokenized=True, # we set this to True as LayoutLMv2 always expects pretokenized inputs
+ )
+
+ # Convert encoding to dict
+ # `Tokens` has type: Tuple[
+ # List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
+ # List[EncodingFast]
+ # ]
+ # with nested dimensions corresponding to batch, overflows, sequence length
+ tokens_and_encodings = [
+ self._convert_encoding(
+ encoding=encoding,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=True
+ if word_labels is not None
+ else return_offsets_mapping, # we use offsets to create the labels
+ return_length=return_length,
+ verbose=verbose,
+ )
+ for encoding in encodings
+ ]
+
+ # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
+ # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
+ # (we say ~ because the number of overflow varies with the example in the batch)
+ #
+ # To match each overflowing sample with the original sample in the batch
+ # we add an overflow_to_sample_mapping array (see below)
+ sanitized_tokens = {}
+ for key in tokens_and_encodings[0][0]:
+ stack = [e for item, _ in tokens_and_encodings for e in item[key]]
+ sanitized_tokens[key] = stack
+ sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
+
+ # If returning overflowing tokens, we need to return a mapping
+ # from the batch idx to the original sample
+ if return_overflowing_tokens:
+ overflow_to_sample_mapping = []
+ for i, (toks, _) in enumerate(tokens_and_encodings):
+ overflow_to_sample_mapping += [i] * len(toks["input_ids"])
+ sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
+
+ for input_ids in sanitized_tokens["input_ids"]:
+ self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
+
+ # create the token boxes
+ token_boxes = []
+ for batch_index in range(len(sanitized_tokens["input_ids"])):
+ if return_overflowing_tokens:
+ original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
+ else:
+ original_index = batch_index
+ token_boxes_example = []
+ for id, sequence_id, word_id in zip(
+ sanitized_tokens["input_ids"][batch_index],
+ sanitized_encodings[batch_index].sequence_ids,
+ sanitized_encodings[batch_index].word_ids,
+ ):
+ if word_id is not None:
+ if is_pair and sequence_id == 0:
+ token_boxes_example.append(self.pad_token_box)
+ else:
+ token_boxes_example.append(boxes[original_index][word_id])
+ else:
+ if id == self.cls_token_id:
+ token_boxes_example.append(self.cls_token_box)
+ elif id == self.sep_token_id:
+ token_boxes_example.append(self.sep_token_box)
+ elif id == self.pad_token_id:
+ token_boxes_example.append(self.pad_token_box)
+ else:
+ raise ValueError("Id not recognized")
+ token_boxes.append(token_boxes_example)
+
+ sanitized_tokens["bbox"] = token_boxes
+
+ # optionally, create the labels
+ if word_labels is not None:
+ labels = []
+ for batch_index in range(len(sanitized_tokens["input_ids"])):
+ if return_overflowing_tokens:
+ original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
+ else:
+ original_index = batch_index
+ labels_example = []
+ for id, offset, word_id in zip(
+ sanitized_tokens["input_ids"][batch_index],
+ sanitized_tokens["offset_mapping"][batch_index],
+ sanitized_encodings[batch_index].word_ids,
+ ):
+ if word_id is not None:
+ if self.only_label_first_subword:
+ if offset[0] == 0:
+ # Use the real label id for the first token of the word, and padding ids for the remaining tokens
+ labels_example.append(word_labels[original_index][word_id])
+ else:
+ labels_example.append(self.pad_token_label)
+ else:
+ labels_example.append(word_labels[original_index][word_id])
+ else:
+ labels_example.append(self.pad_token_label)
+ labels.append(labels_example)
+
+ sanitized_tokens["labels"] = labels
+ # finally, remove offsets if the user didn't want them
+ if not return_offsets_mapping:
+ del sanitized_tokens["offset_mapping"]
+
+ return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
+
+ def _encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[bool] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ # make it a batched input
+ # 2 options:
+ # 1) only text, in case text must be a list of str
+ # 2) text + text_pair, in which case text = str and text_pair a list of str
+ batched_input = [(text, text_pair)] if text_pair else [text]
+ batched_boxes = [boxes]
+ batched_word_labels = [word_labels] if word_labels is not None else None
+ batched_output = self._batch_encode_plus(
+ batched_input,
+ is_pair=bool(text_pair is not None),
+ boxes=batched_boxes,
+ word_labels=batched_word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ # Return tensor is None, then we can remove the leading batch axis
+ # Overflowing tokens are returned as a batch of output so we keep them in this case
+ if return_tensors is None and not return_overflowing_tokens:
+ batched_output = BatchEncoding(
+ {
+ key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
+ for key, value in batched_output.items()
+ },
+ batched_output.encodings,
+ )
+
+ self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
+
+ return batched_output
+
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ """
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+ Args:
+ encoded_inputs:
+ Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+ max_length: maximum length of the returned list and optionally padding length (see below).
+ Will truncate by taking into account the special tokens.
+ padding_strategy: PaddingStrategy to use for padding.
+
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
+ The tokenizer padding sides are defined in self.padding_side:
+
+ - 'left': pads on the left of the sequences
+ - 'right': pads on the right of the sequences
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta).
+ padding_side:
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask:
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+ """
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = len(required_input)
+
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+ # Initialize attention mask if not present.
+ if return_attention_mask and "attention_mask" not in encoded_inputs:
+ encoded_inputs["attention_mask"] = [1] * len(required_input)
+
+ if needs_to_be_padded:
+ difference = max_length - len(required_input)
+ padding_side = padding_side if padding_side is not None else self.padding_side
+ if padding_side == "right":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = (
+ encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
+ )
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
+ encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
+ elif padding_side == "left":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
+ "token_type_ids"
+ ]
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+ else:
+ raise ValueError("Invalid padding strategy:" + str(padding_side))
+
+ return encoded_inputs
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A BERT sequence has the following format:
+
+ - single sequence: `[CLS] X [SEP]`
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+
+ if token_ids_1:
+ output += token_ids_1 + [self.sep_token_id]
+
+ return output
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+ return tuple(files)
+
+
+__all__ = ["LayoutLMv2TokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c776b676f603f9c2d6a2938181e55980e4941048
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/__init__.py
@@ -0,0 +1,34 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_layoutlmv3 import *
+ from .feature_extraction_layoutlmv3 import *
+ from .image_processing_layoutlmv3 import *
+ from .image_processing_layoutlmv3_fast import *
+ from .modeling_layoutlmv3 import *
+ from .modeling_tf_layoutlmv3 import *
+ from .processing_layoutlmv3 import *
+ from .tokenization_layoutlmv3 import *
+ from .tokenization_layoutlmv3_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py
new file mode 100644
index 0000000000000000000000000000000000000000..c845bb43b346a2d00a93193125f8b3d6d72c1ebc
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py
@@ -0,0 +1,294 @@
+# coding=utf-8
+# Copyright 2022 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LayoutLMv3 model configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+from typing import TYPE_CHECKING, Any, Optional
+
+from packaging import version
+
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
+from ...onnx.utils import compute_effective_axis_dimension
+from ...utils import logging
+
+
+if TYPE_CHECKING:
+ from ...processing_utils import ProcessorMixin
+ from ...utils import TensorType
+
+
+logger = logging.get_logger(__name__)
+
+
+class LayoutLMv3Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LayoutLMv3Model`]. It is used to instantiate an
+ LayoutLMv3 model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the LayoutLMv3
+ [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50265):
+ Vocabulary size of the LayoutLMv3 model. Defines the number of different tokens that can be represented by
+ the `inputs_ids` passed when calling [`LayoutLMv3Model`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimension of the encoder layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 2):
+ The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv3Model`].
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+ The epsilon used by the layer normalization layers.
+ max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
+ The maximum value that the 2D position embedding might ever be used with. Typically set this to something
+ large just in case (e.g., 1024).
+ coordinate_size (`int`, *optional*, defaults to `128`):
+ Dimension of the coordinate embeddings.
+ shape_size (`int`, *optional*, defaults to `128`):
+ Dimension of the width and height embeddings.
+ has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
+ Whether or not to use a relative attention bias in the self-attention mechanism.
+ rel_pos_bins (`int`, *optional*, defaults to 32):
+ The number of relative position bins to be used in the self-attention mechanism.
+ max_rel_pos (`int`, *optional*, defaults to 128):
+ The maximum number of relative positions to be used in the self-attention mechanism.
+ max_rel_2d_pos (`int`, *optional*, defaults to 256):
+ The maximum number of relative 2D positions in the self-attention mechanism.
+ rel_2d_pos_bins (`int`, *optional*, defaults to 64):
+ The number of 2D relative position bins in the self-attention mechanism.
+ has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
+ Whether or not to use a spatial attention bias in the self-attention mechanism.
+ visual_embed (`bool`, *optional*, defaults to `True`):
+ Whether or not to add patch embeddings.
+ input_size (`int`, *optional*, defaults to `224`):
+ The size (resolution) of the images.
+ num_channels (`int`, *optional*, defaults to `3`):
+ The number of channels of the images.
+ patch_size (`int`, *optional*, defaults to `16`)
+ The size (resolution) of the patches.
+ classifier_dropout (`float`, *optional*):
+ The dropout ratio for the classification head.
+
+ Example:
+
+ ```python
+ >>> from transformers import LayoutLMv3Config, LayoutLMv3Model
+
+ >>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration
+ >>> configuration = LayoutLMv3Config()
+
+ >>> # Initializing a model (with random weights) from the microsoft/layoutlmv3-base style configuration
+ >>> model = LayoutLMv3Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "layoutlmv3"
+
+ def __init__(
+ self,
+ vocab_size=50265,
+ hidden_size=768,
+ num_hidden_layers=12,
+ num_attention_heads=12,
+ intermediate_size=3072,
+ hidden_act="gelu",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ type_vocab_size=2,
+ initializer_range=0.02,
+ layer_norm_eps=1e-5,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ max_2d_position_embeddings=1024,
+ coordinate_size=128,
+ shape_size=128,
+ has_relative_attention_bias=True,
+ rel_pos_bins=32,
+ max_rel_pos=128,
+ rel_2d_pos_bins=64,
+ max_rel_2d_pos=256,
+ has_spatial_attention_bias=True,
+ text_embed=True,
+ visual_embed=True,
+ input_size=224,
+ num_channels=3,
+ patch_size=16,
+ classifier_dropout=None,
+ **kwargs,
+ ):
+ super().__init__(
+ vocab_size=vocab_size,
+ hidden_size=hidden_size,
+ num_hidden_layers=num_hidden_layers,
+ num_attention_heads=num_attention_heads,
+ intermediate_size=intermediate_size,
+ hidden_act=hidden_act,
+ hidden_dropout_prob=hidden_dropout_prob,
+ attention_probs_dropout_prob=attention_probs_dropout_prob,
+ max_position_embeddings=max_position_embeddings,
+ type_vocab_size=type_vocab_size,
+ initializer_range=initializer_range,
+ layer_norm_eps=layer_norm_eps,
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ **kwargs,
+ )
+ self.max_2d_position_embeddings = max_2d_position_embeddings
+ self.coordinate_size = coordinate_size
+ self.shape_size = shape_size
+ self.has_relative_attention_bias = has_relative_attention_bias
+ self.rel_pos_bins = rel_pos_bins
+ self.max_rel_pos = max_rel_pos
+ self.has_spatial_attention_bias = has_spatial_attention_bias
+ self.rel_2d_pos_bins = rel_2d_pos_bins
+ self.max_rel_2d_pos = max_rel_2d_pos
+ self.text_embed = text_embed
+ self.visual_embed = visual_embed
+ self.input_size = input_size
+ self.num_channels = num_channels
+ self.patch_size = patch_size
+ self.classifier_dropout = classifier_dropout
+
+
+class LayoutLMv3OnnxConfig(OnnxConfig):
+ torch_onnx_minimum_version = version.parse("1.12")
+
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ # The order of inputs is different for question answering and sequence classification
+ if self.task in ["question-answering", "sequence-classification"]:
+ return OrderedDict(
+ [
+ ("input_ids", {0: "batch", 1: "sequence"}),
+ ("attention_mask", {0: "batch", 1: "sequence"}),
+ ("bbox", {0: "batch", 1: "sequence"}),
+ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
+ ]
+ )
+ else:
+ return OrderedDict(
+ [
+ ("input_ids", {0: "batch", 1: "sequence"}),
+ ("bbox", {0: "batch", 1: "sequence"}),
+ ("attention_mask", {0: "batch", 1: "sequence"}),
+ ("pixel_values", {0: "batch", 1: "num_channels"}),
+ ]
+ )
+
+ @property
+ def atol_for_validation(self) -> float:
+ return 1e-5
+
+ @property
+ def default_onnx_opset(self) -> int:
+ return 12
+
+ def generate_dummy_inputs(
+ self,
+ processor: "ProcessorMixin",
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional["TensorType"] = None,
+ num_channels: int = 3,
+ image_width: int = 40,
+ image_height: int = 40,
+ ) -> Mapping[str, Any]:
+ """
+ Generate inputs to provide to the ONNX exporter for the specific framework
+
+ Args:
+ processor ([`ProcessorMixin`]):
+ The processor associated with this model configuration.
+ batch_size (`int`, *optional*, defaults to -1):
+ The batch size to export the model for (-1 means dynamic axis).
+ seq_length (`int`, *optional*, defaults to -1):
+ The sequence length to export the model for (-1 means dynamic axis).
+ is_pair (`bool`, *optional*, defaults to `False`):
+ Indicate if the input is a pair (sentence 1, sentence 2).
+ framework (`TensorType`, *optional*, defaults to `None`):
+ The framework (PyTorch or TensorFlow) that the processor will generate tensors for.
+ num_channels (`int`, *optional*, defaults to 3):
+ The number of channels of the generated images.
+ image_width (`int`, *optional*, defaults to 40):
+ The width of the generated images.
+ image_height (`int`, *optional*, defaults to 40):
+ The height of the generated images.
+
+ Returns:
+ Mapping[str, Any]: holding the kwargs to provide to the model's forward function
+ """
+
+ # A dummy image is used so OCR should not be applied
+ setattr(processor.image_processor, "apply_ocr", False)
+
+ # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
+ batch_size = compute_effective_axis_dimension(
+ batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
+ )
+ # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
+ token_to_add = processor.tokenizer.num_special_tokens_to_add(is_pair)
+ seq_length = compute_effective_axis_dimension(
+ seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
+ )
+ # Generate dummy inputs according to compute batch and sequence
+ dummy_text = [[" ".join([processor.tokenizer.unk_token]) * seq_length]] * batch_size
+
+ # Generate dummy bounding boxes
+ dummy_bboxes = [[[48, 84, 73, 128]]] * batch_size
+
+ # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
+ # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
+ dummy_image = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
+
+ inputs = dict(
+ processor(
+ dummy_image,
+ text=dummy_text,
+ boxes=dummy_bboxes,
+ return_tensors=framework,
+ )
+ )
+
+ return inputs
+
+
+__all__ = ["LayoutLMv3Config", "LayoutLMv3OnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ea779a48f12bf1f0036a3a02cd018adabebc2fc
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py
@@ -0,0 +1,40 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Feature extractor class for LayoutLMv3.
+"""
+
+import warnings
+
+from ...utils import logging
+from ...utils.import_utils import requires
+from .image_processing_layoutlmv3 import LayoutLMv3ImageProcessor
+
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("vision",))
+class LayoutLMv3FeatureExtractor(LayoutLMv3ImageProcessor):
+ def __init__(self, *args, **kwargs) -> None:
+ warnings.warn(
+ "The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
+ " Please use LayoutLMv3ImageProcessor instead.",
+ FutureWarning,
+ )
+ super().__init__(*args, **kwargs)
+
+
+__all__ = ["LayoutLMv3FeatureExtractor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/image_processing_layoutlmv3.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/image_processing_layoutlmv3.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d6b690bb619db4435be428998f9ffc8e50bbb8c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/image_processing_layoutlmv3.py
@@ -0,0 +1,380 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for LayoutLMv3."""
+
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import resize, to_channel_dimension_format, to_pil_image
+from ...image_utils import (
+ IMAGENET_STANDARD_MEAN,
+ IMAGENET_STANDARD_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import (
+ TensorType,
+ filter_out_non_signature_kwargs,
+ is_pytesseract_available,
+ is_vision_available,
+ logging,
+ requires_backends,
+)
+from ...utils.import_utils import requires
+
+
+if is_vision_available():
+ import PIL
+
+# soft dependency
+if is_pytesseract_available():
+ import pytesseract
+
+logger = logging.get_logger(__name__)
+
+
+def normalize_box(box, width, height):
+ return [
+ int(1000 * (box[0] / width)),
+ int(1000 * (box[1] / height)),
+ int(1000 * (box[2] / width)),
+ int(1000 * (box[3] / height)),
+ ]
+
+
+def apply_tesseract(
+ image: np.ndarray,
+ lang: Optional[str],
+ tesseract_config: Optional[str],
+ input_data_format: Optional[Union[ChannelDimension, str]] = None,
+):
+ """Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes."""
+
+ # apply OCR
+ pil_image = to_pil_image(image, input_data_format=input_data_format)
+ image_width, image_height = pil_image.size
+ data = pytesseract.image_to_data(pil_image, lang=lang, output_type="dict", config=tesseract_config)
+ words, left, top, width, height = data["text"], data["left"], data["top"], data["width"], data["height"]
+
+ # filter empty words and corresponding coordinates
+ irrelevant_indices = [idx for idx, word in enumerate(words) if not word.strip()]
+ words = [word for idx, word in enumerate(words) if idx not in irrelevant_indices]
+ left = [coord for idx, coord in enumerate(left) if idx not in irrelevant_indices]
+ top = [coord for idx, coord in enumerate(top) if idx not in irrelevant_indices]
+ width = [coord for idx, coord in enumerate(width) if idx not in irrelevant_indices]
+ height = [coord for idx, coord in enumerate(height) if idx not in irrelevant_indices]
+
+ # turn coordinates into (left, top, left+width, top+height) format
+ actual_boxes = []
+ for x, y, w, h in zip(left, top, width, height):
+ actual_box = [x, y, x + w, y + h]
+ actual_boxes.append(actual_box)
+
+ # finally, normalize the bounding boxes
+ normalized_boxes = []
+ for box in actual_boxes:
+ normalized_boxes.append(normalize_box(box, image_width, image_height))
+
+ assert len(words) == len(normalized_boxes), "Not as many words as there are bounding boxes"
+
+ return words, normalized_boxes
+
+
+@requires(backends=("vision",))
+class LayoutLMv3ImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a LayoutLMv3 image processor.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to `(size["height"], size["width"])`. Can be
+ overridden by `do_resize` in `preprocess`.
+ size (`dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
+ Size of the image after resizing. Can be overridden by `size` in `preprocess`.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+ Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image's pixel values by the specified `rescale_value`. Can be overridden by
+ `do_rescale` in `preprocess`.
+ rescale_factor (`float`, *optional*, defaults to 1 / 255):
+ Value by which the image's pixel values are rescaled. Can be overridden by `rescale_factor` in
+ `preprocess`.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+ method.
+ image_mean (`Iterable[float]` or `float`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`Iterable[float]` or `float`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
+ Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+ number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+ apply_ocr (`bool`, *optional*, defaults to `True`):
+ Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by
+ the `apply_ocr` parameter in the `preprocess` method.
+ ocr_lang (`str`, *optional*):
+ The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
+ used. Can be overridden by the `ocr_lang` parameter in the `preprocess` method.
+ tesseract_config (`str`, *optional*):
+ Any additional custom configuration flags that are forwarded to the `config` parameter when calling
+ Tesseract. For example: '--psm 6'. Can be overridden by the `tesseract_config` parameter in the
+ `preprocess` method.
+ """
+
+ model_input_names = ["pixel_values"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ do_rescale: bool = True,
+ rescale_value: float = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, Iterable[float]]] = None,
+ image_std: Optional[Union[float, Iterable[float]]] = None,
+ apply_ocr: bool = True,
+ ocr_lang: Optional[str] = None,
+ tesseract_config: Optional[str] = "",
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"height": 224, "width": 224}
+ size = get_size_dict(size)
+
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_value
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
+ self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
+ self.apply_ocr = apply_ocr
+ self.ocr_lang = ocr_lang
+ self.tesseract_config = tesseract_config
+
+ # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image to `(size["height"], size["width"])`.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+ `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
+ data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the output image. If unset, the channel dimension format of the input
+ image is used. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+
+ Returns:
+ `np.ndarray`: The resized image.
+ """
+ size = get_size_dict(size)
+ if "height" not in size or "width" not in size:
+ raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
+ output_size = (size["height"], size["width"])
+ return resize(
+ image,
+ size=output_size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample=None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, Iterable[float]]] = None,
+ image_std: Optional[Union[float, Iterable[float]]] = None,
+ apply_ocr: Optional[bool] = None,
+ ocr_lang: Optional[str] = None,
+ tesseract_config: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: ChannelDimension = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> PIL.Image.Image:
+ """
+ Preprocess an image or batch of images.
+
+ Args:
+ images (`ImageInput`):
+ Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Desired size of the output image after applying `resize`.
+ resample (`int`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` filters.
+ Only has an effect if `do_resize` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image pixel values between [0, 1].
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to apply to the image pixel values. Only has an effect if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `Iterable[float]`, *optional*, defaults to `self.image_mean`):
+ Mean values to be used for normalization. Only has an effect if `do_normalize` is set to `True`.
+ image_std (`float` or `Iterable[float]`, *optional*, defaults to `self.image_std`):
+ Standard deviation values to be used for normalization. Only has an effect if `do_normalize` is set to
+ `True`.
+ apply_ocr (`bool`, *optional*, defaults to `self.apply_ocr`):
+ Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes.
+ ocr_lang (`str`, *optional*, defaults to `self.ocr_lang`):
+ The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
+ used.
+ tesseract_config (`str`, *optional*, defaults to `self.tesseract_config`):
+ Any additional custom configuration flags that are forwarded to the `config` parameter when calling
+ Tesseract.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ size = get_size_dict(size)
+ resample = resample if resample is not None else self.resample
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ apply_ocr = apply_ocr if apply_ocr is not None else self.apply_ocr
+ ocr_lang = ocr_lang if ocr_lang is not None else self.ocr_lang
+ tesseract_config = tesseract_config if tesseract_config is not None else self.tesseract_config
+ images = make_list_of_images(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if do_rescale and is_scaled_image(images[0]):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ # Tesseract OCR to get words + normalized bounding boxes
+ if apply_ocr:
+ requires_backends(self, "pytesseract")
+ words_batch = []
+ boxes_batch = []
+ for image in images:
+ words, boxes = apply_tesseract(image, ocr_lang, tesseract_config, input_data_format=input_data_format)
+ words_batch.append(words)
+ boxes_batch.append(boxes)
+
+ if do_resize:
+ images = [
+ self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ if do_rescale:
+ images = [
+ self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ if do_normalize:
+ images = [
+ self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ images = [
+ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
+ ]
+
+ data = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors)
+
+ if apply_ocr:
+ data["words"] = words_batch
+ data["boxes"] = boxes_batch
+ return data
+
+
+__all__ = ["LayoutLMv3ImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/image_processing_layoutlmv3_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/image_processing_layoutlmv3_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7580bb528da3ac8b0558990643753f0862be605
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/image_processing_layoutlmv3_fast.py
@@ -0,0 +1,158 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for LayoutLMv3."""
+
+from typing import Optional, Union
+
+from ...image_processing_utils_fast import BaseImageProcessorFast, BatchFeature, DefaultFastImageProcessorKwargs
+from ...image_transforms import ChannelDimension, group_images_by_shape, reorder_images
+from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ImageInput, PILImageResampling, SizeDict
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+ logging,
+ requires_backends,
+)
+from .image_processing_layoutlmv3 import apply_tesseract
+
+
+logger = logging.get_logger(__name__)
+
+if is_torch_available():
+ import torch
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+class LayoutLMv3FastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ """
+ Args:
+ apply_ocr (`bool`, *optional*, defaults to `True`):
+ Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes. Can be overridden by
+ the `apply_ocr` parameter in the `preprocess` method.
+ ocr_lang (`str`, *optional*):
+ The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
+ used. Can be overridden by the `ocr_lang` parameter in the `preprocess` method.
+ tesseract_config (`str`, *optional*):
+ Any additional custom configuration flags that are forwarded to the `config` parameter when calling
+ Tesseract. For example: '--psm 6'. Can be overridden by the `tesseract_config` parameter in the
+ `preprocess` method.
+ """
+
+ apply_ocr: Optional[bool]
+ ocr_lang: Optional[str]
+ tesseract_config: Optional[str]
+
+
+@auto_docstring
+class LayoutLMv3ImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BILINEAR
+ image_mean = IMAGENET_STANDARD_MEAN
+ image_std = IMAGENET_STANDARD_STD
+ size = {"height": 224, "width": 224}
+ do_resize = True
+ do_rescale = True
+ do_normalize = True
+ apply_ocr = True
+ ocr_lang = None
+ tesseract_config = ""
+ valid_kwargs = LayoutLMv3FastImageProcessorKwargs
+
+ def __init__(self, **kwargs: Unpack[LayoutLMv3FastImageProcessorKwargs]):
+ super().__init__(**kwargs)
+
+ @auto_docstring
+ def preprocess(self, images: ImageInput, **kwargs: Unpack[LayoutLMv3FastImageProcessorKwargs]) -> BatchFeature:
+ return super().preprocess(images, **kwargs)
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ do_resize: bool,
+ size: SizeDict,
+ interpolation: Optional["F.InterpolationMode"],
+ do_center_crop: bool,
+ crop_size: SizeDict,
+ do_rescale: bool,
+ rescale_factor: float,
+ do_normalize: bool,
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ apply_ocr: bool,
+ ocr_lang: Optional[str],
+ tesseract_config: Optional[str],
+ return_tensors: Optional[Union[str, TensorType]],
+ disable_grouping: Optional[bool],
+ **kwargs,
+ ) -> BatchFeature:
+ # Tesseract OCR to get words + normalized bounding boxes
+ if apply_ocr:
+ requires_backends(self, "pytesseract")
+ words_batch = []
+ boxes_batch = []
+ for image in images:
+ if image.is_cuda:
+ logger.warning_once(
+ "apply_ocr can only be performed on cpu. Tensors will be transferred to cpu before processing."
+ )
+ words, boxes = apply_tesseract(
+ image.cpu(), ocr_lang, tesseract_config, input_data_format=ChannelDimension.FIRST
+ )
+ words_batch.append(words)
+ boxes_batch.append(boxes)
+
+ # Group images by size for batched resizing
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ resized_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_resize:
+ stacked_images = self.resize(image=stacked_images, size=size, interpolation=interpolation)
+ resized_images_grouped[shape] = stacked_images
+ resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+ # Group images by size for further processing
+ # Needed in case do_resize is False, or resize returns images with different sizes
+ grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+ processed_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_center_crop:
+ stacked_images = self.center_crop(stacked_images, crop_size)
+ # Fused rescale and normalize
+ stacked_images = self.rescale_and_normalize(
+ stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ processed_images_grouped[shape] = stacked_images
+
+ processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+ processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+
+ data = BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
+
+ if apply_ocr:
+ data["words"] = words_batch
+ data["boxes"] = boxes_batch
+
+ return data
+
+
+__all__ = ["LayoutLMv3ImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd8b525bb4275f19a4e28578b61971475e8bc4be
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py
@@ -0,0 +1,1254 @@
+# coding=utf-8
+# Copyright 2022 Microsoft Research and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch LayoutLMv3 model."""
+
+import collections
+import math
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import apply_chunking_to_forward
+from ...utils import (
+ auto_docstring,
+ logging,
+ torch_int,
+)
+from .configuration_layoutlmv3 import LayoutLMv3Config
+
+
+logger = logging.get_logger(__name__)
+
+
+class LayoutLMv3PatchEmbeddings(nn.Module):
+ """LayoutLMv3 image (patch) embeddings. This class also automatically interpolates the position embeddings for varying
+ image sizes."""
+
+ def __init__(self, config):
+ super().__init__()
+
+ image_size = (
+ config.input_size
+ if isinstance(config.input_size, collections.abc.Iterable)
+ else (config.input_size, config.input_size)
+ )
+ patch_size = (
+ config.patch_size
+ if isinstance(config.patch_size, collections.abc.Iterable)
+ else (config.patch_size, config.patch_size)
+ )
+ self.patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
+ self.proj = nn.Conv2d(config.num_channels, config.hidden_size, kernel_size=patch_size, stride=patch_size)
+
+ def forward(self, pixel_values, position_embedding=None):
+ embeddings = self.proj(pixel_values)
+
+ if position_embedding is not None:
+ # interpolate the position embedding to the corresponding size
+ position_embedding = position_embedding.view(1, self.patch_shape[0], self.patch_shape[1], -1)
+ position_embedding = position_embedding.permute(0, 3, 1, 2)
+ patch_height, patch_width = embeddings.shape[2], embeddings.shape[3]
+ position_embedding = F.interpolate(position_embedding, size=(patch_height, patch_width), mode="bicubic")
+ embeddings = embeddings + position_embedding
+
+ embeddings = embeddings.flatten(2).transpose(1, 2)
+ return embeddings
+
+
+class LayoutLMv3TextEmbeddings(nn.Module):
+ """
+ LayoutLMv3 text embeddings. Same as `RobertaEmbeddings` but with added spatial (layout) embeddings.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+ self.register_buffer(
+ "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+ )
+
+ self.padding_idx = config.pad_token_id
+ self.position_embeddings = nn.Embedding(
+ config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
+ )
+
+ self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
+ self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
+ self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
+ self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
+
+ def calculate_spatial_position_embeddings(self, bbox):
+ try:
+ left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
+ upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
+ right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
+ lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
+ except IndexError as e:
+ raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e
+
+ h_position_embeddings = self.h_position_embeddings(torch.clip(bbox[:, :, 3] - bbox[:, :, 1], 0, 1023))
+ w_position_embeddings = self.w_position_embeddings(torch.clip(bbox[:, :, 2] - bbox[:, :, 0], 0, 1023))
+
+ # below is the difference between LayoutLMEmbeddingsV2 (torch.cat) and LayoutLMEmbeddingsV1 (add)
+ spatial_position_embeddings = torch.cat(
+ [
+ left_position_embeddings,
+ upper_position_embeddings,
+ right_position_embeddings,
+ lower_position_embeddings,
+ h_position_embeddings,
+ w_position_embeddings,
+ ],
+ dim=-1,
+ )
+ return spatial_position_embeddings
+
+ def create_position_ids_from_input_ids(self, input_ids, padding_idx):
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
+ symbols are ignored. This is modified from fairseq's `utils.make_positions`.
+ """
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+ mask = input_ids.ne(padding_idx).int()
+ incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask
+ return incremental_indices.long() + padding_idx
+
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+ """
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+ """
+ input_shape = inputs_embeds.size()[:-1]
+ sequence_length = input_shape[1]
+
+ position_ids = torch.arange(
+ self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+ )
+ return position_ids.unsqueeze(0).expand(input_shape)
+
+ def forward(
+ self,
+ input_ids=None,
+ bbox=None,
+ token_type_ids=None,
+ position_ids=None,
+ inputs_embeds=None,
+ ):
+ if position_ids is None:
+ if input_ids is not None:
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
+ position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx).to(
+ input_ids.device
+ )
+ else:
+ position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
+
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = inputs_embeds + token_type_embeddings
+ position_embeddings = self.position_embeddings(position_ids)
+ embeddings += position_embeddings
+
+ spatial_position_embeddings = self.calculate_spatial_position_embeddings(bbox)
+
+ embeddings = embeddings + spatial_position_embeddings
+
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+@auto_docstring
+class LayoutLMv3PreTrainedModel(PreTrainedModel):
+ config: LayoutLMv3Config
+ base_model_prefix = "layoutlmv3"
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, LayoutLMv3Model):
+ if self.config.visual_embed:
+ module.cls_token.data.zero_()
+ module.pos_embed.data.zero_()
+
+
+class LayoutLMv3SelfAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+ self.has_relative_attention_bias = config.has_relative_attention_bias
+ self.has_spatial_attention_bias = config.has_spatial_attention_bias
+
+ def cogview_attention(self, attention_scores, alpha=32):
+ """
+ https://huggingface.co/papers/2105.13290 Section 2.4 Stabilization of training: Precision Bottleneck Relaxation
+ (PB-Relax). A replacement of the original nn.Softmax(dim=-1)(attention_scores). Seems the new attention_probs
+ will result in a slower speed and a little bias. Can use torch.allclose(standard_attention_probs,
+ cogview_attention_probs, atol=1e-08) for comparison. The smaller atol (e.g., 1e-08), the better.
+ """
+ scaled_attention_scores = attention_scores / alpha
+ max_value = scaled_attention_scores.amax(dim=(-1)).unsqueeze(-1)
+ new_attention_scores = (scaled_attention_scores - max_value) * alpha
+ return nn.Softmax(dim=-1)(new_attention_scores)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ rel_pos=None,
+ rel_2d_pos=None,
+ ):
+ batch_size, seq_length, _ = hidden_states.shape
+ query_layer = (
+ self.query(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ key_layer = (
+ self.key(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ value_layer = (
+ self.value(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ # The attention scores QT K/√d could be significantly larger than input elements, and result in overflow.
+ # Changing the computational order into QT(K/√d) alleviates the problem. (https://huggingface.co/papers/2105.13290)
+ attention_scores = torch.matmul(query_layer / math.sqrt(self.attention_head_size), key_layer.transpose(-1, -2))
+
+ if self.has_relative_attention_bias and self.has_spatial_attention_bias:
+ attention_scores += (rel_pos + rel_2d_pos) / math.sqrt(self.attention_head_size)
+ elif self.has_relative_attention_bias:
+ attention_scores += rel_pos / math.sqrt(self.attention_head_size)
+
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ # Use the trick of the CogView paper to stabilize training
+ attention_probs = self.cogview_attention(attention_scores)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+ return outputs
+
+
+# Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput
+class LayoutLMv3SelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+# Copied from transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2Attention with LayoutLMv2->LayoutLMv3
+class LayoutLMv3Attention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.self = LayoutLMv3SelfAttention(config)
+ self.output = LayoutLMv3SelfOutput(config)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ rel_pos=None,
+ rel_2d_pos=None,
+ ):
+ self_outputs = self.self(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions,
+ rel_pos=rel_pos,
+ rel_2d_pos=rel_2d_pos,
+ )
+ attention_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.layoutlmv2.modeling_layoutlmv2.LayoutLMv2Layer with LayoutLMv2->LayoutLMv3
+class LayoutLMv3Layer(GradientCheckpointingLayer):
+ def __init__(self, config):
+ super().__init__()
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = LayoutLMv3Attention(config)
+ self.intermediate = LayoutLMv3Intermediate(config)
+ self.output = LayoutLMv3Output(config)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ rel_pos=None,
+ rel_2d_pos=None,
+ ):
+ self_attention_outputs = self.attention(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions=output_attentions,
+ rel_pos=rel_pos,
+ rel_2d_pos=rel_2d_pos,
+ )
+ attention_output = self_attention_outputs[0]
+
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ layer_output = apply_chunking_to_forward(
+ self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+ )
+ outputs = (layer_output,) + outputs
+
+ return outputs
+
+ def feed_forward_chunk(self, attention_output):
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+
+class LayoutLMv3Encoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList([LayoutLMv3Layer(config) for _ in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ self.has_relative_attention_bias = config.has_relative_attention_bias
+ self.has_spatial_attention_bias = config.has_spatial_attention_bias
+
+ if self.has_relative_attention_bias:
+ self.rel_pos_bins = config.rel_pos_bins
+ self.max_rel_pos = config.max_rel_pos
+ self.rel_pos_bias = nn.Linear(self.rel_pos_bins, config.num_attention_heads, bias=False)
+
+ if self.has_spatial_attention_bias:
+ self.max_rel_2d_pos = config.max_rel_2d_pos
+ self.rel_2d_pos_bins = config.rel_2d_pos_bins
+ self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False)
+ self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False)
+
+ def relative_position_bucket(self, relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+ ret = 0
+ if bidirectional:
+ num_buckets //= 2
+ ret += (relative_position > 0).long() * num_buckets
+ n = torch.abs(relative_position)
+ else:
+ n = torch.max(-relative_position, torch.zeros_like(relative_position))
+ # now n is in the range [0, inf)
+
+ # half of the buckets are for exact increments in positions
+ max_exact = num_buckets // 2
+ is_small = n < max_exact
+
+ # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ val_if_large = max_exact + (
+ torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
+ ).to(torch.long)
+ val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
+
+ ret += torch.where(is_small, n, val_if_large)
+ return ret
+
+ def _cal_1d_pos_emb(self, position_ids):
+ rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1)
+
+ rel_pos = self.relative_position_bucket(
+ rel_pos_mat,
+ num_buckets=self.rel_pos_bins,
+ max_distance=self.max_rel_pos,
+ )
+ # Since this is a simple indexing operation that is independent of the input,
+ # no need to track gradients for this operation
+ #
+ # Without this no_grad context, training speed slows down significantly
+ with torch.no_grad():
+ rel_pos = self.rel_pos_bias.weight.t()[rel_pos].permute(0, 3, 1, 2)
+ rel_pos = rel_pos.contiguous()
+ return rel_pos
+
+ def _cal_2d_pos_emb(self, bbox):
+ position_coord_x = bbox[:, :, 0]
+ position_coord_y = bbox[:, :, 3]
+ rel_pos_x_2d_mat = position_coord_x.unsqueeze(-2) - position_coord_x.unsqueeze(-1)
+ rel_pos_y_2d_mat = position_coord_y.unsqueeze(-2) - position_coord_y.unsqueeze(-1)
+ rel_pos_x = self.relative_position_bucket(
+ rel_pos_x_2d_mat,
+ num_buckets=self.rel_2d_pos_bins,
+ max_distance=self.max_rel_2d_pos,
+ )
+ rel_pos_y = self.relative_position_bucket(
+ rel_pos_y_2d_mat,
+ num_buckets=self.rel_2d_pos_bins,
+ max_distance=self.max_rel_2d_pos,
+ )
+ # Since this is a simple indexing operation that is independent of the input,
+ # no need to track gradients for this operation
+ #
+ # Without this no_grad context, training speed slows down significantly
+ with torch.no_grad():
+ rel_pos_x = self.rel_pos_x_bias.weight.t()[rel_pos_x].permute(0, 3, 1, 2)
+ rel_pos_y = self.rel_pos_y_bias.weight.t()[rel_pos_y].permute(0, 3, 1, 2)
+ rel_pos_x = rel_pos_x.contiguous()
+ rel_pos_y = rel_pos_y.contiguous()
+ rel_2d_pos = rel_pos_x + rel_pos_y
+ return rel_2d_pos
+
+ def forward(
+ self,
+ hidden_states,
+ bbox=None,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ output_hidden_states=False,
+ return_dict=True,
+ position_ids=None,
+ patch_height=None,
+ patch_width=None,
+ ):
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+
+ rel_pos = self._cal_1d_pos_emb(position_ids) if self.has_relative_attention_bias else None
+ rel_2d_pos = self._cal_2d_pos_emb(bbox) if self.has_spatial_attention_bias else None
+
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ output_attentions,
+ rel_pos=rel_pos,
+ rel_2d_pos=rel_2d_pos,
+ )
+
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ hidden_states,
+ all_hidden_states,
+ all_self_attentions,
+ ]
+ if v is not None
+ )
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+
+
+# Copied from transformers.models.roberta.modeling_roberta.RobertaIntermediate
+class LayoutLMv3Intermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.roberta.modeling_roberta.RobertaOutput
+class LayoutLMv3Output(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+@auto_docstring
+class LayoutLMv3Model(LayoutLMv3PreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.config = config
+
+ if config.text_embed:
+ self.embeddings = LayoutLMv3TextEmbeddings(config)
+
+ if config.visual_embed:
+ # use the default pre-training parameters for fine-tuning (e.g., input_size)
+ # when the input_size is larger in fine-tuning, we will interpolate the position embeddings in forward
+ self.patch_embed = LayoutLMv3PatchEmbeddings(config)
+
+ size = int(config.input_size / config.patch_size)
+ self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+ self.pos_embed = nn.Parameter(torch.zeros(1, size * size + 1, config.hidden_size))
+ self.pos_drop = nn.Dropout(p=0.0)
+
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
+ self.init_visual_bbox(image_size=(size, size))
+
+ self.norm = nn.LayerNorm(config.hidden_size, eps=1e-6)
+
+ self.encoder = LayoutLMv3Encoder(config)
+
+ self.init_weights()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ def init_visual_bbox(self, image_size=(14, 14), max_len=1000):
+ """
+ Create the bounding boxes for the visual (patch) tokens.
+ """
+ visual_bbox_x = torch.div(
+ torch.arange(0, max_len * (image_size[1] + 1), max_len), image_size[1], rounding_mode="trunc"
+ )
+ visual_bbox_y = torch.div(
+ torch.arange(0, max_len * (image_size[0] + 1), max_len), image_size[0], rounding_mode="trunc"
+ )
+ visual_bbox = torch.stack(
+ [
+ visual_bbox_x[:-1].repeat(image_size[0], 1),
+ visual_bbox_y[:-1].repeat(image_size[1], 1).transpose(0, 1),
+ visual_bbox_x[1:].repeat(image_size[0], 1),
+ visual_bbox_y[1:].repeat(image_size[1], 1).transpose(0, 1),
+ ],
+ dim=-1,
+ ).view(-1, 4)
+
+ cls_token_box = torch.tensor([[0 + 1, 0 + 1, max_len - 1, max_len - 1]])
+ self.visual_bbox = torch.cat([cls_token_box, visual_bbox], dim=0)
+
+ def calculate_visual_bbox(self, device, dtype, batch_size):
+ visual_bbox = self.visual_bbox.repeat(batch_size, 1, 1)
+ visual_bbox = visual_bbox.to(device).type(dtype)
+ return visual_bbox
+
+ def forward_image(self, pixel_values):
+ embeddings = self.patch_embed(pixel_values)
+
+ # add [CLS] token
+ batch_size, seq_len, _ = embeddings.size()
+ cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+ embeddings = torch.cat((cls_tokens, embeddings), dim=1)
+
+ # add position embeddings
+ if self.pos_embed is not None:
+ embeddings = embeddings + self.pos_embed
+
+ embeddings = self.pos_drop(embeddings)
+ embeddings = self.norm(embeddings)
+
+ return embeddings
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, token_sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
+ token. See `pixel_values` for `patch_sequence_length`.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ bbox (`torch.LongTensor` of shape `(batch_size, token_sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner.
+
+ Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
+ token. See `pixel_values` for `patch_sequence_length`.
+ token_type_ids (`torch.LongTensor` of shape `(batch_size, token_sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
+ token. See `pixel_values` for `patch_sequence_length`.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`torch.LongTensor` of shape `(batch_size, token_sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
+ token. See `pixel_values` for `patch_sequence_length`.
+
+ [What are position IDs?](../glossary#position-ids)
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, token_sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
+ model's internal embedding lookup matrix.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, AutoModel
+ >>> from datasets import load_dataset
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
+ >>> model = AutoModel.from_pretrained("microsoft/layoutlmv3-base")
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> image = example["image"]
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = processor(image, words, boxes=boxes, return_tensors="pt")
+
+ >>> outputs = model(**encoding)
+ >>> last_hidden_states = outputs.last_hidden_state
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ batch_size, seq_length = input_shape
+ device = input_ids.device
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ batch_size, seq_length = input_shape
+ device = inputs_embeds.device
+ elif pixel_values is not None:
+ batch_size = len(pixel_values)
+ device = pixel_values.device
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds or pixel_values")
+
+ if input_ids is not None or inputs_embeds is not None:
+ if attention_mask is None:
+ attention_mask = torch.ones(((batch_size, seq_length)), device=device)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+ if bbox is None:
+ bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device)
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ bbox=bbox,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ )
+
+ final_bbox = final_position_ids = None
+ patch_height = patch_width = None
+ if pixel_values is not None:
+ patch_height, patch_width = (
+ torch_int(pixel_values.shape[2] / self.config.patch_size),
+ torch_int(pixel_values.shape[3] / self.config.patch_size),
+ )
+ visual_embeddings = self.forward_image(pixel_values)
+ visual_attention_mask = torch.ones(
+ (batch_size, visual_embeddings.shape[1]), dtype=torch.long, device=device
+ )
+ if attention_mask is not None:
+ attention_mask = torch.cat([attention_mask, visual_attention_mask], dim=1)
+ else:
+ attention_mask = visual_attention_mask
+
+ if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
+ if self.config.has_spatial_attention_bias:
+ visual_bbox = self.calculate_visual_bbox(device, dtype=torch.long, batch_size=batch_size)
+ if bbox is not None:
+ final_bbox = torch.cat([bbox, visual_bbox], dim=1)
+ else:
+ final_bbox = visual_bbox
+
+ visual_position_ids = torch.arange(
+ 0, visual_embeddings.shape[1], dtype=torch.long, device=device
+ ).repeat(batch_size, 1)
+ if input_ids is not None or inputs_embeds is not None:
+ position_ids = torch.arange(0, input_shape[1], device=device).unsqueeze(0)
+ position_ids = position_ids.expand(input_shape)
+ final_position_ids = torch.cat([position_ids, visual_position_ids], dim=1)
+ else:
+ final_position_ids = visual_position_ids
+
+ if input_ids is not None or inputs_embeds is not None:
+ embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)
+ else:
+ embedding_output = visual_embeddings
+
+ embedding_output = self.LayerNorm(embedding_output)
+ embedding_output = self.dropout(embedding_output)
+ elif self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
+ if self.config.has_spatial_attention_bias:
+ final_bbox = bbox
+ if self.config.has_relative_attention_bias:
+ position_ids = self.embeddings.position_ids[:, : input_shape[1]]
+ position_ids = position_ids.expand_as(input_ids)
+ final_position_ids = position_ids
+
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+ attention_mask, None, device, dtype=embedding_output.dtype
+ )
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ bbox=final_bbox,
+ position_ids=final_position_ids,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ patch_height=patch_height,
+ patch_width=patch_width,
+ )
+
+ sequence_output = encoder_outputs[0]
+
+ if not return_dict:
+ return (sequence_output,) + encoder_outputs[1:]
+
+ return BaseModelOutput(
+ last_hidden_state=sequence_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+class LayoutLMv3ClassificationHead(nn.Module):
+ """
+ Head for sentence-level classification tasks. Reference: RobertaClassificationHead
+ """
+
+ def __init__(self, config, pool_feature=False):
+ super().__init__()
+ self.pool_feature = pool_feature
+ if pool_feature:
+ self.dense = nn.Linear(config.hidden_size * 3, config.hidden_size)
+ else:
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ classifier_dropout = (
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.dropout = nn.Dropout(classifier_dropout)
+ self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+ def forward(self, x):
+ x = self.dropout(x)
+ x = self.dense(x)
+ x = torch.tanh(x)
+ x = self.dropout(x)
+ x = self.out_proj(x)
+ return x
+
+
+@auto_docstring(
+ custom_intro="""
+ LayoutLMv3 Model with a token classification head on top (a linear layer on top of the final hidden states) e.g.
+ for sequence labeling (information extraction) tasks such as [FUNSD](https://guillaumejaume.github.io/FUNSD/),
+ [SROIE](https://rrc.cvc.uab.es/?ch=13), [CORD](https://github.com/clovaai/cord) and
+ [Kleister-NDA](https://github.com/applicaai/kleister-nda).
+ """
+)
+class LayoutLMv3ForTokenClassification(LayoutLMv3PreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.layoutlmv3 = LayoutLMv3Model(config)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ if config.num_labels < 10:
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+ else:
+ self.classifier = LayoutLMv3ClassificationHead(config, pool_feature=False)
+
+ self.init_weights()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ pixel_values: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, TokenClassifierOutput]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, AutoModelForTokenClassification
+ >>> from datasets import load_dataset
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
+ >>> model = AutoModelForTokenClassification.from_pretrained("microsoft/layoutlmv3-base", num_labels=7)
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> image = example["image"]
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+ >>> word_labels = example["ner_tags"]
+
+ >>> encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt")
+
+ >>> outputs = model(**encoding)
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlmv3(
+ input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ pixel_values=pixel_values,
+ )
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+ # only take the text part of the output representations
+ sequence_output = outputs[0][:, :seq_length]
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class LayoutLMv3ForQuestionAnswering(LayoutLMv3PreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.layoutlmv3 = LayoutLMv3Model(config)
+ self.qa_outputs = LayoutLMv3ClassificationHead(config, pool_feature=False)
+
+ self.init_weights()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ pixel_values: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, QuestionAnsweringModelOutput]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, AutoModelForQuestionAnswering
+ >>> from datasets import load_dataset
+ >>> import torch
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
+ >>> model = AutoModelForQuestionAnswering.from_pretrained("microsoft/layoutlmv3-base")
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> image = example["image"]
+ >>> question = "what's his name?"
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
+ >>> start_positions = torch.tensor([1])
+ >>> end_positions = torch.tensor([3])
+
+ >>> outputs = model(**encoding, start_positions=start_positions, end_positions=end_positions)
+ >>> loss = outputs.loss
+ >>> start_scores = outputs.start_logits
+ >>> end_scores = outputs.end_logits
+ ```"""
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlmv3(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ bbox=bbox,
+ pixel_values=pixel_values,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[1:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ LayoutLMv3 Model with a sequence classification head on top (a linear layer on top of the final hidden state of the
+ [CLS] token) e.g. for document image classification tasks such as the
+ [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
+ """
+)
+class LayoutLMv3ForSequenceClassification(LayoutLMv3PreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.config = config
+ self.layoutlmv3 = LayoutLMv3Model(config)
+ self.classifier = LayoutLMv3ClassificationHead(config, pool_feature=False)
+
+ self.init_weights()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ pixel_values: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, SequenceClassifierOutput]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, AutoModelForSequenceClassification
+ >>> from datasets import load_dataset
+ >>> import torch
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
+ >>> model = AutoModelForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base")
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> image = example["image"]
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = processor(image, words, boxes=boxes, return_tensors="pt")
+ >>> sequence_label = torch.tensor([1])
+
+ >>> outputs = model(**encoding, labels=sequence_label)
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlmv3(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ bbox=bbox,
+ pixel_values=pixel_values,
+ )
+
+ sequence_output = outputs[0][:, 0, :]
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "LayoutLMv3ForQuestionAnswering",
+ "LayoutLMv3ForSequenceClassification",
+ "LayoutLMv3ForTokenClassification",
+ "LayoutLMv3Model",
+ "LayoutLMv3PreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0586d58835ee372bf2c307564fc8593c3c3b57e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py
@@ -0,0 +1,1767 @@
+# coding=utf-8
+# Copyright 2022 Microsoft Research and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TF 2.0 LayoutLMv3 model."""
+
+from __future__ import annotations
+
+import collections
+import math
+
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_outputs import (
+ TFBaseModelOutput,
+ TFQuestionAnsweringModelOutput,
+ TFSequenceClassifierOutput,
+ TFTokenClassifierOutput,
+)
+from ...modeling_tf_utils import (
+ TFPreTrainedModel,
+ TFQuestionAnsweringLoss,
+ TFSequenceClassificationLoss,
+ TFTokenClassificationLoss,
+ get_initializer,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds
+from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
+from .configuration_layoutlmv3 import LayoutLMv3Config
+
+
+_CONFIG_FOR_DOC = "LayoutLMv3Config"
+
+_DUMMY_INPUT_IDS = [
+ [7, 6, 1],
+ [1, 2, 0],
+]
+
+_DUMMY_BBOX = [
+ [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]],
+ [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]],
+]
+
+
+LARGE_NEGATIVE = -1e8
+
+
+class TFLayoutLMv3PatchEmbeddings(keras.layers.Layer):
+ """LayoutLMv3 image (patch) embeddings."""
+
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(**kwargs)
+ patch_sizes = (
+ config.patch_size
+ if isinstance(config.patch_size, collections.abc.Iterable)
+ else (config.patch_size, config.patch_size)
+ )
+ self.proj = keras.layers.Conv2D(
+ filters=config.hidden_size,
+ kernel_size=patch_sizes,
+ strides=patch_sizes,
+ padding="valid",
+ data_format="channels_last",
+ use_bias=True,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="proj",
+ )
+ self.hidden_size = config.hidden_size
+ self.num_patches = (config.input_size**2) // (patch_sizes[0] * patch_sizes[1])
+ self.config = config
+
+ def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
+ # When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format.
+ # So change the input format from `NCHW` to `NHWC`.
+ pixel_values = tf.transpose(pixel_values, perm=[0, 2, 3, 1])
+
+ embeddings = self.proj(pixel_values)
+ embeddings = tf.reshape(embeddings, (-1, self.num_patches, self.hidden_size))
+ return embeddings
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "proj", None) is not None:
+ with tf.name_scope(self.proj.name):
+ self.proj.build([None, None, None, self.config.num_channels])
+
+
+class TFLayoutLMv3TextEmbeddings(keras.layers.Layer):
+ """
+ LayoutLMv3 text embeddings. Same as `RobertaEmbeddings` but with added spatial (layout) embeddings.
+ """
+
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(**kwargs)
+ self.word_embeddings = keras.layers.Embedding(
+ config.vocab_size,
+ config.hidden_size,
+ embeddings_initializer=get_initializer(config.initializer_range),
+ name="word_embeddings",
+ )
+ self.token_type_embeddings = keras.layers.Embedding(
+ config.type_vocab_size,
+ config.hidden_size,
+ embeddings_initializer=get_initializer(config.initializer_range),
+ name="token_type_embeddings",
+ )
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.padding_token_index = config.pad_token_id
+ self.position_embeddings = keras.layers.Embedding(
+ config.max_position_embeddings,
+ config.hidden_size,
+ embeddings_initializer=get_initializer(config.initializer_range),
+ name="position_embeddings",
+ )
+ self.x_position_embeddings = keras.layers.Embedding(
+ config.max_2d_position_embeddings,
+ config.coordinate_size,
+ embeddings_initializer=get_initializer(config.initializer_range),
+ name="x_position_embeddings",
+ )
+ self.y_position_embeddings = keras.layers.Embedding(
+ config.max_2d_position_embeddings,
+ config.coordinate_size,
+ embeddings_initializer=get_initializer(config.initializer_range),
+ name="y_position_embeddings",
+ )
+ self.h_position_embeddings = keras.layers.Embedding(
+ config.max_2d_position_embeddings,
+ config.shape_size,
+ embeddings_initializer=get_initializer(config.initializer_range),
+ name="h_position_embeddings",
+ )
+ self.w_position_embeddings = keras.layers.Embedding(
+ config.max_2d_position_embeddings,
+ config.shape_size,
+ embeddings_initializer=get_initializer(config.initializer_range),
+ name="w_position_embeddings",
+ )
+ self.max_2d_positions = config.max_2d_position_embeddings
+ self.config = config
+
+ def calculate_spatial_position_embeddings(self, bbox: tf.Tensor) -> tf.Tensor:
+ try:
+ left_position_ids = bbox[:, :, 0]
+ upper_position_ids = bbox[:, :, 1]
+ right_position_ids = bbox[:, :, 2]
+ lower_position_ids = bbox[:, :, 3]
+ except IndexError as exception:
+ raise IndexError("Bounding box is not of shape (batch_size, seq_length, 4).") from exception
+
+ try:
+ left_position_embeddings = self.x_position_embeddings(left_position_ids)
+ upper_position_embeddings = self.y_position_embeddings(upper_position_ids)
+ right_position_embeddings = self.x_position_embeddings(right_position_ids)
+ lower_position_embeddings = self.y_position_embeddings(lower_position_ids)
+ except IndexError as exception:
+ raise IndexError(
+ f"The `bbox` coordinate values should be within 0-{self.max_2d_positions} range."
+ ) from exception
+
+ max_position_id = self.max_2d_positions - 1
+ h_position_embeddings = self.h_position_embeddings(
+ tf.clip_by_value(bbox[:, :, 3] - bbox[:, :, 1], 0, max_position_id)
+ )
+ w_position_embeddings = self.w_position_embeddings(
+ tf.clip_by_value(bbox[:, :, 2] - bbox[:, :, 0], 0, max_position_id)
+ )
+
+ # LayoutLMv1 sums the spatial embeddings, but LayoutLMv3 concatenates them.
+ spatial_position_embeddings = tf.concat(
+ [
+ left_position_embeddings,
+ upper_position_embeddings,
+ right_position_embeddings,
+ lower_position_embeddings,
+ h_position_embeddings,
+ w_position_embeddings,
+ ],
+ axis=-1,
+ )
+ return spatial_position_embeddings
+
+ def create_position_ids_from_inputs_embeds(self, inputs_embds: tf.Tensor) -> tf.Tensor:
+ """
+ We are provided embeddings directly. We cannot infer which are padded, so just generate sequential position
+ ids.
+ """
+ input_shape = tf.shape(inputs_embds)
+ sequence_length = input_shape[1]
+ start_index = self.padding_token_index + 1
+ end_index = self.padding_token_index + sequence_length + 1
+ position_ids = tf.range(start_index, end_index, dtype=tf.int32)
+ batch_size = input_shape[0]
+ position_ids = tf.reshape(position_ids, (1, sequence_length))
+ position_ids = tf.tile(position_ids, (batch_size, 1))
+ return position_ids
+
+ def create_position_ids_from_input_ids(self, input_ids: tf.Tensor) -> tf.Tensor:
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_token_index + 1.
+ """
+ mask = tf.cast(tf.not_equal(input_ids, self.padding_token_index), input_ids.dtype)
+ position_ids = tf.cumsum(mask, axis=1) * mask
+ position_ids = position_ids + self.padding_token_index
+ return position_ids
+
+ def create_position_ids(self, input_ids: tf.Tensor, inputs_embeds: tf.Tensor) -> tf.Tensor:
+ if input_ids is None:
+ return self.create_position_ids_from_inputs_embeds(inputs_embeds)
+ else:
+ return self.create_position_ids_from_input_ids(input_ids)
+
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ bbox: tf.Tensor | None = None,
+ token_type_ids: tf.Tensor | None = None,
+ position_ids: tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> tf.Tensor:
+ if position_ids is None:
+ position_ids = self.create_position_ids(input_ids, inputs_embeds)
+
+ if input_ids is not None:
+ input_shape = tf.shape(input_ids)
+ else:
+ input_shape = tf.shape(inputs_embeds)[:-1]
+
+ if token_type_ids is None:
+ token_type_ids = tf.zeros(input_shape, dtype=position_ids.dtype)
+
+ if inputs_embeds is None:
+ check_embeddings_within_bounds(input_ids, self.word_embeddings.input_dim)
+ inputs_embeds = self.word_embeddings(input_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = inputs_embeds + token_type_embeddings
+ position_embeddings = self.position_embeddings(position_ids)
+ embeddings += position_embeddings
+
+ spatial_position_embeddings = self.calculate_spatial_position_embeddings(bbox)
+
+ embeddings += spatial_position_embeddings
+
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings, training=training)
+ return embeddings
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "word_embeddings", None) is not None:
+ with tf.name_scope(self.word_embeddings.name):
+ self.word_embeddings.build(None)
+ if getattr(self, "token_type_embeddings", None) is not None:
+ with tf.name_scope(self.token_type_embeddings.name):
+ self.token_type_embeddings.build(None)
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+ if getattr(self, "position_embeddings", None) is not None:
+ with tf.name_scope(self.position_embeddings.name):
+ self.position_embeddings.build(None)
+ if getattr(self, "x_position_embeddings", None) is not None:
+ with tf.name_scope(self.x_position_embeddings.name):
+ self.x_position_embeddings.build(None)
+ if getattr(self, "y_position_embeddings", None) is not None:
+ with tf.name_scope(self.y_position_embeddings.name):
+ self.y_position_embeddings.build(None)
+ if getattr(self, "h_position_embeddings", None) is not None:
+ with tf.name_scope(self.h_position_embeddings.name):
+ self.h_position_embeddings.build(None)
+ if getattr(self, "w_position_embeddings", None) is not None:
+ with tf.name_scope(self.w_position_embeddings.name):
+ self.w_position_embeddings.build(None)
+
+
+class TFLayoutLMv3SelfAttention(keras.layers.Layer):
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(**kwargs)
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+ self.attention_score_normaliser = math.sqrt(self.attention_head_size)
+
+ self.query = keras.layers.Dense(
+ self.all_head_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="query",
+ )
+ self.key = keras.layers.Dense(
+ self.all_head_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="key",
+ )
+ self.value = keras.layers.Dense(
+ self.all_head_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="value",
+ )
+
+ self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob)
+ self.has_relative_attention_bias = config.has_relative_attention_bias
+ self.has_spatial_attention_bias = config.has_spatial_attention_bias
+ self.config = config
+
+ def transpose_for_scores(self, x: tf.Tensor):
+ shape = tf.shape(x)
+ new_shape = (
+ shape[0], # batch_size
+ shape[1], # seq_length
+ self.num_attention_heads,
+ self.attention_head_size,
+ )
+ x = tf.reshape(x, new_shape)
+ return tf.transpose(x, perm=[0, 2, 1, 3]) # batch_size, num_heads, seq_length, attention_head_size
+
+ def cogview_attention(self, attention_scores: tf.Tensor, alpha: float | int = 32):
+ """
+ https://huggingface.co/papers/2105.13290 Section 2.4 Stabilization of training: Precision Bottleneck Relaxation
+ (PB-Relax). A replacement of the original keras.layers.Softmax(axis=-1)(attention_scores). Seems the new
+ attention_probs will result in a slower speed and a little bias. Can use
+ tf.debugging.assert_near(standard_attention_probs, cogview_attention_probs, atol=1e-08) for comparison. The
+ smaller atol (e.g., 1e-08), the better.
+ """
+ scaled_attention_scores = attention_scores / alpha
+ max_value = tf.expand_dims(tf.reduce_max(scaled_attention_scores, axis=-1), axis=-1)
+ new_attention_scores = (scaled_attention_scores - max_value) * alpha
+ return tf.math.softmax(new_attention_scores, axis=-1)
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: tf.Tensor | None,
+ head_mask: tf.Tensor | None,
+ output_attentions: bool,
+ rel_pos: tf.Tensor | None = None,
+ rel_2d_pos: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> tuple[tf.Tensor] | tuple[tf.Tensor, tf.Tensor]:
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
+ query_layer = self.transpose_for_scores(self.query(hidden_states))
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ normalised_query_layer = query_layer / self.attention_score_normaliser
+ transposed_key_layer = tf.transpose(
+ key_layer, perm=[0, 1, 3, 2]
+ ) # batch_size, num_heads, attention_head_size, seq_length
+ attention_scores = tf.matmul(normalised_query_layer, transposed_key_layer)
+
+ if self.has_relative_attention_bias and self.has_spatial_attention_bias:
+ attention_scores += (rel_pos + rel_2d_pos) / self.attention_score_normaliser
+ elif self.has_relative_attention_bias:
+ attention_scores += rel_pos / self.attention_score_normaliser
+
+ if attention_mask is not None:
+ # Apply the attention mask (is precomputed for all layers in TFLayoutLMv3Model call() function)
+ attention_scores += attention_mask
+
+ # Normalize the attention scores to probabilities.
+ # Use the trick of CogView paper to stabilize training.
+ attention_probs = self.cogview_attention(attention_scores)
+
+ attention_probs = self.dropout(attention_probs, training=training)
+
+ # Mask heads if we want to.
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ context_layer = tf.matmul(attention_probs, value_layer)
+ context_layer = tf.transpose(
+ context_layer, perm=[0, 2, 1, 3]
+ ) # batch_size, seq_length, num_heads, attention_head_size
+ shape = tf.shape(context_layer)
+ context_layer = tf.reshape(
+ context_layer, (shape[0], shape[1], self.all_head_size)
+ ) # batch_size, seq_length, num_heads * attention_head_size
+
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "query", None) is not None:
+ with tf.name_scope(self.query.name):
+ self.query.build([None, None, self.config.hidden_size])
+ if getattr(self, "key", None) is not None:
+ with tf.name_scope(self.key.name):
+ self.key.build([None, None, self.config.hidden_size])
+ if getattr(self, "value", None) is not None:
+ with tf.name_scope(self.value.name):
+ self.value.build([None, None, self.config.hidden_size])
+
+
+# Copied from models.roberta.modeling_tf_roberta.TFRobertaSelfOutput
+class TFLayoutLMv3SelfOutput(keras.layers.Layer):
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.dropout(inputs=hidden_states, training=training)
+ hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+class TFLayoutLMv3Attention(keras.layers.Layer):
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(**kwargs)
+ self.self_attention = TFLayoutLMv3SelfAttention(config, name="self")
+ self.self_output = TFLayoutLMv3SelfOutput(config, name="output")
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: tf.Tensor | None,
+ head_mask: tf.Tensor | None,
+ output_attentions: bool,
+ rel_pos: tf.Tensor | None = None,
+ rel_2d_pos: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> tuple[tf.Tensor] | tuple[tf.Tensor, tf.Tensor]:
+ self_outputs = self.self_attention(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions,
+ rel_pos,
+ rel_2d_pos,
+ training=training,
+ )
+ attention_output = self.self_output(self_outputs[0], hidden_states, training=training)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self_attention", None) is not None:
+ with tf.name_scope(self.self_attention.name):
+ self.self_attention.build(None)
+ if getattr(self, "self_output", None) is not None:
+ with tf.name_scope(self.self_output.name):
+ self.self_output.build(None)
+
+
+# Copied from models.roberta.modeling_tf_bert.TFRobertaIntermediate
+class TFLayoutLMv3Intermediate(keras.layers.Layer):
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = get_tf_activation(config.hidden_act)
+ else:
+ self.intermediate_act_fn = config.hidden_act
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+
+
+# Copied from models.roberta.modeling_tf_bert.TFRobertaOutput
+class TFLayoutLMv3Output(keras.layers.Layer):
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.dropout(inputs=hidden_states, training=training)
+ hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.intermediate_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+class TFLayoutLMv3Layer(keras.layers.Layer):
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(**kwargs)
+ self.attention = TFLayoutLMv3Attention(config, name="attention")
+ self.intermediate = TFLayoutLMv3Intermediate(config, name="intermediate")
+ self.bert_output = TFLayoutLMv3Output(config, name="output")
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: tf.Tensor | None,
+ head_mask: tf.Tensor | None,
+ output_attentions: bool,
+ rel_pos: tf.Tensor | None = None,
+ rel_2d_pos: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> tuple[tf.Tensor] | tuple[tf.Tensor, tf.Tensor]:
+ self_attention_outputs = self.attention(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions=output_attentions,
+ rel_pos=rel_pos,
+ rel_2d_pos=rel_2d_pos,
+ training=training,
+ )
+ attention_output = self_attention_outputs[0]
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.bert_output(intermediate_output, attention_output, training=training)
+ outputs = (layer_output,) + outputs
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "attention", None) is not None:
+ with tf.name_scope(self.attention.name):
+ self.attention.build(None)
+ if getattr(self, "intermediate", None) is not None:
+ with tf.name_scope(self.intermediate.name):
+ self.intermediate.build(None)
+ if getattr(self, "bert_output", None) is not None:
+ with tf.name_scope(self.bert_output.name):
+ self.bert_output.build(None)
+
+
+class TFLayoutLMv3Encoder(keras.layers.Layer):
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.layer = [TFLayoutLMv3Layer(config, name=f"layer.{i}") for i in range(config.num_hidden_layers)]
+
+ self.has_relative_attention_bias = config.has_relative_attention_bias
+ self.has_spatial_attention_bias = config.has_spatial_attention_bias
+
+ if self.has_relative_attention_bias:
+ self.rel_pos_bins = config.rel_pos_bins
+ self.max_rel_pos = config.max_rel_pos
+ self.rel_pos_bias = keras.layers.Dense(
+ units=config.num_attention_heads,
+ kernel_initializer=get_initializer(config.initializer_range),
+ use_bias=False,
+ name="rel_pos_bias",
+ )
+
+ if self.has_spatial_attention_bias:
+ self.max_rel_2d_pos = config.max_rel_2d_pos
+ self.rel_2d_pos_bins = config.rel_2d_pos_bins
+ self.rel_pos_x_bias = keras.layers.Dense(
+ units=config.num_attention_heads,
+ kernel_initializer=get_initializer(config.initializer_range),
+ use_bias=False,
+ name="rel_pos_x_bias",
+ )
+ self.rel_pos_y_bias = keras.layers.Dense(
+ units=config.num_attention_heads,
+ kernel_initializer=get_initializer(config.initializer_range),
+ use_bias=False,
+ name="rel_pos_y_bias",
+ )
+
+ def relative_position_bucket(self, relative_positions: tf.Tensor, num_buckets: int, max_distance: int):
+ # the negative relative positions are assigned to the interval [0, num_buckets / 2]
+ # we deal with this by assigning absolute relative positions to the interval [0, num_buckets / 2]
+ # and then offsetting the positive relative positions by num_buckets / 2 at the end
+ num_buckets = num_buckets // 2
+ buckets = tf.abs(relative_positions)
+
+ # half of the buckets are for exact increments in positions
+ max_exact_buckets = num_buckets // 2
+ is_small = buckets < max_exact_buckets
+
+ # the other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ buckets_log_ratio = tf.math.log(tf.cast(buckets, tf.float32) / max_exact_buckets)
+ distance_log_ratio = math.log(max_distance / max_exact_buckets)
+ buckets_big_offset = (
+ buckets_log_ratio / distance_log_ratio * (num_buckets - max_exact_buckets)
+ ) # scale is [0, num_buckets - max_exact_buckets]
+ buckets_big = max_exact_buckets + buckets_big_offset # scale is [max_exact_buckets, num_buckets]
+ buckets_big = tf.cast(buckets_big, buckets.dtype)
+ buckets_big = tf.minimum(buckets_big, num_buckets - 1)
+
+ return (tf.cast(relative_positions > 0, buckets.dtype) * num_buckets) + tf.where(
+ is_small, buckets, buckets_big
+ )
+
+ def _cal_pos_emb(
+ self,
+ dense_layer: keras.layers.Dense,
+ position_ids: tf.Tensor,
+ num_buckets: int,
+ max_distance: int,
+ ):
+ rel_pos_matrix = tf.expand_dims(position_ids, axis=-2) - tf.expand_dims(position_ids, axis=-1)
+ rel_pos = self.relative_position_bucket(rel_pos_matrix, num_buckets, max_distance)
+ rel_pos_one_hot = tf.one_hot(rel_pos, depth=num_buckets, dtype=self.compute_dtype)
+ embedding = dense_layer(rel_pos_one_hot)
+ # batch_size, seq_length, seq_length, num_heads --> batch_size, num_heads, seq_length, seq_length
+ embedding = tf.transpose(embedding, [0, 3, 1, 2])
+ embedding = tf.cast(embedding, dtype=self.compute_dtype)
+ return embedding
+
+ def _cal_1d_pos_emb(self, position_ids: tf.Tensor):
+ return self._cal_pos_emb(self.rel_pos_bias, position_ids, self.rel_pos_bins, self.max_rel_pos)
+
+ def _cal_2d_pos_emb(self, bbox: tf.Tensor):
+ position_coord_x = bbox[:, :, 0] # left
+ position_coord_y = bbox[:, :, 3] # bottom
+ rel_pos_x = self._cal_pos_emb(
+ self.rel_pos_x_bias,
+ position_coord_x,
+ self.rel_2d_pos_bins,
+ self.max_rel_2d_pos,
+ )
+ rel_pos_y = self._cal_pos_emb(
+ self.rel_pos_y_bias,
+ position_coord_y,
+ self.rel_2d_pos_bins,
+ self.max_rel_2d_pos,
+ )
+ rel_2d_pos = rel_pos_x + rel_pos_y
+ return rel_2d_pos
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ bbox: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ position_ids: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> TFBaseModelOutput | tuple[tf.Tensor] | tuple[tf.Tensor, tf.Tensor] | tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+
+ rel_pos = self._cal_1d_pos_emb(position_ids) if self.has_relative_attention_bias else None
+ rel_2d_pos = self._cal_2d_pos_emb(bbox) if self.has_spatial_attention_bias else None
+
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ output_attentions,
+ rel_pos=rel_pos,
+ rel_2d_pos=rel_2d_pos,
+ training=training,
+ )
+
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if return_dict:
+ return TFBaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+ else:
+ return tuple(
+ value for value in [hidden_states, all_hidden_states, all_self_attentions] if value is not None
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "rel_pos_bias", None) is not None:
+ with tf.name_scope(self.rel_pos_bias.name):
+ self.rel_pos_bias.build([None, None, self.rel_pos_bins])
+ if getattr(self, "rel_pos_x_bias", None) is not None:
+ with tf.name_scope(self.rel_pos_x_bias.name):
+ self.rel_pos_x_bias.build([None, None, self.rel_2d_pos_bins])
+ if getattr(self, "rel_pos_y_bias", None) is not None:
+ with tf.name_scope(self.rel_pos_y_bias.name):
+ self.rel_pos_y_bias.build([None, None, self.rel_2d_pos_bins])
+ if getattr(self, "layer", None) is not None:
+ for layer in self.layer:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+@keras_serializable
+class TFLayoutLMv3MainLayer(keras.layers.Layer):
+ config_class = LayoutLMv3Config
+
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+
+ if config.text_embed:
+ self.embeddings = TFLayoutLMv3TextEmbeddings(config, name="embeddings")
+
+ if config.visual_embed:
+ self.patch_embed = TFLayoutLMv3PatchEmbeddings(config, name="patch_embed")
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob, name="dropout")
+
+ if config.has_relative_attention_bias or config.has_spatial_attention_bias:
+ image_size = config.input_size // config.patch_size
+ self.init_visual_bbox(image_size=(image_size, image_size))
+
+ self.norm = keras.layers.LayerNormalization(epsilon=1e-6, name="norm")
+
+ self.encoder = TFLayoutLMv3Encoder(config, name="encoder")
+
+ def build(self, input_shape=None):
+ if self.config.visual_embed:
+ image_size = self.config.input_size // self.config.patch_size
+ self.cls_token = self.add_weight(
+ shape=(1, 1, self.config.hidden_size),
+ initializer="zeros",
+ trainable=True,
+ dtype=tf.float32,
+ name="cls_token",
+ )
+ self.pos_embed = self.add_weight(
+ shape=(1, image_size * image_size + 1, self.config.hidden_size),
+ initializer="zeros",
+ trainable=True,
+ dtype=tf.float32,
+ name="pos_embed",
+ )
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "encoder", None) is not None:
+ with tf.name_scope(self.encoder.name):
+ self.encoder.build(None)
+ if getattr(self, "embeddings", None) is not None:
+ with tf.name_scope(self.embeddings.name):
+ self.embeddings.build(None)
+ if getattr(self, "patch_embed", None) is not None:
+ with tf.name_scope(self.patch_embed.name):
+ self.patch_embed.build(None)
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+ if getattr(self, "dropout", None) is not None:
+ with tf.name_scope(self.dropout.name):
+ self.dropout.build(None)
+ if getattr(self, "norm", None) is not None:
+ with tf.name_scope(self.norm.name):
+ self.norm.build([None, None, self.config.hidden_size])
+
+ def get_input_embeddings(self) -> keras.layers.Layer:
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value: tf.Variable):
+ self.embeddings.word_embeddings.weight = value
+
+ # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ raise NotImplementedError
+
+ def init_visual_bbox(self, image_size: tuple[int, int], max_len: int = 1000):
+ # We should not hardcode max_len to 1000, but it is done by the reference implementation,
+ # so we keep it for compatibility with the pretrained weights. The more correct approach
+ # would have been to pass on max_len=config.max_2d_position_embeddings - 1.
+ height, width = image_size
+
+ visual_bbox_x = tf.range(0, max_len * (width + 1), max_len) // width
+ visual_bbox_x = tf.expand_dims(visual_bbox_x, axis=0)
+ visual_bbox_x = tf.tile(visual_bbox_x, [width, 1]) # (width, width + 1)
+
+ visual_bbox_y = tf.range(0, max_len * (height + 1), max_len) // height
+ visual_bbox_y = tf.expand_dims(visual_bbox_y, axis=1)
+ visual_bbox_y = tf.tile(visual_bbox_y, [1, height]) # (height + 1, height)
+
+ visual_bbox = tf.stack(
+ [visual_bbox_x[:, :-1], visual_bbox_y[:-1], visual_bbox_x[:, 1:], visual_bbox_y[1:]],
+ axis=-1,
+ )
+ visual_bbox = tf.reshape(visual_bbox, [-1, 4])
+
+ cls_token_box = tf.constant([[1, 1, max_len - 1, max_len - 1]], dtype=tf.int32)
+ self.visual_bbox = tf.concat([cls_token_box, visual_bbox], axis=0)
+
+ def calculate_visual_bbox(self, batch_size: int, dtype: tf.DType):
+ visual_bbox = tf.expand_dims(self.visual_bbox, axis=0)
+ visual_bbox = tf.tile(visual_bbox, [batch_size, 1, 1])
+ visual_bbox = tf.cast(visual_bbox, dtype=dtype)
+ return visual_bbox
+
+ def embed_image(self, pixel_values: tf.Tensor) -> tf.Tensor:
+ embeddings = self.patch_embed(pixel_values)
+
+ # add [CLS] token
+ batch_size = tf.shape(embeddings)[0]
+ cls_tokens = tf.tile(self.cls_token, [batch_size, 1, 1])
+ embeddings = tf.concat([cls_tokens, embeddings], axis=1)
+
+ # add position embeddings
+ if getattr(self, "pos_embed", None) is not None:
+ embeddings += self.pos_embed
+
+ embeddings = self.norm(embeddings)
+ return embeddings
+
+ def get_extended_attention_mask(self, attention_mask: tf.Tensor) -> tf.Tensor:
+ # Adapted from transformers.modelling_utils.ModuleUtilsMixin.get_extended_attention_mask
+
+ n_dims = len(attention_mask.shape)
+
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ if n_dims == 3:
+ extended_attention_mask = tf.expand_dims(attention_mask, axis=1)
+ elif n_dims == 2:
+ # Provided a padding mask of dimensions [batch_size, seq_length].
+ # Make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length].
+ extended_attention_mask = tf.expand_dims(attention_mask, axis=1) # (batch_size, 1, seq_length)
+ extended_attention_mask = tf.expand_dims(extended_attention_mask, axis=1) # (batch_size, 1, 1, seq_length)
+ else:
+ raise ValueError(f"Wrong shape for attention_mask (shape {attention_mask.shape}).")
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and -10000.0 for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+ extended_attention_mask = tf.cast(extended_attention_mask, self.compute_dtype)
+ extended_attention_mask = (1.0 - extended_attention_mask) * LARGE_NEGATIVE
+
+ return extended_attention_mask
+
+ def get_head_mask(self, head_mask: tf.Tensor | None) -> tf.Tensor | list[tf.Tensor | None]:
+ if head_mask is None:
+ return [None] * self.config.num_hidden_layers
+
+ n_dims = tf.rank(head_mask)
+ if n_dims == 1:
+ # Gets a tensor with masks for each head (H).
+ head_mask = tf.expand_dims(head_mask, axis=0) # 1, num_heads
+ head_mask = tf.expand_dims(head_mask, axis=0) # 1, 1, num_heads
+ head_mask = tf.expand_dims(head_mask, axis=-1) # 1, 1, num_heads, 1
+ head_mask = tf.expand_dims(head_mask, axis=-1) # 1, 1, num_heads, 1, 1
+ head_mask = tf.tile(
+ head_mask, [self.config.num_hidden_layers, 1, 1, 1, 1]
+ ) # seq_length, 1, num_heads, 1, 1
+ elif n_dims == 2:
+ # Gets a tensor with masks for each layer (L) and head (H).
+ head_mask = tf.expand_dims(head_mask, axis=1) # seq_length, 1, num_heads
+ head_mask = tf.expand_dims(head_mask, axis=-1) # seq_length, 1, num_heads, 1
+ head_mask = tf.expand_dims(head_mask, axis=-1) # seq_length, 1, num_heads, 1, 1
+ elif n_dims != 5:
+ raise ValueError(f"Wrong shape for head_mask (shape {head_mask.shape}).")
+ assert tf.rank(head_mask) == 5, f"Got head_mask rank of {tf.rank(head_mask)}, but require 5."
+ head_mask = tf.cast(head_mask, self.compute_dtype)
+ return head_mask
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ bbox: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ token_type_ids: tf.Tensor | None = None,
+ position_ids: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ pixel_values: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ) -> TFBaseModelOutput | tuple[tf.Tensor] | tuple[tf.Tensor, tf.Tensor] | tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
+ # This method can be called with a variety of modalities:
+ # 1. text + layout
+ # 2. text + layout + image
+ # 3. image
+ # The complexity of this method is mostly just due to handling of these different modalities.
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ if input_ids is not None:
+ input_shape = tf.shape(input_ids)
+ batch_size = input_shape[0]
+ seq_length = input_shape[1]
+ elif inputs_embeds is not None:
+ input_shape = tf.shape(inputs_embeds)
+ batch_size = input_shape[0]
+ seq_length = input_shape[1]
+ elif pixel_values is not None:
+ batch_size = tf.shape(pixel_values)[0]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds or pixel_values")
+
+ # Determine which integer dtype to use.
+ if input_ids is not None:
+ int_dtype = input_ids.dtype
+ elif bbox is not None:
+ int_dtype = bbox.dtype
+ elif attention_mask is not None:
+ int_dtype = attention_mask.dtype
+ elif token_type_ids is not None:
+ int_dtype = token_type_ids.dtype
+ else:
+ int_dtype = tf.int32
+
+ if input_ids is not None or inputs_embeds is not None:
+ if attention_mask is None:
+ attention_mask = tf.ones((batch_size, seq_length), dtype=int_dtype)
+ if token_type_ids is None:
+ token_type_ids = tf.zeros((batch_size, seq_length), dtype=int_dtype)
+ if bbox is None:
+ bbox = tf.zeros((batch_size, seq_length, 4), dtype=int_dtype)
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ bbox=bbox,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ training=training,
+ )
+
+ final_bbox = None
+ final_position_ids = None
+ if pixel_values is not None:
+ # embed image
+ visual_embeddings = self.embed_image(pixel_values)
+
+ # calculate attention mask
+ visual_attention_mask = tf.ones((batch_size, tf.shape(visual_embeddings)[1]), dtype=int_dtype)
+ if attention_mask is None:
+ attention_mask = visual_attention_mask
+ else:
+ attention_mask = tf.concat([attention_mask, visual_attention_mask], axis=1)
+
+ # calculate bounding boxes
+ if self.config.has_spatial_attention_bias:
+ visual_bbox = self.calculate_visual_bbox(batch_size, int_dtype)
+ if bbox is None:
+ final_bbox = visual_bbox
+ else:
+ final_bbox = tf.concat([bbox, visual_bbox], axis=1)
+
+ # calculate position IDs
+ if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
+ visual_position_ids = tf.range(0, tf.shape(visual_embeddings)[1], dtype=int_dtype)
+ visual_position_ids = tf.expand_dims(visual_position_ids, axis=0)
+ visual_position_ids = tf.tile(visual_position_ids, [batch_size, 1])
+
+ if input_ids is not None or inputs_embeds is not None:
+ position_ids = tf.expand_dims(tf.range(0, seq_length, dtype=int_dtype), axis=0)
+ position_ids = tf.tile(position_ids, [batch_size, 1])
+ final_position_ids = tf.concat([position_ids, visual_position_ids], axis=1)
+ else:
+ final_position_ids = visual_position_ids
+
+ # calculate embeddings
+ if input_ids is None and inputs_embeds is None:
+ embedding_output = visual_embeddings
+ else:
+ embedding_output = tf.concat([embedding_output, visual_embeddings], axis=1)
+ embedding_output = self.LayerNorm(embedding_output)
+ embedding_output = self.dropout(embedding_output, training=training)
+
+ elif self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias:
+ if self.config.has_relative_attention_bias:
+ position_ids = tf.expand_dims(tf.range(0, seq_length, dtype=int_dtype), axis=0)
+ position_ids = tf.tile(position_ids, [batch_size, 1])
+ final_position_ids = position_ids
+
+ if self.config.has_spatial_attention_bias:
+ final_bbox = bbox
+
+ extended_attention_mask = self.get_extended_attention_mask(attention_mask)
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape batch_size x num_heads x seq_length x seq_length
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask)
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ bbox=final_bbox,
+ position_ids=final_position_ids,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = encoder_outputs[0]
+
+ if not return_dict:
+ return (sequence_output,) + encoder_outputs[1:]
+
+ return TFBaseModelOutput(
+ last_hidden_state=sequence_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+ return TFBaseModelOutput(
+ last_hidden_state=sequence_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+class TFLayoutLMv3PreTrainedModel(TFPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = LayoutLMv3Config
+ base_model_prefix = "layoutlmv3"
+
+ @property
+ def input_signature(self):
+ sig = super().input_signature
+ sig["bbox"] = tf.TensorSpec((None, None, 4), tf.int32, name="bbox")
+ return sig
+
+
+LAYOUTLMV3_START_DOCSTRING = r"""
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Parameters:
+ config ([`LayoutLMv3Config`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+LAYOUTLMV3_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
+ token. See `pixel_values` for `patch_sequence_length`.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ bbox (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner.
+
+ Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
+ token. See `pixel_values` for `patch_sequence_length`.
+
+ pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
+ Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size,
+ config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height /
+ config.patch_size) * (width / config.patch_size))`.
+
+ attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
+ token. See `pixel_values` for `patch_sequence_length`.
+
+ [What are attention masks?](../glossary#attention-mask)
+ token_type_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
+ token. See `pixel_values` for `patch_sequence_length`.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS]
+ token. See `pixel_values` for `patch_sequence_length`.
+
+ [What are position IDs?](../glossary#position-ids)
+ head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare LayoutLMv3 Model transformer outputting raw hidden-states without any specific head on top.",
+ LAYOUTLMV3_START_DOCSTRING,
+)
+class TFLayoutLMv3Model(TFLayoutLMv3PreTrainedModel):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [r"position_ids"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.layoutlmv3 = TFLayoutLMv3MainLayer(config, name="layoutlmv3")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ bbox: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ token_type_ids: tf.Tensor | None = None,
+ position_ids: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ pixel_values: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ) -> TFBaseModelOutput | tuple[tf.Tensor] | tuple[tf.Tensor, tf.Tensor] | tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
+ r"""
+ Returns:
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, TFAutoModel
+ >>> from datasets import load_dataset
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
+ >>> model = TFAutoModel.from_pretrained("microsoft/layoutlmv3-base")
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> image = example["image"]
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = processor(image, words, boxes=boxes, return_tensors="tf")
+
+ >>> outputs = model(**encoding)
+ >>> last_hidden_states = outputs.last_hidden_state
+ ```"""
+
+ outputs = self.layoutlmv3(
+ input_ids=input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ pixel_values=pixel_values,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layoutlmv3", None) is not None:
+ with tf.name_scope(self.layoutlmv3.name):
+ self.layoutlmv3.build(None)
+
+
+class TFLayoutLMv3ClassificationHead(keras.layers.Layer):
+ """
+ Head for sentence-level classification tasks. Reference: RobertaClassificationHead
+ """
+
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(
+ config.hidden_size,
+ activation="tanh",
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="dense",
+ )
+ classifier_dropout = (
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.dropout = keras.layers.Dropout(
+ classifier_dropout,
+ name="dropout",
+ )
+ self.out_proj = keras.layers.Dense(
+ config.num_labels,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="out_proj",
+ )
+ self.config = config
+
+ def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
+ outputs = self.dropout(inputs, training=training)
+ outputs = self.dense(outputs)
+ outputs = self.dropout(outputs, training=training)
+ outputs = self.out_proj(outputs)
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "dropout", None) is not None:
+ with tf.name_scope(self.dropout.name):
+ self.dropout.build(None)
+ if getattr(self, "out_proj", None) is not None:
+ with tf.name_scope(self.out_proj.name):
+ self.out_proj.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ LayoutLMv3 Model with a sequence classification head on top (a linear layer on top of the final hidden state of the
+ [CLS] token) e.g. for document image classification tasks such as the
+ [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.
+ """,
+ LAYOUTLMV3_START_DOCSTRING,
+)
+class TFLayoutLMv3ForSequenceClassification(TFLayoutLMv3PreTrainedModel, TFSequenceClassificationLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [r"position_ids"]
+
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(config, **kwargs)
+ self.config = config
+ self.layoutlmv3 = TFLayoutLMv3MainLayer(config, name="layoutlmv3")
+ self.classifier = TFLayoutLMv3ClassificationHead(config, name="classifier")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ token_type_ids: tf.Tensor | None = None,
+ position_ids: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ labels: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ bbox: tf.Tensor | None = None,
+ pixel_values: tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> (
+ TFSequenceClassifierOutput
+ | tuple[tf.Tensor]
+ | tuple[tf.Tensor, tf.Tensor]
+ | tuple[tf.Tensor, tf.Tensor, tf.Tensor]
+ | tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]
+ ):
+ """
+ Returns:
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, TFAutoModelForSequenceClassification
+ >>> from datasets import load_dataset
+ >>> import tensorflow as tf
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
+ >>> model = TFAutoModelForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base")
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> image = example["image"]
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = processor(image, words, boxes=boxes, return_tensors="tf")
+ >>> sequence_label = tf.convert_to_tensor([1])
+
+ >>> outputs = model(**encoding, labels=sequence_label)
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+ ```"""
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlmv3(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ bbox=bbox,
+ pixel_values=pixel_values,
+ training=training,
+ )
+ sequence_output = outputs[0][:, 0, :]
+ logits = self.classifier(sequence_output, training=training)
+
+ loss = None if labels is None else self.hf_compute_loss(labels, logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFSequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layoutlmv3", None) is not None:
+ with tf.name_scope(self.layoutlmv3.name):
+ self.layoutlmv3.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build(None)
+
+
+@add_start_docstrings(
+ """
+ LayoutLMv3 Model with a token classification head on top (a linear layer on top of the final hidden states) e.g.
+ for sequence labeling (information extraction) tasks such as [FUNSD](https://guillaumejaume.github.io/FUNSD/),
+ [SROIE](https://rrc.cvc.uab.es/?ch=13), [CORD](https://github.com/clovaai/cord) and
+ [Kleister-NDA](https://github.com/applicaai/kleister-nda).
+ """,
+ LAYOUTLMV3_START_DOCSTRING,
+)
+class TFLayoutLMv3ForTokenClassification(TFLayoutLMv3PreTrainedModel, TFTokenClassificationLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [r"position_ids"]
+
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(config, **kwargs)
+ self.num_labels = config.num_labels
+
+ self.layoutlmv3 = TFLayoutLMv3MainLayer(config, name="layoutlmv3")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob, name="dropout")
+ if config.num_labels < 10:
+ self.classifier = keras.layers.Dense(
+ config.num_labels,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="classifier",
+ )
+ else:
+ self.classifier = TFLayoutLMv3ClassificationHead(config, name="classifier")
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ bbox: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ token_type_ids: tf.Tensor | None = None,
+ position_ids: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ labels: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ pixel_values: tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> (
+ TFTokenClassifierOutput
+ | tuple[tf.Tensor]
+ | tuple[tf.Tensor, tf.Tensor]
+ | tuple[tf.Tensor, tf.Tensor, tf.Tensor]
+ | tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]
+ ):
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+
+ Returns:
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, TFAutoModelForTokenClassification
+ >>> from datasets import load_dataset
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
+ >>> model = TFAutoModelForTokenClassification.from_pretrained("microsoft/layoutlmv3-base", num_labels=7)
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> image = example["image"]
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+ >>> word_labels = example["ner_tags"]
+
+ >>> encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="tf")
+
+ >>> outputs = model(**encoding)
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+ ```"""
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlmv3(
+ input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ pixel_values=pixel_values,
+ training=training,
+ )
+ if input_ids is not None:
+ input_shape = tf.shape(input_ids)
+ else:
+ input_shape = tf.shape(inputs_embeds)[:-1]
+
+ seq_length = input_shape[1]
+ # only take the text part of the output representations
+ sequence_output = outputs[0][:, :seq_length]
+ sequence_output = self.dropout(sequence_output, training=training)
+ logits = self.classifier(sequence_output)
+
+ loss = None if labels is None else self.hf_compute_loss(labels, logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFTokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layoutlmv3", None) is not None:
+ with tf.name_scope(self.layoutlmv3.name):
+ self.layoutlmv3.build(None)
+ if getattr(self, "dropout", None) is not None:
+ with tf.name_scope(self.dropout.name):
+ self.dropout.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ LayoutLMv3 Model with a span classification head on top for extractive question-answering tasks such as
+ [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the text part of the hidden-states output to
+ compute `span start logits` and `span end logits`).
+ """,
+ LAYOUTLMV3_START_DOCSTRING,
+)
+class TFLayoutLMv3ForQuestionAnswering(TFLayoutLMv3PreTrainedModel, TFQuestionAnsweringLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [r"position_ids"]
+
+ def __init__(self, config: LayoutLMv3Config, **kwargs):
+ super().__init__(config, **kwargs)
+
+ self.num_labels = config.num_labels
+
+ self.layoutlmv3 = TFLayoutLMv3MainLayer(config, name="layoutlmv3")
+ self.qa_outputs = TFLayoutLMv3ClassificationHead(config, name="qa_outputs")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ token_type_ids: tf.Tensor | None = None,
+ position_ids: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ start_positions: tf.Tensor | None = None,
+ end_positions: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ bbox: tf.Tensor | None = None,
+ pixel_values: tf.Tensor | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ) -> (
+ TFQuestionAnsweringModelOutput
+ | tuple[tf.Tensor]
+ | tuple[tf.Tensor, tf.Tensor]
+ | tuple[tf.Tensor, tf.Tensor, tf.Tensor]
+ | tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]
+ ):
+ r"""
+ start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the start of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+ are not taken into account for computing the loss.
+ end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the end of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+ are not taken into account for computing the loss.
+
+ Returns:
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, TFAutoModelForQuestionAnswering
+ >>> from datasets import load_dataset
+ >>> import tensorflow as tf
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
+ >>> model = TFAutoModelForQuestionAnswering.from_pretrained("microsoft/layoutlmv3-base")
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> image = example["image"]
+ >>> question = "what's his name?"
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = processor(image, question, words, boxes=boxes, return_tensors="tf")
+ >>> start_positions = tf.convert_to_tensor([1])
+ >>> end_positions = tf.convert_to_tensor([3])
+
+ >>> outputs = model(**encoding, start_positions=start_positions, end_positions=end_positions)
+ >>> loss = outputs.loss
+ >>> start_scores = outputs.start_logits
+ >>> end_scores = outputs.end_logits
+ ```"""
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.layoutlmv3(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ bbox=bbox,
+ pixel_values=pixel_values,
+ training=training,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output, training=training)
+ start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
+ start_logits = tf.squeeze(input=start_logits, axis=-1)
+ end_logits = tf.squeeze(input=end_logits, axis=-1)
+
+ loss = None
+
+ if start_positions is not None and end_positions is not None:
+ labels = {"start_position": start_positions, "end_position": end_positions}
+ loss = self.hf_compute_loss(labels, logits=(start_logits, end_logits))
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFQuestionAnsweringModelOutput(
+ loss=loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layoutlmv3", None) is not None:
+ with tf.name_scope(self.layoutlmv3.name):
+ self.layoutlmv3.build(None)
+ if getattr(self, "qa_outputs", None) is not None:
+ with tf.name_scope(self.qa_outputs.name):
+ self.qa_outputs.build(None)
+
+
+__all__ = [
+ "TFLayoutLMv3ForQuestionAnswering",
+ "TFLayoutLMv3ForSequenceClassification",
+ "TFLayoutLMv3ForTokenClassification",
+ "TFLayoutLMv3Model",
+ "TFLayoutLMv3PreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/processing_layoutlmv3.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/processing_layoutlmv3.py
new file mode 100644
index 0000000000000000000000000000000000000000..d38090af3f2ef54447d6d31ce422cca2ad3dca34
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/processing_layoutlmv3.py
@@ -0,0 +1,202 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for LayoutLMv3.
+"""
+
+import warnings
+from typing import Optional, Union
+
+from ...processing_utils import ProcessorMixin
+from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from ...utils import TensorType
+
+
+class LayoutLMv3Processor(ProcessorMixin):
+ r"""
+ Constructs a LayoutLMv3 processor which combines a LayoutLMv3 image processor and a LayoutLMv3 tokenizer into a
+ single processor.
+
+ [`LayoutLMv3Processor`] offers all the functionalities you need to prepare data for the model.
+
+ It first uses [`LayoutLMv3ImageProcessor`] to resize and normalize document images, and optionally applies OCR to
+ get words and normalized bounding boxes. These are then provided to [`LayoutLMv3Tokenizer`] or
+ [`LayoutLMv3TokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`,
+ `attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned
+ into token-level `labels` for token classification tasks (such as FUNSD, CORD).
+
+ Args:
+ image_processor (`LayoutLMv3ImageProcessor`, *optional*):
+ An instance of [`LayoutLMv3ImageProcessor`]. The image processor is a required input.
+ tokenizer (`LayoutLMv3Tokenizer` or `LayoutLMv3TokenizerFast`, *optional*):
+ An instance of [`LayoutLMv3Tokenizer`] or [`LayoutLMv3TokenizerFast`]. The tokenizer is a required input.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "LayoutLMv3ImageProcessor"
+ tokenizer_class = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")
+
+ def __init__(self, image_processor=None, tokenizer=None, **kwargs):
+ feature_extractor = None
+ if "feature_extractor" in kwargs:
+ warnings.warn(
+ "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
+ " instead.",
+ FutureWarning,
+ )
+ feature_extractor = kwargs.pop("feature_extractor")
+
+ image_processor = image_processor if image_processor is not None else feature_extractor
+ if image_processor is None:
+ raise ValueError("You need to specify an `image_processor`.")
+ if tokenizer is None:
+ raise ValueError("You need to specify a `tokenizer`.")
+
+ super().__init__(image_processor, tokenizer)
+
+ def __call__(
+ self,
+ images,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+ text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
+ boxes: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ This method first forwards the `images` argument to [`~LayoutLMv3ImageProcessor.__call__`]. In case
+ [`LayoutLMv3ImageProcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
+ bounding boxes along with the additional arguments to [`~LayoutLMv3Tokenizer.__call__`] and returns the output,
+ together with resized and normalized `pixel_values`. In case [`LayoutLMv3ImageProcessor`] was initialized with
+ `apply_ocr` set to `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along
+ with the additional arguments to [`~LayoutLMv3Tokenizer.__call__`] and returns the output, together with
+ resized and normalized `pixel_values`.
+
+ Please refer to the docstring of the above two methods for more information.
+ """
+ # verify input
+ if self.image_processor.apply_ocr and (boxes is not None):
+ raise ValueError(
+ "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."
+ )
+
+ if self.image_processor.apply_ocr and (word_labels is not None):
+ raise ValueError(
+ "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."
+ )
+
+ # first, apply the image processor
+ features = self.image_processor(images=images, return_tensors=return_tensors)
+
+ # second, apply the tokenizer
+ if text is not None and self.image_processor.apply_ocr and text_pair is None:
+ if isinstance(text, str):
+ text = [text] # add batch dimension (as the image processor always adds a batch dimension)
+ text_pair = features["words"]
+
+ encoded_inputs = self.tokenizer(
+ text=text if text is not None else features["words"],
+ text_pair=text_pair if text_pair is not None else None,
+ boxes=boxes if boxes is not None else features["boxes"],
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ return_tensors=return_tensors,
+ **kwargs,
+ )
+
+ # add pixel values
+ images = features.pop("pixel_values")
+ if return_overflowing_tokens is True:
+ images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"])
+ encoded_inputs["pixel_values"] = images
+
+ return encoded_inputs
+
+ def get_overflowing_images(self, images, overflow_to_sample_mapping):
+ # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
+ images_with_overflow = []
+ for sample_idx in overflow_to_sample_mapping:
+ images_with_overflow.append(images[sample_idx])
+
+ if len(images_with_overflow) != len(overflow_to_sample_mapping):
+ raise ValueError(
+ "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
+ f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
+ )
+
+ return images_with_overflow
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
+ to the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ def model_input_names(self):
+ return ["input_ids", "bbox", "attention_mask", "pixel_values"]
+
+ @property
+ def feature_extractor_class(self):
+ warnings.warn(
+ "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
+ FutureWarning,
+ )
+ return self.image_processor_class
+
+ @property
+ def feature_extractor(self):
+ warnings.warn(
+ "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
+ FutureWarning,
+ )
+ return self.image_processor
+
+
+__all__ = ["LayoutLMv3Processor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/tokenization_layoutlmv3.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/tokenization_layoutlmv3.py
new file mode 100644
index 0000000000000000000000000000000000000000..b69fc57b1743c4b0349f33b3bc1f4a3d35120df3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/tokenization_layoutlmv3.py
@@ -0,0 +1,1487 @@
+# coding=utf-8
+# Copyright The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization class for LayoutLMv3. Same as LayoutLMv2, but RoBERTa-like BPE tokenization instead of WordPiece."""
+
+import json
+import os
+from functools import lru_cache
+from typing import Optional, Union
+
+import regex as re
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...tokenization_utils_base import (
+ BatchEncoding,
+ EncodedInput,
+ PreTokenizedInput,
+ TextInput,
+ TextInputPair,
+ TruncationStrategy,
+)
+from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {
+ "vocab_file": "vocab.json",
+ "merges_file": "merges.txt",
+}
+
+
+LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING = r"""
+ add_special_tokens (`bool`, *optional*, defaults to `True`):
+ Whether or not to encode the sequences with the special tokens relative to their model.
+ padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
+ Activates and controls padding. Accepts the following values:
+
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+ Activates and controls truncation. Accepts the following values:
+
+ - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
+ to the maximum acceptable input length for the model if that argument is not provided. This will
+ truncate token by token, removing a token from the longest sequence in the pair if a pair of
+ sequences (or a batch of pairs) is provided.
+ - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
+ greater than the model maximum admissible input size).
+ max_length (`int`, *optional*):
+ Controls the maximum length to use by one of the truncation/padding parameters.
+
+ If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
+ is required by one of the truncation/padding parameters. If the model has no specific maximum input
+ length (like XLNet) truncation/padding to a maximum length will be deactivated.
+ stride (`int`, *optional*, defaults to 0):
+ If set to a number along with `max_length`, the overflowing tokens returned when
+ `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
+ returned to provide some overlap between truncated and overflowing sequences. The value of this
+ argument defines the number of overlapping tokens.
+ pad_to_multiple_of (`int`, *optional*):
+ If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
+ the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
+ return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+ If set, will return tensors instead of list of python integers. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return Numpy `np.ndarray` objects.
+"""
+
+
+LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
+ add_special_tokens (`bool`, *optional*, defaults to `True`):
+ Whether or not to encode the sequences with the special tokens relative to their model.
+ padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
+ Activates and controls padding. Accepts the following values:
+
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+ Activates and controls truncation. Accepts the following values:
+
+ - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
+ to the maximum acceptable input length for the model if that argument is not provided. This will
+ truncate token by token, removing a token from the longest sequence in the pair if a pair of
+ sequences (or a batch of pairs) is provided.
+ - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
+ greater than the model maximum admissible input size).
+ max_length (`int`, *optional*):
+ Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to
+ `None`, this will use the predefined model maximum length if a maximum length is required by one of the
+ truncation/padding parameters. If the model has no specific maximum input length (like XLNet)
+ truncation/padding to a maximum length will be deactivated.
+ stride (`int`, *optional*, defaults to 0):
+ If set to a number along with `max_length`, the overflowing tokens returned when
+ `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
+ returned to provide some overlap between truncated and overflowing sequences. The value of this
+ argument defines the number of overlapping tokens.
+ pad_to_multiple_of (`int`, *optional*):
+ If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
+ the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors instead of list of python integers. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return Numpy `np.ndarray` objects.
+"""
+
+
+@lru_cache
+# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
+def bytes_to_unicode():
+ """
+ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
+ characters the bpe code barfs on.
+
+ The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
+ if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
+ decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
+ tables between utf-8 bytes and unicode strings.
+ """
+ bs = (
+ list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
+ )
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8 + n)
+ n += 1
+ cs = [chr(n) for n in cs]
+ return dict(zip(bs, cs))
+
+
+# Copied from transformers.models.roberta.tokenization_roberta.get_pairs
+def get_pairs(word):
+ """
+ Return set of symbol pairs in a word.
+
+ Word is represented as tuple of symbols (symbols being variable-length strings).
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+class LayoutLMv3Tokenizer(PreTrainedTokenizer):
+ r"""
+ Construct a LayoutLMv3 tokenizer. Based on [`RoBERTatokenizer`] (Byte Pair Encoding or BPE).
+ [`LayoutLMv3Tokenizer`] can be used to turn words, word-level bounding boxes and optional word labels to
+ token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`, and optional `labels` (for token
+ classification).
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ [`LayoutLMv3Tokenizer`] runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the
+ word-level bounding boxes into token-level bounding boxes.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ errors (`str`, *optional*, defaults to `"replace"`):
+ Paradigm to follow when decoding bytes to UTF-8. See
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ add_prefix_space (`bool`, *optional*, defaults to `True`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
+ cls_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [CLS] token.
+ sep_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [SEP] token.
+ pad_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [PAD] token.
+ pad_token_label (`int`, *optional*, defaults to -100):
+ The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
+ CrossEntropyLoss.
+ only_label_first_subword (`bool`, *optional*, defaults to `True`):
+ Whether or not to only label the first subword, in case word labels are provided.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask", "bbox"]
+
+ def __init__(
+ self,
+ vocab_file,
+ merges_file,
+ errors="replace",
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ add_prefix_space=True,
+ cls_token_box=[0, 0, 0, 0],
+ sep_token_box=[0, 0, 0, 0],
+ pad_token_box=[0, 0, 0, 0],
+ pad_token_label=-100,
+ only_label_first_subword=True,
+ **kwargs,
+ ):
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+ sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
+ cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+ with open(vocab_file, encoding="utf-8") as vocab_handle:
+ self.encoder = json.load(vocab_handle)
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ self.errors = errors # how to handle errors in decoding
+ self.byte_encoder = bytes_to_unicode()
+ self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+ with open(merges_file, encoding="utf-8") as merges_handle:
+ bpe_merges = merges_handle.read().split("\n")[1:-1]
+ bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
+ self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+ self.cache = {}
+ self.add_prefix_space = add_prefix_space
+
+ # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
+ self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
+
+ # additional properties
+ self.cls_token_box = cls_token_box
+ self.sep_token_box = sep_token_box
+ self.pad_token_box = pad_token_box
+ self.pad_token_label = pad_token_label
+ self.only_label_first_subword = only_label_first_subword
+
+ super().__init__(
+ errors=errors,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ add_prefix_space=add_prefix_space,
+ cls_token_box=cls_token_box,
+ sep_token_box=sep_token_box,
+ pad_token_box=pad_token_box,
+ pad_token_label=pad_token_label,
+ only_label_first_subword=only_label_first_subword,
+ **kwargs,
+ )
+
+ @property
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size
+ def vocab_size(self):
+ return len(self.encoder)
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_vocab
+ def get_vocab(self):
+ vocab = dict(self.encoder).copy()
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.bpe
+ def bpe(self, token):
+ if token in self.cache:
+ return self.cache[token]
+ word = tuple(token)
+ pairs = get_pairs(word)
+
+ if not pairs:
+ return token
+
+ while True:
+ bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+ if bigram not in self.bpe_ranks:
+ break
+ first, second = bigram
+ new_word = []
+ i = 0
+ while i < len(word):
+ try:
+ j = word.index(first, i)
+ except ValueError:
+ new_word.extend(word[i:])
+ break
+ else:
+ new_word.extend(word[i:j])
+ i = j
+
+ if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+ new_word.append(first + second)
+ i += 2
+ else:
+ new_word.append(word[i])
+ i += 1
+ new_word = tuple(new_word)
+ word = new_word
+ if len(word) == 1:
+ break
+ else:
+ pairs = get_pairs(word)
+ word = " ".join(word)
+ self.cache[token] = word
+ return word
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._tokenize
+ def _tokenize(self, text):
+ """Tokenize a string."""
+ bpe_tokens = []
+ for token in re.findall(self.pat, text):
+ token = "".join(
+ self.byte_encoder[b] for b in token.encode("utf-8")
+ ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
+ bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
+ return bpe_tokens
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_token_to_id
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_id_to_token
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.decoder.get(index)
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.convert_tokens_to_string
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ text = "".join(tokens)
+ text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
+ return text
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.save_vocabulary
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ merge_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
+ )
+
+ with open(vocab_file, "w", encoding="utf-8") as f:
+ f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+ index = 0
+ with open(merge_file, "w", encoding="utf-8") as writer:
+ writer.write("#version: 0.2\n")
+ for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
+ " Please check that the tokenizer is not corrupted!"
+ )
+ index = token_index
+ writer.write(" ".join(bpe_tokens) + "\n")
+ index += 1
+
+ return vocab_file, merge_file
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.build_inputs_with_special_tokens
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A RoBERTa sequence has the following format:
+
+ - single sequence: ` X `
+ - pair of sequences: ` A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_special_tokens_mask
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is None:
+ return [1] + ([0] * len(token_ids_0)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.create_token_type_ids_from_sequences
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
+ add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
+ # If the text starts with a token that should not be split, no space is added before the text in any case.
+ # It's necessary to match the fast tokenization
+ if (
+ (is_split_into_words or add_prefix_space)
+ and (len(text) > 0 and not text[0].isspace())
+ and sum([text.startswith(no_split_token) for no_split_token in self.added_tokens_encoder]) == 0
+ ):
+ text = " " + text
+ return (text, kwargs)
+
+ @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.__call__
+ def __call__(
+ self,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]],
+ text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
+ boxes: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
+ sequences with word-level normalized bounding boxes and optional labels.
+
+ Args:
+ text (`str`, `List[str]`, `List[List[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
+ (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
+ words).
+ text_pair (`List[str]`, `List[List[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
+ (pretokenized string).
+ boxes (`List[List[int]]`, `List[List[List[int]]]`):
+ Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
+ word_labels (`List[int]`, `List[List[int]]`, *optional*):
+ Word-level integer labels (for token classification tasks such as FUNSD, CORD).
+ """
+
+ # Input type checking for clearer error
+ def _is_valid_text_input(t):
+ if isinstance(t, str):
+ # Strings are fine
+ return True
+ elif isinstance(t, (list, tuple)):
+ # List are fine as long as they are...
+ if len(t) == 0:
+ # ... empty
+ return True
+ elif isinstance(t[0], str):
+ # ... list of strings
+ return True
+ elif isinstance(t[0], (list, tuple)):
+ # ... list with an empty list or with a list of strings
+ return len(t[0]) == 0 or isinstance(t[0][0], str)
+ else:
+ return False
+ else:
+ return False
+
+ if text_pair is not None:
+ # in case text + text_pair are provided, text = questions, text_pair = words
+ if not _is_valid_text_input(text):
+ raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
+ if not isinstance(text_pair, (list, tuple)):
+ raise ValueError(
+ "Words must be of type `List[str]` (single pretokenized example), "
+ "or `List[List[str]]` (batch of pretokenized examples)."
+ )
+ else:
+ # in case only text is provided => must be words
+ if not isinstance(text, (list, tuple)):
+ raise ValueError(
+ "Words must be of type `List[str]` (single pretokenized example), "
+ "or `List[List[str]]` (batch of pretokenized examples)."
+ )
+
+ if text_pair is not None:
+ is_batched = isinstance(text, (list, tuple))
+ else:
+ is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
+
+ words = text if text_pair is None else text_pair
+ if boxes is None:
+ raise ValueError("You must provide corresponding bounding boxes")
+ if is_batched:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide words and boxes for an equal amount of examples")
+ for words_example, boxes_example in zip(words, boxes):
+ if len(words_example) != len(boxes_example):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+ else:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+
+ if is_batched:
+ if text_pair is not None and len(text) != len(text_pair):
+ raise ValueError(
+ f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
+ f" {len(text_pair)}."
+ )
+ batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
+ is_pair = bool(text_pair is not None)
+ return self.batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+ else:
+ return self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.batch_encode_plus
+ def batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[list[int]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._batch_encode_plus
+ def _batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[list[int]]]] = None,
+ word_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast."
+ )
+
+ batch_outputs = self._batch_prepare_for_model(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=return_tensors,
+ verbose=verbose,
+ )
+
+ return BatchEncoding(batch_outputs)
+
+ @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._batch_prepare_for_model
+ def _batch_prepare_for_model(
+ self,
+ batch_text_or_text_pairs,
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
+ adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
+ manages a moving window (with user defined stride) for overflowing tokens.
+
+ Args:
+ batch_ids_pairs: list of tokenized input ids or input ids pairs
+ """
+
+ batch_outputs = {}
+ for idx, example in enumerate(zip(batch_text_or_text_pairs, boxes)):
+ batch_text_or_text_pair, boxes_example = example
+ outputs = self.prepare_for_model(
+ batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair,
+ batch_text_or_text_pair[1] if is_pair else None,
+ boxes_example,
+ word_labels=word_labels[idx] if word_labels is not None else None,
+ add_special_tokens=add_special_tokens,
+ padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=None, # we pad in batch afterward
+ padding_side=None, # we pad in batch afterward
+ return_attention_mask=False, # we pad in batch afterward
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=None, # We convert the whole batch to tensors at the end
+ prepend_batch_axis=False,
+ verbose=verbose,
+ )
+
+ for key, value in outputs.items():
+ if key not in batch_outputs:
+ batch_outputs[key] = []
+ batch_outputs[key].append(value)
+
+ batch_outputs = self.pad(
+ batch_outputs,
+ padding=padding_strategy.value,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
+
+ return batch_outputs
+
+ @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING)
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.encode
+ def encode(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> list[int]:
+ encoded_inputs = self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return encoded_inputs["input_ids"]
+
+ @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.encode_plus
+ def encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
+ `__call__` should be used instead.
+
+ Args:
+ text (`str`, `List[str]`, `List[List[str]]`):
+ The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
+ text_pair (`List[str]` or `List[int]`, *optional*):
+ Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
+ list of list of strings (words of a batch of examples).
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._encode_plus(
+ text=text,
+ boxes=boxes,
+ text_pair=text_pair,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._encode_plus
+ def _encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast. "
+ "More information on available tokenizers at "
+ "https://github.com/huggingface/transformers/pull/2674"
+ )
+
+ return self.prepare_for_model(
+ text=text,
+ text_pair=text_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding_strategy.value,
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ prepend_batch_axis=True,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ verbose=verbose,
+ )
+
+ @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def prepare_for_model(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ prepend_batch_axis: bool = False,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
+ truncates sequences if overflowing while taking into account the special tokens and manages a moving window
+ (with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and
+ *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a
+ combination of arguments will raise an error.
+
+ Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into
+ token-level `labels`. The word label is used for the first token of the word, while remaining tokens are
+ labeled with -100, such that they will be ignored by the loss function.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
+ text_pair (`list[str]` or `list[int]`, *optional*):
+ Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
+ list of list of strings (words of a batch of examples).
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ tokens = []
+ pair_tokens = []
+ token_boxes = []
+ pair_token_boxes = []
+ labels = []
+
+ if text_pair is None:
+ if word_labels is None:
+ # CASE 1: document image classification (training + inference) + CASE 2: token classification (inference)
+ for word, box in zip(text, boxes):
+ if len(word) < 1: # skip empty words
+ continue
+ word_tokens = self.tokenize(word)
+ tokens.extend(word_tokens)
+ token_boxes.extend([box] * len(word_tokens))
+ else:
+ # CASE 2: token classification (training)
+ for word, box, label in zip(text, boxes, word_labels):
+ if len(word) < 1: # skip empty words
+ continue
+ word_tokens = self.tokenize(word)
+ tokens.extend(word_tokens)
+ token_boxes.extend([box] * len(word_tokens))
+ if self.only_label_first_subword:
+ # Use the real label id for the first token of the word, and padding ids for the remaining tokens
+ labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
+ else:
+ labels.extend([label] * len(word_tokens))
+ else:
+ # CASE 3: document visual question answering (inference)
+ # text = question
+ # text_pair = words
+ tokens = self.tokenize(text)
+ token_boxes = [self.pad_token_box for _ in range(len(tokens))]
+
+ for word, box in zip(text_pair, boxes):
+ if len(word) < 1: # skip empty words
+ continue
+ word_tokens = self.tokenize(word)
+ pair_tokens.extend(word_tokens)
+ pair_token_boxes.extend([box] * len(word_tokens))
+
+ # Create ids + pair_ids
+ ids = self.convert_tokens_to_ids(tokens)
+ pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None
+
+ if (
+ return_overflowing_tokens
+ and truncation_strategy == TruncationStrategy.LONGEST_FIRST
+ and pair_ids is not None
+ ):
+ raise ValueError(
+ "Not possible to return overflowing tokens for pair of sequences with the "
+ "`longest_first`. Please select another truncation strategy than `longest_first`, "
+ "for instance `only_second` or `only_first`."
+ )
+
+ # Compute the total size of the returned encodings
+ pair = bool(pair_ids is not None)
+ len_ids = len(ids)
+ len_pair_ids = len(pair_ids) if pair else 0
+ total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
+
+ # Truncation: Handle max sequence length
+ overflowing_tokens = []
+ overflowing_token_boxes = []
+ overflowing_labels = []
+ if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
+ (
+ ids,
+ token_boxes,
+ pair_ids,
+ pair_token_boxes,
+ labels,
+ overflowing_tokens,
+ overflowing_token_boxes,
+ overflowing_labels,
+ ) = self.truncate_sequences(
+ ids,
+ token_boxes,
+ pair_ids=pair_ids,
+ pair_token_boxes=pair_token_boxes,
+ labels=labels,
+ num_tokens_to_remove=total_len - max_length,
+ truncation_strategy=truncation_strategy,
+ stride=stride,
+ )
+
+ if return_token_type_ids and not add_special_tokens:
+ raise ValueError(
+ "Asking to return token_type_ids while setting add_special_tokens to False "
+ "results in an undefined behavior. Please set add_special_tokens to True or "
+ "set return_token_type_ids to None."
+ )
+
+ # Load from model defaults
+ if return_token_type_ids is None:
+ return_token_type_ids = "token_type_ids" in self.model_input_names
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ encoded_inputs = {}
+
+ if return_overflowing_tokens:
+ encoded_inputs["overflowing_tokens"] = overflowing_tokens
+ encoded_inputs["overflowing_token_boxes"] = overflowing_token_boxes
+ encoded_inputs["overflowing_labels"] = overflowing_labels
+ encoded_inputs["num_truncated_tokens"] = total_len - max_length
+
+ # Add special tokens
+ if add_special_tokens:
+ sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
+ token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
+ token_boxes = [self.cls_token_box] + token_boxes + [self.sep_token_box]
+ if pair_token_boxes:
+ pair_token_boxes = [self.sep_token_box] + pair_token_boxes + [self.sep_token_box]
+ token_boxes = token_boxes + pair_token_boxes if pair else token_boxes
+ if labels:
+ labels = [self.pad_token_label] + labels + [self.pad_token_label]
+ else:
+ sequence = ids + pair_ids if pair else ids
+ token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
+ token_boxes = token_boxes + pair_token_boxes if pair else token_boxes
+
+ # Build output dictionary
+ encoded_inputs["input_ids"] = sequence
+ encoded_inputs["bbox"] = token_boxes
+ if return_token_type_ids:
+ encoded_inputs["token_type_ids"] = token_type_ids
+ if return_special_tokens_mask:
+ if add_special_tokens:
+ encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
+ else:
+ encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
+
+ if labels:
+ encoded_inputs["labels"] = labels
+
+ # Check lengths
+ self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
+
+ # Padding
+ if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
+ encoded_inputs = self.pad(
+ encoded_inputs,
+ max_length=max_length,
+ padding=padding_strategy.value,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ if return_length:
+ encoded_inputs["length"] = len(encoded_inputs["input_ids"])
+
+ batch_outputs = BatchEncoding(
+ encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
+ )
+
+ return batch_outputs
+
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.truncate_sequences
+ def truncate_sequences(
+ self,
+ ids: list[int],
+ token_boxes: list[list[int]],
+ pair_ids: Optional[list[int]] = None,
+ pair_token_boxes: Optional[list[list[int]]] = None,
+ labels: Optional[list[int]] = None,
+ num_tokens_to_remove: int = 0,
+ truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
+ stride: int = 0,
+ ) -> tuple[list[int], list[int], list[int]]:
+ """
+ Truncates a sequence pair in-place following the strategy.
+
+ Args:
+ ids (`List[int]`):
+ Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
+ `convert_tokens_to_ids` methods.
+ token_boxes (`List[List[int]]`):
+ Bounding boxes of the first sequence.
+ pair_ids (`List[int]`, *optional*):
+ Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
+ and `convert_tokens_to_ids` methods.
+ pair_token_boxes (`List[List[int]]`, *optional*):
+ Bounding boxes of the second sequence.
+ labels (`List[int]`, *optional*):
+ Labels of the first sequence (for token classification tasks).
+ num_tokens_to_remove (`int`, *optional*, defaults to 0):
+ Number of tokens to remove using the truncation strategy.
+ truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+ The strategy to follow for truncation. Can be:
+
+ - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will truncate
+ token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
+ batch of pairs) is provided.
+ - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
+ than the model maximum admissible input size).
+ stride (`int`, *optional*, defaults to 0):
+ If set to a positive number, the overflowing tokens returned will contain some tokens from the main
+ sequence returned. The value of this argument defines the number of additional tokens.
+
+ Returns:
+ `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
+ overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair
+ of sequences (or a batch of pairs) is provided.
+ """
+ if num_tokens_to_remove <= 0:
+ return ids, token_boxes, pair_ids, pair_token_boxes, labels, [], [], []
+
+ if not isinstance(truncation_strategy, TruncationStrategy):
+ truncation_strategy = TruncationStrategy(truncation_strategy)
+
+ overflowing_tokens = []
+ overflowing_token_boxes = []
+ overflowing_labels = []
+ if truncation_strategy == TruncationStrategy.ONLY_FIRST or (
+ truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None
+ ):
+ if len(ids) > num_tokens_to_remove:
+ window_len = min(len(ids), stride + num_tokens_to_remove)
+ overflowing_tokens = ids[-window_len:]
+ overflowing_token_boxes = token_boxes[-window_len:]
+ overflowing_labels = labels[-window_len:]
+ ids = ids[:-num_tokens_to_remove]
+ token_boxes = token_boxes[:-num_tokens_to_remove]
+ labels = labels[:-num_tokens_to_remove]
+ else:
+ error_msg = (
+ f"We need to remove {num_tokens_to_remove} to truncate the input "
+ f"but the first sequence has a length {len(ids)}. "
+ )
+ if truncation_strategy == TruncationStrategy.ONLY_FIRST:
+ error_msg = (
+ error_msg + "Please select another truncation strategy than "
+ f"{truncation_strategy}, for instance 'longest_first' or 'only_second'."
+ )
+ logger.error(error_msg)
+ elif truncation_strategy == TruncationStrategy.LONGEST_FIRST:
+ logger.warning(
+ "Be aware, overflowing tokens are not returned for the setting you have chosen,"
+ f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
+ "truncation strategy. So the returned list will always be empty even if some "
+ "tokens have been removed."
+ )
+ for _ in range(num_tokens_to_remove):
+ if pair_ids is None or len(ids) > len(pair_ids):
+ ids = ids[:-1]
+ token_boxes = token_boxes[:-1]
+ labels = labels[:-1]
+ else:
+ pair_ids = pair_ids[:-1]
+ pair_token_boxes = pair_token_boxes[:-1]
+ elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
+ if len(pair_ids) > num_tokens_to_remove:
+ window_len = min(len(pair_ids), stride + num_tokens_to_remove)
+ overflowing_tokens = pair_ids[-window_len:]
+ overflowing_token_boxes = pair_token_boxes[-window_len:]
+ pair_ids = pair_ids[:-num_tokens_to_remove]
+ pair_token_boxes = pair_token_boxes[:-num_tokens_to_remove]
+ else:
+ logger.error(
+ f"We need to remove {num_tokens_to_remove} to truncate the input "
+ f"but the second sequence has a length {len(pair_ids)}. "
+ f"Please select another truncation strategy than {truncation_strategy}, "
+ "for instance 'longest_first' or 'only_first'."
+ )
+
+ return (
+ ids,
+ token_boxes,
+ pair_ids,
+ pair_token_boxes,
+ labels,
+ overflowing_tokens,
+ overflowing_token_boxes,
+ overflowing_labels,
+ )
+
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._pad
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ """
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+ Args:
+ encoded_inputs:
+ Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+ max_length: maximum length of the returned list and optionally padding length (see below).
+ Will truncate by taking into account the special tokens.
+ padding_strategy: PaddingStrategy to use for padding.
+
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
+ The tokenizer padding sides are defined in self.padding_side:
+
+ - 'left': pads on the left of the sequences
+ - 'right': pads on the right of the sequences
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta).
+ padding_side:
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask:
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+ """
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = len(required_input)
+
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+ # Initialize attention mask if not present.
+ if return_attention_mask and "attention_mask" not in encoded_inputs:
+ encoded_inputs["attention_mask"] = [1] * len(required_input)
+
+ if needs_to_be_padded:
+ difference = max_length - len(required_input)
+ padding_side = padding_side if padding_side is not None else self.padding_side
+ if padding_side == "right":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = (
+ encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
+ )
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
+ encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
+ elif padding_side == "left":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
+ "token_type_ids"
+ ]
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+ else:
+ raise ValueError("Invalid padding strategy:" + str(padding_side))
+
+ return encoded_inputs
+
+
+__all__ = ["LayoutLMv3Tokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/tokenization_layoutlmv3_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/tokenization_layoutlmv3_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0407638595d8a127cef02e5bf687b34663e9bfa
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutlmv3/tokenization_layoutlmv3_fast.py
@@ -0,0 +1,848 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fast tokenization class for LayoutLMv3. It overwrites 2 methods of the slow tokenizer class, namely _batch_encode_plus
+and _encode_plus, in which the Rust tokenizer is used.
+"""
+
+import json
+from typing import Optional, Union
+
+from tokenizers import processors
+
+from ...tokenization_utils_base import (
+ BatchEncoding,
+ EncodedInput,
+ PaddingStrategy,
+ PreTokenizedInput,
+ TensorType,
+ TextInput,
+ TextInputPair,
+ TruncationStrategy,
+)
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import add_end_docstrings, logging
+from .tokenization_layoutlmv3 import (
+ LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING,
+ LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING,
+ LayoutLMv3Tokenizer,
+)
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
+
+
+class LayoutLMv3TokenizerFast(PreTrainedTokenizerFast):
+ r"""
+ Construct a "fast" LayoutLMv3 tokenizer (backed by HuggingFace's *tokenizers* library). Based on BPE.
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ errors (`str`, *optional*, defaults to `"replace"`):
+ Paradigm to follow when decoding bytes to UTF-8. See
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ add_prefix_space (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
+ trim_offsets (`bool`, *optional*, defaults to `True`):
+ Whether the post processing step should trim offsets to avoid including whitespaces.
+ cls_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [CLS] token.
+ sep_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [SEP] token.
+ pad_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [PAD] token.
+ pad_token_label (`int`, *optional*, defaults to -100):
+ The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
+ CrossEntropyLoss.
+ only_label_first_subword (`bool`, *optional*, defaults to `True`):
+ Whether or not to only label the first subword, in case word labels are provided.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+ slow_tokenizer_class = LayoutLMv3Tokenizer
+
+ def __init__(
+ self,
+ vocab_file=None,
+ merges_file=None,
+ tokenizer_file=None,
+ errors="replace",
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ add_prefix_space=True,
+ trim_offsets=True,
+ cls_token_box=[0, 0, 0, 0],
+ sep_token_box=[0, 0, 0, 0],
+ pad_token_box=[0, 0, 0, 0],
+ pad_token_label=-100,
+ only_label_first_subword=True,
+ **kwargs,
+ ):
+ super().__init__(
+ vocab_file,
+ merges_file,
+ tokenizer_file=tokenizer_file,
+ errors=errors,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ add_prefix_space=add_prefix_space,
+ trim_offsets=trim_offsets,
+ cls_token_box=cls_token_box,
+ sep_token_box=sep_token_box,
+ pad_token_box=pad_token_box,
+ pad_token_label=pad_token_label,
+ only_label_first_subword=only_label_first_subword,
+ **kwargs,
+ )
+
+ tokenizer_component = "post_processor"
+ tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
+ if tokenizer_component_instance:
+ state = json.loads(tokenizer_component_instance.__getstate__())
+
+ # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
+ if "sep" in state:
+ state["sep"] = tuple(state["sep"])
+ if "cls" in state:
+ state["cls"] = tuple(state["cls"])
+
+ changes_to_apply = False
+
+ if state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
+ state["add_prefix_space"] = add_prefix_space
+ changes_to_apply = True
+
+ if state.get("trim_offsets", trim_offsets) != trim_offsets:
+ state["trim_offsets"] = trim_offsets
+ changes_to_apply = True
+
+ if changes_to_apply:
+ component_class = getattr(processors, state.pop("type"))
+ new_value = component_class(**state)
+ setattr(self.backend_tokenizer, tokenizer_component, new_value)
+
+ # additional properties
+ self.cls_token_box = cls_token_box
+ self.sep_token_box = sep_token_box
+ self.pad_token_box = pad_token_box
+ self.pad_token_label = pad_token_label
+ self.only_label_first_subword = only_label_first_subword
+
+ @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.__call__
+ def __call__(
+ self,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]],
+ text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
+ boxes: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
+ sequences with word-level normalized bounding boxes and optional labels.
+
+ Args:
+ text (`str`, `List[str]`, `List[List[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
+ (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
+ words).
+ text_pair (`List[str]`, `List[List[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
+ (pretokenized string).
+ boxes (`List[List[int]]`, `List[List[List[int]]]`):
+ Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
+ word_labels (`List[int]`, `List[List[int]]`, *optional*):
+ Word-level integer labels (for token classification tasks such as FUNSD, CORD).
+ """
+
+ # Input type checking for clearer error
+ def _is_valid_text_input(t):
+ if isinstance(t, str):
+ # Strings are fine
+ return True
+ elif isinstance(t, (list, tuple)):
+ # List are fine as long as they are...
+ if len(t) == 0:
+ # ... empty
+ return True
+ elif isinstance(t[0], str):
+ # ... list of strings
+ return True
+ elif isinstance(t[0], (list, tuple)):
+ # ... list with an empty list or with a list of strings
+ return len(t[0]) == 0 or isinstance(t[0][0], str)
+ else:
+ return False
+ else:
+ return False
+
+ if text_pair is not None:
+ # in case text + text_pair are provided, text = questions, text_pair = words
+ if not _is_valid_text_input(text):
+ raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
+ if not isinstance(text_pair, (list, tuple)):
+ raise ValueError(
+ "Words must be of type `List[str]` (single pretokenized example), "
+ "or `List[List[str]]` (batch of pretokenized examples)."
+ )
+ else:
+ # in case only text is provided => must be words
+ if not isinstance(text, (list, tuple)):
+ raise ValueError(
+ "Words must be of type `List[str]` (single pretokenized example), "
+ "or `List[List[str]]` (batch of pretokenized examples)."
+ )
+
+ if text_pair is not None:
+ is_batched = isinstance(text, (list, tuple))
+ else:
+ is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
+
+ words = text if text_pair is None else text_pair
+ if boxes is None:
+ raise ValueError("You must provide corresponding bounding boxes")
+ if is_batched:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide words and boxes for an equal amount of examples")
+ for words_example, boxes_example in zip(words, boxes):
+ if len(words_example) != len(boxes_example):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+ else:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+
+ if is_batched:
+ if text_pair is not None and len(text) != len(text_pair):
+ raise ValueError(
+ f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
+ f" {len(text_pair)}."
+ )
+ batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
+ is_pair = bool(text_pair is not None)
+ return self.batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+ else:
+ return self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.batch_encode_plus
+ def batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[list[int]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.tokenize
+ def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> list[str]:
+ batched_input = [(text, pair)] if pair else [text]
+ encodings = self._tokenizer.encode_batch(
+ batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
+ )
+
+ return encodings[0].tokens
+
+ @add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.encode_plus
+ def encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
+ `__call__` should be used instead.
+
+ Args:
+ text (`str`, `List[str]`, `List[List[str]]`):
+ The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
+ text_pair (`List[str]` or `List[int]`, *optional*):
+ Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
+ list of list of strings (words of a batch of examples).
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._encode_plus(
+ text=text,
+ boxes=boxes,
+ text_pair=text_pair,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def _batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[list[int]]]] = None,
+ word_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ ) -> BatchEncoding:
+ if not isinstance(batch_text_or_text_pairs, list):
+ raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})")
+
+ # Set the truncation and padding strategy and restore the initial configuration
+ self.set_truncation_and_padding(
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ )
+
+ if is_pair:
+ batch_text_or_text_pairs = [(text.split(), text_pair) for text, text_pair in batch_text_or_text_pairs]
+
+ encodings = self._tokenizer.encode_batch(
+ batch_text_or_text_pairs,
+ add_special_tokens=add_special_tokens,
+ is_pretokenized=True, # we set this to True as LayoutLMv3 always expects pretokenized inputs
+ )
+
+ # Convert encoding to dict
+ # `Tokens` has type: tuple[
+ # list[dict[str, list[list[int]]]] or list[dict[str, 2D-Tensor]],
+ # list[EncodingFast]
+ # ]
+ # with nested dimensions corresponding to batch, overflows, sequence length
+ tokens_and_encodings = [
+ self._convert_encoding(
+ encoding=encoding,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=True
+ if word_labels is not None
+ else return_offsets_mapping, # we use offsets to create the labels
+ return_length=return_length,
+ verbose=verbose,
+ )
+ for encoding in encodings
+ ]
+
+ # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
+ # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
+ # (we say ~ because the number of overflow varies with the example in the batch)
+ #
+ # To match each overflowing sample with the original sample in the batch
+ # we add an overflow_to_sample_mapping array (see below)
+ sanitized_tokens = {}
+ for key in tokens_and_encodings[0][0]:
+ stack = [e for item, _ in tokens_and_encodings for e in item[key]]
+ sanitized_tokens[key] = stack
+ sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
+
+ # If returning overflowing tokens, we need to return a mapping
+ # from the batch idx to the original sample
+ if return_overflowing_tokens:
+ overflow_to_sample_mapping = []
+ for i, (toks, _) in enumerate(tokens_and_encodings):
+ overflow_to_sample_mapping += [i] * len(toks["input_ids"])
+ sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
+
+ for input_ids in sanitized_tokens["input_ids"]:
+ self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
+
+ # create the token boxes
+ token_boxes = []
+ for batch_index in range(len(sanitized_tokens["input_ids"])):
+ if return_overflowing_tokens:
+ original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
+ else:
+ original_index = batch_index
+ token_boxes_example = []
+ for id, sequence_id, word_id in zip(
+ sanitized_tokens["input_ids"][batch_index],
+ sanitized_encodings[batch_index].sequence_ids,
+ sanitized_encodings[batch_index].word_ids,
+ ):
+ if word_id is not None:
+ if is_pair and sequence_id == 0:
+ token_boxes_example.append(self.pad_token_box)
+ else:
+ token_boxes_example.append(boxes[original_index][word_id])
+ else:
+ if id == self.cls_token_id:
+ token_boxes_example.append(self.cls_token_box)
+ elif id == self.sep_token_id:
+ token_boxes_example.append(self.sep_token_box)
+ elif id == self.pad_token_id:
+ token_boxes_example.append(self.pad_token_box)
+ else:
+ raise ValueError("Id not recognized")
+ token_boxes.append(token_boxes_example)
+
+ sanitized_tokens["bbox"] = token_boxes
+
+ # optionally, create the labels
+ if word_labels is not None:
+ labels = []
+ for batch_index in range(len(sanitized_tokens["input_ids"])):
+ if return_overflowing_tokens:
+ original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
+ else:
+ original_index = batch_index
+ labels_example = []
+ previous_token_empty = False
+ for id, offset, word_id in zip(
+ sanitized_tokens["input_ids"][batch_index],
+ sanitized_tokens["offset_mapping"][batch_index],
+ sanitized_encodings[batch_index].word_ids,
+ ):
+ if word_id is not None:
+ if self.only_label_first_subword:
+ if offset[0] == 0 and not previous_token_empty:
+ # Use the real label id for the first token of the word, and padding ids for the remaining tokens
+ labels_example.append(word_labels[original_index][word_id])
+ else:
+ labels_example.append(self.pad_token_label)
+ if offset == (0, 0):
+ previous_token_empty = True
+ else:
+ previous_token_empty = False
+ else:
+ labels_example.append(word_labels[original_index][word_id])
+ else:
+ labels_example.append(self.pad_token_label)
+ labels.append(labels_example)
+
+ sanitized_tokens["labels"] = labels
+ # finally, remove offsets if the user didn't want them
+ if not return_offsets_mapping:
+ del sanitized_tokens["offset_mapping"]
+
+ return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
+
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast._encode_plus
+ def _encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[bool] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ # make it a batched input
+ # 2 options:
+ # 1) only text, in case text must be a list of str
+ # 2) text + text_pair, in which case text = str and text_pair a list of str
+ batched_input = [(text, text_pair)] if text_pair else [text]
+ batched_boxes = [boxes]
+ batched_word_labels = [word_labels] if word_labels is not None else None
+ batched_output = self._batch_encode_plus(
+ batched_input,
+ is_pair=bool(text_pair is not None),
+ boxes=batched_boxes,
+ word_labels=batched_word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ # Return tensor is None, then we can remove the leading batch axis
+ # Overflowing tokens are returned as a batch of output so we keep them in this case
+ if return_tensors is None and not return_overflowing_tokens:
+ batched_output = BatchEncoding(
+ {
+ key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
+ for key, value in batched_output.items()
+ },
+ batched_output.encodings,
+ )
+
+ self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
+
+ return batched_output
+
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast._pad
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ """
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+ Args:
+ encoded_inputs:
+ Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+ max_length: maximum length of the returned list and optionally padding length (see below).
+ Will truncate by taking into account the special tokens.
+ padding_strategy: PaddingStrategy to use for padding.
+
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
+ The tokenizer padding sides are defined in self.padding_side:
+
+ - 'left': pads on the left of the sequences
+ - 'right': pads on the right of the sequences
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta).
+ padding_side:
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask:
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+ """
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = len(required_input)
+
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+ # Initialize attention mask if not present.
+ if return_attention_mask and "attention_mask" not in encoded_inputs:
+ encoded_inputs["attention_mask"] = [1] * len(required_input)
+
+ if needs_to_be_padded:
+ difference = max_length - len(required_input)
+ padding_side = padding_side if padding_side is not None else self.padding_side
+ if padding_side == "right":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = (
+ encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
+ )
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
+ encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
+ elif padding_side == "left":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
+ "token_type_ids"
+ ]
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+ else:
+ raise ValueError("Invalid padding strategy:" + str(padding_side))
+
+ return encoded_inputs
+
+ # Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2_fast.LayoutLMv2TokenizerFast.save_vocabulary
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+ return tuple(files)
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
+ if token_ids_1 is None:
+ return output
+
+ return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Args:
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not:
+ make use of token type ids, therefore a list of zeros is returned.
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+
+__all__ = ["LayoutLMv3TokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f5a662e6afd2c83768e9731d896da83b0b89193
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .processing_layoutxlm import *
+ from .tokenization_layoutxlm import *
+ from .tokenization_layoutxlm_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/processing_layoutxlm.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/processing_layoutxlm.py
new file mode 100644
index 0000000000000000000000000000000000000000..c50b43576c1de96fbd5c835cb6caf30b5d4fad05
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/processing_layoutxlm.py
@@ -0,0 +1,203 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for LayoutXLM.
+"""
+
+import warnings
+from typing import Optional, Union
+
+from ...processing_utils import ProcessorMixin
+from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from ...utils import TensorType
+
+
+class LayoutXLMProcessor(ProcessorMixin):
+ r"""
+ Constructs a LayoutXLM processor which combines a LayoutXLM image processor and a LayoutXLM tokenizer into a single
+ processor.
+
+ [`LayoutXLMProcessor`] offers all the functionalities you need to prepare data for the model.
+
+ It first uses [`LayoutLMv2ImageProcessor`] to resize document images to a fixed size, and optionally applies OCR to
+ get words and normalized bounding boxes. These are then provided to [`LayoutXLMTokenizer`] or
+ [`LayoutXLMTokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`,
+ `attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned
+ into token-level `labels` for token classification tasks (such as FUNSD, CORD).
+
+ Args:
+ image_processor (`LayoutLMv2ImageProcessor`, *optional*):
+ An instance of [`LayoutLMv2ImageProcessor`]. The image processor is a required input.
+ tokenizer (`LayoutXLMTokenizer` or `LayoutXLMTokenizerFast`, *optional*):
+ An instance of [`LayoutXLMTokenizer`] or [`LayoutXLMTokenizerFast`]. The tokenizer is a required input.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "LayoutLMv2ImageProcessor"
+ tokenizer_class = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
+
+ def __init__(self, image_processor=None, tokenizer=None, **kwargs):
+ if "feature_extractor" in kwargs:
+ warnings.warn(
+ "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
+ " instead.",
+ FutureWarning,
+ )
+ feature_extractor = kwargs.pop("feature_extractor")
+
+ image_processor = image_processor if image_processor is not None else feature_extractor
+ if image_processor is None:
+ raise ValueError("You need to specify an `image_processor`.")
+ if tokenizer is None:
+ raise ValueError("You need to specify a `tokenizer`.")
+
+ super().__init__(image_processor, tokenizer)
+
+ def __call__(
+ self,
+ images,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+ text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
+ boxes: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ This method first forwards the `images` argument to [`~LayoutLMv2ImagePrpcessor.__call__`]. In case
+ [`LayoutLMv2ImagePrpcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
+ bounding boxes along with the additional arguments to [`~LayoutXLMTokenizer.__call__`] and returns the output,
+ together with resized `images`. In case [`LayoutLMv2ImagePrpcessor`] was initialized with `apply_ocr` set to
+ `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the additional
+ arguments to [`~LayoutXLMTokenizer.__call__`] and returns the output, together with resized `images``.
+
+ Please refer to the docstring of the above two methods for more information.
+ """
+ # verify input
+ if self.image_processor.apply_ocr and (boxes is not None):
+ raise ValueError(
+ "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."
+ )
+
+ if self.image_processor.apply_ocr and (word_labels is not None):
+ raise ValueError(
+ "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."
+ )
+
+ if return_overflowing_tokens is True and return_offsets_mapping is False:
+ raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")
+
+ # first, apply the image processor
+ features = self.image_processor(images=images, return_tensors=return_tensors)
+
+ # second, apply the tokenizer
+ if text is not None and self.image_processor.apply_ocr and text_pair is None:
+ if isinstance(text, str):
+ text = [text] # add batch dimension (as the image processor always adds a batch dimension)
+ text_pair = features["words"]
+
+ encoded_inputs = self.tokenizer(
+ text=text if text is not None else features["words"],
+ text_pair=text_pair if text_pair is not None else None,
+ boxes=boxes if boxes is not None else features["boxes"],
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ return_tensors=return_tensors,
+ **kwargs,
+ )
+
+ # add pixel values
+ images = features.pop("pixel_values")
+ if return_overflowing_tokens is True:
+ images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"])
+ encoded_inputs["image"] = images
+
+ return encoded_inputs
+
+ def get_overflowing_images(self, images, overflow_to_sample_mapping):
+ # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
+ images_with_overflow = []
+ for sample_idx in overflow_to_sample_mapping:
+ images_with_overflow.append(images[sample_idx])
+
+ if len(images_with_overflow) != len(overflow_to_sample_mapping):
+ raise ValueError(
+ "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
+ f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
+ )
+
+ return images_with_overflow
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
+ to the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ def model_input_names(self):
+ return ["input_ids", "bbox", "attention_mask", "image"]
+
+ @property
+ def feature_extractor_class(self):
+ warnings.warn(
+ "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",
+ FutureWarning,
+ )
+ return self.image_processor_class
+
+ @property
+ def feature_extractor(self):
+ warnings.warn(
+ "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
+ FutureWarning,
+ )
+ return self.image_processor
+
+
+__all__ = ["LayoutXLMProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/tokenization_layoutxlm.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/tokenization_layoutxlm.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dbe3c21a6365b5a20988626cc57520ea37d5fc3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/tokenization_layoutxlm.py
@@ -0,0 +1,1191 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
+"""Tokenization classes for LayoutXLM model."""
+
+import os
+from shutil import copyfile
+from typing import Any, Optional, Union
+
+import sentencepiece as spm
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...tokenization_utils_base import (
+ BatchEncoding,
+ EncodedInput,
+ PreTokenizedInput,
+ TextInput,
+ TextInputPair,
+ TruncationStrategy,
+)
+from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging
+from ...utils.import_utils import requires
+from ..xlm_roberta.tokenization_xlm_roberta import (
+ SPIECE_UNDERLINE,
+ VOCAB_FILES_NAMES,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+LAYOUTXLM_ENCODE_KWARGS_DOCSTRING = r"""
+ add_special_tokens (`bool`, *optional*, defaults to `True`):
+ Whether or not to encode the sequences with the special tokens relative to their model.
+ padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
+ Activates and controls padding. Accepts the following values:
+
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+ Activates and controls truncation. Accepts the following values:
+
+ - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
+ to the maximum acceptable input length for the model if that argument is not provided. This will
+ truncate token by token, removing a token from the longest sequence in the pair if a pair of
+ sequences (or a batch of pairs) is provided.
+ - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
+ greater than the model maximum admissible input size).
+ max_length (`int`, *optional*):
+ Controls the maximum length to use by one of the truncation/padding parameters.
+
+ If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
+ is required by one of the truncation/padding parameters. If the model has no specific maximum input
+ length (like XLNet) truncation/padding to a maximum length will be deactivated.
+ stride (`int`, *optional*, defaults to 0):
+ If set to a number along with `max_length`, the overflowing tokens returned when
+ `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
+ returned to provide some overlap between truncated and overflowing sequences. The value of this
+ argument defines the number of overlapping tokens.
+ pad_to_multiple_of (`int`, *optional*):
+ If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
+ the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
+ return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+ If set, will return tensors instead of list of python integers. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return Numpy `np.ndarray` objects.
+ return_token_type_ids (`bool`, *optional*):
+ Whether to return token type IDs. If left to the default, will return the token type IDs according to
+ the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ return_attention_mask (`bool`, *optional*):
+ Whether to return the attention mask. If left to the default, will return the attention mask according
+ to the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+ [What are attention masks?](../glossary#attention-mask)
+ return_overflowing_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
+ of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
+ of returning overflowing tokens.
+ return_special_tokens_mask (`bool`, *optional*, defaults to `False`):
+ Whether or not to return special tokens mask information.
+ return_offsets_mapping (`bool`, *optional*, defaults to `False`):
+ Whether or not to return `(char_start, char_end)` for each token.
+
+ This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using
+ Python's tokenizer, this method will raise `NotImplementedError`.
+ return_length (`bool`, *optional*, defaults to `False`):
+ Whether or not to return the lengths of the encoded inputs.
+ verbose (`bool`, *optional*, defaults to `True`):
+ Whether or not to print more information and warnings.
+ **kwargs: passed to the `self.tokenize()` method
+
+ Return:
+ [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ - **bbox** -- List of bounding boxes to be fed to a model.
+
+ - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or
+ if *"token_type_ids"* is in `self.model_input_names`).
+
+ [What are token type IDs?](../glossary#token-type-ids)
+
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ - **labels** -- List of labels to be fed to a model. (when `word_labels` is specified).
+ - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
+ `return_overflowing_tokens=True`).
+ - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
+ `return_overflowing_tokens=True`).
+ - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
+ regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
+ - **length** -- The length of the inputs (when `return_length=True`).
+"""
+
+
+@requires(backends=("sentencepiece",))
+class LayoutXLMTokenizer(PreTrainedTokenizer):
+ """
+ Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
+ [SentencePiece](https://github.com/google/sentencepiece).
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ cls_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [CLS] token.
+ sep_token_box (`list[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`):
+ The bounding box to use for the special [SEP] token.
+ pad_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [PAD] token.
+ pad_token_label (`int`, *optional*, defaults to -100):
+ The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
+ CrossEntropyLoss.
+ only_label_first_subword (`bool`, *optional*, defaults to `True`):
+ Whether or not to only label the first subword, in case word labels are provided.
+ sp_model_kwargs (`dict`, *optional*):
+ Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+ SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+ to set:
+
+ - `enable_sampling`: Enable subword regularization.
+ - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+ - `nbest_size = {0,1}`: No sampling is performed.
+ - `nbest_size > 1`: samples from the nbest_size results.
+ - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+ using forward-filtering-and-backward-sampling algorithm.
+
+ - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+ BPE-dropout.
+
+ Attributes:
+ sp_model (`SentencePieceProcessor`):
+ The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ def __init__(
+ self,
+ vocab_file,
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ cls_token_box=[0, 0, 0, 0],
+ sep_token_box=[1000, 1000, 1000, 1000],
+ pad_token_box=[0, 0, 0, 0],
+ pad_token_label=-100,
+ only_label_first_subword=True,
+ sp_model_kwargs: Optional[dict[str, Any]] = None,
+ **kwargs,
+ ) -> None:
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
+
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.Load(str(vocab_file))
+ self.vocab_file = vocab_file
+
+ # Original fairseq vocab and spm vocab must be "aligned":
+ # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
+ # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
+ # fairseq | '' | '' | ' ' | '' | ',' | '.' | '▁' | 's' | '▁de' | '-'
+ # spm | '' | '' | ' ' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
+
+ # Mimic fairseq token-to-id alignment for the first 4 token
+ self.fairseq_tokens_to_ids = {"": 0, "": 1, " ": 2, "": 3}
+
+ # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
+ self.fairseq_offset = 1
+
+ self.fairseq_tokens_to_ids[""] = len(self.sp_model) + self.fairseq_offset
+ self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
+
+ # additional properties
+ self.cls_token_box = cls_token_box
+ self.sep_token_box = sep_token_box
+ self.pad_token_box = pad_token_box
+ self.pad_token_label = pad_token_label
+ self.only_label_first_subword = only_label_first_subword
+
+ super().__init__(
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ cls_token_box=cls_token_box,
+ sep_token_box=sep_token_box,
+ pad_token_box=pad_token_box,
+ pad_token_label=pad_token_label,
+ only_label_first_subword=only_label_first_subword,
+ sp_model_kwargs=self.sp_model_kwargs,
+ **kwargs,
+ )
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+ state["sp_model"] = None
+ state["sp_model_proto"] = self.sp_model.serialized_model_proto()
+ return state
+
+ def __setstate__(self, d):
+ self.__dict__.update(d)
+
+ # for backward compatibility
+ if not hasattr(self, "sp_model_kwargs"):
+ self.sp_model_kwargs = {}
+
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. An XLM-RoBERTa sequence has the following format:
+
+ - single sequence: ` X `
+ - pair of sequences: ` A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is None:
+ return [1] + ([0] * len(token_ids_0)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does
+ not make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+
+ """
+
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ @property
+ def vocab_size(self):
+ return len(self.sp_model) + self.fairseq_offset + 1 # Add the token
+
+ def get_vocab(self):
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ def _tokenize(self, text: str) -> list[str]:
+ return self.sp_model.encode(text, out_type=str)
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ if token in self.fairseq_tokens_to_ids:
+ return self.fairseq_tokens_to_ids[token]
+ spm_id = self.sp_model.PieceToId(token)
+
+ # Need to return unknown token if the SP model returned 0
+ return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ if index in self.fairseq_ids_to_tokens:
+ return self.fairseq_ids_to_tokens[index]
+ return self.sp_model.IdToPiece(index - self.fairseq_offset)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (strings for sub-words) in a single string."""
+ out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
+ return out_string
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+ elif not os.path.isfile(self.vocab_file):
+ with open(out_vocab_file, "wb") as fi:
+ content_spiece_model = self.sp_model.serialized_model_proto()
+ fi.write(content_spiece_model)
+
+ return (out_vocab_file,)
+
+ @add_end_docstrings(LAYOUTXLM_ENCODE_KWARGS_DOCSTRING)
+ def __call__(
+ self,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]],
+ text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
+ boxes: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
+ sequences with word-level normalized bounding boxes and optional labels.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
+ (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
+ words).
+ text_pair (`list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
+ (pretokenized string).
+ boxes (`list[list[int]]`, `list[list[list[int]]]`):
+ Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
+ word_labels (`list[int]`, `list[list[int]]`, *optional*):
+ Word-level integer labels (for token classification tasks such as FUNSD, CORD).
+ """
+
+ # Input type checking for clearer error
+ def _is_valid_text_input(t):
+ if isinstance(t, str):
+ # Strings are fine
+ return True
+ elif isinstance(t, (list, tuple)):
+ # List are fine as long as they are...
+ if len(t) == 0:
+ # ... empty
+ return True
+ elif isinstance(t[0], str):
+ # ... list of strings
+ return True
+ elif isinstance(t[0], (list, tuple)):
+ # ... list with an empty list or with a list of strings
+ return len(t[0]) == 0 or isinstance(t[0][0], str)
+ else:
+ return False
+ else:
+ return False
+
+ if text_pair is not None:
+ # in case text + text_pair are provided, text = questions, text_pair = words
+ if not _is_valid_text_input(text):
+ raise ValueError("text input must of type `str` (single example) or `list[str]` (batch of examples). ")
+ if not isinstance(text_pair, (list, tuple)):
+ raise ValueError(
+ "words must of type `list[str]` (single pretokenized example), "
+ "or `list[list[str]]` (batch of pretokenized examples)."
+ )
+ else:
+ # in case only text is provided => must be words
+ if not isinstance(text, (list, tuple)):
+ raise ValueError(
+ "Words must of type `list[str]` (single pretokenized example), "
+ "or `list[list[str]]` (batch of pretokenized examples)."
+ )
+
+ if text_pair is not None:
+ is_batched = isinstance(text, (list, tuple))
+ else:
+ is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
+
+ words = text if text_pair is None else text_pair
+ if boxes is None:
+ raise ValueError("You must provide corresponding bounding boxes")
+ if is_batched:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide words and boxes for an equal amount of examples")
+ for words_example, boxes_example in zip(words, boxes):
+ if len(words_example) != len(boxes_example):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+ else:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+
+ if is_batched:
+ if text_pair is not None and len(text) != len(text_pair):
+ raise ValueError(
+ f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
+ f" {len(text_pair)}."
+ )
+ batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
+ is_pair = bool(text_pair is not None)
+ return self.batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+ else:
+ return self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def _batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[list[int]]]] = None,
+ word_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast."
+ )
+
+ batch_outputs = self._batch_prepare_for_model(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=return_tensors,
+ verbose=verbose,
+ )
+
+ return BatchEncoding(batch_outputs)
+
+ @add_end_docstrings(LAYOUTXLM_ENCODE_KWARGS_DOCSTRING)
+ def _batch_prepare_for_model(
+ self,
+ batch_text_or_text_pairs,
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
+ adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
+ manages a moving window (with user defined stride) for overflowing tokens
+
+ Args:
+ batch_ids_pairs: list of tokenized input ids or input ids pairs
+ """
+
+ batch_outputs = {}
+ for idx, example in enumerate(zip(batch_text_or_text_pairs, boxes)):
+ batch_text_or_text_pair, boxes_example = example
+ outputs = self.prepare_for_model(
+ batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair,
+ batch_text_or_text_pair[1] if is_pair else None,
+ boxes_example,
+ word_labels=word_labels[idx] if word_labels is not None else None,
+ add_special_tokens=add_special_tokens,
+ padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=None, # we pad in batch afterward
+ padding_side=None, # we pad in batch afterward
+ return_attention_mask=False, # we pad in batch afterward
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=None, # We convert the whole batch to tensors at the end
+ prepend_batch_axis=False,
+ verbose=verbose,
+ )
+
+ for key, value in outputs.items():
+ if key not in batch_outputs:
+ batch_outputs[key] = []
+ batch_outputs[key].append(value)
+
+ batch_outputs = self.pad(
+ batch_outputs,
+ padding=padding_strategy.value,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
+
+ return batch_outputs
+
+ def _encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast. "
+ "More information on available tokenizers at "
+ "https://github.com/huggingface/transformers/pull/2674"
+ )
+
+ return self.prepare_for_model(
+ text=text,
+ text_pair=text_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding_strategy.value,
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ prepend_batch_axis=True,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ verbose=verbose,
+ )
+
+ @add_end_docstrings(LAYOUTXLM_ENCODE_KWARGS_DOCSTRING)
+ def prepare_for_model(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ prepend_batch_axis: bool = False,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
+ truncates sequences if overflowing while taking into account the special tokens and manages a moving window
+ (with user defined stride) for overflowing tokens.
+
+ Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into
+ token-level `labels`. The word label is used for the first token of the word, while remaining tokens are
+ labeled with -100, such that they will be ignored by the loss function.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
+ text_pair (`list[str]` or `list[int]`, *optional*):
+ Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
+ list of list of strings (words of a batch of examples).
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ tokens = []
+ pair_tokens = []
+ token_boxes = []
+ pair_token_boxes = []
+ labels = []
+
+ if text_pair is None:
+ if word_labels is None:
+ # CASE 1: document image classification (training + inference) + CASE 2: token classification (inference)
+ for word, box in zip(text, boxes):
+ if len(word) < 1: # skip empty words
+ continue
+ word_tokens = self.tokenize(word)
+ tokens.extend(word_tokens)
+ token_boxes.extend([box] * len(word_tokens))
+ else:
+ # CASE 2: token classification (training)
+ for word, box, label in zip(text, boxes, word_labels):
+ if len(word) < 1: # skip empty words
+ continue
+ word_tokens = self.tokenize(word)
+ tokens.extend(word_tokens)
+ token_boxes.extend([box] * len(word_tokens))
+ if self.only_label_first_subword:
+ # Use the real label id for the first token of the word, and padding ids for the remaining tokens
+ labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
+ else:
+ labels.extend([label] * len(word_tokens))
+ else:
+ # CASE 3: document visual question answering (inference)
+ # text = question
+ # text_pair = words
+ tokens = self.tokenize(text)
+ token_boxes = [self.pad_token_box for _ in range(len(tokens))] + [self.sep_token_box]
+
+ for word, box in zip(text_pair, boxes):
+ if len(word) < 1: # skip empty words
+ continue
+ word_tokens = self.tokenize(word)
+ pair_tokens.extend(word_tokens)
+ pair_token_boxes.extend([box] * len(word_tokens))
+
+ # Create ids + pair_ids
+ ids = self.convert_tokens_to_ids(tokens)
+ pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None
+
+ # Compute the total size of the returned encodings
+ pair = bool(pair_ids is not None)
+ len_ids = len(ids)
+ len_pair_ids = len(pair_ids) if pair else 0
+ total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
+
+ # Truncation: Handle max sequence length
+ overflowing_tokens = []
+ overflowing_token_boxes = []
+ overflowing_labels = []
+ if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
+ (
+ ids,
+ token_boxes,
+ pair_ids,
+ pair_token_boxes,
+ labels,
+ overflowing_tokens,
+ overflowing_token_boxes,
+ overflowing_labels,
+ ) = self.truncate_sequences(
+ ids,
+ token_boxes,
+ pair_ids=pair_ids,
+ pair_token_boxes=pair_token_boxes,
+ labels=labels,
+ num_tokens_to_remove=total_len - max_length,
+ truncation_strategy=truncation_strategy,
+ stride=stride,
+ )
+
+ if return_token_type_ids and not add_special_tokens:
+ raise ValueError(
+ "Asking to return token_type_ids while setting add_special_tokens to False "
+ "results in an undefined behavior. Please set add_special_tokens to True or "
+ "set return_token_type_ids to None."
+ )
+
+ # Load from model defaults
+ if return_token_type_ids is None:
+ return_token_type_ids = "token_type_ids" in self.model_input_names
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ encoded_inputs = {}
+
+ if return_overflowing_tokens:
+ encoded_inputs["overflowing_tokens"] = overflowing_tokens
+ encoded_inputs["overflowing_token_boxes"] = overflowing_token_boxes
+ encoded_inputs["overflowing_labels"] = overflowing_labels
+ encoded_inputs["num_truncated_tokens"] = total_len - max_length
+
+ # Add special tokens
+ if add_special_tokens:
+ sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
+ token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
+ token_boxes = [self.cls_token_box] + token_boxes + [self.sep_token_box]
+ if pair_token_boxes:
+ pair_token_boxes = pair_token_boxes + [self.sep_token_box]
+ if labels:
+ labels = [self.pad_token_label] + labels + [self.pad_token_label]
+ else:
+ sequence = ids + pair_ids if pair else ids
+ token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
+
+ # Build output dictionary
+ encoded_inputs["input_ids"] = sequence
+ encoded_inputs["bbox"] = token_boxes + pair_token_boxes
+ if return_token_type_ids:
+ encoded_inputs["token_type_ids"] = token_type_ids
+ if return_special_tokens_mask:
+ if add_special_tokens:
+ encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
+ else:
+ encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
+
+ if labels:
+ encoded_inputs["labels"] = labels
+
+ # Check lengths
+ self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
+
+ # Padding
+ if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
+ encoded_inputs = self.pad(
+ encoded_inputs,
+ max_length=max_length,
+ padding=padding_strategy.value,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ if return_length:
+ encoded_inputs["length"] = len(encoded_inputs["input_ids"])
+
+ batch_outputs = BatchEncoding(
+ encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
+ )
+
+ return batch_outputs
+
+ def truncate_sequences(
+ self,
+ ids: list[int],
+ token_boxes: list[list[int]],
+ pair_ids: Optional[list[int]] = None,
+ pair_token_boxes: Optional[list[list[int]]] = None,
+ labels: Optional[list[int]] = None,
+ num_tokens_to_remove: int = 0,
+ truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
+ stride: int = 0,
+ ) -> tuple[list[int], list[int], list[int]]:
+ """
+ Truncates a sequence pair in-place following the strategy.
+
+ Args:
+ ids (`list[int]`):
+ Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
+ `convert_tokens_to_ids` methods.
+ token_boxes (`list[list[int]]`):
+ Bounding boxes of the first sequence.
+ pair_ids (`list[int]`, *optional*):
+ Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
+ and `convert_tokens_to_ids` methods.
+ pair_token_boxes (`list[list[int]]`, *optional*):
+ Bounding boxes of the second sequence.
+ labels (`list[int]`, *optional*):
+ Labels of the first sequence (for token classification tasks).
+ num_tokens_to_remove (`int`, *optional*, defaults to 0):
+ Number of tokens to remove using the truncation strategy.
+ truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+ The strategy to follow for truncation. Can be:
+
+ - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will truncate
+ token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
+ batch of pairs) is provided.
+ - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
+ than the model maximum admissible input size).
+ stride (`int`, *optional*, defaults to 0):
+ If set to a positive number, the overflowing tokens returned will contain some tokens from the main
+ sequence returned. The value of this argument defines the number of additional tokens.
+
+ Returns:
+ `tuple[list[int], list[int], list[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
+ overflowing tokens.
+ """
+ if num_tokens_to_remove <= 0:
+ return ids, token_boxes, pair_ids, pair_token_boxes, labels, [], [], []
+
+ if not isinstance(truncation_strategy, TruncationStrategy):
+ truncation_strategy = TruncationStrategy(truncation_strategy)
+
+ overflowing_tokens = []
+ overflowing_token_boxes = []
+ overflowing_labels = []
+ if truncation_strategy == TruncationStrategy.LONGEST_FIRST:
+ for _ in range(num_tokens_to_remove):
+ if pair_ids is None or len(ids) > len(pair_ids):
+ if not overflowing_tokens:
+ window_len = min(len(ids), stride + 1)
+ else:
+ window_len = 1
+ overflowing_tokens.extend(ids[-window_len:])
+ overflowing_token_boxes.extend(token_boxes[-window_len:])
+ overflowing_labels.extend(labels[-window_len:])
+ ids = ids[:-1]
+ token_boxes = token_boxes[:-1]
+ labels = labels[:-1]
+ else:
+ if not overflowing_tokens:
+ window_len = min(len(pair_ids), stride + 1)
+ else:
+ window_len = 1
+ overflowing_tokens.extend(pair_ids[-window_len:])
+ overflowing_token_boxes.extend(pair_token_boxes[-window_len:])
+ pair_ids = pair_ids[:-1]
+ pair_token_boxes = pair_token_boxes[:-1]
+ elif truncation_strategy == TruncationStrategy.ONLY_FIRST:
+ if len(ids) > num_tokens_to_remove:
+ window_len = min(len(ids), stride + num_tokens_to_remove)
+ overflowing_tokens = ids[-window_len:]
+ overflowing_token_boxes = token_boxes[-window_len:]
+ overflowing_labels = labels[-window_len:]
+ ids = ids[:-num_tokens_to_remove]
+ token_boxes = token_boxes[:-num_tokens_to_remove]
+ labels = labels[:-num_tokens_to_remove]
+ else:
+ logger.error(
+ f"We need to remove {num_tokens_to_remove} to truncate the input "
+ f"but the first sequence has a length {len(ids)}. "
+ f"Please select another truncation strategy than {truncation_strategy}, "
+ "for instance 'longest_first' or 'only_second'."
+ )
+ elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
+ if len(pair_ids) > num_tokens_to_remove:
+ window_len = min(len(pair_ids), stride + num_tokens_to_remove)
+ overflowing_tokens = pair_ids[-window_len:]
+ overflowing_token_boxes = pair_token_boxes[-window_len:]
+ pair_ids = pair_ids[:-num_tokens_to_remove]
+ pair_token_boxes = pair_token_boxes[:-num_tokens_to_remove]
+ else:
+ logger.error(
+ f"We need to remove {num_tokens_to_remove} to truncate the input "
+ f"but the second sequence has a length {len(pair_ids)}. "
+ f"Please select another truncation strategy than {truncation_strategy}, "
+ "for instance 'longest_first' or 'only_first'."
+ )
+
+ return (
+ ids,
+ token_boxes,
+ pair_ids,
+ pair_token_boxes,
+ labels,
+ overflowing_tokens,
+ overflowing_token_boxes,
+ overflowing_labels,
+ )
+
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ """
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+ Args:
+ encoded_inputs:
+ Dictionary of tokenized inputs (`list[int]`) or batch of tokenized inputs (`list[list[int]]`).
+ max_length: maximum length of the returned list and optionally padding length (see below).
+ Will truncate by taking into account the special tokens.
+ padding_strategy: PaddingStrategy to use for padding.
+
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
+ The tokenizer padding sides are defined in self.padding_side:
+
+ - 'left': pads on the left of the sequences
+ - 'right': pads on the right of the sequences
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta).
+ padding_side (`str`, *optional*):
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask:
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+ """
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = len(required_input)
+
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+ # Initialize attention mask if not present.
+ if return_attention_mask and "attention_mask" not in encoded_inputs:
+ encoded_inputs["attention_mask"] = [1] * len(required_input)
+
+ if needs_to_be_padded:
+ difference = max_length - len(required_input)
+ padding_side = padding_side if padding_side is not None else self.padding_side
+ if padding_side == "right":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = (
+ encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
+ )
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
+ encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
+ elif padding_side == "left":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
+ "token_type_ids"
+ ]
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+ else:
+ raise ValueError("Invalid padding strategy:" + str(padding_side))
+
+ return encoded_inputs
+
+
+__all__ = ["LayoutXLMTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/tokenization_layoutxlm_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/tokenization_layoutxlm_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..6710c6c8cb66ed08da2df391c28ad1db2e6cf81d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/layoutxlm/tokenization_layoutxlm_fast.py
@@ -0,0 +1,815 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
+"""Tokenization classes for LayoutXLM model."""
+
+import os
+from shutil import copyfile
+from typing import Optional, Union
+
+from ...tokenization_utils import AddedToken
+from ...tokenization_utils_base import (
+ BatchEncoding,
+ EncodedInput,
+ PreTokenizedInput,
+ TextInput,
+ TextInputPair,
+ TruncationStrategy,
+)
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import PaddingStrategy, TensorType, add_end_docstrings, is_sentencepiece_available, logging
+from ..xlm_roberta.tokenization_xlm_roberta_fast import (
+ VOCAB_FILES_NAMES,
+)
+
+
+if is_sentencepiece_available():
+ from .tokenization_layoutxlm import LayoutXLMTokenizer
+else:
+ LayoutXLMTokenizer = None
+
+
+logger = logging.get_logger(__name__)
+
+LAYOUTXLM_ENCODE_KWARGS_DOCSTRING = r"""
+ add_special_tokens (`bool`, *optional*, defaults to `True`):
+ Whether or not to encode the sequences with the special tokens relative to their model.
+ padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
+ Activates and controls padding. Accepts the following values:
+
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+ Activates and controls truncation. Accepts the following values:
+
+ - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
+ to the maximum acceptable input length for the model if that argument is not provided. This will
+ truncate token by token, removing a token from the longest sequence in the pair if a pair of
+ sequences (or a batch of pairs) is provided.
+ - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
+ greater than the model maximum admissible input size).
+ max_length (`int`, *optional*):
+ Controls the maximum length to use by one of the truncation/padding parameters.
+
+ If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
+ is required by one of the truncation/padding parameters. If the model has no specific maximum input
+ length (like XLNet) truncation/padding to a maximum length will be deactivated.
+ stride (`int`, *optional*, defaults to 0):
+ If set to a number along with `max_length`, the overflowing tokens returned when
+ `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
+ returned to provide some overlap between truncated and overflowing sequences. The value of this
+ argument defines the number of overlapping tokens.
+ pad_to_multiple_of (`int`, *optional*):
+ If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
+ the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
+ return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+ If set, will return tensors instead of list of python integers. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return Numpy `np.ndarray` objects.
+ return_token_type_ids (`bool`, *optional*):
+ Whether to return token type IDs. If left to the default, will return the token type IDs according to
+ the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ return_attention_mask (`bool`, *optional*):
+ Whether to return the attention mask. If left to the default, will return the attention mask according
+ to the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+ [What are attention masks?](../glossary#attention-mask)
+ return_overflowing_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
+ of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
+ of returning overflowing tokens.
+ return_special_tokens_mask (`bool`, *optional*, defaults to `False`):
+ Whether or not to return special tokens mask information.
+ return_offsets_mapping (`bool`, *optional*, defaults to `False`):
+ Whether or not to return `(char_start, char_end)` for each token.
+
+ This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using
+ Python's tokenizer, this method will raise `NotImplementedError`.
+ return_length (`bool`, *optional*, defaults to `False`):
+ Whether or not to return the lengths of the encoded inputs.
+ verbose (`bool`, *optional*, defaults to `True`):
+ Whether or not to print more information and warnings.
+ **kwargs: passed to the `self.tokenize()` method
+
+ Return:
+ [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ - **bbox** -- List of bounding boxes to be fed to a model.
+
+ - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or
+ if *"token_type_ids"* is in `self.model_input_names`).
+
+ [What are token type IDs?](../glossary#token-type-ids)
+
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ - **labels** -- List of labels to be fed to a model. (when `word_labels` is specified).
+ - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
+ `return_overflowing_tokens=True`).
+ - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
+ `return_overflowing_tokens=True`).
+ - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
+ regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
+ - **length** -- The length of the inputs (when `return_length=True`).
+"""
+
+
+class LayoutXLMTokenizerFast(PreTrainedTokenizerFast):
+ """
+ Construct a "fast" LayoutXLM tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
+ [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
+ [BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ cls_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [CLS] token.
+ sep_token_box (`list[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`):
+ The bounding box to use for the special [SEP] token.
+ pad_token_box (`list[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+ The bounding box to use for the special [PAD] token.
+ pad_token_label (`int`, *optional*, defaults to -100):
+ The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
+ CrossEntropyLoss.
+ only_label_first_subword (`bool`, *optional*, defaults to `True`):
+ Whether or not to only label the first subword, in case word labels are provided.
+ additional_special_tokens (`list[str]`, *optional*, defaults to `["NOTUSED", " NOTUSED"]`):
+ Additional special tokens used by the tokenizer.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+ slow_tokenizer_class = LayoutXLMTokenizer
+
+ def __init__(
+ self,
+ vocab_file=None,
+ tokenizer_file=None,
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ cls_token_box=[0, 0, 0, 0],
+ sep_token_box=[1000, 1000, 1000, 1000],
+ pad_token_box=[0, 0, 0, 0],
+ pad_token_label=-100,
+ only_label_first_subword=True,
+ **kwargs,
+ ):
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+ super().__init__(
+ vocab_file,
+ tokenizer_file=tokenizer_file,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ cls_token_box=cls_token_box,
+ sep_token_box=sep_token_box,
+ pad_token_box=pad_token_box,
+ pad_token_label=pad_token_label,
+ only_label_first_subword=only_label_first_subword,
+ **kwargs,
+ )
+
+ self.vocab_file = vocab_file
+
+ # additional properties
+ self.cls_token_box = cls_token_box
+ self.sep_token_box = sep_token_box
+ self.pad_token_box = pad_token_box
+ self.pad_token_label = pad_token_label
+ self.only_label_first_subword = only_label_first_subword
+
+ @add_end_docstrings(LAYOUTXLM_ENCODE_KWARGS_DOCSTRING)
+ def __call__(
+ self,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]],
+ text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
+ boxes: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
+ word_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
+ sequences with word-level normalized bounding boxes and optional labels.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
+ (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
+ words).
+ text_pair (`list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
+ (pretokenized string).
+ boxes (`list[list[int]]`, `list[list[list[int]]]`):
+ Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
+ word_labels (`list[int]`, `list[list[int]]`, *optional*):
+ Word-level integer labels (for token classification tasks such as FUNSD, CORD).
+ """
+
+ # Input type checking for clearer error
+ def _is_valid_text_input(t):
+ if isinstance(t, str):
+ # Strings are fine
+ return True
+ elif isinstance(t, (list, tuple)):
+ # List are fine as long as they are...
+ if len(t) == 0:
+ # ... empty
+ return True
+ elif isinstance(t[0], str):
+ # ... list of strings
+ return True
+ elif isinstance(t[0], (list, tuple)):
+ # ... list with an empty list or with a list of strings
+ return len(t[0]) == 0 or isinstance(t[0][0], str)
+ else:
+ return False
+ else:
+ return False
+
+ if text_pair is not None:
+ # in case text + text_pair are provided, text = questions, text_pair = words
+ if not _is_valid_text_input(text):
+ raise ValueError("text input must of type `str` (single example) or `list[str]` (batch of examples). ")
+ if not isinstance(text_pair, (list, tuple)):
+ raise ValueError(
+ "words must of type `list[str]` (single pretokenized example), "
+ "or `list[list[str]]` (batch of pretokenized examples)."
+ )
+ else:
+ # in case only text is provided => must be words
+ if not isinstance(text, (list, tuple)):
+ raise ValueError(
+ "Words must of type `list[str]` (single pretokenized example), "
+ "or `list[list[str]]` (batch of pretokenized examples)."
+ )
+
+ if text_pair is not None:
+ is_batched = isinstance(text, (list, tuple))
+ else:
+ is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
+
+ words = text if text_pair is None else text_pair
+ if boxes is None:
+ raise ValueError("You must provide corresponding bounding boxes")
+ if is_batched:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide words and boxes for an equal amount of examples")
+ for words_example, boxes_example in zip(words, boxes):
+ if len(words_example) != len(boxes_example):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+ else:
+ if len(words) != len(boxes):
+ raise ValueError("You must provide as many words as there are bounding boxes")
+
+ if is_batched:
+ if text_pair is not None and len(text) != len(text_pair):
+ raise ValueError(
+ f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
+ f" {len(text_pair)}."
+ )
+ batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
+ is_pair = bool(text_pair is not None)
+ return self.batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+ else:
+ return self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ boxes=boxes,
+ word_labels=word_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> list[str]:
+ batched_input = [(text, pair)] if pair else [text]
+
+ self._tokenizer.encode_special_tokens = kwargs.pop(
+ "split_special_tokens", self._tokenizer.encode_special_tokens
+ )
+
+ encodings = self._tokenizer.encode_batch(
+ batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
+ )
+
+ return encodings[0].tokens
+
+ def _batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ boxes: Optional[list[list[list[int]]]] = None,
+ word_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if not isinstance(batch_text_or_text_pairs, list):
+ raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})")
+
+ # Set the truncation and padding strategy and restore the initial configuration
+ self.set_truncation_and_padding(
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ )
+
+ if is_pair:
+ batch_text_or_text_pairs = [(text.split(), text_pair) for text, text_pair in batch_text_or_text_pairs]
+
+ encodings = self._tokenizer.encode_batch(
+ batch_text_or_text_pairs,
+ add_special_tokens=add_special_tokens,
+ is_pretokenized=True, # we set this to True as LayoutLMv2 always expects pretokenized inputs
+ )
+
+ # Convert encoding to dict
+ # `Tokens` has type: tuple[
+ # list[dict[str, list[list[int]]]] or list[dict[str, 2D-Tensor]],
+ # list[EncodingFast]
+ # ]
+ # with nested dimensions corresponding to batch, overflows, sequence length
+ tokens_and_encodings = [
+ self._convert_encoding(
+ encoding=encoding,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=True
+ if word_labels is not None
+ else return_offsets_mapping, # we use offsets to create the labels
+ return_length=return_length,
+ verbose=verbose,
+ )
+ for encoding in encodings
+ ]
+
+ # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
+ # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
+ # (we say ~ because the number of overflow varies with the example in the batch)
+ #
+ # To match each overflowing sample with the original sample in the batch
+ # we add an overflow_to_sample_mapping array (see below)
+ sanitized_tokens = {}
+ for key in tokens_and_encodings[0][0]:
+ stack = [e for item, _ in tokens_and_encodings for e in item[key]]
+ sanitized_tokens[key] = stack
+ sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
+
+ # If returning overflowing tokens, we need to return a mapping
+ # from the batch idx to the original sample
+ if return_overflowing_tokens:
+ overflow_to_sample_mapping = []
+ for i, (toks, _) in enumerate(tokens_and_encodings):
+ overflow_to_sample_mapping += [i] * len(toks["input_ids"])
+ sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
+
+ for input_ids in sanitized_tokens["input_ids"]:
+ self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
+
+ # create the token boxes
+ token_boxes = []
+ for batch_index in range(len(sanitized_tokens["input_ids"])):
+ if return_overflowing_tokens:
+ original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
+ else:
+ original_index = batch_index
+ token_boxes_example = []
+ for id, sequence_id, word_id in zip(
+ sanitized_tokens["input_ids"][batch_index],
+ sanitized_encodings[batch_index].sequence_ids,
+ sanitized_encodings[batch_index].word_ids,
+ ):
+ if word_id is not None:
+ if is_pair and sequence_id == 0:
+ token_boxes_example.append(self.pad_token_box)
+ else:
+ token_boxes_example.append(boxes[original_index][word_id])
+ else:
+ if id == self.cls_token_id:
+ token_boxes_example.append(self.cls_token_box)
+ elif id == self.sep_token_id:
+ token_boxes_example.append(self.sep_token_box)
+ elif id == self.pad_token_id:
+ token_boxes_example.append(self.pad_token_box)
+ else:
+ raise ValueError("Id not recognized")
+ token_boxes.append(token_boxes_example)
+
+ sanitized_tokens["bbox"] = token_boxes
+
+ # optionally, create the labels
+ if word_labels is not None:
+ labels = []
+ for batch_index in range(len(sanitized_tokens["input_ids"])):
+ if return_overflowing_tokens:
+ original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
+ else:
+ original_index = batch_index
+ labels_example = []
+ for id, offset, word_id in zip(
+ sanitized_tokens["input_ids"][batch_index],
+ sanitized_tokens["offset_mapping"][batch_index],
+ sanitized_encodings[batch_index].word_ids,
+ ):
+ if word_id is not None:
+ if self.only_label_first_subword:
+ if offset[0] == 0:
+ # Use the real label id for the first token of the word, and padding ids for the remaining tokens
+ labels_example.append(word_labels[original_index][word_id])
+ else:
+ labels_example.append(self.pad_token_label)
+ else:
+ labels_example.append(word_labels[original_index][word_id])
+ else:
+ labels_example.append(self.pad_token_label)
+ labels.append(labels_example)
+
+ sanitized_tokens["labels"] = labels
+ # finally, remove offsets if the user didn't want them
+ if not return_offsets_mapping:
+ del sanitized_tokens["offset_mapping"]
+
+ return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
+
+ def _encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ boxes: Optional[list[list[int]]] = None,
+ word_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[bool] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ # make it a batched input
+ # 2 options:
+ # 1) only text, in case text must be a list of str
+ # 2) text + text_pair, in which case text = str and text_pair a list of str
+ batched_input = [(text, text_pair)] if text_pair else [text]
+ batched_boxes = [boxes]
+ batched_word_labels = [word_labels] if word_labels is not None else None
+ batched_output = self._batch_encode_plus(
+ batched_input,
+ is_pair=bool(text_pair is not None),
+ boxes=batched_boxes,
+ word_labels=batched_word_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ # Return tensor is None, then we can remove the leading batch axis
+ # Overflowing tokens are returned as a batch of output so we keep them in this case
+ if return_tensors is None and not return_overflowing_tokens:
+ batched_output = BatchEncoding(
+ {
+ key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
+ for key, value in batched_output.items()
+ },
+ batched_output.encodings,
+ )
+
+ self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
+
+ return batched_output
+
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ """
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+ Args:
+ encoded_inputs:
+ Dictionary of tokenized inputs (`list[int]`) or batch of tokenized inputs (`list[list[int]]`).
+ max_length: maximum length of the returned list and optionally padding length (see below).
+ Will truncate by taking into account the special tokens.
+ padding_strategy: PaddingStrategy to use for padding.
+
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
+ The tokenizer padding sides are defined in self.padding_side:
+
+ - 'left': pads on the left of the sequences
+ - 'right': pads on the right of the sequences
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta).
+ padding_side (`str`, *optional*):
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask:
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+ """
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = len(required_input)
+
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+ # Initialize attention mask if not present.
+ if return_attention_mask and "attention_mask" not in encoded_inputs:
+ encoded_inputs["attention_mask"] = [1] * len(required_input)
+
+ if needs_to_be_padded:
+ difference = max_length - len(required_input)
+ padding_side = padding_side if padding_side is not None else self.padding_side
+ if padding_side == "right":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = (
+ encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
+ )
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
+ encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
+ elif padding_side == "left":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
+ "token_type_ids"
+ ]
+ if "bbox" in encoded_inputs:
+ encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+ else:
+ raise ValueError("Invalid padding strategy:" + str(padding_side))
+
+ return encoded_inputs
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. An XLM-RoBERTa sequence has the following format:
+
+ - single sequence: ` X `
+ - pair of sequences: ` A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does
+ not make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+
+ """
+
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not self.can_save_slow_tokenizer:
+ raise ValueError(
+ "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
+ "tokenizer."
+ )
+
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory.")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+
+ return (out_vocab_file,)
+
+
+__all__ = ["LayoutXLMTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..786ebd36d7b8cc3c7508d5282889a5dd941c41fe
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_led import *
+ from .modeling_led import *
+ from .modeling_tf_led import *
+ from .tokenization_led import *
+ from .tokenization_led_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/configuration_led.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/configuration_led.py
new file mode 100644
index 0000000000000000000000000000000000000000..57809df4aa881f9a4622ce0d1b110e93ff654c4f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/configuration_led.py
@@ -0,0 +1,165 @@
+# coding=utf-8
+# Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LED model configuration"""
+
+from typing import Union
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class LEDConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LEDModel`]. It is used to instantiate an LED
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the LED
+ [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50265):
+ Vocabulary size of the LED model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`LEDModel`] or [`TFLEDModel`].
+ d_model (`int`, *optional*, defaults to 1024):
+ Dimensionality of the layers and the pooler layer.
+ encoder_layers (`int`, *optional*, defaults to 12):
+ Number of encoder layers.
+ decoder_layers (`int`, *optional*, defaults to 12):
+ Number of decoder layers.
+ encoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ decoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ decoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ encoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ classifier_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for classifier.
+ max_encoder_position_embeddings (`int`, *optional*, defaults to 16384):
+ The maximum sequence length that the encoder might ever be used with.
+ max_decoder_position_embeddings (`int`, *optional*, defaults to 16384):
+ The maximum sequence length that the decoder might ever be used with.
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ encoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ decoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models)
+
+ Example:
+
+ ```python
+ >>> from transformers import LEDModel, LEDConfig
+
+ >>> # Initializing a LED allenai/led-base-16384 style configuration
+ >>> configuration = LEDConfig()
+
+ >>> # Initializing a model from the allenai/led-base-16384 style configuration
+ >>> model = LEDModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "led"
+ attribute_map = {
+ "num_attention_heads": "encoder_attention_heads",
+ "hidden_size": "d_model",
+ "attention_probs_dropout_prob": "attention_dropout",
+ "initializer_range": "init_std",
+ }
+
+ def __init__(
+ self,
+ vocab_size=50265,
+ max_encoder_position_embeddings=16384,
+ max_decoder_position_embeddings=1024,
+ encoder_layers=12,
+ encoder_ffn_dim=4096,
+ encoder_attention_heads=16,
+ decoder_layers=12,
+ decoder_ffn_dim=4096,
+ decoder_attention_heads=16,
+ encoder_layerdrop=0.0,
+ decoder_layerdrop=0.0,
+ use_cache=True,
+ is_encoder_decoder=True,
+ activation_function="gelu",
+ d_model=1024,
+ dropout=0.1,
+ attention_dropout=0.0,
+ activation_dropout=0.0,
+ init_std=0.02,
+ decoder_start_token_id=2,
+ classifier_dropout=0.0,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ attention_window: Union[list[int], int] = 512,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_encoder_position_embeddings = max_encoder_position_embeddings
+ self.max_decoder_position_embeddings = max_decoder_position_embeddings
+ self.d_model = d_model
+ self.encoder_ffn_dim = encoder_ffn_dim
+ self.encoder_layers = encoder_layers
+ self.encoder_attention_heads = encoder_attention_heads
+ self.decoder_ffn_dim = decoder_ffn_dim
+ self.decoder_layers = decoder_layers
+ self.decoder_attention_heads = decoder_attention_heads
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.activation_function = activation_function
+ self.init_std = init_std
+ self.encoder_layerdrop = encoder_layerdrop
+ self.decoder_layerdrop = decoder_layerdrop
+ self.classifier_dropout = classifier_dropout
+ self.use_cache = use_cache
+ self.num_hidden_layers = encoder_layers
+ self.attention_window = attention_window
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ is_encoder_decoder=is_encoder_decoder,
+ decoder_start_token_id=decoder_start_token_id,
+ **kwargs,
+ )
+
+
+__all__ = ["LEDConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/modeling_led.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/modeling_led.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b162cfdd957cea8cc4bad68b91ee875fe180295
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/modeling_led.py
@@ -0,0 +1,2536 @@
+# coding=utf-8
+# Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch LED model."""
+
+import math
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import _create_4d_causal_attention_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
+from ...modeling_utils import PreTrainedModel
+from ...utils import ModelOutput, auto_docstring, logging
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_led import LEDConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+ """
+ Shift input ids one token to the right.
+ """
+ shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+ shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
+ shifted_input_ids[:, 0] = decoder_start_token_id
+
+ if pad_token_id is None:
+ raise ValueError("config.pad_token_id has to be defined.")
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+ return shifted_input_ids
+
+
+def _prepare_4d_attention_mask_inverted(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+ """
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+ """
+ bsz, src_len = mask.size()
+ tgt_len = tgt_len if tgt_len is not None else src_len
+
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+ inverted_mask = 1.0 - expanded_mask
+ expanded_attention_mask = inverted_mask.masked_fill(inverted_mask.bool(), torch.finfo(dtype).min)
+
+ # make sure that global_attn_mask is positive
+ expanded_attention_mask = expanded_attention_mask * inverted_mask
+
+ return expanded_attention_mask
+
+
+class LEDLearnedPositionalEmbedding(nn.Embedding):
+ """
+ This module learns positional embeddings up to a fixed maximum size.
+ """
+
+ def __init__(self, num_embeddings: int, embedding_dim: int):
+ super().__init__(num_embeddings, embedding_dim)
+
+ def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0):
+ """`input_ids_shape` is expected to be [bsz x seqlen]."""
+ bsz, seq_len = input_ids_shape[:2]
+ positions = torch.arange(
+ past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
+ )
+ return super().forward(positions)
+
+
+# Copied from transformers.models.longformer.modeling_longformer.LongformerSelfAttention with Longformer->LEDEncoder
+class LEDEncoderSelfAttention(nn.Module):
+ def __init__(self, config, layer_id):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+ self.num_heads = config.num_attention_heads
+ self.head_dim = int(config.hidden_size / config.num_attention_heads)
+ self.embed_dim = config.hidden_size
+
+ self.query = nn.Linear(config.hidden_size, self.embed_dim)
+ self.key = nn.Linear(config.hidden_size, self.embed_dim)
+ self.value = nn.Linear(config.hidden_size, self.embed_dim)
+
+ # separate projection layers for tokens with global attention
+ self.query_global = nn.Linear(config.hidden_size, self.embed_dim)
+ self.key_global = nn.Linear(config.hidden_size, self.embed_dim)
+ self.value_global = nn.Linear(config.hidden_size, self.embed_dim)
+
+ self.dropout = config.attention_probs_dropout_prob
+
+ self.layer_id = layer_id
+ attention_window = config.attention_window[self.layer_id]
+ assert attention_window % 2 == 0, (
+ f"`attention_window` for layer {self.layer_id} has to be an even value. Given {attention_window}"
+ )
+ assert attention_window > 0, (
+ f"`attention_window` for layer {self.layer_id} has to be positive. Given {attention_window}"
+ )
+
+ self.one_sided_attn_window_size = attention_window // 2
+
+ self.config = config
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ layer_head_mask=None,
+ is_index_masked=None,
+ is_index_global_attn=None,
+ is_global_attn=None,
+ output_attentions=False,
+ ):
+ """
+ [`LEDEncoderSelfAttention`] expects *len(hidden_states)* to be multiple of *attention_window*. Padding to
+ *attention_window* happens in [`LEDEncoderModel.forward`] to avoid redoing the padding on each layer.
+
+ The *attention_mask* is changed in [`LEDEncoderModel.forward`] from 0, 1, 2 to:
+
+ - -10000: no attention
+ - 0: local attention
+ - +10000: global attention
+ """
+ hidden_states = hidden_states.transpose(0, 1)
+
+ # project hidden states
+ query_vectors = self.query(hidden_states)
+ key_vectors = self.key(hidden_states)
+ value_vectors = self.value(hidden_states)
+
+ seq_len, batch_size, embed_dim = hidden_states.size()
+ assert embed_dim == self.embed_dim, (
+ f"hidden_states should have embed_dim = {self.embed_dim}, but has {embed_dim}"
+ )
+
+ # normalize query
+ query_vectors /= math.sqrt(self.head_dim)
+
+ query_vectors = query_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
+ key_vectors = key_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
+
+ attn_scores = self._sliding_chunks_query_key_matmul(
+ query_vectors, key_vectors, self.one_sided_attn_window_size
+ )
+
+ # values to pad for attention probs
+ remove_from_windowed_attention_mask = (attention_mask != 0)[:, :, None, None]
+
+ # cast to fp32/fp16 then replace 1's with -inf
+ float_mask = remove_from_windowed_attention_mask.type_as(query_vectors).masked_fill(
+ remove_from_windowed_attention_mask, torch.finfo(query_vectors.dtype).min
+ )
+ # diagonal mask with zeros everywhere and -inf inplace of padding
+ diagonal_mask = self._sliding_chunks_query_key_matmul(
+ float_mask.new_ones(size=float_mask.size()), float_mask, self.one_sided_attn_window_size
+ )
+
+ # pad local attention probs
+ attn_scores += diagonal_mask
+
+ assert list(attn_scores.size()) == [
+ batch_size,
+ seq_len,
+ self.num_heads,
+ self.one_sided_attn_window_size * 2 + 1,
+ ], (
+ f"local_attn_probs should be of size ({batch_size}, {seq_len}, {self.num_heads},"
+ f" {self.one_sided_attn_window_size * 2 + 1}), but is of size {attn_scores.size()}"
+ )
+
+ # compute local attention probs from global attention keys and contact over window dim
+ if is_global_attn:
+ # compute global attn indices required through out forward fn
+ (
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ ) = self._get_global_attn_indices(is_index_global_attn)
+ # calculate global attn probs from global key
+
+ global_key_attn_scores = self._concat_with_global_key_attn_probs(
+ query_vectors=query_vectors,
+ key_vectors=key_vectors,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
+ )
+ # concat to local_attn_probs
+ # (batch_size, seq_len, num_heads, extra attention count + 2*window+1)
+ attn_scores = torch.cat((global_key_attn_scores, attn_scores), dim=-1)
+
+ # free memory
+ del global_key_attn_scores
+
+ attn_probs = nn.functional.softmax(
+ attn_scores, dim=-1, dtype=torch.float32
+ ) # use fp32 for numerical stability
+
+ if layer_head_mask is not None:
+ assert layer_head_mask.size() == (self.num_heads,), (
+ f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+ )
+ attn_probs = layer_head_mask.view(1, 1, -1, 1) * attn_probs
+
+ # softmax sometimes inserts NaN if all positions are masked, replace them with 0
+ attn_probs = torch.masked_fill(attn_probs, is_index_masked[:, :, None, None], 0.0)
+ attn_probs = attn_probs.type_as(attn_scores)
+
+ # free memory
+ del attn_scores
+
+ # apply dropout
+ attn_probs = nn.functional.dropout(attn_probs, p=self.dropout, training=self.training)
+
+ value_vectors = value_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
+
+ # compute local attention output with global attention value and add
+ if is_global_attn:
+ # compute sum of global and local attn
+ attn_output = self._compute_attn_output_with_global_indices(
+ value_vectors=value_vectors,
+ attn_probs=attn_probs,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ )
+ else:
+ # compute local attn only
+ attn_output = self._sliding_chunks_matmul_attn_probs_value(
+ attn_probs, value_vectors, self.one_sided_attn_window_size
+ )
+
+ assert attn_output.size() == (batch_size, seq_len, self.num_heads, self.head_dim), "Unexpected size"
+ attn_output = attn_output.transpose(0, 1).reshape(seq_len, batch_size, embed_dim).contiguous()
+
+ # compute value for global attention and overwrite to attention output
+ # TODO: remove the redundant computation
+ if is_global_attn:
+ global_attn_output, global_attn_probs = self._compute_global_attn_output_from_hidden(
+ hidden_states=hidden_states,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ layer_head_mask=layer_head_mask,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
+ is_index_masked=is_index_masked,
+ )
+
+ # get only non zero global attn output
+ nonzero_global_attn_output = global_attn_output[
+ is_local_index_global_attn_nonzero[0], :, is_local_index_global_attn_nonzero[1]
+ ]
+
+ # overwrite values with global attention
+ attn_output[is_index_global_attn_nonzero[::-1]] = nonzero_global_attn_output.view(
+ len(is_local_index_global_attn_nonzero[0]), -1
+ )
+ # The attention weights for tokens with global attention are
+ # just filler values, they were never used to compute the output.
+ # Fill with 0 now, the correct values are in 'global_attn_probs'.
+ attn_probs[is_index_global_attn_nonzero] = 0
+
+ outputs = (attn_output.transpose(0, 1),)
+
+ if output_attentions:
+ outputs += (attn_probs,)
+
+ return outputs + (global_attn_probs,) if (is_global_attn and output_attentions) else outputs
+
+ @staticmethod
+ def _pad_and_transpose_last_two_dims(hidden_states_padded, padding):
+ """pads rows and then flips rows and columns"""
+ hidden_states_padded = nn.functional.pad(
+ hidden_states_padded, padding
+ ) # padding value is not important because it will be overwritten
+ hidden_states_padded = hidden_states_padded.view(
+ *hidden_states_padded.size()[:-2], hidden_states_padded.size(-1), hidden_states_padded.size(-2)
+ )
+ return hidden_states_padded
+
+ @staticmethod
+ def _pad_and_diagonalize(chunked_hidden_states):
+ """
+ shift every row 1 step right, converting columns into diagonals.
+
+ Example:
+
+ ```python
+ chunked_hidden_states: [
+ 0.4983,
+ 2.6918,
+ -0.0071,
+ 1.0492,
+ -1.8348,
+ 0.7672,
+ 0.2986,
+ 0.0285,
+ -0.7584,
+ 0.4206,
+ -0.0405,
+ 0.1599,
+ 2.0514,
+ -1.1600,
+ 0.5372,
+ 0.2629,
+ ]
+ window_overlap = num_rows = 4
+ ```
+
+ (pad & diagonalize) => [ 0.4983, 2.6918, -0.0071, 1.0492, 0.0000, 0.0000, 0.0000
+ 0.0000, -1.8348, 0.7672, 0.2986, 0.0285, 0.0000, 0.0000 0.0000, 0.0000, -0.7584, 0.4206,
+ -0.0405, 0.1599, 0.0000 0.0000, 0.0000, 0.0000, 2.0514, -1.1600, 0.5372, 0.2629 ]
+ """
+ total_num_heads, num_chunks, window_overlap, hidden_dim = chunked_hidden_states.size()
+ chunked_hidden_states = nn.functional.pad(
+ chunked_hidden_states, (0, window_overlap + 1)
+ ) # total_num_heads x num_chunks x window_overlap x (hidden_dim+window_overlap+1). Padding value is not important because it'll be overwritten
+ chunked_hidden_states = chunked_hidden_states.view(
+ total_num_heads, num_chunks, -1
+ ) # total_num_heads x num_chunks x window_overlap*window_overlap+window_overlap
+ chunked_hidden_states = chunked_hidden_states[
+ :, :, :-window_overlap
+ ] # total_num_heads x num_chunks x window_overlap*window_overlap
+ chunked_hidden_states = chunked_hidden_states.view(
+ total_num_heads, num_chunks, window_overlap, window_overlap + hidden_dim
+ )
+ chunked_hidden_states = chunked_hidden_states[:, :, :, :-1]
+ return chunked_hidden_states
+
+ @staticmethod
+ def _chunk(hidden_states, window_overlap, onnx_export: bool = False):
+ """convert into overlapping chunks. Chunk size = 2w, overlap size = w"""
+ if not onnx_export:
+ # non-overlapping chunks of size = 2w
+ hidden_states = hidden_states.view(
+ hidden_states.size(0),
+ torch.div(hidden_states.size(1), (window_overlap * 2), rounding_mode="trunc"),
+ window_overlap * 2,
+ hidden_states.size(2),
+ )
+ # use `as_strided` to make the chunks overlap with an overlap size = window_overlap
+ chunk_size = list(hidden_states.size())
+ chunk_size[1] = chunk_size[1] * 2 - 1
+
+ chunk_stride = list(hidden_states.stride())
+ chunk_stride[1] = chunk_stride[1] // 2
+ return hidden_states.as_strided(size=chunk_size, stride=chunk_stride)
+
+ # When exporting to ONNX, use this separate logic
+ # have to use slow implementation since as_strided, unfold and 2d-tensor indexing aren't supported (yet) in ONNX export
+
+ # TODO replace this with
+ # > return hidden_states.unfold(dimension=1, size=window_overlap * 2, step=window_overlap).transpose(2, 3)
+ # once `unfold` is supported
+ # the case hidden_states.size(1) == window_overlap * 2 can also simply return hidden_states.unsqueeze(1), but that's control flow
+
+ chunk_size = [
+ hidden_states.size(0),
+ torch.div(hidden_states.size(1), window_overlap, rounding_mode="trunc") - 1,
+ window_overlap * 2,
+ hidden_states.size(2),
+ ]
+
+ overlapping_chunks = torch.empty(chunk_size, device=hidden_states.device)
+ for chunk in range(chunk_size[1]):
+ overlapping_chunks[:, chunk, :, :] = hidden_states[
+ :, chunk * window_overlap : chunk * window_overlap + 2 * window_overlap, :
+ ]
+ return overlapping_chunks
+
+ @staticmethod
+ def _mask_invalid_locations(input_tensor, affected_seq_len) -> torch.Tensor:
+ beginning_mask_2d = input_tensor.new_ones(affected_seq_len, affected_seq_len + 1).tril().flip(dims=[0])
+ beginning_mask = beginning_mask_2d[None, :, None, :]
+ ending_mask = beginning_mask.flip(dims=(1, 3))
+ beginning_input = input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1]
+ beginning_mask = beginning_mask.expand(beginning_input.size())
+ input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1] = torch.full_like(
+ beginning_input, -float("inf")
+ ).where(beginning_mask.bool(), beginning_input)
+ ending_input = input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :]
+ ending_mask = ending_mask.expand(ending_input.size())
+ input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :] = torch.full_like(
+ ending_input, -float("inf")
+ ).where(ending_mask.bool(), ending_input)
+
+ def _sliding_chunks_query_key_matmul(self, query: torch.Tensor, key: torch.Tensor, window_overlap: int):
+ """
+ Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
+ implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained LEDEncoder) with an
+ overlap of size window_overlap
+ """
+ batch_size, seq_len, num_heads, head_dim = query.size()
+ assert seq_len % (window_overlap * 2) == 0, (
+ f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}"
+ )
+ assert query.size() == key.size()
+
+ chunks_count = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
+
+ # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size window_overlap * 2
+ query = query.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
+ key = key.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
+
+ query = self._chunk(query, window_overlap, getattr(self.config, "onnx_export", False))
+ key = self._chunk(key, window_overlap, getattr(self.config, "onnx_export", False))
+
+ # matrix multiplication
+ # bcxd: batch_size * num_heads x chunks x 2window_overlap x head_dim
+ # bcyd: batch_size * num_heads x chunks x 2window_overlap x head_dim
+ # bcxy: batch_size * num_heads x chunks x 2window_overlap x 2window_overlap
+ diagonal_chunked_attention_scores = torch.einsum("bcxd,bcyd->bcxy", (query, key)) # multiply
+
+ # convert diagonals into columns
+ diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(
+ diagonal_chunked_attention_scores, padding=(0, 0, 0, 1)
+ )
+
+ # allocate space for the overall attention matrix where the chunks are combined. The last dimension
+ # has (window_overlap * 2 + 1) columns. The first (window_overlap) columns are the window_overlap lower triangles (attention from a word to
+ # window_overlap previous words). The following column is attention score from each word to itself, then
+ # followed by window_overlap columns for the upper triangle.
+
+ diagonal_attention_scores = diagonal_chunked_attention_scores.new_zeros(
+ (batch_size * num_heads, chunks_count + 1, window_overlap, window_overlap * 2 + 1)
+ )
+
+ # copy parts from diagonal_chunked_attention_scores into the combined matrix of attentions
+ # - copying the main diagonal and the upper triangle
+ diagonal_attention_scores[:, :-1, :, window_overlap:] = diagonal_chunked_attention_scores[
+ :, :, :window_overlap, : window_overlap + 1
+ ]
+ diagonal_attention_scores[:, -1, :, window_overlap:] = diagonal_chunked_attention_scores[
+ :, -1, window_overlap:, : window_overlap + 1
+ ]
+ # - copying the lower triangle
+ diagonal_attention_scores[:, 1:, :, :window_overlap] = diagonal_chunked_attention_scores[
+ :, :, -(window_overlap + 1) : -1, window_overlap + 1 :
+ ]
+
+ diagonal_attention_scores[:, 0, 1:window_overlap, 1:window_overlap] = diagonal_chunked_attention_scores[
+ :, 0, : window_overlap - 1, 1 - window_overlap :
+ ]
+
+ # separate batch_size and num_heads dimensions again
+ diagonal_attention_scores = diagonal_attention_scores.view(
+ batch_size, num_heads, seq_len, 2 * window_overlap + 1
+ ).transpose(2, 1)
+
+ self._mask_invalid_locations(diagonal_attention_scores, window_overlap)
+ return diagonal_attention_scores
+
+ def _sliding_chunks_matmul_attn_probs_value(
+ self, attn_probs: torch.Tensor, value: torch.Tensor, window_overlap: int
+ ):
+ """
+ Same as _sliding_chunks_query_key_matmul but for attn_probs and value tensors. Returned tensor will be of the
+ same shape as `attn_probs`
+ """
+ batch_size, seq_len, num_heads, head_dim = value.size()
+
+ assert seq_len % (window_overlap * 2) == 0
+ assert attn_probs.size()[:3] == value.size()[:3]
+ assert attn_probs.size(3) == 2 * window_overlap + 1
+ chunks_count = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
+ # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size 2 window overlap
+
+ chunked_attn_probs = attn_probs.transpose(1, 2).reshape(
+ batch_size * num_heads,
+ torch.div(seq_len, window_overlap, rounding_mode="trunc"),
+ window_overlap,
+ 2 * window_overlap + 1,
+ )
+
+ # group batch_size and num_heads dimensions into one
+ value = value.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
+
+ # pad seq_len with w at the beginning of the sequence and another window overlap at the end
+ padded_value = nn.functional.pad(value, (0, 0, window_overlap, window_overlap), value=-1)
+
+ # chunk padded_value into chunks of size 3 window overlap and an overlap of size window overlap
+ chunked_value_size = (batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim)
+ chunked_value_stride = padded_value.stride()
+ chunked_value_stride = (
+ chunked_value_stride[0],
+ window_overlap * chunked_value_stride[1],
+ chunked_value_stride[1],
+ chunked_value_stride[2],
+ )
+ chunked_value = padded_value.as_strided(size=chunked_value_size, stride=chunked_value_stride)
+
+ chunked_attn_probs = self._pad_and_diagonalize(chunked_attn_probs)
+
+ context = torch.einsum("bcwd,bcdh->bcwh", (chunked_attn_probs, chunked_value))
+ return context.view(batch_size, num_heads, seq_len, head_dim).transpose(1, 2)
+
+ @staticmethod
+ def _get_global_attn_indices(is_index_global_attn):
+ """compute global attn indices required throughout forward pass"""
+ # helper variable
+ num_global_attn_indices = is_index_global_attn.long().sum(dim=1)
+
+ # max number of global attn indices in batch
+ max_num_global_attn_indices = num_global_attn_indices.max()
+
+ # indices of global attn
+ is_index_global_attn_nonzero = is_index_global_attn.nonzero(as_tuple=True)
+
+ # helper variable
+ is_local_index_global_attn = torch.arange(
+ max_num_global_attn_indices, device=is_index_global_attn.device
+ ) < num_global_attn_indices.unsqueeze(dim=-1)
+
+ # location of the non-padding values within global attention indices
+ is_local_index_global_attn_nonzero = is_local_index_global_attn.nonzero(as_tuple=True)
+
+ # location of the padding values within global attention indices
+ is_local_index_no_global_attn_nonzero = (is_local_index_global_attn == 0).nonzero(as_tuple=True)
+ return (
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ )
+
+ def _concat_with_global_key_attn_probs(
+ self,
+ key_vectors,
+ query_vectors,
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ ):
+ batch_size = key_vectors.shape[0]
+
+ # create only global key vectors
+ key_vectors_only_global = key_vectors.new_zeros(
+ batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim
+ )
+
+ key_vectors_only_global[is_local_index_global_attn_nonzero] = key_vectors[is_index_global_attn_nonzero]
+
+ # (batch_size, seq_len, num_heads, max_num_global_attn_indices)
+ attn_probs_from_global_key = torch.einsum("blhd,bshd->blhs", (query_vectors, key_vectors_only_global))
+
+ # need to transpose since ONNX export only supports consecutive indexing: https://pytorch.org/docs/stable/onnx.html#writes-sets
+ attn_probs_from_global_key = attn_probs_from_global_key.transpose(1, 3)
+ attn_probs_from_global_key[
+ is_local_index_no_global_attn_nonzero[0], is_local_index_no_global_attn_nonzero[1], :, :
+ ] = torch.finfo(attn_probs_from_global_key.dtype).min
+ attn_probs_from_global_key = attn_probs_from_global_key.transpose(1, 3)
+
+ return attn_probs_from_global_key
+
+ def _compute_attn_output_with_global_indices(
+ self,
+ value_vectors,
+ attn_probs,
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ ):
+ batch_size = attn_probs.shape[0]
+
+ # cut local attn probs to global only
+ attn_probs_only_global = attn_probs.narrow(-1, 0, max_num_global_attn_indices)
+ # get value vectors for global only
+ value_vectors_only_global = value_vectors.new_zeros(
+ batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim
+ )
+ value_vectors_only_global[is_local_index_global_attn_nonzero] = value_vectors[is_index_global_attn_nonzero]
+
+ # use `matmul` because `einsum` crashes sometimes with fp16
+ # attn = torch.einsum('blhs,bshd->blhd', (selected_attn_probs, selected_v))
+ # compute attn output only global
+ attn_output_only_global = torch.matmul(
+ attn_probs_only_global.transpose(1, 2).clone(), value_vectors_only_global.transpose(1, 2).clone()
+ ).transpose(1, 2)
+
+ # reshape attn probs
+ attn_probs_without_global = attn_probs.narrow(
+ -1, max_num_global_attn_indices, attn_probs.size(-1) - max_num_global_attn_indices
+ ).contiguous()
+
+ # compute attn output with global
+ attn_output_without_global = self._sliding_chunks_matmul_attn_probs_value(
+ attn_probs_without_global, value_vectors, self.one_sided_attn_window_size
+ )
+ return attn_output_only_global + attn_output_without_global
+
+ def _compute_global_attn_output_from_hidden(
+ self,
+ hidden_states,
+ max_num_global_attn_indices,
+ layer_head_mask,
+ is_local_index_global_attn_nonzero,
+ is_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ is_index_masked,
+ ):
+ seq_len, batch_size = hidden_states.shape[:2]
+
+ # prepare global hidden states
+ global_attn_hidden_states = hidden_states.new_zeros(max_num_global_attn_indices, batch_size, self.embed_dim)
+ global_attn_hidden_states[is_local_index_global_attn_nonzero[::-1]] = hidden_states[
+ is_index_global_attn_nonzero[::-1]
+ ]
+
+ # global key, query, value
+ global_query_vectors_only_global = self.query_global(global_attn_hidden_states)
+ global_key_vectors = self.key_global(hidden_states)
+ global_value_vectors = self.value_global(hidden_states)
+
+ # normalize
+ global_query_vectors_only_global /= math.sqrt(self.head_dim)
+
+ # reshape
+ global_query_vectors_only_global = (
+ global_query_vectors_only_global.contiguous()
+ .view(max_num_global_attn_indices, batch_size * self.num_heads, self.head_dim)
+ .transpose(0, 1)
+ ) # (batch_size * self.num_heads, max_num_global_attn_indices, head_dim)
+ global_key_vectors = (
+ global_key_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
+ ) # batch_size * self.num_heads, seq_len, head_dim)
+ global_value_vectors = (
+ global_value_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
+ ) # batch_size * self.num_heads, seq_len, head_dim)
+
+ # compute attn scores
+ global_attn_scores = torch.bmm(global_query_vectors_only_global, global_key_vectors.transpose(1, 2))
+
+ assert list(global_attn_scores.size()) == [
+ batch_size * self.num_heads,
+ max_num_global_attn_indices,
+ seq_len,
+ ], (
+ "global_attn_scores have the wrong size. Size should be"
+ f" {(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)}, but is"
+ f" {global_attn_scores.size()}."
+ )
+
+ global_attn_scores = global_attn_scores.view(batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
+
+ # need to transpose since ONNX export only supports consecutive indexing: https://pytorch.org/docs/stable/onnx.html#writes-sets
+ global_attn_scores = global_attn_scores.transpose(1, 2)
+ global_attn_scores[
+ is_local_index_no_global_attn_nonzero[0], is_local_index_no_global_attn_nonzero[1], :, :
+ ] = torch.finfo(global_attn_scores.dtype).min
+ global_attn_scores = global_attn_scores.transpose(1, 2)
+
+ global_attn_scores = global_attn_scores.masked_fill(
+ is_index_masked[:, None, None, :],
+ torch.finfo(global_attn_scores.dtype).min,
+ )
+
+ global_attn_scores = global_attn_scores.view(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)
+
+ # compute global attn probs
+ global_attn_probs_float = nn.functional.softmax(
+ global_attn_scores, dim=-1, dtype=torch.float32
+ ) # use fp32 for numerical stability
+
+ # apply layer head masking
+ if layer_head_mask is not None:
+ assert layer_head_mask.size() == (self.num_heads,), (
+ f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+ )
+ global_attn_probs_float = layer_head_mask.view(1, -1, 1, 1) * global_attn_probs_float.view(
+ batch_size, self.num_heads, max_num_global_attn_indices, seq_len
+ )
+ global_attn_probs_float = global_attn_probs_float.view(
+ batch_size * self.num_heads, max_num_global_attn_indices, seq_len
+ )
+
+ global_attn_probs = nn.functional.dropout(
+ global_attn_probs_float.type_as(global_attn_scores), p=self.dropout, training=self.training
+ )
+
+ # global attn output
+ global_attn_output = torch.bmm(global_attn_probs, global_value_vectors)
+
+ assert list(global_attn_output.size()) == [
+ batch_size * self.num_heads,
+ max_num_global_attn_indices,
+ self.head_dim,
+ ], (
+ "global_attn_output tensor has the wrong size. Size should be"
+ f" {(batch_size * self.num_heads, max_num_global_attn_indices, self.head_dim)}, but is"
+ f" {global_attn_output.size()}."
+ )
+
+ global_attn_probs = global_attn_probs.view(batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
+ global_attn_output = global_attn_output.view(
+ batch_size, self.num_heads, max_num_global_attn_indices, self.head_dim
+ )
+ return global_attn_output, global_attn_probs
+
+
+class LEDEncoderAttention(nn.Module):
+ def __init__(self, config, layer_id):
+ super().__init__()
+ self.longformer_self_attn = LEDEncoderSelfAttention(config, layer_id=layer_id)
+ self.output = nn.Linear(config.d_model, config.d_model)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ is_index_masked: Optional[torch.Tensor] = None,
+ is_index_global_attn: Optional[torch.Tensor] = None,
+ is_global_attn: Optional[bool] = None,
+ output_attentions: bool = False,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ self_outputs = self.longformer_self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ is_index_masked=is_index_masked,
+ is_index_global_attn=is_index_global_attn,
+ is_global_attn=is_global_attn,
+ output_attentions=output_attentions,
+ )
+
+ attn_output = self.output(self_outputs[0])
+ outputs = (attn_output,) + self_outputs[1:]
+
+ return outputs
+
+
+class LEDDecoderAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: Optional[float] = 0.0,
+ is_decoder: Optional[bool] = False,
+ bias: Optional[bool] = True,
+ layer_idx: Optional[bool] = None,
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+ if self.head_dim * num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+ f" {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+ self.layer_idx = layer_idx
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ key_value_states: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+ bsz, tgt_len, embed_dim = hidden_states.size()
+
+ # get query proj
+ query_states = self.q_proj(hidden_states) * self.scaling
+
+ if past_key_values is not None:
+ if isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_states from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = key_value_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_states = curr_past_key_value.layers[self.layer_idx].keys
+ value_states = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_states = self.k_proj(current_states)
+ value_states = self.v_proj(current_states)
+ key_states = key_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_states, value_states = curr_past_key_value.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+ query_states = query_states.view(bsz, tgt_len, self.num_heads, self.head_dim).transpose(1, 2)
+ query_states = query_states.reshape(*proj_shape)
+ key_states = key_states.reshape(*proj_shape)
+ value_states = value_states.reshape(*proj_shape)
+
+ src_len = key_states.size(1)
+ attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
+
+ if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
+ attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+ if layer_head_mask is not None:
+ if layer_head_mask.size() != (self.num_heads,):
+ raise ValueError(
+ f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
+ f" {layer_head_mask.size()}"
+ )
+ attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+ attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+ if output_attentions:
+ # this operation is a bit awkward, but it's required to
+ # make sure that attn_weights keeps its gradient.
+ # In order to do so, attn_weights have to be reshaped
+ # twice and have to be reused in the following
+ attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+ attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
+ else:
+ attn_weights_reshaped = None
+
+ attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+ attn_output = torch.bmm(attn_probs, value_states)
+
+ if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = (
+ attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+ .transpose(1, 2)
+ .reshape(bsz, tgt_len, embed_dim)
+ )
+
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights_reshaped, past_key_values
+
+
+class LEDEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: LEDConfig, layer_id: int):
+ super().__init__()
+ self.embed_dim = config.d_model
+ self.self_attn = LEDEncoderAttention(config, layer_id)
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+ self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
+ self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ layer_head_mask: torch.Tensor,
+ is_index_masked=None,
+ is_index_global_attn=None,
+ is_global_attn=None,
+ output_attentions=False,
+ ):
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ *(encoder_attention_heads,)*.
+ """
+ residual = hidden_states
+ attn_outputs = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ is_index_masked=is_index_masked,
+ is_index_global_attn=is_index_global_attn,
+ is_global_attn=is_global_attn,
+ output_attentions=output_attentions,
+ )
+ hidden_states = attn_outputs[0]
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ if hidden_states.dtype == torch.float16 and (
+ torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
+ ):
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+ return (hidden_states,) + attn_outputs[1:]
+
+
+class LEDDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: LEDConfig, layer_idx=None):
+ super().__init__()
+ self.embed_dim = config.d_model
+
+ self.self_attn = LEDDecoderAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ layer_idx=layer_idx,
+ )
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.encoder_attn = LEDDecoderAttention(
+ self.embed_dim,
+ config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ layer_idx=layer_idx,
+ )
+ self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
+ self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = True,
+ cache_position: Optional[torch.Tensor] = None,
+ ):
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
+ encoder_hidden_states (`torch.FloatTensor`):
+ cross attention input to the layer of shape *(batch, seq_len, embed_dim)*
+ encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
+ *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ *(decoder_attention_heads,)*.
+ cross_attn_layer_head_mask (`torch.FloatTensor`): mask for encoder attention heads in a given layer of
+ size *(decoder_attention_heads,)*.
+ past_key_values (`Tuple(torch.FloatTensor)`): cached past key and value projection states
+ output_attentions (`bool`): Whether the base model outputs attentions.
+ This requires the attentions tensor to be reshaped in this function.
+ """
+ residual = hidden_states
+
+ # Self-Attention
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Cross-Attention Block
+ cross_attn_present_key_value = None
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+
+ hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ layer_head_mask=cross_attn_layer_head_mask,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ if use_cache:
+ outputs += (past_key_values,)
+
+ return outputs
+
+
+class LEDClassificationHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(
+ self,
+ input_dim: int,
+ inner_dim: int,
+ num_classes: int,
+ pooler_dropout: float,
+ ):
+ super().__init__()
+ self.dense = nn.Linear(input_dim, inner_dim)
+ self.dropout = nn.Dropout(p=pooler_dropout)
+ self.out_proj = nn.Linear(inner_dim, num_classes)
+
+ def forward(self, hidden_states: torch.Tensor):
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.dense(hidden_states)
+ hidden_states = torch.tanh(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.out_proj(hidden_states)
+ return hidden_states
+
+
+@auto_docstring
+class LEDPreTrainedModel(PreTrainedModel):
+ config: LEDConfig
+ base_model_prefix = "led"
+ supports_gradient_checkpointing = True
+
+ def _init_weights(self, module):
+ std = self.config.init_std
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+ @property
+ def dummy_inputs(self):
+ pad_token = self.config.pad_token_id
+ input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
+ dummy_inputs = {
+ "attention_mask": input_ids.ne(pad_token),
+ "input_ids": input_ids,
+ }
+ return dummy_inputs
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for LEDEncoder's outputs, with potential hidden states, local and global attentions.
+ """
+)
+# Copied from transformers.models.longformer.modeling_longformer.LongformerBaseModelOutput with Longformer->LEDEncoder
+class LEDEncoderBaseModelOutput(ModelOutput):
+ r"""
+ attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ last_hidden_state: torch.FloatTensor
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
+ decoding.
+ """
+)
+class LEDSeq2SeqModelOutput(ModelOutput):
+ r"""
+ last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the decoder of the model.
+
+ If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
+ hidden_size)` is output.
+ past_key_values (`list[torch.FloatTensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ List of `torch.FloatTensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size,
+ num_heads, sequence_length, embed_size_per_head)`).
+
+ Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
+ used (see `past_key_values` input) to speed up sequential decoding.
+ encoder_global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ decoder_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ decoder_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ cross_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for sequence-to-sequence language models outputs.
+ """
+)
+class LEDSeq2SeqLMOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`list[torch.FloatTensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ List of `torch.FloatTensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size,
+ num_heads, sequence_length, embed_size_per_head)`).
+
+ Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
+ used (see `past_key_values` input) to speed up sequential decoding.
+ encoder_global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ decoder_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ decoder_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ cross_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of sequence-to-sequence sentence classification models.
+ """
+)
+class LEDSeq2SeqSequenceClassifierOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided):
+ Classification (or regression if config.num_labels==1) loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+ Classification (or regression if config.num_labels==1) scores (before SoftMax).
+ past_key_values (`list[torch.FloatTensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ List of `torch.FloatTensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size,
+ num_heads, sequence_length, embed_size_per_head)`).
+
+ Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
+ used (see `past_key_values` input) to speed up sequential decoding.
+ encoder_global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ decoder_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ decoder_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ cross_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of sequence-to-sequence question answering models.
+ """
+)
+class LEDSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
+ past_key_values (`list[torch.FloatTensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ List of `torch.FloatTensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size,
+ num_heads, sequence_length, embed_size_per_head)`).
+
+ Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
+ used (see `past_key_values` input) to speed up sequential decoding.
+ encoder_global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ start_logits: Optional[torch.FloatTensor] = None
+ end_logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ decoder_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ decoder_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ cross_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ encoder_global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+class LEDEncoder(LEDPreTrainedModel):
+ """
+ Transformer encoder consisting of *config.encoder_layers* self-attention layers. Each layer is a
+ [`LEDEncoderLayer`].
+
+ Args:
+ config: LEDConfig
+ embed_tokens (nn.Embedding): output embedding
+ """
+
+ def __init__(self, config: LEDConfig, embed_tokens: Optional[nn.Embedding] = None):
+ super().__init__(config)
+
+ self.dropout = config.dropout
+ self.layerdrop = config.encoder_layerdrop
+
+ embed_dim = config.d_model
+ self.padding_idx = config.pad_token_id
+ self.max_source_positions = config.max_encoder_position_embeddings
+
+ if isinstance(config.attention_window, int):
+ if config.attention_window % 2 != 0:
+ raise ValueError("`config.attention_window` has to be an even value")
+ if config.attention_window <= 0:
+ raise ValueError("`config.attention_window` has to be positive")
+ config.attention_window = [config.attention_window] * config.num_hidden_layers # one value per layer
+ else:
+ if len(config.attention_window) != config.num_hidden_layers:
+ raise ValueError(
+ "`len(config.attention_window)` should equal `config.num_hidden_layers`. "
+ f"Expected {config.num_hidden_layers}, given {len(config.attention_window)}"
+ )
+
+ if embed_tokens is not None:
+ self.embed_tokens = embed_tokens
+ else:
+ self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
+
+ self.embed_positions = LEDLearnedPositionalEmbedding(
+ self.max_source_positions,
+ embed_dim,
+ )
+ self.layers = nn.ModuleList([LEDEncoderLayer(config, i) for i in range(config.encoder_layers)])
+ self.layernorm_embedding = nn.LayerNorm(embed_dim)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def _merge_to_attention_mask(self, attention_mask: torch.Tensor, global_attention_mask: torch.Tensor):
+ # longformer self-attention expects attention mask to have 0 (no attn), 1 (local attn), 2 (global attn)
+ # (global_attention_mask + 1) => 1 for local attention, 2 for global attention
+ # => final attention_mask => 0 for no attention, 1 for local attention 2 for global attention
+ if attention_mask is not None:
+ attention_mask = attention_mask * (global_attention_mask + 1)
+ else:
+ # simply use `global_attention_mask` as `attention_mask`
+ # if no `attention_mask` is given
+ attention_mask = global_attention_mask + 1
+ return attention_mask
+
+ def _pad_to_window_size(
+ self,
+ input_ids: torch.Tensor,
+ attention_mask: torch.Tensor,
+ inputs_embeds: torch.Tensor,
+ pad_token_id: int,
+ ):
+ """A helper function to pad tokens and mask to work with implementation of Longformer self-attention."""
+ # padding
+ attention_window = (
+ self.config.attention_window
+ if isinstance(self.config.attention_window, int)
+ else max(self.config.attention_window)
+ )
+
+ if attention_window % 2 != 0:
+ raise ValueError(f"`attention_window` should be an even value. Given {attention_window}")
+ input_shape = input_ids.shape if input_ids is not None else inputs_embeds.shape
+ batch_size, seq_len = input_shape[:2]
+
+ padding_len = (attention_window - seq_len % attention_window) % attention_window
+ if padding_len > 0:
+ logger.warning_once(
+ f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of "
+ f"`config.attention_window`: {attention_window}"
+ )
+ if input_ids is not None:
+ input_ids = nn.functional.pad(input_ids, (0, padding_len), value=pad_token_id)
+ if inputs_embeds is not None:
+ input_ids_padding = inputs_embeds.new_full(
+ (batch_size, padding_len),
+ self.config.pad_token_id,
+ dtype=torch.long,
+ )
+ inputs_embeds_padding = self.embed_tokens(input_ids_padding)
+ inputs_embeds = torch.cat([inputs_embeds, inputs_embeds_padding], dim=-2)
+
+ attention_mask = nn.functional.pad(
+ attention_mask, (0, padding_len), value=False
+ ) # no attention on the padding tokens
+
+ return padding_len, input_ids, attention_mask, inputs_embeds
+
+ def forward(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ global_attention_mask=None,
+ head_mask=None,
+ inputs_embeds=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention for the encoder.
+ Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is
+ important for task-specific finetuning because it makes the model more flexible at representing the
+ task. For example, for classification, the token should be given global attention. For QA, all
+ question tokens should also have global attention. Please refer to the [Longformer
+ paper](https://huggingface.co/papers/2004.05150) for more details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+ head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # check input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is None and inputs_embeds is None:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ # create default attention_mask
+ if attention_mask is None:
+ attention_mask = torch.ones(inputs_embeds.size()[:-1], device=inputs_embeds.device, dtype=torch.long)
+
+ # merge `global_attention_mask` and `attention_mask`
+ if global_attention_mask is not None:
+ attention_mask = self._merge_to_attention_mask(attention_mask, global_attention_mask)
+
+ # pad input if necessary
+ padding_len, input_ids, attention_mask, inputs_embeds = self._pad_to_window_size(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ pad_token_id=self.config.pad_token_id,
+ )
+
+ # retrieve input_shape
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+
+ # convert attention_mask to float
+ if attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, seq_len]; 1 -> 0.0; 0 -> "-inf"
+ attention_mask = _prepare_4d_attention_mask_inverted(attention_mask, inputs_embeds.dtype)[:, 0, 0, :]
+
+ # get masking tensors
+ is_index_masked = attention_mask < 0
+ is_index_global_attn = attention_mask > 0
+ is_global_attn = is_index_global_attn.flatten().any().item()
+
+ embed_pos = self.embed_positions(input_shape)
+
+ hidden_states = inputs_embeds + embed_pos
+ hidden_states = self.layernorm_embedding(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+ all_global_attentions = () if (output_attentions and is_global_attn) else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ if head_mask.size()[0] != len(self.layers):
+ raise ValueError(
+ f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = torch.rand([])
+
+ if self.training and (dropout_probability < self.layerdrop): # skip the layer
+ layer_outputs = (None, None, None)
+ else:
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ is_index_masked=is_index_masked,
+ is_index_global_attn=is_index_global_attn,
+ is_global_attn=is_global_attn,
+ output_attentions=output_attentions,
+ )
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ # bzs x seq_len x num_attn_heads x (num_global_attn + attention_window_len + 1) => bzs x num_attn_heads x seq_len x (num_global_attn + attention_window_len + 1)
+ all_attentions = all_attentions + (layer_outputs[1].transpose(1, 2),)
+
+ if is_global_attn:
+ # bzs x num_attn_heads x num_global_attn x seq_len => bzs x num_attn_heads x seq_len x num_global_attn
+ all_global_attentions = all_global_attentions + (layer_outputs[2].transpose(2, 3),)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ # undo padding
+ if padding_len > 0:
+ # unpad `hidden_states` because the calling function is expecting a length == input_ids.size(1)
+ hidden_states = hidden_states[:, :-padding_len]
+ if output_hidden_states:
+ encoder_states = tuple(state[:, :-padding_len] for state in encoder_states)
+
+ if output_attentions:
+ all_attentions = tuple(state[:, :, :-padding_len, :] for state in all_attentions)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, encoder_states, all_attentions, all_global_attentions] if v is not None
+ )
+ return LEDEncoderBaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=encoder_states,
+ attentions=all_attentions,
+ global_attentions=all_global_attentions,
+ )
+
+
+class LEDDecoder(LEDPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`LEDDecoderLayer`]
+
+ Args:
+ config: LEDConfig
+ embed_tokens (nn.Embedding): output embedding
+ """
+
+ def __init__(self, config: LEDConfig, embed_tokens: Optional[nn.Embedding] = None):
+ super().__init__(config)
+ self.dropout = config.dropout
+ self.layerdrop = config.decoder_layerdrop
+ self.padding_idx = config.pad_token_id
+ self.max_target_positions = config.max_decoder_position_embeddings
+
+ if embed_tokens is not None:
+ self.embed_tokens = embed_tokens
+ else:
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
+
+ self.embed_positions = LEDLearnedPositionalEmbedding(
+ self.max_target_positions,
+ config.d_model,
+ )
+ self.layers = nn.ModuleList([LEDDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)])
+ self.layernorm_embedding = nn.LayerNorm(config.d_model)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ global_attention_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ head_mask=None,
+ cross_attn_head_mask=None,
+ past_key_values=None,
+ inputs_embeds=None,
+ use_cache=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ cache_position=None,
+ ):
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention. Tokens with
+ global attention attends to all other tokens, and all other tokens attend to them. This is important
+ for task-specific finetuning because it makes the model more flexible at representing the task. For
+ example, for classification, the token should be given global attention. For QA, all question
+ tokens should also have global attention. Please refer to the [Longformer
+ paper](https://huggingface.co/papers/2004.05150) for more details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+ shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+ cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+ that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+ all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ if use_cache and past_key_values is None:
+ past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
+ if use_cache and isinstance(past_key_values, tuple):
+ logger.warning_once(
+ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
+ "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
+ "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
+ )
+ past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
+
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+ # create causal mask
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ combined_attention_mask = None
+ if input_shape[-1] > 1:
+ combined_attention_mask = _create_4d_causal_attention_mask(
+ input_shape, inputs_embeds.dtype, inputs_embeds.device, past_key_values_length=past_key_values_length
+ )
+
+ if attention_mask is not None and combined_attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ combined_attention_mask = combined_attention_mask + _prepare_4d_attention_mask_inverted(
+ attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+ )
+
+ # expand encoder attention mask
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask_inverted(
+ encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+ )
+
+ # embed positions
+ positions = self.embed_positions(input_shape, past_key_values_length)
+
+ hidden_states = inputs_embeds + positions
+ hidden_states = self.layernorm_embedding(hidden_states)
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attentions = () if output_attentions else None
+
+ # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
+ for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
+ if attn_mask is not None:
+ if attn_mask.size()[0] != len(self.layers):
+ raise ValueError(
+ f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ if self.training:
+ dropout_probability = torch.rand([])
+ if dropout_probability < self.layerdrop:
+ continue
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ combined_attention_mask,
+ encoder_hidden_states, # as a positional argument for gradient checkpointing
+ encoder_attention_mask=encoder_attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+ all_cross_attentions += (layer_outputs[2],)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions]
+ if v is not None
+ )
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+@auto_docstring
+class LEDModel(LEDPreTrainedModel):
+ _tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]
+
+ def __init__(self, config: LEDConfig):
+ super().__init__(config)
+
+ padding_idx, vocab_size = config.pad_token_id, config.vocab_size
+ self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
+
+ self.encoder = LEDEncoder(config, self.shared)
+ self.decoder = LEDDecoder(config, self.shared)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, value):
+ self.shared = value
+ self.encoder.embed_tokens = self.shared
+ self.decoder.embed_tokens = self.shared
+
+ def get_encoder(self):
+ return self.encoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ global_attention_mask: Optional[torch.FloatTensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple[torch.Tensor], LEDSeq2SeqModelOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`LedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ LED uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should read [`modeling_led._prepare_decoder_inputs`] and modify
+ to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more information on the
+ default strategy.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention for the encoder.
+ Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is
+ important for task-specific finetuning because it makes the model more flexible at representing the task.
+ For example, for classification, the token should be given global attention. For QA, all question
+ tokens should also have global attention. Please refer to the [Longformer
+ paper](https://huggingface.co/papers/2004.05150) for more details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # Using this like Bart, as LED is derived from it. So far
+ # No checkpoint on the hub exists that uses that in practice.
+ # https://github.com/huggingface/transformers/blob/ac3cb660cad283163f7c73cad511124e845ca388/src/transformers/models/bart/modeling_bart.py#L1153
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ decoder_input_ids = shift_tokens_right(
+ input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
+ )
+
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ global_attention_mask=global_attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ # If the user passed a tuple for encoder_outputs, we wrap it in a LEDEncoderBaseModelOutput when return_dict=False
+ elif return_dict and not isinstance(encoder_outputs, LEDEncoderBaseModelOutput):
+ encoder_outputs = LEDEncoderBaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ global_attentions=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
+ )
+
+ # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return LEDSeq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ encoder_global_attentions=encoder_outputs.global_attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The LED Model with a language modeling head. Can be used for summarization.
+ """
+)
+class LEDForConditionalGeneration(LEDPreTrainedModel, GenerationMixin):
+ base_model_prefix = "led"
+ _keys_to_ignore_on_load_missing = ["final_logits_bias"]
+ _tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"]
+
+ def __init__(self, config: LEDConfig):
+ super().__init__(config)
+ self.led = LEDModel(config)
+ self.register_buffer("final_logits_bias", torch.zeros((1, self.led.shared.num_embeddings)))
+ self.lm_head = nn.Linear(config.d_model, self.led.shared.num_embeddings, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_encoder(self):
+ return self.led.get_encoder()
+
+ def get_decoder(self):
+ return self.led.get_decoder()
+
+ def resize_token_embeddings(
+ self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+ ) -> nn.Embedding:
+ new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
+ self._resize_final_logits_bias(new_embeddings.weight.shape[0])
+ return new_embeddings
+
+ def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
+ old_num_tokens = self.final_logits_bias.shape[-1]
+ if new_num_tokens <= old_num_tokens:
+ new_bias = self.final_logits_bias[:, :new_num_tokens]
+ else:
+ extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
+ new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
+ self.register_buffer("final_logits_bias", new_bias)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ global_attention_mask: Optional[torch.FloatTensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple[torch.Tensor], LEDSeq2SeqLMOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`LedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ LED uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should read [`modeling_led._prepare_decoder_inputs`] and modify
+ to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more information on the
+ default strategy.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention for the encoder.
+ Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is
+ important for task-specific finetuning because it makes the model more flexible at representing the task.
+ For example, for classification, the token should be given global attention. For QA, all question
+ tokens should also have global attention. Please refer to the [Longformer
+ paper](https://huggingface.co/papers/2004.05150) for more details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example Summarization:
+
+ ```python
+ >>> import torch
+ >>> from transformers import AutoTokenizer, LEDForConditionalGeneration
+
+ >>> model = LEDForConditionalGeneration.from_pretrained("allenai/led-large-16384-arxiv")
+ >>> tokenizer = AutoTokenizer.from_pretrained("allenai/led-large-16384-arxiv")
+
+ >>> ARTICLE_TO_SUMMARIZE = '''Transformers (Vaswani et al., 2017) have achieved state-of-the-art
+ ... results in a wide range of natural language tasks including generative language modeling
+ ... (Dai et al., 2019; Radford et al., 2019) and discriminative ... language understanding (Devlin et al., 2019).
+ ... This success is partly due to the self-attention component which enables the network to capture contextual
+ ... information from the entire sequence. While powerful, the memory and computational requirements of
+ ... self-attention grow quadratically with sequence length, making it infeasible (or very expensive) to
+ ... process long sequences. To address this limitation, we present Longformer, a modified Transformer
+ ... architecture with a self-attention operation that scales linearly with the sequence length, making it
+ ... versatile for processing long documents (Fig 1). This is an advantage for natural language tasks such as
+ ... long document classification, question answering (QA), and coreference resolution, where existing approaches
+ ... partition or shorten the long context into smaller sequences that fall within the typical 512 token limit
+ ... of BERT-style pretrained models. Such partitioning could potentially result in loss of important
+ ... cross-partition information, and to mitigate this problem, existing methods often rely on complex
+ ... architectures to address such interactions. On the other hand, our proposed Longformer is able to build
+ ... contextual representations of the entire context using multiple layers of attention, reducing the need for
+ ... task-specific architectures.'''
+ >>> inputs = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors="pt")
+
+ >>> # Global attention on the first token (cf. Beltagy et al. 2020)
+ >>> global_attention_mask = torch.zeros_like(inputs)
+ >>> global_attention_mask[:, 0] = 1
+
+ >>> # Generate Summary
+ >>> summary_ids = model.generate(inputs, global_attention_mask=global_attention_mask, num_beams=3, max_length=32)
+ >>> print(tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
+ ```
+
+ Example Conditional generation :
+
+ ```python
+ >>> from transformers import AutoTokenizer, LEDForConditionalGeneration
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("allenai/led-base-16384")
+ >>> TXT = "My friends are but they eat too many carbs."
+
+ >>> model = LEDForConditionalGeneration.from_pretrained("allenai/led-base-16384")
+ >>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
+
+ >>> prediction = model.generate(input_ids)[0]
+ >>> print(tokenizer.decode(prediction, skip_special_tokens=True))
+ ```
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if labels is not None:
+ if use_cache:
+ logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+ use_cache = False
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ decoder_input_ids = shift_tokens_right(
+ labels, self.config.pad_token_id, self.config.decoder_start_token_id
+ )
+
+ outputs = self.led(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ encoder_outputs=encoder_outputs,
+ global_attention_mask=global_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+ lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return LEDSeq2SeqLMOutput(
+ loss=masked_lm_loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ encoder_global_attentions=outputs.encoder_global_attentions,
+ )
+
+ def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+ return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
+
+@auto_docstring(
+ custom_intro="""
+ LED model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
+ tasks.
+ """
+)
+class LEDForSequenceClassification(LEDPreTrainedModel):
+ _tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]
+
+ def __init__(self, config: LEDConfig, **kwargs):
+ warnings.warn(
+ "The `transformers.LEDForSequenceClassification` class is deprecated and will be removed in version 5 of"
+ " Transformers. No actual method were provided in the original paper on how to perform"
+ " sequence classification.",
+ FutureWarning,
+ )
+ super().__init__(config, **kwargs)
+ self.led = LEDModel(config)
+ self.classification_head = LEDClassificationHead(
+ config.d_model,
+ config.d_model,
+ config.num_labels,
+ config.classifier_dropout,
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ global_attention_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], LEDSeq2SeqSequenceClassifierOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`LedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ LED uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should read [`modeling_led._prepare_decoder_inputs`] and modify
+ to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more information on the
+ default strategy.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention for the encoder.
+ Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is
+ important for task-specific finetuning because it makes the model more flexible at representing the task.
+ For example, for classification, the token should be given global attention. For QA, all question
+ tokens should also have global attention. Please refer to the [Longformer
+ paper](https://huggingface.co/papers/2004.05150) for more details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ if labels is not None:
+ use_cache = False
+
+ if input_ids is None and inputs_embeds is not None:
+ raise NotImplementedError(
+ f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
+ )
+
+ outputs = self.led(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ global_attention_mask=global_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ encoder_outputs=encoder_outputs,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = outputs[0] # last hidden state
+
+ eos_mask = input_ids.eq(self.config.eos_token_id).to(hidden_states.device)
+
+ if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
+ raise ValueError("All examples must have the same number of tokens.")
+ sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
+ :, -1, :
+ ]
+ logits = self.classification_head(sentence_representation)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.config.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.config.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return LEDSeq2SeqSequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ encoder_global_attentions=outputs.encoder_global_attentions,
+ )
+
+
+@auto_docstring
+class LEDForQuestionAnswering(LEDPreTrainedModel):
+ _tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ config.num_labels = 2
+ self.num_labels = config.num_labels
+
+ self.led = LEDModel(config)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ global_attention_mask: Optional[torch.FloatTensor] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], LEDSeq2SeqQuestionAnsweringModelOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`LedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ LED uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should read [`modeling_led._prepare_decoder_inputs`] and modify
+ to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more information on the
+ default strategy.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention for the encoder.
+ Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is
+ important for task-specific finetuning because it makes the model more flexible at representing the task.
+ For example, for classification, the token should be given global attention. For QA, all question
+ tokens should also have global attention. Please refer to the [Longformer
+ paper](https://huggingface.co/papers/2004.05150) for more details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ if start_positions is not None and end_positions is not None:
+ use_cache = False
+
+ outputs = self.led(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ global_attention_mask=global_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ encoder_outputs=encoder_outputs,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (
+ start_logits,
+ end_logits,
+ ) + outputs[1:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return LEDSeq2SeqQuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ encoder_global_attentions=outputs.encoder_global_attentions,
+ )
+
+
+__all__ = [
+ "LEDForConditionalGeneration",
+ "LEDForQuestionAnswering",
+ "LEDForSequenceClassification",
+ "LEDModel",
+ "LEDPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/modeling_tf_led.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/modeling_tf_led.py
new file mode 100644
index 0000000000000000000000000000000000000000..f499ffac30c9f8324a3d16c1bb4a0ad9638337bc
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/modeling_tf_led.py
@@ -0,0 +1,2663 @@
+# coding=utf-8
+# Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TF 2.0 LED model."""
+
+from __future__ import annotations
+
+import random
+from dataclasses import dataclass
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_outputs import TFBaseModelOutputWithPastAndCrossAttentions
+
+# Public API
+from ...modeling_tf_utils import (
+ TFModelInputType,
+ TFPreTrainedModel,
+ get_initializer,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
+from ...utils import (
+ ModelOutput,
+ add_code_sample_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+ replace_return_docstrings,
+)
+from .configuration_led import LEDConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "allenai/led-base-16384"
+_CONFIG_FOR_DOC = "LEDConfig"
+
+
+LARGE_NEGATIVE = -1e8
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right
+def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
+ pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
+ decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
+ start_tokens = tf.fill(
+ (shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype)
+ )
+ shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids = tf.where(
+ shifted_input_ids == -100,
+ tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)),
+ shifted_input_ids,
+ )
+
+ # "Verify that `labels` has only positive values and -100"
+ assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))
+
+ # Make sure the assertion op is called by wrapping the result in an identity no-op
+ with tf.control_dependencies([assert_gte0]):
+ shifted_input_ids = tf.identity(shifted_input_ids)
+
+ return shifted_input_ids
+
+
+# Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask
+def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
+ """
+ Make causal mask used for bi-directional self-attention.
+ """
+ bsz = input_ids_shape[0]
+ tgt_len = input_ids_shape[1]
+ mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
+ mask_cond = tf.range(shape_list(mask)[-1])
+
+ mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
+
+ if past_key_values_length > 0:
+ mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
+
+ return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
+
+
+# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
+def _expand_mask(mask: tf.Tensor, tgt_len: int | None = None):
+ """
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+ """
+ src_len = shape_list(mask)[1]
+ tgt_len = tgt_len if tgt_len is not None else src_len
+ one_cst = tf.constant(1.0)
+ mask = tf.cast(mask, dtype=one_cst.dtype)
+ expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
+
+ return (one_cst - expanded_mask) * LARGE_NEGATIVE
+
+
+class TFLEDLearnedPositionalEmbedding(keras.layers.Embedding):
+ """
+ This module learns positional embeddings up to a fixed maximum size.
+ """
+
+ def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
+ super().__init__(num_embeddings, embedding_dim, **kwargs)
+
+ def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0):
+ """Input is expected to be of size [bsz x seqlen]."""
+ seq_len = input_shape[1]
+ position_ids = tf.range(seq_len, delta=1, name="range")
+ position_ids += past_key_values_length
+
+ return super().call(tf.cast(position_ids, dtype=tf.int32))
+
+
+# Copied from transformers.models.longformer.modeling_tf_longformer.TFLongformerSelfAttention with TFLongformer->TFLEDEncoder
+class TFLEDEncoderSelfAttention(keras.layers.Layer):
+ def __init__(self, config, layer_id, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads}"
+ )
+
+ self.num_heads = config.num_attention_heads
+ self.head_dim = int(config.hidden_size / config.num_attention_heads)
+ self.embed_dim = config.hidden_size
+ self.query = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="query",
+ )
+ self.key = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="key",
+ )
+ self.value = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="value",
+ )
+
+ # separate projection layers for tokens with global attention
+ self.query_global = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="query_global",
+ )
+ self.key_global = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="key_global",
+ )
+ self.value_global = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="value_global",
+ )
+ self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob)
+ self.global_dropout = keras.layers.Dropout(config.attention_probs_dropout_prob)
+ self.layer_id = layer_id
+ attention_window = config.attention_window[self.layer_id]
+
+ assert attention_window % 2 == 0, (
+ f"`attention_window` for layer {self.layer_id} has to be an even value. Given {attention_window}"
+ )
+ assert attention_window > 0, (
+ f"`attention_window` for layer {self.layer_id} has to be positive. Given {attention_window}"
+ )
+
+ self.one_sided_attn_window_size = attention_window // 2
+
+ def build(self, input_shape=None):
+ if not self.built:
+ with tf.name_scope("query_global"):
+ self.query_global.build((self.config.hidden_size,))
+ with tf.name_scope("key_global"):
+ self.key_global.build((self.config.hidden_size,))
+ with tf.name_scope("value_global"):
+ self.value_global.build((self.config.hidden_size,))
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "query", None) is not None:
+ with tf.name_scope(self.query.name):
+ self.query.build([None, None, self.config.hidden_size])
+ if getattr(self, "key", None) is not None:
+ with tf.name_scope(self.key.name):
+ self.key.build([None, None, self.config.hidden_size])
+ if getattr(self, "value", None) is not None:
+ with tf.name_scope(self.value.name):
+ self.value.build([None, None, self.config.hidden_size])
+ if getattr(self, "query_global", None) is not None:
+ with tf.name_scope(self.query_global.name):
+ self.query_global.build([None, None, self.config.hidden_size])
+ if getattr(self, "key_global", None) is not None:
+ with tf.name_scope(self.key_global.name):
+ self.key_global.build([None, None, self.config.hidden_size])
+ if getattr(self, "value_global", None) is not None:
+ with tf.name_scope(self.value_global.name):
+ self.value_global.build([None, None, self.config.hidden_size])
+
+ def call(
+ self,
+ inputs,
+ training=False,
+ ):
+ """
+ LongformerSelfAttention expects *len(hidden_states)* to be multiple of *attention_window*. Padding to
+ *attention_window* happens in LongformerModel.forward to avoid redoing the padding on each layer.
+
+ The *attention_mask* is changed in [`LongformerModel.forward`] from 0, 1, 2 to:
+
+ - -10000: no attention
+ - 0: local attention
+ - +10000: global attention
+ """
+ # retrieve input args
+ (
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ is_index_masked,
+ is_index_global_attn,
+ is_global_attn,
+ ) = inputs
+
+ # project hidden states
+ query_vectors = self.query(hidden_states)
+ key_vectors = self.key(hidden_states)
+ value_vectors = self.value(hidden_states)
+ batch_size, seq_len, embed_dim = shape_list(hidden_states)
+
+ tf.debugging.assert_equal(
+ embed_dim,
+ self.embed_dim,
+ message=f"hidden_states should have embed_dim = {self.embed_dim}, but has {embed_dim}",
+ )
+
+ # normalize query
+ query_vectors /= tf.math.sqrt(tf.cast(self.head_dim, dtype=query_vectors.dtype))
+ query_vectors = tf.reshape(query_vectors, (batch_size, seq_len, self.num_heads, self.head_dim))
+ key_vectors = tf.reshape(key_vectors, (batch_size, seq_len, self.num_heads, self.head_dim))
+
+ # attn_probs = (batch_size, seq_len, num_heads, window*2+1)
+ attn_scores = self._sliding_chunks_query_key_matmul(
+ query_vectors, key_vectors, self.one_sided_attn_window_size
+ )
+
+ # values to pad for attention probs
+ remove_from_windowed_attention_mask = attention_mask != 0
+ # cast to fp32/fp16 then replace 1's with -inf
+ float_mask = tf.cast(remove_from_windowed_attention_mask, dtype=query_vectors.dtype) * LARGE_NEGATIVE
+
+ # diagonal mask with zeros everywhere and -inf inplace of padding
+ diagonal_mask = self._sliding_chunks_query_key_matmul(
+ tf.ones(shape_list(attention_mask)),
+ float_mask,
+ self.one_sided_attn_window_size,
+ )
+
+ # pad local attention probs
+ attn_scores += diagonal_mask
+
+ tf.debugging.assert_equal(
+ shape_list(attn_scores),
+ [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + 1],
+ message=(
+ f"attn_probs should be of size ({batch_size}, {seq_len}, {self.num_heads},"
+ f" {self.one_sided_attn_window_size * 2 + 1}), but is of size {shape_list(attn_scores)}"
+ ),
+ )
+
+ # compute global attn indices required through out forward fn
+ (
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ ) = self._get_global_attn_indices(is_index_global_attn)
+
+ # this function is only relevant for global attention
+ if is_global_attn:
+ attn_scores = self._concat_with_global_key_attn_probs(
+ attn_scores=attn_scores,
+ query_vectors=query_vectors,
+ key_vectors=key_vectors,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
+ )
+
+ attn_probs = stable_softmax(attn_scores, axis=-1)
+
+ # softmax sometimes inserts NaN if all positions are masked, replace them with 0
+ # Make sure to create a mask with the proper shape:
+ # if is_global_attn==True => [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + max_num_global_attn_indices + 1]
+ # if is_global_attn==False => [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + 1]
+ if is_global_attn:
+ masked_index = tf.tile(
+ is_index_masked[:, :, None, None],
+ (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + max_num_global_attn_indices + 1),
+ )
+ else:
+ masked_index = tf.tile(
+ is_index_masked[:, :, None, None],
+ (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + 1),
+ )
+ attn_probs = tf.where(
+ masked_index,
+ tf.zeros(shape_list(masked_index), dtype=attn_probs.dtype),
+ attn_probs,
+ )
+
+ if layer_head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(layer_head_mask),
+ [self.num_heads],
+ message=(
+ f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
+ f" {shape_list(layer_head_mask)}"
+ ),
+ )
+
+ attn_probs = tf.reshape(layer_head_mask, (1, 1, -1, 1)) * attn_probs
+
+ # apply dropout
+ attn_probs = self.dropout(attn_probs, training=training)
+ value_vectors = tf.reshape(value_vectors, (batch_size, seq_len, self.num_heads, self.head_dim))
+
+ # if global attention, compute sum of global and local attn
+
+ if is_global_attn:
+ attn_output = self._compute_attn_output_with_global_indices(
+ value_vectors=value_vectors,
+ attn_probs=attn_probs,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ )
+ else:
+ attn_output = self._sliding_chunks_matmul_attn_probs_value(
+ attn_probs, value_vectors, self.one_sided_attn_window_size
+ )
+
+ tf.debugging.assert_equal(
+ shape_list(attn_output), [batch_size, seq_len, self.num_heads, self.head_dim], message="Unexpected size"
+ )
+
+ attn_output = tf.reshape(attn_output, (batch_size, seq_len, embed_dim))
+
+ # compute value for global attention and overwrite to attention output
+ if is_global_attn:
+ attn_output, global_attn_probs = self._compute_global_attn_output_from_hidden(
+ attn_output=attn_output,
+ hidden_states=hidden_states,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ layer_head_mask=layer_head_mask,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
+ is_index_masked=is_index_masked,
+ training=training,
+ )
+ else:
+ # Leave attn_output unchanged
+ global_attn_probs = tf.zeros((batch_size, self.num_heads, max_num_global_attn_indices, seq_len))
+
+ # make sure that local attention probabilities are set to 0 for indices of global attn
+ # Make sure to create a mask with the proper shape:
+ # if is_global_attn==True => [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + max_num_global_attn_indices + 1]
+ # if is_global_attn==False => [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + 1]
+ if is_global_attn:
+ masked_global_attn_index = tf.tile(
+ is_index_global_attn[:, :, None, None],
+ (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + max_num_global_attn_indices + 1),
+ )
+ else:
+ masked_global_attn_index = tf.tile(
+ is_index_global_attn[:, :, None, None],
+ (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + 1),
+ )
+ attn_probs = tf.where(
+ masked_global_attn_index,
+ tf.zeros(shape_list(masked_global_attn_index), dtype=attn_probs.dtype),
+ attn_probs,
+ )
+
+ outputs = (attn_output, attn_probs, global_attn_probs)
+
+ return outputs
+
+ def _sliding_chunks_query_key_matmul(self, query, key, window_overlap):
+ """
+ Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
+ implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer) with an
+ overlap of size window_overlap
+ """
+ batch_size, seq_len, num_heads, head_dim = shape_list(query)
+
+ tf.debugging.assert_equal(
+ seq_len % (window_overlap * 2),
+ 0,
+ message=f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}",
+ )
+ tf.debugging.assert_equal(
+ shape_list(query),
+ shape_list(key),
+ message=(
+ f"Shape of query and key should be equal, but got query: {shape_list(query)} and key:"
+ f" {shape_list(key)}"
+ ),
+ )
+
+ chunks_count = seq_len // window_overlap - 1
+
+ # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size window_overlap * 2
+ query = tf.reshape(
+ tf.transpose(query, (0, 2, 1, 3)),
+ (batch_size * num_heads, seq_len, head_dim),
+ )
+ key = tf.reshape(tf.transpose(key, (0, 2, 1, 3)), (batch_size * num_heads, seq_len, head_dim))
+ chunked_query = self._chunk(query, window_overlap)
+ chunked_key = self._chunk(key, window_overlap)
+
+ # matrix multiplication
+ # bcxd: batch_size * num_heads x chunks x 2window_overlap x head_dim
+ # bcyd: batch_size * num_heads x chunks x 2window_overlap x head_dim
+ # bcxy: batch_size * num_heads x chunks x 2window_overlap x 2window_overlap
+ chunked_query = tf.cast(chunked_query, dtype=chunked_key.dtype)
+ chunked_attention_scores = tf.einsum("bcxd,bcyd->bcxy", chunked_query, chunked_key) # multiply
+
+ # convert diagonals into columns
+ paddings = tf.convert_to_tensor([[0, 0], [0, 0], [0, 1], [0, 0]])
+ diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(chunked_attention_scores, paddings)
+
+ # allocate space for the overall attention matrix where the chunks are combined. The last dimension
+ # has (window_overlap * 2 + 1) columns. The first (window_overlap) columns are the window_overlap lower triangles (attention from a word to
+ # window_overlap previous words). The following column is attention score from each word to itself, then
+ # followed by window_overlap columns for the upper triangle.
+
+ # copy parts from diagonal_chunked_attention_scores into the combined matrix of attentions
+ # - copying the main diagonal and the upper triangle
+ # TODO: This code is most likely not very efficient and should be improved
+ diagonal_attn_scores_up_triang = tf.concat(
+ [
+ diagonal_chunked_attention_scores[:, :, :window_overlap, : window_overlap + 1],
+ diagonal_chunked_attention_scores[:, -1:, window_overlap:, : window_overlap + 1],
+ ],
+ axis=1,
+ )
+
+ # - copying the lower triangle
+ diagonal_attn_scores_low_triang = tf.concat(
+ [
+ tf.zeros(
+ (batch_size * num_heads, 1, window_overlap, window_overlap),
+ dtype=diagonal_chunked_attention_scores.dtype,
+ ),
+ diagonal_chunked_attention_scores[:, :, -(window_overlap + 1) : -1, window_overlap + 1 :],
+ ],
+ axis=1,
+ )
+ diagonal_attn_scores_first_chunk = tf.concat(
+ [
+ tf.roll(
+ diagonal_chunked_attention_scores,
+ shift=[1, window_overlap],
+ axis=[2, 3],
+ )[:, :, :window_overlap, :window_overlap],
+ tf.zeros(
+ (batch_size * num_heads, 1, window_overlap, window_overlap),
+ dtype=diagonal_chunked_attention_scores.dtype,
+ ),
+ ],
+ axis=1,
+ )
+ first_chunk_mask = (
+ tf.tile(
+ tf.range(chunks_count + 1, dtype=tf.int64)[None, :, None, None],
+ (batch_size * num_heads, 1, window_overlap, window_overlap),
+ )
+ < 1
+ )
+ diagonal_attn_scores_low_triang = tf.where(
+ first_chunk_mask,
+ diagonal_attn_scores_first_chunk,
+ diagonal_attn_scores_low_triang,
+ )
+
+ # merging upper and lower triangle
+ diagonal_attention_scores = tf.concat(
+ [diagonal_attn_scores_low_triang, diagonal_attn_scores_up_triang], axis=-1
+ )
+
+ # separate batch_size and num_heads dimensions again
+ diagonal_attention_scores = tf.transpose(
+ tf.reshape(
+ diagonal_attention_scores,
+ (batch_size, num_heads, seq_len, 2 * window_overlap + 1),
+ ),
+ (0, 2, 1, 3),
+ )
+
+ diagonal_attention_scores = self._mask_invalid_locations(diagonal_attention_scores, window_overlap)
+
+ return diagonal_attention_scores
+
+ @staticmethod
+ def _mask_invalid_locations(input_tensor, window_overlap):
+ # create correct upper triangle bool mask
+ mask_2d_upper = tf.reverse(
+ tf.linalg.band_part(tf.ones(shape=(window_overlap, window_overlap + 1)), -1, 0),
+ axis=[0],
+ )
+
+ # pad to full matrix
+ padding = tf.convert_to_tensor(
+ [[0, shape_list(input_tensor)[1] - window_overlap], [0, shape_list(input_tensor)[3] - window_overlap - 1]]
+ )
+
+ # create lower mask
+ mask_2d = tf.pad(mask_2d_upper, padding)
+
+ # combine with upper mask
+ mask_2d = mask_2d + tf.reverse(mask_2d, axis=[0, 1])
+
+ # broadcast to full matrix
+ mask_4d = tf.tile(mask_2d[None, :, None, :], (shape_list(input_tensor)[0], 1, 1, 1))
+
+ # inf tensor used for masking
+ inf_tensor = -float("inf") * tf.ones_like(input_tensor)
+
+ # mask
+ input_tensor = tf.where(tf.math.greater(mask_4d, 0), inf_tensor, input_tensor)
+
+ return input_tensor
+
+ def _sliding_chunks_matmul_attn_probs_value(self, attn_probs, value, window_overlap):
+ """
+ Same as _sliding_chunks_query_key_matmul but for attn_probs and value tensors. Returned tensor will be of the
+ same shape as `attn_probs`
+ """
+
+ batch_size, seq_len, num_heads, head_dim = shape_list(value)
+
+ tf.debugging.assert_equal(
+ seq_len % (window_overlap * 2), 0, message="Seq_len has to be multiple of 2 * window_overlap"
+ )
+ tf.debugging.assert_equal(
+ shape_list(attn_probs)[:3],
+ shape_list(value)[:3],
+ message="value and attn_probs must have same dims (except head_dim)",
+ )
+ tf.debugging.assert_equal(
+ shape_list(attn_probs)[3],
+ 2 * window_overlap + 1,
+ message="attn_probs last dim has to be 2 * window_overlap + 1",
+ )
+
+ chunks_count = seq_len // window_overlap - 1
+
+ # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size 2 window overlap
+ chunked_attn_probs = tf.reshape(
+ tf.transpose(attn_probs, (0, 2, 1, 3)),
+ (
+ batch_size * num_heads,
+ seq_len // window_overlap,
+ window_overlap,
+ 2 * window_overlap + 1,
+ ),
+ )
+
+ # group batch_size and num_heads dimensions into one
+ value = tf.reshape(
+ tf.transpose(value, (0, 2, 1, 3)),
+ (batch_size * num_heads, seq_len, head_dim),
+ )
+
+ # pad seq_len with w at the beginning of the sequence and another window overlap at the end
+ paddings = tf.convert_to_tensor([[0, 0], [window_overlap, window_overlap], [0, 0]])
+ padded_value = tf.pad(value, paddings, constant_values=-1)
+
+ # chunk padded_value into chunks of size 3 window overlap and an overlap of size window overlap
+ frame_size = 3 * window_overlap * head_dim
+ frame_hop_size = (shape_list(padded_value)[1] * head_dim - frame_size) // chunks_count
+ chunked_value = tf.signal.frame(
+ tf.reshape(padded_value, (batch_size * num_heads, -1)),
+ frame_size,
+ frame_hop_size,
+ )
+ chunked_value = tf.reshape(
+ chunked_value,
+ (batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim),
+ )
+
+ tf.debugging.assert_equal(
+ shape_list(chunked_value),
+ [batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim],
+ message="Chunked value has the wrong shape",
+ )
+
+ chunked_attn_probs = self._pad_and_diagonalize(chunked_attn_probs)
+ context = tf.einsum("bcwd,bcdh->bcwh", chunked_attn_probs, chunked_value)
+ context = tf.transpose(
+ tf.reshape(context, (batch_size, num_heads, seq_len, head_dim)),
+ (0, 2, 1, 3),
+ )
+
+ return context
+
+ @staticmethod
+ def _pad_and_transpose_last_two_dims(hidden_states_padded, paddings):
+ """pads rows and then flips rows and columns"""
+ hidden_states_padded = tf.pad(
+ hidden_states_padded, paddings
+ ) # padding value is not important because it will be overwritten
+ batch_size, chunk_size, seq_length, hidden_dim = shape_list(hidden_states_padded)
+ hidden_states_padded = tf.reshape(hidden_states_padded, (batch_size, chunk_size, hidden_dim, seq_length))
+
+ return hidden_states_padded
+
+ @staticmethod
+ def _pad_and_diagonalize(chunked_hidden_states):
+ """
+ shift every row 1 step right, converting columns into diagonals.
+
+ Example:
+
+ ```python
+ chunked_hidden_states: [
+ 0.4983,
+ 2.6918,
+ -0.0071,
+ 1.0492,
+ -1.8348,
+ 0.7672,
+ 0.2986,
+ 0.0285,
+ -0.7584,
+ 0.4206,
+ -0.0405,
+ 0.1599,
+ 2.0514,
+ -1.1600,
+ 0.5372,
+ 0.2629,
+ ]
+ window_overlap = num_rows = 4
+ ```
+
+ (pad & diagonalize) => [ 0.4983, 2.6918, -0.0071, 1.0492, 0.0000, 0.0000, 0.0000
+ 0.0000, -1.8348, 0.7672, 0.2986, 0.0285, 0.0000, 0.0000 0.0000, 0.0000, -0.7584, 0.4206,
+ -0.0405, 0.1599, 0.0000 0.0000, 0.0000, 0.0000, 2.0514, -1.1600, 0.5372, 0.2629 ]
+ """
+ total_num_heads, num_chunks, window_overlap, hidden_dim = shape_list(chunked_hidden_states)
+ paddings = tf.convert_to_tensor([[0, 0], [0, 0], [0, 0], [0, window_overlap + 1]])
+ chunked_hidden_states = tf.pad(
+ chunked_hidden_states, paddings
+ ) # total_num_heads x num_chunks x window_overlap x (hidden_dim+window_overlap+1). Padding value is not important because it'll be overwritten
+ chunked_hidden_states = tf.reshape(
+ chunked_hidden_states, (total_num_heads, num_chunks, -1)
+ ) # total_num_heads x num_chunks x window_overlapL+window_overlapwindow_overlap+window_overlap
+ chunked_hidden_states = chunked_hidden_states[
+ :, :, :-window_overlap
+ ] # total_num_heads x num_chunks x window_overlapL+window_overlapwindow_overlap
+ chunked_hidden_states = tf.reshape(
+ chunked_hidden_states,
+ (total_num_heads, num_chunks, window_overlap, window_overlap + hidden_dim),
+ ) # total_num_heads x num_chunks, window_overlap x hidden_dim+window_overlap
+ chunked_hidden_states = chunked_hidden_states[:, :, :, :-1]
+
+ return chunked_hidden_states
+
+ @staticmethod
+ def _chunk(hidden_states, window_overlap):
+ """convert into overlapping chunks. Chunk size = 2w, overlap size = w"""
+ batch_size, seq_length, hidden_dim = shape_list(hidden_states)
+ num_output_chunks = 2 * (seq_length // (2 * window_overlap)) - 1
+
+ # define frame size and frame stride (similar to convolution)
+ frame_hop_size = window_overlap * hidden_dim
+ frame_size = 2 * frame_hop_size
+ hidden_states = tf.reshape(hidden_states, (batch_size, seq_length * hidden_dim))
+
+ # chunk with overlap
+ chunked_hidden_states = tf.signal.frame(hidden_states, frame_size, frame_hop_size)
+
+ tf.debugging.assert_equal(
+ shape_list(chunked_hidden_states),
+ [batch_size, num_output_chunks, frame_size],
+ message=(
+ "Make sure chunking is correctly applied. `Chunked hidden states should have output dimension"
+ f" {[batch_size, frame_size, num_output_chunks]}, but got {shape_list(chunked_hidden_states)}."
+ ),
+ )
+
+ chunked_hidden_states = tf.reshape(
+ chunked_hidden_states,
+ (batch_size, num_output_chunks, 2 * window_overlap, hidden_dim),
+ )
+
+ return chunked_hidden_states
+
+ @staticmethod
+ def _get_global_attn_indices(is_index_global_attn):
+ """compute global attn indices required throughout forward pass"""
+ # helper variable
+ num_global_attn_indices = tf.math.count_nonzero(is_index_global_attn, axis=1)
+ num_global_attn_indices = tf.cast(num_global_attn_indices, dtype=tf.constant(1).dtype)
+
+ # max number of global attn indices in batch
+ max_num_global_attn_indices = tf.reduce_max(num_global_attn_indices)
+
+ # indices of global attn
+ is_index_global_attn_nonzero = tf.where(is_index_global_attn)
+
+ # helper variable
+ is_local_index_global_attn = tf.range(max_num_global_attn_indices) < tf.expand_dims(
+ num_global_attn_indices, axis=-1
+ )
+
+ # location of the non-padding values within global attention indices
+ is_local_index_global_attn_nonzero = tf.where(is_local_index_global_attn)
+
+ # location of the padding values within global attention indices
+ is_local_index_no_global_attn_nonzero = tf.where(tf.math.logical_not(is_local_index_global_attn))
+
+ return (
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ )
+
+ def _concat_with_global_key_attn_probs(
+ self,
+ attn_scores,
+ key_vectors,
+ query_vectors,
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ ):
+ batch_size = shape_list(key_vectors)[0]
+
+ # select global key vectors
+ global_key_vectors = tf.gather_nd(key_vectors, is_index_global_attn_nonzero)
+
+ # create only global key vectors
+ key_vectors_only_global = tf.scatter_nd(
+ is_local_index_global_attn_nonzero,
+ global_key_vectors,
+ shape=(
+ batch_size,
+ max_num_global_attn_indices,
+ self.num_heads,
+ self.head_dim,
+ ),
+ )
+
+ # (batch_size, seq_len, num_heads, max_num_global_attn_indices)
+ attn_probs_from_global_key = tf.einsum("blhd,bshd->blhs", query_vectors, key_vectors_only_global)
+
+ # (batch_size, max_num_global_attn_indices, seq_len, num_heads)
+ attn_probs_from_global_key_trans = tf.transpose(attn_probs_from_global_key, (0, 3, 1, 2))
+ mask_shape = (shape_list(is_local_index_no_global_attn_nonzero)[0],) + tuple(
+ shape_list(attn_probs_from_global_key_trans)[-2:]
+ )
+ mask = tf.ones(mask_shape) * -10000.0
+ mask = tf.cast(mask, dtype=attn_probs_from_global_key_trans.dtype)
+
+ # scatter mask
+ attn_probs_from_global_key_trans = tf.tensor_scatter_nd_update(
+ attn_probs_from_global_key_trans,
+ is_local_index_no_global_attn_nonzero,
+ mask,
+ )
+
+ # (batch_size, seq_len, num_heads, max_num_global_attn_indices)
+ attn_probs_from_global_key = tf.transpose(attn_probs_from_global_key_trans, (0, 2, 3, 1))
+
+ # concat to attn_probs
+ # (batch_size, seq_len, num_heads, extra attention count + 2*window+1)
+ attn_scores = tf.concat((attn_probs_from_global_key, attn_scores), axis=-1)
+
+ return attn_scores
+
+ def _compute_attn_output_with_global_indices(
+ self,
+ value_vectors,
+ attn_probs,
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ ):
+ batch_size = shape_list(attn_probs)[0]
+
+ # cut local attn probs to global only
+ attn_probs_only_global = attn_probs[:, :, :, :max_num_global_attn_indices]
+
+ # select global value vectors
+ global_value_vectors = tf.gather_nd(value_vectors, is_index_global_attn_nonzero)
+
+ # create only global value vectors
+ value_vectors_only_global = tf.scatter_nd(
+ is_local_index_global_attn_nonzero,
+ global_value_vectors,
+ shape=(
+ batch_size,
+ max_num_global_attn_indices,
+ self.num_heads,
+ self.head_dim,
+ ),
+ )
+
+ # compute attn output only global
+ attn_output_only_global = tf.einsum("blhs,bshd->blhd", attn_probs_only_global, value_vectors_only_global)
+
+ # reshape attn probs
+ attn_probs_without_global = attn_probs[:, :, :, max_num_global_attn_indices:]
+
+ # compute attn output with global
+ attn_output_without_global = self._sliding_chunks_matmul_attn_probs_value(
+ attn_probs_without_global, value_vectors, self.one_sided_attn_window_size
+ )
+
+ return attn_output_only_global + attn_output_without_global
+
+ def _compute_global_attn_output_from_hidden(
+ self,
+ attn_output,
+ hidden_states,
+ max_num_global_attn_indices,
+ layer_head_mask,
+ is_local_index_global_attn_nonzero,
+ is_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ is_index_masked,
+ training,
+ ):
+ batch_size, seq_len = shape_list(hidden_states)[:2]
+
+ # prepare global hidden states
+ global_attn_hidden_states = tf.gather_nd(hidden_states, is_index_global_attn_nonzero)
+ global_attn_hidden_states = tf.scatter_nd(
+ is_local_index_global_attn_nonzero,
+ global_attn_hidden_states,
+ shape=(batch_size, max_num_global_attn_indices, self.embed_dim),
+ )
+
+ # global key, query, value
+ global_query_vectors_only_global = self.query_global(global_attn_hidden_states)
+ global_key_vectors = self.key_global(hidden_states)
+ global_value_vectors = self.value_global(hidden_states)
+
+ # normalize
+ global_query_vectors_only_global /= tf.math.sqrt(
+ tf.cast(self.head_dim, dtype=global_query_vectors_only_global.dtype)
+ )
+ global_query_vectors_only_global = self.reshape_and_transpose(global_query_vectors_only_global, batch_size)
+ global_key_vectors = self.reshape_and_transpose(global_key_vectors, batch_size)
+ global_value_vectors = self.reshape_and_transpose(global_value_vectors, batch_size)
+
+ # compute attn scores
+ global_attn_scores = tf.matmul(global_query_vectors_only_global, global_key_vectors, transpose_b=True)
+
+ tf.debugging.assert_equal(
+ shape_list(global_attn_scores),
+ [batch_size * self.num_heads, max_num_global_attn_indices, seq_len],
+ message=(
+ "global_attn_scores have the wrong size. Size should be"
+ f" {(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)}, but is"
+ f" {shape_list(global_attn_scores)}."
+ ),
+ )
+
+ global_attn_scores = tf.reshape(
+ global_attn_scores,
+ (batch_size, self.num_heads, max_num_global_attn_indices, seq_len),
+ )
+ global_attn_scores_trans = tf.transpose(global_attn_scores, (0, 2, 1, 3))
+ mask_shape = (shape_list(is_local_index_no_global_attn_nonzero)[0],) + tuple(
+ shape_list(global_attn_scores_trans)[-2:]
+ )
+ global_attn_mask = tf.ones(mask_shape) * -10000.0
+ global_attn_mask = tf.cast(global_attn_mask, dtype=global_attn_scores_trans.dtype)
+
+ # scatter mask
+ global_attn_scores_trans = tf.tensor_scatter_nd_update(
+ global_attn_scores_trans,
+ is_local_index_no_global_attn_nonzero,
+ global_attn_mask,
+ )
+ global_attn_scores = tf.transpose(global_attn_scores_trans, (0, 2, 1, 3))
+
+ # mask global attn scores
+ attn_mask = tf.tile(is_index_masked[:, None, None, :], (1, shape_list(global_attn_scores)[1], 1, 1))
+ global_attn_scores = tf.where(attn_mask, -10000.0, global_attn_scores)
+ global_attn_scores = tf.reshape(
+ global_attn_scores,
+ (batch_size * self.num_heads, max_num_global_attn_indices, seq_len),
+ )
+
+ # compute global attn probs
+ global_attn_probs_float = stable_softmax(global_attn_scores, axis=-1)
+
+ # apply layer head masking
+ if layer_head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(layer_head_mask),
+ [self.num_heads],
+ message=(
+ f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
+ f" {shape_list(layer_head_mask)}"
+ ),
+ )
+ global_attn_probs_float = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
+ global_attn_probs_float, (batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
+ )
+ global_attn_probs_float = tf.reshape(
+ global_attn_probs_float, (batch_size * self.num_heads, max_num_global_attn_indices, seq_len)
+ )
+
+ # dropout
+ global_attn_probs = self.global_dropout(global_attn_probs_float, training=training)
+
+ # global attn output
+ global_attn_output = tf.matmul(global_attn_probs, global_value_vectors)
+
+ tf.debugging.assert_equal(
+ shape_list(global_attn_output),
+ [batch_size * self.num_heads, max_num_global_attn_indices, self.head_dim],
+ message=(
+ "global_attn_output tensor has the wrong size. Size should be"
+ f" {(batch_size * self.num_heads, max_num_global_attn_indices, self.head_dim)}, but is"
+ f" {shape_list(global_attn_output)}."
+ ),
+ )
+
+ global_attn_output = tf.reshape(
+ global_attn_output,
+ (batch_size, self.num_heads, max_num_global_attn_indices, self.head_dim),
+ )
+
+ # get only non zero global attn output
+ nonzero_global_attn_output = tf.gather_nd(
+ tf.transpose(global_attn_output, (0, 2, 1, 3)),
+ is_local_index_global_attn_nonzero,
+ )
+ nonzero_global_attn_output = tf.reshape(
+ nonzero_global_attn_output,
+ (shape_list(is_local_index_global_attn_nonzero)[0], -1),
+ )
+
+ # overwrite values with global attention
+ attn_output = tf.tensor_scatter_nd_update(
+ attn_output, is_index_global_attn_nonzero, nonzero_global_attn_output
+ )
+
+ global_attn_probs = tf.reshape(
+ global_attn_probs, (batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
+ )
+
+ return attn_output, global_attn_probs
+
+ def reshape_and_transpose(self, vector, batch_size):
+ return tf.reshape(
+ tf.transpose(
+ tf.reshape(vector, (batch_size, -1, self.num_heads, self.head_dim)),
+ (0, 2, 1, 3),
+ ),
+ (batch_size * self.num_heads, -1, self.head_dim),
+ )
+
+
+class TFLEDEncoderAttention(keras.layers.Layer):
+ def __init__(self, config, layer_id, **kwargs):
+ super().__init__(**kwargs)
+ self.longformer_self_attn = TFLEDEncoderSelfAttention(config, layer_id=layer_id, name="longformer_self_attn")
+ self.output_dense = keras.layers.Dense(config.d_model, use_bias=True, name="output")
+ self.config = config
+
+ def call(self, inputs, training=False):
+ (
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ is_index_masked,
+ is_index_global_attn,
+ is_global_attn,
+ ) = inputs
+
+ self_outputs = self.longformer_self_attn(
+ [hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn],
+ training=training,
+ )
+
+ attention_output = self.output_dense(self_outputs[0], training=training)
+ outputs = (attention_output,) + self_outputs[1:]
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "longformer_self_attn", None) is not None:
+ with tf.name_scope(self.longformer_self_attn.name):
+ self.longformer_self_attn.build(None)
+ if getattr(self, "output_dense", None) is not None:
+ with tf.name_scope(self.output_dense.name):
+ self.output_dense.build([None, None, self.config.d_model])
+
+
+class TFLEDDecoderAttention(keras.layers.Layer):
+ """Multi-headed attention from "Attention Is All You Need"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: float = 0.0,
+ is_decoder: bool = False,
+ bias: bool = True,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.embed_dim = embed_dim
+
+ self.num_heads = num_heads
+ self.dropout = keras.layers.Dropout(dropout)
+ self.head_dim = embed_dim // num_heads
+ assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+
+ self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
+ self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
+ self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
+ self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
+
+ def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
+ return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ key_value_states: tf.Tensor | None = None,
+ past_key_value: tuple[tuple[tf.Tensor]] | None = None,
+ attention_mask: tf.Tensor | None = None,
+ layer_head_mask: tf.Tensor | None = None,
+ training=False,
+ ) -> tuple[tf.Tensor, tf.Tensor | None]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+ bsz, tgt_len, embed_dim = shape_list(hidden_states)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states) * self.scaling
+ # get key, value proj
+ if is_cross_attention and past_key_value is not None:
+ # reuse k,v, cross_attentions
+ key_states = past_key_value[0]
+ value_states = past_key_value[1]
+ elif is_cross_attention:
+ # cross_attentions
+ key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
+ value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
+ elif past_key_value is not None:
+ # reuse k, v, self_attention
+ key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+ value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+ key_states = tf.concat([past_key_value[0], key_states], axis=2)
+ value_states = tf.concat([past_key_value[1], value_states], axis=2)
+ else:
+ # self_attention
+ key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+ value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+
+ if self.is_decoder:
+ # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
+ # Further calls to cross_attention layer can then reuse all cross-attention
+ # key/value_states (first "if" case)
+ # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
+ # all previous decoder key/value_states. Further calls to uni-directional self-attention
+ # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+ # if encoder bi-directional self-attention `past_key_value` is always `None`
+ past_key_value = (key_states, value_states)
+
+ proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+ query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
+ key_states = tf.reshape(key_states, proj_shape)
+ value_states = tf.reshape(value_states, proj_shape)
+
+ src_len = shape_list(key_states)[1]
+ attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
+
+ tf.debugging.assert_equal(
+ shape_list(attn_weights),
+ [bsz * self.num_heads, tgt_len, src_len],
+ message=(
+ f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
+ f" {shape_list(attn_weights)}"
+ ),
+ )
+
+ if attention_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(attention_mask),
+ [bsz, 1, tgt_len, src_len],
+ message=(
+ f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
+ f" {shape_list(attention_mask)}"
+ ),
+ )
+
+ attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + tf.cast(
+ attention_mask, dtype=attn_weights.dtype
+ )
+ attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
+
+ attn_weights = stable_softmax(attn_weights, axis=-1)
+
+ if layer_head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(layer_head_mask),
+ [self.num_heads],
+ message=(
+ f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
+ f" {shape_list(layer_head_mask)}"
+ ),
+ )
+
+ attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
+ attn_weights, (bsz, self.num_heads, tgt_len, src_len)
+ )
+ attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
+
+ attn_probs = self.dropout(attn_weights, training=training)
+
+ attn_output = tf.matmul(attn_probs, value_states)
+
+ tf.debugging.assert_equal(
+ shape_list(attn_output),
+ [bsz * self.num_heads, tgt_len, self.head_dim],
+ message=(
+ f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
+ f" {shape_list(attn_output)}"
+ ),
+ )
+
+ attn_output = tf.transpose(
+ tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
+ )
+ attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
+
+ attn_output = self.out_proj(attn_output)
+ attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
+
+ return attn_output, attn_weights, past_key_value
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "k_proj", None) is not None:
+ with tf.name_scope(self.k_proj.name):
+ self.k_proj.build([None, None, self.embed_dim])
+ if getattr(self, "q_proj", None) is not None:
+ with tf.name_scope(self.q_proj.name):
+ self.q_proj.build([None, None, self.embed_dim])
+ if getattr(self, "v_proj", None) is not None:
+ with tf.name_scope(self.v_proj.name):
+ self.v_proj.build([None, None, self.embed_dim])
+ if getattr(self, "out_proj", None) is not None:
+ with tf.name_scope(self.out_proj.name):
+ self.out_proj.build([None, None, self.embed_dim])
+
+
+class TFLEDEncoderLayer(keras.layers.Layer):
+ def __init__(self, config: LEDConfig, layer_id: int, **kwargs):
+ super().__init__(**kwargs)
+ self.embed_dim = config.d_model
+ self.self_attn = TFLEDEncoderAttention(config, layer_id, name="self_attn")
+ self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
+ self.dropout = keras.layers.Dropout(config.dropout)
+ self.activation_fn = get_tf_activation(config.activation_function)
+ self.activation_dropout = keras.layers.Dropout(config.activation_dropout)
+ self.fc1 = keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
+ self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2")
+ self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+ self.config = config
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: tf.Tensor,
+ layer_head_mask: tf.Tensor,
+ is_index_masked: tf.Tensor,
+ is_index_global_attn: tf.Tensor,
+ is_global_attn: bool,
+ training=False,
+ ):
+ """
+ Args:
+ hidden_states (`tf.Tensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
+ attention_mask (`tf.Tensor`): attention mask of size
+ *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
+ layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
+ *(config.encoder_attention_heads,)*.
+ """
+ residual = hidden_states
+ layer_outputs = self.self_attn(
+ [hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn],
+ training=training,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ tf.debugging.assert_equal(
+ shape_list(hidden_states),
+ shape_list(residual),
+ message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
+ )
+
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = self.activation_dropout(hidden_states, training=training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ return (hidden_states,) + layer_outputs[1:]
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self_attn", None) is not None:
+ with tf.name_scope(self.self_attn.name):
+ self.self_attn.build(None)
+ if getattr(self, "self_attn_layer_norm", None) is not None:
+ with tf.name_scope(self.self_attn_layer_norm.name):
+ self.self_attn_layer_norm.build([None, None, self.embed_dim])
+ if getattr(self, "fc1", None) is not None:
+ with tf.name_scope(self.fc1.name):
+ self.fc1.build([None, None, self.embed_dim])
+ if getattr(self, "fc2", None) is not None:
+ with tf.name_scope(self.fc2.name):
+ self.fc2.build([None, None, self.config.encoder_ffn_dim])
+ if getattr(self, "final_layer_norm", None) is not None:
+ with tf.name_scope(self.final_layer_norm.name):
+ self.final_layer_norm.build([None, None, self.embed_dim])
+
+
+class TFLEDDecoderLayer(keras.layers.Layer):
+ def __init__(self, config: LEDConfig, **kwargs):
+ super().__init__(**kwargs)
+ self.embed_dim = config.d_model
+ self.self_attn = TFLEDDecoderAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ name="self_attn",
+ is_decoder=True,
+ )
+ self.dropout = keras.layers.Dropout(config.dropout)
+ self.activation_fn = get_tf_activation(config.activation_function)
+ self.activation_dropout = keras.layers.Dropout(config.activation_dropout)
+
+ self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
+ self.encoder_attn = TFLEDDecoderAttention(
+ self.embed_dim,
+ config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ name="encoder_attn",
+ is_decoder=True,
+ )
+ self.encoder_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
+ self.fc1 = keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
+ self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2")
+ self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+ self.config = config
+
+ def call(
+ self,
+ hidden_states,
+ attention_mask: tf.Tensor | None = None,
+ encoder_hidden_states: tf.Tensor | None = None,
+ encoder_attention_mask: tf.Tensor | None = None,
+ layer_head_mask: tf.Tensor | None = None,
+ encoder_layer_head_mask: tf.Tensor | None = None,
+ past_key_value: tuple[tf.Tensor] | None = None,
+ training=False,
+ ) -> tuple[tf.Tensor, tf.Tensor, tf.Tensor, tuple[tuple[tf.Tensor]]]:
+ """
+ Args:
+ hidden_states (`tf.Tensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
+ attention_mask (`tf.Tensor`): attention mask of size
+ *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
+ encoder_hidden_states (`tf.Tensor`):
+ cross attention input to the layer of shape *(batch, seq_len, embed_dim)*
+ encoder_attention_mask (`tf.Tensor`): encoder attention mask of size
+ *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
+ layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
+ *(config.encoder_attention_heads,)*.
+ encoder_layer_head_mask (`tf.Tensor`): mask for encoder attention heads in a given layer of
+ size *(config.encoder_attention_heads,)*.
+ past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states
+ """
+ residual = hidden_states
+
+ # Self-Attention
+ # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+ self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+ # add present self-attn cache to positions 1,2 of present_key_value tuple
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_value=self_attn_past_key_value,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ )
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Cross-Attention Block
+ cross_attn_present_key_value = None
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+
+ # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
+ cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
+ hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ layer_head_mask=encoder_layer_head_mask,
+ past_key_value=cross_attn_past_key_value,
+ )
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ # add cross-attn to positions 3,4 of present_key_value tuple
+ present_key_value = present_key_value + cross_attn_present_key_value
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = self.activation_dropout(hidden_states, training=training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ return (
+ hidden_states,
+ self_attn_weights,
+ cross_attn_weights,
+ present_key_value,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self_attn", None) is not None:
+ with tf.name_scope(self.self_attn.name):
+ self.self_attn.build(None)
+ if getattr(self, "self_attn_layer_norm", None) is not None:
+ with tf.name_scope(self.self_attn_layer_norm.name):
+ self.self_attn_layer_norm.build([None, None, self.embed_dim])
+ if getattr(self, "encoder_attn", None) is not None:
+ with tf.name_scope(self.encoder_attn.name):
+ self.encoder_attn.build(None)
+ if getattr(self, "encoder_attn_layer_norm", None) is not None:
+ with tf.name_scope(self.encoder_attn_layer_norm.name):
+ self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+ if getattr(self, "fc1", None) is not None:
+ with tf.name_scope(self.fc1.name):
+ self.fc1.build([None, None, self.embed_dim])
+ if getattr(self, "fc2", None) is not None:
+ with tf.name_scope(self.fc2.name):
+ self.fc2.build([None, None, self.config.decoder_ffn_dim])
+ if getattr(self, "final_layer_norm", None) is not None:
+ with tf.name_scope(self.final_layer_norm.name):
+ self.final_layer_norm.build([None, None, self.embed_dim])
+
+
+class TFLEDPreTrainedModel(TFPreTrainedModel):
+ config_class = LEDConfig
+ base_model_prefix = "led"
+
+ @property
+ def input_signature(self):
+ sig = super().input_signature
+ sig["global_attention_mask"] = tf.TensorSpec((None, None), tf.int32, name="global_attention_mask")
+ return sig
+
+
+@dataclass
+# Copied from transformers.models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutput with TFLongformer->TFLEDEncoder
+class TFLEDEncoderBaseModelOutput(ModelOutput):
+ """
+ Base class for Longformer's outputs, with potential hidden states, local and global attentions.
+
+ Args:
+ last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the model.
+ hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+ attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`, where `x`
+ is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ last_hidden_state: tf.Tensor | None = None
+ hidden_states: tuple[tf.Tensor, ...] | None = None
+ attentions: tuple[tf.Tensor, ...] | None = None
+ global_attentions: tuple[tf.Tensor, ...] | None = None
+
+
+@dataclass
+class TFLEDSeq2SeqModelOutput(ModelOutput):
+ """
+ Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
+ decoding.
+
+ Args:
+ last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the decoder of the model.
+
+ If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
+ hidden_size)` is output.
+ past_key_values (`list[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
+ sequence_length, embed_size_per_head)`).
+
+ Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
+ used (see `past_key_values` input) to speed up sequential decoding.
+ decoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
+ decoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`.
+
+ Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
+ self-attention heads.
+ cross_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`.
+
+ Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
+ weighted average in the cross-attention heads.
+ encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder of the model.
+ encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
+ encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`.
+
+ Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
+ self-attention heads.
+ encoder_global_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`, where `x`
+ is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ last_hidden_state: tf.Tensor | None = None
+ past_key_values: list[tf.Tensor] | None = None
+ decoder_hidden_states: tuple[tf.Tensor, ...] | None = None
+ decoder_attentions: tuple[tf.Tensor, ...] | None = None
+ cross_attentions: tuple[tf.Tensor, ...] | None = None
+ encoder_last_hidden_state: tf.Tensor | None = None
+ encoder_hidden_states: tuple[tf.Tensor, ...] | None = None
+ encoder_attentions: tuple[tf.Tensor, ...] | None = None
+ encoder_global_attentions: tuple[tf.Tensor, ...] | None = None
+
+
+@dataclass
+class TFLEDSeq2SeqLMOutput(ModelOutput):
+ """
+ Base class for sequence-to-sequence language models outputs.
+
+ Args:
+ loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss.
+ logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`list[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
+ sequence_length, embed_size_per_head)`).
+
+ Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
+ used (see `past_key_values` input) to speed up sequential decoding.
+ decoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
+ decoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`.
+
+ Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
+ self-attention heads.
+ cross_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`.
+
+ Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
+ weighted average in the cross-attention heads.
+ encoder_last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder of the model.
+ encoder_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
+ encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`.
+
+ Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
+ self-attention heads.
+ encoder_global_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`, where `x`
+ is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: tf.Tensor | None = None
+ logits: tf.Tensor | None = None
+ past_key_values: list[tf.Tensor] | None = None
+ decoder_hidden_states: tuple[tf.Tensor, ...] | None = None
+ decoder_attentions: tuple[tf.Tensor, ...] | None = None
+ cross_attentions: tuple[tf.Tensor, ...] | None = None
+ encoder_last_hidden_state: tf.Tensor | None = None
+ encoder_hidden_states: tuple[tf.Tensor, ...] | None = None
+ encoder_attentions: tuple[tf.Tensor, ...] | None = None
+ encoder_global_attentions: tuple[tf.Tensor, ...] | None = None
+
+
+LED_START_DOCSTRING = r"""
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Args:
+ config ([`LEDConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+LED_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`tf.Tensor` of shape `({0})`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `({0})`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`LedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ LED uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+ decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
+ head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ encoder_outputs (`tf.Tensor`, *optional*):
+ hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
+ of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
+ past_key_values (`tuple[tuple[tf.Tensor]]` of length `config.n_layers`)
+ contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`). Set to `False` during training, `True` during generation
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+ config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+ used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+ eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+"""
+
+
+@keras_serializable
+class TFLEDEncoder(keras.layers.Layer):
+ config_class = LEDConfig
+ """
+ Transformer encoder consisting of *config.encoder_layers* self-attention layers. Each layer is a
+ [`TFLEDEncoderLayer`].
+
+ Args:
+ config: LEDConfig
+ """
+
+ def __init__(self, config: LEDConfig, embed_tokens: keras.layers.Embedding | None = None, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.dropout = keras.layers.Dropout(config.dropout)
+ if config.encoder_layerdrop > 0:
+ logger.warning("Layerdrop is currently disabled in TFLED models.")
+ self.layerdrop = 0.0
+ self.padding_idx = config.pad_token_id
+
+ if isinstance(config.attention_window, int):
+ assert config.attention_window % 2 == 0, "`config.attention_window` has to be an even value"
+ assert config.attention_window > 0, "`config.attention_window` has to be positive"
+ config.attention_window = [config.attention_window] * config.num_hidden_layers # one value per layer
+ else:
+ assert len(config.attention_window) == config.num_hidden_layers, (
+ "`len(config.attention_window)` should equal `config.num_hidden_layers`. "
+ f"Expected {config.num_hidden_layers}, given {len(config.attention_window)}"
+ )
+
+ self.attention_window = config.attention_window
+ self.embed_tokens = embed_tokens
+ self.embed_positions = TFLEDLearnedPositionalEmbedding(
+ config.max_encoder_position_embeddings,
+ config.d_model,
+ name="embed_positions",
+ )
+ self.layers = [TFLEDEncoderLayer(config, i, name=f"layers.{i}") for i in range(config.encoder_layers)]
+ self.layernorm_embedding = keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
+ self.embed_dim = config.d_model
+
+ def get_embed_tokens(self):
+ return self.embed_tokens
+
+ def set_embed_tokens(self, embed_tokens):
+ self.embed_tokens = embed_tokens
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids=None,
+ inputs_embeds=None,
+ attention_mask=None,
+ global_attention_mask=None,
+ head_mask=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ training=False,
+ ):
+ """
+ Args:
+ input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`tf.Tensor` of shape `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
+ inputs_embeds = self.embed_tokens(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if attention_mask is None:
+ attention_mask = tf.fill(input_shape, 1)
+
+ # merge `global_attention_mask` and `attention_mask`
+ if global_attention_mask is not None:
+ attention_mask = attention_mask * tf.cast((global_attention_mask + 1), dtype=attention_mask.dtype)
+
+ padding_len, input_ids, attention_mask, inputs_embeds = self._pad_to_window_size(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ pad_token_id=self.padding_idx,
+ )
+
+ input_shape = shape_list(attention_mask)
+ # is index masked or global attention
+ is_index_masked = tf.math.less(tf.cast(attention_mask, tf.int8), 1)
+ is_index_global_attn = tf.math.greater(tf.cast(attention_mask, tf.int8), 1)
+ is_global_attn = tf.math.reduce_any(is_index_global_attn)
+
+ embed_pos = self.embed_positions(input_shape)
+ hidden_states = inputs_embeds + embed_pos
+ hidden_states = self.layernorm_embedding(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+
+ # check attention mask and invert
+ if attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _expand_mask(attention_mask)[:, 0, 0, :]
+ attention_mask = attention_mask[:, :, None, None]
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = all_global_attentions = () if output_attentions else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(head_mask)[0],
+ len(self.layers),
+ message=(
+ f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
+ f" {shape_list(head_mask)[0]}."
+ ),
+ )
+
+ # encoder layers
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ hidden_states_to_add = self.compute_hidden_states(hidden_states, padding_len)
+ encoder_states = encoder_states + (hidden_states_to_add,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = random.uniform(0, 1)
+ if training and (dropout_probability < self.layerdrop): # skip the layer
+ continue
+
+ layer_outputs = encoder_layer(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=head_mask[idx] if head_mask is not None else None,
+ is_index_masked=is_index_masked,
+ is_index_global_attn=is_index_global_attn,
+ is_global_attn=is_global_attn,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ # bzs x seq_len x num_attn_heads x (num_global_attn + attention_window_len + 1) => bzs x num_attn_heads x seq_len x (num_global_attn + attention_window_len + 1)
+ all_attentions = all_attentions + (tf.transpose(layer_outputs[1], (0, 2, 1, 3)),)
+
+ # bzs x num_attn_heads x num_global_attn x seq_len => bzs x num_attn_heads x seq_len x num_global_attn
+ all_global_attentions = all_global_attentions + (tf.transpose(layer_outputs[2], (0, 1, 3, 2)),)
+
+ # undo padding
+ # unpad `hidden_states` because the calling function is expecting a length == input_ids.size(1)
+ hidden_states = self.compute_hidden_states(hidden_states, padding_len)
+
+ # undo padding
+ if output_attentions:
+ all_attentions = (
+ tuple(state[:, :, :-padding_len, :] for state in all_attentions) if padding_len > 0 else all_attentions
+ )
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+ return TFLEDEncoderBaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=encoder_states,
+ attentions=all_attentions,
+ global_attentions=all_global_attentions,
+ )
+
+ @tf.function
+ def compute_hidden_states(self, hidden_states, padding_len):
+ return hidden_states[:, :-padding_len] if padding_len > 0 else hidden_states
+
+ def _pad_to_window_size(
+ self,
+ input_ids,
+ attention_mask,
+ inputs_embeds,
+ pad_token_id,
+ ):
+ """A helper function to pad tokens and mask to work with implementation of Longformer selfattention."""
+ # padding
+ attention_window = (
+ self.attention_window if isinstance(self.attention_window, int) else max(self.attention_window)
+ )
+
+ assert attention_window % 2 == 0, f"`attention_window` should be an even value. Given {attention_window}"
+
+ input_shape = shape_list(input_ids) if input_ids is not None else shape_list(inputs_embeds)
+ batch_size, seq_len = input_shape[:2]
+ padding_len = (attention_window - seq_len % attention_window) % attention_window
+
+ if padding_len > 0:
+ logger.warning_once(
+ f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of "
+ f"`config.attention_window`: {attention_window}"
+ )
+
+ paddings = tf.convert_to_tensor([[0, 0], [0, padding_len]])
+
+ if input_ids is not None:
+ input_ids = tf.pad(input_ids, paddings, constant_values=pad_token_id)
+
+ if inputs_embeds is not None:
+ if padding_len > 0:
+ input_ids_padding = tf.fill((batch_size, padding_len), pad_token_id)
+ inputs_embeds_padding = self.embed_tokens(input_ids_padding)
+ inputs_embeds = tf.concat([inputs_embeds, inputs_embeds_padding], axis=-2)
+
+ attention_mask = tf.pad(attention_mask, paddings, constant_values=False) # no attention on the padding tokens
+
+ return (
+ padding_len,
+ input_ids,
+ attention_mask,
+ inputs_embeds,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embed_positions", None) is not None:
+ with tf.name_scope(self.embed_positions.name):
+ self.embed_positions.build(None)
+ if getattr(self, "layernorm_embedding", None) is not None:
+ with tf.name_scope(self.layernorm_embedding.name):
+ self.layernorm_embedding.build([None, None, self.embed_dim])
+ if getattr(self, "layers", None) is not None:
+ for layer in self.layers:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+@keras_serializable
+class TFLEDDecoder(keras.layers.Layer):
+ config_class = LEDConfig
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFLEDDecoderLayer`]
+
+ Args:
+ config: LEDConfig
+ embed_tokens: output embedding
+ """
+
+ def __init__(self, config: LEDConfig, embed_tokens: keras.layers.Embedding | None = None, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.padding_idx = config.pad_token_id
+ self.embed_tokens = embed_tokens
+ if config.decoder_layerdrop > 0:
+ logger.warning("Layerdrop is currently disabled in TFLED models.")
+ self.layerdrop = 0.0
+ self.embed_positions = TFLEDLearnedPositionalEmbedding(
+ config.max_decoder_position_embeddings,
+ config.d_model,
+ name="embed_positions",
+ )
+ self.layers = [TFLEDDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
+ self.layernorm_embedding = keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
+
+ self.dropout = keras.layers.Dropout(config.dropout)
+
+ def set_embed_tokens(self, embed_tokens):
+ self.embed_tokens = embed_tokens
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids=None,
+ inputs_embeds=None,
+ attention_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ head_mask=None,
+ encoder_head_mask=None,
+ past_key_values=None,
+ use_cache=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ training=False,
+ ):
+ r"""
+ Args:
+ input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ [What are attention masks?](../glossary#attention-mask)
+ encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`tf.Tensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ encoder_head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention
+ on hidden heads. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`tuple[tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+ Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
+ decoding. If `past_key_values` are used, the user can optionally input only the last
+ `decoder_input_ids` (those that don't have their past key value states given to this model) of shape
+ `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
+
+ # embed positions
+ positions = self.embed_positions(input_shape, past_key_values_length)
+
+ if inputs_embeds is None:
+ check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ hidden_states = inputs_embeds
+
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ if input_shape[-1] > 1:
+ combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
+ else:
+ combined_attention_mask = _expand_mask(
+ tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
+ )
+
+ if attention_mask is not None and input_shape[-1] > 1:
+ combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1])
+
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1])
+
+ hidden_states = self.layernorm_embedding(hidden_states + positions)
+ hidden_states = self.dropout(hidden_states, training=training)
+
+ # decoder layers
+ all_hidden_states = ()
+ all_self_attns = ()
+ all_cross_attentions = ()
+ present_key_values = ()
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(head_mask)[0],
+ len(self.layers),
+ message=(
+ f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
+ f" {shape_list(head_mask)[0]}."
+ ),
+ )
+
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ dropout_probability = random.uniform(0, 1)
+
+ if training and (dropout_probability < self.layerdrop):
+ continue
+
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+ hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
+ hidden_states,
+ attention_mask=combined_attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ layer_head_mask=head_mask[idx] if head_mask is not None else None,
+ encoder_layer_head_mask=encoder_head_mask[idx] if encoder_head_mask is not None else None,
+ past_key_value=past_key_value,
+ )
+
+ if use_cache:
+ present_key_values += (present_key_value,)
+
+ if output_attentions:
+ all_self_attns += (layer_self_attn,)
+ all_cross_attentions += (layer_cross_attn,)
+
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ else:
+ all_hidden_states = None
+
+ all_self_attns = all_self_attns if output_attentions else None
+ all_cross_attentions = all_cross_attentions if output_attentions else None
+
+ present_key_values = present_key_values if use_cache else None
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attentions]
+ if v is not None
+ )
+ else:
+ return TFBaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=present_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embed_positions", None) is not None:
+ with tf.name_scope(self.embed_positions.name):
+ self.embed_positions.build(None)
+ if getattr(self, "layernorm_embedding", None) is not None:
+ with tf.name_scope(self.layernorm_embedding.name):
+ self.layernorm_embedding.build([None, None, self.config.d_model])
+ if getattr(self, "layers", None) is not None:
+ for layer in self.layers:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+@keras_serializable
+class TFLEDMainLayer(keras.layers.Layer):
+ config_class = LEDConfig
+
+ def __init__(self, config: LEDConfig, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.shared = keras.layers.Embedding(
+ input_dim=config.vocab_size,
+ output_dim=config.d_model,
+ embeddings_initializer=keras.initializers.TruncatedNormal(stddev=self.config.init_std),
+ name="led.shared",
+ )
+ # Additional attribute to specify the expected name scope of the layer (for loading/storing weights)
+ self.shared.load_weight_prefix = "led.shared"
+
+ self.encoder = TFLEDEncoder(config, self.shared, name="encoder")
+ self.decoder = TFLEDDecoder(config, self.shared, name="decoder")
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, new_embeddings):
+ self.shared = new_embeddings
+ self.encoder.embed_tokens = self.shared
+ self.decoder.embed_tokens = self.shared
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ decoder_input_ids=None,
+ decoder_attention_mask=None,
+ head_mask=None,
+ decoder_head_mask=None,
+ encoder_outputs: tuple | TFLEDEncoderBaseModelOutput | None = None,
+ global_attention_mask=None,
+ past_key_values=None,
+ inputs_embeds=None,
+ decoder_inputs_embeds=None,
+ use_cache=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ training=False,
+ **kwargs,
+ ):
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ use_cache = False
+
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ global_attention_mask=global_attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ # If the user passed a tuple for encoder_outputs, we wrap it in a TFLEDEncoderBaseModelOutput when return_dict=True
+ elif return_dict and not isinstance(encoder_outputs, TFLEDEncoderBaseModelOutput):
+ encoder_outputs = TFLEDEncoderBaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+ # If the user passed a TFLEDEncoderBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
+ elif not return_dict and not isinstance(encoder_outputs, tuple):
+ encoder_outputs = encoder_outputs.to_tuple()
+
+ decoder_outputs = self.decoder(
+ decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ encoder_head_mask=head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return TFLEDSeq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ encoder_global_attentions=encoder_outputs.global_attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ # The shared/tied weights expect to be in the model base namespace
+ # Adding "/" to the end (not the start!) of a tf.name_scope puts it in the root namespace rather than
+ # the current one.
+ with tf.name_scope(self.shared.load_weight_prefix + "/" + self.shared.name + "/"):
+ self.shared.build(None)
+ if getattr(self, "encoder", None) is not None:
+ with tf.name_scope(self.encoder.name):
+ self.encoder.build(None)
+ if getattr(self, "decoder", None) is not None:
+ with tf.name_scope(self.decoder.name):
+ self.decoder.build(None)
+
+
+@add_start_docstrings(
+ "The bare LED Model outputting raw hidden-states without any specific head on top.",
+ LED_START_DOCSTRING,
+)
+class TFLEDModel(TFLEDPreTrainedModel):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.led = TFLEDMainLayer(config, name="led")
+
+ def get_encoder(self):
+ return self.led.encoder
+
+ def get_decoder(self):
+ return self.led.decoder
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LED_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFLEDSeq2SeqModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: tf.Tensor | None = None,
+ decoder_input_ids: tf.Tensor | None = None,
+ decoder_attention_mask: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ decoder_head_mask: tf.Tensor | None = None,
+ encoder_outputs: tf.Tensor | None = None,
+ global_attention_mask: tf.Tensor | None = None,
+ past_key_values: tuple[tuple[tf.Tensor]] | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ decoder_inputs_embeds: tf.Tensor | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ **kwargs,
+ ) -> tuple[tf.Tensor] | TFLEDSeq2SeqModelOutput:
+ outputs = self.led(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ encoder_outputs=encoder_outputs,
+ global_attention_mask=global_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ return outputs
+
+ def serving_output(self, output):
+ pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
+ dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
+ dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
+ cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
+ enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
+ enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
+ enc_g_attns = tf.convert_to_tensor(output.encoder_global_attentions) if self.config.output_attentions else None
+
+ return TFLEDSeq2SeqModelOutput(
+ last_hidden_state=output.last_hidden_state,
+ past_key_values=pkv,
+ decoder_hidden_states=dec_hs,
+ decoder_attentions=dec_attns,
+ cross_attentions=cross_attns,
+ encoder_last_hidden_state=output.encoder_last_hidden_state,
+ encoder_hidden_states=enc_hs,
+ encoder_attentions=enc_attns,
+ encoder_global_attentions=enc_g_attns,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "led", None) is not None:
+ with tf.name_scope(self.led.name):
+ self.led.build(None)
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
+class BiasLayer(keras.layers.Layer):
+ """
+ Bias as a layer. It is used for serialization purposes: `keras.Model.save_weights` stores on a per-layer basis,
+ so all weights have to be registered in a layer.
+ """
+
+ def __init__(self, shape, initializer, trainable, name, **kwargs):
+ super().__init__(name=name, **kwargs)
+ # Note: the name of this variable will NOT be scoped when serialized, i.e. it will not be in the format of
+ # "outer_layer/inner_layer/.../name:0". Instead, it will be "name:0". For further details, see:
+ # https://github.com/huggingface/transformers/pull/18833#issuecomment-1233090214
+ self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable)
+
+ def call(self, x):
+ return x + self.bias
+
+
+@add_start_docstrings(
+ "The LED Model with a language modeling head. Can be used for summarization.",
+ LED_START_DOCSTRING,
+)
+class TFLEDForConditionalGeneration(TFLEDPreTrainedModel):
+ _keys_to_ignore_on_load_unexpected = [
+ r"led.encoder.embed_tokens.weight",
+ r"led.decoder.embed_tokens.weight",
+ ]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.led = TFLEDMainLayer(config, name="led")
+ self.use_cache = config.use_cache
+ # final_bias_logits is registered as a buffer in pytorch, so not trainable for the sake of consistency.
+ self.bias_layer = BiasLayer(
+ name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
+ )
+
+ # TODO (Joao): investigate why LED has numerical issues in XLA generate
+ self.supports_xla_generation = False
+
+ def get_decoder(self):
+ return self.led.decoder
+
+ def get_encoder(self):
+ return self.led.encoder
+
+ def get_bias(self):
+ return {"final_logits_bias": self.bias_layer.bias}
+
+ def set_bias(self, value):
+ # Replaces the existing layers containing bias for correct (de)serialization.
+ vocab_size = value["final_logits_bias"].shape[-1]
+ self.bias_layer = BiasLayer(
+ name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False
+ )
+ self.bias_layer.bias.assign(value["final_logits_bias"])
+
+ def get_output_embeddings(self):
+ return self.get_input_embeddings()
+
+ def set_output_embeddings(self, value):
+ self.set_input_embeddings(value)
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LED_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=TFLEDSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ decoder_input_ids: np.ndarray | tf.Tensor | None = None,
+ decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ decoder_head_mask: np.ndarray | tf.Tensor | None = None,
+ encoder_outputs: TFLEDEncoderBaseModelOutput | None = None,
+ global_attention_mask: np.ndarray | tf.Tensor | None = None,
+ past_key_values: tuple[tuple[np.ndarray | tf.Tensor]] | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ decoder_inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> tuple[tf.Tensor] | TFLEDSeq2SeqLMOutput:
+ """
+ Returns:
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, TFLEDForConditionalGeneration
+ >>> import tensorflow as tf
+
+ >>> mname = "allenai/led-base-16384"
+ >>> tokenizer = AutoTokenizer.from_pretrained(mname)
+ >>> TXT = "My friends are but they eat too many carbs."
+ >>> model = TFLEDForConditionalGeneration.from_pretrained(mname)
+ >>> batch = tokenizer([TXT], return_tensors="tf")
+ >>> logits = model(inputs=batch.input_ids).logits
+ >>> probs = tf.nn.softmax(logits[0])
+ >>> # probs[5] is associated with the mask token
+ ```"""
+
+ if labels is not None:
+ use_cache = False
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ decoder_input_ids = shift_tokens_right(
+ labels, self.config.pad_token_id, self.config.decoder_start_token_id
+ )
+
+ outputs = self.led(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ encoder_outputs=encoder_outputs,
+ global_attention_mask=global_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ lm_logits = tf.matmul(outputs[0], self.led.shared.weights, transpose_b=True)
+ lm_logits = self.bias_layer(lm_logits)
+ masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+ return TFLEDSeq2SeqLMOutput(
+ loss=masked_lm_loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values, # index 1 of d outputs
+ decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs
+ decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs
+ cross_attentions=outputs.cross_attentions, # index 4 of d outputs
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs
+ encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out
+ encoder_attentions=outputs.encoder_attentions, # 2 of e out
+ encoder_global_attentions=outputs.encoder_global_attentions,
+ )
+
+ def serving_output(self, output):
+ pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
+ dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
+ dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
+ cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
+ enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
+ enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
+ enc_g_attns = tf.convert_to_tensor(output.encoder_global_attentions) if self.config.output_attentions else None
+
+ return TFLEDSeq2SeqLMOutput(
+ logits=output.logits,
+ past_key_values=pkv,
+ decoder_hidden_states=dec_hs,
+ decoder_attentions=dec_attns,
+ cross_attentions=cross_attns,
+ encoder_last_hidden_state=output.encoder_last_hidden_state,
+ encoder_hidden_states=enc_hs,
+ encoder_attentions=enc_attns,
+ encoder_global_attentions=enc_g_attns,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ decoder_input_ids,
+ past_key_values=None,
+ attention_mask=None,
+ head_mask=None,
+ decoder_head_mask=None,
+ use_cache=None,
+ encoder_outputs=None,
+ **kwargs,
+ ):
+ # cut decoder_input_ids if past is used
+ if past_key_values is not None:
+ decoder_input_ids = decoder_input_ids[:, -1:]
+
+ return {
+ "input_ids": None, # encoder_outputs is defined. input_ids not needed
+ "encoder_outputs": encoder_outputs,
+ "past_key_values": past_key_values,
+ "decoder_input_ids": decoder_input_ids,
+ "attention_mask": attention_mask,
+ "head_mask": head_mask,
+ "decoder_head_mask": decoder_head_mask,
+ "use_cache": use_cache, # change this to avoid caching (presumably for debugging)
+ }
+
+ def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
+ return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
+ def hf_compute_loss(self, labels, logits):
+ """CrossEntropyLoss that ignores pad tokens"""
+ loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=keras.losses.Reduction.NONE)
+ if self.config.tf_legacy_loss:
+ melted_labels = tf.reshape(labels, (-1,))
+ active_loss = tf.not_equal(melted_labels, self.config.pad_token_id)
+ reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss)
+ labels = tf.boolean_mask(melted_labels, active_loss)
+ return loss_fn(labels, reduced_logits)
+
+ # Clip negative labels to zero here to avoid NaNs and errors - those positions will get masked later anyway
+ unmasked_loss = loss_fn(tf.nn.relu(labels), logits)
+ # make sure only non-padding labels affect the loss
+ loss_mask = tf.cast(labels != self.config.pad_token_id, dtype=unmasked_loss.dtype)
+ masked_loss = unmasked_loss * loss_mask
+ reduced_masked_loss = tf.reduce_sum(masked_loss) / tf.reduce_sum(loss_mask)
+ return tf.reshape(reduced_masked_loss, (1,))
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "led", None) is not None:
+ with tf.name_scope(self.led.name):
+ self.led.build(None)
+ if getattr(self, "bias_layer", None) is not None:
+ with tf.name_scope(self.bias_layer.name):
+ self.bias_layer.build(None)
+
+
+__all__ = ["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/tokenization_led.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/tokenization_led.py
new file mode 100644
index 0000000000000000000000000000000000000000..d110ac30d969e1958a872e8bdc186df0822a25b5
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/tokenization_led.py
@@ -0,0 +1,454 @@
+# coding=utf-8
+# Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for LED."""
+
+import json
+import os
+from functools import lru_cache
+from typing import Optional, Union
+
+import regex as re
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...tokenization_utils_base import BatchEncoding, EncodedInput
+from ...utils import PaddingStrategy, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
+
+# See all LED models at https://huggingface.co/models?filter=LED
+
+
+@lru_cache
+# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
+def bytes_to_unicode():
+ """
+ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
+ characters the bpe code barfs on.
+
+ The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
+ if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
+ decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
+ tables between utf-8 bytes and unicode strings.
+ """
+ bs = (
+ list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
+ )
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8 + n)
+ n += 1
+ cs = [chr(n) for n in cs]
+ return dict(zip(bs, cs))
+
+
+# Copied from transformers.models.bart.tokenization_bart.get_pairs
+def get_pairs(word):
+ """
+ Return set of symbol pairs in a word.
+
+ Word is represented as tuple of symbols (symbols being variable-length strings).
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+class LEDTokenizer(PreTrainedTokenizer):
+ """
+ Constructs a LED tokenizer, which is smilar to the ROBERTa tokenizer, using byte-level Byte-Pair-Encoding.
+
+ This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
+ be encoded differently whether it is at the beginning of the sentence (without space) or not:
+
+ ```python
+ >>> from transformers import LEDTokenizer
+
+ >>> tokenizer = LEDTokenizer.from_pretrained("allenai/led-base-16384")
+ >>> tokenizer("Hello world")["input_ids"]
+ [0, 31414, 232, 2]
+
+ >>> tokenizer(" Hello world")["input_ids"]
+ [0, 20920, 232, 2]
+ ```
+
+ You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
+ call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
+
+
+
+ When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
+
+
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ errors (`str`, *optional*, defaults to `"replace"`):
+ Paradigm to follow when decoding bytes to UTF-8. See
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ add_prefix_space (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. (BART tokenizer detect beginning of words by the preceding space).
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.__init__
+ def __init__(
+ self,
+ vocab_file,
+ merges_file,
+ errors="replace",
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ add_prefix_space=False,
+ **kwargs,
+ ):
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+ sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
+ cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+ with open(vocab_file, encoding="utf-8") as vocab_handle:
+ self.encoder = json.load(vocab_handle)
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ self.errors = errors # how to handle errors in decoding
+ self.byte_encoder = bytes_to_unicode()
+ self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+ with open(merges_file, encoding="utf-8") as merges_handle:
+ bpe_merges = merges_handle.read().split("\n")[1:-1]
+ bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
+ self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+ self.cache = {}
+ self.add_prefix_space = add_prefix_space
+
+ # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
+ self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
+
+ super().__init__(
+ errors=errors,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ add_prefix_space=add_prefix_space,
+ **kwargs,
+ )
+
+ @property
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
+ def vocab_size(self):
+ return len(self.encoder)
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.get_vocab
+ def get_vocab(self):
+ return dict(self.encoder, **self.added_tokens_encoder)
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.bpe
+ def bpe(self, token):
+ if token in self.cache:
+ return self.cache[token]
+ word = tuple(token)
+ pairs = get_pairs(word)
+
+ if not pairs:
+ return token
+
+ while True:
+ bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+ if bigram not in self.bpe_ranks:
+ break
+ first, second = bigram
+ new_word = []
+ i = 0
+ while i < len(word):
+ try:
+ j = word.index(first, i)
+ except ValueError:
+ new_word.extend(word[i:])
+ break
+ else:
+ new_word.extend(word[i:j])
+ i = j
+
+ if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+ new_word.append(first + second)
+ i += 2
+ else:
+ new_word.append(word[i])
+ i += 1
+ new_word = tuple(new_word)
+ word = new_word
+ if len(word) == 1:
+ break
+ else:
+ pairs = get_pairs(word)
+ word = " ".join(word)
+ self.cache[token] = word
+ return word
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer._tokenize
+ def _tokenize(self, text):
+ """Tokenize a string."""
+ bpe_tokens = []
+ for token in re.findall(self.pat, text):
+ token = "".join(
+ self.byte_encoder[b] for b in token.encode("utf-8")
+ ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
+ bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
+ return bpe_tokens
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer._convert_token_to_id
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer._convert_id_to_token
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.decoder.get(index)
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.convert_tokens_to_string
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ text = "".join(tokens)
+ text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
+ return text
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.save_vocabulary
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ merge_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
+ )
+
+ with open(vocab_file, "w", encoding="utf-8") as f:
+ f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+ index = 0
+ with open(merge_file, "w", encoding="utf-8") as writer:
+ writer.write("#version: 0.2\n")
+ for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
+ " Please check that the tokenizer is not corrupted!"
+ )
+ index = token_index
+ writer.write(" ".join(bpe_tokens) + "\n")
+ index += 1
+
+ return vocab_file, merge_file
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.build_inputs_with_special_tokens with BART->LED
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A LED sequence has the following format:
+
+ - single sequence: ` X `
+ - pair of sequences: ` A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.get_special_tokens_mask
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is None:
+ return [1] + ([0] * len(token_ids_0)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.create_token_type_ids_from_sequences with BART->LED
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. LED does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.prepare_for_tokenization
+ def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
+ add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
+ if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
+ text = " " + text
+ return (text, kwargs)
+
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ encoded_inputs = super()._pad(
+ encoded_inputs=encoded_inputs,
+ max_length=max_length,
+ padding_strategy=padding_strategy,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ if return_attention_mask and "global_attention_mask" in encoded_inputs:
+ required_input = encoded_inputs[self.model_input_names[0]]
+ # `global_attention_mask` need to have the same length as other (sequential) inputs.
+ needs_to_be_padded = len(encoded_inputs["global_attention_mask"]) != len(required_input)
+
+ if needs_to_be_padded:
+ difference = len(required_input) - len(encoded_inputs["global_attention_mask"])
+
+ if self.padding_side == "right":
+ # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
+ encoded_inputs["global_attention_mask"] = (
+ encoded_inputs["global_attention_mask"] + [-1] * difference
+ )
+ elif self.padding_side == "left":
+ encoded_inputs["global_attention_mask"] = [-1] * difference + encoded_inputs[
+ "global_attention_mask"
+ ]
+ else:
+ raise ValueError("Invalid padding strategy:" + str(self.padding_side))
+
+ return encoded_inputs
+
+
+__all__ = ["LEDTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/tokenization_led_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/tokenization_led_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..baea10f23516fef914d70fef87bd3d17bce19baa
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/led/tokenization_led_fast.py
@@ -0,0 +1,322 @@
+# coding=utf-8
+# Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for LED."""
+
+import json
+from typing import Optional, Union
+
+from tokenizers import processors
+
+from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import PaddingStrategy, logging
+from .tokenization_led import LEDTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
+
+
+class LEDTokenizerFast(PreTrainedTokenizerFast):
+ r"""
+ Construct a "fast" LED tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer,
+ using byte-level Byte-Pair-Encoding.
+
+ This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
+ be encoded differently whether it is at the beginning of the sentence (without space) or not:
+
+ ```python
+ >>> from transformers import LEDTokenizerFast
+
+ >>> tokenizer = LEDTokenizerFast.from_pretrained("allenai/led-base-16384")
+ >>> tokenizer("Hello world")["input_ids"]
+ [0, 31414, 232, 2]
+
+ >>> tokenizer(" Hello world")["input_ids"]
+ [0, 20920, 232, 2]
+ ```
+
+ You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
+ call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
+
+
+
+ When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
+
+
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ errors (`str`, *optional*, defaults to `"replace"`):
+ Paradigm to follow when decoding bytes to UTF-8. See
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ add_prefix_space (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. (LED tokenizer detect beginning of words by the preceding space).
+ trim_offsets (`bool`, *optional*, defaults to `True`):
+ Whether the post processing step should trim offsets to avoid including whitespaces.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ slow_tokenizer_class = LEDTokenizer
+ model_input_names = ["input_ids", "attention_mask"]
+
+ # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.__init__
+ def __init__(
+ self,
+ vocab_file=None,
+ merges_file=None,
+ tokenizer_file=None,
+ errors="replace",
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ add_prefix_space=False,
+ trim_offsets=True,
+ **kwargs,
+ ):
+ # we have to specify that this tokens is special otherwise adding it will reset the normalized flag to `False` in `add_special_tokens`
+ mask_token = (
+ AddedToken(mask_token, lstrip=True, normalized=True, special=True)
+ if isinstance(mask_token, str)
+ else mask_token
+ )
+ super().__init__(
+ vocab_file,
+ merges_file,
+ tokenizer_file=tokenizer_file,
+ errors=errors,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ add_prefix_space=add_prefix_space,
+ trim_offsets=trim_offsets,
+ **kwargs,
+ )
+
+ # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
+ tokenizer_component = "post_processor"
+ tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
+ if tokenizer_component_instance:
+ state = json.loads(tokenizer_component_instance.__getstate__())
+
+ # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
+ if "sep" in state:
+ state["sep"] = tuple(state["sep"])
+ if "cls" in state:
+ state["cls"] = tuple(state["cls"])
+
+ changes_to_apply = False
+
+ if state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
+ state["add_prefix_space"] = add_prefix_space
+ changes_to_apply = True
+
+ if state.get("trim_offsets", trim_offsets) != trim_offsets:
+ state["trim_offsets"] = trim_offsets
+ changes_to_apply = True
+
+ if changes_to_apply:
+ component_class = getattr(processors, state.pop("type"))
+ new_value = component_class(**state)
+ setattr(self.backend_tokenizer, tokenizer_component, new_value)
+
+ @property
+ # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
+ def mask_token(self) -> str:
+ """
+ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
+ having been set.
+
+ LED tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
+ comprise the space before the **.
+ """
+ if self._mask_token is None:
+ if self.verbose:
+ logger.error("Using mask_token, but it is not set yet.")
+ return None
+ return str(self._mask_token)
+
+ @mask_token.setter
+ def mask_token(self, value):
+ """
+ Overriding the default behavior of the mask token to have it eat the space before it.
+
+ This is needed to preserve backward compatibility with all the previously used models based on LED.
+ """
+ # Mask token behave like a normal word, i.e. include the space before it
+ # So we set lstrip to True
+ value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value
+ self._mask_token = value
+
+ # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast._batch_encode_plus
+ def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
+ is_split_into_words = kwargs.get("is_split_into_words", False)
+
+ if is_split_into_words and not self.add_prefix_space:
+ raise ValueError(
+ f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
+ "to use it with pretokenized inputs."
+ )
+
+ return super()._batch_encode_plus(*args, **kwargs)
+
+ # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast._encode_plus
+ def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
+ is_split_into_words = kwargs.get("is_split_into_words", False)
+
+ if is_split_into_words and not self.add_prefix_space:
+ raise ValueError(
+ f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
+ "to use it with pretokenized inputs."
+ )
+
+ return super()._encode_plus(*args, **kwargs)
+
+ # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.save_vocabulary
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+ return tuple(files)
+
+ # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.build_inputs_with_special_tokens
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
+ if token_ids_1 is None:
+ return output
+
+ return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
+
+ # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.create_token_type_ids_from_sequences with BART->LED
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. LED does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ # Copied from transformers.models.led.tokenization_led.LEDTokenizer._pad
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ encoded_inputs = super()._pad(
+ encoded_inputs=encoded_inputs,
+ max_length=max_length,
+ padding_strategy=padding_strategy,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ if return_attention_mask and "global_attention_mask" in encoded_inputs:
+ required_input = encoded_inputs[self.model_input_names[0]]
+ # `global_attention_mask` need to have the same length as other (sequential) inputs.
+ needs_to_be_padded = len(encoded_inputs["global_attention_mask"]) != len(required_input)
+
+ if needs_to_be_padded:
+ difference = len(required_input) - len(encoded_inputs["global_attention_mask"])
+
+ if self.padding_side == "right":
+ # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
+ encoded_inputs["global_attention_mask"] = (
+ encoded_inputs["global_attention_mask"] + [-1] * difference
+ )
+ elif self.padding_side == "left":
+ encoded_inputs["global_attention_mask"] = [-1] * difference + encoded_inputs[
+ "global_attention_mask"
+ ]
+ else:
+ raise ValueError("Invalid padding strategy:" + str(self.padding_side))
+
+ return encoded_inputs
+
+
+__all__ = ["LEDTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3ae097b66a7f23a97cc24eda5ae80051bcd475c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_levit import *
+ from .feature_extraction_levit import *
+ from .image_processing_levit import *
+ from .image_processing_levit_fast import *
+ from .modeling_levit import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/configuration_levit.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/configuration_levit.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0d63ed8e37177e0779fe3d22b027a69f1f80838
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/configuration_levit.py
@@ -0,0 +1,144 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LeViT model configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+
+from packaging import version
+
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class LevitConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LevitModel`]. It is used to instantiate a LeViT
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the LeViT
+ [facebook/levit-128S](https://huggingface.co/facebook/levit-128S) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ image_size (`int`, *optional*, defaults to 224):
+ The size of the input image.
+ num_channels (`int`, *optional*, defaults to 3):
+ Number of channels in the input image.
+ kernel_size (`int`, *optional*, defaults to 3):
+ The kernel size for the initial convolution layers of patch embedding.
+ stride (`int`, *optional*, defaults to 2):
+ The stride size for the initial convolution layers of patch embedding.
+ padding (`int`, *optional*, defaults to 1):
+ The padding size for the initial convolution layers of patch embedding.
+ patch_size (`int`, *optional*, defaults to 16):
+ The patch size for embeddings.
+ hidden_sizes (`list[int]`, *optional*, defaults to `[128, 256, 384]`):
+ Dimension of each of the encoder blocks.
+ num_attention_heads (`list[int]`, *optional*, defaults to `[4, 8, 12]`):
+ Number of attention heads for each attention layer in each block of the Transformer encoder.
+ depths (`list[int]`, *optional*, defaults to `[4, 4, 4]`):
+ The number of layers in each encoder block.
+ key_dim (`list[int]`, *optional*, defaults to `[16, 16, 16]`):
+ The size of key in each of the encoder blocks.
+ drop_path_rate (`int`, *optional*, defaults to 0):
+ The dropout probability for stochastic depths, used in the blocks of the Transformer encoder.
+ mlp_ratios (`list[int]`, *optional*, defaults to `[2, 2, 2]`):
+ Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
+ encoder blocks.
+ attention_ratios (`list[int]`, *optional*, defaults to `[2, 2, 2]`):
+ Ratio of the size of the output dimension compared to input dimension of attention layers.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+
+ Example:
+
+ ```python
+ >>> from transformers import LevitConfig, LevitModel
+
+ >>> # Initializing a LeViT levit-128S style configuration
+ >>> configuration = LevitConfig()
+
+ >>> # Initializing a model (with random weights) from the levit-128S style configuration
+ >>> model = LevitModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "levit"
+
+ def __init__(
+ self,
+ image_size=224,
+ num_channels=3,
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ patch_size=16,
+ hidden_sizes=[128, 256, 384],
+ num_attention_heads=[4, 8, 12],
+ depths=[4, 4, 4],
+ key_dim=[16, 16, 16],
+ drop_path_rate=0,
+ mlp_ratio=[2, 2, 2],
+ attention_ratio=[2, 2, 2],
+ initializer_range=0.02,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.image_size = image_size
+ self.num_channels = num_channels
+ self.kernel_size = kernel_size
+ self.stride = stride
+ self.padding = padding
+ self.hidden_sizes = hidden_sizes
+ self.num_attention_heads = num_attention_heads
+ self.depths = depths
+ self.key_dim = key_dim
+ self.drop_path_rate = drop_path_rate
+ self.patch_size = patch_size
+ self.attention_ratio = attention_ratio
+ self.mlp_ratio = mlp_ratio
+ self.initializer_range = initializer_range
+ self.down_ops = [
+ ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
+ ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
+ ]
+
+
+# Copied from transformers.models.vit.configuration_vit.ViTOnnxConfig
+class LevitOnnxConfig(OnnxConfig):
+ torch_onnx_minimum_version = version.parse("1.11")
+
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ return OrderedDict(
+ [
+ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
+ ]
+ )
+
+ @property
+ def atol_for_validation(self) -> float:
+ return 1e-4
+
+
+__all__ = ["LevitConfig", "LevitOnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/convert_levit_timm_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/convert_levit_timm_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d5731bf7befec4f1e984b0f538f55552992ba5b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/convert_levit_timm_to_pytorch.py
@@ -0,0 +1,181 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert LeViT checkpoints from timm."""
+
+import argparse
+import json
+from collections import OrderedDict
+from functools import partial
+from pathlib import Path
+from typing import Optional
+
+import timm
+import torch
+from huggingface_hub import hf_hub_download
+
+from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger()
+
+
+def convert_weight_and_push(
+ hidden_sizes: int, name: str, config: LevitConfig, save_directory: Path, push_to_hub: bool = True
+):
+ print(f"Converting {name}...")
+
+ with torch.no_grad():
+ if hidden_sizes == 128:
+ if name[-1] == "S":
+ from_model = timm.create_model("levit_128s", pretrained=True)
+ else:
+ from_model = timm.create_model("levit_128", pretrained=True)
+ if hidden_sizes == 192:
+ from_model = timm.create_model("levit_192", pretrained=True)
+ if hidden_sizes == 256:
+ from_model = timm.create_model("levit_256", pretrained=True)
+ if hidden_sizes == 384:
+ from_model = timm.create_model("levit_384", pretrained=True)
+
+ from_model.eval()
+ our_model = LevitForImageClassificationWithTeacher(config).eval()
+ huggingface_weights = OrderedDict()
+
+ weights = from_model.state_dict()
+ og_keys = list(from_model.state_dict().keys())
+ new_keys = list(our_model.state_dict().keys())
+ print(len(og_keys), len(new_keys))
+ for i in range(len(og_keys)):
+ huggingface_weights[new_keys[i]] = weights[og_keys[i]]
+ our_model.load_state_dict(huggingface_weights)
+
+ x = torch.randn((2, 3, 224, 224))
+ out1 = from_model(x)
+ out2 = our_model(x).logits
+
+ assert torch.allclose(out1, out2), "The model logits don't match the original one."
+
+ checkpoint_name = name
+ print(checkpoint_name)
+
+ if push_to_hub:
+ our_model.save_pretrained(save_directory / checkpoint_name)
+ image_processor = LevitImageProcessor()
+ image_processor.save_pretrained(save_directory / checkpoint_name)
+
+ print(f"Pushed {checkpoint_name}")
+
+
+def convert_weights_and_push(save_directory: Path, model_name: Optional[str] = None, push_to_hub: bool = True):
+ filename = "imagenet-1k-id2label.json"
+ num_labels = 1000
+ expected_shape = (1, num_labels)
+
+ repo_id = "huggingface/label-files"
+ num_labels = num_labels
+ id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
+ id2label = {int(k): v for k, v in id2label.items()}
+
+ id2label = id2label
+ label2id = {v: k for k, v in id2label.items()}
+
+ ImageNetPreTrainedConfig = partial(LevitConfig, num_labels=num_labels, id2label=id2label, label2id=label2id)
+
+ names_to_hidden_sizes = {
+ "levit-128S": 128,
+ "levit-128": 128,
+ "levit-192": 192,
+ "levit-256": 256,
+ "levit-384": 384,
+ }
+
+ names_to_config = {
+ "levit-128S": ImageNetPreTrainedConfig(
+ hidden_sizes=[128, 256, 384],
+ num_attention_heads=[4, 6, 8],
+ depths=[2, 3, 4],
+ key_dim=[16, 16, 16],
+ drop_path_rate=0,
+ ),
+ "levit-128": ImageNetPreTrainedConfig(
+ hidden_sizes=[128, 256, 384],
+ num_attention_heads=[4, 8, 12],
+ depths=[4, 4, 4],
+ key_dim=[16, 16, 16],
+ drop_path_rate=0,
+ ),
+ "levit-192": ImageNetPreTrainedConfig(
+ hidden_sizes=[192, 288, 384],
+ num_attention_heads=[3, 5, 6],
+ depths=[4, 4, 4],
+ key_dim=[32, 32, 32],
+ drop_path_rate=0,
+ ),
+ "levit-256": ImageNetPreTrainedConfig(
+ hidden_sizes=[256, 384, 512],
+ num_attention_heads=[4, 6, 8],
+ depths=[4, 4, 4],
+ key_dim=[32, 32, 32],
+ drop_path_rate=0,
+ ),
+ "levit-384": ImageNetPreTrainedConfig(
+ hidden_sizes=[384, 512, 768],
+ num_attention_heads=[6, 9, 12],
+ depths=[4, 4, 4],
+ key_dim=[32, 32, 32],
+ drop_path_rate=0.1,
+ ),
+ }
+
+ if model_name:
+ convert_weight_and_push(
+ names_to_hidden_sizes[model_name], model_name, names_to_config[model_name], save_directory, push_to_hub
+ )
+ else:
+ for model_name, config in names_to_config.items():
+ convert_weight_and_push(names_to_hidden_sizes[model_name], model_name, config, save_directory, push_to_hub)
+ return config, expected_shape
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--model_name",
+ default=None,
+ type=str,
+ help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path",
+ default="levit-dump-folder/",
+ type=Path,
+ required=False,
+ help="Path to the output PyTorch model directory.",
+ )
+ parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
+ parser.add_argument(
+ "--no-push_to_hub",
+ dest="push_to_hub",
+ action="store_false",
+ help="Do not push model and image processor to the hub",
+ )
+
+ args = parser.parse_args()
+ pytorch_dump_folder_path: Path = args.pytorch_dump_folder_path
+ pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
+ convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/feature_extraction_levit.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/feature_extraction_levit.py
new file mode 100644
index 0000000000000000000000000000000000000000..d634239b24500fd279d41101188937548df4bfa2
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/feature_extraction_levit.py
@@ -0,0 +1,38 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Feature extractor class for LeViT."""
+
+import warnings
+
+from ...utils import logging
+from ...utils.import_utils import requires
+from .image_processing_levit import LevitImageProcessor
+
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("vision",))
+class LevitFeatureExtractor(LevitImageProcessor):
+ def __init__(self, *args, **kwargs) -> None:
+ warnings.warn(
+ "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
+ " use LevitImageProcessor instead.",
+ FutureWarning,
+ )
+ super().__init__(*args, **kwargs)
+
+
+__all__ = ["LevitFeatureExtractor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/image_processing_levit.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/image_processing_levit.py
new file mode 100644
index 0000000000000000000000000000000000000000..7832dbe5f43b4985017337ebed3b85c0fc3df5c2
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/image_processing_levit.py
@@ -0,0 +1,312 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for LeViT."""
+
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import (
+ get_resize_output_image_size,
+ resize,
+ to_channel_dimension_format,
+)
+from ...image_utils import (
+ IMAGENET_DEFAULT_MEAN,
+ IMAGENET_DEFAULT_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import TensorType, filter_out_non_signature_kwargs, logging
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("vision",))
+class LevitImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a LeViT image processor.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Wwhether to resize the shortest edge of the input to int(256/224 *`size`). Can be overridden by the
+ `do_resize` parameter in the `preprocess` method.
+ size (`dict[str, int]`, *optional*, defaults to `{"shortest_edge": 224}`):
+ Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will
+ be resized to `(size["height"], size["width"])`. If size is a dict with key "shortest_edge", the shortest
+ edge value `c` is rescaled to `int(c * (256/224))`. The smaller edge of the image will be matched to this
+ value i.e, if height > width, then image will be rescaled to `(size["shortest_egde"] * height / width,
+ size["shortest_egde"])`. Can be overridden by the `size` parameter in the `preprocess` method.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+ Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
+ `preprocess` method.
+ do_center_crop (`bool`, *optional*, defaults to `True`):
+ Whether or not to center crop the input to `(crop_size["height"], crop_size["width"])`. Can be overridden
+ by the `do_center_crop` parameter in the `preprocess` method.
+ crop_size (`Dict`, *optional*, defaults to `{"height": 224, "width": 224}`):
+ Desired image size after `center_crop`. Can be overridden by the `crop_size` parameter in the `preprocess`
+ method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
+ `do_rescale` parameter in the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
+ `preprocess` method.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
+ `preprocess` method.
+ image_mean (`list[int]`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`list[int]`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
+ Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+ number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+ """
+
+ model_input_names = ["pixel_values"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ do_center_crop: bool = True,
+ crop_size: Optional[dict[str, int]] = None,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN,
+ image_std: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"shortest_edge": 224}
+ size = get_size_dict(size, default_to_square=False)
+ crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
+ crop_size = get_size_dict(crop_size, param_name="crop_size")
+
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.do_center_crop = do_center_crop
+ self.crop_size = crop_size
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
+ self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
+
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image.
+
+ If size is a dict with keys "width" and "height", the image will be resized to `(size["height"],
+ size["width"])`.
+
+ If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to `int(c * (256/224))`.
+ The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled
+ to `(size["shortest_egde"] * height / width, size["shortest_egde"])`.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Size of the output image after resizing. If size is a dict with keys "width" and "height", the image
+ will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value
+ `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value
+ i.e, if height > width, then image will be rescaled to (size * height / width, size).
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ Resampling filter to use when resiizing the image.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ size_dict = get_size_dict(size, default_to_square=False)
+ # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
+ if "shortest_edge" in size:
+ shortest_edge = int((256 / 224) * size["shortest_edge"])
+ output_size = get_resize_output_image_size(
+ image, size=shortest_edge, default_to_square=False, input_data_format=input_data_format
+ )
+ size_dict = {"height": output_size[0], "width": output_size[1]}
+ if "height" not in size_dict or "width" not in size_dict:
+ raise ValueError(
+ f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"
+ )
+ return resize(
+ image,
+ size=(size_dict["height"], size_dict["width"]),
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[dict[str, int]] = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, Iterable[float]]] = None,
+ image_std: Optional[Union[float, Iterable[float]]] = None,
+ return_tensors: Optional[TensorType] = None,
+ data_format: ChannelDimension = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> BatchFeature:
+ """
+ Preprocess an image or batch of images to be used as input to a LeViT model.
+
+ Args:
+ images (`ImageInput`):
+ Image or batch of images to preprocess. Expects a single or batch of images with pixel values ranging
+ from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the output image after resizing. If size is a dict with keys "width" and "height", the image
+ will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value
+ `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value
+ i.e, if height > width, then image will be rescaled to (size * height / width, size).
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ Resampling filter to use when resiizing the image.
+ do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
+ Whether to center crop the image.
+ crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`):
+ Size of the output image after center cropping. Crops images to (crop_size["height"],
+ crop_size["width"]).
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image pixel values by `rescaling_factor` - typical to values between 0 and 1.
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Factor to rescale the image pixel values by.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image pixel values by `image_mean` and `image_std`.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Mean to normalize the image pixel values by.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Standard deviation to normalize the image pixel values by.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`str` or `ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. If unset, the channel dimension format of the input
+ image is used. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ resample = resample if resample is not None else self.resample
+ do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+
+ size = size if size is not None else self.size
+ size = get_size_dict(size, default_to_square=False)
+ crop_size = crop_size if crop_size is not None else self.crop_size
+ crop_size = get_size_dict(crop_size, param_name="crop_size")
+ images = make_list_of_images(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if do_rescale and is_scaled_image(images[0]):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ if do_resize:
+ images = [self.resize(image, size, resample, input_data_format=input_data_format) for image in images]
+
+ if do_center_crop:
+ images = [self.center_crop(image, crop_size, input_data_format=input_data_format) for image in images]
+
+ if do_rescale:
+ images = [self.rescale(image, rescale_factor, input_data_format=input_data_format) for image in images]
+
+ if do_normalize:
+ images = [
+ self.normalize(image, image_mean, image_std, input_data_format=input_data_format) for image in images
+ ]
+
+ images = [
+ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
+ ]
+
+ data = {"pixel_values": images}
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+
+__all__ = ["LevitImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/image_processing_levit_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/image_processing_levit_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d8774ade8f3c22cf40960bdf2116d2e45cfccf7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/image_processing_levit_fast.py
@@ -0,0 +1,98 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for LeViT."""
+
+from ...image_processing_utils_fast import BaseImageProcessorFast, SizeDict
+from ...image_transforms import (
+ ChannelDimension,
+ get_resize_output_image_size,
+)
+from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
+from ...utils import auto_docstring, is_torch_available, is_torchvision_available, is_torchvision_v2_available
+
+
+if is_torch_available():
+ import torch
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+@auto_docstring
+class LevitImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BICUBIC
+ image_mean = IMAGENET_DEFAULT_MEAN
+ image_std = IMAGENET_DEFAULT_STD
+ size = {"shortest_edge": 224}
+ default_to_square = False
+ crop_size = {"height": 224, "width": 224}
+ do_resize = True
+ do_center_crop = True
+ do_rescale = True
+ do_normalize = True
+ do_convert_rgb = None
+
+ def resize(
+ self,
+ image: torch.Tensor,
+ size: SizeDict,
+ interpolation: "F.InterpolationMode" = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ """
+ Resize an image.
+
+ If size is a dict with keys "width" and "height", the image will be resized to `(size["height"],
+ size["width"])`.
+
+ If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to `int(c * (256/224))`.
+ The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled
+ to `(size["shortest_egde"] * height / width, size["shortest_egde"])`.
+
+ Args:
+ image (`torch.Tensor`):
+ Image to resize.
+ size (`SizeDict`):
+ Size of the output image after resizing. If size is a dict with keys "width" and "height", the image
+ will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value
+ `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value
+ i.e, if height > width, then image will be rescaled to (size * height / width, size).
+ interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BICUBIC`):
+ Resampling filter to use when resiizing the image.
+ """
+ interpolation = interpolation if interpolation is not None else F.InterpolationMode.BICUBIC
+ if size.shortest_edge:
+ shortest_edge = int((256 / 224) * size["shortest_edge"])
+ new_size = get_resize_output_image_size(
+ image, size=shortest_edge, default_to_square=False, input_data_format=ChannelDimension.FIRST
+ )
+ elif size.height and size.width:
+ new_size = (size.height, size.width)
+ else:
+ raise ValueError(
+ f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size.keys()} {size.keys()}."
+ )
+ return F.resize(
+ image,
+ size=new_size,
+ interpolation=interpolation,
+ **kwargs,
+ )
+
+
+__all__ = ["LevitImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/modeling_levit.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/modeling_levit.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc275a1c4c40418641fc041416223f630fa8188c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/levit/modeling_levit.py
@@ -0,0 +1,677 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch LeViT model."""
+
+import itertools
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...modeling_outputs import (
+ BaseModelOutputWithNoAttention,
+ BaseModelOutputWithPoolingAndNoAttention,
+ ImageClassifierOutputWithNoAttention,
+ ModelOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, logging
+from .configuration_levit import LevitConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Output type of [`LevitForImageClassificationWithTeacher`].
+ """
+)
+class LevitForImageClassificationWithTeacherOutput(ModelOutput):
+ r"""
+ logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+ Prediction scores as the average of the `cls_logits` and `distillation_logits`.
+ cls_logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+ Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the
+ class token).
+ distillation_logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+ Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the
+ distillation token).
+ """
+
+ logits: Optional[torch.FloatTensor] = None
+ cls_logits: Optional[torch.FloatTensor] = None
+ distillation_logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+
+
+class LevitConvEmbeddings(nn.Module):
+ """
+ LeViT Conv Embeddings with Batch Norm, used in the initial patch embedding layer.
+ """
+
+ def __init__(
+ self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, groups=1, bn_weight_init=1
+ ):
+ super().__init__()
+ self.convolution = nn.Conv2d(
+ in_channels, out_channels, kernel_size, stride, padding, dilation=dilation, groups=groups, bias=False
+ )
+ self.batch_norm = nn.BatchNorm2d(out_channels)
+
+ def forward(self, embeddings):
+ embeddings = self.convolution(embeddings)
+ embeddings = self.batch_norm(embeddings)
+ return embeddings
+
+
+class LevitPatchEmbeddings(nn.Module):
+ """
+ LeViT patch embeddings, for final embeddings to be passed to transformer blocks. It consists of multiple
+ `LevitConvEmbeddings`.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.embedding_layer_1 = LevitConvEmbeddings(
+ config.num_channels, config.hidden_sizes[0] // 8, config.kernel_size, config.stride, config.padding
+ )
+ self.activation_layer_1 = nn.Hardswish()
+
+ self.embedding_layer_2 = LevitConvEmbeddings(
+ config.hidden_sizes[0] // 8, config.hidden_sizes[0] // 4, config.kernel_size, config.stride, config.padding
+ )
+ self.activation_layer_2 = nn.Hardswish()
+
+ self.embedding_layer_3 = LevitConvEmbeddings(
+ config.hidden_sizes[0] // 4, config.hidden_sizes[0] // 2, config.kernel_size, config.stride, config.padding
+ )
+ self.activation_layer_3 = nn.Hardswish()
+
+ self.embedding_layer_4 = LevitConvEmbeddings(
+ config.hidden_sizes[0] // 2, config.hidden_sizes[0], config.kernel_size, config.stride, config.padding
+ )
+ self.num_channels = config.num_channels
+
+ def forward(self, pixel_values):
+ num_channels = pixel_values.shape[1]
+ if num_channels != self.num_channels:
+ raise ValueError(
+ "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
+ )
+ embeddings = self.embedding_layer_1(pixel_values)
+ embeddings = self.activation_layer_1(embeddings)
+ embeddings = self.embedding_layer_2(embeddings)
+ embeddings = self.activation_layer_2(embeddings)
+ embeddings = self.embedding_layer_3(embeddings)
+ embeddings = self.activation_layer_3(embeddings)
+ embeddings = self.embedding_layer_4(embeddings)
+ return embeddings.flatten(2).transpose(1, 2)
+
+
+class MLPLayerWithBN(nn.Module):
+ def __init__(self, input_dim, output_dim, bn_weight_init=1):
+ super().__init__()
+ self.linear = nn.Linear(in_features=input_dim, out_features=output_dim, bias=False)
+ self.batch_norm = nn.BatchNorm1d(output_dim)
+
+ def forward(self, hidden_state):
+ hidden_state = self.linear(hidden_state)
+ hidden_state = self.batch_norm(hidden_state.flatten(0, 1)).reshape_as(hidden_state)
+ return hidden_state
+
+
+class LevitSubsample(nn.Module):
+ def __init__(self, stride, resolution):
+ super().__init__()
+ self.stride = stride
+ self.resolution = resolution
+
+ def forward(self, hidden_state):
+ batch_size, _, channels = hidden_state.shape
+ hidden_state = hidden_state.view(batch_size, self.resolution, self.resolution, channels)[
+ :, :: self.stride, :: self.stride
+ ].reshape(batch_size, -1, channels)
+ return hidden_state
+
+
+class LevitAttention(nn.Module):
+ def __init__(self, hidden_sizes, key_dim, num_attention_heads, attention_ratio, resolution):
+ super().__init__()
+ self.num_attention_heads = num_attention_heads
+ self.scale = key_dim**-0.5
+ self.key_dim = key_dim
+ self.attention_ratio = attention_ratio
+ self.out_dim_keys_values = attention_ratio * key_dim * num_attention_heads + key_dim * num_attention_heads * 2
+ self.out_dim_projection = attention_ratio * key_dim * num_attention_heads
+
+ self.queries_keys_values = MLPLayerWithBN(hidden_sizes, self.out_dim_keys_values)
+ self.activation = nn.Hardswish()
+ self.projection = MLPLayerWithBN(self.out_dim_projection, hidden_sizes, bn_weight_init=0)
+
+ points = list(itertools.product(range(resolution), range(resolution)))
+ len_points = len(points)
+ attention_offsets, indices = {}, []
+ for p1 in points:
+ for p2 in points:
+ offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
+ if offset not in attention_offsets:
+ attention_offsets[offset] = len(attention_offsets)
+ indices.append(attention_offsets[offset])
+
+ self.attention_bias_cache = {}
+ self.attention_biases = torch.nn.Parameter(torch.zeros(num_attention_heads, len(attention_offsets)))
+ self.register_buffer(
+ "attention_bias_idxs", torch.LongTensor(indices).view(len_points, len_points), persistent=False
+ )
+
+ @torch.no_grad()
+ def train(self, mode=True):
+ super().train(mode)
+ if mode and self.attention_bias_cache:
+ self.attention_bias_cache = {} # clear ab cache
+
+ def get_attention_biases(self, device):
+ if self.training:
+ return self.attention_biases[:, self.attention_bias_idxs]
+ else:
+ device_key = str(device)
+ if device_key not in self.attention_bias_cache:
+ self.attention_bias_cache[device_key] = self.attention_biases[:, self.attention_bias_idxs]
+ return self.attention_bias_cache[device_key]
+
+ def forward(self, hidden_state):
+ batch_size, seq_length, _ = hidden_state.shape
+ queries_keys_values = self.queries_keys_values(hidden_state)
+ query, key, value = queries_keys_values.view(batch_size, seq_length, self.num_attention_heads, -1).split(
+ [self.key_dim, self.key_dim, self.attention_ratio * self.key_dim], dim=3
+ )
+ query = query.permute(0, 2, 1, 3)
+ key = key.permute(0, 2, 1, 3)
+ value = value.permute(0, 2, 1, 3)
+
+ attention = query @ key.transpose(-2, -1) * self.scale + self.get_attention_biases(hidden_state.device)
+ attention = attention.softmax(dim=-1)
+ hidden_state = (attention @ value).transpose(1, 2).reshape(batch_size, seq_length, self.out_dim_projection)
+ hidden_state = self.projection(self.activation(hidden_state))
+ return hidden_state
+
+
+class LevitAttentionSubsample(nn.Module):
+ def __init__(
+ self,
+ input_dim,
+ output_dim,
+ key_dim,
+ num_attention_heads,
+ attention_ratio,
+ stride,
+ resolution_in,
+ resolution_out,
+ ):
+ super().__init__()
+ self.num_attention_heads = num_attention_heads
+ self.scale = key_dim**-0.5
+ self.key_dim = key_dim
+ self.attention_ratio = attention_ratio
+ self.out_dim_keys_values = attention_ratio * key_dim * num_attention_heads + key_dim * num_attention_heads
+ self.out_dim_projection = attention_ratio * key_dim * num_attention_heads
+ self.resolution_out = resolution_out
+ # resolution_in is the initial resolution, resolution_out is final resolution after downsampling
+ self.keys_values = MLPLayerWithBN(input_dim, self.out_dim_keys_values)
+ self.queries_subsample = LevitSubsample(stride, resolution_in)
+ self.queries = MLPLayerWithBN(input_dim, key_dim * num_attention_heads)
+ self.activation = nn.Hardswish()
+ self.projection = MLPLayerWithBN(self.out_dim_projection, output_dim)
+
+ self.attention_bias_cache = {}
+
+ points = list(itertools.product(range(resolution_in), range(resolution_in)))
+ points_ = list(itertools.product(range(resolution_out), range(resolution_out)))
+ len_points, len_points_ = len(points), len(points_)
+ attention_offsets, indices = {}, []
+ for p1 in points_:
+ for p2 in points:
+ size = 1
+ offset = (abs(p1[0] * stride - p2[0] + (size - 1) / 2), abs(p1[1] * stride - p2[1] + (size - 1) / 2))
+ if offset not in attention_offsets:
+ attention_offsets[offset] = len(attention_offsets)
+ indices.append(attention_offsets[offset])
+
+ self.attention_biases = torch.nn.Parameter(torch.zeros(num_attention_heads, len(attention_offsets)))
+ self.register_buffer(
+ "attention_bias_idxs", torch.LongTensor(indices).view(len_points_, len_points), persistent=False
+ )
+
+ @torch.no_grad()
+ def train(self, mode=True):
+ super().train(mode)
+ if mode and self.attention_bias_cache:
+ self.attention_bias_cache = {} # clear ab cache
+
+ def get_attention_biases(self, device):
+ if self.training:
+ return self.attention_biases[:, self.attention_bias_idxs]
+ else:
+ device_key = str(device)
+ if device_key not in self.attention_bias_cache:
+ self.attention_bias_cache[device_key] = self.attention_biases[:, self.attention_bias_idxs]
+ return self.attention_bias_cache[device_key]
+
+ def forward(self, hidden_state):
+ batch_size, seq_length, _ = hidden_state.shape
+ key, value = (
+ self.keys_values(hidden_state)
+ .view(batch_size, seq_length, self.num_attention_heads, -1)
+ .split([self.key_dim, self.attention_ratio * self.key_dim], dim=3)
+ )
+ key = key.permute(0, 2, 1, 3)
+ value = value.permute(0, 2, 1, 3)
+
+ query = self.queries(self.queries_subsample(hidden_state))
+ query = query.view(batch_size, self.resolution_out**2, self.num_attention_heads, self.key_dim).permute(
+ 0, 2, 1, 3
+ )
+
+ attention = query @ key.transpose(-2, -1) * self.scale + self.get_attention_biases(hidden_state.device)
+ attention = attention.softmax(dim=-1)
+ hidden_state = (attention @ value).transpose(1, 2).reshape(batch_size, -1, self.out_dim_projection)
+ hidden_state = self.projection(self.activation(hidden_state))
+ return hidden_state
+
+
+class LevitMLPLayer(nn.Module):
+ """
+ MLP Layer with `2X` expansion in contrast to ViT with `4X`.
+ """
+
+ def __init__(self, input_dim, hidden_dim):
+ super().__init__()
+ self.linear_up = MLPLayerWithBN(input_dim, hidden_dim)
+ self.activation = nn.Hardswish()
+ self.linear_down = MLPLayerWithBN(hidden_dim, input_dim)
+
+ def forward(self, hidden_state):
+ hidden_state = self.linear_up(hidden_state)
+ hidden_state = self.activation(hidden_state)
+ hidden_state = self.linear_down(hidden_state)
+ return hidden_state
+
+
+class LevitResidualLayer(nn.Module):
+ """
+ Residual Block for LeViT
+ """
+
+ def __init__(self, module, drop_rate):
+ super().__init__()
+ self.module = module
+ self.drop_rate = drop_rate
+
+ def forward(self, hidden_state):
+ if self.training and self.drop_rate > 0:
+ rnd = torch.rand(hidden_state.size(0), 1, 1, device=hidden_state.device)
+ rnd = rnd.ge_(self.drop_rate).div(1 - self.drop_rate).detach()
+ hidden_state = hidden_state + self.module(hidden_state) * rnd
+ return hidden_state
+ else:
+ hidden_state = hidden_state + self.module(hidden_state)
+ return hidden_state
+
+
+class LevitStage(nn.Module):
+ """
+ LeViT Stage consisting of `LevitMLPLayer` and `LevitAttention` layers.
+ """
+
+ def __init__(
+ self,
+ config,
+ idx,
+ hidden_sizes,
+ key_dim,
+ depths,
+ num_attention_heads,
+ attention_ratio,
+ mlp_ratio,
+ down_ops,
+ resolution_in,
+ ):
+ super().__init__()
+ self.layers = []
+ self.config = config
+ self.resolution_in = resolution_in
+ # resolution_in is the initial resolution, resolution_out is final resolution after downsampling
+ for _ in range(depths):
+ self.layers.append(
+ LevitResidualLayer(
+ LevitAttention(hidden_sizes, key_dim, num_attention_heads, attention_ratio, resolution_in),
+ self.config.drop_path_rate,
+ )
+ )
+ if mlp_ratio > 0:
+ hidden_dim = hidden_sizes * mlp_ratio
+ self.layers.append(
+ LevitResidualLayer(LevitMLPLayer(hidden_sizes, hidden_dim), self.config.drop_path_rate)
+ )
+
+ if down_ops[0] == "Subsample":
+ self.resolution_out = (self.resolution_in - 1) // down_ops[5] + 1
+ self.layers.append(
+ LevitAttentionSubsample(
+ *self.config.hidden_sizes[idx : idx + 2],
+ key_dim=down_ops[1],
+ num_attention_heads=down_ops[2],
+ attention_ratio=down_ops[3],
+ stride=down_ops[5],
+ resolution_in=resolution_in,
+ resolution_out=self.resolution_out,
+ )
+ )
+ self.resolution_in = self.resolution_out
+ if down_ops[4] > 0:
+ hidden_dim = self.config.hidden_sizes[idx + 1] * down_ops[4]
+ self.layers.append(
+ LevitResidualLayer(
+ LevitMLPLayer(self.config.hidden_sizes[idx + 1], hidden_dim), self.config.drop_path_rate
+ )
+ )
+
+ self.layers = nn.ModuleList(self.layers)
+
+ def get_resolution(self):
+ return self.resolution_in
+
+ def forward(self, hidden_state):
+ for layer in self.layers:
+ hidden_state = layer(hidden_state)
+ return hidden_state
+
+
+class LevitEncoder(nn.Module):
+ """
+ LeViT Encoder consisting of multiple `LevitStage` stages.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ resolution = self.config.image_size // self.config.patch_size
+ self.stages = []
+ self.config.down_ops.append([""])
+
+ for stage_idx in range(len(config.depths)):
+ stage = LevitStage(
+ config,
+ stage_idx,
+ config.hidden_sizes[stage_idx],
+ config.key_dim[stage_idx],
+ config.depths[stage_idx],
+ config.num_attention_heads[stage_idx],
+ config.attention_ratio[stage_idx],
+ config.mlp_ratio[stage_idx],
+ config.down_ops[stage_idx],
+ resolution,
+ )
+ resolution = stage.get_resolution()
+ self.stages.append(stage)
+
+ self.stages = nn.ModuleList(self.stages)
+
+ def forward(self, hidden_state, output_hidden_states=False, return_dict=True):
+ all_hidden_states = () if output_hidden_states else None
+
+ for stage in self.stages:
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_state,)
+ hidden_state = stage(hidden_state)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_state,)
+ if not return_dict:
+ return tuple(v for v in [hidden_state, all_hidden_states] if v is not None)
+
+ return BaseModelOutputWithNoAttention(last_hidden_state=hidden_state, hidden_states=all_hidden_states)
+
+
+class LevitClassificationLayer(nn.Module):
+ """
+ LeViT Classification Layer
+ """
+
+ def __init__(self, input_dim, output_dim):
+ super().__init__()
+ self.batch_norm = nn.BatchNorm1d(input_dim)
+ self.linear = nn.Linear(input_dim, output_dim)
+
+ def forward(self, hidden_state):
+ hidden_state = self.batch_norm(hidden_state)
+ logits = self.linear(hidden_state)
+ return logits
+
+
+@auto_docstring
+class LevitPreTrainedModel(PreTrainedModel):
+ config: LevitConfig
+ base_model_prefix = "levit"
+ main_input_name = "pixel_values"
+ _no_split_modules = ["LevitResidualLayer"]
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, (nn.BatchNorm1d, nn.BatchNorm2d)):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class LevitModel(LevitPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.config = config
+ self.patch_embeddings = LevitPatchEmbeddings(config)
+ self.encoder = LevitEncoder(config)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ embeddings = self.patch_embeddings(pixel_values)
+ encoder_outputs = self.encoder(
+ embeddings,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ last_hidden_state = encoder_outputs[0]
+
+ # global average pooling, (batch_size, seq_length, hidden_sizes) -> (batch_size, hidden_sizes)
+ pooled_output = last_hidden_state.mean(dim=1)
+
+ if not return_dict:
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPoolingAndNoAttention(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ Levit Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
+ ImageNet.
+ """
+)
+class LevitForImageClassification(LevitPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.config = config
+ self.num_labels = config.num_labels
+ self.levit = LevitModel(config)
+
+ # Classifier head
+ self.classifier = (
+ LevitClassificationLayer(config.hidden_sizes[-1], config.num_labels)
+ if config.num_labels > 0
+ else torch.nn.Identity()
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.levit(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
+
+ sequence_output = outputs[0]
+ sequence_output = sequence_output.mean(1)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return ImageClassifierOutputWithNoAttention(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ LeViT Model transformer with image classification heads on top (a linear layer on top of the final hidden state and
+ a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet. .. warning::
+ This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet
+ supported.
+ """
+)
+class LevitForImageClassificationWithTeacher(LevitPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.config = config
+ self.num_labels = config.num_labels
+ self.levit = LevitModel(config)
+
+ # Classifier head
+ self.classifier = (
+ LevitClassificationLayer(config.hidden_sizes[-1], config.num_labels)
+ if config.num_labels > 0
+ else torch.nn.Identity()
+ )
+ self.classifier_distill = (
+ LevitClassificationLayer(config.hidden_sizes[-1], config.num_labels)
+ if config.num_labels > 0
+ else torch.nn.Identity()
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LevitForImageClassificationWithTeacherOutput]:
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.levit(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
+
+ sequence_output = outputs[0]
+ sequence_output = sequence_output.mean(1)
+ cls_logits, distill_logits = self.classifier(sequence_output), self.classifier_distill(sequence_output)
+ logits = (cls_logits + distill_logits) / 2
+
+ if not return_dict:
+ output = (logits, cls_logits, distill_logits) + outputs[2:]
+ return output
+
+ return LevitForImageClassificationWithTeacherOutput(
+ logits=logits,
+ cls_logits=cls_logits,
+ distillation_logits=distill_logits,
+ hidden_states=outputs.hidden_states,
+ )
+
+
+__all__ = [
+ "LevitForImageClassification",
+ "LevitForImageClassificationWithTeacher",
+ "LevitModel",
+ "LevitPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..239ab879832c57465eb85f910cadd0f373abe46a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_lfm2 import *
+ from .modeling_lfm2 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/configuration_lfm2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/configuration_lfm2.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce331a311a7ebd8c3b4719b35a4a7992b4f60a7f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/configuration_lfm2.py
@@ -0,0 +1,165 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Optional
+
+from ...configuration_utils import PretrainedConfig
+
+
+class Lfm2Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Lfm2Model`]. It is used to instantiate a LFM2
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the LFM2-1.2B model.
+ e.g. [LiquidAI/LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 65536):
+ Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`Lfm2Model`]
+ hidden_size (`int`, *optional*, defaults to 2560):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 12288):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*, defaults to 8):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+ `num_attention_heads`.
+ max_position_embeddings (`int`, *optional*, defaults to 128000):
+ The maximum sequence length that this model might ever be used with. Lfm2 1 supports up to 2048 tokens,
+ Lfm2 2 up to 4096, CodeLfm2 up to 16384.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*, defaults to 0):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ End of stream token id.
+ tie_word_embeddings (`bool`, *optional*, defaults to `True`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 1000000.0):
+ The base period of the RoPE embeddings.
+ conv_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the conv layers.
+ conv_L_cache (`int`, *optional*, defaults to 3):
+ L_cache dim in the conv layers.
+ block_multiple_of (`int`, *optional*, defaults to 256):
+ Multiple for the `intermediate_size`.
+ block_ffn_dim_multiplier (`float`, *optional*, defaults to 1.0):
+ Multiplier for the `intermediate_size`.
+ block_auto_adjust_ff_dim (`bool`, *optional*, defaults to `True`):
+ Whether to adjust the dim of the `intermediate_size`.
+ full_attn_idxs (`Optional`, *optional*):
+ Index of the layers which use attention.
+ layer_types (`Optional`, *optional*):
+ Type of each layers.
+
+ ```python
+ >>> from transformers import Lfm2Model, Lfm2Config
+
+ >>> # Initializing a LFM2 model
+ >>> configuration = Lfm2Config()
+
+ >>> # Initializing a model from the LFM2-1.2B style configuration
+ >>> model = Lfm2Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "lfm2"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size: int = 65536,
+ hidden_size: int = 2560,
+ intermediate_size: int = 12288,
+ num_hidden_layers: int = 32,
+ num_attention_heads: int = 32,
+ num_key_value_heads: int = 8,
+ max_position_embeddings: int = 128_000,
+ initializer_range: float = 0.02,
+ norm_eps: float = 0.00001,
+ use_cache: bool = True,
+ pad_token_id: int = 0,
+ bos_token_id: int = 1,
+ eos_token_id: int = 2,
+ tie_word_embeddings: bool = True,
+ rope_theta: float = 1000000.0,
+ conv_bias: bool = False,
+ conv_L_cache: int = 3,
+ block_multiple_of: int = 256,
+ block_ffn_dim_multiplier: float = 1.0,
+ block_auto_adjust_ff_dim: bool = True,
+ full_attn_idxs: Optional[list[int]] = None,
+ layer_types: Optional[list[str]] = None,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.rope_theta = kwargs.get("theta", rope_theta) # to fit original config keys
+ self.max_position_embeddings = max_position_embeddings
+ self.use_cache = use_cache
+ self.norm_eps = norm_eps
+ self.initializer_range = initializer_range
+
+ # attn operator config
+ self.num_attention_heads = num_attention_heads
+ self.num_key_value_heads = num_key_value_heads
+
+ # custom operator config
+ self.conv_bias = conv_bias
+ self.conv_L_cache = conv_L_cache
+
+ # MLP config
+ self.intermediate_size = kwargs.get("block_ff_dim", intermediate_size) # to fit original config keys
+ self.block_multiple_of = block_multiple_of
+ self.block_ffn_dim_multiplier = block_ffn_dim_multiplier
+ self.block_auto_adjust_ff_dim = block_auto_adjust_ff_dim
+
+ self.layer_types = layer_types
+ if self.layer_types is None:
+ full_attn_idxs = full_attn_idxs if full_attn_idxs is not None else list(range(num_hidden_layers))
+ self.layer_types = ["full_attention" if i in full_attn_idxs else "conv" for i in range(num_hidden_layers)]
+
+ tie_word_embeddings = kwargs.get("tie_embedding", tie_word_embeddings) # to fit original config keys
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+
+__all__ = ["Lfm2Config"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/modeling_lfm2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/modeling_lfm2.py
new file mode 100644
index 0000000000000000000000000000000000000000..092a8b3caa82ea1a45b1b25d11bcb9253b963bc0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/modeling_lfm2.py
@@ -0,0 +1,760 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/lfm2/modular_lfm2.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_lfm2.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Callable, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ...cache_utils import Cache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...masking_utils import create_causal_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import check_model_inputs
+from ...utils.import_utils import is_causal_conv1d_available
+from .configuration_lfm2 import Lfm2Config
+
+
+if is_causal_conv1d_available():
+ from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+else:
+ causal_conv1d_fn, causal_conv1d_update = None, None
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class Lfm2RMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ Lfm2RMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class Lfm2RotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: Lfm2Config, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class Lfm2MLP(nn.Module):
+ def __init__(self, config: Lfm2Config):
+ super().__init__()
+ intermediate_size = config.intermediate_size
+ if config.block_auto_adjust_ff_dim:
+ intermediate_size = int(2 * intermediate_size / 3)
+ # custom dim factor multiplier
+ if config.block_ffn_dim_multiplier is not None:
+ intermediate_size = int(config.block_ffn_dim_multiplier * intermediate_size)
+ intermediate_size = config.block_multiple_of * (
+ (intermediate_size + config.block_multiple_of - 1) // config.block_multiple_of
+ )
+ self.w1 = nn.Linear(config.hidden_size, intermediate_size, bias=False)
+ self.w3 = nn.Linear(config.hidden_size, intermediate_size, bias=False)
+ self.w2 = nn.Linear(intermediate_size, config.hidden_size, bias=False)
+
+ def forward(self, x):
+ return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class Lfm2HybridConvCache:
+ """
+ Attention and conv cache for Lfm2.
+
+ It stores the Key and Value states as a list of tensors, one for each layer.
+ Attention layer cache shape: `[batch_size, num_heads, seq_len, head_dim]`.
+ Conv layer cache shape: `[batch_size, hidden_size, L_cache-1]`.
+ """
+
+ # Override @property existing in Cache
+ max_batch_size = None
+ is_compileable = False
+ key_cache = None
+ value_cache = None
+
+ def __init__(
+ self,
+ config: Lfm2Config,
+ max_batch_size: int,
+ dtype: torch.dtype = torch.float32,
+ device: Union[torch.device, str, None] = None,
+ ):
+ self.key_cache = []
+ self.value_cache = []
+ self.max_batch_size = max_batch_size
+ self.layer_types = config.layer_types
+ self.first_attention_layer = self.layer_types.index("full_attention")
+ self.conv_L_cache = config.conv_L_cache
+ self._dtype = dtype
+
+ self.conv_cache: list[torch.Tensor] = []
+ device = torch.device(device) if device is not None else None
+
+ for _ in range(config.num_hidden_layers):
+ conv_state = torch.zeros(
+ self.max_batch_size,
+ config.hidden_size,
+ self.conv_L_cache,
+ dtype=self._dtype,
+ device=device,
+ )
+ torch._dynamo.mark_static_address(conv_state)
+ self.conv_cache.append(conv_state)
+
+ def update(
+ self,
+ key_states: torch.Tensor,
+ value_states: torch.Tensor,
+ layer_idx: int,
+ cache_kwargs: Optional[dict[str, Any]] = None,
+ ) -> tuple[torch.Tensor, torch.Tensor]:
+ """
+ Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+
+ Parameters:
+ key_states (`torch.Tensor`):
+ The new key states to cache.
+ value_states (`torch.Tensor`):
+ The new value states to cache.
+ layer_idx (`int`):
+ The index of the layer to cache the states for.
+ cache_kwargs (`Dict[str, Any]`, `optional`):
+ Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
+
+ Return:
+ A tuple containing the updated key and value states.
+ """
+ # Update the cache
+ if key_states is not None:
+ if len(self.key_cache) <= layer_idx:
+ # There may be skipped layers, fill them with empty lists
+ for _ in range(len(self.key_cache), layer_idx):
+ self.key_cache.append(torch.tensor([]))
+ self.value_cache.append(torch.tensor([]))
+ self.key_cache.append(key_states)
+ self.value_cache.append(value_states)
+ elif (
+ not self.key_cache[layer_idx].numel() # prefers not t.numel() to len(t) == 0 to export the model
+ ): # fills previously skipped layers; checking for tensor causes errors
+ self.key_cache[layer_idx] = key_states
+ self.value_cache[layer_idx] = value_states
+ else:
+ self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+ self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+
+ return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+ def reorder_cache(self, beam_idx: torch.LongTensor):
+ """Reorders the cache for beam search, given the selected beam indices."""
+ for layer_idx in range(len(self.key_cache)):
+ device = self.key_cache[layer_idx].device
+ self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
+ device = self.value_cache[layer_idx].device
+ self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
+
+ device = self.conv_cache[layer_idx].device
+ self.conv_cache[layer_idx] = self.conv_cache[layer_idx].index_select(0, beam_idx.to(device))
+
+ def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+ """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+ # take any layer that contains cache and not empty tensor
+ layer_idx = self.first_attention_layer if self.layer_types[layer_idx] != "full_attention" else layer_idx
+ if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
+ return 0
+ return self.key_cache[layer_idx].shape[-2]
+
+ def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]:
+ """
+ Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
+ the given layer at `layer_idx`.
+ The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns (i.e. sliding_window, chunk_size),
+ for each layer.
+ """
+ full_mask_kv_offset = 0
+ query_length = cache_position.shape[0]
+ past_seen_tokens = self.get_seq_length()
+ kv_length = query_length + past_seen_tokens
+ return kv_length, full_mask_kv_offset
+
+ def crop(self, max_length: int):
+ """Crop the cache to the given length"""
+ if max_length < 0:
+ max_length = self.get_seq_length() - abs(max_length)
+
+ if self.get_seq_length() <= max_length:
+ return
+
+ for idx in range(len(self.key_cache)):
+ if self.key_cache[idx].numel():
+ self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
+ self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
+
+ def __len__(self) -> int:
+ return len(self.key_cache)
+
+ def __getitem__(self, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor]:
+ return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+ def reset(self):
+ for layer_idx in range(len(self.conv_cache)):
+ # In-place ops prevent breaking the static address
+ self.conv_cache[layer_idx].zero_()
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class Lfm2Attention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: Lfm2Config, layer_idx: int):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.is_causal = True
+ self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
+ self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.out_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+ self.q_layernorm = Lfm2RMSNorm(self.head_dim, eps=config.norm_eps)
+ self.k_layernorm = Lfm2RMSNorm(self.head_dim, eps=config.norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Lfm2HybridConvCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_layernorm(self.q_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
+ key_states = self.k_layernorm(self.k_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
+ value_states = self.v_proj(hidden_states).view(*hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0,
+ scaling=self.scaling,
+ **kwargs,
+ )
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ output = self.out_proj(attn_output)
+ return output, attn_weights
+
+
+def apply_mask_to_padding_states(hidden_states, attention_mask):
+ """
+ Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66
+ """
+ if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1:
+ dtype = hidden_states.dtype
+ hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+
+ return hidden_states
+
+
+kernel_modules = (causal_conv1d_fn, causal_conv1d_update)
+is_fast_path_available = all(kernel_modules)
+
+
+class Lfm2ShortConv(nn.Module):
+ def __init__(
+ self,
+ config: Lfm2Config,
+ layer_idx: int,
+ ):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.L_cache = config.conv_L_cache
+ self.bias = config.conv_bias
+
+ self.conv = nn.Conv1d(
+ in_channels=config.hidden_size,
+ out_channels=config.hidden_size,
+ kernel_size=self.L_cache,
+ groups=config.hidden_size,
+ bias=self.bias,
+ padding=self.L_cache - 1,
+ )
+ self.in_proj = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=self.bias)
+ self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=self.bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def cuda_kernels_forward(
+ self,
+ x: torch.Tensor,
+ past_key_values: Optional[Lfm2HybridConvCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ ):
+ x = apply_mask_to_padding_states(x, attention_mask)
+ BCx = self.in_proj(x).transpose(-1, -2)
+ B, C, x = BCx.chunk(3, dim=-2)
+
+ Bx = B * x
+
+ conv_weights = self.conv.weight.view(self.conv.weight.size(0), self.conv.weight.size(2))
+ if past_key_values is not None and cache_position[0] > 0:
+ conv_out = causal_conv1d_update(
+ Bx.squeeze(-1),
+ past_key_values.conv_cache[self.layer_idx],
+ conv_weights,
+ self.conv.bias,
+ None,
+ )
+ conv_out = conv_out.unsqueeze(-1)
+ else:
+ if past_key_values is not None:
+ conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
+ past_key_values.conv_cache[self.layer_idx].copy_(conv_state)
+
+ conv_out = causal_conv1d_fn(Bx, conv_weights, self.conv.bias, activation=None)
+
+ y = C * conv_out
+ y = self.out_proj(y.transpose(-1, -2).contiguous())
+ return y
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def slow_forward(
+ self,
+ x: torch.Tensor,
+ past_key_values: Optional[Lfm2HybridConvCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ ):
+ seqlen = x.shape[1]
+
+ x = apply_mask_to_padding_states(x, attention_mask)
+ BCx = self.in_proj(x).transpose(-1, -2)
+ B, C, x = BCx.chunk(3, dim=-2)
+
+ Bx = B * x
+
+ if past_key_values is not None and cache_position[0] > 0:
+ conv_state = past_key_values.conv_cache[self.layer_idx]
+ cache_position = cache_position.clamp(0, self.L_cache - 1)
+ conv_state = conv_state.roll(shifts=-1, dims=-1)
+ conv_state[:, :, cache_position] = Bx.to(device=conv_state.device, dtype=conv_state.dtype)
+ past_key_values.conv_cache[self.layer_idx].copy_(conv_state)
+ conv_out = torch.sum(conv_state.to(Bx.device) * self.conv.weight[:, 0, :], dim=-1)
+ if self.bias:
+ conv_out += self.conv.bias
+
+ conv_out = conv_out.unsqueeze(-1)
+ else:
+ if past_key_values is not None:
+ conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
+ past_key_values.conv_cache[self.layer_idx].copy_(conv_state)
+
+ conv_out = self.conv(Bx)[..., :seqlen]
+
+ y = C * conv_out
+ y = y.transpose(-1, -2).contiguous()
+ y = self.out_proj(y)
+ return y
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ past_key_values: Optional[Lfm2HybridConvCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ ):
+ if is_fast_path_available and "cuda" in hidden_states.device.type and not torch._dynamo.is_compiling():
+ return self.cuda_kernels_forward(hidden_states, past_key_values, cache_position, attention_mask)
+ return self.slow_forward(hidden_states, past_key_values, cache_position, attention_mask)
+
+
+class Lfm2DecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: Lfm2Config, layer_idx: int):
+ super().__init__()
+ self.is_attention_layer = config.layer_types[layer_idx] == "full_attention"
+
+ if self.is_attention_layer:
+ self.self_attn = Lfm2Attention(config, layer_idx)
+ else:
+ self.conv = Lfm2ShortConv(config, layer_idx)
+ self.feed_forward = Lfm2MLP(config)
+ self.operator_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
+ self.ffn_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[torch.Tensor]] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ residual = hidden_states
+ if self.is_attention_layer:
+ hidden_states, _ = self.self_attn(
+ hidden_states=self.operator_norm(hidden_states),
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ else:
+ hidden_states = self.conv(
+ hidden_states=self.operator_norm(hidden_states),
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ attention_mask=attention_mask,
+ )
+ hidden_states = hidden_states + residual
+ hidden_states = hidden_states + self.feed_forward(self.ffn_norm(hidden_states))
+
+ return hidden_states
+
+
+@auto_docstring
+class Lfm2PreTrainedModel(PreTrainedModel):
+ config: Lfm2Config
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["Lfm2DecoderLayer"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+ _can_compile_fullgraph = False
+ _supports_attention_backend = True
+ _can_record_outputs = {
+ "hidden_states": Lfm2DecoderLayer,
+ "attentions": Lfm2Attention,
+ }
+
+
+@auto_docstring
+class Lfm2Model(Lfm2PreTrainedModel):
+ def __init__(self, config: Lfm2Config):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [Lfm2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.rotary_emb = Lfm2RotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+ self.pos_emb = Lfm2RotaryEmbedding(config)
+ self.embedding_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Lfm2HybridConvCache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> BaseModelOutputWithPast:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if use_cache and past_key_values is None:
+ batch_size = inputs_embeds.shape[0]
+ past_key_values = Lfm2HybridConvCache(
+ config=self.config, max_batch_size=batch_size, dtype=self.dtype, device=self.device
+ )
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = create_causal_mask(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+ position_embeddings = self.pos_emb(hidden_states, position_ids)
+
+ # decoder layers
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ hidden_states = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+
+ hidden_states = self.embedding_norm(hidden_states)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+@auto_docstring
+class Lfm2ForCausalLM(Lfm2PreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+ _tp_plan = {"lm_head": "colwise_rep"}
+ _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = Lfm2Model(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> CausalLMOutputWithPast:
+ r"""
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, Lfm2ForCausalLM
+
+ >>> model = Lfm2ForCausalLM.from_pretrained("meta-lfm2/Lfm2-2-7b-hf")
+ >>> tokenizer = AutoTokenizer.from_pretrained("meta-lfm2/Lfm2-2-7b-hf")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ outputs: BaseModelOutputWithPast = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = ["Lfm2ForCausalLM", "Lfm2Model", "Lfm2PreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/modular_lfm2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/modular_lfm2.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d3791cbe3b1f0f15afb9a9bd1c3534780c3e6c3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lfm2/modular_lfm2.py
@@ -0,0 +1,520 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Callable, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ...masking_utils import create_causal_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, logging
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.import_utils import is_causal_conv1d_available
+from ..bamba.modeling_bamba import apply_mask_to_padding_states
+from ..llama.modeling_llama import (
+ LlamaAttention,
+ LlamaForCausalLM,
+ LlamaModel,
+ LlamaPreTrainedModel,
+ LlamaRMSNorm,
+ LlamaRotaryEmbedding,
+ apply_rotary_pos_emb,
+ eager_attention_forward,
+)
+from .configuration_lfm2 import Lfm2Config
+
+
+if is_causal_conv1d_available():
+ from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+else:
+ causal_conv1d_fn, causal_conv1d_update = None, None
+
+
+kernel_modules = (causal_conv1d_fn, causal_conv1d_update)
+is_fast_path_available = all(kernel_modules)
+
+
+logger = logging.get_logger(__name__)
+
+
+class Lfm2RMSNorm(LlamaRMSNorm):
+ pass
+
+
+class Lfm2RotaryEmbedding(LlamaRotaryEmbedding):
+ pass
+
+
+class Lfm2MLP(nn.Module):
+ def __init__(self, config: Lfm2Config):
+ super().__init__()
+ intermediate_size = config.intermediate_size
+ if config.block_auto_adjust_ff_dim:
+ intermediate_size = int(2 * intermediate_size / 3)
+ # custom dim factor multiplier
+ if config.block_ffn_dim_multiplier is not None:
+ intermediate_size = int(config.block_ffn_dim_multiplier * intermediate_size)
+ intermediate_size = config.block_multiple_of * (
+ (intermediate_size + config.block_multiple_of - 1) // config.block_multiple_of
+ )
+ self.w1 = nn.Linear(config.hidden_size, intermediate_size, bias=False)
+ self.w3 = nn.Linear(config.hidden_size, intermediate_size, bias=False)
+ self.w2 = nn.Linear(intermediate_size, config.hidden_size, bias=False)
+
+ def forward(self, x):
+ return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class Lfm2HybridConvCache:
+ """
+ Attention and conv cache for Lfm2.
+
+ It stores the Key and Value states as a list of tensors, one for each layer.
+ Attention layer cache shape: `[batch_size, num_heads, seq_len, head_dim]`.
+ Conv layer cache shape: `[batch_size, hidden_size, L_cache-1]`.
+ """
+
+ # Override @property existing in Cache
+ max_batch_size = None
+ is_compileable = False
+ key_cache = None
+ value_cache = None
+
+ def __init__(
+ self,
+ config: Lfm2Config,
+ max_batch_size: int,
+ dtype: torch.dtype = torch.float32,
+ device: Union[torch.device, str, None] = None,
+ ):
+ self.key_cache = []
+ self.value_cache = []
+ self.max_batch_size = max_batch_size
+ self.layer_types = config.layer_types
+ self.first_attention_layer = self.layer_types.index("full_attention")
+ self.conv_L_cache = config.conv_L_cache
+ self._dtype = dtype
+
+ self.conv_cache: list[torch.Tensor] = []
+ device = torch.device(device) if device is not None else None
+
+ for _ in range(config.num_hidden_layers):
+ conv_state = torch.zeros(
+ self.max_batch_size,
+ config.hidden_size,
+ self.conv_L_cache,
+ dtype=self._dtype,
+ device=device,
+ )
+ torch._dynamo.mark_static_address(conv_state)
+ self.conv_cache.append(conv_state)
+
+ def update(
+ self,
+ key_states: torch.Tensor,
+ value_states: torch.Tensor,
+ layer_idx: int,
+ cache_kwargs: Optional[dict[str, Any]] = None,
+ ) -> tuple[torch.Tensor, torch.Tensor]:
+ """
+ Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+
+ Parameters:
+ key_states (`torch.Tensor`):
+ The new key states to cache.
+ value_states (`torch.Tensor`):
+ The new value states to cache.
+ layer_idx (`int`):
+ The index of the layer to cache the states for.
+ cache_kwargs (`Dict[str, Any]`, `optional`):
+ Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
+
+ Return:
+ A tuple containing the updated key and value states.
+ """
+ # Update the cache
+ if key_states is not None:
+ if len(self.key_cache) <= layer_idx:
+ # There may be skipped layers, fill them with empty lists
+ for _ in range(len(self.key_cache), layer_idx):
+ self.key_cache.append(torch.tensor([]))
+ self.value_cache.append(torch.tensor([]))
+ self.key_cache.append(key_states)
+ self.value_cache.append(value_states)
+ elif (
+ not self.key_cache[layer_idx].numel() # prefers not t.numel() to len(t) == 0 to export the model
+ ): # fills previously skipped layers; checking for tensor causes errors
+ self.key_cache[layer_idx] = key_states
+ self.value_cache[layer_idx] = value_states
+ else:
+ self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+ self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+
+ return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+ def reorder_cache(self, beam_idx: torch.LongTensor):
+ """Reorders the cache for beam search, given the selected beam indices."""
+ for layer_idx in range(len(self.key_cache)):
+ device = self.key_cache[layer_idx].device
+ self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
+ device = self.value_cache[layer_idx].device
+ self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
+
+ device = self.conv_cache[layer_idx].device
+ self.conv_cache[layer_idx] = self.conv_cache[layer_idx].index_select(0, beam_idx.to(device))
+
+ def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+ """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+ # take any layer that contains cache and not empty tensor
+ layer_idx = self.first_attention_layer if self.layer_types[layer_idx] != "full_attention" else layer_idx
+ if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
+ return 0
+ return self.key_cache[layer_idx].shape[-2]
+
+ def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]:
+ """
+ Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
+ the given layer at `layer_idx`.
+ The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns (i.e. sliding_window, chunk_size),
+ for each layer.
+ """
+ full_mask_kv_offset = 0
+ query_length = cache_position.shape[0]
+ past_seen_tokens = self.get_seq_length()
+ kv_length = query_length + past_seen_tokens
+ return kv_length, full_mask_kv_offset
+
+ def crop(self, max_length: int):
+ """Crop the cache to the given length"""
+ if max_length < 0:
+ max_length = self.get_seq_length() - abs(max_length)
+
+ if self.get_seq_length() <= max_length:
+ return
+
+ for idx in range(len(self.key_cache)):
+ if self.key_cache[idx].numel():
+ self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
+ self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
+
+ def __len__(self) -> int:
+ return len(self.key_cache)
+
+ def __getitem__(self, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor]:
+ return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+ def reset(self):
+ for layer_idx in range(len(self.conv_cache)):
+ # In-place ops prevent breaking the static address
+ self.conv_cache[layer_idx].zero_()
+
+
+class Lfm2Attention(LlamaAttention):
+ def __init__(self, config: Lfm2Config, layer_idx: int):
+ super().__init__(config, layer_idx)
+ self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
+ self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.out_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+ self.q_layernorm = Lfm2RMSNorm(self.head_dim, eps=config.norm_eps)
+ self.k_layernorm = Lfm2RMSNorm(self.head_dim, eps=config.norm_eps)
+ del self.o_proj
+ del self.attention_dropout
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Lfm2HybridConvCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_layernorm(self.q_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
+ key_states = self.k_layernorm(self.k_proj(hidden_states).view(*hidden_shape)).transpose(1, 2)
+ value_states = self.v_proj(hidden_states).view(*hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0,
+ scaling=self.scaling,
+ **kwargs,
+ )
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ output = self.out_proj(attn_output)
+ return output, attn_weights
+
+
+class Lfm2ShortConv(nn.Module):
+ def __init__(
+ self,
+ config: Lfm2Config,
+ layer_idx: int,
+ ):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.L_cache = config.conv_L_cache
+ self.bias = config.conv_bias
+
+ self.conv = nn.Conv1d(
+ in_channels=config.hidden_size,
+ out_channels=config.hidden_size,
+ kernel_size=self.L_cache,
+ groups=config.hidden_size,
+ bias=self.bias,
+ padding=self.L_cache - 1,
+ )
+ self.in_proj = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=self.bias)
+ self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=self.bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def cuda_kernels_forward(
+ self,
+ x: torch.Tensor,
+ past_key_values: Optional[Lfm2HybridConvCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ ):
+ x = apply_mask_to_padding_states(x, attention_mask)
+ BCx = self.in_proj(x).transpose(-1, -2)
+ B, C, x = BCx.chunk(3, dim=-2)
+
+ Bx = B * x
+
+ conv_weights = self.conv.weight.view(self.conv.weight.size(0), self.conv.weight.size(2))
+ if past_key_values is not None and cache_position[0] > 0:
+ conv_out = causal_conv1d_update(
+ Bx.squeeze(-1),
+ past_key_values.conv_cache[self.layer_idx],
+ conv_weights,
+ self.conv.bias,
+ None,
+ )
+ conv_out = conv_out.unsqueeze(-1)
+ else:
+ if past_key_values is not None:
+ conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
+ past_key_values.conv_cache[self.layer_idx].copy_(conv_state)
+
+ conv_out = causal_conv1d_fn(Bx, conv_weights, self.conv.bias, activation=None)
+
+ y = C * conv_out
+ y = self.out_proj(y.transpose(-1, -2).contiguous())
+ return y
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def slow_forward(
+ self,
+ x: torch.Tensor,
+ past_key_values: Optional[Lfm2HybridConvCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ ):
+ seqlen = x.shape[1]
+
+ x = apply_mask_to_padding_states(x, attention_mask)
+ BCx = self.in_proj(x).transpose(-1, -2)
+ B, C, x = BCx.chunk(3, dim=-2)
+
+ Bx = B * x
+
+ if past_key_values is not None and cache_position[0] > 0:
+ conv_state = past_key_values.conv_cache[self.layer_idx]
+ cache_position = cache_position.clamp(0, self.L_cache - 1)
+ conv_state = conv_state.roll(shifts=-1, dims=-1)
+ conv_state[:, :, cache_position] = Bx.to(device=conv_state.device, dtype=conv_state.dtype)
+ past_key_values.conv_cache[self.layer_idx].copy_(conv_state)
+ conv_out = torch.sum(conv_state.to(Bx.device) * self.conv.weight[:, 0, :], dim=-1)
+ if self.bias:
+ conv_out += self.conv.bias
+
+ conv_out = conv_out.unsqueeze(-1)
+ else:
+ if past_key_values is not None:
+ conv_state = nn.functional.pad(Bx, (self.L_cache - Bx.shape[-1], 0))
+ past_key_values.conv_cache[self.layer_idx].copy_(conv_state)
+
+ conv_out = self.conv(Bx)[..., :seqlen]
+
+ y = C * conv_out
+ y = y.transpose(-1, -2).contiguous()
+ y = self.out_proj(y)
+ return y
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ past_key_values: Optional[Lfm2HybridConvCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ ):
+ if is_fast_path_available and "cuda" in hidden_states.device.type and not torch._dynamo.is_compiling():
+ return self.cuda_kernels_forward(hidden_states, past_key_values, cache_position, attention_mask)
+ return self.slow_forward(hidden_states, past_key_values, cache_position, attention_mask)
+
+
+class Lfm2DecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: Lfm2Config, layer_idx: int):
+ super().__init__()
+ self.is_attention_layer = config.layer_types[layer_idx] == "full_attention"
+
+ if self.is_attention_layer:
+ self.self_attn = Lfm2Attention(config, layer_idx)
+ else:
+ self.conv = Lfm2ShortConv(config, layer_idx)
+ self.feed_forward = Lfm2MLP(config)
+ self.operator_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
+ self.ffn_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[torch.Tensor]] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ residual = hidden_states
+ if self.is_attention_layer:
+ hidden_states, _ = self.self_attn(
+ hidden_states=self.operator_norm(hidden_states),
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ else:
+ hidden_states = self.conv(
+ hidden_states=self.operator_norm(hidden_states),
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ attention_mask=attention_mask,
+ )
+ hidden_states = hidden_states + residual
+ hidden_states = hidden_states + self.feed_forward(self.ffn_norm(hidden_states))
+
+ return hidden_states
+
+
+class Lfm2PreTrainedModel(LlamaPreTrainedModel):
+ _can_compile_fullgraph = False
+
+
+class Lfm2Model(LlamaModel):
+ def __init__(self, config: Lfm2Config):
+ super().__init__(config)
+ self.pos_emb = Lfm2RotaryEmbedding(config)
+ self.embedding_norm = Lfm2RMSNorm(config.hidden_size, eps=config.norm_eps)
+ del self.norm
+ del self.rotary_emv
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Lfm2HybridConvCache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> BaseModelOutputWithPast:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if use_cache and past_key_values is None:
+ batch_size = inputs_embeds.shape[0]
+ past_key_values = Lfm2HybridConvCache(
+ config=self.config, max_batch_size=batch_size, dtype=self.dtype, device=self.device
+ )
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = create_causal_mask(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+ position_embeddings = self.pos_emb(hidden_states, position_ids)
+
+ # decoder layers
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ hidden_states = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+
+ hidden_states = self.embedding_norm(hidden_states)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+class Lfm2ForCausalLM(LlamaForCausalLM):
+ pass
+
+
+__all__ = ["Lfm2ForCausalLM", "Lfm2Model", "Lfm2PreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..190e4e4329419f92da2141c2027f8212d6178e6f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_lightglue import *
+ from .image_processing_lightglue import *
+ from .modeling_lightglue import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/configuration_lightglue.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/configuration_lightglue.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0f228ae4b468dcd3e575d713d1ad0874d0c4303
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/configuration_lightglue.py
@@ -0,0 +1,155 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/lightglue/modular_lightglue.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_lightglue.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from ...configuration_utils import PretrainedConfig
+from ..auto import CONFIG_MAPPING, AutoConfig
+from ..superpoint import SuperPointConfig
+
+
+class LightGlueConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LightGlueForKeypointMatching`]. It is used to
+ instantiate a LightGlue model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the LightGlue
+ [ETH-CVG/lightglue_superpoint](https://huggingface.co/ETH-CVG/lightglue_superpoint) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ keypoint_detector_config (`Union[AutoConfig, dict]`, *optional*, defaults to `SuperPointConfig`):
+ The config object or dictionary of the keypoint detector.
+ descriptor_dim (`int`, *optional*, defaults to 256):
+ The dimension of the descriptors.
+ num_hidden_layers (`int`, *optional*, defaults to 9):
+ The number of self and cross attention layers.
+ num_attention_heads (`int`, *optional*, defaults to 4):
+ The number of heads in the multi-head attention.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details checkout [this
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+ `num_attention_heads`.
+ depth_confidence (`float`, *optional*, defaults to 0.95):
+ The confidence threshold used to perform early stopping
+ width_confidence (`float`, *optional*, defaults to 0.99):
+ The confidence threshold used to prune points
+ filter_threshold (`float`, *optional*, defaults to 0.1):
+ The confidence threshold used to filter matches
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ hidden_act (`str`, *optional*, defaults to `"gelu"`):
+ The activation function to be used in the hidden layers.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ attention_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ trust_remote_code (`bool`, *optional*, defaults to `False`):
+ Whether to trust remote code when using other models than SuperPoint as keypoint detector.
+
+ Examples:
+ ```python
+ >>> from transformers import LightGlueConfig, LightGlueForKeypointMatching
+
+ >>> # Initializing a LightGlue style configuration
+ >>> configuration = LightGlueConfig()
+
+ >>> # Initializing a model from the LightGlue style configuration
+ >>> model = LightGlueForKeypointMatching(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+ """
+
+ model_type = "lightglue"
+ sub_configs = {"keypoint_detector_config": AutoConfig}
+
+ def __init__(
+ self,
+ keypoint_detector_config: SuperPointConfig = None,
+ descriptor_dim: int = 256,
+ num_hidden_layers: int = 9,
+ num_attention_heads: int = 4,
+ num_key_value_heads=None,
+ depth_confidence: float = 0.95,
+ width_confidence: float = 0.99,
+ filter_threshold: float = 0.1,
+ initializer_range: float = 0.02,
+ hidden_act: str = "gelu",
+ attention_dropout=0.0,
+ attention_bias=True,
+ trust_remote_code: bool = False,
+ **kwargs,
+ ):
+ # LightGlue can be used with other models than SuperPoint as keypoint detector
+ # We provide the trust_remote_code argument to allow the use of other models
+ # that are not registered in the CONFIG_MAPPING dictionary (for example DISK)
+ self.trust_remote_code = trust_remote_code
+
+ if descriptor_dim % num_attention_heads != 0:
+ raise ValueError("descriptor_dim % num_heads is different from zero")
+
+ self.descriptor_dim = descriptor_dim
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+
+ self.depth_confidence = depth_confidence
+ self.width_confidence = width_confidence
+ self.filter_threshold = filter_threshold
+ self.initializer_range = initializer_range
+
+ # Keypoint Detector is forced into eager attention mode because SuperPoint does not have Attention
+ # See https://github.com/huggingface/transformers/pull/31718#discussion_r2109733153
+ if isinstance(keypoint_detector_config, dict):
+ keypoint_detector_config["model_type"] = keypoint_detector_config.get("model_type", "superpoint")
+ if keypoint_detector_config["model_type"] not in CONFIG_MAPPING:
+ keypoint_detector_config = AutoConfig.from_pretrained(
+ keypoint_detector_config["_name_or_path"], trust_remote_code=self.trust_remote_code
+ )
+ else:
+ keypoint_detector_config = CONFIG_MAPPING[keypoint_detector_config["model_type"]](
+ **keypoint_detector_config, attn_implementation="eager"
+ )
+
+ if keypoint_detector_config is None:
+ keypoint_detector_config = CONFIG_MAPPING["superpoint"](attn_implementation="eager")
+
+ self.keypoint_detector_config = keypoint_detector_config
+
+ self.hidden_size = descriptor_dim
+ self.intermediate_size = descriptor_dim * 2
+ self.hidden_act = hidden_act
+ self.attention_dropout = attention_dropout
+ self.attention_bias = attention_bias
+ super().__init__(**kwargs)
+
+
+__all__ = ["LightGlueConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/convert_lightglue_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/convert_lightglue_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..feb7c790113db2239fdb34dfbe35fad85e95b63d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/convert_lightglue_to_hf.py
@@ -0,0 +1,280 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import gc
+import os
+import re
+
+import torch
+from datasets import load_dataset
+
+from transformers import (
+ AutoModelForKeypointDetection,
+ LightGlueForKeypointMatching,
+ LightGlueImageProcessor,
+)
+from transformers.models.lightglue.configuration_lightglue import LightGlueConfig
+
+
+DEFAULT_CHECKPOINT_URL = "https://github.com/cvg/LightGlue/releases/download/v0.1_arxiv/superpoint_lightglue.pth"
+
+
+def prepare_imgs():
+ dataset = load_dataset("hf-internal-testing/image-matching-test-dataset", split="train")
+ image0 = dataset[0]["image"]
+ image1 = dataset[1]["image"]
+ image2 = dataset[2]["image"]
+ # [image1, image1] on purpose to test the model early stopping
+ return [[image2, image0], [image1, image1]]
+
+
+def verify_model_outputs(model, device):
+ images = prepare_imgs()
+ preprocessor = LightGlueImageProcessor()
+ inputs = preprocessor(images=images, return_tensors="pt").to(device)
+ model.to(device)
+ with torch.no_grad():
+ outputs = model(**inputs, output_hidden_states=True, output_attentions=True)
+
+ predicted_matches_values = outputs.matches[0, 0, 20:30]
+ predicted_matching_scores_values = outputs.matching_scores[0, 0, 20:30]
+
+ predicted_number_of_matches = torch.sum(outputs.matches[0][0] != -1).item()
+
+ expected_max_number_keypoints = 866
+ expected_matches_shape = torch.Size((len(images), 2, expected_max_number_keypoints))
+ expected_matching_scores_shape = torch.Size((len(images), 2, expected_max_number_keypoints))
+
+ expected_matches_values = torch.tensor([-1, -1, 5, -1, -1, 19, -1, 10, -1, 11], dtype=torch.int64).to(device)
+ expected_matching_scores_values = torch.tensor([0, 0, 0.2997, 0, 0, 0.6762, 0, 0.8826, 0, 0.5583]).to(device)
+
+ expected_number_of_matches = 140
+
+ assert outputs.matches.shape == expected_matches_shape
+ assert outputs.matching_scores.shape == expected_matching_scores_shape
+
+ assert torch.allclose(predicted_matches_values, expected_matches_values, atol=1e-2)
+ assert torch.allclose(predicted_matching_scores_values, expected_matching_scores_values, atol=1e-2)
+
+ assert predicted_number_of_matches == expected_number_of_matches
+
+
+ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
+ r"posenc.Wr": r"positional_encoder.projector",
+ r"self_attn.(\d+).Wqkv": r"transformer_layers.\1.self_attention.Wqkv",
+ r"self_attn.(\d+).out_proj": r"transformer_layers.\1.self_attention.o_proj",
+ r"self_attn.(\d+).ffn.0": r"transformer_layers.\1.self_mlp.fc1",
+ r"self_attn.(\d+).ffn.1": r"transformer_layers.\1.self_mlp.layer_norm",
+ r"self_attn.(\d+).ffn.3": r"transformer_layers.\1.self_mlp.fc2",
+ r"cross_attn.(\d+).to_qk": r"transformer_layers.\1.cross_attention.to_qk",
+ r"cross_attn.(\d+).to_v": r"transformer_layers.\1.cross_attention.v_proj",
+ r"cross_attn.(\d+).to_out": r"transformer_layers.\1.cross_attention.o_proj",
+ r"cross_attn.(\d+).ffn.0": r"transformer_layers.\1.cross_mlp.fc1",
+ r"cross_attn.(\d+).ffn.1": r"transformer_layers.\1.cross_mlp.layer_norm",
+ r"cross_attn.(\d+).ffn.3": r"transformer_layers.\1.cross_mlp.fc2",
+ r"log_assignment.(\d+).matchability": r"match_assignment_layers.\1.matchability",
+ r"log_assignment.(\d+).final_proj": r"match_assignment_layers.\1.final_projection",
+ r"token_confidence.(\d+).token.0": r"token_confidence.\1.token",
+}
+
+
+def convert_old_keys_to_new_keys(state_dict_keys: list[str]):
+ """
+ This function should be applied only once, on the concatenated keys to efficiently rename using
+ the key mappings.
+ """
+ output_dict = {}
+ if state_dict_keys is not None:
+ old_text = "\n".join(state_dict_keys)
+ new_text = old_text
+ for pattern, replacement in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items():
+ if replacement is None:
+ new_text = re.sub(pattern, "", new_text) # an empty line
+ continue
+ new_text = re.sub(pattern, replacement, new_text)
+ output_dict = dict(zip(old_text.split("\n"), new_text.split("\n")))
+ return output_dict
+
+
+def add_keypoint_detector_state_dict(lightglue_state_dict):
+ keypoint_detector = AutoModelForKeypointDetection.from_pretrained("magic-leap-community/superpoint")
+ keypoint_detector_state_dict = keypoint_detector.state_dict()
+ for k, v in keypoint_detector_state_dict.items():
+ lightglue_state_dict[f"keypoint_detector.{k}"] = v
+ return lightglue_state_dict
+
+
+def split_weights(state_dict):
+ for i in range(9):
+ # Remove unused r values
+ log_assignment_r_key = f"log_assignment.{i}.r"
+ if state_dict.get(log_assignment_r_key, None) is not None:
+ state_dict.pop(log_assignment_r_key)
+
+ Wqkv_weight = state_dict.pop(f"transformer_layers.{i}.self_attention.Wqkv.weight")
+ Wqkv_bias = state_dict.pop(f"transformer_layers.{i}.self_attention.Wqkv.bias")
+ Wqkv_weight = Wqkv_weight.reshape(256, 3, 256)
+ Wqkv_bias = Wqkv_bias.reshape(256, 3)
+ query_weight, key_weight, value_weight = Wqkv_weight[:, 0], Wqkv_weight[:, 1], Wqkv_weight[:, 2]
+ query_bias, key_bias, value_bias = Wqkv_bias[:, 0], Wqkv_bias[:, 1], Wqkv_bias[:, 2]
+ state_dict[f"transformer_layers.{i}.self_attention.q_proj.weight"] = query_weight
+ state_dict[f"transformer_layers.{i}.self_attention.k_proj.weight"] = key_weight
+ state_dict[f"transformer_layers.{i}.self_attention.v_proj.weight"] = value_weight
+ state_dict[f"transformer_layers.{i}.self_attention.q_proj.bias"] = query_bias
+ state_dict[f"transformer_layers.{i}.self_attention.k_proj.bias"] = key_bias
+ state_dict[f"transformer_layers.{i}.self_attention.v_proj.bias"] = value_bias
+
+ to_qk_weight = state_dict.pop(f"transformer_layers.{i}.cross_attention.to_qk.weight")
+ to_qk_bias = state_dict.pop(f"transformer_layers.{i}.cross_attention.to_qk.bias")
+ state_dict[f"transformer_layers.{i}.cross_attention.q_proj.weight"] = to_qk_weight
+ state_dict[f"transformer_layers.{i}.cross_attention.q_proj.bias"] = to_qk_bias
+ state_dict[f"transformer_layers.{i}.cross_attention.k_proj.weight"] = to_qk_weight
+ state_dict[f"transformer_layers.{i}.cross_attention.k_proj.bias"] = to_qk_bias
+
+ return state_dict
+
+
+@torch.no_grad()
+def write_model(
+ model_path,
+ checkpoint_url,
+ organization,
+ safe_serialization=True,
+ push_to_hub=False,
+):
+ os.makedirs(model_path, exist_ok=True)
+
+ # ------------------------------------------------------------
+ # LightGlue config
+ # ------------------------------------------------------------
+
+ config = LightGlueConfig(
+ descriptor_dim=256,
+ num_hidden_layers=9,
+ num_attention_heads=4,
+ )
+ config.architectures = ["LightGlueForKeypointMatching"]
+ config.save_pretrained(model_path)
+ print("Model config saved successfully...")
+
+ # ------------------------------------------------------------
+ # Convert weights
+ # ------------------------------------------------------------
+
+ print(f"Fetching all parameters from the checkpoint at {checkpoint_url}...")
+ original_state_dict = torch.hub.load_state_dict_from_url(checkpoint_url)
+
+ print("Converting model...")
+ all_keys = list(original_state_dict.keys())
+ new_keys = convert_old_keys_to_new_keys(all_keys)
+
+ state_dict = {}
+ for key in all_keys:
+ new_key = new_keys[key]
+ state_dict[new_key] = original_state_dict.pop(key).contiguous().clone()
+
+ del original_state_dict
+ gc.collect()
+ state_dict = split_weights(state_dict)
+ state_dict = add_keypoint_detector_state_dict(state_dict)
+
+ print("Loading the checkpoint in a LightGlue model...")
+ device = "cuda"
+ with torch.device(device):
+ model = LightGlueForKeypointMatching(config)
+ model.load_state_dict(state_dict)
+ print("Checkpoint loaded successfully...")
+ del model.config._name_or_path
+
+ print("Saving the model...")
+ model.save_pretrained(model_path, safe_serialization=safe_serialization)
+ del state_dict, model
+
+ # Safety check: reload the converted model
+ gc.collect()
+ print("Reloading the model to check if it's saved correctly.")
+ model = LightGlueForKeypointMatching.from_pretrained(model_path)
+ print("Model reloaded successfully.")
+
+ model_name = "lightglue"
+ if "superpoint" in checkpoint_url:
+ model_name += "_superpoint"
+ if checkpoint_url == DEFAULT_CHECKPOINT_URL:
+ print("Checking the model outputs...")
+ verify_model_outputs(model, device)
+ print("Model outputs verified successfully.")
+
+ if push_to_hub:
+ print("Pushing model to the hub...")
+ model.push_to_hub(
+ repo_id=f"{organization}/{model_name}",
+ commit_message="Add model",
+ )
+ config.push_to_hub(repo_id=f"{organization}/{model_name}", commit_message="Add config")
+
+ write_image_processor(model_path, model_name, organization, push_to_hub=push_to_hub)
+
+
+def write_image_processor(save_dir, model_name, organization, push_to_hub=False):
+ if "superpoint" in model_name:
+ image_processor = LightGlueImageProcessor(do_grayscale=True)
+ else:
+ image_processor = LightGlueImageProcessor()
+ image_processor.save_pretrained(save_dir)
+
+ if push_to_hub:
+ print("Pushing image processor to the hub...")
+ image_processor.push_to_hub(
+ repo_id=f"{organization}/{model_name}",
+ commit_message="Add image processor",
+ )
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--checkpoint_url",
+ default=DEFAULT_CHECKPOINT_URL,
+ type=str,
+ help="URL of the original LightGlue checkpoint you'd like to convert.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path",
+ default=None,
+ type=str,
+ required=True,
+ help="Path to the output PyTorch model directory.",
+ )
+ parser.add_argument("--save_model", action="store_true", help="Save model to local")
+ parser.add_argument(
+ "--push_to_hub",
+ action="store_true",
+ help="Push model and image preprocessor to the hub",
+ )
+ parser.add_argument(
+ "--organization",
+ default="ETH-CVG",
+ type=str,
+ help="Hub organization in which you want the model to be uploaded.",
+ )
+
+ args = parser.parse_args()
+ write_model(
+ args.pytorch_dump_folder_path,
+ args.checkpoint_url,
+ args.organization,
+ safe_serialization=True,
+ push_to_hub=args.push_to_hub,
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/image_processing_lightglue.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/image_processing_lightglue.py
new file mode 100644
index 0000000000000000000000000000000000000000..c389929eea309350436957c10266b53c914751d4
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/image_processing_lightglue.py
@@ -0,0 +1,526 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/lightglue/modular_lightglue.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_lightglue.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import warnings
+from typing import Optional, Union
+
+import numpy as np
+import torch
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import resize, to_channel_dimension_format
+from ...image_utils import (
+ ChannelDimension,
+ ImageInput,
+ ImageType,
+ PILImageResampling,
+ get_image_type,
+ infer_channel_dimension_format,
+ is_pil_image,
+ is_scaled_image,
+ is_valid_image,
+ is_vision_available,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import TensorType, is_matplotlib_available, logging, requires_backends
+from ...utils.import_utils import requires
+from .modeling_lightglue import LightGlueKeypointMatchingOutput
+
+
+if is_vision_available():
+ from PIL import Image, ImageDraw
+
+if is_vision_available():
+ import PIL
+
+logger = logging.get_logger(__name__)
+
+
+def is_grayscale(
+ image: np.ndarray,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+):
+ if input_data_format == ChannelDimension.FIRST:
+ if image.shape[0] == 1:
+ return True
+ return np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...])
+ elif input_data_format == ChannelDimension.LAST:
+ if image.shape[-1] == 1:
+ return True
+ return np.all(image[..., 0] == image[..., 1]) and np.all(image[..., 1] == image[..., 2])
+
+
+def convert_to_grayscale(
+ image: ImageInput,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+) -> ImageInput:
+ """
+ Converts an image to grayscale format using the NTSC formula. Only support numpy and PIL Image. TODO support torch
+ and tensorflow grayscale conversion
+
+ This function is supposed to return a 1-channel image, but it returns a 3-channel image with the same value in each
+ channel, because of an issue that is discussed in :
+ https://github.com/huggingface/transformers/pull/25786#issuecomment-1730176446
+
+ Args:
+ image (Image):
+ The image to convert.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image.
+ """
+ requires_backends(convert_to_grayscale, ["vision"])
+
+ if isinstance(image, np.ndarray):
+ if is_grayscale(image, input_data_format=input_data_format):
+ return image
+ if input_data_format == ChannelDimension.FIRST:
+ gray_image = image[0, ...] * 0.2989 + image[1, ...] * 0.5870 + image[2, ...] * 0.1140
+ gray_image = np.stack([gray_image] * 3, axis=0)
+ elif input_data_format == ChannelDimension.LAST:
+ gray_image = image[..., 0] * 0.2989 + image[..., 1] * 0.5870 + image[..., 2] * 0.1140
+ gray_image = np.stack([gray_image] * 3, axis=-1)
+ return gray_image
+
+ if not isinstance(image, PIL.Image.Image):
+ return image
+
+ image = image.convert("L")
+ return image
+
+
+def validate_and_format_image_pairs(images: ImageInput):
+ error_message = (
+ "Input images must be a one of the following :",
+ " - A pair of PIL images.",
+ " - A pair of 3D arrays.",
+ " - A list of pairs of PIL images.",
+ " - A list of pairs of 3D arrays.",
+ )
+
+ def _is_valid_image(image):
+ """images is a PIL Image or a 3D array."""
+ return is_pil_image(image) or (
+ is_valid_image(image) and get_image_type(image) != ImageType.PIL and len(image.shape) == 3
+ )
+
+ if isinstance(images, list):
+ if len(images) == 2 and all((_is_valid_image(image)) for image in images):
+ return images
+ if all(
+ isinstance(image_pair, list)
+ and len(image_pair) == 2
+ and all(_is_valid_image(image) for image in image_pair)
+ for image_pair in images
+ ):
+ return [image for image_pair in images for image in image_pair]
+ raise ValueError(error_message)
+
+
+@requires(backends=("torch",))
+class LightGlueImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a LightGlue image processor.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden
+ by `do_resize` in the `preprocess` method.
+ size (`dict[str, int]` *optional*, defaults to `{"height": 480, "width": 640}`):
+ Resolution of the output image after `resize` is applied. Only has an effect if `do_resize` is set to
+ `True`. Can be overridden by `size` in the `preprocess` method.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
+ Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
+ the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
+ method.
+ do_grayscale (`bool`, *optional*, defaults to `True`):
+ Whether to convert the image to grayscale. Can be overridden by `do_grayscale` in the `preprocess` method.
+ """
+
+ model_input_names = ["pixel_values"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ do_rescale: bool = True,
+ rescale_factor: float = 1 / 255,
+ do_grayscale: bool = True,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"height": 480, "width": 640}
+ size = get_size_dict(size, default_to_square=False)
+
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_grayscale = do_grayscale
+
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ):
+ """
+ Resize an image.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Dictionary of the form `{"height": int, "width": int}`, specifying the size of the output image.
+ data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the output image. If not provided, it will be inferred from the input
+ image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ size = get_size_dict(size, default_to_square=False)
+
+ return resize(
+ image,
+ size=(size["height"], size["width"]),
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ def preprocess(
+ self,
+ images,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_grayscale: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: ChannelDimension = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> BatchFeature:
+ """
+ Preprocess an image or batch of images.
+
+ Args:
+ images (`ImageInput`):
+ Image pairs to preprocess. Expects either a list of 2 images or a list of list of 2 images list with
+ pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set
+ `do_rescale=False`.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the output image after `resize` has been applied. If `size["shortest_edge"]` >= 384, the image
+ is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the
+ image will be matched to `int(size["shortest_edge"]/ crop_pct)`, after which the image is cropped to
+ `(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`.
+ resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of `PILImageResampling`, filters. Only
+ has an effect if `do_resize` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image values between [0 - 1].
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_grayscale (`bool`, *optional*, defaults to `self.do_grayscale`):
+ Whether to convert the image to grayscale.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ resample = resample if resample is not None else self.resample
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_grayscale = do_grayscale if do_grayscale is not None else self.do_grayscale
+
+ size = size if size is not None else self.size
+ size = get_size_dict(size, default_to_square=False)
+
+ # Validate and convert the input images into a flattened list of images for all subsequent processing steps.
+ images = validate_and_format_image_pairs(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ validate_preprocess_arguments(
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ )
+
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if is_scaled_image(images[0]) and do_rescale:
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ all_images = []
+ for image in images:
+ if do_resize:
+ image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+
+ if do_rescale:
+ image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+
+ if do_grayscale:
+ image = convert_to_grayscale(image, input_data_format=input_data_format)
+
+ image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+ all_images.append(image)
+
+ # Convert back the flattened list of images into a list of pairs of images.
+ image_pairs = [all_images[i : i + 2] for i in range(0, len(all_images), 2)]
+
+ data = {"pixel_values": image_pairs}
+
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+ def post_process_keypoint_matching(
+ self,
+ outputs: LightGlueKeypointMatchingOutput,
+ target_sizes: Union[TensorType, list[tuple]],
+ threshold: float = 0.0,
+ ) -> list[dict[str, torch.Tensor]]:
+ """
+ Converts the raw output of [`KeypointMatchingOutput`] into lists of keypoints, scores and descriptors
+ with coordinates absolute to the original image sizes.
+ Args:
+ outputs ([`KeypointMatchingOutput`]):
+ Raw outputs of the model.
+ target_sizes (`torch.Tensor` or `list[tuple[tuple[int, int]]]`, *optional*):
+ Tensor of shape `(batch_size, 2, 2)` or list of tuples of tuples (`tuple[int, int]`) containing the
+ target size `(height, width)` of each image in the batch. This must be the original image size (before
+ any processing).
+ threshold (`float`, *optional*, defaults to 0.0):
+ Threshold to filter out the matches with low scores.
+ Returns:
+ `list[Dict]`: A list of dictionaries, each dictionary containing the keypoints in the first and second image
+ of the pair, the matching scores and the matching indices.
+ """
+ if outputs.mask.shape[0] != len(target_sizes):
+ raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the mask")
+ if not all(len(target_size) == 2 for target_size in target_sizes):
+ raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
+
+ if isinstance(target_sizes, list):
+ image_pair_sizes = torch.tensor(target_sizes, device=outputs.mask.device)
+ else:
+ if target_sizes.shape[1] != 2 or target_sizes.shape[2] != 2:
+ raise ValueError(
+ "Each element of target_sizes must contain the size (h, w) of each image of the batch"
+ )
+ image_pair_sizes = target_sizes
+
+ keypoints = outputs.keypoints.clone()
+ keypoints = keypoints * image_pair_sizes.flip(-1).reshape(-1, 2, 1, 2)
+ keypoints = keypoints.to(torch.int32)
+
+ results = []
+ for mask_pair, keypoints_pair, matches, scores in zip(
+ outputs.mask, keypoints, outputs.matches[:, 0], outputs.matching_scores[:, 0]
+ ):
+ mask0 = mask_pair[0] > 0
+ mask1 = mask_pair[1] > 0
+ keypoints0 = keypoints_pair[0][mask0]
+ keypoints1 = keypoints_pair[1][mask1]
+ matches0 = matches[mask0]
+ scores0 = scores[mask0]
+
+ # Filter out matches with low scores
+ valid_matches = torch.logical_and(scores0 > threshold, matches0 > -1)
+
+ matched_keypoints0 = keypoints0[valid_matches]
+ matched_keypoints1 = keypoints1[matches0[valid_matches]]
+ matching_scores = scores0[valid_matches]
+
+ results.append(
+ {
+ "keypoints0": matched_keypoints0,
+ "keypoints1": matched_keypoints1,
+ "matching_scores": matching_scores,
+ }
+ )
+
+ return results
+
+ def visualize_keypoint_matching(
+ self,
+ images: ImageInput,
+ keypoint_matching_output: list[dict[str, torch.Tensor]],
+ ) -> list["Image.Image"]:
+ """
+ Plots the image pairs side by side with the detected keypoints as well as the matching between them.
+
+ Args:
+ images (`ImageInput`):
+ Image pairs to plot. Same as `LightGlueImageProcessor.preprocess`. Expects either a list of 2
+ images or a list of list of 2 images list with pixel values ranging from 0 to 255.
+ keypoint_matching_output (List[Dict[str, torch.Tensor]]]):
+ A post processed keypoint matching output
+
+ Returns:
+ `List[PIL.Image.Image]`: A list of PIL images, each containing the image pairs side by side with the detected
+ keypoints as well as the matching between them.
+ """
+ images = validate_and_format_image_pairs(images)
+ images = [to_numpy_array(image) for image in images]
+ image_pairs = [images[i : i + 2] for i in range(0, len(images), 2)]
+
+ results = []
+ for image_pair, pair_output in zip(image_pairs, keypoint_matching_output):
+ height0, width0 = image_pair[0].shape[:2]
+ height1, width1 = image_pair[1].shape[:2]
+ plot_image = np.zeros((max(height0, height1), width0 + width1, 3), dtype=np.uint8)
+ plot_image[:height0, :width0] = image_pair[0]
+ plot_image[:height1, width0:] = image_pair[1]
+
+ plot_image_pil = Image.fromarray(plot_image)
+ draw = ImageDraw.Draw(plot_image_pil)
+
+ keypoints0_x, keypoints0_y = pair_output["keypoints0"].unbind(1)
+ keypoints1_x, keypoints1_y = pair_output["keypoints1"].unbind(1)
+ for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
+ keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, pair_output["matching_scores"]
+ ):
+ color = self._get_color(matching_score)
+ draw.line(
+ (keypoint0_x, keypoint0_y, keypoint1_x + width0, keypoint1_y),
+ fill=color,
+ width=3,
+ )
+ draw.ellipse((keypoint0_x - 2, keypoint0_y - 2, keypoint0_x + 2, keypoint0_y + 2), fill="black")
+ draw.ellipse(
+ (keypoint1_x + width0 - 2, keypoint1_y - 2, keypoint1_x + width0 + 2, keypoint1_y + 2),
+ fill="black",
+ )
+
+ results.append(plot_image_pil)
+ return results
+
+ def _get_color(self, score):
+ """Maps a score to a color."""
+ r = int(255 * (1 - score))
+ g = int(255 * score)
+ b = 0
+ return (r, g, b)
+
+ def plot_keypoint_matching(self, images: ImageInput, keypoint_matching_output: LightGlueKeypointMatchingOutput):
+ """
+ Plots the image pairs side by side with the detected keypoints as well as the matching between them. Requires
+ matplotlib to be installed.
+
+ .. deprecated::
+ `plot_keypoint_matching` is deprecated and will be removed in a future version. Use `visualize_keypoint_matching` instead.
+
+ Args:
+ images (`ImageInput`):
+ Image pairs to plot. Same as `LightGlueImageProcessor.preprocess`. Expects either a list of 2 images or
+ a list of list of 2 images list with pixel values ranging from 0 to 255.
+ keypoint_matching_output ([`LightGlueKeypointMatchingOutput`]):
+ Raw outputs of the model.
+ """
+ warnings.warn(
+ "`plot_keypoint_matching` is deprecated and will be removed in transformers v. "
+ "Use `visualize_keypoint_matching` instead.",
+ FutureWarning,
+ )
+
+ if is_matplotlib_available():
+ import matplotlib.pyplot as plt
+ else:
+ raise ImportError("Please install matplotlib to use `plot_keypoint_matching` method")
+
+ images = validate_and_format_image_pairs(images)
+ images = [to_numpy_array(image) for image in images]
+ image_pairs = [images[i : i + 2] for i in range(0, len(images), 2)]
+
+ for image_pair, pair_output in zip(image_pairs, keypoint_matching_output):
+ height0, width0 = image_pair[0].shape[:2]
+ height1, width1 = image_pair[1].shape[:2]
+ plot_image = np.zeros((max(height0, height1), width0 + width1, 3))
+ plot_image[:height0, :width0] = image_pair[0] / 255.0
+ plot_image[:height1, width0:] = image_pair[1] / 255.0
+ plt.imshow(plot_image)
+ plt.axis("off")
+
+ keypoints0_x, keypoints0_y = pair_output["keypoints0"].unbind(1)
+ keypoints1_x, keypoints1_y = pair_output["keypoints1"].unbind(1)
+ for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
+ keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, pair_output["matching_scores"]
+ ):
+ plt.plot(
+ [keypoint0_x, keypoint1_x + width0],
+ [keypoint0_y, keypoint1_y],
+ color=plt.get_cmap("RdYlGn")(matching_score.item()),
+ alpha=0.9,
+ linewidth=0.5,
+ )
+ plt.scatter(keypoint0_x, keypoint0_y, c="black", s=2)
+ plt.scatter(keypoint1_x + width0, keypoint1_y, c="black", s=2)
+ plt.show()
+
+
+__all__ = ["LightGlueImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/modeling_lightglue.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/modeling_lightglue.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee3b39ed9e861855f54083112d41ef7f0873112c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/modeling_lightglue.py
@@ -0,0 +1,916 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/lightglue/modular_lightglue.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_lightglue.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from typing import Callable, Optional, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch.nn.utils.rnn import pad_sequence
+
+from ...activations import ACT2FN
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import ModelOutput, TransformersKwargs, auto_docstring
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import can_return_tuple
+from ..auto.modeling_auto import AutoModelForKeypointDetection
+from .configuration_lightglue import LightGlueConfig
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of LightGlue keypoint matching models. Due to the nature of keypoint detection and matching,
+ the number of keypoints is not fixed and can vary from image to image, which makes batching non-trivial. In the
+ batch of images, the maximum number of matches is set as the dimension of the matches and matching scores. The mask
+ tensor is used to indicate which values in the keypoints, matches, matching_scores and prune tensors are keypoint
+ matching information.
+ """
+)
+class LightGlueKeypointMatchingOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*):
+ Loss computed during training.
+ matches (`torch.FloatTensor` of shape `(batch_size, 2, num_matches)`):
+ Index of keypoint matched in the other image.
+ matching_scores (`torch.FloatTensor` of shape `(batch_size, 2, num_matches)`):
+ Scores of predicted matches.
+ keypoints (`torch.FloatTensor` of shape `(batch_size, num_keypoints, 2)`):
+ Absolute (x, y) coordinates of predicted keypoints in a given image.
+ prune (`torch.IntTensor` of shape `(batch_size, num_keypoints)`):
+ Pruning mask indicating which keypoints are removed and at which layer.
+ mask (`torch.BoolTensor` of shape `(batch_size, num_keypoints)`):
+ Mask indicating which values in matches, matching_scores, keypoints and prune are keypoint matching
+ information.
+ hidden_states (`Tuple[torch.FloatTensor, ...]`, *optional*):
+ Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, 2, num_channels,
+ num_keypoints)` returned when `output_hidden_states=True` is passed or when
+ `config.output_hidden_states=True`
+ attentions (`Tuple[torch.FloatTensor, ...]`, *optional*):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, 2, num_heads, num_keypoints,
+ num_keypoints)` returned when `output_attentions=True` is passed or when
+ `config.output_attentions=True`
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ matches: Optional[torch.FloatTensor] = None
+ matching_scores: Optional[torch.FloatTensor] = None
+ keypoints: Optional[torch.FloatTensor] = None
+ prune: Optional[torch.IntTensor] = None
+ mask: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+class LightGluePositionalEncoder(nn.Module):
+ def __init__(self, config: LightGlueConfig):
+ super().__init__()
+ self.projector = nn.Linear(2, config.descriptor_dim // config.num_attention_heads // 2, bias=False)
+
+ def forward(
+ self, keypoints: torch.Tensor, output_hidden_states: Optional[bool] = False
+ ) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
+ projected_keypoints = self.projector(keypoints)
+ embeddings = projected_keypoints.repeat_interleave(2, dim=-1)
+ cosines = torch.cos(embeddings)
+ sines = torch.sin(embeddings)
+ embeddings = (cosines, sines)
+ output = (embeddings, projected_keypoints) if output_hidden_states else (embeddings,)
+ return output
+
+
+def rotate_half(x):
+ # Split and rotate. Note that this function is different from e.g. Llama.
+ x1 = x[..., ::2]
+ x2 = x[..., 1::2]
+ rot_x = torch.stack([-x2, x1], dim=-1).flatten(-2)
+ return rot_x
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ dtype = q.dtype
+ q = q.float()
+ k = k.float()
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed.to(dtype=dtype), k_embed.to(dtype=dtype)
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class LightGlueAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: LightGlueConfig, layer_idx: int):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = config.attention_dropout
+ self.is_causal = True
+
+ self.q_proj = nn.Linear(
+ config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.k_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.v_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.o_proj = nn.Linear(
+ config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+ )
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ is_cross_attention = encoder_hidden_states is not None
+ current_states = encoder_hidden_states if is_cross_attention else hidden_states
+ current_attention_mask = encoder_attention_mask if is_cross_attention else attention_mask
+
+ key_states = self.k_proj(current_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.v_proj(current_states).view(hidden_shape).transpose(1, 2)
+
+ if position_embeddings is not None:
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ current_attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class LightGlueMLP(nn.Module):
+ def __init__(self, config: LightGlueConfig):
+ super().__init__()
+ self.config = config
+ self.activation_fn = ACT2FN[config.hidden_act]
+ self.fc1 = nn.Linear(config.intermediate_size, config.intermediate_size)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.layer_norm = nn.LayerNorm(config.intermediate_size, elementwise_affine=True)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.layer_norm(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+class LightGlueTransformerLayer(nn.Module):
+ def __init__(self, config: LightGlueConfig, layer_idx: int):
+ super().__init__()
+ self.self_attention = LightGlueAttention(config, layer_idx)
+ self.self_mlp = LightGlueMLP(config)
+ self.cross_attention = LightGlueAttention(config, layer_idx)
+ self.cross_mlp = LightGlueMLP(config)
+
+ def forward(
+ self,
+ descriptors: torch.Tensor,
+ keypoints: torch.Tensor,
+ attention_mask: torch.Tensor,
+ output_hidden_states: Optional[bool] = False,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor, Optional[tuple[torch.Tensor]], Optional[tuple[torch.Tensor]]]:
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (descriptors,)
+
+ batch_size, num_keypoints, descriptor_dim = descriptors.shape
+
+ # Self attention block
+ attention_output, self_attentions = self.self_attention(
+ descriptors,
+ position_embeddings=keypoints,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+ intermediate_states = torch.cat([descriptors, attention_output], dim=-1)
+ output_states = self.self_mlp(intermediate_states)
+ self_attention_descriptors = descriptors + output_states
+
+ if output_hidden_states:
+ self_attention_hidden_states = (intermediate_states, output_states)
+
+ # Reshape hidden_states to group by image_pairs :
+ # (batch_size, num_keypoints, descriptor_dim) -> (batch_size, 2, num_keypoints, descriptor_dim)
+ # Flip dimension 1 to perform cross attention :
+ # (image0, image1) -> (image1, image0)
+ # Reshape back to original shape :
+ # (batch_size, 2, num_keypoints, descriptor_dim) -> (batch_size, num_keypoints, descriptor_dim)
+ encoder_hidden_states = (
+ self_attention_descriptors.reshape(-1, 2, num_keypoints, descriptor_dim)
+ .flip(1)
+ .reshape(batch_size, num_keypoints, descriptor_dim)
+ )
+ # Same for mask
+ encoder_attention_mask = (
+ attention_mask.reshape(-1, 2, 1, 1, num_keypoints).flip(1).reshape(batch_size, 1, 1, num_keypoints)
+ if attention_mask is not None
+ else None
+ )
+
+ # Cross attention block
+ cross_attention_output, cross_attentions = self.cross_attention(
+ self_attention_descriptors,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ output_attentions=output_attentions,
+ )
+ cross_intermediate_states = torch.cat([self_attention_descriptors, cross_attention_output], dim=-1)
+ cross_output_states = self.cross_mlp(cross_intermediate_states)
+ descriptors = self_attention_descriptors + cross_output_states
+
+ if output_hidden_states:
+ cross_attention_hidden_states = (cross_intermediate_states, cross_output_states)
+ all_hidden_states = (
+ all_hidden_states
+ + (self_attention_descriptors.reshape(batch_size, num_keypoints, descriptor_dim),)
+ + self_attention_hidden_states
+ + (descriptors.reshape(batch_size, num_keypoints, descriptor_dim),)
+ + cross_attention_hidden_states
+ )
+
+ if output_attentions:
+ all_attentions = all_attentions + (self_attentions,) + (cross_attentions,)
+
+ return descriptors, all_hidden_states, all_attentions
+
+
+def sigmoid_log_double_softmax(
+ similarity: torch.Tensor, matchability0: torch.Tensor, matchability1: torch.Tensor
+) -> torch.Tensor:
+ """create the log assignment matrix from logits and similarity"""
+ batch_size, num_keypoints_0, num_keypoints_1 = similarity.shape
+ certainties = nn.functional.logsigmoid(matchability0) + nn.functional.logsigmoid(matchability1).transpose(1, 2)
+ scores0 = nn.functional.log_softmax(similarity, 2)
+ scores1 = nn.functional.log_softmax(similarity.transpose(-1, -2).contiguous(), 2).transpose(-1, -2)
+ scores = similarity.new_full((batch_size, num_keypoints_0 + 1, num_keypoints_1 + 1), 0)
+ scores[:, :num_keypoints_0, :num_keypoints_1] = scores0 + scores1 + certainties
+ scores[:, :-1, -1] = nn.functional.logsigmoid(-matchability0.squeeze(-1))
+ scores[:, -1, :-1] = nn.functional.logsigmoid(-matchability1.squeeze(-1))
+ return scores
+
+
+class LightGlueMatchAssignmentLayer(nn.Module):
+ def __init__(self, config: LightGlueConfig):
+ super().__init__()
+
+ self.descriptor_dim = config.descriptor_dim
+ self.final_projection = nn.Linear(self.descriptor_dim, self.descriptor_dim, bias=True)
+ self.matchability = nn.Linear(self.descriptor_dim, 1, bias=True)
+
+ def forward(self, descriptors: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+ batch_size, num_keypoints, descriptor_dim = descriptors.shape
+ # Final projection and similarity computation
+ m_descriptors = self.final_projection(descriptors)
+ m_descriptors = m_descriptors / torch.tensor(self.descriptor_dim, device=m_descriptors.device) ** 0.25
+ m_descriptors = m_descriptors.reshape(batch_size // 2, 2, num_keypoints, descriptor_dim)
+ m_descriptors0 = m_descriptors[:, 0]
+ m_descriptors1 = m_descriptors[:, 1]
+ similarity = m_descriptors0 @ m_descriptors1.transpose(-1, -2)
+ if mask is not None:
+ mask = mask.reshape(batch_size // 2, 2, num_keypoints)
+ mask0 = mask[:, 0].unsqueeze(-1)
+ mask1 = mask[:, 1].unsqueeze(-1).transpose(-1, -2)
+ mask = mask0 * mask1
+ similarity = similarity.masked_fill(mask == 0, torch.finfo(similarity.dtype).min)
+
+ # Compute matchability of descriptors
+ matchability = self.matchability(descriptors)
+ matchability = matchability.reshape(batch_size // 2, 2, num_keypoints, 1)
+ matchability_0 = matchability[:, 0]
+ matchability_1 = matchability[:, 1]
+
+ # Compute scores from similarity and matchability
+ scores = sigmoid_log_double_softmax(similarity, matchability_0, matchability_1)
+ return scores
+
+ def get_matchability(self, descriptors: torch.Tensor) -> torch.Tensor:
+ """Get matchability of descriptors as a probability"""
+ matchability = self.matchability(descriptors)
+ matchability = nn.functional.sigmoid(matchability).squeeze(-1)
+ return matchability
+
+
+class LightGlueTokenConfidenceLayer(nn.Module):
+ def __init__(self, config: LightGlueConfig):
+ super().__init__()
+
+ self.token = nn.Linear(config.descriptor_dim, 1)
+
+ def forward(self, descriptors: torch.Tensor) -> torch.Tensor:
+ token = self.token(descriptors.detach())
+ token = nn.functional.sigmoid(token).squeeze(-1)
+ return token
+
+
+@auto_docstring
+class LightGluePreTrainedModel(PreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config: LightGlueConfig
+ base_model_prefix = "lightglue"
+ main_input_name = "pixel_values"
+ supports_gradient_checkpointing = False
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+
+def get_matches_from_scores(scores: torch.Tensor, threshold: float) -> tuple[torch.Tensor, torch.Tensor]:
+ """obtain matches from a score matrix [Bx M+1 x N+1]"""
+ batch_size, _, _ = scores.shape
+ # For each keypoint, get the best match
+ max0 = scores[:, :-1, :-1].max(2)
+ max1 = scores[:, :-1, :-1].max(1)
+ matches0 = max0.indices
+ matches1 = max1.indices
+
+ # Mutual check for matches
+ indices0 = torch.arange(matches0.shape[1], device=matches0.device)[None]
+ indices1 = torch.arange(matches1.shape[1], device=matches1.device)[None]
+ mutual0 = indices0 == matches1.gather(1, matches0)
+ mutual1 = indices1 == matches0.gather(1, matches1)
+
+ # Get matching scores and filter based on mutual check and thresholding
+ max0 = max0.values.exp()
+ zero = max0.new_tensor(0)
+ matching_scores0 = torch.where(mutual0, max0, zero)
+ matching_scores1 = torch.where(mutual1, matching_scores0.gather(1, matches1), zero)
+ valid0 = mutual0 & (matching_scores0 > threshold)
+ valid1 = mutual1 & valid0.gather(1, matches1)
+
+ # Filter matches based on mutual check and thresholding of scores
+ matches0 = torch.where(valid0, matches0, -1)
+ matches1 = torch.where(valid1, matches1, -1)
+ matches = torch.stack([matches0, matches1]).transpose(0, 1).reshape(batch_size * 2, -1)
+ matching_scores = torch.stack([matching_scores0, matching_scores1]).transpose(0, 1).reshape(batch_size * 2, -1)
+
+ return matches, matching_scores
+
+
+def normalize_keypoints(keypoints: torch.Tensor, height: int, width: int) -> torch.Tensor:
+ """
+ Normalize keypoints locations based on image image_shape
+
+ Args:
+ keypoints (`torch.Tensor` of shape `(batch_size, num_keypoints, 2)`):
+ Keypoints locations in (x, y) format.
+ height (`int`):
+ Image height.
+ width (`int`):
+ Image width.
+
+ Returns:
+ Normalized keypoints locations of shape (`torch.Tensor` of shape `(batch_size, num_keypoints, 2)`).
+ """
+ size = torch.tensor([width, height], device=keypoints.device, dtype=keypoints.dtype)[None]
+ shift = size / 2
+ scale = size.max(-1).values / 2
+ keypoints = (keypoints - shift[..., None, :]) / scale[..., None, None]
+ return keypoints
+
+
+@auto_docstring(
+ custom_intro="""
+ LightGlue model taking images as inputs and outputting the matching of them.
+ """
+)
+class LightGlueForKeypointMatching(LightGluePreTrainedModel):
+ """
+ LightGlue is a model matching keypoints in images by leveraging detections from a keypoint detector such as
+ SuperPoint. It is based on the SuperGlue architecture and is designed to be lightweight and efficient.
+ It consists of :
+ 1. Keypoint Encoder
+ 2. A Graph Neural Network with self and cross attention layers
+ 3. Matching Assignment layers
+
+ The correspondence ids use -1 to indicate non-matching points.
+
+ Philipp Lindenberger, Paul-Edouard Sarlin and Marc Pollefeys. LightGlue: Local Feature Matching at Light Speed.
+ In ICCV 2023. https://arxiv.org/pdf/2306.13643.pdf
+ """
+
+ def __init__(self, config: LightGlueConfig):
+ super().__init__(config)
+ self.keypoint_detector = AutoModelForKeypointDetection.from_config(
+ config.keypoint_detector_config, trust_remote_code=config.trust_remote_code
+ )
+
+ self.keypoint_detector_descriptor_dim = config.keypoint_detector_config.descriptor_decoder_dim
+ self.descriptor_dim = config.descriptor_dim
+ self.num_layers = config.num_hidden_layers
+ self.filter_threshold = config.filter_threshold
+ self.depth_confidence = config.depth_confidence
+ self.width_confidence = config.width_confidence
+
+ if self.descriptor_dim != self.keypoint_detector_descriptor_dim:
+ self.input_projection = nn.Linear(self.keypoint_detector_descriptor_dim, self.descriptor_dim, bias=True)
+ else:
+ self.input_projection = nn.Identity()
+
+ self.positional_encoder = LightGluePositionalEncoder(config)
+
+ self.transformer_layers = nn.ModuleList(
+ [LightGlueTransformerLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]
+ )
+ self.match_assignment_layers = nn.ModuleList(
+ [LightGlueMatchAssignmentLayer(config) for _ in range(config.num_hidden_layers)]
+ )
+ self.token_confidence = nn.ModuleList(
+ [LightGlueTokenConfidenceLayer(config) for _ in range(config.num_hidden_layers - 1)]
+ )
+
+ self.post_init()
+
+ def _get_confidence_threshold(self, layer_index: int) -> float:
+ """scaled confidence threshold for a given layer"""
+ threshold = 0.8 + 0.1 * np.exp(-4.0 * layer_index / self.num_layers)
+ return np.clip(threshold, 0, 1)
+
+ def _keypoint_processing(
+ self, descriptors: torch.Tensor, keypoints: torch.Tensor, output_hidden_states: Optional[bool] = False
+ ) -> tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+ descriptors = descriptors.detach().contiguous()
+ projected_descriptors = self.input_projection(descriptors)
+ keypoint_encoding_output = self.positional_encoder(keypoints, output_hidden_states=output_hidden_states)
+ return projected_descriptors, keypoint_encoding_output
+
+ def _get_early_stopped_image_pairs(
+ self, keypoint_confidences: torch.Tensor, layer_index: int, mask: torch.Tensor, num_points: torch.Tensor
+ ) -> torch.Tensor:
+ """evaluate whether we should stop inference based on the confidence of the keypoints"""
+ batch_size, _ = mask.shape
+ if layer_index < self.num_layers - 1:
+ # If the current layer is not the last layer, we compute the confidence of the keypoints and check
+ # if we should stop the forward pass through the transformer layers for each pair of images.
+ keypoint_confidences = keypoint_confidences.masked_fill(mask == 0, 1)
+ keypoint_confidences = keypoint_confidences.reshape(batch_size // 2, -1)
+ threshold = self._get_confidence_threshold(layer_index)
+ ratio_confident = 1.0 - (keypoint_confidences < threshold).float().sum(dim=1) / num_points
+ early_stopped_pairs = ratio_confident > self.depth_confidence
+ else:
+ # If the current layer is the last layer, we stop the forward pass through the transformer layers for
+ # all pairs of images.
+ early_stopped_pairs = torch.ones(batch_size, dtype=torch.bool)
+ return early_stopped_pairs
+
+ def _get_keypoint_matching(self, descriptors, mask, layer_index, early_stops=None):
+ if early_stops is not None:
+ descriptors = descriptors[early_stops]
+ mask = mask[early_stops]
+ scores = self.match_assignment_layers[layer_index](descriptors, mask)
+ matches, matching_scores = get_matches_from_scores(scores, self.filter_threshold)
+ return matches, matching_scores
+
+ def _get_pruning_mask(self, confidences: torch.Tensor, scores: torch.Tensor, layer_index: int) -> torch.Tensor:
+ """mask points which should be removed"""
+ keep = scores > (1 - self.width_confidence)
+ if confidences is not None: # Low-confidence points are never pruned.
+ keep |= confidences <= self._get_confidence_threshold(layer_index)
+ return keep
+
+ def _do_layer_keypoint_pruning(
+ self,
+ descriptors: torch.Tensor,
+ keypoints: torch.Tensor,
+ mask: torch.Tensor,
+ indices: torch.Tensor,
+ prune_output: torch.Tensor,
+ keypoint_confidences: torch.Tensor,
+ layer_index: int,
+ ):
+ """
+ For a given layer, prune keypoints based on the confidence of the keypoints and the matchability of the
+ descriptors.
+ """
+ batch_size, _, _ = descriptors.shape
+ descriptors_matchability = self.match_assignment_layers[layer_index].get_matchability(descriptors)
+ pruned_keypoints_mask = self._get_pruning_mask(keypoint_confidences, descriptors_matchability, layer_index)
+ pruned_keypoints_mask = pruned_keypoints_mask.masked_fill(mask == 0, torch.tensor(False))
+
+ # For each image, we extract the pruned indices and the corresponding descriptors and keypoints.
+ pruned_descriptors, pruned_keypoints_0, pruned_keypoints_1, pruned_mask, pruned_indices = (
+ [t[mask] for t, mask in zip(tensor, pruned_keypoints_mask)]
+ for tensor in [descriptors, keypoints[0], keypoints[1], pruned_keypoints_mask, indices]
+ )
+ for i in range(batch_size):
+ prune_output[i, pruned_indices[i]] += 1
+
+ # Pad the pruned descriptors, keypoints, indices and mask to have the same shape across the batch.
+ pruned_descriptors, pruned_keypoints_0, pruned_keypoints_1, pruned_mask = (
+ pad_sequence(pruned_tensor, batch_first=True)
+ for pruned_tensor in [pruned_descriptors, pruned_keypoints_0, pruned_keypoints_1, pruned_mask]
+ )
+ pruned_keypoints = (pruned_keypoints_0, pruned_keypoints_1)
+ pruned_indices = pad_sequence(pruned_indices, batch_first=True, padding_value=-1)
+
+ return pruned_descriptors, pruned_keypoints, pruned_indices, pruned_mask, prune_output
+
+ def _concat_early_stopped_outputs(
+ self,
+ early_stops_indices,
+ final_pruned_keypoints_indices,
+ final_pruned_keypoints_iterations,
+ matches,
+ matching_scores,
+ ):
+ early_stops_indices = torch.stack(early_stops_indices)
+ matches, final_pruned_keypoints_indices = (
+ pad_sequence(tensor, batch_first=True, padding_value=-1)
+ for tensor in [matches, final_pruned_keypoints_indices]
+ )
+ matching_scores, final_pruned_keypoints_iterations = (
+ pad_sequence(tensor, batch_first=True, padding_value=0)
+ for tensor in [matching_scores, final_pruned_keypoints_iterations]
+ )
+ matches, matching_scores, final_pruned_keypoints_indices, final_pruned_keypoints_iterations = (
+ tensor[early_stops_indices]
+ for tensor in [
+ matches,
+ matching_scores,
+ final_pruned_keypoints_indices,
+ final_pruned_keypoints_iterations,
+ ]
+ )
+ return final_pruned_keypoints_indices, final_pruned_keypoints_iterations, matches, matching_scores
+
+ def _do_final_keypoint_pruning(
+ self,
+ indices: torch.Tensor,
+ matches: torch.Tensor,
+ matching_scores: torch.Tensor,
+ num_keypoints: torch.Tensor,
+ ) -> tuple[torch.Tensor, torch.Tensor]:
+ # (batch_size, num_keypoints) -> (batch_size // 2, 2, num_keypoints) -> 2 * (batch_size // 2, num_keypoints) to
+ # have tensors from
+ batch_size, _ = indices.shape
+ indices, matches, matching_scores = (
+ tensor.reshape(batch_size // 2, 2, -1) for tensor in [indices, matches, matching_scores]
+ )
+ indices0 = indices[:, 0]
+ indices1 = indices[:, 1]
+ matches0 = matches[:, 0]
+ matches1 = matches[:, 1]
+ matching_scores0 = matching_scores[:, 0]
+ matching_scores1 = matching_scores[:, 1]
+
+ # Prepare final matches and matching scores
+ _matches = torch.full((batch_size // 2, 2, num_keypoints), -1, device=indices.device, dtype=matches.dtype)
+ _matching_scores = torch.zeros(
+ (batch_size // 2, 2, num_keypoints), device=indices.device, dtype=matching_scores.dtype
+ )
+ # Fill the matches and matching scores for each image pair
+ for i in range(batch_size // 2):
+ _matches[i, 0, indices0[i]] = torch.where(
+ matches0[i] == -1, -1, indices1[i].gather(0, matches0[i].clamp(min=0))
+ )
+ _matches[i, 1, indices1[i]] = torch.where(
+ matches1[i] == -1, -1, indices0[i].gather(0, matches1[i].clamp(min=0))
+ )
+ _matching_scores[i, 0, indices0[i]] = matching_scores0[i]
+ _matching_scores[i, 1, indices1[i]] = matching_scores1[i]
+ return _matches, _matching_scores
+
+ def _match_image_pair(
+ self,
+ keypoints: torch.Tensor,
+ descriptors: torch.Tensor,
+ height: int,
+ width: int,
+ mask: torch.Tensor = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, tuple, tuple]:
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ if keypoints.shape[2] == 0: # no keypoints
+ shape = keypoints.shape[:-1]
+ return (
+ keypoints.new_full(shape, -1, dtype=torch.int),
+ keypoints.new_zeros(shape),
+ keypoints.new_zeros(shape),
+ all_hidden_states,
+ all_attentions,
+ )
+
+ device = keypoints.device
+ batch_size, _, initial_num_keypoints, _ = keypoints.shape
+ num_points_per_pair = torch.sum(mask.reshape(batch_size, -1), dim=1)
+ # (batch_size, 2, num_keypoints, 2) -> (batch_size * 2, num_keypoints, 2)
+ keypoints = keypoints.reshape(batch_size * 2, initial_num_keypoints, 2)
+ mask = mask.reshape(batch_size * 2, initial_num_keypoints) if mask is not None else None
+ descriptors = descriptors.reshape(batch_size * 2, initial_num_keypoints, self.keypoint_detector_descriptor_dim)
+ image_indices = torch.arange(batch_size * 2, device=device)
+ # Keypoint normalization
+ keypoints = normalize_keypoints(keypoints, height, width)
+
+ descriptors, keypoint_encoding_output = self._keypoint_processing(
+ descriptors, keypoints, output_hidden_states=output_hidden_states
+ )
+
+ keypoints = keypoint_encoding_output[0]
+
+ # Early stop consists of stopping the forward pass through the transformer layers when the confidence of the
+ # keypoints is above a certain threshold.
+ do_early_stop = self.depth_confidence > 0
+ # Keypoint pruning consists of removing keypoints from the input of the transformer layers when the confidence of
+ # the keypoints is below a certain threshold.
+ do_keypoint_pruning = self.width_confidence > 0
+
+ early_stops_indices = []
+ matches = []
+ matching_scores = []
+ final_pruned_keypoints_indices = []
+ final_pruned_keypoints_iterations = []
+
+ pruned_keypoints_indices = torch.arange(0, initial_num_keypoints, device=device).expand(batch_size * 2, -1)
+ pruned_keypoints_iterations = torch.ones_like(pruned_keypoints_indices)
+
+ for layer_index in range(self.num_layers):
+ input_shape = descriptors.size()
+ if mask is not None:
+ extended_attention_mask = self.get_extended_attention_mask(mask, input_shape)
+ else:
+ extended_attention_mask = torch.ones((batch_size, input_shape[-2]), device=keypoints.device)
+ layer_output = self.transformer_layers[layer_index](
+ descriptors,
+ keypoints,
+ attention_mask=extended_attention_mask,
+ output_hidden_states=output_hidden_states,
+ output_attentions=output_attentions,
+ )
+ descriptors, hidden_states, attention = layer_output
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + hidden_states
+ if output_attentions:
+ all_attentions = all_attentions + attention
+
+ if do_early_stop:
+ if layer_index < self.num_layers - 1:
+ # Get the confidence of the keypoints for the current layer
+ keypoint_confidences = self.token_confidence[layer_index](descriptors)
+
+ # Determine which pairs of images should be early stopped based on the confidence of the keypoints for
+ # the current layer.
+ early_stopped_pairs = self._get_early_stopped_image_pairs(
+ keypoint_confidences, layer_index, mask, num_points=num_points_per_pair
+ )
+ else:
+ # Early stopping always occurs at the last layer
+ early_stopped_pairs = torch.ones(batch_size, dtype=torch.bool)
+
+ if torch.any(early_stopped_pairs):
+ # If a pair of images is considered early stopped, we compute the matches for the remaining
+ # keypoints and stop the forward pass through the transformer layers for this pair of images.
+ early_stops = early_stopped_pairs.repeat_interleave(2)
+ early_stopped_image_indices = image_indices[early_stops]
+ early_stopped_matches, early_stopped_matching_scores = self._get_keypoint_matching(
+ descriptors, mask, layer_index, early_stops=early_stops
+ )
+ early_stops_indices.extend(list(early_stopped_image_indices))
+ matches.extend(list(early_stopped_matches))
+ matching_scores.extend(list(early_stopped_matching_scores))
+ if do_keypoint_pruning:
+ final_pruned_keypoints_indices.extend(list(pruned_keypoints_indices[early_stops]))
+ final_pruned_keypoints_iterations.extend(list(pruned_keypoints_iterations[early_stops]))
+
+ # Remove image pairs that have been early stopped from the forward pass
+ num_points_per_pair = num_points_per_pair[~early_stopped_pairs]
+ descriptors, keypoints_0, keypoint_1, mask, image_indices = tuple(
+ tensor[~early_stops]
+ for tensor in [descriptors, keypoints[0], keypoints[1], mask, image_indices]
+ )
+ keypoints = (keypoints_0, keypoint_1)
+ if do_keypoint_pruning:
+ pruned_keypoints_indices, pruned_keypoints_iterations, keypoint_confidences = tuple(
+ tensor[~early_stops]
+ for tensor in [
+ pruned_keypoints_indices,
+ pruned_keypoints_iterations,
+ keypoint_confidences,
+ ]
+ )
+ # If all pairs of images are early stopped, we stop the forward pass through the transformer
+ # layers for all pairs of images.
+ if torch.all(early_stopped_pairs):
+ break
+
+ if do_keypoint_pruning:
+ # Prune keypoints from the input of the transformer layers for the next iterations if the confidence of
+ # the keypoints is below a certain threshold.
+ descriptors, keypoints, pruned_keypoints_indices, mask, pruned_keypoints_iterations = (
+ self._do_layer_keypoint_pruning(
+ descriptors,
+ keypoints,
+ mask,
+ pruned_keypoints_indices,
+ pruned_keypoints_iterations,
+ keypoint_confidences,
+ layer_index,
+ )
+ )
+
+ if do_early_stop and do_keypoint_pruning:
+ # Concatenate early stopped outputs together and perform final keypoint pruning
+ final_pruned_keypoints_indices, final_pruned_keypoints_iterations, matches, matching_scores = (
+ self._concat_early_stopped_outputs(
+ early_stops_indices,
+ final_pruned_keypoints_indices,
+ final_pruned_keypoints_iterations,
+ matches,
+ matching_scores,
+ )
+ )
+ matches, matching_scores = self._do_final_keypoint_pruning(
+ final_pruned_keypoints_indices,
+ matches,
+ matching_scores,
+ initial_num_keypoints,
+ )
+ else:
+ matches, matching_scores = self._get_keypoint_matching(descriptors, mask, self.num_layers - 1)
+ final_pruned_keypoints_iterations = torch.ones_like(matching_scores) * self.num_layers
+
+ final_pruned_keypoints_iterations = final_pruned_keypoints_iterations.reshape(
+ batch_size, 2, initial_num_keypoints
+ )
+
+ return (
+ matches,
+ matching_scores,
+ final_pruned_keypoints_iterations,
+ all_hidden_states,
+ all_attentions,
+ )
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: torch.FloatTensor,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ ) -> Union[tuple, LightGlueKeypointMatchingOutput]:
+ loss = None
+ if labels is not None:
+ raise ValueError("LightGlue is not trainable, no labels should be provided.")
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ if pixel_values.ndim != 5 or pixel_values.size(1) != 2:
+ raise ValueError("Input must be a 5D tensor of shape (batch_size, 2, num_channels, height, width)")
+
+ batch_size, _, channels, height, width = pixel_values.shape
+ pixel_values = pixel_values.reshape(batch_size * 2, channels, height, width)
+ keypoint_detections = self.keypoint_detector(pixel_values)
+
+ keypoints, _, descriptors, mask = keypoint_detections[:4]
+ keypoints = keypoints.reshape(batch_size, 2, -1, 2).to(pixel_values)
+ descriptors = descriptors.reshape(batch_size, 2, -1, self.keypoint_detector_descriptor_dim).to(pixel_values)
+ mask = mask.reshape(batch_size, 2, -1)
+
+ absolute_keypoints = keypoints.clone()
+ absolute_keypoints[:, :, :, 0] = absolute_keypoints[:, :, :, 0] * width
+ absolute_keypoints[:, :, :, 1] = absolute_keypoints[:, :, :, 1] * height
+
+ matches, matching_scores, prune, hidden_states, attentions = self._match_image_pair(
+ absolute_keypoints,
+ descriptors,
+ height,
+ width,
+ mask=mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ )
+
+ return LightGlueKeypointMatchingOutput(
+ loss=loss,
+ matches=matches,
+ matching_scores=matching_scores,
+ keypoints=keypoints,
+ prune=prune,
+ mask=mask,
+ hidden_states=hidden_states,
+ attentions=attentions,
+ )
+
+
+__all__ = ["LightGluePreTrainedModel", "LightGlueForKeypointMatching"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/modular_lightglue.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/modular_lightglue.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b487256aa5997ac9b5f792e7add970669e3c753
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lightglue/modular_lightglue.py
@@ -0,0 +1,1074 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import warnings
+from dataclasses import dataclass
+from typing import Callable, Optional, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch.nn.utils.rnn import pad_sequence
+
+from ...configuration_utils import PretrainedConfig
+from ...image_utils import ImageInput, is_vision_available, to_numpy_array
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import ModelOutput, TensorType, auto_docstring, is_matplotlib_available, logging
+from ...utils.generic import can_return_tuple
+from ..auto import CONFIG_MAPPING, AutoConfig
+from ..auto.modeling_auto import AutoModelForKeypointDetection
+from ..clip.modeling_clip import CLIPMLP
+from ..cohere.modeling_cohere import apply_rotary_pos_emb
+from ..llama.modeling_llama import LlamaAttention, eager_attention_forward
+from ..superglue.image_processing_superglue import SuperGlueImageProcessor, validate_and_format_image_pairs
+from ..superpoint import SuperPointConfig
+
+
+if is_vision_available():
+ from PIL import Image, ImageDraw
+
+
+logger = logging.get_logger(__name__)
+
+
+class LightGlueConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LightGlueForKeypointMatching`]. It is used to
+ instantiate a LightGlue model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the LightGlue
+ [ETH-CVG/lightglue_superpoint](https://huggingface.co/ETH-CVG/lightglue_superpoint) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ keypoint_detector_config (`Union[AutoConfig, dict]`, *optional*, defaults to `SuperPointConfig`):
+ The config object or dictionary of the keypoint detector.
+ descriptor_dim (`int`, *optional*, defaults to 256):
+ The dimension of the descriptors.
+ num_hidden_layers (`int`, *optional*, defaults to 9):
+ The number of self and cross attention layers.
+ num_attention_heads (`int`, *optional*, defaults to 4):
+ The number of heads in the multi-head attention.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details checkout [this
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+ `num_attention_heads`.
+ depth_confidence (`float`, *optional*, defaults to 0.95):
+ The confidence threshold used to perform early stopping
+ width_confidence (`float`, *optional*, defaults to 0.99):
+ The confidence threshold used to prune points
+ filter_threshold (`float`, *optional*, defaults to 0.1):
+ The confidence threshold used to filter matches
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ hidden_act (`str`, *optional*, defaults to `"gelu"`):
+ The activation function to be used in the hidden layers.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ attention_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ trust_remote_code (`bool`, *optional*, defaults to `False`):
+ Whether to trust remote code when using other models than SuperPoint as keypoint detector.
+
+ Examples:
+ ```python
+ >>> from transformers import LightGlueConfig, LightGlueForKeypointMatching
+
+ >>> # Initializing a LightGlue style configuration
+ >>> configuration = LightGlueConfig()
+
+ >>> # Initializing a model from the LightGlue style configuration
+ >>> model = LightGlueForKeypointMatching(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+ """
+
+ model_type = "lightglue"
+ sub_configs = {"keypoint_detector_config": AutoConfig}
+
+ def __init__(
+ self,
+ keypoint_detector_config: SuperPointConfig = None,
+ descriptor_dim: int = 256,
+ num_hidden_layers: int = 9,
+ num_attention_heads: int = 4,
+ num_key_value_heads=None,
+ depth_confidence: float = 0.95,
+ width_confidence: float = 0.99,
+ filter_threshold: float = 0.1,
+ initializer_range: float = 0.02,
+ hidden_act: str = "gelu",
+ attention_dropout=0.0,
+ attention_bias=True,
+ trust_remote_code: bool = False,
+ **kwargs,
+ ):
+ # LightGlue can be used with other models than SuperPoint as keypoint detector
+ # We provide the trust_remote_code argument to allow the use of other models
+ # that are not registered in the CONFIG_MAPPING dictionary (for example DISK)
+ self.trust_remote_code = trust_remote_code
+
+ if descriptor_dim % num_attention_heads != 0:
+ raise ValueError("descriptor_dim % num_heads is different from zero")
+
+ self.descriptor_dim = descriptor_dim
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+
+ self.depth_confidence = depth_confidence
+ self.width_confidence = width_confidence
+ self.filter_threshold = filter_threshold
+ self.initializer_range = initializer_range
+
+ # Keypoint Detector is forced into eager attention mode because SuperPoint does not have Attention
+ # See https://github.com/huggingface/transformers/pull/31718#discussion_r2109733153
+ if isinstance(keypoint_detector_config, dict):
+ keypoint_detector_config["model_type"] = keypoint_detector_config.get("model_type", "superpoint")
+ if keypoint_detector_config["model_type"] not in CONFIG_MAPPING:
+ keypoint_detector_config = AutoConfig.from_pretrained(
+ keypoint_detector_config["_name_or_path"], trust_remote_code=self.trust_remote_code
+ )
+ else:
+ keypoint_detector_config = CONFIG_MAPPING[keypoint_detector_config["model_type"]](
+ **keypoint_detector_config, attn_implementation="eager"
+ )
+
+ if keypoint_detector_config is None:
+ keypoint_detector_config = CONFIG_MAPPING["superpoint"](attn_implementation="eager")
+
+ self.keypoint_detector_config = keypoint_detector_config
+
+ self.hidden_size = descriptor_dim
+ self.intermediate_size = descriptor_dim * 2
+ self.hidden_act = hidden_act
+ self.attention_dropout = attention_dropout
+ self.attention_bias = attention_bias
+ super().__init__(**kwargs)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of LightGlue keypoint matching models. Due to the nature of keypoint detection and matching,
+ the number of keypoints is not fixed and can vary from image to image, which makes batching non-trivial. In the
+ batch of images, the maximum number of matches is set as the dimension of the matches and matching scores. The mask
+ tensor is used to indicate which values in the keypoints, matches, matching_scores and prune tensors are keypoint
+ matching information.
+ """
+)
+class LightGlueKeypointMatchingOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*):
+ Loss computed during training.
+ matches (`torch.FloatTensor` of shape `(batch_size, 2, num_matches)`):
+ Index of keypoint matched in the other image.
+ matching_scores (`torch.FloatTensor` of shape `(batch_size, 2, num_matches)`):
+ Scores of predicted matches.
+ keypoints (`torch.FloatTensor` of shape `(batch_size, num_keypoints, 2)`):
+ Absolute (x, y) coordinates of predicted keypoints in a given image.
+ prune (`torch.IntTensor` of shape `(batch_size, num_keypoints)`):
+ Pruning mask indicating which keypoints are removed and at which layer.
+ mask (`torch.BoolTensor` of shape `(batch_size, num_keypoints)`):
+ Mask indicating which values in matches, matching_scores, keypoints and prune are keypoint matching
+ information.
+ hidden_states (`Tuple[torch.FloatTensor, ...]`, *optional*):
+ Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, 2, num_channels,
+ num_keypoints)` returned when `output_hidden_states=True` is passed or when
+ `config.output_hidden_states=True`
+ attentions (`Tuple[torch.FloatTensor, ...]`, *optional*):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, 2, num_heads, num_keypoints,
+ num_keypoints)` returned when `output_attentions=True` is passed or when
+ `config.output_attentions=True`
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ matches: Optional[torch.FloatTensor] = None
+ matching_scores: Optional[torch.FloatTensor] = None
+ keypoints: Optional[torch.FloatTensor] = None
+ prune: Optional[torch.IntTensor] = None
+ mask: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+class LightGlueImageProcessor(SuperGlueImageProcessor):
+ def post_process_keypoint_matching(
+ self,
+ outputs: LightGlueKeypointMatchingOutput,
+ target_sizes: Union[TensorType, list[tuple]],
+ threshold: float = 0.0,
+ ) -> list[dict[str, torch.Tensor]]:
+ return super().post_process_keypoint_matching(outputs, target_sizes, threshold)
+
+ # Copied from transformers.models.efficientloftr.image_processing_efficientloftr.EfficientLoFTRImageProcessor.visualize_keypoint_matching with EfficientLoFTR->LightGlue
+ def visualize_keypoint_matching(
+ self,
+ images: ImageInput,
+ keypoint_matching_output: list[dict[str, torch.Tensor]],
+ ) -> list["Image.Image"]:
+ """
+ Plots the image pairs side by side with the detected keypoints as well as the matching between them.
+
+ Args:
+ images (`ImageInput`):
+ Image pairs to plot. Same as `LightGlueImageProcessor.preprocess`. Expects either a list of 2
+ images or a list of list of 2 images list with pixel values ranging from 0 to 255.
+ keypoint_matching_output (List[Dict[str, torch.Tensor]]]):
+ A post processed keypoint matching output
+
+ Returns:
+ `List[PIL.Image.Image]`: A list of PIL images, each containing the image pairs side by side with the detected
+ keypoints as well as the matching between them.
+ """
+ images = validate_and_format_image_pairs(images)
+ images = [to_numpy_array(image) for image in images]
+ image_pairs = [images[i : i + 2] for i in range(0, len(images), 2)]
+
+ results = []
+ for image_pair, pair_output in zip(image_pairs, keypoint_matching_output):
+ height0, width0 = image_pair[0].shape[:2]
+ height1, width1 = image_pair[1].shape[:2]
+ plot_image = np.zeros((max(height0, height1), width0 + width1, 3), dtype=np.uint8)
+ plot_image[:height0, :width0] = image_pair[0]
+ plot_image[:height1, width0:] = image_pair[1]
+
+ plot_image_pil = Image.fromarray(plot_image)
+ draw = ImageDraw.Draw(plot_image_pil)
+
+ keypoints0_x, keypoints0_y = pair_output["keypoints0"].unbind(1)
+ keypoints1_x, keypoints1_y = pair_output["keypoints1"].unbind(1)
+ for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
+ keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, pair_output["matching_scores"]
+ ):
+ color = self._get_color(matching_score)
+ draw.line(
+ (keypoint0_x, keypoint0_y, keypoint1_x + width0, keypoint1_y),
+ fill=color,
+ width=3,
+ )
+ draw.ellipse((keypoint0_x - 2, keypoint0_y - 2, keypoint0_x + 2, keypoint0_y + 2), fill="black")
+ draw.ellipse(
+ (keypoint1_x + width0 - 2, keypoint1_y - 2, keypoint1_x + width0 + 2, keypoint1_y + 2),
+ fill="black",
+ )
+
+ results.append(plot_image_pil)
+ return results
+
+ # Copied from transformers.models.efficientloftr.image_processing_efficientloftr.EfficientLoFTRImageProcessor._get_color
+ def _get_color(self, score):
+ """Maps a score to a color."""
+ r = int(255 * (1 - score))
+ g = int(255 * score)
+ b = 0
+ return (r, g, b)
+
+ def plot_keypoint_matching(self, images: ImageInput, keypoint_matching_output: LightGlueKeypointMatchingOutput):
+ """
+ Plots the image pairs side by side with the detected keypoints as well as the matching between them. Requires
+ matplotlib to be installed.
+
+ .. deprecated::
+ `plot_keypoint_matching` is deprecated and will be removed in a future version. Use `visualize_keypoint_matching` instead.
+
+ Args:
+ images (`ImageInput`):
+ Image pairs to plot. Same as `LightGlueImageProcessor.preprocess`. Expects either a list of 2 images or
+ a list of list of 2 images list with pixel values ranging from 0 to 255.
+ keypoint_matching_output ([`LightGlueKeypointMatchingOutput`]):
+ Raw outputs of the model.
+ """
+ warnings.warn(
+ "`plot_keypoint_matching` is deprecated and will be removed in transformers v. "
+ "Use `visualize_keypoint_matching` instead.",
+ FutureWarning,
+ )
+
+ if is_matplotlib_available():
+ import matplotlib.pyplot as plt
+ else:
+ raise ImportError("Please install matplotlib to use `plot_keypoint_matching` method")
+
+ images = validate_and_format_image_pairs(images)
+ images = [to_numpy_array(image) for image in images]
+ image_pairs = [images[i : i + 2] for i in range(0, len(images), 2)]
+
+ for image_pair, pair_output in zip(image_pairs, keypoint_matching_output):
+ height0, width0 = image_pair[0].shape[:2]
+ height1, width1 = image_pair[1].shape[:2]
+ plot_image = np.zeros((max(height0, height1), width0 + width1, 3))
+ plot_image[:height0, :width0] = image_pair[0] / 255.0
+ plot_image[:height1, width0:] = image_pair[1] / 255.0
+ plt.imshow(plot_image)
+ plt.axis("off")
+
+ keypoints0_x, keypoints0_y = pair_output["keypoints0"].unbind(1)
+ keypoints1_x, keypoints1_y = pair_output["keypoints1"].unbind(1)
+ for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
+ keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, pair_output["matching_scores"]
+ ):
+ plt.plot(
+ [keypoint0_x, keypoint1_x + width0],
+ [keypoint0_y, keypoint1_y],
+ color=plt.get_cmap("RdYlGn")(matching_score.item()),
+ alpha=0.9,
+ linewidth=0.5,
+ )
+ plt.scatter(keypoint0_x, keypoint0_y, c="black", s=2)
+ plt.scatter(keypoint1_x + width0, keypoint1_y, c="black", s=2)
+ plt.show()
+
+
+class LightGluePositionalEncoder(nn.Module):
+ def __init__(self, config: LightGlueConfig):
+ super().__init__()
+ self.projector = nn.Linear(2, config.descriptor_dim // config.num_attention_heads // 2, bias=False)
+
+ def forward(
+ self, keypoints: torch.Tensor, output_hidden_states: Optional[bool] = False
+ ) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
+ projected_keypoints = self.projector(keypoints)
+ embeddings = projected_keypoints.repeat_interleave(2, dim=-1)
+ cosines = torch.cos(embeddings)
+ sines = torch.sin(embeddings)
+ embeddings = (cosines, sines)
+ output = (embeddings, projected_keypoints) if output_hidden_states else (embeddings,)
+ return output
+
+
+class LightGlueAttention(LlamaAttention):
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ is_cross_attention = encoder_hidden_states is not None
+ current_states = encoder_hidden_states if is_cross_attention else hidden_states
+ current_attention_mask = encoder_attention_mask if is_cross_attention else attention_mask
+
+ key_states = self.k_proj(current_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.v_proj(current_states).view(hidden_shape).transpose(1, 2)
+
+ if position_embeddings is not None:
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ current_attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class LightGlueMLP(CLIPMLP):
+ def __init__(self, config: LightGlueConfig):
+ super().__init__(config)
+ self.fc1 = nn.Linear(config.intermediate_size, config.intermediate_size)
+ self.layer_norm = nn.LayerNorm(config.intermediate_size, elementwise_affine=True)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.layer_norm(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+class LightGlueTransformerLayer(nn.Module):
+ def __init__(self, config: LightGlueConfig, layer_idx: int):
+ super().__init__()
+ self.self_attention = LightGlueAttention(config, layer_idx)
+ self.self_mlp = LightGlueMLP(config)
+ self.cross_attention = LightGlueAttention(config, layer_idx)
+ self.cross_mlp = LightGlueMLP(config)
+
+ def forward(
+ self,
+ descriptors: torch.Tensor,
+ keypoints: torch.Tensor,
+ attention_mask: torch.Tensor,
+ output_hidden_states: Optional[bool] = False,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor, Optional[tuple[torch.Tensor]], Optional[tuple[torch.Tensor]]]:
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (descriptors,)
+
+ batch_size, num_keypoints, descriptor_dim = descriptors.shape
+
+ # Self attention block
+ attention_output, self_attentions = self.self_attention(
+ descriptors,
+ position_embeddings=keypoints,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+ intermediate_states = torch.cat([descriptors, attention_output], dim=-1)
+ output_states = self.self_mlp(intermediate_states)
+ self_attention_descriptors = descriptors + output_states
+
+ if output_hidden_states:
+ self_attention_hidden_states = (intermediate_states, output_states)
+
+ # Reshape hidden_states to group by image_pairs :
+ # (batch_size, num_keypoints, descriptor_dim) -> (batch_size, 2, num_keypoints, descriptor_dim)
+ # Flip dimension 1 to perform cross attention :
+ # (image0, image1) -> (image1, image0)
+ # Reshape back to original shape :
+ # (batch_size, 2, num_keypoints, descriptor_dim) -> (batch_size, num_keypoints, descriptor_dim)
+ encoder_hidden_states = (
+ self_attention_descriptors.reshape(-1, 2, num_keypoints, descriptor_dim)
+ .flip(1)
+ .reshape(batch_size, num_keypoints, descriptor_dim)
+ )
+ # Same for mask
+ encoder_attention_mask = (
+ attention_mask.reshape(-1, 2, 1, 1, num_keypoints).flip(1).reshape(batch_size, 1, 1, num_keypoints)
+ if attention_mask is not None
+ else None
+ )
+
+ # Cross attention block
+ cross_attention_output, cross_attentions = self.cross_attention(
+ self_attention_descriptors,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ output_attentions=output_attentions,
+ )
+ cross_intermediate_states = torch.cat([self_attention_descriptors, cross_attention_output], dim=-1)
+ cross_output_states = self.cross_mlp(cross_intermediate_states)
+ descriptors = self_attention_descriptors + cross_output_states
+
+ if output_hidden_states:
+ cross_attention_hidden_states = (cross_intermediate_states, cross_output_states)
+ all_hidden_states = (
+ all_hidden_states
+ + (self_attention_descriptors.reshape(batch_size, num_keypoints, descriptor_dim),)
+ + self_attention_hidden_states
+ + (descriptors.reshape(batch_size, num_keypoints, descriptor_dim),)
+ + cross_attention_hidden_states
+ )
+
+ if output_attentions:
+ all_attentions = all_attentions + (self_attentions,) + (cross_attentions,)
+
+ return descriptors, all_hidden_states, all_attentions
+
+
+def sigmoid_log_double_softmax(
+ similarity: torch.Tensor, matchability0: torch.Tensor, matchability1: torch.Tensor
+) -> torch.Tensor:
+ """create the log assignment matrix from logits and similarity"""
+ batch_size, num_keypoints_0, num_keypoints_1 = similarity.shape
+ certainties = nn.functional.logsigmoid(matchability0) + nn.functional.logsigmoid(matchability1).transpose(1, 2)
+ scores0 = nn.functional.log_softmax(similarity, 2)
+ scores1 = nn.functional.log_softmax(similarity.transpose(-1, -2).contiguous(), 2).transpose(-1, -2)
+ scores = similarity.new_full((batch_size, num_keypoints_0 + 1, num_keypoints_1 + 1), 0)
+ scores[:, :num_keypoints_0, :num_keypoints_1] = scores0 + scores1 + certainties
+ scores[:, :-1, -1] = nn.functional.logsigmoid(-matchability0.squeeze(-1))
+ scores[:, -1, :-1] = nn.functional.logsigmoid(-matchability1.squeeze(-1))
+ return scores
+
+
+class LightGlueMatchAssignmentLayer(nn.Module):
+ def __init__(self, config: LightGlueConfig):
+ super().__init__()
+
+ self.descriptor_dim = config.descriptor_dim
+ self.final_projection = nn.Linear(self.descriptor_dim, self.descriptor_dim, bias=True)
+ self.matchability = nn.Linear(self.descriptor_dim, 1, bias=True)
+
+ def forward(self, descriptors: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+ batch_size, num_keypoints, descriptor_dim = descriptors.shape
+ # Final projection and similarity computation
+ m_descriptors = self.final_projection(descriptors)
+ m_descriptors = m_descriptors / torch.tensor(self.descriptor_dim, device=m_descriptors.device) ** 0.25
+ m_descriptors = m_descriptors.reshape(batch_size // 2, 2, num_keypoints, descriptor_dim)
+ m_descriptors0 = m_descriptors[:, 0]
+ m_descriptors1 = m_descriptors[:, 1]
+ similarity = m_descriptors0 @ m_descriptors1.transpose(-1, -2)
+ if mask is not None:
+ mask = mask.reshape(batch_size // 2, 2, num_keypoints)
+ mask0 = mask[:, 0].unsqueeze(-1)
+ mask1 = mask[:, 1].unsqueeze(-1).transpose(-1, -2)
+ mask = mask0 * mask1
+ similarity = similarity.masked_fill(mask == 0, torch.finfo(similarity.dtype).min)
+
+ # Compute matchability of descriptors
+ matchability = self.matchability(descriptors)
+ matchability = matchability.reshape(batch_size // 2, 2, num_keypoints, 1)
+ matchability_0 = matchability[:, 0]
+ matchability_1 = matchability[:, 1]
+
+ # Compute scores from similarity and matchability
+ scores = sigmoid_log_double_softmax(similarity, matchability_0, matchability_1)
+ return scores
+
+ def get_matchability(self, descriptors: torch.Tensor) -> torch.Tensor:
+ """Get matchability of descriptors as a probability"""
+ matchability = self.matchability(descriptors)
+ matchability = nn.functional.sigmoid(matchability).squeeze(-1)
+ return matchability
+
+
+class LightGlueTokenConfidenceLayer(nn.Module):
+ def __init__(self, config: LightGlueConfig):
+ super().__init__()
+
+ self.token = nn.Linear(config.descriptor_dim, 1)
+
+ def forward(self, descriptors: torch.Tensor) -> torch.Tensor:
+ token = self.token(descriptors.detach())
+ token = nn.functional.sigmoid(token).squeeze(-1)
+ return token
+
+
+@auto_docstring
+class LightGluePreTrainedModel(PreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config: LightGlueConfig
+ base_model_prefix = "lightglue"
+ main_input_name = "pixel_values"
+ supports_gradient_checkpointing = False
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+
+def get_matches_from_scores(scores: torch.Tensor, threshold: float) -> tuple[torch.Tensor, torch.Tensor]:
+ """obtain matches from a score matrix [Bx M+1 x N+1]"""
+ batch_size, _, _ = scores.shape
+ # For each keypoint, get the best match
+ max0 = scores[:, :-1, :-1].max(2)
+ max1 = scores[:, :-1, :-1].max(1)
+ matches0 = max0.indices
+ matches1 = max1.indices
+
+ # Mutual check for matches
+ indices0 = torch.arange(matches0.shape[1], device=matches0.device)[None]
+ indices1 = torch.arange(matches1.shape[1], device=matches1.device)[None]
+ mutual0 = indices0 == matches1.gather(1, matches0)
+ mutual1 = indices1 == matches0.gather(1, matches1)
+
+ # Get matching scores and filter based on mutual check and thresholding
+ max0 = max0.values.exp()
+ zero = max0.new_tensor(0)
+ matching_scores0 = torch.where(mutual0, max0, zero)
+ matching_scores1 = torch.where(mutual1, matching_scores0.gather(1, matches1), zero)
+ valid0 = mutual0 & (matching_scores0 > threshold)
+ valid1 = mutual1 & valid0.gather(1, matches1)
+
+ # Filter matches based on mutual check and thresholding of scores
+ matches0 = torch.where(valid0, matches0, -1)
+ matches1 = torch.where(valid1, matches1, -1)
+ matches = torch.stack([matches0, matches1]).transpose(0, 1).reshape(batch_size * 2, -1)
+ matching_scores = torch.stack([matching_scores0, matching_scores1]).transpose(0, 1).reshape(batch_size * 2, -1)
+
+ return matches, matching_scores
+
+
+def normalize_keypoints(keypoints: torch.Tensor, height: int, width: int) -> torch.Tensor:
+ """
+ Normalize keypoints locations based on image image_shape
+
+ Args:
+ keypoints (`torch.Tensor` of shape `(batch_size, num_keypoints, 2)`):
+ Keypoints locations in (x, y) format.
+ height (`int`):
+ Image height.
+ width (`int`):
+ Image width.
+
+ Returns:
+ Normalized keypoints locations of shape (`torch.Tensor` of shape `(batch_size, num_keypoints, 2)`).
+ """
+ size = torch.tensor([width, height], device=keypoints.device, dtype=keypoints.dtype)[None]
+ shift = size / 2
+ scale = size.max(-1).values / 2
+ keypoints = (keypoints - shift[..., None, :]) / scale[..., None, None]
+ return keypoints
+
+
+@auto_docstring(
+ custom_intro="""
+ LightGlue model taking images as inputs and outputting the matching of them.
+ """
+)
+class LightGlueForKeypointMatching(LightGluePreTrainedModel):
+ """
+ LightGlue is a model matching keypoints in images by leveraging detections from a keypoint detector such as
+ SuperPoint. It is based on the SuperGlue architecture and is designed to be lightweight and efficient.
+ It consists of :
+ 1. Keypoint Encoder
+ 2. A Graph Neural Network with self and cross attention layers
+ 3. Matching Assignment layers
+
+ The correspondence ids use -1 to indicate non-matching points.
+
+ Philipp Lindenberger, Paul-Edouard Sarlin and Marc Pollefeys. LightGlue: Local Feature Matching at Light Speed.
+ In ICCV 2023. https://arxiv.org/pdf/2306.13643.pdf
+ """
+
+ def __init__(self, config: LightGlueConfig):
+ super().__init__(config)
+ self.keypoint_detector = AutoModelForKeypointDetection.from_config(
+ config.keypoint_detector_config, trust_remote_code=config.trust_remote_code
+ )
+
+ self.keypoint_detector_descriptor_dim = config.keypoint_detector_config.descriptor_decoder_dim
+ self.descriptor_dim = config.descriptor_dim
+ self.num_layers = config.num_hidden_layers
+ self.filter_threshold = config.filter_threshold
+ self.depth_confidence = config.depth_confidence
+ self.width_confidence = config.width_confidence
+
+ if self.descriptor_dim != self.keypoint_detector_descriptor_dim:
+ self.input_projection = nn.Linear(self.keypoint_detector_descriptor_dim, self.descriptor_dim, bias=True)
+ else:
+ self.input_projection = nn.Identity()
+
+ self.positional_encoder = LightGluePositionalEncoder(config)
+
+ self.transformer_layers = nn.ModuleList(
+ [LightGlueTransformerLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]
+ )
+ self.match_assignment_layers = nn.ModuleList(
+ [LightGlueMatchAssignmentLayer(config) for _ in range(config.num_hidden_layers)]
+ )
+ self.token_confidence = nn.ModuleList(
+ [LightGlueTokenConfidenceLayer(config) for _ in range(config.num_hidden_layers - 1)]
+ )
+
+ self.post_init()
+
+ def _get_confidence_threshold(self, layer_index: int) -> float:
+ """scaled confidence threshold for a given layer"""
+ threshold = 0.8 + 0.1 * np.exp(-4.0 * layer_index / self.num_layers)
+ return np.clip(threshold, 0, 1)
+
+ def _keypoint_processing(
+ self, descriptors: torch.Tensor, keypoints: torch.Tensor, output_hidden_states: Optional[bool] = False
+ ) -> tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+ descriptors = descriptors.detach().contiguous()
+ projected_descriptors = self.input_projection(descriptors)
+ keypoint_encoding_output = self.positional_encoder(keypoints, output_hidden_states=output_hidden_states)
+ return projected_descriptors, keypoint_encoding_output
+
+ def _get_early_stopped_image_pairs(
+ self, keypoint_confidences: torch.Tensor, layer_index: int, mask: torch.Tensor, num_points: torch.Tensor
+ ) -> torch.Tensor:
+ """evaluate whether we should stop inference based on the confidence of the keypoints"""
+ batch_size, _ = mask.shape
+ if layer_index < self.num_layers - 1:
+ # If the current layer is not the last layer, we compute the confidence of the keypoints and check
+ # if we should stop the forward pass through the transformer layers for each pair of images.
+ keypoint_confidences = keypoint_confidences.masked_fill(mask == 0, 1)
+ keypoint_confidences = keypoint_confidences.reshape(batch_size // 2, -1)
+ threshold = self._get_confidence_threshold(layer_index)
+ ratio_confident = 1.0 - (keypoint_confidences < threshold).float().sum(dim=1) / num_points
+ early_stopped_pairs = ratio_confident > self.depth_confidence
+ else:
+ # If the current layer is the last layer, we stop the forward pass through the transformer layers for
+ # all pairs of images.
+ early_stopped_pairs = torch.ones(batch_size, dtype=torch.bool)
+ return early_stopped_pairs
+
+ def _get_keypoint_matching(self, descriptors, mask, layer_index, early_stops=None):
+ if early_stops is not None:
+ descriptors = descriptors[early_stops]
+ mask = mask[early_stops]
+ scores = self.match_assignment_layers[layer_index](descriptors, mask)
+ matches, matching_scores = get_matches_from_scores(scores, self.filter_threshold)
+ return matches, matching_scores
+
+ def _get_pruning_mask(self, confidences: torch.Tensor, scores: torch.Tensor, layer_index: int) -> torch.Tensor:
+ """mask points which should be removed"""
+ keep = scores > (1 - self.width_confidence)
+ if confidences is not None: # Low-confidence points are never pruned.
+ keep |= confidences <= self._get_confidence_threshold(layer_index)
+ return keep
+
+ def _do_layer_keypoint_pruning(
+ self,
+ descriptors: torch.Tensor,
+ keypoints: torch.Tensor,
+ mask: torch.Tensor,
+ indices: torch.Tensor,
+ prune_output: torch.Tensor,
+ keypoint_confidences: torch.Tensor,
+ layer_index: int,
+ ):
+ """
+ For a given layer, prune keypoints based on the confidence of the keypoints and the matchability of the
+ descriptors.
+ """
+ batch_size, _, _ = descriptors.shape
+ descriptors_matchability = self.match_assignment_layers[layer_index].get_matchability(descriptors)
+ pruned_keypoints_mask = self._get_pruning_mask(keypoint_confidences, descriptors_matchability, layer_index)
+ pruned_keypoints_mask = pruned_keypoints_mask.masked_fill(mask == 0, torch.tensor(False))
+
+ # For each image, we extract the pruned indices and the corresponding descriptors and keypoints.
+ pruned_descriptors, pruned_keypoints_0, pruned_keypoints_1, pruned_mask, pruned_indices = (
+ [t[mask] for t, mask in zip(tensor, pruned_keypoints_mask)]
+ for tensor in [descriptors, keypoints[0], keypoints[1], pruned_keypoints_mask, indices]
+ )
+ for i in range(batch_size):
+ prune_output[i, pruned_indices[i]] += 1
+
+ # Pad the pruned descriptors, keypoints, indices and mask to have the same shape across the batch.
+ pruned_descriptors, pruned_keypoints_0, pruned_keypoints_1, pruned_mask = (
+ pad_sequence(pruned_tensor, batch_first=True)
+ for pruned_tensor in [pruned_descriptors, pruned_keypoints_0, pruned_keypoints_1, pruned_mask]
+ )
+ pruned_keypoints = (pruned_keypoints_0, pruned_keypoints_1)
+ pruned_indices = pad_sequence(pruned_indices, batch_first=True, padding_value=-1)
+
+ return pruned_descriptors, pruned_keypoints, pruned_indices, pruned_mask, prune_output
+
+ def _concat_early_stopped_outputs(
+ self,
+ early_stops_indices,
+ final_pruned_keypoints_indices,
+ final_pruned_keypoints_iterations,
+ matches,
+ matching_scores,
+ ):
+ early_stops_indices = torch.stack(early_stops_indices)
+ matches, final_pruned_keypoints_indices = (
+ pad_sequence(tensor, batch_first=True, padding_value=-1)
+ for tensor in [matches, final_pruned_keypoints_indices]
+ )
+ matching_scores, final_pruned_keypoints_iterations = (
+ pad_sequence(tensor, batch_first=True, padding_value=0)
+ for tensor in [matching_scores, final_pruned_keypoints_iterations]
+ )
+ matches, matching_scores, final_pruned_keypoints_indices, final_pruned_keypoints_iterations = (
+ tensor[early_stops_indices]
+ for tensor in [
+ matches,
+ matching_scores,
+ final_pruned_keypoints_indices,
+ final_pruned_keypoints_iterations,
+ ]
+ )
+ return final_pruned_keypoints_indices, final_pruned_keypoints_iterations, matches, matching_scores
+
+ def _do_final_keypoint_pruning(
+ self,
+ indices: torch.Tensor,
+ matches: torch.Tensor,
+ matching_scores: torch.Tensor,
+ num_keypoints: torch.Tensor,
+ ) -> tuple[torch.Tensor, torch.Tensor]:
+ # (batch_size, num_keypoints) -> (batch_size // 2, 2, num_keypoints) -> 2 * (batch_size // 2, num_keypoints) to
+ # have tensors from
+ batch_size, _ = indices.shape
+ indices, matches, matching_scores = (
+ tensor.reshape(batch_size // 2, 2, -1) for tensor in [indices, matches, matching_scores]
+ )
+ indices0 = indices[:, 0]
+ indices1 = indices[:, 1]
+ matches0 = matches[:, 0]
+ matches1 = matches[:, 1]
+ matching_scores0 = matching_scores[:, 0]
+ matching_scores1 = matching_scores[:, 1]
+
+ # Prepare final matches and matching scores
+ _matches = torch.full((batch_size // 2, 2, num_keypoints), -1, device=indices.device, dtype=matches.dtype)
+ _matching_scores = torch.zeros(
+ (batch_size // 2, 2, num_keypoints), device=indices.device, dtype=matching_scores.dtype
+ )
+ # Fill the matches and matching scores for each image pair
+ for i in range(batch_size // 2):
+ _matches[i, 0, indices0[i]] = torch.where(
+ matches0[i] == -1, -1, indices1[i].gather(0, matches0[i].clamp(min=0))
+ )
+ _matches[i, 1, indices1[i]] = torch.where(
+ matches1[i] == -1, -1, indices0[i].gather(0, matches1[i].clamp(min=0))
+ )
+ _matching_scores[i, 0, indices0[i]] = matching_scores0[i]
+ _matching_scores[i, 1, indices1[i]] = matching_scores1[i]
+ return _matches, _matching_scores
+
+ def _match_image_pair(
+ self,
+ keypoints: torch.Tensor,
+ descriptors: torch.Tensor,
+ height: int,
+ width: int,
+ mask: torch.Tensor = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, tuple, tuple]:
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ if keypoints.shape[2] == 0: # no keypoints
+ shape = keypoints.shape[:-1]
+ return (
+ keypoints.new_full(shape, -1, dtype=torch.int),
+ keypoints.new_zeros(shape),
+ keypoints.new_zeros(shape),
+ all_hidden_states,
+ all_attentions,
+ )
+
+ device = keypoints.device
+ batch_size, _, initial_num_keypoints, _ = keypoints.shape
+ num_points_per_pair = torch.sum(mask.reshape(batch_size, -1), dim=1)
+ # (batch_size, 2, num_keypoints, 2) -> (batch_size * 2, num_keypoints, 2)
+ keypoints = keypoints.reshape(batch_size * 2, initial_num_keypoints, 2)
+ mask = mask.reshape(batch_size * 2, initial_num_keypoints) if mask is not None else None
+ descriptors = descriptors.reshape(batch_size * 2, initial_num_keypoints, self.keypoint_detector_descriptor_dim)
+ image_indices = torch.arange(batch_size * 2, device=device)
+ # Keypoint normalization
+ keypoints = normalize_keypoints(keypoints, height, width)
+
+ descriptors, keypoint_encoding_output = self._keypoint_processing(
+ descriptors, keypoints, output_hidden_states=output_hidden_states
+ )
+
+ keypoints = keypoint_encoding_output[0]
+
+ # Early stop consists of stopping the forward pass through the transformer layers when the confidence of the
+ # keypoints is above a certain threshold.
+ do_early_stop = self.depth_confidence > 0
+ # Keypoint pruning consists of removing keypoints from the input of the transformer layers when the confidence of
+ # the keypoints is below a certain threshold.
+ do_keypoint_pruning = self.width_confidence > 0
+
+ early_stops_indices = []
+ matches = []
+ matching_scores = []
+ final_pruned_keypoints_indices = []
+ final_pruned_keypoints_iterations = []
+
+ pruned_keypoints_indices = torch.arange(0, initial_num_keypoints, device=device).expand(batch_size * 2, -1)
+ pruned_keypoints_iterations = torch.ones_like(pruned_keypoints_indices)
+
+ for layer_index in range(self.num_layers):
+ input_shape = descriptors.size()
+ if mask is not None:
+ extended_attention_mask = self.get_extended_attention_mask(mask, input_shape)
+ else:
+ extended_attention_mask = torch.ones((batch_size, input_shape[-2]), device=keypoints.device)
+ layer_output = self.transformer_layers[layer_index](
+ descriptors,
+ keypoints,
+ attention_mask=extended_attention_mask,
+ output_hidden_states=output_hidden_states,
+ output_attentions=output_attentions,
+ )
+ descriptors, hidden_states, attention = layer_output
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + hidden_states
+ if output_attentions:
+ all_attentions = all_attentions + attention
+
+ if do_early_stop:
+ if layer_index < self.num_layers - 1:
+ # Get the confidence of the keypoints for the current layer
+ keypoint_confidences = self.token_confidence[layer_index](descriptors)
+
+ # Determine which pairs of images should be early stopped based on the confidence of the keypoints for
+ # the current layer.
+ early_stopped_pairs = self._get_early_stopped_image_pairs(
+ keypoint_confidences, layer_index, mask, num_points=num_points_per_pair
+ )
+ else:
+ # Early stopping always occurs at the last layer
+ early_stopped_pairs = torch.ones(batch_size, dtype=torch.bool)
+
+ if torch.any(early_stopped_pairs):
+ # If a pair of images is considered early stopped, we compute the matches for the remaining
+ # keypoints and stop the forward pass through the transformer layers for this pair of images.
+ early_stops = early_stopped_pairs.repeat_interleave(2)
+ early_stopped_image_indices = image_indices[early_stops]
+ early_stopped_matches, early_stopped_matching_scores = self._get_keypoint_matching(
+ descriptors, mask, layer_index, early_stops=early_stops
+ )
+ early_stops_indices.extend(list(early_stopped_image_indices))
+ matches.extend(list(early_stopped_matches))
+ matching_scores.extend(list(early_stopped_matching_scores))
+ if do_keypoint_pruning:
+ final_pruned_keypoints_indices.extend(list(pruned_keypoints_indices[early_stops]))
+ final_pruned_keypoints_iterations.extend(list(pruned_keypoints_iterations[early_stops]))
+
+ # Remove image pairs that have been early stopped from the forward pass
+ num_points_per_pair = num_points_per_pair[~early_stopped_pairs]
+ descriptors, keypoints_0, keypoint_1, mask, image_indices = tuple(
+ tensor[~early_stops]
+ for tensor in [descriptors, keypoints[0], keypoints[1], mask, image_indices]
+ )
+ keypoints = (keypoints_0, keypoint_1)
+ if do_keypoint_pruning:
+ pruned_keypoints_indices, pruned_keypoints_iterations, keypoint_confidences = tuple(
+ tensor[~early_stops]
+ for tensor in [
+ pruned_keypoints_indices,
+ pruned_keypoints_iterations,
+ keypoint_confidences,
+ ]
+ )
+ # If all pairs of images are early stopped, we stop the forward pass through the transformer
+ # layers for all pairs of images.
+ if torch.all(early_stopped_pairs):
+ break
+
+ if do_keypoint_pruning:
+ # Prune keypoints from the input of the transformer layers for the next iterations if the confidence of
+ # the keypoints is below a certain threshold.
+ descriptors, keypoints, pruned_keypoints_indices, mask, pruned_keypoints_iterations = (
+ self._do_layer_keypoint_pruning(
+ descriptors,
+ keypoints,
+ mask,
+ pruned_keypoints_indices,
+ pruned_keypoints_iterations,
+ keypoint_confidences,
+ layer_index,
+ )
+ )
+
+ if do_early_stop and do_keypoint_pruning:
+ # Concatenate early stopped outputs together and perform final keypoint pruning
+ final_pruned_keypoints_indices, final_pruned_keypoints_iterations, matches, matching_scores = (
+ self._concat_early_stopped_outputs(
+ early_stops_indices,
+ final_pruned_keypoints_indices,
+ final_pruned_keypoints_iterations,
+ matches,
+ matching_scores,
+ )
+ )
+ matches, matching_scores = self._do_final_keypoint_pruning(
+ final_pruned_keypoints_indices,
+ matches,
+ matching_scores,
+ initial_num_keypoints,
+ )
+ else:
+ matches, matching_scores = self._get_keypoint_matching(descriptors, mask, self.num_layers - 1)
+ final_pruned_keypoints_iterations = torch.ones_like(matching_scores) * self.num_layers
+
+ final_pruned_keypoints_iterations = final_pruned_keypoints_iterations.reshape(
+ batch_size, 2, initial_num_keypoints
+ )
+
+ return (
+ matches,
+ matching_scores,
+ final_pruned_keypoints_iterations,
+ all_hidden_states,
+ all_attentions,
+ )
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: torch.FloatTensor,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ ) -> Union[tuple, LightGlueKeypointMatchingOutput]:
+ loss = None
+ if labels is not None:
+ raise ValueError("LightGlue is not trainable, no labels should be provided.")
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ if pixel_values.ndim != 5 or pixel_values.size(1) != 2:
+ raise ValueError("Input must be a 5D tensor of shape (batch_size, 2, num_channels, height, width)")
+
+ batch_size, _, channels, height, width = pixel_values.shape
+ pixel_values = pixel_values.reshape(batch_size * 2, channels, height, width)
+ keypoint_detections = self.keypoint_detector(pixel_values)
+
+ keypoints, _, descriptors, mask = keypoint_detections[:4]
+ keypoints = keypoints.reshape(batch_size, 2, -1, 2).to(pixel_values)
+ descriptors = descriptors.reshape(batch_size, 2, -1, self.keypoint_detector_descriptor_dim).to(pixel_values)
+ mask = mask.reshape(batch_size, 2, -1)
+
+ absolute_keypoints = keypoints.clone()
+ absolute_keypoints[:, :, :, 0] = absolute_keypoints[:, :, :, 0] * width
+ absolute_keypoints[:, :, :, 1] = absolute_keypoints[:, :, :, 1] * height
+
+ matches, matching_scores, prune, hidden_states, attentions = self._match_image_pair(
+ absolute_keypoints,
+ descriptors,
+ height,
+ width,
+ mask=mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ )
+
+ return LightGlueKeypointMatchingOutput(
+ loss=loss,
+ matches=matches,
+ matching_scores=matching_scores,
+ keypoints=keypoints,
+ prune=prune,
+ mask=mask,
+ hidden_states=hidden_states,
+ attentions=attentions,
+ )
+
+
+__all__ = ["LightGluePreTrainedModel", "LightGlueForKeypointMatching", "LightGlueConfig", "LightGlueImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lilt/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lilt/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8666fa5cdcf6f98c1a162c7189110ba56c611284
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lilt/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_lilt import *
+ from .modeling_lilt import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lilt/configuration_lilt.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lilt/configuration_lilt.py
new file mode 100644
index 0000000000000000000000000000000000000000..940fad4aa8107d957e8a5d6f61d47fa11a08dfd0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lilt/configuration_lilt.py
@@ -0,0 +1,131 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LiLT configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class LiltConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LiltModel`]. It is used to instantiate a LiLT
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the LiLT
+ [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) architecture.
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 30522):
+ Vocabulary size of the LiLT model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`LiltModel`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimensionality of the encoder layers and the pooler layer. Should be a multiple of 24.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 2):
+ The vocabulary size of the `token_type_ids` passed when calling [`LiltModel`].
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+ position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
+ Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
+ positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
+ [Self-Attention with Relative Position Representations (Shaw et al.)](https://huggingface.co/papers/1803.02155).
+ For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
+ with Better Relative Position Embeddings (Huang et al.)](https://huggingface.co/papers/2009.13658).
+ classifier_dropout (`float`, *optional*):
+ The dropout ratio for the classification head.
+ channel_shrink_ratio (`int`, *optional*, defaults to 4):
+ The shrink ratio compared to the `hidden_size` for the channel dimension of the layout embeddings.
+ max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
+ The maximum value that the 2D position embedding might ever be used with. Typically set this to something
+ large just in case (e.g., 1024).
+
+ Examples:
+
+ ```python
+ >>> from transformers import LiltConfig, LiltModel
+
+ >>> # Initializing a LiLT SCUT-DLVCLab/lilt-roberta-en-base style configuration
+ >>> configuration = LiltConfig()
+ >>> # Randomly initializing a model from the SCUT-DLVCLab/lilt-roberta-en-base style configuration
+ >>> model = LiltModel(configuration)
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "lilt"
+
+ def __init__(
+ self,
+ vocab_size=30522,
+ hidden_size=768,
+ num_hidden_layers=12,
+ num_attention_heads=12,
+ intermediate_size=3072,
+ hidden_act="gelu",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ type_vocab_size=2,
+ initializer_range=0.02,
+ layer_norm_eps=1e-12,
+ pad_token_id=0,
+ position_embedding_type="absolute",
+ classifier_dropout=None,
+ channel_shrink_ratio=4,
+ max_2d_position_embeddings=1024,
+ **kwargs,
+ ):
+ super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.hidden_act = hidden_act
+ self.intermediate_size = intermediate_size
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.max_position_embeddings = max_position_embeddings
+ self.type_vocab_size = type_vocab_size
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.position_embedding_type = position_embedding_type
+ self.classifier_dropout = classifier_dropout
+ self.channel_shrink_ratio = channel_shrink_ratio
+ self.max_2d_position_embeddings = max_2d_position_embeddings
+
+
+__all__ = ["LiltConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lilt/modeling_lilt.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lilt/modeling_lilt.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3bcbf31f0354b2e61a9153b805218b695354b09
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lilt/modeling_lilt.py
@@ -0,0 +1,1093 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch LiLT model."""
+
+import math
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPooling,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import auto_docstring, logging
+from .configuration_lilt import LiltConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class LiltTextEmbeddings(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+ self.register_buffer(
+ "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+ )
+ self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+
+ # End copy
+ self.padding_idx = config.pad_token_id
+ self.position_embeddings = nn.Embedding(
+ config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
+ )
+
+ def forward(
+ self,
+ input_ids=None,
+ token_type_ids=None,
+ position_ids=None,
+ inputs_embeds=None,
+ ):
+ if position_ids is None:
+ if input_ids is not None:
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
+ position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx).to(
+ input_ids.device
+ )
+ else:
+ position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
+
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = inputs_embeds + token_type_embeddings
+ if self.position_embedding_type == "absolute":
+ position_embeddings = self.position_embeddings(position_ids)
+ embeddings += position_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings, position_ids
+
+ def create_position_ids_from_input_ids(self, input_ids, padding_idx):
+ """
+ Args:
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
+ symbols are ignored. This is modified from fairseq's `utils.make_positions`.
+ x: torch.Tensor x:
+ Returns: torch.Tensor
+ """
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+ mask = input_ids.ne(padding_idx).int()
+ incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask
+ return incremental_indices.long() + padding_idx
+
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+ """
+ Args:
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.:
+ inputs_embeds: torch.Tensor
+ Returns: torch.Tensor
+ """
+ input_shape = inputs_embeds.size()[:-1]
+ sequence_length = input_shape[1]
+
+ position_ids = torch.arange(
+ self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+ )
+ return position_ids.unsqueeze(0).expand(input_shape)
+
+
+class LiltLayoutEmbeddings(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ # we divide the hidden_size by 6 here as there are 6 different layout embeddings,
+ # namely left_position, upper_position, right_position, lower_position, height, width
+ self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
+ self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
+ self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
+ self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.hidden_size // 6)
+
+ self.padding_idx = config.pad_token_id
+ self.box_position_embeddings = nn.Embedding(
+ config.max_position_embeddings,
+ config.hidden_size // config.channel_shrink_ratio,
+ padding_idx=self.padding_idx,
+ )
+ self.box_linear_embeddings = nn.Linear(
+ in_features=config.hidden_size, out_features=config.hidden_size // config.channel_shrink_ratio
+ )
+ self.LayerNorm = nn.LayerNorm(config.hidden_size // config.channel_shrink_ratio, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, bbox=None, position_ids=None):
+ try:
+ left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
+ upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
+ right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
+ lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
+ except IndexError as e:
+ raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e
+
+ h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
+ w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
+
+ spatial_position_embeddings = torch.cat(
+ [
+ left_position_embeddings,
+ upper_position_embeddings,
+ right_position_embeddings,
+ lower_position_embeddings,
+ h_position_embeddings,
+ w_position_embeddings,
+ ],
+ dim=-1,
+ )
+ spatial_position_embeddings = self.box_linear_embeddings(spatial_position_embeddings)
+ box_position_embeddings = self.box_position_embeddings(position_ids)
+
+ spatial_position_embeddings = spatial_position_embeddings + box_position_embeddings
+
+ spatial_position_embeddings = self.LayerNorm(spatial_position_embeddings)
+ spatial_position_embeddings = self.dropout(spatial_position_embeddings)
+
+ return spatial_position_embeddings
+
+
+class LiltSelfAttention(nn.Module):
+ def __init__(self, config, position_embedding_type=None, layer_idx=None):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.layout_query = nn.Linear(
+ config.hidden_size // config.channel_shrink_ratio, self.all_head_size // config.channel_shrink_ratio
+ )
+ self.layout_key = nn.Linear(
+ config.hidden_size // config.channel_shrink_ratio, self.all_head_size // config.channel_shrink_ratio
+ )
+ self.layout_value = nn.Linear(
+ config.hidden_size // config.channel_shrink_ratio, self.all_head_size // config.channel_shrink_ratio
+ )
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+ self.position_embedding_type = position_embedding_type or getattr(
+ config, "position_embedding_type", "absolute"
+ )
+ if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+ self.max_position_embeddings = config.max_position_embeddings
+ self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
+
+ self.channel_shrink_ratio = config.channel_shrink_ratio
+ self.layer_idx = layer_idx
+
+ def transpose_for_scores(self, x, r=1):
+ new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size // r)
+ x = x.view(*new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward(
+ self,
+ hidden_states,
+ layout_inputs,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ ):
+ layout_value_layer = self.transpose_for_scores(self.layout_value(layout_inputs), r=self.channel_shrink_ratio)
+ layout_key_layer = self.transpose_for_scores(self.layout_key(layout_inputs), r=self.channel_shrink_ratio)
+ layout_query_layer = self.transpose_for_scores(self.layout_query(layout_inputs), r=self.channel_shrink_ratio)
+
+ mixed_query_layer = self.query(hidden_states)
+
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
+ query_layer = self.transpose_for_scores(mixed_query_layer)
+
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+ layout_attention_scores = torch.matmul(layout_query_layer, layout_key_layer.transpose(-1, -2))
+
+ if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+ seq_length = hidden_states.size()[1]
+ position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
+ position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
+ distance = position_ids_l - position_ids_r
+ positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
+ positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
+
+ if self.position_embedding_type == "relative_key":
+ relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+ attention_scores = attention_scores + relative_position_scores
+ elif self.position_embedding_type == "relative_key_query":
+ relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+ relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
+ attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
+
+ tmp_attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ tmp_layout_attention_scores = layout_attention_scores / math.sqrt(
+ self.attention_head_size // self.channel_shrink_ratio
+ )
+ attention_scores = tmp_attention_scores + tmp_layout_attention_scores
+ layout_attention_scores = tmp_layout_attention_scores + tmp_attention_scores
+
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+ layout_attention_scores = layout_attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ layout_attention_probs = nn.Softmax(dim=-1)(layout_attention_scores)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ layout_attention_probs = self.dropout(layout_attention_probs)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ layout_attention_probs = layout_attention_probs * head_mask
+
+ layout_context_layer = torch.matmul(layout_attention_probs, layout_value_layer)
+
+ layout_context_layer = layout_context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = layout_context_layer.size()[:-2] + (self.all_head_size // self.channel_shrink_ratio,)
+ layout_context_layer = layout_context_layer.view(*new_context_layer_shape)
+
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+
+ outputs = (
+ ((context_layer, layout_context_layer), attention_probs)
+ if output_attentions
+ else ((context_layer, layout_context_layer),)
+ )
+
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
+class LiltSelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class LiltAttention(nn.Module):
+ def __init__(self, config, position_embedding_type=None, layer_idx=None):
+ super().__init__()
+ self.self = LiltSelfAttention(config, position_embedding_type=position_embedding_type, layer_idx=layer_idx)
+ self.output = LiltSelfOutput(config)
+ self.pruned_heads = set()
+
+ ori_hidden_size = config.hidden_size
+ config.hidden_size = config.hidden_size // config.channel_shrink_ratio
+ self.layout_output = LiltSelfOutput(config)
+ config.hidden_size = ori_hidden_size
+
+ # Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+ )
+
+ # Prune linear layers
+ self.self.query = prune_linear_layer(self.self.query, index)
+ self.self.key = prune_linear_layer(self.self.key, index)
+ self.self.value = prune_linear_layer(self.self.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+ self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ layout_inputs: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor]:
+ self_outputs = self.self(
+ hidden_states,
+ layout_inputs,
+ attention_mask,
+ head_mask,
+ output_attentions,
+ )
+ attention_output = self.output(self_outputs[0][0], hidden_states)
+ layout_attention_output = self.layout_output(self_outputs[0][1], layout_inputs)
+ outputs = ((attention_output, layout_attention_output),) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate
+class LiltIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOutput
+class LiltOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class LiltLayer(GradientCheckpointingLayer):
+ def __init__(self, config, layer_idx=None):
+ super().__init__()
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = LiltAttention(config, layer_idx=layer_idx)
+ self.intermediate = LiltIntermediate(config)
+ self.output = LiltOutput(config)
+
+ ori_hidden_size = config.hidden_size
+ ori_intermediate_size = config.intermediate_size
+ config.hidden_size = config.hidden_size // config.channel_shrink_ratio
+ config.intermediate_size = config.intermediate_size // config.channel_shrink_ratio
+ self.layout_intermediate = LiltIntermediate(config)
+ self.layout_output = LiltOutput(config)
+ config.hidden_size = ori_hidden_size
+ config.intermediate_size = ori_intermediate_size
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ layout_inputs: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor]:
+ self_attention_outputs = self.attention(
+ hidden_states,
+ layout_inputs,
+ attention_mask,
+ head_mask,
+ output_attentions=output_attentions,
+ )
+ attention_output = self_attention_outputs[0][0]
+ layout_attention_output = self_attention_outputs[0][1]
+
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ layer_output = apply_chunking_to_forward(
+ self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+ )
+ layout_layer_output = apply_chunking_to_forward(
+ self.layout_feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, layout_attention_output
+ )
+ outputs = ((layer_output, layout_layer_output),) + outputs
+
+ return outputs
+
+ # Copied from transformers.models.bert.modeling_bert.BertLayer.feed_forward_chunk
+ def feed_forward_chunk(self, attention_output):
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+ def layout_feed_forward_chunk(self, attention_output):
+ intermediate_output = self.layout_intermediate(attention_output)
+ layer_output = self.layout_output(intermediate_output, attention_output)
+ return layer_output
+
+
+class LiltEncoder(nn.Module):
+ # Copied from transformers.models.bert.modeling_bert.BertEncoder.__init__ with Bert->Lilt
+ def __init__(self, config, layer_idx=None):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList([LiltLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ layout_inputs: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ output_hidden_states: Optional[bool] = False,
+ return_dict: Optional[bool] = True,
+ ) -> Union[tuple[torch.Tensor], BaseModelOutput]:
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+
+ layer_outputs = layer_module(
+ hidden_states,
+ layout_inputs,
+ attention_mask,
+ layer_head_mask,
+ output_attentions,
+ )
+
+ hidden_states = layer_outputs[0][0]
+ layout_inputs = layer_outputs[0][1]
+
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ hidden_states,
+ all_hidden_states,
+ all_self_attentions,
+ ]
+ if v is not None
+ )
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler
+class LiltPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+@auto_docstring
+class LiltPreTrainedModel(PreTrainedModel):
+ config: LiltConfig
+ base_model_prefix = "lilt"
+ supports_gradient_checkpointing = True
+ _no_split_modules = []
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, nn.Linear):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class LiltModel(LiltPreTrainedModel):
+ def __init__(self, config, add_pooling_layer=True):
+ r"""
+ add_pooling_layer (bool, *optional*, defaults to `True`):
+ Whether to add a pooling layer
+ """
+ super().__init__(config)
+ self.config = config
+
+ self.embeddings = LiltTextEmbeddings(config)
+ self.layout_embeddings = LiltLayoutEmbeddings(config)
+ self.encoder = LiltEncoder(config)
+
+ self.pooler = LiltPooler(config) if add_pooling_layer else None
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ bbox: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], BaseModelOutputWithPooling]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, AutoModel
+ >>> from datasets import load_dataset
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
+ >>> model = AutoModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
+
+ >>> outputs = model(**encoding)
+ >>> last_hidden_states = outputs.last_hidden_state
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ batch_size, seq_length = input_shape
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if bbox is None:
+ bbox = torch.zeros(input_shape + (4,), dtype=torch.long, device=device)
+
+ if attention_mask is None:
+ attention_mask = torch.ones(((batch_size, seq_length)), device=device)
+
+ if token_type_ids is None:
+ if hasattr(self.embeddings, "token_type_ids"):
+ buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
+ buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
+ token_type_ids = buffered_token_type_ids_expanded
+ else:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+ embedding_output, position_ids = self.embeddings(
+ input_ids=input_ids,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ )
+
+ layout_embedding_output = self.layout_embeddings(bbox=bbox, position_ids=position_ids)
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ layout_embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ LiLT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
+ output) e.g. for GLUE tasks.
+ """
+)
+class LiltForSequenceClassification(LiltPreTrainedModel):
+ # Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification.__init__ with Roberta->Lilt, roberta->lilt
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.config = config
+
+ self.lilt = LiltModel(config, add_pooling_layer=False)
+ self.classifier = LiltClassificationHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
+ >>> from datasets import load_dataset
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
+ >>> model = AutoModelForSequenceClassification.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
+
+ >>> outputs = model(**encoding)
+ >>> predicted_class_idx = outputs.logits.argmax(-1).item()
+ >>> predicted_class = model.config.id2label[predicted_class_idx]
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.lilt(
+ input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = outputs[0]
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class LiltForTokenClassification(LiltPreTrainedModel):
+ # Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification.__init__ with Roberta->Lilt, roberta->lilt
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.lilt = LiltModel(config, add_pooling_layer=False)
+ classifier_dropout = (
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.dropout = nn.Dropout(classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], TokenClassifierOutput]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, AutoModelForTokenClassification
+ >>> from datasets import load_dataset
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
+ >>> model = AutoModelForTokenClassification.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
+
+ >>> outputs = model(**encoding)
+ >>> predicted_class_indices = outputs.logits.argmax(-1)
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.lilt(
+ input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+# Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->Lilt
+class LiltClassificationHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ classifier_dropout = (
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.dropout = nn.Dropout(classifier_dropout)
+ self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+ def forward(self, features, **kwargs):
+ x = features[:, 0, :] # take token (equiv. to [CLS])
+ x = self.dropout(x)
+ x = self.dense(x)
+ x = torch.tanh(x)
+ x = self.dropout(x)
+ x = self.out_proj(x)
+ return x
+
+
+@auto_docstring
+class LiltForQuestionAnswering(LiltPreTrainedModel):
+ # Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering.__init__ with Roberta->Lilt, roberta->lilt
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.lilt = LiltModel(config, add_pooling_layer=False)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ bbox: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+ r"""
+ bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*):
+ Bounding boxes of each input sequence tokens. Selected in the range `[0,
+ config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1)
+ format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
+ y1) represents the position of the lower right corner. See [Overview](#Overview) for normalization.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+ >>> from datasets import load_dataset
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
+ >>> model = AutoModelForQuestionAnswering.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
+
+ >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
+ >>> example = dataset[0]
+ >>> words = example["tokens"]
+ >>> boxes = example["bboxes"]
+
+ >>> encoding = tokenizer(words, boxes=boxes, return_tensors="pt")
+
+ >>> outputs = model(**encoding)
+
+ >>> answer_start_index = outputs.start_logits.argmax()
+ >>> answer_end_index = outputs.end_logits.argmax()
+
+ >>> predict_answer_tokens = encoding.input_ids[0, answer_start_index : answer_end_index + 1]
+ >>> predicted_answer = tokenizer.decode(predict_answer_tokens)
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.lilt(
+ input_ids,
+ bbox=bbox,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "LiltForQuestionAnswering",
+ "LiltForSequenceClassification",
+ "LiltForTokenClassification",
+ "LiltModel",
+ "LiltPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0677bb91435c51222e72e7f0296bb6ccb7323d9c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_llama import *
+ from .modeling_flax_llama import *
+ from .modeling_llama import *
+ from .tokenization_llama import *
+ from .tokenization_llama_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/configuration_llama.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/configuration_llama.py
new file mode 100644
index 0000000000000000000000000000000000000000..0065cf56b3bf75e176f54bbcec37d1a4efdf6723
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/configuration_llama.py
@@ -0,0 +1,225 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LLaMA model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...modeling_rope_utils import rope_config_validation
+
+
+class LlamaConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the LLaMA-7B.
+ e.g. [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`LlamaModel`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 11008):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+ `num_attention_heads`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
+ Llama 2 up to 4096, CodeLlama up to 16384.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ End of stream token id.
+ pretraining_tp (`int`, *optional*, defaults to 1):
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+ document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
+ understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
+ results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+ and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+ accordingly.
+ Expected contents:
+ `rope_type` (`str`):
+ The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+ 'llama3'], with 'default' being the original RoPE implementation.
+ `factor` (`float`, *optional*):
+ Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+ most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+ original maximum pre-trained length.
+ `original_max_position_embeddings` (`int`, *optional*):
+ Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+ pretraining.
+ `attention_factor` (`float`, *optional*):
+ Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+ computation. If unspecified, it defaults to value recommended by the implementation, using the
+ `factor` field to infer the suggested value.
+ `beta_fast` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+ ramp function. If unspecified, it defaults to 32.
+ `beta_slow` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+ ramp function. If unspecified, it defaults to 1.
+ `short_factor` (`list[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+ size divided by the number of attention heads divided by 2
+ `long_factor` (`list[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+ size divided by the number of attention heads divided by 2
+ `low_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+ `high_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+ attention_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ mlp_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
+ head_dim (`int`, *optional*):
+ The attention head dimension. If None, it will default to hidden_size // num_attention_heads
+
+ ```python
+ >>> from transformers import LlamaModel, LlamaConfig
+
+ >>> # Initializing a LLaMA llama-7b style configuration
+ >>> configuration = LlamaConfig()
+
+ >>> # Initializing a model from the llama-7b style configuration
+ >>> model = LlamaModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "llama"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ # Default tensor parallel plan for base model `LlamaModel`
+ base_model_tp_plan = {
+ "layers.*.self_attn.q_proj": "colwise",
+ "layers.*.self_attn.k_proj": "colwise",
+ "layers.*.self_attn.v_proj": "colwise",
+ "layers.*.self_attn.o_proj": "rowwise",
+ "layers.*.mlp.gate_proj": "colwise",
+ "layers.*.mlp.up_proj": "colwise",
+ "layers.*.mlp.down_proj": "rowwise",
+ }
+ base_model_pp_plan = {
+ "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+ "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+ "norm": (["hidden_states"], ["hidden_states"]),
+ }
+
+ def __init__(
+ self,
+ vocab_size=32000,
+ hidden_size=4096,
+ intermediate_size=11008,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=None,
+ hidden_act="silu",
+ max_position_embeddings=2048,
+ initializer_range=0.02,
+ rms_norm_eps=1e-6,
+ use_cache=True,
+ pad_token_id=None,
+ bos_token_id=1,
+ eos_token_id=2,
+ pretraining_tp=1,
+ tie_word_embeddings=False,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ attention_bias=False,
+ attention_dropout=0.0,
+ mlp_bias=False,
+ head_dim=None,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.pretraining_tp = pretraining_tp
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.mlp_bias = mlp_bias
+ self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
+ # Validate the correctness of rotary position embeddings parameters
+ # BC: if there is a 'type' field, copy it it to 'rope_type'.
+ if self.rope_scaling is not None and "type" in self.rope_scaling:
+ self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+ rope_config_validation(self)
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+
+__all__ = ["LlamaConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/convert_llama_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/convert_llama_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed7a89f6f32e7955285476ad38e60aa8a098cbf8
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/convert_llama_weights_to_hf.py
@@ -0,0 +1,605 @@
+# Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import gc
+import json
+import os
+import tempfile
+import warnings
+
+import torch
+from tokenizers import AddedToken, processors
+
+from transformers import GenerationConfig, LlamaConfig, LlamaForCausalLM, LlamaTokenizer, PreTrainedTokenizerFast
+from transformers.convert_slow_tokenizer import TikTokenConverter
+
+
+try:
+ from transformers import LlamaTokenizerFast
+except ImportError as e:
+ warnings.warn(e)
+ warnings.warn(
+ "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
+ )
+ LlamaTokenizerFast = None
+
+"""
+Sample usage:
+
+```
+python src/transformers/models/llama/convert_llama_weights_to_hf.py \
+ --input_dir /path/to/downloaded/llama/weights --model_size 1B --llama_version 3.2 --output_dir /output/path
+```
+
+Thereafter, models can be loaded via:
+
+```py
+from transformers import LlamaForCausalLM, LlamaTokenizer
+
+model = LlamaForCausalLM.from_pretrained("/output/path")
+tokenizer = LlamaTokenizer.from_pretrained("/output/path")
+```
+
+Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
+come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
+
+If you want your tokenizer to add a bos automatically you should update the tokenizer._tokenizers.post_processor:
+
+```py
+from tokenizers import processors
+bos = "<|begin_of_text|>"
+tokenizer._tokenizers.post_processor = processors.Sequence(
+ [
+ processors.ByteLevel(trim_offsets=False),
+ processors.TemplateProcessing(
+ single=f"{bos}:0 $A:0",
+ pair=f"{bos}:0 $A:0 {bos}:1 $B:1",
+ special_tokens=[
+ (bos, tokenizer.encode(bos)),
+ ],
+ ),
+ ]
+)
+```
+"""
+
+NUM_SHARDS = {
+ "1B": 1,
+ "3B": 1,
+ "7B": 1,
+ "8B": 1,
+ "8Bf": 1,
+ "7Bf": 1,
+ "13B": 2,
+ "13Bf": 2,
+ "34B": 4,
+ "30B": 4,
+ "65B": 8,
+ "70B": 8,
+ "70Bf": 8,
+ "405B": 8,
+ "405B-MP16": 16,
+}
+
+CONTEXT_LENGTH_FOR_VERSION = {"Guard-3": 131072, "3.2": 131072, "3.1": 131072, "3": 8192, "2": 4096, "1": 2048}
+
+BOS_ADDED_TOKEN = AddedToken(
+ "<|begin_of_text|>", single_word=False, lstrip=False, rstrip=False, normalized=False, special=True
+)
+EOS_ADDED_TOKEN = AddedToken(
+ "<|end_of_text|>", single_word=False, lstrip=False, rstrip=False, normalized=False, special=True
+)
+EOT_ADDED_TOKEN = AddedToken(
+ "<|eot_id|>", single_word=False, lstrip=False, rstrip=False, normalized=False, special=True
+)
+
+DEFAULT_LLAMA_SPECIAL_TOKENS = {
+ "3": [
+ "<|begin_of_text|>",
+ "<|end_of_text|>",
+ "<|reserved_special_token_0|>",
+ "<|reserved_special_token_1|>",
+ "<|reserved_special_token_2|>",
+ "<|reserved_special_token_3|>",
+ "<|start_header_id|>",
+ "<|end_header_id|>",
+ "<|reserved_special_token_4|>",
+ "<|eot_id|>", # end of turn
+ ]
+ + [f"<|reserved_special_token_{i}|>" for i in range(5, 256 - 5)],
+ "3.1": [
+ "<|begin_of_text|>",
+ "<|end_of_text|>",
+ "<|reserved_special_token_0|>",
+ "<|reserved_special_token_1|>",
+ "<|finetune_right_pad_id|>",
+ "<|reserved_special_token_2|>",
+ "<|start_header_id|>",
+ "<|end_header_id|>",
+ "<|eom_id|>", # end of message
+ "<|eot_id|>", # end of turn
+ "<|python_tag|>",
+ ]
+ + [f"<|reserved_special_token_{i}|>" for i in range(3, 256 - 8)],
+ "3.2": [
+ "<|begin_of_text|>",
+ "<|end_of_text|>",
+ "<|reserved_special_token_0|>",
+ "<|reserved_special_token_1|>",
+ "<|finetune_right_pad_id|>",
+ "<|reserved_special_token_2|>",
+ "<|start_header_id|>",
+ "<|end_header_id|>",
+ "<|eom_id|>", # end of message
+ "<|eot_id|>", # end of turn
+ "<|python_tag|>",
+ ]
+ + [f"<|reserved_special_token_{i}|>" for i in range(3, 256 - 8)],
+ "Guard-3": [
+ "<|begin_of_text|>",
+ "<|end_of_text|>",
+ "<|reserved_special_token_0|>",
+ "<|reserved_special_token_1|>",
+ "<|finetune_right_pad_id|>",
+ "<|reserved_special_token_2|>",
+ "<|start_header_id|>",
+ "<|end_header_id|>",
+ "<|eom_id|>", # end of message
+ "<|eot_id|>", # end of turn
+ "<|python_tag|>",
+ ]
+ + [f"<|reserved_special_token_{i}|>" for i in range(3, 256 - 8)],
+}
+
+
+def is_llama_3(version):
+ return version in ["3", "3.1", "3.2", "Guard-3"]
+
+
+def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
+ return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
+
+
+def read_json(path):
+ with open(path, "r") as f:
+ return json.load(f)
+
+
+def write_json(text, path):
+ with open(path, "w") as f:
+ json.dump(text, f)
+
+
+def write_model(
+ model_path,
+ input_base_path,
+ model_size=None,
+ safe_serialization=True,
+ llama_version="1",
+ vocab_size=None,
+ num_shards=None,
+ instruct=False,
+ push_to_hub=False,
+):
+ print("Converting the model.")
+ params = read_json(os.path.join(input_base_path, "params.json"))
+ num_shards = NUM_SHARDS[model_size] if num_shards is None else num_shards
+ params = params.get("model", params)
+ n_layers = params["n_layers"]
+ n_heads = params["n_heads"]
+ n_heads_per_shard = n_heads // num_shards
+ dim = params["dim"]
+ dims_per_head = dim // n_heads
+ base = params.get("rope_theta", 10000.0)
+ inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
+ if base > 10000.0 and not is_llama_3(llama_version):
+ max_position_embeddings = 16384
+ else:
+ max_position_embeddings = CONTEXT_LENGTH_FOR_VERSION[llama_version]
+
+ if params.get("n_kv_heads", None) is not None:
+ num_key_value_heads = params["n_kv_heads"] # for GQA / MQA
+ num_key_value_heads_per_shard = num_key_value_heads // num_shards
+ key_value_dim = dims_per_head * num_key_value_heads
+ else: # compatibility with other checkpoints
+ num_key_value_heads = n_heads
+ num_key_value_heads_per_shard = n_heads_per_shard
+ key_value_dim = dim
+
+ # permute for sliced rotary
+ def permute(w, n_heads, dim1=dim, dim2=dim):
+ return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
+
+ with tempfile.TemporaryDirectory() as tmp_model_path:
+ print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
+ # Load weights
+ if num_shards == 1:
+ # Not sharded
+ # (The sharded implementation would also work, but this is simpler.)
+ loaded = torch.load(
+ os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu", weights_only=True
+ )
+ else:
+ # Sharded
+ checkpoint_list = sorted([file for file in os.listdir(input_base_path) if file.endswith(".pth")])
+ print("Loading in order:", checkpoint_list)
+ loaded = [
+ torch.load(os.path.join(input_base_path, file), map_location="cpu", weights_only=True)
+ for file in checkpoint_list
+ ]
+ param_count = 0
+ index_dict = {"weight_map": {}}
+ for layer_i in range(n_layers):
+ filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
+ if num_shards == 1:
+ # Unsharded
+ state_dict = {
+ f"model.layers.{layer_i}.self_attn.q_proj.weight": permute(
+ loaded[f"layers.{layer_i}.attention.wq.weight"], n_heads=n_heads
+ ),
+ f"model.layers.{layer_i}.self_attn.k_proj.weight": permute(
+ loaded[f"layers.{layer_i}.attention.wk.weight"],
+ n_heads=num_key_value_heads,
+ dim1=key_value_dim,
+ ),
+ f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"layers.{layer_i}.attention.wv.weight"],
+ f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"layers.{layer_i}.attention.wo.weight"],
+ f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w1.weight"],
+ f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w2.weight"],
+ f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w3.weight"],
+ f"model.layers.{layer_i}.input_layernorm.weight": loaded[
+ f"layers.{layer_i}.attention_norm.weight"
+ ],
+ f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[
+ f"layers.{layer_i}.ffn_norm.weight"
+ ],
+ }
+ else:
+ # Sharded
+ # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
+ # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
+ # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
+
+ state_dict = {
+ f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
+ f"layers.{layer_i}.attention_norm.weight"
+ ].clone(),
+ f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
+ f"layers.{layer_i}.ffn_norm.weight"
+ ].clone(),
+ }
+ state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
+ torch.cat(
+ [
+ loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(
+ n_heads_per_shard, dims_per_head, dim
+ )
+ for i in range(len(loaded))
+ ],
+ dim=0,
+ ).reshape(dim, dim),
+ n_heads=n_heads,
+ )
+ state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
+ torch.cat(
+ [
+ loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
+ num_key_value_heads_per_shard, dims_per_head, dim
+ )
+ for i in range(len(loaded))
+ ],
+ dim=0,
+ ).reshape(key_value_dim, dim),
+ num_key_value_heads,
+ key_value_dim,
+ dim,
+ )
+ state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
+ [
+ loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(
+ num_key_value_heads_per_shard, dims_per_head, dim
+ )
+ for i in range(len(loaded))
+ ],
+ dim=0,
+ ).reshape(key_value_dim, dim)
+
+ state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
+ [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(len(loaded))], dim=1
+ )
+ state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
+ [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(len(loaded))], dim=0
+ )
+ state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
+ [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(len(loaded))], dim=1
+ )
+ state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
+ [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(len(loaded))], dim=0
+ )
+
+ state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
+ for k, v in state_dict.items():
+ index_dict["weight_map"][k] = filename
+ param_count += v.numel()
+ torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+ filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
+ if num_shards == 1:
+ # Unsharded
+ state_dict = {
+ "model.embed_tokens.weight": loaded["tok_embeddings.weight"],
+ "model.norm.weight": loaded["norm.weight"],
+ "lm_head.weight": loaded["output.weight"],
+ }
+ else:
+ concat_dim = 0 if is_llama_3(llama_version) else 1
+ state_dict = {
+ "model.norm.weight": loaded[0]["norm.weight"],
+ "model.embed_tokens.weight": torch.cat(
+ [loaded[i]["tok_embeddings.weight"] for i in range(len(loaded))], dim=concat_dim
+ ),
+ "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(len(loaded))], dim=0),
+ }
+
+ for k, v in state_dict.items():
+ index_dict["weight_map"][k] = filename
+ param_count += v.numel()
+ torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+ # Write configs
+ index_dict["metadata"] = {"total_size": param_count * 2}
+ write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
+ ffn_dim_multiplier = params.get("ffn_dim_multiplier", 1)
+ multiple_of = params.get("multiple_of", 256)
+
+ if is_llama_3(llama_version):
+ bos_token_id = 128000
+
+ if instruct:
+ eos_token_id = [128001, 128008, 128009]
+ else:
+ eos_token_id = 128001
+ else:
+ bos_token_id = 1
+ eos_token_id = 2
+
+ if llama_version in ["3.1", "3.2", "Guard-3"]:
+ rope_scaling = {
+ "factor": 32.0 if llama_version == "3.2" else 8.0,
+ "low_freq_factor": 1.0,
+ "high_freq_factor": 4.0,
+ "original_max_position_embeddings": 8192,
+ "rope_type": "llama3",
+ }
+ else:
+ rope_scaling = None
+
+ config = LlamaConfig(
+ hidden_size=dim,
+ intermediate_size=compute_intermediate_size(dim, ffn_dim_multiplier, multiple_of),
+ num_attention_heads=params["n_heads"],
+ num_hidden_layers=params["n_layers"],
+ rms_norm_eps=params["norm_eps"],
+ num_key_value_heads=num_key_value_heads,
+ vocab_size=vocab_size,
+ rope_theta=base,
+ rope_scaling=rope_scaling,
+ max_position_embeddings=max_position_embeddings,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=llama_version in ["3.2"],
+ )
+
+ config.save_pretrained(tmp_model_path)
+
+ generation_config = GenerationConfig(
+ do_sample=True,
+ temperature=0.6,
+ top_p=0.9,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ )
+ generation_config.save_pretrained(tmp_model_path)
+
+ # Make space so we can load the model properly now.
+ del state_dict
+ del loaded
+ gc.collect()
+
+ print("Loading the checkpoint in a Llama model.")
+ model = LlamaForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.bfloat16)
+
+ # Avoid saving this as part of the config.
+ del model.config._name_or_path
+ model.config.torch_dtype = torch.float16
+
+ print("Saving in the Transformers format.")
+ if push_to_hub:
+ print("Pushing to the hub.")
+ model.push_to_hub(model_path, safe_serialization=safe_serialization, private=True, use_temp_dir=True)
+ else:
+ print("Saving to disk.")
+ model.save_pretrained(model_path, safe_serialization=safe_serialization)
+
+
+class Llama3Converter(TikTokenConverter):
+ def __init__(self, vocab_file, special_tokens=None, instruct=False, llama_version="3.2", **kwargs):
+ super().__init__(vocab_file, additional_special_tokens=special_tokens, **kwargs)
+ tokenizer = self.converted()
+
+ # References for chat templates in instruct models
+ templates_for_version = {
+ "2": ("meta-llama/Llama-2-7b-chat-hf", "f5db02db724555f92da89c216ac04704f23d4590"),
+ "3": ("meta-llama/Meta-Llama-3-8B-Instruct", "5f0b02c75b57c5855da9ae460ce51323ea669d8a"),
+ "3.1": ("meta-llama/Llama-3.1-8B-Instruct", "0e9e39f249a16976918f6564b8830bc894c89659"),
+ "3.2": ("meta-llama/Llama-3.2-1B-Instruct", "e9f8effbab1cbdc515c11ee6e098e3d5a9f51e14"),
+ "Guard-3": ("meta-llama/Llama-Guard-3-1B", "acf7aafa60f0410f8f42b1fa35e077d705892029"),
+ }
+
+ # Add chat_template only if instruct is True.
+ # Prevents a null chat_template, which triggers
+ # a parsing warning in the Hub.
+ additional_kwargs = {}
+ if instruct or llama_version in ["Guard-3"]:
+ model_id, revision = templates_for_version.get(llama_version, (None, None))
+ if model_id is not None:
+ from transformers import AutoTokenizer
+
+ t = AutoTokenizer.from_pretrained(model_id, revision=revision)
+ additional_kwargs["chat_template"] = t.chat_template
+
+ self.converted_tokenizer = PreTrainedTokenizerFast(
+ tokenizer_object=tokenizer,
+ bos_token="<|begin_of_text|>",
+ eos_token="<|end_of_text|>" if not instruct else "<|eot_id|>",
+ model_input_names=["input_ids", "attention_mask"],
+ model_max_length=CONTEXT_LENGTH_FOR_VERSION[llama_version],
+ clean_up_tokenization_spaces=True,
+ **additional_kwargs,
+ )
+ self.update_post_processor(self.converted_tokenizer)
+ # finer special_tokens_map.json
+ self.converted_tokenizer._bos_token = BOS_ADDED_TOKEN
+ self.converted_tokenizer._eos_token = EOT_ADDED_TOKEN if instruct else EOS_ADDED_TOKEN
+
+ # We can't do this while building the tokenizer because we have no easy access to the bos token id
+ def update_post_processor(self, tokenizer):
+ tokenizer._tokenizer.post_processor = processors.Sequence(
+ [
+ processors.ByteLevel(trim_offsets=False),
+ processors.TemplateProcessing(
+ single="<|begin_of_text|> $A",
+ pair="<|begin_of_text|>:0 $A:0 <|begin_of_text|>:1 $B:1",
+ special_tokens=[
+ ("<|begin_of_text|>", tokenizer.convert_tokens_to_ids("<|begin_of_text|>")),
+ ],
+ ),
+ ]
+ )
+
+
+def write_tokenizer(
+ tokenizer_path, input_tokenizer_path, llama_version="2", special_tokens=None, instruct=False, push_to_hub=False
+):
+ print("Converting the tokenizer.")
+ tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
+ if is_llama_3(llama_version):
+ tokenizer = Llama3Converter(
+ input_tokenizer_path,
+ special_tokens,
+ instruct,
+ llama_version,
+ ).converted_tokenizer
+ else:
+ try:
+ tokenizer = tokenizer_class(input_tokenizer_path)
+ except Exception:
+ raise ValueError(
+ "Failed to instantiate tokenizer. Please, make sure you have sentencepiece and protobuf installed."
+ )
+
+ if push_to_hub:
+ print(f"Pushing a {tokenizer_class.__name__} to the Hub repo - {tokenizer_path}.")
+ tokenizer.push_to_hub(tokenizer_path, private=True, use_temp_dir=True)
+ else:
+ print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
+ tokenizer.save_pretrained(tokenizer_path)
+ return tokenizer
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--input_dir",
+ help="Location of Llama weights, which contains tokenizer.model and model folders",
+ )
+ parser.add_argument(
+ "--model_size",
+ default=None,
+ help="'f' Deprecated in favor of `num_shards`: models correspond to the finetuned versions, and are specific to the Llama2 official release. For more details on Llama2, check out the original repo: https://huggingface.co/meta-llama",
+ )
+ parser.add_argument(
+ "--output_dir",
+ help="Location to write HF model and tokenizer",
+ )
+ parser.add_argument(
+ "--push_to_hub",
+ help="Whether or not to push the model to the hub at `output_dir` instead of saving it locally.",
+ action="store_true",
+ default=False,
+ )
+ parser.add_argument(
+ "--safe_serialization", action="store_true", default=True, help="Whether or not to save using `safetensors`."
+ )
+ # Different Llama versions used different default values for max_position_embeddings, hence the need to be able to specify which version is being used.
+ parser.add_argument(
+ "--llama_version",
+ choices=["1", "2", "3", "3.1", "3.2", "Guard-3"],
+ default="1",
+ type=str,
+ help="Version of the Llama model to convert. Currently supports Llama1 and Llama2. Controls the context size",
+ )
+ parser.add_argument(
+ "--num_shards",
+ default=None,
+ type=int,
+ help="The number of individual shards used for the model. Does not have to be the same as the number of consolidated_xx.pth",
+ )
+ parser.add_argument(
+ "--special_tokens",
+ default=None,
+ type=list[str],
+ help="The list of special tokens that should be added to the model.",
+ )
+ parser.add_argument(
+ "--instruct",
+ action="store_true",
+ default=False,
+ help="Whether the model is an instruct model or not. Will affect special tokens and chat template.",
+ )
+ args = parser.parse_args()
+ if args.model_size is None and args.num_shards is None:
+ raise ValueError("You have to set at least `num_shards` if you are not giving the `model_size`")
+ if args.special_tokens is None:
+ # no special tokens by default
+ args.special_tokens = DEFAULT_LLAMA_SPECIAL_TOKENS.get(str(args.llama_version), [])
+
+ spm_path = os.path.join(args.input_dir, "tokenizer.model")
+ vocab_size = len(
+ write_tokenizer(
+ args.output_dir,
+ spm_path,
+ llama_version=args.llama_version,
+ special_tokens=args.special_tokens,
+ instruct=args.instruct,
+ push_to_hub=args.push_to_hub,
+ )
+ )
+
+ if args.model_size != "tokenizer_only":
+ write_model(
+ model_path=args.output_dir,
+ input_base_path=args.input_dir,
+ model_size=args.model_size,
+ safe_serialization=args.safe_serialization,
+ llama_version=args.llama_version,
+ vocab_size=vocab_size,
+ num_shards=args.num_shards,
+ instruct=args.instruct,
+ push_to_hub=args.push_to_hub,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/modeling_flax_llama.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/modeling_flax_llama.py
new file mode 100644
index 0000000000000000000000000000000000000000..63e34e996ade323a9d8ad4a91ac67b8229e4d825
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/modeling_flax_llama.py
@@ -0,0 +1,747 @@
+# coding=utf-8
+# Copyright 2023 Meta AI, EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Flax LLaMA model."""
+
+from functools import partial
+from typing import Optional
+
+import flax.linen as nn
+import jax
+import jax.numpy as jnp
+import numpy as np
+from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
+from flax.linen import combine_masks, make_causal_mask
+from flax.linen.attention import dot_product_attention_weights
+from flax.traverse_util import flatten_dict, unflatten_dict
+from jax import lax
+
+from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput
+from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring
+from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
+from .configuration_llama import LlamaConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlamaConfig"
+_CHECKPOINT_FOR_DOC = "afmck/testing-llama-tiny"
+_REAL_CHECKPOINT_FOR_DOC = "openlm-research/open_llama_3b_v2"
+
+LLAMA_START_DOCSTRING = r"""
+
+ This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a Flax Linen
+ [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
+ regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
+
+ Finally, this model supports inherent JAX features such as:
+
+ - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
+ - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
+ - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
+ - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
+
+ Parameters:
+ config ([`LlamaConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
+ dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
+ The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16`, or
+ `jax.numpy.bfloat16`.
+
+ This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
+ specified all the computation will be performed with the given `dtype`.
+
+ **Note that this only specifies the dtype of the computation and does not influence the dtype of model
+ parameters.**
+
+ If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
+ [`~FlaxPreTrainedModel.to_bf16`].
+"""
+
+LLAMA_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`numpy.ndarray` of shape `(batch_size, input_ids_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`numpy.ndarray` of shape `(batch_size, input_ids_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
+ Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
+ auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+def create_sinusoidal_positions(num_pos, dim):
+ inv_freq = 1.0 / (10000 ** (np.arange(0, dim, 2) / dim))
+ freqs = np.einsum("i , j -> i j", np.arange(num_pos), inv_freq).astype("float32")
+
+ emb = np.concatenate((freqs, freqs), axis=-1)
+ out = np.concatenate((np.sin(emb)[:, None, :], np.cos(emb)[:, None, :]), axis=-1)
+ return jnp.array(out[:, :, :num_pos])
+
+
+def rotate_half(tensor):
+ """Rotates half the hidden dims of the input."""
+ rotate_half_tensor = jnp.concatenate(
+ (-tensor[..., tensor.shape[-1] // 2 :], tensor[..., : tensor.shape[-1] // 2]), axis=-1
+ )
+ return rotate_half_tensor
+
+
+def apply_rotary_pos_emb(tensor, sin_pos, cos_pos):
+ return (tensor * cos_pos) + (rotate_half(tensor) * sin_pos)
+
+
+class FlaxLlamaRMSNorm(nn.Module):
+ config: LlamaConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.epsilon = self.config.rms_norm_eps
+ self.weight = self.param("weight", lambda _, shape: jnp.ones(shape), self.config.hidden_size)
+
+ def __call__(self, hidden_states):
+ variance = jnp.asarray(hidden_states, dtype=jnp.float32)
+ variance = jnp.power(variance, 2)
+ variance = variance.mean(-1, keepdims=True)
+ # use `jax.numpy.sqrt` as `jax.lax.rsqrt` does not match `torch.rsqrt`
+ hidden_states = hidden_states / jnp.sqrt(variance + self.epsilon)
+
+ return self.weight * jnp.asarray(hidden_states, dtype=self.dtype)
+
+
+class FlaxLlamaRotaryEmbedding(nn.Module):
+ config: LlamaConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ head_dim = self.config.hidden_size // self.config.num_attention_heads
+ self.sincos = create_sinusoidal_positions(self.config.max_position_embeddings, head_dim)
+
+ def __call__(self, key, query, position_ids):
+ sincos = self.sincos[position_ids]
+ sin_pos, cos_pos = jnp.split(sincos, 2, axis=-1)
+
+ key = apply_rotary_pos_emb(key, sin_pos, cos_pos)
+ query = apply_rotary_pos_emb(query, sin_pos, cos_pos)
+
+ key = jnp.asarray(key, dtype=self.dtype)
+ query = jnp.asarray(query, dtype=self.dtype)
+
+ return key, query
+
+
+class FlaxLlamaAttention(nn.Module):
+ config: LlamaConfig
+ dtype: jnp.dtype = jnp.float32
+ causal: bool = True
+ is_cross_attention: bool = False
+
+ def setup(self):
+ config = self.config
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.embed_dim // self.num_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.attention_softmax_in_fp32 = self.dtype is not jnp.float32
+
+ dense = partial(
+ nn.Dense,
+ use_bias=config.attention_bias,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
+ )
+
+ self.q_proj = dense(self.num_heads * self.head_dim)
+ self.k_proj = dense(self.num_key_value_heads * self.head_dim)
+ self.v_proj = dense(self.num_key_value_heads * self.head_dim)
+ self.o_proj = dense(self.embed_dim)
+ self.causal_mask = make_causal_mask(jnp.ones((1, config.max_position_embeddings), dtype="bool"), dtype="bool")
+ self.rotary_emb = FlaxLlamaRotaryEmbedding(config, dtype=self.dtype)
+
+ def _split_heads(self, hidden_states, num_heads):
+ return hidden_states.reshape(hidden_states.shape[:2] + (num_heads, self.head_dim))
+
+ def _merge_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
+
+ @nn.compact
+ # Copied from transformers.models.gpt_neo.modeling_flax_gpt_neo.FlaxGPTNeoSelfAttention._concatenate_to_cache
+ def _concatenate_to_cache(self, key, value, query, attention_mask):
+ """
+ This function takes projected key, value states from a single input token and concatenates the states to cached
+ states from previous steps. This function is slightly adapted from the official Flax repository:
+ https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
+ """
+ # detect if we're initializing by absence of existing cache data.
+ is_initialized = self.has_variable("cache", "cached_key")
+ cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
+ cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
+ cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
+
+ if is_initialized:
+ *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
+ # update key, value caches with our new 1d spatial slices
+ cur_index = cache_index.value
+ indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
+ key = lax.dynamic_update_slice(cached_key.value, key, indices)
+ value = lax.dynamic_update_slice(cached_value.value, value, indices)
+ cached_key.value = key
+ cached_value.value = value
+ num_updated_cache_vectors = query.shape[1]
+ cache_index.value = cache_index.value + num_updated_cache_vectors
+ # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
+ pad_mask = jnp.broadcast_to(
+ jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
+ tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
+ )
+ attention_mask = combine_masks(pad_mask, attention_mask)
+ return key, value, attention_mask
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask,
+ position_ids,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ ):
+ query = self.q_proj(hidden_states)
+ key = self.k_proj(hidden_states)
+ value = self.v_proj(hidden_states)
+
+ query = self._split_heads(query, self.num_heads)
+ key = self._split_heads(key, self.num_key_value_heads)
+ value = self._split_heads(value, self.num_key_value_heads)
+
+ key, query = self.rotary_emb(key, query, position_ids)
+
+ query_length, key_length = query.shape[1], key.shape[1]
+
+ if self.has_variable("cache", "cached_key"):
+ mask_shift = self.variables["cache"]["cache_index"]
+ max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
+ causal_mask = lax.dynamic_slice(
+ self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
+ )
+ else:
+ causal_mask = self.causal_mask[:, :, :query_length, :key_length]
+
+ batch_size = hidden_states.shape[0]
+ causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
+
+ attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
+ attention_mask = combine_masks(attention_mask, causal_mask)
+
+ dropout_rng = None
+ if not deterministic and self.config.attention_dropout > 0.0:
+ dropout_rng = self.make_rng("dropout")
+
+ # During fast autoregressive decoding, we feed one position at a time,
+ # and cache the keys and values step by step.
+ if self.has_variable("cache", "cached_key") or init_cache:
+ key, value, attention_mask = self._concatenate_to_cache(key, value, query, attention_mask)
+
+ key = jnp.repeat(key, self.num_key_value_groups, axis=2)
+ value = jnp.repeat(value, self.num_key_value_groups, axis=2)
+
+ # transform boolean mask into float mask
+ attention_bias = lax.select(
+ attention_mask > 0,
+ jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
+ jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
+ )
+
+ # usual dot product attention
+ attention_dtype = jnp.float32 if self.attention_softmax_in_fp32 else self.dtype
+ attn_weights = dot_product_attention_weights(
+ query,
+ key,
+ bias=attention_bias,
+ dropout_rng=dropout_rng,
+ dropout_rate=self.config.attention_dropout,
+ deterministic=deterministic,
+ dtype=attention_dtype,
+ )
+
+ if self.attention_softmax_in_fp32:
+ attn_weights = attn_weights.astype(self.dtype)
+
+ attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value)
+ attn_output = self._merge_heads(attn_output)
+ attn_output = self.o_proj(attn_output)
+
+ outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
+ return outputs
+
+
+class FlaxLlamaMLP(nn.Module):
+ config: LlamaConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ embed_dim = self.config.hidden_size
+ inner_dim = self.config.intermediate_size if self.config.intermediate_size is not None else 4 * embed_dim
+
+ kernel_init = jax.nn.initializers.normal(self.config.initializer_range)
+ self.act = ACT2FN[self.config.hidden_act]
+
+ self.gate_proj = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init)
+ self.down_proj = nn.Dense(embed_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init)
+ self.up_proj = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init)
+
+ def __call__(self, hidden_states):
+ up_proj_states = self.up_proj(hidden_states)
+ gate_states = self.act(self.gate_proj(hidden_states))
+
+ hidden_states = self.down_proj(up_proj_states * gate_states)
+ return hidden_states
+
+
+class FlaxLlamaDecoderLayer(nn.Module):
+ config: LlamaConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.input_layernorm = FlaxLlamaRMSNorm(self.config, dtype=self.dtype)
+ self.self_attn = FlaxLlamaAttention(self.config, dtype=self.dtype)
+ self.post_attention_layernorm = FlaxLlamaRMSNorm(self.config, dtype=self.dtype)
+ self.mlp = FlaxLlamaMLP(self.config, dtype=self.dtype)
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_ids=None,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ ):
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+ outputs = self.self_attn(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ )
+ # residual connection
+ attn_output = outputs[0]
+ hidden_states = residual + attn_output
+
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ # residual connection
+ hidden_states = residual + hidden_states
+
+ return (hidden_states,) + outputs[1:]
+
+
+# Copied from transformers.models.gpt_neo.modeling_flax_gpt_neo.FlaxGPTNeoPreTrainedModel with GPTNeo->Llama, GPT_NEO->LLAMA, transformer->model
+class FlaxLlamaPreTrainedModel(FlaxPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = LlamaConfig
+ base_model_prefix = "model"
+ module_class: nn.Module = None
+
+ def __init__(
+ self,
+ config: LlamaConfig,
+ input_shape: tuple = (1, 1),
+ seed: int = 0,
+ dtype: jnp.dtype = jnp.float32,
+ _do_init: bool = True,
+ **kwargs,
+ ):
+ module = self.module_class(config=config, dtype=dtype, **kwargs)
+ super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
+
+ def init_weights(self, rng: jax.random.PRNGKey, input_shape: tuple, params: FrozenDict = None) -> FrozenDict:
+ # init input tensors
+ input_ids = jnp.zeros(input_shape, dtype="i4")
+ attention_mask = jnp.ones_like(input_ids)
+ position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
+ params_rng, dropout_rng = jax.random.split(rng)
+ rngs = {"params": params_rng, "dropout": dropout_rng}
+
+ random_params = self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False)["params"]
+
+ if params is not None:
+ random_params = flatten_dict(unfreeze(random_params))
+ params = flatten_dict(unfreeze(params))
+ for missing_key in self._missing_keys:
+ params[missing_key] = random_params[missing_key]
+ self._missing_keys = set()
+ return freeze(unflatten_dict(params))
+ else:
+ return random_params
+
+ def init_cache(self, batch_size, max_length):
+ r"""
+ Args:
+ batch_size (`int`):
+ batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
+ max_length (`int`):
+ maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
+ cache.
+ """
+ # init input variables to retrieve cache
+ input_ids = jnp.ones((batch_size, max_length))
+ attention_mask = jnp.ones_like(input_ids)
+ position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
+
+ init_variables = self.module.init(
+ jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
+ )
+ return unfreeze(init_variables["cache"])
+
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+ def __call__(
+ self,
+ input_ids,
+ attention_mask=None,
+ position_ids=None,
+ params: Optional[dict] = None,
+ past_key_values: Optional[dict] = None,
+ dropout_rng: jax.random.PRNGKey = None,
+ train: bool = False,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ batch_size, sequence_length = input_ids.shape
+
+ if position_ids is None:
+ if past_key_values is not None:
+ raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
+
+ position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+
+ if attention_mask is None:
+ attention_mask = jnp.ones((batch_size, sequence_length))
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ inputs = {"params": params or self.params}
+
+ # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be changed by FlaxLlamaAttention module
+ if past_key_values:
+ inputs["cache"] = past_key_values
+ mutable = ["cache"]
+ else:
+ mutable = False
+
+ outputs = self.module.apply(
+ inputs,
+ jnp.array(input_ids, dtype="i4"),
+ jnp.array(attention_mask, dtype="i4"),
+ jnp.array(position_ids, dtype="i4"),
+ not train,
+ False,
+ output_attentions,
+ output_hidden_states,
+ return_dict,
+ rngs=rngs,
+ mutable=mutable,
+ )
+
+ # add updated cache to model output
+ if past_key_values is not None and return_dict:
+ outputs, past_key_values = outputs
+ outputs["past_key_values"] = unfreeze(past_key_values["cache"])
+ return outputs
+ elif past_key_values is not None and not return_dict:
+ outputs, past_key_values = outputs
+ outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
+
+ return outputs
+
+
+class FlaxLlamaLayerCollection(nn.Module):
+ config: LlamaConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.blocks = [
+ FlaxLlamaDecoderLayer(self.config, dtype=self.dtype, name=str(i))
+ for i in range(self.config.num_hidden_layers)
+ ]
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_ids=None,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = False,
+ ):
+ all_attentions = () if output_attentions else None
+ all_hidden_states = () if output_hidden_states else None
+
+ for block in self.blocks:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ layer_outputs = block(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ )
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions += (layer_outputs[1],)
+
+ # this contains possible `None` values - `FlaxLlamaModule` will filter them out
+ outputs = (hidden_states, all_hidden_states, all_attentions)
+
+ return outputs
+
+
+class FlaxLlamaModule(nn.Module):
+ config: LlamaConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.hidden_size = self.config.hidden_size
+ embedding_init = jax.nn.initializers.normal(stddev=self.config.initializer_range)
+ self.embed_tokens = nn.Embed(
+ self.config.vocab_size,
+ self.hidden_size,
+ embedding_init=embedding_init,
+ dtype=self.dtype,
+ )
+ self.layers = FlaxLlamaLayerCollection(self.config, dtype=self.dtype)
+ self.norm = FlaxLlamaRMSNorm(self.config, dtype=self.dtype)
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask=None,
+ position_ids=None,
+ deterministic=True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ):
+ input_embeds = self.embed_tokens(input_ids.astype("i4"))
+
+ outputs = self.layers(
+ input_embeds,
+ position_ids=position_ids,
+ attention_mask=attention_mask,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ hidden_states = self.norm(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = outputs[1] + (hidden_states,)
+ outputs = (hidden_states, all_hidden_states) + outputs[2:]
+ else:
+ outputs = (hidden_states,) + outputs[1:]
+
+ if not return_dict:
+ return tuple(v for v in outputs if v is not None)
+
+ return FlaxBaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=outputs[1],
+ attentions=outputs[-1],
+ )
+
+
+@add_start_docstrings(
+ "The bare Llama Model transformer outputting raw hidden-states without any specific head on top.",
+ LLAMA_START_DOCSTRING,
+)
+class FlaxLlamaModel(FlaxLlamaPreTrainedModel):
+ module_class = FlaxLlamaModule
+
+
+append_call_sample_docstring(
+ FlaxLlamaModel,
+ _CHECKPOINT_FOR_DOC,
+ FlaxBaseModelOutput,
+ _CONFIG_FOR_DOC,
+ real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
+)
+
+
+class FlaxLlamaForCausalLMModule(nn.Module):
+ config: LlamaConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.model = FlaxLlamaModule(self.config, dtype=self.dtype)
+ self.lm_head = nn.Dense(
+ self.config.vocab_size,
+ use_bias=False,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
+ )
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask=None,
+ position_ids=None,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ):
+ outputs = self.model(
+ input_ids,
+ position_ids=position_ids,
+ attention_mask=attention_mask,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ lm_logits = self.lm_head(hidden_states)
+
+ if not return_dict:
+ return (lm_logits,) + outputs[1:]
+
+ return FlaxCausalLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
+
+
+@add_start_docstrings(
+ """
+ The Llama Model transformer with a language modeling head (linear layer) on top.
+ """,
+ LLAMA_START_DOCSTRING,
+)
+# Copied from transformers.models.gptj.modeling_flax_gptj.FlaxGPTJForCausalLM with GPTJ->Llama
+class FlaxLlamaForCausalLM(FlaxLlamaPreTrainedModel):
+ module_class = FlaxLlamaForCausalLMModule
+
+ def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
+ # initializing the cache
+ batch_size, seq_length = input_ids.shape
+
+ past_key_values = self.init_cache(batch_size, max_length)
+ # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
+ # But since Llama uses a causal mask, those positions are masked anyways.
+ # Thus we can create a single static attention_mask here, which is more efficient for compilation
+ extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
+ if attention_mask is not None:
+ position_ids = attention_mask.cumsum(axis=-1) - 1
+ extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
+ else:
+ position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
+
+ return {
+ "past_key_values": past_key_values,
+ "attention_mask": extended_attention_mask,
+ "position_ids": position_ids,
+ }
+
+ def update_inputs_for_generation(self, model_outputs, model_kwargs):
+ model_kwargs["past_key_values"] = model_outputs.past_key_values
+ model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
+ return model_kwargs
+
+
+append_call_sample_docstring(
+ FlaxLlamaForCausalLM,
+ _CHECKPOINT_FOR_DOC,
+ FlaxCausalLMOutput,
+ _CONFIG_FOR_DOC,
+ real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
+)
+
+
+__all__ = ["FlaxLlamaForCausalLM", "FlaxLlamaModel", "FlaxLlamaPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/modeling_llama.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/modeling_llama.py
new file mode 100644
index 0000000000000000000000000000000000000000..0facccb23704fd52233f7b89bdcbcbd61ac16037
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/modeling_llama.py
@@ -0,0 +1,511 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Callable, Optional, Union
+
+import torch
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...masking_utils import create_causal_mask
+from ...modeling_layers import (
+ GenericForQuestionAnswering,
+ GenericForSequenceClassification,
+ GenericForTokenClassification,
+ GradientCheckpointingLayer,
+)
+from ...modeling_outputs import (
+ BaseModelOutputWithPast,
+ CausalLMOutputWithPast,
+)
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import check_model_inputs
+from .configuration_llama import LlamaConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class LlamaRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ LlamaRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class LlamaRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: LlamaConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class LlamaMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+ return down_proj
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class LlamaAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: LlamaConfig, layer_idx: int):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = config.attention_dropout
+ self.is_causal = True
+
+ self.q_proj = nn.Linear(
+ config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.k_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.v_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.o_proj = nn.Linear(
+ config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+ )
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> tuple[torch.Tensor, torch.Tensor]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class LlamaDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: LlamaConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = LlamaAttention(config=config, layer_idx=layer_idx)
+
+ self.mlp = LlamaMLP(config)
+ self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> torch.Tensor:
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+ # Self Attention
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+ return hidden_states
+
+
+@auto_docstring
+class LlamaPreTrainedModel(PreTrainedModel):
+ config: LlamaConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["LlamaDecoderLayer"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ _can_compile_fullgraph = True
+ _supports_attention_backend = True
+ _can_record_outputs = {
+ "hidden_states": LlamaDecoderLayer,
+ "attentions": LlamaAttention,
+ }
+
+
+@auto_docstring
+class LlamaModel(LlamaPreTrainedModel):
+ def __init__(self, config: LlamaConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.rotary_emb = LlamaRotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> BaseModelOutputWithPast:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache(config=self.config)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position: torch.Tensor = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = create_causal_mask(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ hidden_states = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+
+ hidden_states = self.norm(hidden_states)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+@auto_docstring
+class LlamaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+ _tp_plan = {"lm_head": "colwise_rep"}
+ _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = LlamaModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> CausalLMOutputWithPast:
+ r"""
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
+
+ >>> model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
+ >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ outputs: BaseModelOutputWithPast = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+class LlamaForSequenceClassification(GenericForSequenceClassification, LlamaPreTrainedModel): ...
+
+
+class LlamaForQuestionAnswering(GenericForQuestionAnswering, LlamaPreTrainedModel):
+ base_model_prefix = "transformer" # For BC, where `transformer` was used instead of `model`
+
+
+class LlamaForTokenClassification(GenericForTokenClassification, LlamaPreTrainedModel): ...
+
+
+__all__ = [
+ "LlamaForCausalLM",
+ "LlamaModel",
+ "LlamaPreTrainedModel",
+ "LlamaForSequenceClassification",
+ "LlamaForQuestionAnswering",
+ "LlamaForTokenClassification",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/tokenization_llama.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/tokenization_llama.py
new file mode 100644
index 0000000000000000000000000000000000000000..971c7c49ddafbc3346987a3c7d704f9607a28cdf
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/tokenization_llama.py
@@ -0,0 +1,415 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tokenization classes for LLaMA."""
+
+import os
+from shutil import copyfile
+from typing import TYPE_CHECKING, Any, Optional
+
+import sentencepiece as spm
+
+from ...convert_slow_tokenizer import import_protobuf
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...utils import logging
+from ...utils.import_utils import requires
+
+
+if TYPE_CHECKING:
+ from ...tokenization_utils_base import TextInput
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
+
+SPIECE_UNDERLINE = "▁"
+
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<>\n", "\n< >\n\n"
+
+DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
+answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
+ that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
+correct. If you don't know the answer to a question, please don't share false information.""" # fmt: skip
+
+
+@requires(backends=("sentencepiece",))
+class LlamaTokenizer(PreTrainedTokenizer):
+ """
+ Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
+ no padding token in the original model.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+ eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `" "`):
+ The end of sequence token.
+ pad_token (`str` or `tokenizers.AddedToken`, *optional*):
+ A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
+ attention mechanisms or loss computation.
+ sp_model_kwargs (`dict[str, Any]`, `Optional`, *optional*):
+ Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+ SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+ to set:
+
+ - `enable_sampling`: Enable subword regularization.
+ - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+ - `nbest_size = {0,1}`: No sampling is performed.
+ - `nbest_size > 1`: samples from the nbest_size results.
+ - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+ using forward-filtering-and-backward-sampling algorithm.
+
+ - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+ BPE-dropout.
+
+ add_bos_token (`bool`, *optional*, defaults to `True`):
+ Whether or not to add an `bos_token` at the start of sequences.
+ add_eos_token (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an `eos_token` at the end of sequences.
+ clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+ Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+ extra spaces.
+ use_default_system_prompt (`bool`, *optional*, defaults to `False`):
+ Whether or not the default system prompt for Llama should be used.
+ spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not to add spaces between special tokens.
+ legacy (`bool`, *optional*):
+ Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
+ and #25224 which includes fixes to properly handle tokens that appear after special tokens.
+ Make sure to also set `from_slow` to `True`.
+ A simple example:
+
+ - `legacy=True`:
+ ```python
+ >>> from transformers import LlamaTokenizerFast
+
+ >>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=True, from_slow=True)
+ >>> tokenizer.encode("Hello .") # 869 is '▁.'
+ [1, 15043, 29871, 1, 869]
+ ```
+ - `legacy=False`:
+ ```python
+ >>> from transformers import LlamaTokenizerFast
+
+ >>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=False, from_slow=True)
+ >>> tokenizer.encode("Hello .") # 29889 is '.'
+ [1, 15043, 29871, 1, 29889]
+ ```
+ Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
+ add_prefix_space (`bool`, *optional*, defaults to `True`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. Again, this should be set with `from_slow=True` to make sure it's taken into account.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ def __init__(
+ self,
+ vocab_file,
+ unk_token="",
+ bos_token="",
+ eos_token=" ",
+ pad_token=None,
+ sp_model_kwargs: Optional[dict[str, Any]] = None,
+ add_bos_token=True,
+ add_eos_token=False,
+ clean_up_tokenization_spaces=False,
+ use_default_system_prompt=False,
+ spaces_between_special_tokens=False,
+ legacy=None,
+ add_prefix_space=True,
+ **kwargs,
+ ):
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+ bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
+ eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
+ unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
+
+ if legacy is None:
+ logger.warning_once(
+ f"You are using the default legacy behaviour of the {self.__class__}. This is"
+ " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
+ " If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
+ " means, and thoroughly read the reason why this was added as explained in"
+ " https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file"
+ " you can ignore this message"
+ )
+ legacy = True
+
+ self.legacy = legacy
+ self.vocab_file = vocab_file
+ self.add_bos_token = add_bos_token
+ self.add_eos_token = add_eos_token
+ self.use_default_system_prompt = use_default_system_prompt
+ self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
+ self.add_prefix_space = add_prefix_space
+
+ super().__init__(
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ add_bos_token=add_bos_token,
+ add_eos_token=add_eos_token,
+ sp_model_kwargs=self.sp_model_kwargs,
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+ use_default_system_prompt=use_default_system_prompt,
+ spaces_between_special_tokens=spaces_between_special_tokens,
+ legacy=legacy,
+ add_prefix_space=add_prefix_space,
+ **kwargs,
+ )
+
+ @property
+ def unk_token_length(self):
+ return len(self.sp_model.encode(str(self.unk_token)))
+
+ # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
+ def get_spm_processor(self, from_slow=False):
+ tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ if self.legacy or from_slow: # no dependency on protobuf
+ tokenizer.Load(self.vocab_file)
+ return tokenizer
+
+ with open(self.vocab_file, "rb") as f:
+ sp_model = f.read()
+ model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
+ model = model_pb2.ModelProto.FromString(sp_model)
+ normalizer_spec = model_pb2.NormalizerSpec()
+ normalizer_spec.add_dummy_prefix = False
+ model.normalizer_spec.MergeFrom(normalizer_spec)
+ sp_model = model.SerializeToString()
+ tokenizer.LoadFromSerializedProto(sp_model)
+ return tokenizer
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+ state["sp_model"] = None
+ state["sp_model_proto"] = self.sp_model.serialized_model_proto()
+ return state
+
+ def __setstate__(self, d):
+ self.__dict__.update(d)
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+ @property
+ def vocab_size(self):
+ """Returns vocab size"""
+ return self.sp_model.get_piece_size()
+
+ def get_vocab(self):
+ """Returns vocab as a dict"""
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
+ def tokenize(self, text: "TextInput", **kwargs) -> list[str]:
+ """
+ Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
+ first token is special.
+ """
+ if self.legacy or len(text) == 0:
+ return super().tokenize(text, **kwargs)
+
+ text = text.replace(SPIECE_UNDERLINE, " ")
+ if self.add_prefix_space:
+ text = SPIECE_UNDERLINE + text
+
+ tokens = super().tokenize(text, **kwargs)
+
+ if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
+ tokens = tokens[1:]
+ return tokens
+
+ # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
+ def _tokenize(self, text, **kwargs):
+ """
+ Returns a tokenized string.
+
+ We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
+ SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
+ `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
+ `unk_token`. Here is an example with `unk_token = ""` and `unk_token_length = 4`.
+ `self.tokenizer.sp_model.encode(" Hey", out_type = str)[4:]`.
+ """
+ if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
+ return self.sp_model.encode(text, out_type=str)
+
+ # 1. Encode string + prefix ex: " Hey"
+ tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
+ # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
+ return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.sp_model.piece_to_id(token)
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ token = self.sp_model.IdToPiece(index)
+ return token
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ # since we manually add the prefix space, we have to remove it when decoding
+ if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
+ tokens[0] = tokens[0][1:]
+
+ current_sub_tokens = []
+ out_string = ""
+ prev_is_special = False
+ for i, token in enumerate(tokens):
+ # make sure that special tokens are not decoded using sentencepiece model
+ if token in self.all_special_tokens:
+ if not prev_is_special and i != 0 and self.legacy:
+ out_string += " "
+ out_string += self.sp_model.decode(current_sub_tokens) + token
+ prev_is_special = True
+ current_sub_tokens = []
+ else:
+ if prev_is_special and i == 1 and self.add_prefix_space and not token.startswith(SPIECE_UNDERLINE):
+ out_string += " "
+ current_sub_tokens.append(token)
+ prev_is_special = False
+ out_string += self.sp_model.decode(current_sub_tokens)
+ return out_string
+
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> tuple[str]:
+ """
+ Save the vocabulary and special tokens file to a directory.
+
+ Args:
+ save_directory (`str`):
+ The directory in which to save the vocabulary.
+
+ Returns:
+ `Tuple(str)`: Paths to the files saved.
+ """
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+ elif not os.path.isfile(self.vocab_file):
+ with open(out_vocab_file, "wb") as fi:
+ content_spiece_model = self.sp_model.serialized_model_proto()
+ fi.write(content_spiece_model)
+
+ return (out_vocab_file,)
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+ output = bos_token_id + token_ids_0 + eos_token_id
+
+ if token_ids_1 is not None:
+ output = output + bos_token_id + token_ids_1 + eos_token_id
+
+ return output
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ bos_token_id = [1] if self.add_bos_token else []
+ eos_token_id = [1] if self.add_eos_token else []
+
+ if token_ids_1 is None:
+ return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
+ return (
+ bos_token_id
+ + ([0] * len(token_ids_0))
+ + eos_token_id
+ + bos_token_id
+ + ([0] * len(token_ids_1))
+ + eos_token_id
+ )
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
+ sequence pair mask has the following format:
+
+ ```
+ 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+ | first sequence | second sequence |
+ ```
+
+ if token_ids_1 is None, only returns the first portion of the mask (0s).
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of ids.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+ """
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+ output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
+
+ if token_ids_1 is not None:
+ output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
+
+ return output
+
+
+__all__ = ["LlamaTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/tokenization_llama_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/tokenization_llama_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..212e65404e827e9d91a779e56cab5af7d2c97d34
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama/tokenization_llama_fast.py
@@ -0,0 +1,251 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+from shutil import copyfile
+from typing import Optional
+
+from tokenizers import processors
+
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import is_sentencepiece_available, logging
+
+
+if is_sentencepiece_available():
+ from .tokenization_llama import LlamaTokenizer
+else:
+ LlamaTokenizer = None
+
+logger = logging.get_logger(__name__)
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}
+
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<>\n", "\n< >\n\n"
+
+# fmt: off
+DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
+answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
+ that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
+correct. If you don't know the answer to a question, please don't share false information."""
+# fmt: on
+
+
+class LlamaTokenizerFast(PreTrainedTokenizerFast):
+ """
+ Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+ This uses notably ByteFallback and no normalization.
+
+ ```python
+ >>> from transformers import LlamaTokenizerFast
+
+ >>> tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
+ >>> tokenizer.encode("Hello this is a test")
+ [1, 15043, 445, 338, 263, 1243]
+ ```
+
+ If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
+ call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
+ values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
+ [post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.
+
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`, *optional*):
+ [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
+ contains the vocabulary necessary to instantiate a tokenizer.
+ tokenizer_file (`str`, *optional*):
+ [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
+ contains everything needed to load the tokenizer.
+ clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+ Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+ extra spaces.
+ unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+ eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `" "`):
+ The end of sequence token.
+ add_bos_token (`bool`, *optional*, defaults to `True`):
+ Whether or not to add an `bos_token` at the start of sequences.
+ add_eos_token (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an `eos_token` at the end of sequences.
+ use_default_system_prompt (`bool`, *optional*, defaults to `False`):
+ Whether or not the default system prompt for Llama should be used
+ legacy (`bool`, *optional*):
+ Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
+ and #25224 which includes fixes to properly handle tokens that appear after special tokens.
+ Make sure to also set `from_slow` to `True`.
+ A simple example:
+
+ - `legacy=True`:
+ ```python
+ >>> from transformers import LlamaTokenizerFast
+
+ >>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=True, from_slow=True)
+ >>> tokenizer.encode("Hello .") # 869 is '▁.'
+ [1, 15043, 29871, 1, 869]
+ ```
+ - `legacy=False`:
+ ```python
+ >>> from transformers import LlamaTokenizerFast
+
+ >>> tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b", legacy=False, from_slow=True)
+ >>> tokenizer.encode("Hello .") # 29889 is '.'
+ [1, 15043, 29871, 1, 29889]
+ ```
+ Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
+ add_prefix_space (`bool`, *optional*):
+ Whether or not the tokenizer should automatically add a prefix space
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ slow_tokenizer_class = LlamaTokenizer
+ padding_side = "left"
+ model_input_names = ["input_ids", "attention_mask"]
+
+ def __init__(
+ self,
+ vocab_file=None,
+ tokenizer_file=None,
+ clean_up_tokenization_spaces=False,
+ unk_token="",
+ bos_token="",
+ eos_token=" ",
+ add_bos_token=True,
+ add_eos_token=False,
+ use_default_system_prompt=False,
+ legacy=None,
+ add_prefix_space=None,
+ **kwargs,
+ ):
+ if legacy is None:
+ logger.warning_once(
+ f"You are using the default legacy behaviour of the {self.__class__}. This is"
+ " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
+ " If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
+ " means, and thoroughly read the reason why this was added as explained in"
+ " https://github.com/huggingface/transformers/pull/24565 - if you loaded a llama tokenizer from a GGUF file"
+ " you can ignore this message."
+ )
+ legacy = True
+ self.legacy = legacy
+
+ if add_prefix_space is not None:
+ kwargs["from_slow"] = True
+
+ super().__init__(
+ vocab_file=vocab_file,
+ tokenizer_file=tokenizer_file,
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+ unk_token=unk_token,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ add_bos_token=add_bos_token,
+ add_eos_token=add_eos_token,
+ use_default_system_prompt=use_default_system_prompt,
+ add_prefix_space=add_prefix_space,
+ legacy=legacy,
+ **kwargs,
+ )
+ self._add_bos_token = add_bos_token
+ self._add_eos_token = add_eos_token
+ self.update_post_processor()
+ self.use_default_system_prompt = use_default_system_prompt
+ self.vocab_file = vocab_file
+
+ def update_post_processor(self):
+ """
+ Updates the underlying post processor with the current `bos_token` and `eos_token`.
+ """
+ bos = self.bos_token
+ bos_token_id = self.bos_token_id
+ if bos is None and self.add_bos_token:
+ raise ValueError("add_bos_token = True but bos_token = None")
+
+ eos = self.eos_token
+ eos_token_id = self.eos_token_id
+ if eos is None and self.add_eos_token:
+ raise ValueError("add_eos_token = True but eos_token = None")
+
+ single = f"{(bos + ':0 ') if self.add_bos_token else ''}$A:0{(' ' + eos + ':0') if self.add_eos_token else ''}"
+ pair = f"{single}{(' ' + bos + ':1') if self.add_bos_token else ''} $B:1{(' ' + eos + ':1') if self.add_eos_token else ''}"
+
+ special_tokens = []
+ if self.add_bos_token:
+ special_tokens.append((bos, bos_token_id))
+ if self.add_eos_token:
+ special_tokens.append((eos, eos_token_id))
+ self._tokenizer.post_processor = processors.TemplateProcessing(
+ single=single, pair=pair, special_tokens=special_tokens
+ )
+
+ @property
+ def add_eos_token(self):
+ return self._add_eos_token
+
+ @property
+ def add_bos_token(self):
+ return self._add_bos_token
+
+ @add_eos_token.setter
+ def add_eos_token(self, value):
+ self._add_eos_token = value
+ self.update_post_processor()
+
+ @add_bos_token.setter
+ def add_bos_token(self, value):
+ self._add_bos_token = value
+ self.update_post_processor()
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not self.can_save_slow_tokenizer:
+ raise ValueError(
+ "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
+ "tokenizer."
+ )
+
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+
+ return (out_vocab_file,)
+
+ # TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
+ # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.build_inputs_with_special_tokens
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+ output = bos_token_id + token_ids_0 + eos_token_id
+
+ if token_ids_1 is not None:
+ output = output + bos_token_id + token_ids_1 + eos_token_id
+
+ return output
+
+
+__all__ = ["LlamaTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..59fe1686cec1bbad80233353820eb1517e7bb27b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_llama4 import *
+ from .image_processing_llama4_fast import *
+ from .modeling_llama4 import *
+ from .processing_llama4 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/configuration_llama4.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/configuration_llama4.py
new file mode 100644
index 0000000000000000000000000000000000000000..d74107c2610a517e7fe52a85d5bf17bdb8d43712
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/configuration_llama4.py
@@ -0,0 +1,466 @@
+# coding=utf-8
+# Copyright 2025 The LLAMA4 and HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from ...configuration_utils import PretrainedConfig, layer_type_validation
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Llama4VisionConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Llama4VisionModel`]. It is used to instantiate a
+ Llama4 vision model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Llama4 109B.
+
+ e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimensionality of the encoder layers and the pooler layer.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
+ num_hidden_layers (`int`, *optional*, defaults to 34):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_channels (`int`, *optional*, defaults to 3):
+ Number of channels in the input image.
+ intermediate_size (`int`, *optional*, defaults to 5632):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ vision_output_dim (`int`, *optional*, defaults to 7680):
+ Dimensionality of the vision model output. Includes output of transformer
+ encoder with intermediate layers and global transformer encoder.
+ image_size (`int`, *optional*, defaults to 448):
+ The size (resolution) of each image *tile*.
+ patch_size (`int`, *optional*, defaults to 14):
+ The size (resolution) of each patch.
+ norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the layer normalization layers.
+ vision_feature_layer (``, *optional*, defaults to -1): TODO
+ vision_feature_select_strategy (`int`, *optional*, defaults to `"default"`): TODO
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ pixel_shuffle_ratio (`int`, *optional*, defaults to 0.5): TODO
+ projector_input_dim (`int`, *optional*, defaults to 4096): TODO
+ projector_output_dim (`int`, *optional*, defaults to 4096): TODO
+ multi_modal_projector_bias (`int`, *optional*, defaults to `False`): TODO
+ projector_dropout (`int`, *optional*, defaults to 0.0): TODO
+ attention_dropout (`int`, *optional*, defaults to 0.0): TODO
+ rope_theta (`int`, *optional*, defaults to 10000): TODO
+ """
+
+ base_model_tp_plan = {
+ "model.layers.*.self_attn.q_proj": "colwise",
+ "model.layers.*.self_attn.k_proj": "colwise",
+ "model.layers.*.self_attn.v_proj": "colwise",
+ "model.layers.*.self_attn.o_proj": "rowwise",
+ "vision_adapter.mlp.fc1": "colwise",
+ "vision_adapter.mlp.fc2": "rowwise",
+ "patch_embedding.linear": "colwise_rep",
+ }
+ model_type = "llama4_vision_model"
+ base_config_key = "vision_config"
+
+ def __init__(
+ self,
+ hidden_size: int = 768,
+ hidden_act: str = "gelu",
+ num_hidden_layers: int = 34,
+ num_attention_heads: int = 16,
+ num_channels: int = 3,
+ intermediate_size: int = 5632,
+ vision_output_dim: int = 7680,
+ image_size: int = 448,
+ patch_size: int = 14,
+ norm_eps: float = 1e-5,
+ vision_feature_layer=-1,
+ vision_feature_select_strategy="default",
+ initializer_range: float = 0.02,
+ pixel_shuffle_ratio=0.5,
+ projector_input_dim=4096,
+ projector_output_dim=4096,
+ multi_modal_projector_bias=False,
+ projector_dropout=0.0,
+ attention_dropout=0.0,
+ rope_theta=10000,
+ **kwargs,
+ ):
+ self.hidden_size = hidden_size
+ self.hidden_act = hidden_act
+ self.num_hidden_layers = num_hidden_layers
+ self.num_channels = num_channels
+ self.intermediate_size = intermediate_size
+ self.image_size = image_size
+ self.vision_output_dim = vision_output_dim
+ self.patch_size = patch_size
+ self.norm_eps = norm_eps
+ self.num_attention_heads = num_attention_heads
+ self.initializer_range = initializer_range
+ self.pixel_shuffle_ratio = pixel_shuffle_ratio
+ self.projector_input_dim = projector_input_dim
+ self.projector_output_dim = projector_output_dim
+ self.multi_modal_projector_bias = multi_modal_projector_bias
+ self.projector_dropout = projector_dropout
+ self.attention_dropout = attention_dropout
+ self.vision_feature_layer = vision_feature_layer
+ self.vision_feature_select_strategy = vision_feature_select_strategy
+ self.rope_theta = rope_theta
+ super().__init__(**kwargs)
+
+
+class Llama4TextConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Llama4TextModel`]. It is used to instantiate a
+ Llama4 text model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Llama4 109B.
+
+ e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 202048):
+ Vocabulary size of the Llama4 text model. Defines the maximum number of different tokens that can be represented
+ by the `inputs_ids` passed when calling [`Llama4TextModel`].
+ hidden_size (`int`, *optional*, defaults to 5120):
+ Dimensionality of the embeddings and hidden states.
+ intermediate_size (`int`, *optional*, defaults to 8192):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ intermediate_size_mlp (`int`, *optional*, defaults to 16384): TODO
+ num_hidden_layers (`int`, *optional*, defaults to 48):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 40):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_key_value_heads (`int`, *optional*, defaults to 8):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If not
+ specified, will default to `num_attention_heads`.
+ head_dim (`int`, *optional*, defaults to 128): TODO
+ hidden_act (`str` or `Callable`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the encoder and pooler.
+ max_position_embeddings (`int`, *optional*, defaults to 131072):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions.
+ pad_token_id (`int`, *optional*, defaults to 128004):
+ The id of the padding token.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ The id of the beginning of sentence token.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ The id of the end of sentence token.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to `500000.0`):
+ The base period of the RoPE embeddings.
+ attention_dropout (`int`, *optional*, defaults to 0.0): TODO
+ num_experts_per_tok (`int`, *optional*, defaults to 1): TODO
+ num_local_experts (`int`, *optional*, defaults to 16): TODO
+ moe_layers (`int`, *optional*): TODO
+ interleave_moe_layer_step (`int`, *optional*, defaults to 1): TODO
+ use_qk_norm (`int`, *optional*, defaults to `True`): TODO
+ output_router_logits (`int`, *optional*, defaults to `False`): TODO
+ router_aux_loss_coef (`int`, *optional*, defaults to 0.001): TODO
+ router_jitter_noise (`int`, *optional*, defaults to 0.0): TODO
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+ and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+ accordingly.
+ Expected contents:
+ `rope_type` (`str`):
+ The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+ 'llama3'], with 'default' being the original RoPE implementation.
+ `factor` (`float`, *optional*):
+ Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+ most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+ original maximum pre-trained length.
+ `original_max_position_embeddings` (`int`, *optional*):
+ Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+ pretraining.
+ `attention_factor` (`float`, *optional*):
+ Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+ computation. If unspecified, it defaults to value recommended by the implementation, using the
+ `factor` field to infer the suggested value.
+ `beta_fast` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+ ramp function. If unspecified, it defaults to 32.
+ `beta_slow` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+ ramp function. If unspecified, it defaults to 1.
+ `short_factor` (`list[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+ size divided by the number of attention heads divided by 2
+ `long_factor` (`list[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+ size divided by the number of attention heads divided by 2
+ `low_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+ `high_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+
+
+ no_rope_layers (`list[int]`, *optional*):
+ List with at least the same length as the number of layers in the model.
+ A `1` at an index position indicates that the corresponding layer will use RoPE,
+ while a `0` indicates that it's a NoPE layer.
+ no_rope_layer_interval (`int`, *optional*, defaults to 4):
+ If `no_rope_layers` is `None`, it will be created using a NoPE layer every
+ `no_rope_layer_interval` layers.
+ attention_chunk_size (`int`, *optional*, defaults to 8192):
+
+ layer_types (`list`, *optional*):
+ Attention pattern for each layer.
+ attn_temperature_tuning (`bool`, *optional*, defaults to `True`):
+ Whether to dynamically scale the attention temperature for each query token based on sequence length.
+ Recommended for long sequences (e.g., >32k tokens) to maintain stable output results.
+ floor_scale (`int`, *optional*, defaults to 8192): TODO
+ attn_scale (`int`, *optional*, defaults to 0.1): TODO
+
+ Example:
+ """
+
+ model_type = "llama4_text"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ base_model_tp_plan = {
+ "layers.*.self_attn.q_proj": "colwise",
+ "layers.*.self_attn.k_proj": "colwise",
+ "layers.*.self_attn.v_proj": "colwise",
+ "layers.*.self_attn.o_proj": "rowwise",
+ "layers.*.feed_forward.shared_expert.gate_proj": "local_colwise",
+ "layers.*.feed_forward.shared_expert.up_proj": "local_colwise",
+ "layers.*.feed_forward.shared_expert.down_proj": "local_rowwise",
+ "layers.*.feed_forward.experts.gate_up_proj": "local_packed_rowwise", # row because not linear
+ "layers.*.feed_forward.experts.down_proj": "local_colwise", # col because not linear
+ "layers.*.feed_forward.experts": "local",
+ "layers.*.feed_forward.gate_proj": "local_colwise",
+ "layers.*.feed_forward.up_proj": "local_colwise",
+ "layers.*.feed_forward.down_proj": "local_rowwise",
+ "layers.*.feed_forward": "gather",
+ }
+ base_model_ep_plan = {
+ "layers.*.self_attn.q_proj": "colwise",
+ "layers.*.self_attn.k_proj": "colwise",
+ "layers.*.self_attn.v_proj": "colwise",
+ "layers.*.self_attn.o_proj": "rowwise",
+ "layers.*.feed_forward.experts.gate_up_proj": "grouped_gemm", # row because not linear
+ "layers.*.feed_forward.experts.down_proj": "grouped_gemm", # col because not linear
+ "layers.*.feed_forward.experts": "gather", # all reduce
+ "layers.*.feed_forward.gate_proj": "local_colwise",
+ "layers.*.feed_forward.up_proj": "local_colwise",
+ "layers.*.feed_forward.down_proj": "local_rowwise",
+ "layers.*.feed_forward.router": "ep_router",
+ }
+
+ def __init__(
+ self,
+ vocab_size=202048,
+ hidden_size=5120,
+ intermediate_size=8192,
+ intermediate_size_mlp=16384,
+ num_hidden_layers=48,
+ num_attention_heads=40,
+ num_key_value_heads=8,
+ head_dim=128,
+ hidden_act="silu",
+ max_position_embeddings=4096 * 32,
+ initializer_range=0.02,
+ rms_norm_eps=1e-5,
+ use_cache=True,
+ pad_token_id=None,
+ bos_token_id=1,
+ eos_token_id=2,
+ tie_word_embeddings=False,
+ rope_theta=500000,
+ attention_dropout=0.0,
+ num_experts_per_tok=1,
+ num_local_experts=16,
+ moe_layers=None,
+ interleave_moe_layer_step=1,
+ use_qk_norm=True,
+ output_router_logits=False,
+ router_aux_loss_coef=0.001,
+ router_jitter_noise=0.0,
+ rope_scaling=None,
+ no_rope_layers=None,
+ no_rope_layer_interval=4,
+ attention_chunk_size=8192,
+ layer_types=None,
+ attn_temperature_tuning=True,
+ floor_scale=8192,
+ attn_scale=0.1,
+ **kwargs,
+ ):
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+ self.attn_temperature_tuning = attn_temperature_tuning
+ self.attn_scale = attn_scale
+ self.floor_scale = floor_scale
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.intermediate_size_mlp = intermediate_size_mlp
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.rope_scaling = rope_scaling
+ self.attention_bias = False
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.attention_dropout = attention_dropout
+ self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
+ self.use_qk_norm = use_qk_norm
+
+ self.num_experts_per_tok = num_experts_per_tok
+ self.num_local_experts = num_local_experts
+
+ self.output_router_logits = output_router_logits
+ self.router_aux_loss_coef = router_aux_loss_coef
+ self.router_jitter_noise = router_jitter_noise
+
+ # Backwards compatibility
+ if no_rope_layers == []:
+ no_rope_layers = None
+
+ default_no_rope_layers = [
+ int((layer_idx + 1) % no_rope_layer_interval != 0) for layer_idx in range(self.num_hidden_layers)
+ ]
+
+ self.no_rope_layers = no_rope_layers if no_rope_layers else default_no_rope_layers
+
+ self.interleave_moe_layer_step = interleave_moe_layer_step
+ self.moe_layers = (
+ moe_layers
+ if moe_layers is not None
+ else list(range(interleave_moe_layer_step - 1, num_hidden_layers, interleave_moe_layer_step))
+ )
+ self.attention_chunk_size = attention_chunk_size
+
+ self.layer_types = layer_types
+ if layer_types is None:
+ self.layer_types = [
+ "chunked_attention" if no_rope else "full_attention" for no_rope in self.no_rope_layers
+ ]
+ layer_type_validation(self.layer_types)
+
+
+class Llama4Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Llama4Model`]. It is used to instantiate an
+ Llama4 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Llama4 109B.
+
+ e.g. [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vision_config (`Llama4VisionConfig`, *optional*):
+ The Llama4 Vision config.
+ text_config (`Llama4TextConfig`, *optional*):
+ The Llama4 Text config.
+ boi_token_index (`int`, *optional*, defaults to 200080):
+ The begin-of-image token index to wrap the image prompt.
+ eoi_token_index (`int`, *optional*, defaults to 200081):
+ The end-of-image token index to wrap the image prompt.
+ image_token_index (`int`, *optional*, defaults to 200092):
+ The image token index to encode the image prompt.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether the model's input and output word embeddings should be tied.
+
+ ```python
+ >>> from transformers import Llama4Model, Llama4Config
+
+ >>> # Initializing a Llama4 7B style configuration
+ >>> configuration = Llama4Config()
+
+ >>> # Initializing a model from the Llama4 7B style configuration
+ >>> model = Llama4Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "llama4"
+ attribute_map = {
+ "image_token_id": "image_token_index",
+ "boi_token_id": "boi_token_index",
+ "eoi_token_id": "eoi_token_index",
+ }
+ sub_configs = {"text_config": Llama4TextConfig, "vision_config": Llama4VisionConfig}
+ base_model_tp_plan = {
+ "multi_modal_projector.linear_1": "colwise_rep",
+ }
+
+ def __init__(
+ self,
+ vision_config=None,
+ text_config=None,
+ boi_token_index=200080,
+ eoi_token_index=200081,
+ image_token_index=200092,
+ tie_word_embeddings=False,
+ **kwargs,
+ ):
+ if vision_config is None:
+ self.vision_config = Llama4VisionConfig()
+ logger.info("vision_config is None, using default llama4 vision config")
+ elif isinstance(vision_config, dict):
+ self.vision_config = Llama4VisionConfig(**vision_config)
+ elif isinstance(vision_config, Llama4VisionConfig):
+ self.vision_config = vision_config
+
+ self.boi_token_index = boi_token_index
+ self.eoi_token_index = eoi_token_index
+ self.image_token_index = image_token_index
+ if text_config is None:
+ self.text_config = Llama4TextConfig()
+ logger.info("text_config is None, using default llama4 text config")
+ elif isinstance(text_config, dict):
+ self.text_config = Llama4TextConfig(**text_config)
+ elif isinstance(text_config, Llama4TextConfig):
+ self.text_config = text_config
+
+ super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+__all__ = ["Llama4Config", "Llama4TextConfig", "Llama4VisionConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/convert_llama4_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/convert_llama4_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bf105272a25471ddec1d1e1e0018b7e3d0f0853
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/convert_llama4_weights_to_hf.py
@@ -0,0 +1,743 @@
+import argparse
+import gc
+import io
+import json
+import os
+import re
+from typing import Optional
+
+import torch
+from tokenizers import AddedToken, processors
+from tqdm import tqdm
+
+from transformers import (
+ GenerationConfig,
+ Llama4Config,
+ Llama4ForConditionalGeneration,
+ Llama4ImageProcessorFast,
+ Llama4Processor,
+ Llama4TextConfig,
+ Llama4VisionConfig,
+ PreTrainedTokenizerFast,
+)
+from transformers.integrations.tiktoken import TikTokenConverter
+
+
+_OFFLINE_QUANT_COMPATIBLE = os.environ.get("OFFLINE_QUANT_COMPATIBLE", "0") == "1"
+
+torch.serialization.add_safe_globals([io.BytesIO])
+# fmt: off
+# `None` means we drop the key
+
+
+weight_postfix = ".weight" if _OFFLINE_QUANT_COMPATIBLE else ""
+ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
+ # CausalLM keys
+ r"output.weight": r"language_model.lm_head.weight",
+ r"\nnorm.weight": r"\nlanguage_model.model.norm.weight",
+ # Model keys
+ r"tok_embeddings.weight": r"language_model.model.embed_tokens.weight",
+ r"freq_cis": None,
+ r"rope.freqs": None,
+ r"layers.(\d+).attention_norm.weight": r"language_model.model.layers.\1.input_layernorm.weight",
+ r"layers.(\d+).attention.wqkv.layer_norm_weight": r"language_model.model.layers.\1.input_layernorm.weight",
+ r"layers.(\d+).feed_forward.norm.weight": r"language_model.model.layers.\1.post_attention_layernorm.weight",
+ r"layers.(\d+).attention.wo.weight": r"language_model.model.layers.\1.self_attn.o_proj.weight",
+ r"layers.(\d+).attention.wqkv.weight": r"language_model.model.layers.\1.self_attn.qkv_proj.weight",
+
+ # MoE keys: no simple MLPmodel.
+ r"layers.(\d+).feed_forward.experts.moe_w_in_eD_F": r"language_model.model.layers.\1.feed_forward.experts.gate_proj" + weight_postfix, # will be fused with up
+ r"layers.(\d+).feed_forward.experts.moe_w_out_eF_D": r"language_model.model.layers.\1.feed_forward.experts.down_proj" + weight_postfix, # expert win
+ r"layers.(\d+).feed_forward.experts.moe_w_swiglu_eD_F": r"language_model.model.layers.\1.feed_forward.experts.up_proj" + weight_postfix, # fused with up
+ r"layers.(\d+).feed_forward.router_DE": r"language_model.model.layers.\1.feed_forward.router.weight", # used for top
+ r"layers.(\d+).feed_forward.w_in_shared_FD": r"language_model.model.layers.\1.feed_forward.shared_expert.gate_proj", # might need to be fused for efficiency?
+ r"layers.(\d+).feed_forward.w_out_shared_DF": r"language_model.model.layers.\1.feed_forward.shared_expert.down_proj", # might need to be fused for efficiency?
+ r"layers.(\d+).feed_forward.w_swiglu_FD": r"language_model.model.layers.\1.feed_forward.shared_expert.up_proj", # might need to be fused for efficiency?
+ r"layers.(\d+).feed_forward.global_gate_stats_3E": None,
+ # Unused keys in load hooks (explicitly removed)
+ r'layers.(\d+).attention.wqkv._extra_state': None,
+ r'layers.(\d+).attention.wo._extra_state': None,
+ # Key apparently unused in base models
+ r'layers.(\d+).feed_forward.expert_activation_DE': None,
+
+ # MLP layer variant
+ r"layers.(\d+).feed_forward.w1.weight": r"language_model.model.layers.\1.feed_forward.gate_proj.weight", # might need to be fused for efficiency?
+ r"layers.(\d+).feed_forward.w3.weight": r"language_model.model.layers.\1.feed_forward.up_proj.weight", # might need to be fused for efficiency?
+ # r"layers.(\d+).feed_forward.mlp.fc1_weight": r"language_model.model.layers.\1.feed_forward.gate_up_proj.weight",
+ r"layers.(\d+).feed_forward.mlp.fc2_weight": r"language_model.model.layers.\1.feed_forward.down_proj.weight",
+ r"layers.(\d+).feed_forward.w2.weight": r"language_model.model.layers.\1.feed_forward.down_proj.weight",
+ r"layers.(\d+).feed_forward.mlp.layer_norm.weight": r"language_model.model.layers.\1.post_attention_layernorm.weight",
+
+ # Vision encoder mapping
+ r"vision_embeddings.vision_encoder.conv1._linear": r"vision_model.patch_embedding.linear",
+ r'vision_embeddings.vision_adapter.mlp.c_fc': r"vision_model.vision_adapter.mlp.fc1",
+ r'vision_embeddings.vision_adapter.mlp.c_proj': r"vision_model.vision_adapter.mlp.fc2",
+ r"vision_embeddings.vision_encoder.transformer.resblocks.(\d+).attn.wq.(weight|bias)": r"vision_model.model.layers.\1.self_attn.q_proj.\2",
+ r"vision_embeddings.vision_encoder.transformer.resblocks.(\d+).attn.wk.(weight|bias)": r"vision_model.model.layers.\1.self_attn.k_proj.\2",
+ r"vision_embeddings.vision_encoder.transformer.resblocks.(\d+).attn.wv.(weight|bias)": r"vision_model.model.layers.\1.self_attn.v_proj.\2",
+ r"vision_embeddings.vision_encoder.transformer.resblocks.(\d+).attn.wo.(weight|bias)": r"vision_model.model.layers.\1.self_attn.o_proj.\2",
+ r"vision_embeddings.vision_encoder.transformer.resblocks.(\d+).mlp.c_fc": r"vision_model.model.layers.\1.mlp.fc1",
+ r"vision_embeddings.vision_encoder.transformer.resblocks.(\d+).mlp.c_proj": r"vision_model.model.layers.\1.mlp.fc2",
+ r"vision_embeddings.vision_encoder.transformer.resblocks.(\d+).ln_1.(weight|bias)": r"vision_model.model.layers.\1.input_layernorm.\2",
+ r"vision_embeddings.vision_encoder.transformer.resblocks.(\d+).ln_2.(weight|bias)": r"vision_model.model.layers.\1.post_attention_layernorm.\2",
+ # r'vision_embeddings.vision_encoder.ln_(1|2).(weight|bias)': r'vision_model.transformer.vision_encoder.layernorm_\1.\2',
+ r'vision_embeddings.vision_encoder.ln_post': r'vision_model.layernorm_post',
+ r'vision_embeddings.vision_encoder.ln_pre': r'vision_model.layernorm_pre',
+ r'vision_embeddings.vision_encoder.class_embedding': r'vision_model.class_embedding',
+ r"vision_embeddings.vision_encoder.positional_embedding_vlm": r"vision_model.positional_embedding_vlm",
+ r"vision_embeddings.vision_encoder.(?=\w)": r"vision_model.model.",
+ r"vision_projection.weight": r"multi_modal_projector.linear_1.weight",
+}
+# fmt: on
+
+
+def convert_old_keys_to_new_keys(state_dict_keys: Optional[dict] = None):
+ """
+ This function should be applied only once, on the concatenated keys to efficiently rename using
+ the key mappings.
+ """
+ output_dict = {}
+ if state_dict_keys is not None:
+ old_text = "\n".join(state_dict_keys)
+ new_text = old_text
+ for pattern, replacement in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items():
+ if replacement is None:
+ new_text = re.sub(pattern, "", new_text) # an empty line
+ continue
+ new_text = re.sub(pattern, replacement, new_text)
+ output_dict = dict(zip(old_text.split("\n"), new_text.split("\n")))
+ return output_dict
+
+
+def permute_for_rope(input_tensor, n_heads, dim1, dim2):
+ """
+ When you go from the complex ROPE formulation to sin and cos one, you need
+ to permute the query and key weights (to avoid doing it on the fly)
+ """
+ input_tensor = input_tensor.view(n_heads, dim1 // n_heads // 2, 2, dim2)
+ input_tensor = input_tensor.transpose(1, 2).reshape(dim1, dim2)
+ return input_tensor
+
+
+def is_param_same_across_shards(key):
+ """
+ Return `False` if the parameter is different across checkpoint shards
+ and needs to be concatenated.
+ """
+ patterns = [
+ r"language_model.layers.(\d+).(.*)layernorm.weight",
+ r"language_model.norm.weight",
+ r"router.weight",
+ r"feed_forward.global_gate_stats",
+ # not all vision weights are sharded, some are repeated
+ r"vision_model.class_embedding",
+ r"vision_model.positional_embedding_vlm",
+ r"vision_embeddings.vision_encoder.positional_embedding_vlm",
+ r"vision_model.model.layers.(\d+).self_attn.o_proj.bias",
+ r"vision_model.model.layers.(\d+).input_layernorm",
+ r"vision_model.model.layers.(\d+).post_attention_layernorm",
+ r"vision_model.layernorm_pre",
+ r"vision_model.layernorm_post",
+ r"vision_model.model.layers.(\d+).mlp.fc2.bias",
+ r"norm.weight",
+ ] # fmt: skip
+ return any(re.search(pattern, key) for pattern in patterns)
+
+
+def get_concat_dim(key):
+ """
+ Return the dimension to concatenate the weights on.
+ """
+ concat_dim_1 = [
+ # language dim 1 sharded weights
+ "feed_forward.router.weight",
+ "self_attn.o_proj",
+ "experts.gate_proj",
+ "experts.up_proj",
+ "expert.down_proj",
+ # "feed_forward.up_proj",
+ # "feed_forward.gate_proj",
+ "feed_forward.down_proj",
+ "global_gate_stats",
+ # vision dim1 sharded stuff
+ "mlp.fc2.weight", # covers all rowparallels across vis
+ ] # fmt: off
+ if any(re.search(pattern, key) for pattern in concat_dim_1):
+ return 1
+ return 0
+
+
+def compute_intermediate_size(hidden_dim, ffn_exp=4, multiple_of=1024, ffn_dim_multiplier=1.2):
+ hidden_dim = ffn_exp * int(2 * hidden_dim / 3)
+ hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+ hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+ return hidden_dim
+
+
+# Ignore extra info - h/t Aritra
+def safe_load(filename):
+ # Can use weights_only because io.BytesIO was registered, but we still need to skip those objects
+ shard = torch.load(filename, weights_only=True, map_location="cpu", mmap=True)
+ shard = {k: v for k, v in shard.items() if not isinstance(v, io.BytesIO)}
+ return shard
+
+
+# Unpack mlp projections - possibly to be removed when they are fused
+def preprocess_keys(state_dict):
+ new_state_dict = {}
+ for key, value in state_dict.items():
+ if "mlp.fc1_weight" in key:
+ prefix = key.split("mlp.fc1_weight")[0]
+ w1, w3 = value.chunk(2, dim=0)
+ new_state_dict[prefix + "w1.weight"] = w1
+ new_state_dict[prefix + "w3.weight"] = w3
+ else:
+ new_state_dict[key] = value
+ return new_state_dict
+
+
+def max_context_length(model_path, instruct=False):
+ """256K for base, 1M for 128E instruct, 10M for 16E instruct."""
+ if not instruct:
+ return 256 * 1024
+
+ with open(os.path.join(model_path, "params.json"), "r") as f:
+ params = json.load(f)
+ params = params.get("model", params)
+ if params.get("moe_args") is None:
+ return 8192
+ num_experts = params["moe_args"]["num_experts"]
+ return 10485760 if num_experts == 16 else 1048576
+
+
+def write_model(
+ model_path,
+ input_base_path,
+ num_shards,
+ convert_checkpoints,
+ safe_serialization=True,
+ instruct=False,
+):
+ os.makedirs(model_path, exist_ok=True)
+
+ with open(os.path.join(input_base_path, "params.json"), "r") as f:
+ params = json.load(f)
+
+ params = params.get("model", params)
+ torch_dtype = "bfloat16"
+
+ # ------------------------------------------------------------
+ # Text model params and config
+ # ------------------------------------------------------------
+
+ # params from config
+ vocab_size = 202048 # params["vocab_size"] # seems like the lm head is 25256 so padded instead of 202048
+ num_layers = params["n_layers"]
+ dim = params["dim"]
+ num_heads = params["n_heads"]
+ rms_norm_eps = params["norm_eps"]
+ rope_theta = params["rope_theta"]
+ no_rope_layer_interval = params["nope_layer_interval"]
+ attention_chunk_size = params["attention_chunk_size"]
+
+ config_kwargs = {}
+ if params["use_scaled_rope"]:
+ # some constants from original code
+ rope_scaling = {
+ "rope_type": "llama3",
+ "factor": params.get("rope_scaling_factor", 8.0),
+ "low_freq_factor": 1.0,
+ "high_freq_factor": params.get("rope_high_freq_factor", 4.0),
+ "original_max_position_embeddings": 8192,
+ }
+ config_kwargs.update({"rope_scaling": rope_scaling})
+
+ if attention_chunk_size is None:
+ config_kwargs.update({"cache_implementation": "static"})
+
+ # compute additional params for weight conversion
+ num_heads_per_shard = num_heads // num_shards
+ dim_per_head = dim // num_heads
+ intermediate_size_mlp = compute_intermediate_size(
+ dim,
+ ffn_exp=params["ffn_exp"],
+ multiple_of=params["multiple_of"],
+ ffn_dim_multiplier=params["ffn_dim_multiplier"],
+ )
+
+ num_key_value_heads = params["n_kv_heads"] # for GQA / MQA
+
+ if params.get("moe_args", False):
+ num_experts = params["moe_args"]["num_experts"]
+ interleave_moe_layer_step = params["moe_args"].get("interleave_moe_layer_step", 1)
+ else:
+ # Dense model (possibly Llama Guard) - disable all moe layers
+ num_experts = 0
+ interleave_moe_layer_step = 0
+ config_kwargs.update({"moe_layers": []})
+
+ # Ensure all layers are rope if `nope_layer_interval` is None
+ no_rope_layer_interval = params["nope_layer_interval"]
+ no_rope_layer_interval = num_heads * 2 if no_rope_layer_interval is None else no_rope_layer_interval
+
+ bos_token_id = 200000
+ eos_token_id = [200001, 200007, 200008] if instruct else 200001
+ pad_token_id = 200018
+
+ text_config = Llama4TextConfig(
+ num_attention_heads=num_heads,
+ vocab_size=vocab_size,
+ hidden_size=dim,
+ rms_norm_eps=rms_norm_eps,
+ rope_theta=rope_theta,
+ num_hidden_layers=num_layers,
+ intermediate_size=8192,
+ intermediate_size_mlp=intermediate_size_mlp,
+ max_position_embeddings=max_context_length(input_base_path, instruct),
+ num_local_experts=num_experts,
+ interleave_moe_layer_step=interleave_moe_layer_step,
+ use_qk_norm=params["use_qk_norm"],
+ no_rope_layer_interval=no_rope_layer_interval,
+ attention_chunk_size=attention_chunk_size,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ pad_token_id=pad_token_id,
+ tie_word_embeddings=False, # Constant set to False
+ torch_dtype=torch_dtype,
+ for_llm_compressor=_OFFLINE_QUANT_COMPATIBLE,
+ **config_kwargs,
+ )
+ # default vision config from params
+
+ vision_params = params["vision_args"]
+ vision_dim = vision_params["dim"]
+ vision_num_layers = vision_params["n_layers"]
+ image_size = vision_params["image_size"]["height"] # siglip config is outdated
+ vision_num_heads = vision_params["n_heads"]
+
+ vision_output_dim = vision_params["output_dim"]
+
+ vision_config = Llama4VisionConfig(
+ hidden_act="gelu",
+ num_hidden_layers=vision_num_layers,
+ image_size=image_size,
+ num_attention_heads=vision_num_heads,
+ hidden_size=vision_dim,
+ vision_output_dim=vision_output_dim,
+ )
+
+ config = Llama4Config(text_config=text_config, vision_config=vision_config)
+ config.save_pretrained(model_path)
+
+ print("Model config saved successfully...")
+
+ # ------------------------------------------------------------
+ # Convert weights
+ # ------------------------------------------------------------
+
+ if convert_checkpoints:
+ print(f"Fetching all parameters from the checkpoint at {input_base_path}...")
+ if num_shards == 1:
+ if os.path.exists(os.path.join(input_base_path, "consolidated.00.pth")):
+ path = os.path.join(input_base_path, "consolidated.00.pth")
+ else:
+ path = os.path.join(input_base_path, "consolidated.pth")
+ loaded = [safe_load(path)]
+ else:
+ loaded = [
+ safe_load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"))
+ for i in tqdm(range(num_shards), desc="Loading shards", unit="shard")
+ ]
+ loaded = [preprocess_keys(d) for d in loaded]
+
+ all_keys_raw = list(loaded[0].keys())
+ repeated_keys = []
+ sharded_keys = []
+ for _key in all_keys_raw:
+ try:
+ if num_shards == 1 or (loaded[0][_key] == loaded[1][_key]).all():
+ repeated_keys.append(_key)
+ else:
+ sharded_keys.append(_key)
+ except Exception as e:
+ print(f"Encountered exception {e} for {_key}")
+ print("Initializing an empty model")
+ with torch.device("meta"):
+ model = Llama4ForConditionalGeneration(config)
+
+ print("Converting model...")
+ all_keys = list(loaded[0].keys())
+ new_keys = convert_old_keys_to_new_keys(all_keys)
+ state_dict = {}
+ replicated_params = [] # To keep track of replicated weights.
+ for key in tqdm(all_keys, desc="Renaming and processing all keys", unit="key"):
+ new_key = new_keys[key]
+ print(key, new_key)
+ if num_shards > 1 and not is_param_same_across_shards(new_key):
+ current_parameter = [chunk.pop(key) for chunk in loaded if not isinstance(chunk[key], io.BytesIO)]
+ else:
+ print(f"{key} (now {new_key}) is the same across all shards.")
+ replicated_params.append((key, new_key))
+ current_parameter = [loaded[0].pop(key)] if not isinstance(loaded[0][key], io.BytesIO) else []
+
+ if "running_gate_stats_3E" in key:
+ new_keys.pop(new_key)
+ continue
+
+ concat_dim = get_concat_dim(new_key)
+
+ # Post-process the current_parameter.
+ if "qkv_proj" in new_key:
+ queries = []
+ keys = []
+ values = []
+ for param in current_parameter:
+ query, key_, value = param.split(
+ [
+ num_heads * dim_per_head // num_shards,
+ num_key_value_heads * dim_per_head // num_shards,
+ num_key_value_heads * dim_per_head // num_shards,
+ ]
+ )
+ queries.append(query.reshape(num_heads_per_shard, -1, dim))
+ keys.append(key_.reshape(num_key_value_heads // num_shards, -1, dim))
+ values.append(value.reshape(num_key_value_heads // num_shards, -1, dim))
+
+ queries = torch.cat(queries, dim=0).reshape(dim, dim)
+ keys = torch.cat(keys, dim=0).reshape(num_key_value_heads * dim_per_head, dim)
+ values = torch.cat(values, dim=0).reshape(num_key_value_heads * dim_per_head, dim)
+ # queries = permute_for_rope(queries, num_heads, dim, dim)
+ # keys = permute_for_rope(keys, num_key_value_heads, num_key_value_heads*dim_per_head, dim)
+
+ q = new_key.replace("qkv", "q")
+ tqdm.write(f"Processing: {key.ljust(50)} ->\t {q}, {queries.shape}")
+ state_dict[q] = queries
+
+ k = new_key.replace("qkv", "k")
+ tqdm.write(f"Processing: {key.ljust(50)} ->\t {k}, {keys.shape}")
+ state_dict[k] = keys
+
+ v = new_key.replace("qkv", "v")
+ tqdm.write(f"Processing: {key.ljust(50)} ->\t {v}, {values.shape}")
+ state_dict[v] = values
+ elif _OFFLINE_QUANT_COMPATIBLE and "feed_forward.experts." in new_key:
+ # for experts, we need to split expert for offline quantization purpose and don't need to fuse
+ expert_lists = []
+ for k in current_parameter:
+ expert_lists.append(
+ list(k.reshape(num_experts, -1, k.shape[-1]).unbind(0))
+ ) # [#expert * IN, OUT] -> #experts * [IN, OUT]
+ for i in range(num_experts):
+ expert = torch.cat([expert_list[i] for expert_list in expert_lists], dim=concat_dim)
+ expert_key = new_key.replace("experts.", f"experts.{i}.")
+ state_dict[expert_key] = expert.transpose(0, 1).contiguous() # [OUT, IN]
+ tqdm.write(f"Processing: {key.ljust(50)} ->\t {expert_key}, {state_dict[expert_key].shape}")
+ elif re.search(r"(gate|up)_proj", new_key):
+ path = new_key.split(".")
+ gate_key = re.sub(r"(gate|up)_proj", lambda m: "gate_proj", new_key)
+ up_key = re.sub(r"(gate|up)_proj", lambda m: "up_proj", new_key)
+ if gate_key == new_key:
+ state_dict[new_key] = torch.cat(current_parameter, dim=concat_dim)
+ elif new_key == up_key:
+ if "experts" not in new_key:
+ state_dict[new_key] = torch.cat(current_parameter, dim=concat_dim)
+ else:
+ gate_proj = state_dict.pop(gate_key)
+ gate_proj = [
+ gate_proj.reshape(num_experts, -1, 8, 1024)[:, :, k, :].reshape(num_experts, -1, 1024)
+ for k in range(8)
+ ]
+ gate_proj = torch.cat(gate_proj, dim=-1)
+
+ up_proj = [
+ k.reshape(num_experts, -1, 8, 1024).reshape(num_experts, -1, 1024)
+ for k in current_parameter
+ ]
+ up_proj = torch.cat(up_proj, dim=-1)
+
+ gate_up_proj = torch.cat((gate_proj, up_proj), dim=-1)
+ new_key = new_key.replace("up_proj", "gate_up_proj")
+ state_dict[new_key] = gate_up_proj.contiguous()
+
+ tqdm.write(f"Processing: {key.ljust(50)} ->\t {new_key}, {state_dict[new_key].shape}")
+ elif "down_proj" in new_key:
+ current_parameter = torch.cat(current_parameter, dim=concat_dim)
+ if "experts" in new_key:
+ p = []
+ for i in range(8):
+ p += [current_parameter.reshape(8, -1, 5120)[i, :, :].view(num_experts, -1, 5120)]
+ current_parameter = torch.cat(p, dim=1)
+ state_dict[new_key] = current_parameter.contiguous()
+ tqdm.write(f"Processing: {key.ljust(50)} ->\t {new_key}, {state_dict[new_key].shape}")
+ elif "router" in new_key:
+ current_parameter = torch.cat(current_parameter, dim=concat_dim)
+ state_dict[new_key] = current_parameter.transpose(0, 1)
+ elif "lm_head" in new_key:
+ current_parameter = torch.cat(current_parameter, dim=concat_dim).clone()
+ # TODO we need to do better than mean, works for now
+ # if (vocab_size - current_parameter.shape[0]) > 0:
+ # mean_embedding = torch.mean(current_parameter, dim=0)[:, None].repeat(vocab_size-current_parameter.shape[0],1)
+ # print(mean_embedding.shape)
+ # current_parameter = torch.cat((current_parameter, mean_embedding), dim=0)
+ state_dict[new_key] = current_parameter
+ tqdm.write(
+ f"Processing: {key.ljust(50)} ->\t {new_key}, {state_dict[new_key].shape}, concat dim = {concat_dim}"
+ )
+ elif new_key == "vision_model.patch_embedding.linear.weight":
+ current_parameter = torch.cat(current_parameter, dim=concat_dim).clone()
+ # We don't reshape the patch embedding as we're using unfolded convolution as well
+ state_dict[new_key] = current_parameter # .reshape(-1, 3, vision_patch_size, vision_patch_size)
+ # generic concat for weights/select one for biases
+ elif isinstance(current_parameter, list) and len(current_parameter) > 0:
+ if not is_param_same_across_shards(new_key):
+ current_parameter = torch.cat(current_parameter, dim=concat_dim)
+ state_dict[new_key] = current_parameter
+ tqdm.write(
+ f"Processing: {key.ljust(50)} ->\t {new_key}, {state_dict[new_key].shape}, concat dim = {concat_dim}"
+ )
+ elif is_param_same_across_shards(new_key):
+ state_dict[new_key] = current_parameter[0]
+ tqdm.write(
+ f"Processing: {key.ljust(50)} ->\t {new_key}, {state_dict[new_key].shape}, concat dim = {concat_dim}"
+ )
+
+ elif new_key == "":
+ # skip empty keys
+ continue
+ else:
+ # just load the parameter
+ state_dict[new_key] = current_parameter
+ tqdm.write(
+ f"Processing: {key.ljust(50)} ->\t {new_key}, {state_dict[new_key].shape}, concat dim = {concat_dim}"
+ )
+ del loaded
+ gc.collect()
+
+ print("Loading the checkpoint in a Llama4 model.")
+ state_dict.pop("")
+ model.load_state_dict(state_dict, strict=True, assign=True)
+ print("Model reloaded successfully.")
+ print("Saving the model.")
+ model.save_pretrained(model_path, safe_serialization=safe_serialization)
+ del state_dict, model
+
+ # Safety check: reload the converted model
+ gc.collect()
+ print("Reloading the model to check if it's saved correctly.")
+ with torch.no_grad():
+ # TODO test if we can do `tp_plan="auto"``
+ model = Llama4ForConditionalGeneration.from_pretrained(
+ model_path, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="eager"
+ )
+
+ model.generation_config.top_p = 0.9
+ model.generation_config.temperature = 0.6
+ print("Model reloaded successfully.")
+
+ from transformers import AutoTokenizer
+
+ tokenizer = AutoTokenizer.from_pretrained(model_path)
+ inputs = tokenizer(["Roses are red,"], return_tensors="pt").to(model.device)
+ out = model.generate(**inputs, max_new_tokens=4)
+ print(tokenizer.batch_decode(out))
+ # generation config
+ if instruct:
+ print("Saving generation config...")
+ generation_config = GenerationConfig(
+ do_sample=True,
+ temperature=0.6,
+ top_p=0.9,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ pad_token_id=pad_token_id,
+ )
+ generation_config.save_pretrained(model_path)
+
+
+BOS_ADDED_TOKEN = AddedToken(
+ "<|begin_of_text|>", single_word=False, lstrip=False, rstrip=False, normalized=False, special=True
+)
+EOS_ADDED_TOKEN = AddedToken(
+ "<|end_of_text|>", single_word=False, lstrip=False, rstrip=False, normalized=False, special=True
+)
+EOT_ADDED_TOKEN = AddedToken("<|eot|>", single_word=False, lstrip=False, rstrip=False, normalized=False, special=True)
+
+
+def get_reserved_special_tokens(name, count, start_index=0):
+ return [f"<|{name}_reserved_special_token_{i}|>" for i in range(start_index, start_index + count)]
+
+
+# 200005, ..., 200079
+LLAMA4_TEXT_POST_TRAIN_SPECIAL_TOKENS = [
+ "<|header_start|>",
+ "<|header_end|>",
+ "<|eom|>",
+ "<|eot|>",
+ "<|step|>",
+ "<|text_post_train_reserved_special_token_0|>",
+ "<|text_post_train_reserved_special_token_1|>",
+ "<|text_post_train_reserved_special_token_2|>",
+ "<|text_post_train_reserved_special_token_3|>",
+ "<|text_post_train_reserved_special_token_4|>",
+ "<|text_post_train_reserved_special_token_5|>",
+ "<|python_start|>",
+ "<|python_end|>",
+ "<|finetune_right_pad|>",
+] + get_reserved_special_tokens(
+ "text_post_train", 61, 8
+) # <|text_post_train_reserved_special_token_8|>, ..., <|text_post_train_reserved_special_token_68|>
+
+# 200080, ..., 201133
+LLAMA4_VISION_SPECIAL_TOKENS = [
+ "<|image_start|>",
+ "<|image_end|>",
+ "<|vision_reserved_special_token_0|>",
+ "<|vision_reserved_special_token_1|>",
+ "<|tile_x_separator|>",
+ "<|tile_y_separator|>",
+ "<|vision_reserved_special_token_2|>",
+ "<|vision_reserved_special_token_3|>",
+ "<|vision_reserved_special_token_4|>",
+ "<|vision_reserved_special_token_5|>",
+ "<|image|>",
+ "<|vision_reserved_special_token_6|>",
+ "<|patch|>",
+] + get_reserved_special_tokens(
+ "vision", 1041, 7
+) # <|vision_reserved_special_token_7|>, ..., <|vision_reserved_special_token_1047|>
+
+LLAMA4_SPECIAL_TOKENS = LLAMA4_TEXT_POST_TRAIN_SPECIAL_TOKENS + LLAMA4_VISION_SPECIAL_TOKENS
+
+BASIC_SPECIAL_TOKENS = [
+ "<|begin_of_text|>",
+ "<|end_of_text|>",
+ "<|fim_prefix|>",
+ "<|fim_middle|>",
+ "<|fim_suffix|>",
+]
+
+
+class Llama4Converter(TikTokenConverter):
+ def __init__(
+ self,
+ vocab_file,
+ special_tokens: list[str],
+ pattern: str,
+ model_max_length: int = 0,
+ chat_template: Optional[str] = None,
+ **kwargs,
+ ):
+ super().__init__(vocab_file, pattern=pattern)
+ self.additional_special_tokens = special_tokens
+ tokenizer = self.converted()
+ if chat_template is not None:
+ kwargs["chat_template"] = chat_template
+
+ self.converted_tokenizer = PreTrainedTokenizerFast(
+ tokenizer_object=tokenizer,
+ model_input_names=["input_ids", "attention_mask"],
+ model_max_length=model_max_length,
+ **kwargs,
+ )
+
+ instruct = chat_template is not None
+ self.update_post_processor(self.converted_tokenizer)
+ # finer special_tokens_map.json
+ self.converted_tokenizer._bos_token = BOS_ADDED_TOKEN
+ self.converted_tokenizer._eos_token = EOT_ADDED_TOKEN if instruct else EOS_ADDED_TOKEN
+
+ # We can't do this while building the tokenizer because we have no easy access to the bos token id
+ def update_post_processor(self, tokenizer):
+ tokenizer._tokenizer.post_processor = processors.Sequence(
+ [
+ processors.ByteLevel(trim_offsets=False),
+ processors.TemplateProcessing(
+ single="<|begin_of_text|> $A",
+ pair="<|begin_of_text|>:0 $A:0 <|begin_of_text|>:1 $B:1",
+ special_tokens=[
+ ("<|begin_of_text|>", tokenizer.convert_tokens_to_ids("<|begin_of_text|>")),
+ ],
+ ),
+ ]
+ )
+
+
+O200K_PATTERN = r"""[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+""" # noqa: E501
+
+
+def write_tokenizer(args):
+ tokenizer_path = os.path.join(args.input_dir, "tokenizer.model")
+ chat_template = "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %} \n {%- if messages[0]['content'] is string %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- else %}\n {#- FIXME: The processor requires an array, always. #}\n {%- set system_message = messages[0]['content'][0]['text']|trim %}\n {%- endif %}\n {%- set messages = messages[1:] %}\n {%- set user_supplied_system_message = true %}\n{%- else %}\n {%- set system_message = \"\" %}\n {%- set user_supplied_system_message = false %}\n{%- endif %}\n\n{#- System message if the user supplied one #}\n{%- if user_supplied_system_message %}\n {{- \"<|header_start|>system<|header_end|>\n\n\" }}\n {%- if tools is not none %}\n {{- \"Environment: ipython\n\" }}\n {%- endif %}\n {%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\n\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\n\n\" }}\n {%- endfor %}\n {%- endif %}\n {{- system_message }}\n {{- \"<|eot|>\" }}\n{%- endif %}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|header_start|>user<|header_end|>\n\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\n\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\n\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\n\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|header_start|>' + message['role'] + '<|header_end|>\n\n' }}\n {%- if message['content'] is string %}\n {{- message['content'] }}\n {%- else %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {{- '<|image|>' }}\n {%- elif content['type'] == 'text' %}\n {{- content['text'] }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {{- \"<|eot|>\" }}\n {%- elif 'tool_calls' in message and message.tool_calls|length > 0 %}\n {{- '<|header_start|>assistant<|header_end|>\n\n' -}}\n {{- '<|python_start|>' }}\n {%- if message['content'] is string %}\n {{- message['content'] }}\n {%- else %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {{- '<|image|>' }}\n {%- elif content['type'] == 'text' %}\n {{- content['text'] }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {{- '<|python_end|>' }}\n {%- for tool_call in message.tool_calls %}\n {{- '{\"name\": \"' + tool_call.function.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.function.arguments | tojson }}\n {{- \"}\" }}\n {%- endfor %}\n {{- \"<|eot|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|header_start|>ipython<|header_end|>\n\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|header_start|>assistant<|header_end|>\n\n' }}\n{%- endif %}\n"
+
+ special_tokens = BASIC_SPECIAL_TOKENS + LLAMA4_SPECIAL_TOKENS
+ converter = Llama4Converter(
+ vocab_file=tokenizer_path,
+ pattern=O200K_PATTERN,
+ special_tokens=special_tokens,
+ chat_template=chat_template if args.instruct else None,
+ bos_token="<|begin_of_text|>",
+ eos_token="<|end_of_text|>" if not args.instruct else "<|eot|>",
+ pad_token="<|finetune_right_pad_id|>",
+ model_max_length=max_context_length(args.input_dir, args.instruct),
+ )
+ tokenizer = converter.converted_tokenizer
+
+ image_processor = Llama4ImageProcessorFast()
+ processor = Llama4Processor(
+ image_processor=image_processor,
+ tokenizer=tokenizer,
+ chat_template=tokenizer.chat_template,
+ )
+ processor.save_pretrained(args.output_dir)
+ del processor
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--input_dir",
+ type=str,
+ help="Location of the local folder copied from the Hub.",
+ )
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ help="Location to write HF model and tokenizer",
+ )
+ parser.add_argument(
+ "--safe_serialization", default=True, type=bool, help="Whether or not to save using `safetensors`."
+ )
+ parser.add_argument(
+ "--special_tokens",
+ default=None,
+ type=list[str],
+ help="The list of special tokens that should be added to the model.",
+ )
+ parser.add_argument(
+ "--num_shards",
+ default=8,
+ type=int,
+ help="The number of individual shards used for the model. Does not have to be the same as the number of consolidated_xx.pth",
+ )
+ parser.add_argument(
+ "--instruct",
+ action="store_true",
+ help="Whether the model is an instruct model",
+ )
+ parser.add_argument(
+ "--convert_checkpoints",
+ action="store_true",
+ help="Whether to convert the original weights (or skip if previously converted)",
+ )
+
+ args = parser.parse_args()
+ write_tokenizer(args)
+
+ write_model(
+ model_path=args.output_dir,
+ input_base_path=args.input_dir,
+ safe_serialization=args.safe_serialization,
+ num_shards=args.num_shards,
+ instruct=args.instruct,
+ convert_checkpoints=args.convert_checkpoints,
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/image_processing_llama4_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/image_processing_llama4_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..257893980111d9fcb0333eec0646e7cc26540bad
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/image_processing_llama4_fast.py
@@ -0,0 +1,465 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for Got-OCR-2."""
+
+import math
+from collections import defaultdict
+from functools import lru_cache
+from typing import Optional, Union
+
+from ...image_processing_utils import BatchFeature
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_utils import ImageInput, PILImageResampling, SizeDict
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+)
+
+
+if is_torch_available():
+ import torch
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+def get_factors(dividend: int) -> set[int]:
+ """
+ Calculate all factors of a given number, i.e. a divisor that leaves
+ no remainder. For example, if dividend=12, it will return {1, 2, 3, 4, 6, 12}.
+
+ Args:
+ dividend (int): The number to find factors for.
+
+ Returns:
+ set: A set containing all factors of the number.
+ """
+ factors_set = set()
+
+ for i in range(1, int(dividend**0.5) + 1):
+ if dividend % i == 0:
+ factors_set.add(i)
+ factors_set.add(dividend // i)
+ return factors_set
+
+
+def get_max_res_without_distortion(
+ image_size: tuple[int, int],
+ target_size: tuple[int, int],
+) -> tuple[int, int]:
+ """
+ Determines the maximum resolution to which an image can be resized to without distorting its
+ aspect ratio, based on the target resolution.
+
+ Args:
+ image_size (tuple[int, int]): The original resolution of the image (height, width).
+ target_resolution (tuple[int, int]): The desired resolution to fit the image into (height, width).
+ Returns:
+ tuple[int, int]: The optimal dimensions (height, width) to which the image should be resized.
+ Example:
+ >>> _get_max_res_without_distortion([200, 300], target_size = [450, 200])
+ (134, 200)
+ >>> _get_max_res_without_distortion([800, 600], target_size = [450, 1300])
+ (450, 338)
+ """
+
+ original_height, original_width = image_size
+ target_height, target_width = target_size
+
+ scale_w = target_width / original_width
+ scale_h = target_height / original_height
+
+ if scale_w < scale_h:
+ new_width = target_width
+ new_height = min(math.floor(original_height * scale_w), target_height)
+ else:
+ new_height = target_height
+ new_width = min(math.floor(original_width * scale_h), target_width)
+
+ return new_height, new_width
+
+
+def split_to_tiles(images: torch.Tensor, num_tiles_height: int, num_tiles_width: int) -> torch.Tensor:
+ # Split image into number of required tiles (width x height)
+ batch_size, num_channels, height, width = images.size()
+ images = images.view(
+ batch_size,
+ num_channels,
+ num_tiles_height,
+ height // num_tiles_height,
+ num_tiles_width,
+ width // num_tiles_width,
+ )
+ # Permute dimensions to reorder the axes
+ image = images.permute(0, 2, 4, 1, 3, 5).contiguous()
+ # Reshape into the desired output shape (batch_size * 4, num_channels, width/2, height/2)
+ image = image.view(
+ batch_size,
+ num_tiles_width * num_tiles_height,
+ num_channels,
+ height // num_tiles_height,
+ width // num_tiles_width,
+ )
+ return image
+
+
+@lru_cache(maxsize=1)
+def find_supported_resolutions(max_num_chunks: int, patch_size: SizeDict) -> torch.Tensor:
+ """
+ Computes all of the allowed resolutions for a fixed number of chunks
+ and patch_size. Useful for when dividing an image into chunks.
+
+ Args:
+ max_num_chunks (int): Maximum number of chunks for processing.
+ patch_size (int): Size of the side of the patch.
+
+ Returns:
+ torch.Tensor: List of possible resolutions as tuples (height, width).
+
+ Example:
+ >>> max_num_chunks = 5
+ >>> patch_size = 224
+ >>> find_supported_resolutions(max_num_chunks, patch_size)
+ tensor([(224, 896), (448, 448), (224, 224), (896, 224), (224, 672),
+ (672, 224), (224, 448), (448, 224)])
+
+ Given max_num_chunks=4, patch_size=224, it will create a dictionary:
+ {
+ 0.25: [(1, 4)],
+ 1.0: [(2, 2), (1, 1)],
+ 4.0: [(4, 1)],
+ 0.33: [(1, 3)],
+ 3.0: [(3, 1)],
+ 0.5: [(1, 2)],
+ 2.0: [(2, 1)]
+ }
+
+ and return the resolutions multiplied by the patch_size:
+ [(1*224, 4*224), (2*224, 2*224), ..., (2*224, 1*224)]
+ """
+ height, width = patch_size.height, patch_size.width
+ if height != width:
+ raise ValueError("`size` must be square.")
+
+ patch_size = height
+
+ asp_dict = defaultdict(list)
+ for chunk_size in range(max_num_chunks, 0, -1):
+ _factors = sorted(get_factors(chunk_size))
+ _asp_ratios = [(factor, chunk_size // factor) for factor in _factors]
+ for height, width in _asp_ratios:
+ ratio_float = height / width
+ asp_dict[ratio_float].append((height, width))
+
+ # get the resolutions multiplied by the patch_size
+ possible_resolutions = []
+ for value in asp_dict.values():
+ for height, depth in value:
+ possible_resolutions.append((height * patch_size, depth * patch_size))
+
+ return possible_resolutions
+
+
+def pad_to_best_fit(
+ images: "torch.Tensor",
+ target_size: tuple[int, int],
+ background_color: Union[int, tuple[int, int, int]] = 0,
+) -> "torch.Tensor":
+ """
+ Pads an image to fit the target size.
+
+ Args:
+ images (`np.ndarray`):
+ The images to pad.
+ background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+ The color to use for the padding. Can be an integer for single channel or a
+ tuple of integers representing for multi-channel images. If passed as integer
+ in mutli-channel mode, it will default to `0` in subsequent channels.
+ Returns:
+ `torch.Tensor`: The padded images.
+ """
+
+ num_channels = images.shape[1] if len(images.shape) == 4 else images.shape[0]
+ if isinstance(background_color, int):
+ background_color = [background_color] + [0] * (num_channels - 1)
+ elif len(background_color) != num_channels:
+ raise ValueError(
+ f"background_color must have no more than {num_channels} elements to match the number of channels"
+ )
+
+ height, width = images.shape[-2:]
+ target_height, target_width = target_size
+ paste_x_right = target_width - width
+ paste_y_right = target_height - height
+ padded_images = F.pad(images, padding=[0, 0, paste_x_right, paste_y_right], fill=background_color)
+
+ return padded_images
+
+
+def get_best_fit(
+ image_size: tuple[int, int],
+ possible_resolutions: torch.Tensor,
+ resize_to_max_canvas: bool = False,
+) -> tuple[int, int]:
+ """
+ Determines the best canvas possible from a list of possible resolutions to, without distortion,
+ resize an image to.
+
+ For each possible resolution, calculates the scaling factors for
+ width and height, and selects the smallest one, which is the limiting side.
+ E.g. to match the canvas you can upscale height by 2x, and width by 1.5x,
+ therefore, the maximum upscaling you can do is min(2, 1.5) = 1.5.
+
+ If upscaling is possible (any of the scaling factors is greater than 1),
+ then picks the smallest upscaling factor > 1, unless resize_to_max_canvas is True.
+
+ If upscaling is not possible, then picks the largest scaling factor <= 1, i.e.
+ reduce downscaling as much as possible.
+
+ If there are multiple resolutions with the same max scale, we pick the one with the lowest area,
+ to minimize padding. E.g., the same image can be upscaled to 224x224 and 224x448, but the latter
+ has more padding.
+
+ Args:
+ image_size (tuple[int, int]): A tuple containing the height and width of the image.
+ possible_resolutions (torch.Tensor): A tensor of shape (N, 2) where each
+ row represents a possible resolution (height, width).
+ resize_to_max_canvas (bool): If True, will return the largest upscaling resolution.
+
+ Returns:
+ list[int]: The best resolution [height, width] for the given image.
+
+ Example:
+ >>> image_size = (200, 300)
+ >>> possible_resolutions = torch.tensor([[224, 672],
+ ... [672, 224],
+ ... [224, 448],
+ ... [448, 224],
+ ... [224, 224]])
+ >>> get_best_fit(image_size, possible_resolutions)
+ [224, 448]
+
+ We have:
+ scale_w = tensor([2.2400, 0.7467, 1.4933, 0.7467, 0.7467])
+ scale_h = tensor([1.1200, 3.3600, 1.1200, 2.2400, 1.1200])
+ scales = tensor([1.1200, 0.7467, 1.1200, 0.7467, 0.7467])
+ Only one of the scales > 1:
+ upscaling_possible = tensor([1.1200, 1.1200])
+ smallest_rescale = tensor(1.1200)
+ So we pick the resolution with the smallest smallest area:
+ areas = tensor([150528, 100352]) # [672, 224], [224, 448]
+ optimal_canvas = tensor([224, 448])
+ """
+
+ original_height, original_width = image_size
+
+ # get all possible resolutions heights/widths
+ target_heights, target_widths = (
+ possible_resolutions[:, 0],
+ possible_resolutions[:, 1],
+ )
+
+ # get scaling factors to resize the image without distortion
+ scale_w = target_widths / original_width
+ scale_h = target_heights / original_height
+
+ # get the min scale between width and height (limiting side -> no distortion)
+ scales = torch.where(scale_h > scale_w, scale_w, scale_h)
+
+ # filter only scales that allow upscaling
+ upscaling_options = scales[scales >= 1]
+ if len(upscaling_options) > 0:
+ if resize_to_max_canvas:
+ selected_scale = torch.max(upscaling_options)
+ else:
+ selected_scale = torch.min(upscaling_options)
+ else:
+ # no upscaling possible,
+ # get the minimum downscaling (max scale for scales<1)
+ downscaling_options = scales[scales < 1]
+ selected_scale = torch.max(downscaling_options)
+
+ # get all resolutions that support this scaling factor,
+ # e.g. you can upscale to 224x224, 224x448, 224x672 without distortion
+ chosen_canvas = possible_resolutions[scales == selected_scale]
+
+ # if there are multiple resolutions,
+ # get the one with minimum area to reduce padding
+ if len(chosen_canvas) > 1:
+ areas = chosen_canvas[:, 0] * chosen_canvas[:, 1]
+ optimal_idx = torch.argmin(areas)
+ optimal_canvas = chosen_canvas[optimal_idx]
+ else:
+ optimal_canvas = chosen_canvas[0]
+
+ return optimal_canvas
+
+
+class Llama4ImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ """
+ max_patches (`int`, *optional*, defaults to 16):
+ The maximum number of patches to be extracted from the image.
+ Can be overridden by the `max_patches` parameter in the `preprocess` method.
+ resize_to_max_canvas (`bool`, *optional*, defaults to False):
+ Whether to resize the image to the maximum canvas size.
+ If True, picks the canvas the allows the largest resizing without distortion.
+ If False, downsample as little as possible, including no resizing at all,
+ but never upsample, unless the image is smaller than the patch size.
+ """
+
+ max_patches: Optional[int]
+ resize_to_max_canvas: Optional[bool]
+
+
+@auto_docstring
+class Llama4ImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BILINEAR
+ image_mean = [0.5, 0.5, 0.5]
+ image_std = [0.5, 0.5, 0.5]
+ size = {"height": 336, "width": 336}
+ do_resize = True
+ do_rescale = True
+ do_normalize = True
+ do_convert_rgb = True
+ max_patches = 16
+ resize_to_max_canvas = False
+ valid_kwargs = Llama4ImageProcessorKwargs
+
+ def __init__(self, **kwargs: Unpack[Llama4ImageProcessorKwargs]):
+ super().__init__(**kwargs)
+
+ # Disable compilation here as conversion to bfloat16 causes differences in the output of the compiled and non-compiled versions
+ @torch.compiler.disable
+ def rescale_and_normalize(
+ self,
+ images: "torch.Tensor",
+ do_rescale: bool,
+ rescale_factor: float,
+ do_normalize: bool,
+ image_mean: Union[float, list[float]],
+ image_std: Union[float, list[float]],
+ ) -> "torch.Tensor":
+ """
+ Rescale and normalize images.
+ Override to rescale and normalize the images in torch.bfloat16 as in the original implementation
+ """
+ if do_rescale and do_normalize:
+ images = images.to(dtype=torch.bfloat16) * rescale_factor
+ images = self.normalize(images, image_mean, image_std)
+ elif do_rescale:
+ images = images * rescale_factor
+ elif do_normalize:
+ images = self.normalize(images, image_mean, image_std)
+
+ return images
+
+ @auto_docstring
+ def preprocess(self, images: ImageInput, **kwargs: Unpack[Llama4ImageProcessorKwargs]) -> BatchFeature:
+ return super().preprocess(images, **kwargs)
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ size: SizeDict,
+ max_patches: int,
+ resize_to_max_canvas: bool,
+ interpolation: Optional["F.InterpolationMode"],
+ do_rescale: bool,
+ rescale_factor: float,
+ do_normalize: bool,
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ disable_grouping: Optional[bool],
+ return_tensors: Optional[Union[str, TensorType]],
+ **kwargs,
+ ) -> BatchFeature:
+ possible_resolutions = find_supported_resolutions(max_num_chunks=max_patches, patch_size=size)
+ possible_resolutions = torch.tensor(possible_resolutions, device=images[0].device)
+ # process images by batch, grouped by shape
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ grouped_processed_images = {}
+ grouped_aspect_ratios = {}
+ for shape, stacked_images in grouped_images.items():
+ image_size = stacked_images.shape[-2:]
+ target_size = get_best_fit(image_size, possible_resolutions, resize_to_max_canvas=resize_to_max_canvas)
+ # If target_size requires upscaling, we might want to limit the upscaling to max_upscaling_size
+ max_upscaling_size = None if resize_to_max_canvas else size.height
+ if max_upscaling_size is not None:
+ new_target_height = min(max(image_size[0], max_upscaling_size), target_size[0])
+ new_target_width = min(max(image_size[1], max_upscaling_size), target_size[1])
+ target_size_without_distortion = (new_target_height, new_target_width)
+
+ # resize to target_size while preserving aspect ratio
+ new_size_without_distortion = get_max_res_without_distortion(image_size, target_size_without_distortion)
+ new_size_without_distortion = SizeDict(
+ height=max(new_size_without_distortion[0], 1), width=max(new_size_without_distortion[1], 1)
+ )
+ processed_images = self.resize(
+ stacked_images,
+ new_size_without_distortion,
+ interpolation=interpolation,
+ )
+
+ # pad to target_size to be able to split into tiles
+ processed_images = pad_to_best_fit(processed_images, target_size)
+ processed_images = self.rescale_and_normalize(
+ processed_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+
+ ratio_h, ratio_w = (
+ target_size[0] // size.height,
+ target_size[1] // size.width,
+ )
+ # split into tiles
+ processed_images = split_to_tiles(processed_images, ratio_h, ratio_w)
+ grouped_processed_images[shape] = processed_images
+ grouped_aspect_ratios[shape] = torch.tensor(
+ [[ratio_h, ratio_w]] * stacked_images.shape[0], device=images[0].device
+ )
+
+ # add a global tile to the processed tile if there are more than one tile
+ if ratio_h * ratio_w > 1:
+ global_tiles = self.resize(
+ stacked_images,
+ size,
+ interpolation=interpolation,
+ )
+ global_tiles = self.rescale_and_normalize(
+ global_tiles, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ grouped_processed_images[shape] = torch.cat([processed_images, global_tiles.unsqueeze(1)], dim=1)
+ processed_images = reorder_images(grouped_processed_images, grouped_images_index)
+ aspect_ratios_list = reorder_images(grouped_aspect_ratios, grouped_images_index)
+
+ processed_images = torch.cat(processed_images, dim=0) if return_tensors else processed_images
+ aspect_ratios = torch.stack(aspect_ratios_list, dim=0) if return_tensors else aspect_ratios_list
+ return BatchFeature(
+ data={"pixel_values": processed_images, "aspect_ratios": aspect_ratios}, tensor_type=return_tensors
+ )
+
+
+__all__ = ["Llama4ImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/modeling_llama4.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/modeling_llama4.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cdd6ae865d36c7a74786dd58da9e6b15d176372
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/modeling_llama4.py
@@ -0,0 +1,1409 @@
+# coding=utf-8
+# Copyright 2025 The LLAMA4 and HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+from dataclasses import dataclass
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformers.models.llama4.configuration_llama4 import Llama4VisionConfig
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...masking_utils import create_causal_mask, create_chunked_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, CausalLMOutputWithPast, ModelOutput
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import check_model_inputs
+from .configuration_llama4 import Llama4Config, Llama4TextConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class Llama4TextExperts(nn.Module):
+ def __init__(self, config: Llama4TextConfig):
+ super().__init__()
+ self.num_experts = config.num_local_experts
+ self.intermediate_size = config.intermediate_size
+ self.hidden_size = config.hidden_size
+ self.expert_dim = self.intermediate_size
+ self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_size, 2 * self.expert_dim))
+ self.down_proj = nn.Parameter(torch.empty((self.num_experts, self.expert_dim, self.hidden_size)))
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ """
+ This should really not be run on a single machine, as we are reaching compute bound:
+ - the inputs are expected to be "sorted" per expert already.
+ - the weights are viewed with another dim, to match num_expert, 1, shape * num_tokens, shape
+
+ Args:
+ hidden_states (torch.Tensor): (batch_size * token_num, hidden_size)
+ selected_experts (torch.Tensor): (batch_size * token_num, top_k)
+ routing_weights (torch.Tensor): (batch_size * token_num, top_k)
+ Returns:
+ torch.Tensor
+ """
+ hidden_states = hidden_states.view(self.gate_up_proj.shape[0], -1, self.hidden_size)
+ gate_up = torch.bmm(hidden_states, self.gate_up_proj)
+ gate, up = gate_up.chunk(2, dim=-1) # not supported for DTensors
+ next_states = torch.bmm((up * self.act_fn(gate)), self.down_proj)
+ next_states = next_states.view(-1, self.hidden_size)
+ return next_states
+
+
+# Phi3MLP
+class Llama4TextMLP(nn.Module):
+ def __init__(self, config, intermediate_size=None):
+ super().__init__()
+
+ if intermediate_size is None:
+ intermediate_size = config.intermediate_size
+
+ self.config = config
+ self.gate_proj = nn.Linear(config.hidden_size, intermediate_size, bias=False)
+ self.up_proj = nn.Linear(config.hidden_size, intermediate_size, bias=False)
+ self.down_proj = nn.Linear(intermediate_size, config.hidden_size, bias=False)
+ self.activation_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ down_proj = self.activation_fn(self.gate_proj(x)) * self.up_proj(x)
+ return self.down_proj(down_proj)
+
+
+class Llama4TextL2Norm(torch.nn.Module):
+ def __init__(self, eps: float = 1e-6):
+ super().__init__()
+ self.eps = eps
+
+ def _norm(self, x):
+ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+ def forward(self, x):
+ return self._norm(x.float()).type_as(x)
+
+ def extra_repr(self):
+ return f"eps={self.eps}"
+
+
+class Llama4TextRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-5):
+ """
+ Llama4RMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.eps = eps
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+
+ def _norm(self, x):
+ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+ def forward(self, x):
+ output = self._norm(x.float()).type_as(x)
+ return output * self.weight
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.eps}"
+
+
+class Llama4Router(nn.Linear):
+ def __init__(self, config):
+ super().__init__(config.hidden_size, config.num_local_experts, bias=False)
+ self.num_experts = config.num_local_experts
+ self.top_k = config.num_experts_per_tok
+
+ def forward(self, hidden_states):
+ router_logits = super().forward(hidden_states)
+ router_top_value, router_indices = torch.topk(router_logits, self.top_k, dim=1)
+ router_scores = torch.full_like(router_logits, float("-inf")).scatter_(1, router_indices, router_top_value)
+ router_scores = torch.nn.functional.sigmoid(router_scores.float()).to(router_scores.dtype)
+ return router_scores, router_logits
+
+
+@use_kernel_forward_from_hub("Llama4TextMoe")
+class Llama4TextMoe(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.top_k = config.num_experts_per_tok
+ self.hidden_dim = config.hidden_size
+ self.num_experts = config.num_local_experts
+ self.experts = Llama4TextExperts(config)
+ self.router = Llama4Router(config)
+ self.shared_expert = Llama4TextMLP(config)
+
+ def forward(self, hidden_states):
+ hidden_states = hidden_states.reshape(-1, self.hidden_dim)
+ router_scores, router_logits = self.router(hidden_states)
+ routed_in = hidden_states.repeat(router_scores.shape[1], 1)
+ routed_in = routed_in * router_scores.reshape(-1, 1)
+ routed_out = self.experts(routed_in)
+ out = self.shared_expert(hidden_states)
+ out.add_(routed_out.reshape(router_scores.shape[1], -1, routed_out.shape[-1]).sum(dim=0))
+ return out, router_logits
+
+
+class Llama4TextRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: Llama4TextConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ self.rope_type = "llama3" if config.rope_scaling is not None else "default"
+
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.to(x.device) @ position_ids_expanded).transpose(1, 2)
+ freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # Convert to complex representation
+ freqs_cis = freqs_cis * self.attention_scaling
+
+ return freqs_cis
+
+
+def apply_rotary_emb(
+ xq: torch.Tensor,
+ xk: torch.Tensor,
+ freqs_cis: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+ xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+ xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+ xq_out = torch.view_as_real(xq_ * freqs_cis[:, :, None, :]).flatten(3)
+ xk_out = torch.view_as_real(xk_ * freqs_cis[:, :, None, :]).flatten(3)
+ return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Adapted from transformers.models.llama.modeling_llama.eager_attention_forward -> llama4 doesn't cast attn weights to fp32
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs,
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+# Adapted from transformers.models.llama.modeling_llama.eager_attention_forward -> llama4 doesn't cast attn weights to fp32
+def vision_eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs,
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * module.head_dim**-0.5
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class Llama4TextAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: Llama4TextConfig, layer_idx):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+ self.num_attention_heads = config.num_attention_heads
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.attn_scale = config.attn_scale
+ self.floor_scale = config.floor_scale
+ self.attn_temperature_tuning = config.attn_temperature_tuning
+ self.attention_dropout = config.attention_dropout
+ self.is_causal = True
+ self.use_rope = config.no_rope_layers[layer_idx]
+ self.q_proj = nn.Linear(
+ config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.k_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.v_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.o_proj = nn.Linear(
+ config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+ )
+ if self.config.use_qk_norm and self.use_rope:
+ self.qk_norm = Llama4TextL2Norm(config.rms_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape)
+ key_states = self.k_proj(hidden_states).view(*input_shape, -1, self.head_dim)
+ value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ if self.use_rope: # the 16E model skips rope for long context on certain layers
+ query_states, key_states = apply_rotary_emb(
+ query_states, key_states, position_embeddings.to(query_states.device)
+ )
+
+ if hasattr(self, "qk_norm"): # the 128E model does not use qk_norm
+ query_states = self.qk_norm(query_states)
+ key_states = self.qk_norm(key_states)
+
+ # Use temperature tuning from https://huggingface.co/papers/2501.19399) to NoROPE layers
+ if self.attn_temperature_tuning and not self.use_rope:
+ attn_scales = (
+ torch.log(torch.floor((cache_position.float() + 1.0) / self.floor_scale) + 1.0) * self.attn_scale + 1.0
+ )
+ attn_scales = attn_scales.view((1, input_shape[-1], 1, 1)).expand((*input_shape, 1, 1)) # batch size > 1
+ query_states = (query_states * attn_scales).to(query_states.dtype)
+
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class Llama4TextDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config, layer_idx):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.layer_idx = layer_idx
+ self.attention_type = config.layer_types[layer_idx]
+ self.self_attn = Llama4TextAttention(config, layer_idx)
+ self.is_moe_layer = layer_idx in config.moe_layers
+ if self.is_moe_layer: # the 128E model interleaves dense / sparse
+ self.feed_forward = Llama4TextMoe(config)
+ else:
+ self.feed_forward = Llama4TextMLP(config, intermediate_size=config.intermediate_size_mlp)
+
+ self.input_layernorm = Llama4TextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = Llama4TextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[torch.Tensor]] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ attention_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = residual + attention_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.feed_forward(hidden_states)
+ if self.is_moe_layer:
+ hidden_states, _ = hidden_states
+ hidden_states = residual + hidden_states.view(residual.shape)
+ return hidden_states
+
+
+@auto_docstring
+class Llama4PreTrainedModel(PreTrainedModel):
+ config: Llama4Config
+ supports_gradient_checkpointing = True
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = False
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ _can_compile_fullgraph = True
+ _supports_attention_backend = True
+
+ def _init_weights(self, module):
+ std = (
+ self.config.initializer_range
+ if hasattr(self.config, "initializer_range")
+ else self.config.text_config.initializer_range
+ )
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+ elif isinstance(module, Llama4TextRMSNorm):
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, Llama4TextExperts):
+ module.gate_up_proj.data.normal_(mean=0.0, std=std)
+ module.down_proj.data.normal_(mean=0.0, std=std)
+ elif isinstance(module, Llama4VisionModel):
+ module.class_embedding.data.normal_(std=module.scale)
+ module.positional_embedding_vlm.data.normal_(std=module.scale)
+
+
+@auto_docstring
+class Llama4TextModel(Llama4PreTrainedModel):
+ _no_split_modules = ["Llama4TextDecoderLayer"]
+ base_model_prefix = "model"
+ config: Llama4TextConfig
+ _can_record_outputs = {
+ "attentions": Llama4TextAttention,
+ "hidden_states": Llama4TextDecoderLayer,
+ "router_logits": Llama4TextMoe,
+ }
+
+ def __init__(self, config: Llama4TextConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [Llama4TextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.norm = Llama4TextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.rotary_emb = Llama4TextRotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids.to(self.embed_tokens.weight.device))
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache(config=self.config)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ # It may already have been prepared by e.g. `generate`
+ if not isinstance(causal_mask_mapping := attention_mask, dict):
+ # Prepare mask arguments
+ mask_kwargs = {
+ "config": self.config,
+ "input_embeds": inputs_embeds,
+ "attention_mask": attention_mask,
+ "cache_position": cache_position,
+ "past_key_values": past_key_values,
+ "position_ids": position_ids,
+ }
+ # Create the masks
+ causal_mask_mapping = {
+ "full_attention": create_causal_mask(**mask_kwargs),
+ "chunked_attention": create_chunked_causal_mask(**mask_kwargs),
+ }
+
+ hidden_states = inputs_embeds
+
+ # create position embeddings to be shared across the decoder layers
+ freq_cis = self.rotary_emb(hidden_states, position_ids)
+
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ hidden_states = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask_mapping[decoder_layer.attention_type],
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=freq_cis,
+ **kwargs,
+ )
+ hidden_states = self.norm(hidden_states)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values if use_cache else None,
+ )
+
+
+class Llama4ForCausalLM(Llama4PreTrainedModel, GenerationMixin):
+ _no_split_modules = ["Llama4TextDecoderLayer"]
+ base_model_prefix = "language_model"
+ _tied_weights_keys = ["lm_head.weight"]
+ _tp_plan = {"lm_head": "colwise_rep"}
+ config: Llama4TextConfig
+
+ def __init__(self, config: Llama4TextConfig):
+ super().__init__(config)
+ self.model = Llama4TextModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, CausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, Llama4ForCausalLM
+
+ >>> model = Llama4ForCausalLM.from_pretrained("meta-llama4/Llama4-2-7b-hf")
+ >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama4/Llama4-2-7b-hf")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Llava causal language model (or autoregressive) outputs.
+ """
+)
+class Llama4CausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size (batch_size, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: torch.FloatTensor = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+
+class Llama4VisionMLP2(torch.nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.fc1 = nn.Linear(self.intermediate_size, config.projector_input_dim, bias=False)
+ self.fc2 = nn.Linear(config.projector_output_dim, config.projector_output_dim, bias=False)
+ self.activation_fn = nn.GELU() # ACT2FN[config.hidden_act]
+ self.dropout = config.projector_dropout
+
+ def forward(self, hidden_states):
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = F.dropout(hidden_states, p=self.dropout, training=self.training)
+ return self.activation_fn(self.fc2(hidden_states))
+
+
+class Llama4MultiModalProjector(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.linear_1 = nn.Linear(
+ config.vision_config.vision_output_dim,
+ config.text_config.hidden_size,
+ bias=False,
+ )
+
+ def forward(self, image_features):
+ hidden_states = self.linear_1(image_features)
+ return hidden_states
+
+
+def pixel_shuffle(input_tensor, shuffle_ratio):
+ # input_tensor: [batch_size, num_patches, channels]
+ batch_size, num_patches, channels = input_tensor.shape
+ patch_size = int(math.sqrt(num_patches))
+
+ input_tensor = input_tensor.view(batch_size, patch_size, patch_size, -1)
+ batch_size, height, width, channels = input_tensor.size()
+
+ reshaped_tensor = input_tensor.view(batch_size, height, int(width * shuffle_ratio), int(channels / shuffle_ratio))
+ reshaped_tensor = reshaped_tensor.permute(0, 2, 1, 3).contiguous()
+
+ reshaped_tensor = reshaped_tensor.view(
+ batch_size, int(height * shuffle_ratio), int(width * shuffle_ratio), int(channels / (shuffle_ratio**2))
+ )
+ reshaped_tensor = reshaped_tensor.permute(0, 2, 1, 3).contiguous()
+
+ output_tensor = reshaped_tensor.view(batch_size, -1, reshaped_tensor.shape[-1])
+ return output_tensor
+
+
+class Llama4VisionPixelShuffleMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.pixel_shuffle_ratio = config.pixel_shuffle_ratio
+ self.inner_dim = int(config.projector_input_dim // (self.pixel_shuffle_ratio**2))
+ self.output_dim = config.projector_output_dim
+ self.mlp = Llama4VisionMLP2(config)
+
+ def forward(self, encoded_patches: torch.Tensor) -> torch.Tensor:
+ encoded_patches = pixel_shuffle(encoded_patches, self.pixel_shuffle_ratio)
+ return self.mlp(encoded_patches)
+
+
+# TODO there is a different RoPE for vision encoder, defined as below
+def reshape_for_broadcast(freqs_ci: torch.Tensor, query: torch.Tensor):
+ ndim = query.ndim
+ shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(query.shape)]
+ return freqs_ci.view(*shape)
+
+
+def vision_apply_rotary_emb(
+ query: torch.Tensor,
+ key: torch.Tensor,
+ freqs_ci: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+ query_ = torch.view_as_complex(query.float().reshape(*query.shape[:-1], -1, 2))
+ key_ = torch.view_as_complex(key.float().reshape(*key.shape[:-1], -1, 2))
+ freqs_ci = reshape_for_broadcast(freqs_ci=freqs_ci, query=query_) # freqs_ci[:,:,None,:]
+ freqs_ci = freqs_ci.to(query_.device)
+ query_out = torch.view_as_real(query_ * freqs_ci).flatten(3)
+ key_out = torch.view_as_real(key_ * freqs_ci).flatten(3)
+ return query_out.type_as(query), key_out.type_as(key) # but this drops to 8e-3
+
+
+class Llama4VisionAttention(nn.Module):
+ def __init__(self, config: Llama4VisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = config.hidden_size // config.num_attention_heads
+ self.num_key_value_groups = 1
+ self.attention_dropout = config.attention_dropout
+ self.scaling = self.head_dim**-0.5
+
+ self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=True)
+ self.k_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=True)
+ self.v_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=True)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.embed_dim, bias=True)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ freqs_ci: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape)
+ key_states = self.k_proj(hidden_states).view(hidden_shape)
+ value_states = self.v_proj(hidden_states).view(hidden_shape)
+
+ query_states, key_states = vision_apply_rotary_emb(query_states, key_states, freqs_ci=freqs_ci)
+
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ attention_interface: Callable = vision_eager_attention_forward
+ # flex disable because breaks on TP 8, embed is 88 not power of 2
+ if self.config._attn_implementation not in ["eager", "flex_attention"]:
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ None,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=None, # TODO Might be enforced here for TP compatibility as scaling is not just sqrt(head_dim)
+ is_causal=False, # HAS TO BE ENFORCED
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class Llama4VisionMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.activation_fn = nn.GELU() # ACT2FN[config.hidden_act]
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=True)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=True)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+class Llama4VisionEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: Llama4VisionConfig):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = Llama4VisionAttention(config)
+ self.mlp = Llama4VisionMLP(config)
+
+ self.input_layernorm = nn.LayerNorm(config.hidden_size)
+ self.post_attention_layernorm = nn.LayerNorm(config.hidden_size)
+
+ def forward(
+ self,
+ hidden_state: torch.Tensor,
+ freqs_ci: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ ):
+ # Self Attention
+ residual = hidden_state
+
+ hidden_state = self.input_layernorm(hidden_state)
+
+ hidden_state, attn_weights = self.self_attn(
+ hidden_state,
+ freqs_ci=freqs_ci,
+ attention_mask=attention_mask,
+ )
+ hidden_state = residual + hidden_state
+
+ # Feed forward
+ residual = hidden_state
+ hidden_state = self.post_attention_layernorm(hidden_state)
+ hidden_state = self.mlp(hidden_state)
+ hidden_state = residual + hidden_state
+
+ outputs = (hidden_state,)
+
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs
+
+
+class Llama4VisionEncoder(nn.Module):
+ """
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+ [`Llama4VisionEncoderLayer`].
+
+ Args:
+ config: Llama4VisionConfig
+ """
+
+ def __init__(self, config: Llama4VisionConfig):
+ super().__init__()
+ self.config = config
+ self.layers = nn.ModuleList([Llama4VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+ self.config = config
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ freqs_ci: torch.Tensor, # TODO move this to an attribute instead of keeping it around
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutput]:
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ for encoder_layer in self.layers:
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ layer_outputs = encoder_layer(
+ hidden_state=hidden_states,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ freqs_ci=freqs_ci,
+ )
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ hidden_states = layer_outputs[0]
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+ )
+
+
+class Llama4UnfoldConvolution(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ kernel_size = config.patch_size
+ if isinstance(kernel_size, int):
+ kernel_size = (kernel_size, kernel_size)
+ self.unfold = torch.nn.Unfold(kernel_size=kernel_size, stride=config.patch_size)
+ self.linear = nn.Linear(
+ config.num_channels * kernel_size[0] * kernel_size[1],
+ config.hidden_size,
+ bias=False,
+ )
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.unfold(hidden_states)
+ hidden_states = hidden_states.permute(0, 2, 1)
+ hidden_states = self.linear(hidden_states)
+ return hidden_states
+
+
+class Llama4VisionRotaryEmbedding(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ idx = config.image_size // config.patch_size
+ img_idx = torch.arange(idx**2, dtype=torch.int32).reshape(idx**2, 1)
+ img_idx = torch.cat([img_idx, img_idx[:1]], dim=0)
+ img_idx[-1, -1] = -2 # ID_CLS_TOKEN
+ frequencies_x = img_idx % idx # get the coordinates of the 2d matrix along x
+ frequencies_y = img_idx // idx # get the coordinates of the 2d matrix along y
+ freq_dim = config.hidden_size // config.num_attention_heads // 2
+ rope_freq = 1.0 / (config.rope_theta ** (torch.arange(0, freq_dim, 2)[: (freq_dim // 2)].float() / freq_dim))
+ freqs_x = ((frequencies_x + 1)[..., None] * rope_freq[None, None, :]).repeat_interleave(2, dim=-1)
+ freqs_y = ((frequencies_y + 1)[..., None] * rope_freq[None, None, :]).repeat_interleave(2, dim=-1)
+ freqs = torch.cat([freqs_x, freqs_y], dim=-1).float().contiguous()[..., ::2]
+ freqs = freqs.masked_fill(img_idx.reshape(-1, 1, 1) < 0, 0)
+ freq_cis = torch.view_as_complex(torch.stack([torch.cos(freqs), torch.sin(freqs)], dim=-1))
+ self.freqs_ci = freq_cis # idx**2, idx**2, idx * 2
+
+ def forward(self, hidden_states):
+ return self.freqs_ci.to(hidden_states.device)
+
+
+class Llama4VisionModel(Llama4PreTrainedModel):
+ base_model_prefix = "vision_model"
+ _no_split_modules = ["Llama4VisionEncoderLayer"]
+ config: Llama4VisionConfig
+
+ def __init__(self, config: Llama4VisionConfig):
+ super().__init__(config)
+ self.image_size = config.image_size
+ self.patch_size = config.patch_size
+ self.hidden_size = config.hidden_size
+ self.num_channels = config.num_channels
+
+ self.num_patches = (self.image_size // self.patch_size) ** 2 + 1
+ self.scale = config.hidden_size**-0.5
+
+ self.patch_embedding = Llama4UnfoldConvolution(config)
+
+ self.class_embedding = nn.Parameter(self.scale * torch.randn(self.hidden_size))
+ self.positional_embedding_vlm = nn.Parameter(self.scale * torch.randn(self.num_patches, self.hidden_size))
+ self.rotary_embedding = Llama4VisionRotaryEmbedding(config)
+
+ # layer norms
+ self.layernorm_pre = nn.LayerNorm(self.hidden_size)
+ self.layernorm_post = nn.LayerNorm(self.hidden_size)
+
+ # encoders
+ self.model = Llama4VisionEncoder(config)
+ self.vision_adapter = Llama4VisionPixelShuffleMLP(config)
+ self.post_init()
+
+ def get_input_embeddings(self):
+ """
+ This function is used to fetch the first embedding layer to activate grads on inputs.
+ """
+ return self.patch_embedding
+
+ def forward(
+ self,
+ pixel_values: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[BaseModelOutput, tuple[torch.Tensor, ...]]:
+ r"""
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import AutoProcessor, MllamaVisionModel
+
+ >>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
+ >>> model = MllamaVisionModel.from_pretrained(checkpoint)
+ >>> processor = AutoProcessor.from_pretrained(checkpoint)
+
+ >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> inputs = processor(images=image, return_tensors="pt")
+
+ >>> output = model(**inputs)
+
+ >>> print(output.last_hidden_state.shape)
+ torch.Size([1, 1, 4, 1025, 7680])
+ ```
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # num_concurrent_media and num_chunks are both currently 1
+ batch_size_times_num_tiles, num_channels, height, width = pixel_values.shape
+ num_concurrent_media = 1
+ num_chunks = 1
+ hidden_state = self.patch_embedding(pixel_values)
+ _, num_patches, hidden_dim = hidden_state.shape
+
+ # Add cls token
+ hidden_state = hidden_state.reshape(
+ batch_size_times_num_tiles * num_concurrent_media * num_chunks, num_patches, hidden_dim
+ )
+ class_embedding = self.class_embedding.expand(hidden_state.shape[0], 1, hidden_state.shape[-1])
+ hidden_state = torch.cat([hidden_state, class_embedding], dim=1)
+ num_patches += 1
+
+ # Position embeddings
+ hidden_state = hidden_state.reshape(
+ batch_size_times_num_tiles * num_concurrent_media, num_chunks, num_patches, hidden_dim
+ )
+ positional_embedding = self.positional_embedding_vlm.to(dtype=hidden_state.dtype, device=hidden_state.device)
+ hidden_state = hidden_state + positional_embedding
+
+ hidden_state = self.layernorm_pre(hidden_state)
+
+ hidden_state = hidden_state.view(batch_size_times_num_tiles, -1, hidden_dim)
+ freqs_ci = self.rotary_embedding(pixel_values)
+
+ output = self.model(
+ hidden_state,
+ attention_mask=None,
+ output_hidden_states=output_hidden_states,
+ output_attentions=output_attentions,
+ freqs_ci=freqs_ci,
+ )
+
+ hidden_state = output.last_hidden_state
+
+ hidden_state = self.layernorm_post(hidden_state)
+
+ hidden_state = hidden_state[:, :-1, :]
+
+ # now, we use Llama4VisionPixelShuffle + mlp to project embeddings
+ hidden_state = self.vision_adapter(hidden_state)
+
+ hidden_states = output.hidden_states if output_hidden_states else None
+
+ if output_attentions:
+ attentions = output[2]
+ else:
+ attentions = None
+
+ if not return_dict:
+ return tuple(v for v in [hidden_state, hidden_states, attentions] if v is not None)
+
+ return BaseModelOutput(
+ last_hidden_state=hidden_state,
+ hidden_states=hidden_states,
+ attentions=attentions,
+ )
+
+
+class Llama4ForConditionalGeneration(Llama4PreTrainedModel, GenerationMixin):
+ _no_split_modules = ["Llama4TextDecoderLayer", "Llama4VisionEncoderLayer"]
+ _tp_plan = {}
+ base_model_prefix = ""
+ config: Llama4Config
+
+ def __init__(self, config: Llama4Config):
+ super().__init__(config)
+ self.vision_model = Llama4VisionModel(config.vision_config)
+
+ self.multi_modal_projector = Llama4MultiModalProjector(config)
+ self.language_model = Llama4ForCausalLM(config.text_config)
+ self.vocab_size = config.text_config.vocab_size
+ self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def get_output_embeddings(self):
+ return self.language_model.get_output_embeddings()
+
+ def set_output_embeddings(self, new_embeddings):
+ self.language_model.set_output_embeddings(new_embeddings)
+
+ def set_decoder(self, decoder):
+ self.language_model.set_decoder(decoder)
+
+ def get_decoder(self):
+ return self.language_model.get_decoder()
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ vision_feature_layer: Union[int, list[int]],
+ vision_feature_select_strategy: str,
+ **kwargs,
+ ):
+ """
+ Obtains image last hidden states from the vision tower and apply al projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
+ The tensors corresponding to the input images.
+ vision_feature_layer (`Union[int, list[int]]`):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ Returns:
+ image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
+ """
+ if vision_feature_select_strategy not in ["default", "full"]:
+ raise ValueError(f"Unexpected select feature strategy: {self.vision_feature_select_strategy}")
+ kwargs = {k: v for k, v in kwargs.items() if v is not None}
+ image_outputs = self.vision_model(pixel_values, output_hidden_states=False, **kwargs)
+ hidden_state = image_outputs.last_hidden_state
+ return hidden_state
+
+ def get_placeholder_mask(
+ self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
+ ):
+ """
+ Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if inputs_embeds[special_image_mask].numel() != image_features.numel():
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {image_features.shape[0]}"
+ )
+ return special_image_mask
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ image_sizes: torch.Tensor = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, Llama4CausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import AutoProcessor, LlavaForConditionalGeneration
+
+ >>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf")
+ >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
+
+ >>> prompt = "USER: \nWhat's the content of the image? ASSISTANT:"
+ >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(**inputs, max_new_tokens=15)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "USER: \nWhat's the content of the image? ASSISTANT: The image features a busy city street with a stop sign prominently displayed"
+ ```"""
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer
+ if vision_feature_layer is not None
+ else self.config.vision_config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_config.vision_feature_select_strategy
+ )
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if pixel_values is not None and inputs_embeds is not None:
+ raise ValueError(
+ "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
+ )
+
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ if pixel_values is not None:
+ image_features = self.get_image_features(
+ pixel_values=pixel_values,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ image_sizes=image_sizes,
+ )
+
+ vision_flat = image_features.view(-1, image_features.size(-1))
+ projected_vision_flat = self.multi_modal_projector(vision_flat).to(
+ inputs_embeds.device, inputs_embeds.dtype
+ )
+ special_image_mask = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=projected_vision_flat
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, projected_vision_flat)
+
+ outputs = self.language_model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ logits = outputs[0]
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ if attention_mask is not None:
+ # we use the input attention mask to shift the logits and labels, because it is 2D.
+ # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
+ shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(logits.device)
+ shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
+ shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
+ else:
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = nn.CrossEntropyLoss()
+ loss = loss_fct(
+ shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
+ )
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return Llama4CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=image_features if pixel_values is not None else None,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ inputs_embeds=None,
+ pixel_values=None,
+ attention_mask=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+
+ model_inputs = self.language_model.prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ if cache_position[0] == 0:
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ model_inputs["pixel_values"] = pixel_values
+
+ return model_inputs
+
+
+__all__ = [
+ "Llama4PreTrainedModel",
+ "Llama4TextModel",
+ "Llama4VisionModel",
+ "Llama4ForCausalLM",
+ "Llama4ForConditionalGeneration",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/processing_llama4.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/processing_llama4.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ff76403f436a6c2298bd18942ad02f0c9c7cb11
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llama4/processing_llama4.py
@@ -0,0 +1,258 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from typing import Optional, Union
+
+from transformers.processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+from ...image_processing_utils import BatchFeature
+from ...image_utils import ImageInput, make_flat_list_of_images
+
+
+class Llama4ImagesKwargs(ImagesKwargs, total=False):
+ max_patches: Optional[int]
+ resize_to_max_canvas: Optional[bool]
+
+
+class Llama4ProcessorKwargs(ProcessingKwargs, total=False):
+ images_kwargs: Llama4ImagesKwargs
+ _defaults = {
+ "text_kwargs": {
+ "padding_side": "left",
+ },
+ }
+
+
+chat_template = "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %} \n {%- if messages[0]['content'] is string %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- else %}\n {#- FIXME: The processor requires an array, always. #}\n {%- set system_message = messages[0]['content'][0]['text']|trim %}\n {%- endif %}\n {%- set messages = messages[1:] %}\n {%- set user_supplied_system_message = true %}\n{%- else %}\n {%- set system_message = \"\" %}\n {%- set user_supplied_system_message = false %}\n{%- endif %}\n\n{#- System message if the user supplied one #}\n{%- if user_supplied_system_message %}\n {{- \"<|header_start|>system<|header_end|>\n\n\" }}\n {%- if tools is not none %}\n {{- \"Environment: ipython\n\" }}\n {%- endif %}\n {%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\n\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\n\n\" }}\n {%- endfor %}\n {%- endif %}\n {{- system_message }}\n {{- \"<|eot|>\" }}\n{%- endif %}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|header_start|>user<|header_end|>\n\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\n\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\n\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\n\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|header_start|>' + message['role'] + '<|header_end|>\n\n' }}\n {%- if message['content'] is string %}\n {{- message['content'] }}\n {%- else %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {{- '<|image|>' }}\n {%- elif content['type'] == 'text' %}\n {{- content['text'] }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {{- \"<|eot|>\" }}\n {%- elif 'tool_calls' in message and message.tool_calls|length > 0 %}\n {{- '<|header_start|>assistant<|header_end|>\n\n' -}}\n {{- '<|python_start|>' }}\n {%- if message['content'] is string %}\n {{- message['content'] }}\n {%- else %}\n {%- for content in message['content'] %}\n {%- if content['type'] == 'image' %}\n {{- '<|image|>' }}\n {%- elif content['type'] == 'text' %}\n {{- content['text'] }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {{- '<|python_end|>' }}\n {%- for tool_call in message.tool_calls %}\n {{- '{\"name\": \"' + tool_call.function.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.function.arguments | tojson }}\n {{- \"}\" }}\n {%- endfor %}\n {{- \"<|eot|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|header_start|>ipython<|header_end|>\n\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|header_start|>assistant<|header_end|>\n\n' }}\n{%- endif %}\n"
+
+
+class Llama4Processor(ProcessorMixin):
+ r"""
+ Constructs a Llama4 processor which wraps a [`AutoImageProcessor`] and
+ [`PretrainedTokenizerFast`] tokenizer into a single processor that inherits both the image processor and
+ tokenizer functionalities. See the [`~Llama4Processor.__call__`] and [`~Llama4Processor.decode`] for more information.
+ Args:
+ image_processor ([`AutoImageProcessor`], *optional*):
+ The image processor is a required input.
+ tokenizer ([`PreTrainedTokenizer`, `PreTrainedTokenizerFast`], *optional*):
+ The tokenizer is a required input.
+ patch_size (`int`, *optional*, defaults to 28):
+ The size of image patches for tokenization.
+ img_size (`int`, *optional*, defaults to 364):
+ The size of the image to be tokenized. This should correspond to the size given to the image processor.
+ image_token (`str`, *optional*, defaults to `"<|image|>"`):
+ The token to be used to represent an image in the text.
+ downsample_factor (`int`, *optional*, defaults to 1):
+ The factor by which to scale the patch size.
+ start_of_img_token (`str`, *optional*, defaults to `"<|START_OF_IMG|>"`):
+ The token to be used to represent the start of an image in the text.
+ end_of_img_token (`str`, *optional*, defaults to `"<|END_OF_IMG|>"`):
+ The token to be used to represent the end of an image in the text.
+ img_patch_token (`str`, *optional*, defaults to `"<|IMG_PATCH|>"`):
+ The token to be used to represent an image patch in the text.
+ img_line_break_token (`str`, *optional*, defaults to `"<|IMG_LINE_BREAK|>"`):
+ The token to be used to represent a line break in the text.
+ tile_token (`str`, *optional*, defaults to `"TILE"`):
+ The token to be used to represent an image patch in the text.
+ tile_global_token (`str`, *optional*, defaults to `"TILE_GLOBAL"`):
+ The token to be used to represent the cover image in the text.
+ chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+ in a chat into a tokenizable string.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "AutoImageProcessor"
+ tokenizer_class = "AutoTokenizer"
+
+ def __init__(
+ self,
+ image_processor=None,
+ tokenizer=None,
+ patch_size: int = 14,
+ pixel_shuffle_ratio: float = 0.5,
+ fake_image_token="<|image|>",
+ image_token="<|image|>",
+ start_of_image_token="<|image_start|>",
+ end_of_image_token="<|image_end|>",
+ patch_token="<|patch|>",
+ tile_x_separator_token="<|tile_x_separator|>",
+ tile_y_separator_token="<|tile_y_separator|>",
+ chat_template=chat_template,
+ **kwargs,
+ ):
+ super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+ self.downsample_ratio = int(round(1.0 / (pixel_shuffle_ratio**2)))
+ self.patch_size = patch_size
+
+ self.fake_image_token = fake_image_token
+ self.image_token = image_token
+ self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
+ self.start_of_img_token = start_of_image_token
+ self.end_of_img_token = end_of_image_token
+ self.img_patch_token = patch_token
+ self.tile_token = tile_x_separator_token
+ self.tile_global_token = tile_y_separator_token
+
+ def _prompt_split_image(self, aspect_ratio, num_patches_per_chunk):
+ """
+ Create a structured string representation of image tokens
+
+ Args:
+ num_patches: Number of patches in the image
+
+ Returns:
+ String with appropriate image tokens
+ """
+ img_string = "<|image_start|>"
+ ratio_h, ratio_w = aspect_ratio
+ if ratio_h * ratio_w > 1:
+ for yy in range(ratio_h):
+ for xx in range(ratio_w):
+ img_string += "<|patch|>" * num_patches_per_chunk
+ if xx < ratio_w - 1:
+ img_string += "<|tile_x_separator|>"
+
+ img_string += "<|tile_y_separator|>"
+
+ img_string += "<|image|>"
+ img_string += "<|patch|>" * num_patches_per_chunk
+ img_string += "<|image_end|>"
+
+ return img_string
+
+ def __call__(
+ self,
+ images: Optional[ImageInput] = None,
+ text: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]] = None,
+ audio=None,
+ videos=None,
+ **kwargs: Unpack[Llama4ProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+ and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] to encode the text.
+ To prepare the vision inputs, this method forwards the `images` and `kwargs` arguments to
+ Llama4ImageProcessor's [`~Llama4ImageProcessor.__call__`] if `images` is not `None`.
+
+ Args:
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors of a particular framework. Acceptable values are:
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return NumPy `np.ndarray` objects.
+ - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+ `None`).
+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+ """
+ if text is None:
+ raise ValueError("You have to specify text.")
+
+ output_kwargs = self._merge_kwargs(
+ Llama4ProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+
+ if not isinstance(text, (list, tuple)):
+ text = [text]
+
+ # Process images
+ image_inputs = {}
+ if images is not None:
+ images = make_flat_list_of_images(images)
+ image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
+ image_height, image_width = image_inputs["pixel_values"][0].shape[-2:]
+ num_patches_per_chunk = int(
+ (image_height // self.patch_size) * (image_width // self.patch_size) // self.downsample_ratio
+ )
+ aspect_ratios = image_inputs.pop("aspect_ratios")
+
+ total_placeholders = sum(prompt.count(self.fake_image_token) for prompt in text)
+ if total_placeholders != len(images):
+ raise ValueError(
+ f"Found {total_placeholders} placeholders across the batch, "
+ f"but have {len(images)} flattened images."
+ )
+
+ image_index = 0
+ processed_text = []
+ for prompt in text:
+ placeholder_count = prompt.count(self.fake_image_token)
+ if placeholder_count == 0:
+ # do nothing if there is no image
+ processed_text.append(prompt)
+ continue
+ prompt_splits = prompt.split(self.fake_image_token)
+ new_prompt = []
+ for local_image_index, split_part in enumerate(prompt_splits):
+ new_prompt.append(split_part)
+ if local_image_index < placeholder_count:
+ tokens_for_this_image = self._prompt_split_image(
+ aspect_ratios[image_index], num_patches_per_chunk
+ )
+ image_index += 1
+ new_prompt.append(tokens_for_this_image)
+ processed_text.append("".join(new_prompt))
+
+ if image_index != len(images):
+ raise ValueError("Number of image placeholders in the prompt does not match the number of images.")
+
+ text = processed_text
+
+ return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+ text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+ self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
+
+ return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+ return list(tokenizer_input_names) + list(image_processor_input_names)
+
+
+__all__ = ["Llama4Processor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..aadd45dc13ed4074437cc6f224b0348de110f292
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_llava import *
+ from .image_processing_llava_fast import *
+ from .modeling_llava import *
+ from .processing_llava import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/configuration_llava.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/configuration_llava.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ae710c011986f422d965adcdb758b29e3113690
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/configuration_llava.py
@@ -0,0 +1,137 @@
+# coding=utf-8
+# Copyright 2023 Microsoft Research & University of Wisconsin-Madison and the HuggingFace Inc. team. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Llava model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class LlavaConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LlavaForConditionalGeneration`]. It is used to instantiate an
+ Llava model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Llava-9B.
+
+ e.g. [llava-hf/llava-9b](https://huggingface.co/llava-hf/llava-9b)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`):
+ The config object or dictionary of the vision backbone.
+ text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
+ The config object or dictionary of the text backbone.
+ image_token_index (`int`, *optional*, defaults to 32000):
+ The image token index to encode the image prompt.
+ projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
+ The activation function used by the multimodal projector.
+ vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`.
+ vision_feature_layer (`Union[int, list[int]]`, *optional*, defaults to -2):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ image_seq_length (`int`, *optional*, defaults to 576):
+ Sequence length of one image embedding.
+ multimodal_projector_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use bias in the multimodal projector.
+
+ Example:
+
+ ```python
+ >>> from transformers import LlavaForConditionalGeneration, LlavaConfig, CLIPVisionConfig, LlamaConfig
+
+ >>> # Initializing a CLIP-vision config
+ >>> vision_config = CLIPVisionConfig()
+
+ >>> # Initializing a Llama config
+ >>> text_config = LlamaConfig()
+
+ >>> # Initializing a Llava llava-1.5-7b style configuration
+ >>> configuration = LlavaConfig(vision_config, text_config)
+
+ >>> # Initializing a model from the llava-1.5-7b style configuration
+ >>> model = LlavaForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "llava"
+ attribute_map = {
+ "image_token_id": "image_token_index",
+ }
+ sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
+
+ def __init__(
+ self,
+ vision_config=None,
+ text_config=None,
+ image_token_index=32000,
+ projector_hidden_act="gelu",
+ vision_feature_select_strategy="default",
+ vision_feature_layer=-2,
+ image_seq_length=576,
+ multimodal_projector_bias=True,
+ **kwargs,
+ ):
+ self.image_token_index = image_token_index
+ self.projector_hidden_act = projector_hidden_act
+ self.image_seq_length = image_seq_length
+
+ if vision_feature_select_strategy not in ["default", "full"]:
+ raise ValueError(
+ "vision_feature_select_strategy should be one of 'default', 'full'."
+ f"Got: {vision_feature_select_strategy}"
+ )
+
+ self.vision_feature_select_strategy = vision_feature_select_strategy
+ self.vision_feature_layer = vision_feature_layer
+
+ if isinstance(vision_config, dict):
+ vision_config["model_type"] = vision_config.get("model_type", "clip_vision_model")
+ vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
+ elif vision_config is None:
+ vision_config = CONFIG_MAPPING["clip_vision_model"](
+ intermediate_size=4096,
+ hidden_size=1024,
+ patch_size=14,
+ image_size=336,
+ num_hidden_layers=24,
+ num_attention_heads=16,
+ vocab_size=32000,
+ projection_dim=768,
+ )
+
+ self.vision_config = vision_config
+
+ if isinstance(text_config, dict):
+ text_config["model_type"] = text_config.get("model_type", "llama")
+ text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+ elif text_config is None:
+ text_config = CONFIG_MAPPING["llama"]()
+
+ self.text_config = text_config
+ self.multimodal_projector_bias = multimodal_projector_bias
+
+ super().__init__(**kwargs)
+
+
+__all__ = ["LlavaConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/convert_llava_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/convert_llava_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d9609cb63a77655e1a44cb1f6f16242f6b6cd09
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/convert_llava_weights_to_hf.py
@@ -0,0 +1,202 @@
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import glob
+
+import torch
+from huggingface_hub import file_exists, hf_hub_download, snapshot_download
+from safetensors import safe_open
+
+from transformers import (
+ AddedToken,
+ AutoConfig,
+ AutoImageProcessor,
+ AutoTokenizer,
+ LlavaConfig,
+ LlavaForConditionalGeneration,
+ LlavaProcessor,
+ SiglipVisionConfig,
+)
+
+
+EPILOG_TXT = """Example:
+ python transformers/src/transformers/models/llava/convert_llava_weights_to_hf.py --text_model_id lmsys/vicuna-7b-v1.5 --vision_model_id openai/clip-vit-large-patch14-336 --output_hub_path org/llava-v1.5-7b-conv --old_state_dict_id liuhaotian/llava-v1.5-7b
+
+Example for creating the old state dict file with Python:
+
+ import torch
+ from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM
+
+ # load model
+ kwargs = {"device_map": "auto", "torch_dtype": torch.float16}
+ model = LlavaLlamaForCausalLM.from_pretrained("liuhaotian/llava-v1.5-7b", **kwargs)
+
+ # load vision tower
+ model.get_vision_tower().load_model()
+
+ # Save state dict
+ torch.save(model.state_dict(), "tmp/hf_models/llava-v1.5-7b/model_state_dict.bin")
+"""
+
+KEYS_TO_MODIFY_MAPPING = {
+ "model.vision_tower.": "",
+ ".vision_resampler": "", # all lmms-lab models do avg pooling, so no vision_resampler
+ "model.mm_projector": "multi_modal_projector",
+ "model": "model.model",
+ "vision_model.model": "vision_model",
+ "lm_head": "language_model.lm_head",
+ "model.model": "language_model.model",
+ "multi_modal_projector.0": "multi_modal_projector.linear_1",
+ "multi_modal_projector.2": "multi_modal_projector.linear_2",
+}
+
+
+def load_original_state_dict(model_id):
+ directory_path = snapshot_download(repo_id=model_id, allow_patterns=["*.safetensors"])
+
+ original_state_dict = {}
+ for path in glob.glob(f"{directory_path}/*"):
+ if path.endswith(".safetensors"):
+ with safe_open(path, framework="pt", device="cpu") as f:
+ for key in f.keys():
+ original_state_dict[key] = f.get_tensor(key)
+
+ # tied weights so lm.head is not saved. Let's clone to load state dict
+ if "lm_head.weight" not in original_state_dict:
+ original_state_dict["lm_head.weight"] = original_state_dict["model.embed_tokens.weight"].clone()
+
+ if "model.image_newline" in original_state_dict:
+ # not used in the original implementation because "merge_type=flat"
+ del original_state_dict["model.image_newline"]
+ return original_state_dict
+
+
+# used only for llava-interlave
+# for ex: Qwen/Qwen1.5-0.5B-Chat google/siglip-so400m-patch14-384 lmms-lab/llava-next-interleave-qwen-0.5b
+def convert_state_dict_to_hf(state_dict):
+ new_state_dict = {}
+ for key, value in state_dict.items():
+ if key.endswith(".inv_freq"):
+ continue
+ for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
+ if key_to_modify in key:
+ key = key.replace(key_to_modify, new_key)
+
+ new_state_dict[key] = value
+ return new_state_dict
+
+
+def convert_llava_llama_to_hf(text_model_id, vision_model_id, output_hub_path, old_state_dict_id):
+ torch.set_default_dtype(torch.float16)
+ text_config = AutoConfig.from_pretrained(text_model_id)
+
+ tokenizer = AutoTokenizer.from_pretrained(text_model_id)
+ tokenizer.add_tokens(AddedToken("", special=True, normalized=False), special_tokens=True)
+ if "Qwen" not in text_model_id: # qwen already has a pad token
+ tokenizer.add_special_tokens({"pad_token": ""})
+
+ image_processor = AutoImageProcessor.from_pretrained(vision_model_id)
+ processor = LlavaProcessor(tokenizer=tokenizer, image_processor=image_processor)
+
+ if "siglip" in vision_model_id:
+ vision_config = SiglipVisionConfig(
+ hidden_size=1152,
+ image_size=384,
+ intermediate_size=4304,
+ num_attention_heads=16,
+ num_hidden_layers=26,
+ patch_size=14,
+ vision_use_head=False,
+ ).to_dict()
+ else:
+ vision_config = None
+
+ config = LlavaConfig(
+ text_config=text_config,
+ vision_config=vision_config,
+ )
+
+ # llms-lab interleave models do not use any selection strategy except for last hidden state
+ if "Qwen" in text_model_id:
+ config.image_token_id = 151646
+ if "siglip" in vision_model_id:
+ config.vision_feature_select_strategy = "full"
+ config.vision_feature_layer = -1
+ else:
+ config.pad_token_id = 32001
+ config.image_token_id = 32000
+
+ with torch.device("meta"):
+ model = LlavaForConditionalGeneration(config)
+
+ # Some llava variants like microsoft/llava-med-v1.5-mistral-7b use safetensors to store weights
+ if file_exists(old_state_dict_id, "model_state_dict.bin"):
+ state_dict_path = hf_hub_download(old_state_dict_id, "model_state_dict.bin")
+ state_dict = torch.load(state_dict_path, map_location="cpu", weights_only=True)
+ else:
+ state_dict = load_original_state_dict(old_state_dict_id)
+
+ state_dict = convert_state_dict_to_hf(state_dict)
+ model.load_state_dict(state_dict, strict=True, assign=True)
+
+ pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data
+ mu = torch.mean(pre_expansion_embeddings, dim=0).float()
+ n = pre_expansion_embeddings.size()[0]
+ sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n
+ dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma)
+
+ # We add an image token so we resize the model and pad to 64 for performance reasons
+ pad_shape = 64
+ vocab_size = config.text_config.vocab_size
+ model.resize_token_embeddings(config.text_config.vocab_size + 2, pad_shape)
+ model.language_model.model.embed_tokens.weight.data[vocab_size:] = torch.stack(
+ tuple(dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[vocab_size:].shape[0])),
+ dim=0,
+ )
+ model.language_model.lm_head.weight.data[vocab_size:] = torch.stack(
+ tuple(dist.sample() for _ in range(model.language_model.lm_head.weight.data[vocab_size:].shape[0])),
+ dim=0,
+ )
+
+ model.push_to_hub(output_hub_path)
+ processor.push_to_hub(output_hub_path)
+
+
+def main():
+ parser = argparse.ArgumentParser(
+ epilog=EPILOG_TXT,
+ formatter_class=argparse.RawDescriptionHelpFormatter,
+ )
+ parser.add_argument(
+ "--text_model_id",
+ help="Hub location of the text model",
+ )
+ parser.add_argument(
+ "--vision_model_id",
+ help="Hub location of the vision model",
+ )
+ parser.add_argument(
+ "--output_hub_path",
+ help="Location on the hub of the converted model",
+ )
+ parser.add_argument(
+ "--old_state_dict_id",
+ help="Location on the hub of the raw state dict of the original model. The filename needs to be `model_state_dict.bin`",
+ )
+ args = parser.parse_args()
+ convert_llava_llama_to_hf(args.text_model_id, args.vision_model_id, args.output_hub_path, args.old_state_dict_id)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/image_processing_llava.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/image_processing_llava.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa737bd9b9c696068c0bc616f2deec65cbee82c2
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/image_processing_llava.py
@@ -0,0 +1,436 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for LLaVa."""
+
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import (
+ convert_to_rgb,
+ get_resize_output_image_size,
+ resize,
+ to_channel_dimension_format,
+)
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ get_image_size,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_kwargs,
+ validate_preprocess_arguments,
+)
+from ...utils import TensorType, is_vision_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+if is_vision_available():
+ import PIL
+
+
+class LlavaImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a LLaVa image processor.
+
+ Args:
+ do_pad (`bool`, *optional*, defaults to `False`):
+ Whether to pad the image to a square based on the longest edge.
+ The padding value is determined by the `image_mean` parameter.
+ Can be overridden by `do_pad` in the `preprocess` method.
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
+ `do_resize` in the `preprocess` method.
+ size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
+ Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
+ method.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+ Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
+ do_center_crop (`bool`, *optional*, defaults to `True`):
+ Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the
+ `preprocess` method.
+ crop_size (`dict[str, int]` *optional*, defaults to 224):
+ Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
+ method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
+ the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
+ method.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`float` or `list[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
+ Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+ number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+ Can be overridden by the `image_std` parameter in the `preprocess` method.
+ do_convert_rgb (`bool`, *optional*, defaults to `True`):
+ Whether to convert the image to RGB.
+ """
+
+ model_input_names = ["pixel_values"]
+
+ def __init__(
+ self,
+ do_pad: bool = False,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ do_center_crop: bool = True,
+ crop_size: Optional[dict[str, int]] = None,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_convert_rgb: bool = True,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"shortest_edge": 224}
+ size = get_size_dict(size, default_to_square=False)
+ crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
+ crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")
+
+ self.do_pad = do_pad
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.do_center_crop = do_center_crop
+ self.crop_size = crop_size
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
+ self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+ self.do_convert_rgb = do_convert_rgb
+ self._valid_processor_keys = [
+ "images",
+ "do_pad",
+ "do_resize",
+ "size",
+ "resample",
+ "do_center_crop",
+ "crop_size",
+ "do_rescale",
+ "rescale_factor",
+ "do_normalize",
+ "image_mean",
+ "image_std",
+ "do_convert_rgb",
+ "return_tensors",
+ "data_format",
+ "input_data_format",
+ ]
+
+ def pad_to_square(
+ self,
+ image: np.ndarray,
+ background_color: Union[int, tuple[int, int, int]] = 0,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.array:
+ """
+ Pads an image to a square based on the longest edge.
+
+ Args:
+ image (`np.ndarray`):
+ The image to pad.
+ background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+ The color to use for the padding. Can be an integer for single channel or a
+ tuple of integers representing for multi-channel images. If passed as integer
+ in mutli-channel mode, it will default to `0` in subsequent channels.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use same as the input image.
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use the inferred format of the input image.
+
+ Returns:
+ `np.ndarray`: The padded image.
+ """
+ height, width = get_image_size(image, input_data_format)
+ num_channels = image.shape[0] if input_data_format == ChannelDimension.FIRST else image.shape[-1]
+
+ if height == width:
+ image = (
+ to_channel_dimension_format(image, data_format, input_data_format)
+ if data_format is not None
+ else image
+ )
+ return image
+
+ max_dim = max(height, width)
+
+ # Ensure background_color is the correct shape
+ if isinstance(background_color, int):
+ background_color = [background_color]
+ elif len(background_color) != num_channels:
+ raise ValueError(
+ f"background_color must have no more than {num_channels} elements to match the number of channels"
+ )
+
+ if input_data_format == ChannelDimension.FIRST:
+ result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype)
+ for i, color in enumerate(background_color):
+ result[i, :, :] = color
+ if width > height:
+ start = (max_dim - height) // 2
+ result[:, start : start + height, :] = image
+ else:
+ start = (max_dim - width) // 2
+ result[:, :, start : start + width] = image
+ else:
+ result = np.zeros((max_dim, max_dim, num_channels), dtype=image.dtype)
+ for i, color in enumerate(background_color):
+ result[:, :, i] = color
+ if width > height:
+ start = (max_dim - height) // 2
+ result[start : start + height, :, :] = image
+ else:
+ start = (max_dim - width) // 2
+ result[:, start : start + width, :] = image
+
+ image = (
+ to_channel_dimension_format(result, data_format, input_data_format) if data_format is not None else result
+ )
+ return image
+
+ # Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
+ resized to keep the input aspect ratio.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Size of the output image.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ Resampling filter to use when resiizing the image.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ default_to_square = True
+ if "shortest_edge" in size:
+ size = size["shortest_edge"]
+ default_to_square = False
+ elif "height" in size and "width" in size:
+ size = (size["height"], size["width"])
+ else:
+ raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
+ output_size = get_resize_output_image_size(
+ image,
+ size=size,
+ default_to_square=default_to_square,
+ input_data_format=input_data_format,
+ )
+ return resize(
+ image,
+ size=output_size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ def preprocess(
+ self,
+ images: ImageInput,
+ do_pad: Optional[bool] = None,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: Optional[PILImageResampling] = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[int] = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_convert_rgb: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> PIL.Image.Image:
+ """
+ Preprocess an image or batch of images.
+
+ Args:
+ images (`ImageInput`):
+ Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ do_pad (`bool`, *optional*, defaults to `self.do_pad`):
+ Whether to pad the image to a square based on the longest edge.
+ The padding value is determined by the `image_mean` parameter.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio.
+ resample (`int`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+ has an effect if `do_resize` is set to `True`.
+ do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
+ Whether to center crop the image.
+ crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`):
+ Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image.
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+ `True`.
+ do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+ Whether to convert the image to RGB.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ do_pad = do_pad if do_pad is not None else self.do_pad
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ size = get_size_dict(size, param_name="size", default_to_square=False)
+ resample = resample if resample is not None else self.resample
+ do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
+ crop_size = crop_size if crop_size is not None else self.crop_size
+ crop_size = get_size_dict(crop_size, param_name="crop_size", default_to_square=True)
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+ validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
+
+ images = make_list_of_images(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+ # we don't pass `do_pad` here since LLaVa uses a custom padding to a square
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ if do_convert_rgb:
+ images = [convert_to_rgb(image) for image in images]
+
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if is_scaled_image(images[0]) and do_rescale:
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ processed_images = []
+ for image in images:
+ if do_pad:
+ image = self.pad_to_square(
+ image=image,
+ background_color=tuple(int(x * 255) for x in self.image_mean),
+ input_data_format=input_data_format,
+ )
+
+ if do_resize:
+ image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+
+ if do_center_crop:
+ image = self.center_crop(image=image, size=crop_size, input_data_format=input_data_format)
+
+ if do_rescale:
+ image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+
+ if do_normalize:
+ image = self.normalize(
+ image=image, mean=image_mean, std=image_std, input_data_format=input_data_format
+ )
+
+ image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+ processed_images.append(image)
+
+ return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
+
+
+__all__ = ["LlavaImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/image_processing_llava_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/image_processing_llava_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a1502f78d4434dd3ea7ead4ec518bf373f3d239
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/image_processing_llava_fast.py
@@ -0,0 +1,191 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for LLaVa."""
+
+from typing import Optional, Union
+
+from ...image_processing_utils import BatchFeature
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ SizeDict,
+ get_image_size,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+ is_vision_available,
+)
+
+
+if is_vision_available():
+ from ...image_utils import PILImageResampling
+
+if is_torch_available():
+ import torch
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+class LlavaFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ """
+ Args:
+ do_pad (`bool`, *optional*):
+ Whether to pad the image to a square based on the longest edge.
+ """
+
+ do_pad: Optional[bool]
+
+
+@auto_docstring
+class LlavaImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BICUBIC
+ image_mean = OPENAI_CLIP_MEAN
+ image_std = OPENAI_CLIP_STD
+ size = {"shortest_edge": 224}
+ default_to_square = False
+ crop_size = {"height": 224, "width": 224}
+ do_pad = False
+ do_resize = True
+ do_center_crop = True
+ do_rescale = True
+ do_normalize = True
+ do_convert_rgb = True
+ valid_kwargs = LlavaFastImageProcessorKwargs
+
+ def __init__(self, **kwargs: Unpack[LlavaFastImageProcessorKwargs]) -> None:
+ super().__init__(**kwargs)
+
+ @auto_docstring
+ def preprocess(self, images: ImageInput, **kwargs: Unpack[LlavaFastImageProcessorKwargs]) -> BatchFeature:
+ return super().preprocess(images, **kwargs)
+
+ def pad_to_square(
+ self,
+ images: "torch.Tensor",
+ background_color: Union[int, tuple[int, int, int]] = 0,
+ ) -> "torch.Tensor":
+ """
+ Pads an image to a square based on the longest edge.
+
+ Args:
+ images (`np.ndarray`):
+ The images to pad.
+ background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+ The color to use for the padding. Can be an integer for single channel or a
+ tuple of integers representing for multi-channel images. If passed as integer
+ in mutli-channel mode, it will default to `0` in subsequent channels.
+ Returns:
+ `torch.Tensor`: The padded images.
+ """
+ height, width = get_image_size(images, ChannelDimension.FIRST)
+
+ if height == width:
+ return images
+
+ num_channels = images.shape[1] if len(images.shape) == 4 else images.shape[0]
+ if isinstance(background_color, int):
+ background_color = [background_color] + [0] * (num_channels - 1)
+ elif len(background_color) != num_channels:
+ raise ValueError(
+ f"background_color must have no more than {num_channels} elements to match the number of channels"
+ )
+
+ max_dim = max(height, width)
+ paste_x_left = (max_dim - width) // 2
+ paste_y_left = (max_dim - height) // 2
+ paste_x_right = max_dim - width - paste_x_left
+ paste_y_right = max_dim - height - paste_y_left
+ padded_images = F.pad(
+ images, padding=[paste_x_left, paste_y_left, paste_x_right, paste_y_right], fill=background_color
+ )
+
+ return padded_images
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ do_resize: bool,
+ size: SizeDict,
+ interpolation: Optional["F.InterpolationMode"],
+ do_pad: bool,
+ do_center_crop: bool,
+ crop_size: SizeDict,
+ do_rescale: bool,
+ rescale_factor: float,
+ do_normalize: bool,
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ disable_grouping: Optional[bool],
+ return_tensors: Optional[Union[str, TensorType]],
+ ) -> BatchFeature:
+ # Group images by size for batched resizing
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ resized_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_pad:
+ stacked_images = self.pad_to_square(
+ images=stacked_images, background_color=tuple(int(x * 255) for x in self.image_mean)
+ )
+ resized_images_grouped[shape] = stacked_images
+ padded_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+ # Group images by size for batched resizing
+ # Needed in case do_pad is False, or padding returns images with different sizes
+ grouped_images, grouped_images_index = group_images_by_shape(padded_images, disable_grouping=disable_grouping)
+ resized_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_resize:
+ stacked_images = self.resize(image=stacked_images, size=size, interpolation=interpolation)
+ resized_images_grouped[shape] = stacked_images
+ resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+ # Group images by size for further processing
+ # Needed in case do_resize is False, or resize returns images with different sizes
+ grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+ processed_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_center_crop:
+ stacked_images = self.center_crop(stacked_images, crop_size)
+ # Fused rescale and normalize
+ stacked_images = self.rescale_and_normalize(
+ stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ processed_images_grouped[shape] = stacked_images
+
+ processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+ processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+
+ return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
+
+
+__all__ = ["LlavaImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/modeling_llava.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/modeling_llava.py
new file mode 100644
index 0000000000000000000000000000000000000000..f81555279410099734a5b2117a7ba5774355439b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/modeling_llava.py
@@ -0,0 +1,514 @@
+# coding=utf-8
+# Copyright 2023 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Llava model."""
+
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache
+from ...generation import GenerationMixin
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_outputs import BaseModelOutputWithPast, ModelOutput
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ..auto import AutoModel
+from .configuration_llava import LlavaConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Llava outputs, with hidden states and attentions.
+ """
+)
+class LlavaModelOutputWithPast(BaseModelOutputWithPast):
+ r"""
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Llava causal language model (or autoregressive) outputs.
+ """
+)
+class LlavaCausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+
+class LlavaMultiModalProjector(nn.Module):
+ def __init__(self, config: LlavaConfig):
+ super().__init__()
+ # We have hidden_size * the number of vision feature layers
+ num_feature_layers = 1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer)
+ self.linear_1 = nn.Linear(
+ config.vision_config.hidden_size * num_feature_layers,
+ config.text_config.hidden_size,
+ bias=config.multimodal_projector_bias,
+ )
+ self.act = ACT2FN[config.projector_hidden_act]
+ self.linear_2 = nn.Linear(
+ config.text_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
+ )
+
+ def forward(self, image_features):
+ hidden_states = self.linear_1(image_features)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.linear_2(hidden_states)
+ return hidden_states
+
+
+@auto_docstring
+class LlavaPreTrainedModel(PreTrainedModel):
+ config: LlavaConfig
+ base_model_prefix = ""
+ supports_gradient_checkpointing = True
+ _skip_keys_device_placement = "past_key_values"
+
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+ _supports_flex_attn = True
+ _supports_attention_backend = True
+
+
+@auto_docstring(
+ custom_intro="""
+ The Llava model which consists of a vision backbone and a language model, without a language modeling head.
+ """
+)
+class LlavaModel(LlavaPreTrainedModel):
+ _checkpoint_conversion_mapping = {"language_model.model": "language_model"}
+
+ def __init__(self, config: LlavaConfig):
+ super().__init__(config)
+ self.vision_tower = AutoModel.from_config(config.vision_config)
+
+ self.multi_modal_projector = LlavaMultiModalProjector(config)
+ self.language_model = AutoModel.from_config(config.text_config)
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def set_decoder(self, decoder):
+ self.language_model = decoder
+
+ def get_decoder(self):
+ return self.language_model
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ **kwargs,
+ ):
+ """
+ Obtains image last hidden states from the vision tower and apply multimodal projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
+ The tensors corresponding to the input images.
+ vision_feature_layer (`Union[int, list[int]]`, *optional*):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`, *optional*):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ Returns:
+ image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
+ """
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ if vision_feature_select_strategy not in ["default", "full"]:
+ raise ValueError(f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}")
+
+ kwargs = {k: v for k, v in kwargs.items() if v is not None}
+ # this is not memory efficient at all (output_hidden_states=True) will save all the hidden states.
+ image_outputs = self.vision_tower(pixel_values, output_hidden_states=True, **kwargs)
+
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
+ if vision_feature_select_strategy == "default":
+ selected_image_feature = selected_image_feature[:, 1:]
+ else:
+ hs_pool = [image_outputs.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ # For default; crop CLS from each hidden state in the hidden state pool
+ if vision_feature_select_strategy == "default":
+ hs_pool = [hs[:, 1:] for hs in hs_pool]
+ selected_image_feature = torch.cat(hs_pool, dim=-1)
+
+ image_features = self.multi_modal_projector(selected_image_feature)
+
+ if "image_sizes" in kwargs:
+ split_sizes = [
+ (height // self.vision_tower.patch_size) * (width // self.vision_tower.patch_size)
+ for height, width in kwargs["image_sizes"]
+ ]
+ image_features = torch.split(image_features.squeeze(0), split_sizes)
+ else:
+ image_features = list(image_features)
+ return image_features
+
+ def get_placeholder_mask(
+ self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
+ ):
+ """
+ Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ n_image_features = image_features.shape[0] * image_features.shape[1]
+ if inputs_embeds[special_image_mask].numel() != image_features.numel():
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+ )
+ return special_image_mask
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ image_sizes: torch.Tensor = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, LlavaModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ if pixel_values is not None:
+ image_features = self.get_image_features(
+ pixel_values=pixel_values,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ image_sizes=image_sizes,
+ )
+ image_features = torch.cat(image_features, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+ special_image_mask = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+ outputs = self.language_model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return LlavaModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=image_features if pixel_values is not None else None,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The LLAVA model which consists of a vision backbone and a language model.
+ """
+)
+class LlavaForConditionalGeneration(LlavaPreTrainedModel, GenerationMixin):
+ _checkpoint_conversion_mapping = {
+ "^language_model.model": "model.language_model",
+ "^vision_tower": "model.vision_tower",
+ "^multi_modal_projector": "model.multi_modal_projector",
+ "^language_model.lm_head": "lm_head",
+ }
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: LlavaConfig):
+ super().__init__(config)
+ self.model = LlavaModel(config)
+ self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ def get_output_embeddings(self) -> nn.Module:
+ return self.lm_head
+
+ def set_decoder(self, decoder):
+ self.model.set_decoder(decoder)
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ **kwargs,
+ ):
+ return self.model.get_image_features(
+ pixel_values=pixel_values,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ **kwargs,
+ )
+
+ # Make modules available through conditional class for BC
+ @property
+ def language_model(self):
+ return self.model.language_model
+
+ @property
+ def vision_tower(self):
+ return self.model.vision_tower
+
+ @property
+ def multi_modal_projector(self):
+ return self.model.multi_modal_projector
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ image_sizes: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, LlavaCausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import AutoProcessor, LlavaForConditionalGeneration
+
+ >>> model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf")
+ >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
+
+ >>> prompt = "USER: \nWhat's the content of the image? ASSISTANT:"
+ >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(**inputs, max_new_tokens=15)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "USER: \nWhat's the content of the image? ASSISTANT: The image features a busy city street with a stop sign prominently displayed"
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ image_sizes=image_sizes,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return LlavaCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ inputs_embeds=None,
+ pixel_values=None,
+ attention_mask=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ if cache_position[0] == 0:
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ model_inputs["pixel_values"] = pixel_values
+
+ return model_inputs
+
+
+__all__ = ["LlavaForConditionalGeneration", "LlavaPreTrainedModel", "LlavaModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/processing_llava.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/processing_llava.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bac76cb44fc9486349cb7448e35a59449fdf49f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava/processing_llava.py
@@ -0,0 +1,235 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for Llava.
+"""
+
+from typing import Union
+
+import numpy as np
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_utils import ImageInput, get_image_size, to_numpy_array
+from ...processing_utils import (
+ MultiModalData,
+ ProcessingKwargs,
+ ProcessorMixin,
+ Unpack,
+)
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class LlavaProcessorKwargs(ProcessingKwargs, total=False):
+ _defaults = {
+ "text_kwargs": {"padding": False, "return_mm_token_type_ids": False},
+ "images_kwargs": {},
+ }
+
+
+class LlavaProcessor(ProcessorMixin):
+ r"""
+ Constructs a LLaVa processor which wraps a LLaVa image processor and a LLaMa tokenizer into a single processor.
+
+ [`LlavaProcessor`] offers all the functionalities of [`LlavaImageProcessor`] and [`LlamaTokenizerFast`]. See the
+ [`~LlavaProcessor.__call__`] and [`~LlavaProcessor.decode`] for more information.
+
+ Args:
+ image_processor ([`LlavaImageProcessor`], *optional*):
+ The image processor is a required input.
+ tokenizer ([`LlamaTokenizerFast`], *optional*):
+ The tokenizer is a required input.
+ patch_size (`int`, *optional*):
+ Patch size from the vision tower.
+ vision_feature_select_strategy (`str`, *optional*):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Should be same as in model's config
+ chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+ in a chat into a tokenizable string.
+ image_token (`str`, *optional*, defaults to `""`):
+ Special token used to denote image location.
+ num_additional_image_tokens (`int`, *optional*, defaults to 0):
+ Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other
+ extra tokens appended, no need to set this arg.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "AutoImageProcessor"
+ tokenizer_class = "AutoTokenizer"
+
+ def __init__(
+ self,
+ image_processor=None,
+ tokenizer=None,
+ patch_size=None,
+ vision_feature_select_strategy=None,
+ chat_template=None,
+ image_token="", # set the default and let users change if they have peculiar special tokens in rare cases
+ num_additional_image_tokens=0,
+ **kwargs,
+ ):
+ self.patch_size = patch_size
+ self.num_additional_image_tokens = num_additional_image_tokens
+ self.vision_feature_select_strategy = vision_feature_select_strategy
+ self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
+ self.image_token_id = tokenizer.encode(self.image_token, add_special_tokens=False)[0]
+ super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+ def __call__(
+ self,
+ images: ImageInput = None,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+ audio=None,
+ videos=None,
+ **kwargs: Unpack[LlavaProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+ and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
+ the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+ CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
+ of the above two methods for more information.
+
+ Args:
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors of a particular framework. Acceptable values are:
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return NumPy `np.ndarray` objects.
+ - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+ `None`).
+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+ """
+ if images is None and text is None:
+ raise ValueError("You have to specify at least one of `images` or `text`.")
+
+ output_kwargs = self._merge_kwargs(
+ LlavaProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+ if images is not None:
+ image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+ else:
+ image_inputs = {}
+
+ if isinstance(text, str):
+ text = [text]
+ elif not isinstance(text, list) and not isinstance(text[0], str):
+ raise TypeError("Invalid input text. Please provide a string, or a list of strings")
+
+ # try to expand inputs in processing if we have the necessary parts
+ prompt_strings = text
+ if image_inputs.get("pixel_values") is not None:
+ # Replace the image token with the expanded image token sequence
+ pixel_values = image_inputs["pixel_values"]
+ height, width = get_image_size(to_numpy_array(pixel_values[0]))
+ num_image_tokens = (height // self.patch_size) * (
+ width // self.patch_size
+ ) + self.num_additional_image_tokens
+ if self.vision_feature_select_strategy == "default":
+ num_image_tokens -= 1
+
+ prompt_strings = []
+ for sample in text:
+ sample = sample.replace(self.image_token, self.image_token * num_image_tokens)
+ prompt_strings.append(sample)
+
+ return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+ return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+ text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
+ self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
+
+ if return_mm_token_type_ids:
+ array_ids = np.array(text_inputs["input_ids"])
+ mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+ mm_token_type_ids[array_ids == self.image_token_id] = 1
+ text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
+ return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+
+ def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+ """
+ Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+ Args:
+ image_sizes (`list[list[int]]`, *optional*):
+ The input sizes formatted as (height, width) per each image.
+
+ Returns:
+ `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+ input modalities, along with other useful data.
+ """
+
+ vision_data = {}
+ if image_sizes is not None:
+ images_kwargs = LlavaProcessorKwargs._defaults.get("images_kwargs", {})
+ images_kwargs.update(kwargs)
+ crop_size = images_kwargs.get("crop_size", None) or self.image_processor.crop_size
+ resized_height, resized_width = crop_size["height"], crop_size["width"]
+
+ num_image_tokens = (resized_height // self.patch_size) * (resized_width // self.patch_size)
+ num_image_tokens += self.num_additional_image_tokens
+ if self.vision_feature_select_strategy == "default":
+ num_image_tokens -= 1
+
+ num_image_tokens = [num_image_tokens] * len(image_sizes)
+ num_image_patches = [1] * len(image_sizes)
+ vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+ return MultiModalData(**vision_data)
+
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+__all__ = ["LlavaProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c8429dc7e80c1ced93d7fa79b1b36d472eec26e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_llava_next import *
+ from .image_processing_llava_next import *
+ from .image_processing_llava_next_fast import *
+ from .modeling_llava_next import *
+ from .processing_llava_next import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/configuration_llava_next.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/configuration_llava_next.py
new file mode 100644
index 0000000000000000000000000000000000000000..17ea71b1aa6421c8e2007cbb9399a69e9894288e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/configuration_llava_next.py
@@ -0,0 +1,150 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Llava-NeXT model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class LlavaNextConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LlavaNextForConditionalGeneration`]. It is used to instantiate an
+ Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the [llava-hf/llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf)
+ model.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`):
+ The config object or dictionary of the vision backbone.
+ text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
+ The config object or dictionary of the text backbone.
+ image_token_index (`int`, *optional*, defaults to 32000):
+ The image token index to encode the image prompt.
+ projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
+ The activation function used by the multimodal projector.
+ vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
+ If `"full"`, the full vision features are used.
+ vision_feature_layer (`Union[int, list[int]]`, *optional*, defaults to -2):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ image_grid_pinpoints (`List`, *optional*, defaults to `[[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]`):
+ A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list
+ of the form `(height, width)`.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether the model's input and output word embeddings should be tied.
+ image_seq_length (`int`, *optional*, defaults to 576):
+ Sequence length of one image embedding.
+ multimodal_projector_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use bias in the multimodal projector.
+
+ Example:
+
+ ```python
+ >>> from transformers import LlavaNextForConditionalGeneration, LlavaNextConfig, CLIPVisionConfig, LlamaConfig
+
+ >>> # Initializing a CLIP-vision config
+ >>> vision_config = CLIPVisionConfig()
+
+ >>> # Initializing a Llama config
+ >>> text_config = LlamaConfig()
+
+ >>> # Initializing a Llava-Next llava-hf/llava-v1.6-mistral-7b-hf style configuration
+ >>> configuration = LlavaNextConfig(vision_config, text_config)
+
+ >>> # Initializing a model from the llava-hf/llava-v1.6-mistral-7b-hf style configuration
+ >>> model = LlavaNextForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "llava_next"
+ attribute_map = {
+ "image_token_id": "image_token_index",
+ }
+ sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
+
+ def __init__(
+ self,
+ vision_config=None,
+ text_config=None,
+ image_token_index=32000,
+ projector_hidden_act="gelu",
+ vision_feature_select_strategy="default",
+ vision_feature_layer=-2,
+ image_grid_pinpoints=None,
+ tie_word_embeddings=False,
+ image_seq_length=576,
+ multimodal_projector_bias=True,
+ **kwargs,
+ ):
+ self.image_token_index = image_token_index
+ self.projector_hidden_act = projector_hidden_act
+ self.image_seq_length = image_seq_length
+ self.multimodal_projector_bias = multimodal_projector_bias
+
+ if vision_feature_select_strategy not in ["default", "full"]:
+ raise ValueError(
+ "vision_feature_select_strategy should be one of 'default', 'full'."
+ f"Got: {vision_feature_select_strategy}"
+ )
+
+ self.vision_feature_select_strategy = vision_feature_select_strategy
+ self.vision_feature_layer = vision_feature_layer
+ image_grid_pinpoints = (
+ image_grid_pinpoints
+ if image_grid_pinpoints is not None
+ else [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
+ )
+ self.image_grid_pinpoints = image_grid_pinpoints
+
+ if isinstance(vision_config, dict):
+ vision_config["model_type"] = vision_config.get("model_type", "clip_vision_model")
+ vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
+ elif vision_config is None:
+ vision_config = CONFIG_MAPPING["clip_vision_model"](
+ intermediate_size=4096,
+ hidden_size=1024,
+ patch_size=14,
+ image_size=336,
+ num_hidden_layers=24,
+ num_attention_heads=16,
+ vocab_size=32000,
+ projection_dim=768,
+ )
+
+ self.vision_config = vision_config
+
+ if isinstance(text_config, dict):
+ text_config["model_type"] = text_config.get("model_type", "llama")
+ text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+ elif text_config is None:
+ text_config = CONFIG_MAPPING["llama"]()
+
+ self.text_config = text_config
+
+ super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+__all__ = ["LlavaNextConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/convert_llava_next_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/convert_llava_next_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..41fc226783652321429b9c1befc58c2185f25c20
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/convert_llava_next_weights_to_hf.py
@@ -0,0 +1,394 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Convert LLaVa-NeXT (LLaVa-1.6) checkpoints from the original repository.
+
+URL: https://github.com/haotian-liu/LLaVA/tree/main.
+
+
+The command used to obtain original logits is the following:
+python llava/eval/run_llava.py --model-path "liuhaotian/llava-v1.6-mistral-7b" --image-file "images/llava_v1_5_radar.jpg" --query "What is shown in this image?" --max_new_tokens 100 --temperature 0
+
+Note: logits are tested with torch==2.1.2.
+"""
+
+import argparse
+import gc
+import glob
+import json
+from pathlib import Path
+
+import requests
+import torch
+from accelerate import init_empty_weights
+from huggingface_hub import hf_hub_download, snapshot_download
+from PIL import Image
+from safetensors import safe_open
+
+from transformers import (
+ AddedToken,
+ AutoConfig,
+ AutoTokenizer,
+ LlavaNextConfig,
+ LlavaNextForConditionalGeneration,
+ LlavaNextImageProcessor,
+ LlavaNextProcessor,
+)
+
+
+KEYS_TO_MODIFY_MAPPING = {
+ "model.vision_tower.": "",
+ "model.mm_projector": "multi_modal_projector",
+ "model": "model.model",
+ "vision_model.model": "vision_model",
+ "lm_head": "language_model.lm_head",
+ "model.model": "language_model.model",
+ "multi_modal_projector.0": "multi_modal_projector.linear_1",
+ "multi_modal_projector.2": "multi_modal_projector.linear_2",
+ "language_model.model.image_newline": "image_newline",
+}
+
+
+def load_original_state_dict(model_id):
+ directory_path = snapshot_download(repo_id=model_id, allow_patterns=["*.safetensors"])
+
+ original_state_dict = {}
+ for path in glob.glob(f"{directory_path}/*"):
+ if path.endswith(".safetensors"):
+ with safe_open(path, framework="pt", device="cpu") as f:
+ for key in f.keys():
+ original_state_dict[key] = f.get_tensor(key)
+
+ return original_state_dict
+
+
+def convert_state_dict_to_hf(state_dict):
+ new_state_dict = {}
+ for key, value in state_dict.items():
+ if key.endswith(".inv_freq"):
+ continue
+ for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
+ if key_to_modify in key:
+ key = key.replace(key_to_modify, new_key)
+
+ new_state_dict[key] = value.to(torch.float16)
+ return new_state_dict
+
+
+def load_image():
+ url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
+ image = Image.open(requests.get(url, stream=True).raw)
+ return image
+
+
+def convert_llava_to_hf(model_id, pytorch_dump_folder_path, push_to_hub=False):
+ # load original config
+ filepath = hf_hub_download(repo_id=model_id, filename="config.json", repo_type="model")
+ # read json
+ with open(filepath) as f:
+ data = json.load(f)
+ print(data)
+
+ if model_id == "liuhaotian/llava-v1.6-mistral-7b":
+ text_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
+ image_token_id = 32000
+ elif model_id == "liuhaotian/llava-v1.6-vicuna-7b":
+ text_model_id = "lmsys/vicuna-7b-v1.5"
+ image_token_id = 32000
+ elif model_id == "liuhaotian/llava-v1.6-vicuna-13b":
+ text_model_id = "lmsys/vicuna-13b-v1.5"
+ image_token_id = 32000
+ elif model_id == "liuhaotian/llava-v1.6-34b":
+ text_model_id = "NousResearch/Nous-Hermes-2-Yi-34B"
+ image_token_id = 64000
+ elif model_id == "lmms-lab/llama3-llava-next-8b":
+ text_model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+ image_token_id = 128256
+ elif model_id == "lmms-lab/llava-next-72b":
+ text_model_id = "Qwen/Qwen1.5-72B-Chat"
+ image_token_id = 151646
+ elif model_id == "lmms-lab/llava-next-110b":
+ text_model_id = "Qwen/Qwen1.5-110B-Chat"
+ image_token_id = 151646
+
+ vision_model_id = data["mm_vision_tower"]
+
+ torch.set_default_dtype(torch.float16)
+ text_config = AutoConfig.from_pretrained(text_model_id)
+
+ use_fast = model_id != "liuhaotian/llava-v1.6-34b"
+ tokenizer = AutoTokenizer.from_pretrained(text_model_id, use_fast=use_fast)
+ tokenizer.add_tokens(AddedToken("", special=True, normalized=False), special_tokens=True)
+
+ if model_id in ("liuhaotian/llava-v1.6-mistral-7b", "lmms-lab/llama3-llava-next-8b"):
+ # Mistral-7B doesn't have a padding token set yet
+ tokenizer.add_special_tokens({"pad_token": ""})
+
+ image_processor = LlavaNextImageProcessor.from_pretrained(vision_model_id)
+ processor = LlavaNextProcessor(tokenizer=tokenizer, image_processor=image_processor)
+
+ config = LlavaNextConfig(
+ text_config=text_config.to_dict(),
+ image_grid_pinpoints=image_processor.image_grid_pinpoints,
+ use_image_newline_parameter=True,
+ image_token_id=image_token_id,
+ )
+
+ with init_empty_weights():
+ model = LlavaNextForConditionalGeneration(config)
+
+ # load original state dict
+ state_dict = load_original_state_dict(model_id)
+ state_dict = convert_state_dict_to_hf(state_dict)
+ model.load_state_dict(state_dict, assign=True)
+ model.eval()
+
+ pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data
+ mu = torch.mean(pre_expansion_embeddings, dim=0).float()
+ n = pre_expansion_embeddings.size()[0]
+ sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n
+ dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma)
+
+ # We add an image token so we resize the model
+ # Pad to 64 for performance reasons
+ # Qwen-based models have extra unused space in the vocab size already, so no need to resize
+ if model_id not in ["lmms-lab/llava-next-72b", "lmms-lab/llava-next-110b"]:
+ pad_shape = 64
+ vocab_size = config.text_config.vocab_size
+ if model_id == "liuhaotian/llava-v1.6-34b":
+ # this one has 3 additional tokens, namely <|startoftext|>, <|endoftext|> and
+ num_tokens = vocab_size + 3
+ else:
+ # this one has 2 additional tokens, namely and
+ num_tokens = vocab_size + 2
+ model.resize_token_embeddings(num_tokens, pad_to_multiple_of=pad_shape)
+ model.language_model.model.embed_tokens.weight.data[vocab_size:] = torch.stack(
+ tuple(
+ dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[vocab_size:].shape[0])
+ ),
+ dim=0,
+ )
+ model.language_model.lm_head.weight.data[vocab_size:] = torch.stack(
+ tuple(dist.sample() for _ in range(model.language_model.lm_head.weight.data[vocab_size:].shape[0])),
+ dim=0,
+ )
+
+ print(f"Saving model and processor for {model_id} to {pytorch_dump_folder_path}")
+ Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+ model.save_pretrained(pytorch_dump_folder_path)
+ processor.save_pretrained(pytorch_dump_folder_path)
+
+ # Make space so we can load the model properly now.
+ del state_dict
+ gc.collect()
+
+ # Load everything back for inference tests in float32 because prev script was written as that
+ # Though it's mostly loaded in fp16 as original weights are in fp16
+ model = LlavaNextForConditionalGeneration.from_pretrained(pytorch_dump_folder_path, device_map="auto")
+ processor = LlavaNextProcessor.from_pretrained(pytorch_dump_folder_path)
+ device = model.device
+
+ # prepare inputs
+ image = load_image()
+ if model_id == "liuhaotian/llava-v1.6-mistral-7b":
+ prompt = "[INST] \nWhat is shown in this image? [/INST]"
+ elif model_id in ["liuhaotian/llava-v1.6-vicuna-7b", "liuhaotian/llava-v1.6-vicuna-13b"]:
+ prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: \nWhat is shown in this image? ASSISTANT:"
+ elif model_id == "liuhaotian/llava-v1.6-34b":
+ prompt = "<|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n\nWhat is shown in this image?<|im_end|><|im_start|>assistant\n"
+ elif model_id == "lmms-lab/llama3-llava-next-8b":
+ prompt = "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.<|eot_id|><|start_header_id|><|start_header_id|>user<|end_header_id|>\n\n\nWhat is shown in this image?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+ elif model_id in ["lmms-lab/llava-next-72b", "lmms-lab/llava-next-110b"]:
+ prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n\nWhat is shown in this image?<|im_end|>\n<|im_start|>assistant\n"
+
+ inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+ # verify inputs
+ filepath = hf_hub_download(repo_id="nielsr/test-image", filename="llava_1_6_pixel_values.pt", repo_type="dataset")
+ original_pixel_values = torch.load(filepath, map_location="cpu", weights_only=True)
+ assert torch.allclose(original_pixel_values, inputs.pixel_values.half())
+
+ if model_id == "liuhaotian/llava-v1.6-mistral-7b":
+ filepath = hf_hub_download(repo_id="nielsr/test-image", filename="llava_1_6_input_ids.pt", repo_type="dataset")
+ original_input_ids = torch.load(filepath, map_location="cpu", weights_only=True)
+ # replace -200 by image_token_id (since we use token ID = 32000 for the image token)
+ original_input_ids[original_input_ids == -200] = image_token_id
+ assert original_input_ids[0].tolist() == inputs.input_ids[0].tolist()
+
+ elif model_id == "liuhaotian/llava-v1.6-34b":
+ filepath = hf_hub_download(
+ repo_id="nielsr/test-image", filename="llava_1_6_34b_input_ids.pt", repo_type="dataset"
+ )
+ original_input_ids = torch.load(filepath, map_location="cpu", weights_only=True)
+ # replace -200 by image_token_id
+ original_input_ids[original_input_ids == -200] = image_token_id
+
+ assert original_input_ids[0].tolist() == inputs.input_ids[0].tolist()
+
+ image_sizes = torch.tensor([[899, 1024]])
+ assert image_sizes[0].tolist() == inputs.image_sizes[0].tolist()
+
+ # verify single forward pass
+ print("Single forward pass")
+ with torch.inference_mode():
+ inputs = inputs.to(device)
+ outputs = model(**inputs)
+ print("Shape of logits:", outputs.logits.shape)
+ print("First values of logits:", outputs.logits[0, :3, :3])
+
+ if model_id == "liuhaotian/llava-v1.6-mistral-7b":
+ expected_slice = torch.tensor(
+ [[-4.8555, -4.6992, -0.1996], [-10.5703, -10.7344, -2.7246], [-7.0391, -7.3672, -0.2634]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "liuhaotian/llava-v1.6-vicuna-7b":
+ expected_slice = torch.tensor(
+ [[1.4883, 0.9976, -0.6992], [-9.7031, -5.7031, -1.5557], [-5.1328, -5.5586, 8.8281]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "liuhaotian/llava-v1.6-vicuna-13b":
+ expected_slice = torch.tensor(
+ [[-0.9614, 7.3125, 0.2106], [-7.2695, -8.5469, 3.6211], [-6.3750, -8.1875, 5.4688]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "liuhaotian/llava-v1.6-34b":
+ expected_slice = torch.tensor(
+ [[-9.0859, -9.1406, 5.9453], [-5.9570, -5.9766, 2.2754], [-5.7305, -5.7539, 4.0000]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "lmms-lab/llama3-llava-next-8b":
+ expected_slice = torch.tensor(
+ [[-3.9648, 1.1396, 3.3145], [-5.3594, -1.5654, -1.9619], [-12.3750, -10.6797, -9.3125]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "lmms-lab/llava-next-72b":
+ # Not yet checked against reference
+ expected_slice = torch.tensor(
+ [[3.7148, 3.9277, 3.4395], [-0.4341, 1.1387, 6.5117], [3.2324, 3.4688, 4.1133]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "lmms-lab/llava-next-110b":
+ # Not yet checked against reference
+ expected_slice = torch.tensor(
+ [[-2.5449, -1.6738, -2.0371], [1.0811, 3.4961, 5.0312], [1.7803, 2.5137, 2.4277]],
+ dtype=torch.float32,
+ device=device,
+ )
+ else:
+ raise ValueError(f"Model {model_id} not supported")
+
+ assert torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4)
+ print("Logits are ok!")
+
+ # verify generation
+ output_ids = model.generate(
+ **inputs,
+ max_new_tokens=100,
+ use_cache=True,
+ )
+
+ generated_text = processor.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
+
+ print("Generated text:", repr(generated_text))
+
+ if model_id == "liuhaotian/llava-v1.6-mistral-7b":
+ expected_text = '[INST] \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot that displays data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point.\n\nIn this particular radar chart, there are several axes labeled with different metrics or benchmarks, such as "MMM-Vet," "MMM-Bench," "LLaVA-Bench," "SLED-Bench," "'
+ elif model_id == "liuhaotian/llava-v1.6-vicuna-7b":
+ expected_text = """A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human\'s questions. USER: \nWhat is shown in this image? ASSISTANT: The image appears to be a graphical representation of a benchmarking study comparing the performance of various models or systems. It\'s a scatter plot with a circular layout, where each point represents a different model or system, and the axes represent different metrics or dimensions of comparison.\n\nThe metrics are likely related to machine learning or artificial intelligence performance, as indicated by the terms like "BLIP-2," "Instruct BLIP," "POE," "QWA," "V"""
+ elif model_id == "liuhaotian/llava-v1.6-vicuna-13b":
+ expected_text = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: \nWhat is shown in this image? ASSISTANT: The image appears to be a radar chart, also known as a spider chart or star chart, which is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point.\n\nIn this particular radar chart, there are several variables represented:\n\n- MM-Vet\n- LLa-Va-Bench\n- SEED-Bench\n- MM"
+ elif model_id == "liuhaotian/llava-v1.6-34b":
+ expected_text = "<|im_start|> system\nAnswer the questions. <|im_start|> user\n\nWhat is shown in this image? <|im_start|> assistant\nThe image appears to be a radar chart, also known as a spider chart, which is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point.\n\nIn this particular chart, there are several datasets represented by different colors and labeled with various acronyms such as MM-Vet, LLaVA-Bench, SEED-Bench, MM-Bench-CN, MM-"
+ elif model_id == "lmms-lab/llama3-llava-next-8b":
+ expected_text = 'system\n\nYou are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.user\n\n\nWhat is shown in this image?assistant\n\n\nThe image shows a radar chart, also known as a spider chart or a web chart, which is a type of graph used to display multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. Each axis represents a different variable, and the values are plotted along each axis and connected to form a polygon.\n\nIn this particular radar chart, there are several axes labeled with different variables, such as "MM-Vet," "LL'
+ elif model_id == "lmms-lab/llava-next-72b":
+ expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image displays a radar chart, also known as a spider chart or a star chart, which is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. Each axis represents a different variable, and the value of each variable is represented by the distance from the center of the chart to the point where the axis intersects with the line representing that variable's value.\n\nIn this particular chart, there are several axes"
+ elif model_id == "lmms-lab/llava-next-110b":
+ expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image shows a radar chart comparing the performance of different models on various visual question answering (VQA) benchmarks. Each colored line represents a different model, and the distance from the center of the chart indicates the score or performance level of the model on a particular benchmark. The benchmarks are labeled around the edges of the chart, and include VQA v2, GQA, VizWiz, TextVQA, MMBench-CN, MME, and others. The chart allows for a"
+ else:
+ raise ValueError(f"Model {model_id} not supported")
+
+ assert generated_text == expected_text
+ print("Generated text is ok!")
+
+ # verify batched generation
+ print("Batched generation...")
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ cats_image = Image.open(requests.get(url, stream=True).raw)
+
+ inputs = processor(
+ images=[image, cats_image],
+ text=[prompt, prompt],
+ padding=True,
+ return_tensors="pt",
+ ).to(device)
+
+ for k, v in inputs.items():
+ print(k, v.shape)
+
+ print("Image sizes:", inputs.image_sizes)
+
+ # make sure image_sizes are the same
+ # as otherwise batched generation doesn't work
+ inputs.image_sizes[1] = inputs.image_sizes[0]
+
+ print("Batched generation...")
+ output_ids = model.generate(
+ **inputs,
+ max_new_tokens=20,
+ use_cache=True,
+ )
+
+ outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+ print(outputs)
+
+ if push_to_hub:
+ checkpoint_name = model_id.split("/")[-1]
+ print(f"Pushing to repo llava-hf/{checkpoint_name}-hf")
+ model.push_to_hub(f"llava-hf/{checkpoint_name}-hf")
+ processor.push_to_hub(f"llava-hf/{checkpoint_name}-hf")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--model_id",
+ help="Hub location of the model to convert",
+ default="liuhaotian/llava-v1.6-mistral-7b",
+ choices=[
+ "liuhaotian/llava-v1.6-mistral-7b",
+ "liuhaotian/llava-v1.6-vicuna-7b",
+ "liuhaotian/llava-v1.6-vicuna-13b",
+ "liuhaotian/llava-v1.6-34b",
+ "lmms-lab/llama3-llava-next-8b",
+ "lmms-lab/llava-next-72b",
+ "lmms-lab/llava-next-110b",
+ ],
+ required=False,
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", type=str, required=True, help="Path to the output PyTorch model directory."
+ )
+ parser.add_argument(
+ "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
+ )
+ args = parser.parse_args()
+
+ convert_llava_to_hf(args.model_id, args.pytorch_dump_folder_path, args.push_to_hub)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/image_processing_llava_next.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/image_processing_llava_next.py
new file mode 100644
index 0000000000000000000000000000000000000000..21c47701405d8dde1989ff428b11cad1d1373c1f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/image_processing_llava_next.py
@@ -0,0 +1,724 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for LLaVa-NeXT."""
+
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import (
+ BaseImageProcessor,
+ BatchFeature,
+ get_patch_output_size,
+ get_size_dict,
+ select_best_resolution,
+)
+from ...image_transforms import (
+ PaddingMode,
+ convert_to_rgb,
+ get_resize_output_image_size,
+ pad,
+ resize,
+ to_channel_dimension_format,
+)
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ get_image_size,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_flat_list_of_images,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import TensorType, is_vision_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+if is_vision_available():
+ from PIL import Image
+
+
+def divide_to_patches(image: np.array, patch_size: int, input_data_format) -> list[np.array]:
+ """
+ Divides an image into patches of a specified size.
+
+ Args:
+ image (`np.array`):
+ The input image.
+ patch_size (`int`):
+ The size of each patch.
+ input_data_format (`ChannelDimension` or `str`):
+ The channel dimension format of the input image.
+
+ Returns:
+ list: A list of np.array representing the patches.
+ """
+ patches = []
+ height, width = get_image_size(image, channel_dim=input_data_format)
+ for i in range(0, height, patch_size):
+ for j in range(0, width, patch_size):
+ if input_data_format == ChannelDimension.LAST:
+ patch = image[i : i + patch_size, j : j + patch_size]
+ else:
+ patch = image[:, i : i + patch_size, j : j + patch_size]
+ patches.append(patch)
+
+ return patches
+
+
+def expand_to_square(image: np.array, background_color, input_data_format) -> np.array:
+ """
+ Expands an image to a square by adding a background color.
+ """
+
+ height, width = get_image_size(image, channel_dim=input_data_format)
+ if width == height:
+ return image
+ elif width > height:
+ result = np.ones((width, width, image.shape[2]), dtype=image.dtype) * background_color
+ result[(width - height) // 2 : (width - height) // 2 + height, :] = image
+ return result
+ else:
+ result = np.ones((height, height, image.shape[2]), dtype=image.dtype) * background_color
+ result[:, (height - width) // 2 : (height - width) // 2 + width] = image
+ return result
+
+
+class LlavaNextImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a LLaVa-NeXT image processor. Based on [`CLIPImageProcessor`] with incorporation of additional techniques
+ for processing high resolution images as explained in the [LLaVa paper](https://huggingface.co/papers/2310.03744).
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
+ `do_resize` in the `preprocess` method.
+ size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
+ Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
+ method.
+ image_grid_pinpoints (`List` *optional*, defaults to `[[672, 336], [336, 672], [672, 672], [336, 1008], [1008, 336]]`):
+ A list of possible resolutions to use for processing high resolution images. The best resolution is selected
+ based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
+ method.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+ Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
+ do_center_crop (`bool`, *optional*, defaults to `True`):
+ Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the
+ `preprocess` method.
+ crop_size (`dict[str, int]` *optional*, defaults to 224):
+ Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
+ method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
+ the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
+ method.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`float` or `list[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
+ Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+ number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+ Can be overridden by the `image_std` parameter in the `preprocess` method.
+ do_pad (`bool`, *optional*, defaults to `True`):
+ Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
+ number of patches in the batch. Padding will be applied to the bottom and right with zeros.
+ do_convert_rgb (`bool`, *optional*, defaults to `True`):
+ Whether to convert the image to RGB.
+ """
+
+ model_input_names = ["pixel_values", "image_sizes"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ image_grid_pinpoints: Optional[list] = None,
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ do_center_crop: bool = True,
+ crop_size: Optional[dict[str, int]] = None,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_pad: Optional[bool] = True,
+ do_convert_rgb: bool = True,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"shortest_edge": 224}
+ size = get_size_dict(size, default_to_square=False)
+ image_grid_pinpoints = (
+ image_grid_pinpoints
+ if image_grid_pinpoints is not None
+ else [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
+ )
+ crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
+ crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")
+
+ self.do_resize = do_resize
+ self.size = size
+ self.image_grid_pinpoints = image_grid_pinpoints
+ self.resample = resample
+ self.do_center_crop = do_center_crop
+ self.crop_size = crop_size
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
+ self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+ self.do_pad = do_pad
+ self.do_convert_rgb = do_convert_rgb
+
+ # Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize with CLIP->LLaVa
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
+ resized to keep the input aspect ratio.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Size of the output image.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ Resampling filter to use when resiizing the image.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ default_to_square = True
+ if "shortest_edge" in size:
+ size = size["shortest_edge"]
+ default_to_square = False
+ elif "height" in size and "width" in size:
+ size = (size["height"], size["width"])
+ else:
+ raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
+ output_size = get_resize_output_image_size(
+ image,
+ size=size,
+ default_to_square=default_to_square,
+ input_data_format=input_data_format,
+ )
+
+ return resize(
+ image,
+ size=output_size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ def pad(
+ self,
+ image: np.ndarray,
+ padding: Union[int, tuple[int, int], Iterable[tuple[int, int]]],
+ mode: PaddingMode = PaddingMode.CONSTANT,
+ constant_values: Union[float, Iterable[float]] = 0.0,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Pads the `image` with the specified `padding` and `mode`. Padding can be in the (`height`, `width`)
+ dimension of in the (`num_patches`) dimension. In the second case an iterable if tuples is expected
+ as input.
+
+ Args:
+ image (`np.ndarray`):
+ The image to pad.
+ padding (`int` or `tuple[int, int]` or `Iterable[tuple[int, int]]`):
+ Padding to apply to the edges of the height, width axes. Can be one of three formats:
+ - `((before_height, after_height), (before_width, after_width))` unique pad widths for each axis.
+ - `((before, after),)` yields same before and after pad for height and width.
+ - `(pad,)` or int is a shortcut for before = after = pad width for all axes.
+ mode (`PaddingMode`):
+ The padding mode to use. Can be one of:
+ - `"constant"`: pads with a constant value.
+ - `"reflect"`: pads with the reflection of the vector mirrored on the first and last values of the
+ vector along each axis.
+ - `"replicate"`: pads with the replication of the last value on the edge of the array along each axis.
+ - `"symmetric"`: pads with the reflection of the vector mirrored along the edge of the array.
+ constant_values (`float` or `Iterable[float]`, *optional*):
+ The value to use for the padding if `mode` is `"constant"`.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use same as the input image.
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use the inferred format of the input image.
+
+ Returns:
+ `np.ndarray`: The padded image.
+
+ """
+
+ # call the general `pad` if padding on `height/width`, otherwise it's the `num_patched` dim
+ if isinstance(padding, int) or len(padding) != 4:
+ return pad(image, padding, mode, constant_values, data_format, input_data_format)
+
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(image)
+ if mode == PaddingMode.CONSTANT:
+ image = np.pad(image, padding, mode="constant", constant_values=constant_values)
+ elif mode == PaddingMode.REFLECT:
+ image = np.pad(image, padding, mode="reflect")
+ elif mode == PaddingMode.REPLICATE:
+ image = np.pad(image, padding, mode="edge")
+ elif mode == PaddingMode.SYMMETRIC:
+ image = np.pad(image, padding, mode="symmetric")
+ else:
+ raise ValueError(f"Invalid padding mode: {mode}")
+ image = (
+ to_channel_dimension_format(image, data_format, input_data_format) if data_format is not None else image
+ )
+ return image
+
+ def _preprocess(
+ self,
+ images: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[int] = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> Image.Image:
+ """
+ Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
+
+ Args:
+ images (`ImageInput`):
+ Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio.
+ resample (`int`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+ has an effect if `do_resize` is set to `True`.
+ do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
+ Whether to center crop the image.
+ crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`):
+ Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image.
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+ `True`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ images = make_list_of_images(images)
+
+ all_images = []
+ for image in images:
+ if do_resize:
+ image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+
+ if do_center_crop:
+ image = self.center_crop(image=image, size=crop_size, input_data_format=input_data_format)
+
+ if do_rescale:
+ image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+
+ if do_normalize:
+ image = self.normalize(
+ image=image, mean=image_mean, std=image_std, input_data_format=input_data_format
+ )
+
+ all_images.append(image)
+ images = [
+ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+ for image in all_images
+ ]
+
+ return images
+
+ def _resize_for_patching(
+ self, image: np.array, target_resolution: tuple, resample, input_data_format: ChannelDimension
+ ) -> np.array:
+ """
+ Resizes an image to a target resolution while maintaining aspect ratio.
+
+ Args:
+ image (np.array):
+ The input image.
+ target_resolution (tuple):
+ The target resolution (height, width) of the image.
+ resample (`PILImageResampling`):
+ Resampling filter to use if resizing the image.
+ input_data_format (`ChannelDimension` or `str`):
+ The channel dimension format of the input image.
+
+ Returns:
+ np.array: The resized and padded image.
+ """
+ new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
+
+ # Resize the image
+ resized_image = resize(image, (new_height, new_width), resample=resample, input_data_format=input_data_format)
+
+ return resized_image
+
+ def _get_padding_size(self, original_resolution: tuple, target_resolution: tuple):
+ original_height, original_width = original_resolution
+ target_height, target_width = target_resolution
+ paste_x, r_x = divmod(target_width - original_width, 2)
+ paste_y, r_y = divmod(target_height - original_height, 2)
+ return (paste_y, paste_y + r_y), (paste_x, paste_x + r_x)
+
+ def _pad_for_patching(
+ self, image: np.array, target_resolution: tuple, input_data_format: ChannelDimension
+ ) -> np.array:
+ """
+ Pad an image to a target resolution while maintaining aspect ratio.
+ """
+ new_resolution = get_patch_output_size(image, target_resolution, input_data_format)
+ padding = self._get_padding_size(new_resolution, target_resolution)
+
+ padded_image = self.pad(image, padding=padding)
+
+ return padded_image
+
+ def get_image_patches(
+ self,
+ image: np.array,
+ grid_pinpoints,
+ size: tuple,
+ patch_size: int,
+ resample: PILImageResampling,
+ data_format: ChannelDimension,
+ input_data_format: ChannelDimension,
+ ) -> list[np.array]:
+ """
+ Process an image with variable resolutions by dividing it into patches.
+
+ Args:
+ image (np.array):
+ The input image to be processed.
+ grid_pinpoints (List):
+ A string representation of a list of possible resolutions.
+ size (`tuple`):
+ Size to resize the original image to.
+ patch_size (`int`):
+ Size of the patches to divide the image into.
+ resample (`PILImageResampling`):
+ Resampling filter to use if resizing the image.
+ data_format (`ChannelDimension` or `str`):
+ The channel dimension format for the output image.
+ input_data_format (`ChannelDimension` or `str`):
+ The channel dimension format of the input image.
+
+ Returns:
+ list[np.array]: A list of NumPy arrays containing the processed image patches.
+ """
+ if not isinstance(grid_pinpoints, list):
+ raise TypeError("grid_pinpoints must be a list of possible resolutions.")
+
+ possible_resolutions = grid_pinpoints
+
+ image_size = get_image_size(image, channel_dim=input_data_format)
+ best_resolution = select_best_resolution(image_size, possible_resolutions)
+ resized_image = self._resize_for_patching(
+ image, best_resolution, resample=resample, input_data_format=input_data_format
+ )
+ padded_image = self._pad_for_patching(resized_image, best_resolution, input_data_format=input_data_format)
+
+ patches = divide_to_patches(padded_image, patch_size=patch_size, input_data_format=input_data_format)
+
+ # make sure that all patches are in the input data format
+ patches = [
+ to_channel_dimension_format(patch, channel_dim=data_format, input_channel_dim=input_data_format)
+ for patch in patches
+ ]
+
+ resized_original_image = resize(
+ image,
+ size=size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+
+ image_patches = [resized_original_image] + patches
+
+ return image_patches
+
+ def _pad_for_batching(
+ self,
+ pixel_values: list[np.ndarray],
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ """
+ Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
+
+ Args:
+ pixel_values (`list[np.ndarray]`):
+ An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`)
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use same as the input image.
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use the inferred format of the input image.
+
+ Returns:
+ list[`np.ndarray`]: The padded images.
+ """
+ max_patch = max(len(x) for x in pixel_values)
+ pixel_values = [
+ self.pad(
+ image,
+ padding=((0, max_patch - image.shape[0]), (0, 0), (0, 0), (0, 0)),
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ for image in pixel_values
+ ]
+
+ return pixel_values
+
+ def preprocess(
+ self,
+ images: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ image_grid_pinpoints: Optional[list] = None,
+ resample: PILImageResampling = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[int] = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_pad: Optional[bool] = None,
+ do_convert_rgb: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ """
+ Args:
+ images (`ImageInput`):
+ Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio.
+ image_grid_pinpoints (`List` *optional*, defaults to `self.image_grid_pinpoints`):
+ A list of possible resolutions to use for processing high resolution images. The best resolution is
+ selected based on the original size of the image.
+ resample (`int`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+ has an effect if `do_resize` is set to `True`.
+ do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
+ Whether to center crop the image.
+ crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`):
+ Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image.
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+ `True`.
+ do_pad (`bool`, *optional*, defaults to `self.do_pad`):
+ Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
+ number of patches in the batch. Padding will be applied to the bottom and right with zeros.
+ do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+ Whether to convert the image to RGB.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+
+ """
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ size = get_size_dict(size, param_name="size", default_to_square=False)
+ image_grid_pinpoints = image_grid_pinpoints if image_grid_pinpoints is not None else self.image_grid_pinpoints
+ resample = resample if resample is not None else self.resample
+ do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
+ crop_size = crop_size if crop_size is not None else self.crop_size
+ crop_size = get_size_dict(crop_size, param_name="crop_size", default_to_square=True)
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ do_pad = do_pad if do_pad is not None else self.do_pad
+ do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+ images = make_flat_list_of_images(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ if do_convert_rgb:
+ images = [convert_to_rgb(image) for image in images]
+
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if do_rescale and is_scaled_image(images[0]):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ new_images = []
+ image_sizes = [get_image_size(image, channel_dim=input_data_format) for image in images]
+ for image in images:
+ # convert image into a list of patches
+ # we intentionally use the same data format as the input data format
+ image_patches = self.get_image_patches(
+ image,
+ image_grid_pinpoints,
+ size=(size["shortest_edge"], size["shortest_edge"])
+ if "shortest_edge" in size
+ else (min(size["height"], size["width"]), min(size["height"], size["width"])),
+ patch_size=crop_size["height"],
+ resample=resample,
+ data_format=input_data_format,
+ input_data_format=input_data_format,
+ )
+
+ # preprocess patches
+ pixel_values = self._preprocess(
+ image_patches,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ pixel_values = np.array(pixel_values)
+ new_images.append(pixel_values)
+
+ if do_pad:
+ processed_images = self._pad_for_batching(new_images)
+
+ return BatchFeature(
+ data={"pixel_values": processed_images, "image_sizes": image_sizes}, tensor_type=return_tensors
+ )
+
+
+__all__ = ["LlavaNextImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/image_processing_llava_next_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/image_processing_llava_next_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dda73507006ce01dc24f7889b4bd8b7d7ea62b6
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/image_processing_llava_next_fast.py
@@ -0,0 +1,295 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for LLaVa-NeXT."""
+
+from typing import Optional, Union
+
+from ...image_processing_utils import BatchFeature, get_patch_output_size, select_best_resolution
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ divide_to_patches,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ SizeDict,
+ get_image_size,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+)
+
+
+if is_torch_available():
+ import torch
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+class LlavaNextFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ """
+ image_grid_pinpoints (`list[list[int]]`, *optional*):
+ A list of possible resolutions to use for processing high resolution images. The best resolution is selected
+ based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
+ method.
+ do_pad (`bool`, *optional*):
+ Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
+ number of patches in the batch. Padding will be applied to the bottom and right with zeros.
+ """
+
+ image_grid_pinpoints: Optional[list[list[int]]]
+ do_pad: Optional[bool]
+
+
+@auto_docstring
+class LlavaNextImageProcessorFast(BaseImageProcessorFast):
+ # To be checked against the slow image processor
+ # None values left after checking can be removed
+ resample = PILImageResampling.BICUBIC
+ image_mean = OPENAI_CLIP_MEAN
+ image_std = OPENAI_CLIP_STD
+ size = {"shortest_edge": 224}
+ default_to_square = False
+ crop_size = {"height": 224, "width": 224}
+ do_resize = True
+ do_center_crop = True
+ do_rescale = True
+ do_normalize = True
+ do_convert_rgb = True
+ do_pad = True
+ image_grid_pinpoints = [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
+ valid_kwargs = LlavaNextFastImageProcessorKwargs
+
+ def __init__(self, **kwargs: Unpack[LlavaNextFastImageProcessorKwargs]):
+ super().__init__(**kwargs)
+
+ @auto_docstring
+ def preprocess(self, images: ImageInput, **kwargs: Unpack[LlavaNextFastImageProcessorKwargs]) -> BatchFeature:
+ return super().preprocess(images, **kwargs)
+
+ def _resize_for_patching(
+ self,
+ image: "torch.Tensor",
+ target_resolution: tuple,
+ interpolation: "F.InterpolationMode",
+ input_data_format: ChannelDimension,
+ ) -> "torch.Tensor":
+ """
+ Resizes an image to a target resolution while maintaining aspect ratio.
+
+ Args:
+ image ("torch.Tensor"):
+ The input image.
+ target_resolution (tuple):
+ The target resolution (height, width) of the image.
+ interpolation (`InterpolationMode`):
+ Resampling filter to use if resizing the image.
+ input_data_format (`ChannelDimension` or `str`):
+ The channel dimension format of the input image.
+
+ Returns:
+ "torch.Tensor": The resized and padded image.
+ """
+ new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
+
+ # Resize the image
+ resized_image = self.resize(
+ image=image,
+ size=SizeDict(height=new_height, width=new_width),
+ interpolation=interpolation,
+ )
+
+ return resized_image
+
+ def _get_padding_size(self, original_resolution: tuple, target_resolution: tuple):
+ original_height, original_width = original_resolution
+ target_height, target_width = target_resolution
+ paste_x, r_x = divmod(target_width - original_width, 2)
+ paste_y, r_y = divmod(target_height - original_height, 2)
+ return [paste_x, paste_y, paste_x + r_x, paste_y + r_y]
+
+ def _pad_for_patching(
+ self, image: "torch.Tensor", target_resolution: tuple, input_data_format: ChannelDimension
+ ) -> "torch.Tensor":
+ """
+ Pad an image to a target resolution while maintaining aspect ratio.
+ """
+ new_resolution = get_patch_output_size(image, target_resolution, input_data_format)
+ padding = self._get_padding_size(new_resolution, target_resolution)
+
+ padded_image = F.pad(image, padding=padding)
+
+ return padded_image
+
+ def _get_image_patches(
+ self,
+ image: "torch.Tensor",
+ grid_pinpoints,
+ size: tuple,
+ patch_size: int,
+ interpolation: "F.InterpolationMode",
+ ) -> list["torch.Tensor"]:
+ """
+ Process an image with variable resolutions by dividing it into patches.
+
+ Args:
+ image ("torch.Tensor"):
+ The input image to be processed.
+ grid_pinpoints (List):
+ A string representation of a list of possible resolutions.
+ size (`tuple`):
+ Size to resize the original image to.
+ patch_size (`int`):
+ Size of the patches to divide the image into.
+ interpolation (`"InterpolationMode"`):
+ Resampling filter to use if resizing the image.
+
+ Returns:
+ list["torch.Tensor"]: A list of NumPy arrays containing the processed image patches.
+ """
+ if not isinstance(grid_pinpoints, list):
+ raise TypeError("grid_pinpoints must be a list of possible resolutions.")
+
+ possible_resolutions = grid_pinpoints
+
+ image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST)
+ best_resolution = select_best_resolution(image_size, possible_resolutions)
+ resized_image = self._resize_for_patching(
+ image, best_resolution, interpolation=interpolation, input_data_format=ChannelDimension.FIRST
+ )
+ padded_image = self._pad_for_patching(resized_image, best_resolution, input_data_format=ChannelDimension.FIRST)
+ patches = divide_to_patches(padded_image, patch_size=patch_size)
+ resized_original_image = F.resize(image, size=size, interpolation=interpolation)
+
+ image_patches = [resized_original_image] + patches
+
+ return image_patches
+
+ def _pad_for_batching(
+ self,
+ pixel_values: list["torch.Tensor"],
+ ) -> list["torch.Tensor"]:
+ """
+ Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
+
+ Args:
+ pixel_values (`list[torch.Tensor]`):
+ An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`)
+
+ Returns:
+ list[`torch.Tensor`]: The padded images.
+ """
+ max_patch = max(len(x) for x in pixel_values)
+ pixel_values = [
+ torch.nn.functional.pad(image, pad=[0, 0, 0, 0, 0, 0, 0, max_patch - image.shape[0]])
+ for image in pixel_values
+ ]
+
+ return pixel_values
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ do_resize: bool,
+ size: SizeDict,
+ image_grid_pinpoints: list[list[int]],
+ interpolation: Optional["F.InterpolationMode"],
+ do_center_crop: bool,
+ crop_size: SizeDict,
+ do_rescale: bool,
+ rescale_factor: float,
+ do_normalize: bool,
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ do_pad: bool,
+ disable_grouping: Optional[bool],
+ return_tensors: Optional[Union[str, TensorType]],
+ **kwargs,
+ ) -> BatchFeature:
+ processed_images = []
+ image_sizes = []
+ # Determine the size tuple
+ if size and size.height and size.width:
+ size_tuple = (size.height, size.width)
+ else:
+ size_tuple = (size.shortest_edge, size.shortest_edge)
+
+ # Determine the patch size
+ if crop_size and crop_size.height:
+ patch_size = crop_size.height
+ elif size and size.height:
+ patch_size = size.height
+ else:
+ patch_size = size.shortest_edge
+
+ for image in images:
+ image_patches = self._get_image_patches(
+ image,
+ image_grid_pinpoints,
+ size=size_tuple,
+ patch_size=patch_size,
+ interpolation=interpolation,
+ )
+
+ # Group images by size for batched processing
+ processed_image_patches_grouped = {}
+ grouped_image_patches, grouped_image_patches_index = group_images_by_shape(
+ image_patches, disable_grouping=disable_grouping
+ )
+ for shape, stacked_image_patches in grouped_image_patches.items():
+ if do_resize:
+ stacked_image_patches = self.resize(
+ image=stacked_image_patches,
+ size=size,
+ interpolation=interpolation,
+ )
+ if do_center_crop:
+ stacked_image_patches = self.center_crop(stacked_image_patches, crop_size)
+ # Fused rescale and normalize
+ stacked_image_patches = self.rescale_and_normalize(
+ stacked_image_patches, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ processed_image_patches_grouped[shape] = stacked_image_patches
+ processed_image_patches = reorder_images(processed_image_patches_grouped, grouped_image_patches_index)
+ processed_image_patches = (
+ torch.stack(processed_image_patches, dim=0) if return_tensors else processed_image_patches
+ )
+ processed_images.append(processed_image_patches)
+ image_sizes.append(get_image_size(image, ChannelDimension.FIRST))
+
+ if do_pad:
+ processed_images = self._pad_for_batching(processed_images)
+ processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+ return BatchFeature(
+ data={"pixel_values": processed_images, "image_sizes": image_sizes}, tensor_type=return_tensors
+ )
+
+
+__all__ = ["LlavaNextImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/modeling_llava_next.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/modeling_llava_next.py
new file mode 100644
index 0000000000000000000000000000000000000000..888fd9b9e64e4e427ff10c346b47ea4b1929a316
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/modeling_llava_next.py
@@ -0,0 +1,797 @@
+# coding=utf-8
+# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Llava-NeXT model."""
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import numpy as np
+import torch
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache
+from ...generation import GenerationMixin
+from ...image_processing_utils import select_best_resolution
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_outputs import BaseModelOutputWithPast, ModelOutput
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ..auto import AutoModel
+from .configuration_llava_next import LlavaNextConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
+ """
+ Calculate the shape of the image patch grid after the preprocessing for images of any resolution.
+
+ Args:
+ image_size (`tuple`):
+ The size of the input image in the format (width, height).
+ grid_pinpoints (`List`):
+ A list containing possible resolutions. Each item in the list should be a tuple or list
+ of the form `(height, width)`.
+ patch_size (`int`):
+ The size of each image patch.
+
+ Returns:
+ tuple: The shape of the image patch grid in the format (width, height).
+ """
+ if not isinstance(grid_pinpoints, list):
+ raise TypeError("grid_pinpoints should be a list of tuples or lists")
+
+ # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
+ if not isinstance(image_size, (list, tuple)):
+ if not isinstance(image_size, (torch.Tensor, np.ndarray)):
+ raise TypeError(
+ f"image_size invalid type: {type(image_size)} not valid, should be either list, tuple, np.ndarray or tensor"
+ )
+ image_size = image_size.tolist()
+
+ height, width = select_best_resolution(image_size, grid_pinpoints)
+ return height // patch_size, width // patch_size
+
+
+def image_size_to_num_patches(image_size, grid_pinpoints, patch_size: int):
+ """
+ Calculate the number of patches after the preprocessing for images of any resolution.
+
+ Args:
+ image_size (`torch.LongTensor` or `np.ndarray` or `tuple[int, int]`):
+ The size of the input image in the format (height, width). ?
+ grid_pinpoints (`List`):
+ A list containing possible resolutions. Each item in the list should be a tuple or list
+ of the form `(height, width)`.
+ patch_size (`int`):
+ The size of each image patch.
+
+ Returns:
+ int: the number of patches
+ """
+ if not isinstance(grid_pinpoints, list):
+ raise TypeError("grid_pinpoints should be a list of tuples or lists")
+
+ # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
+ if not isinstance(image_size, (list, tuple)):
+ if not isinstance(image_size, (torch.Tensor, np.ndarray)):
+ raise TypeError(f"image_size invalid type {type(image_size)} with value {image_size}")
+ image_size = image_size.tolist()
+
+ best_resolution = select_best_resolution(image_size, grid_pinpoints)
+ height, width = best_resolution
+ num_patches = 0
+ # consider change to ceil(height/patch_size)*ceil(width/patch_size) + 1
+ for i in range(0, height, patch_size):
+ for j in range(0, width, patch_size):
+ num_patches += 1
+ # add the base patch
+ num_patches += 1
+ return num_patches
+
+
+def unpad_image(tensor, original_size):
+ """
+ Unpads a PyTorch tensor of a padded and resized image.
+
+ Args:
+ tensor (`torch.Tensor`):
+ The image tensor, assumed to be of shape (num_channels, height, width).
+ original_size (`tuple`):
+ The original size of the image (height, width).
+
+ Returns:
+ `torch.Tensor`: The unpadded image tensor.
+ """
+ if not isinstance(original_size, (list, tuple)):
+ if not isinstance(original_size, (torch.Tensor, np.ndarray)):
+ raise TypeError(
+ f"image_size invalid type: {type(original_size)} not valid, should be either list, tuple, np.ndarray or tensor"
+ )
+ original_size = original_size.tolist()
+ original_height, original_width = original_size
+ current_height, current_width = tensor.shape[1:]
+
+ original_aspect_ratio = original_width / original_height
+ current_aspect_ratio = current_width / current_height
+
+ if original_aspect_ratio > current_aspect_ratio:
+ scale_factor = current_width / original_width
+ new_height = int(round(original_height * scale_factor, 7))
+ padding = (current_height - new_height) // 2
+ unpadded_tensor = tensor[:, padding : current_height - padding, :]
+ else:
+ scale_factor = current_height / original_height
+ new_width = int(round(original_width * scale_factor, 7))
+ padding = (current_width - new_width) // 2
+ unpadded_tensor = tensor[:, :, padding : current_width - padding]
+
+ return unpadded_tensor
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Llava outputs, with hidden states and attentions.
+ """
+)
+class LlavaNextModelOutputWithPast(BaseModelOutputWithPast):
+ r"""
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for LlavaNext causal language model (or autoregressive) outputs.
+ """
+)
+class LlavaNextCausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+
+# Copied from transformers.models.llava.modeling_llava.LlavaMultiModalProjector with Llava->LlavaNext
+class LlavaNextMultiModalProjector(nn.Module):
+ def __init__(self, config: LlavaNextConfig):
+ super().__init__()
+ # We have hidden_size * the number of vision feature layers
+ num_feature_layers = 1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer)
+ self.linear_1 = nn.Linear(
+ config.vision_config.hidden_size * num_feature_layers,
+ config.text_config.hidden_size,
+ bias=config.multimodal_projector_bias,
+ )
+ self.act = ACT2FN[config.projector_hidden_act]
+ self.linear_2 = nn.Linear(
+ config.text_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
+ )
+
+ def forward(self, image_features):
+ hidden_states = self.linear_1(image_features)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.linear_2(hidden_states)
+ return hidden_states
+
+
+@auto_docstring
+class LlavaNextPreTrainedModel(PreTrainedModel):
+ config: LlavaNextConfig
+ base_model_prefix = ""
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["LlamaDecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+ _supports_flex_attn = True
+ _supports_attention_backend = True
+
+ def _init_weights(self, module):
+ std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range)
+
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, LlavaNextModel):
+ embed_std = 1 / math.sqrt(self.config.text_config.hidden_size)
+ module.image_newline.data.normal_(mean=0.0, std=embed_std)
+
+
+@auto_docstring(
+ custom_intro="""
+ The Llava-Next model which consists of a vision backbone and a language model without language modeling head.
+ """
+)
+class LlavaNextModel(LlavaNextPreTrainedModel):
+ _checkpoint_conversion_mapping = {"language_model.model": "language_model"}
+
+ def __init__(self, config: LlavaNextConfig):
+ super().__init__(config)
+ self.vision_tower = AutoModel.from_config(config.vision_config)
+
+ self.multi_modal_projector = LlavaNextMultiModalProjector(config)
+ embed_std = 1 / math.sqrt(config.text_config.hidden_size)
+ self.image_newline = nn.Parameter(torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std)
+
+ self.vocab_size = config.text_config.vocab_size
+ self.language_model = AutoModel.from_config(config.text_config)
+ self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def set_decoder(self, decoder):
+ self.language_model = decoder
+
+ def get_decoder(self):
+ return self.language_model
+
+ def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None):
+ """
+ Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors.
+
+ Args:
+ image_features (`list[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`)
+ List of image feature tensor, each contains all the visual feature of all patches.
+ image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
+ Actual image size of each images (H, W).
+ vision_feature_select_strategy (`str`)
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ image_newline (`torch.Tensor` of shape `(embed_dim)`)
+ New line embedding vector.
+ Returns:
+ image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`)
+ feature_lens (`list[int]`)
+ token length of each image in image_features
+ """
+ new_image_features = []
+ feature_lens = []
+ for image_idx, image_feature in enumerate(image_features):
+ if image_feature.shape[0] > 1:
+ base_image_feature = image_feature[0]
+ image_feature = image_feature[1:]
+ height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
+
+ num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+ image_sizes[image_idx],
+ self.config.image_grid_pinpoints,
+ self.config.vision_config.image_size,
+ )
+
+ if (
+ np.prod(image_feature.shape) % (num_patch_height * num_patch_width * height * width) != 0
+ and vision_feature_select_strategy == "default"
+ ):
+ logger.warning_once(
+ "Image feature shape does not line up with the provided patch size. "
+ "You may be using the `default` vision_feature_select_strategy with a"
+ " visual encoder that does not have CLS."
+ )
+
+ image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
+ image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
+ image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+ image_feature = unpad_image(image_feature, image_sizes[image_idx])
+ if image_newline is not None:
+ image_feature = torch.cat(
+ (
+ image_feature,
+ image_newline[:, None, None]
+ .expand(*image_feature.shape[:-1], 1)
+ .to(image_feature.device, image_feature.dtype),
+ ),
+ dim=-1,
+ )
+ image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+ image_feature = torch.cat((base_image_feature, image_feature), dim=0)
+ else:
+ image_feature = image_feature[0]
+ if image_newline is not None:
+ image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0)
+ new_image_features.append(image_feature)
+ feature_lens.append(image_feature.size(0))
+ feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device)
+ return new_image_features, feature_lens
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ ):
+ """
+ Obtains image last hidden states from the vision tower and apply multimodal projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`)
+ The tensors corresponding to the input images.
+ image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
+ Actual image size of each images (H, W).
+ vision_feature_layer (`Union[int, list[int]]`, *optional*):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`, *optional*):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ Returns:
+ image_features (list[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches
+ and are of shape `(num_patches, image_length, embed_dim)`).
+ """
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ # ! infer image_num_patches from image_sizes
+ image_num_patches = [
+ image_size_to_num_patches(
+ image_size=imsize,
+ grid_pinpoints=self.config.image_grid_pinpoints,
+ patch_size=self.config.vision_config.image_size,
+ )
+ for imsize in image_sizes
+ ]
+ if pixel_values.dim() == 5:
+ # stacked if input is (batch_size, num_patches, num_channels, height, width)
+ _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)]
+ pixel_values = torch.cat(_pixel_values_list, dim=0)
+ elif pixel_values.dim() != 4:
+ # otherwise has to be stacked from list of (num_patches, num_channels, height, width)
+ raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")
+
+ image_features = self.vision_tower(pixel_values, output_hidden_states=True)
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_image_feature = image_features.hidden_states[vision_feature_layer]
+ else:
+ hs_pool = [image_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ selected_image_feature = torch.cat(hs_pool, dim=-1)
+
+ if vision_feature_select_strategy == "default":
+ selected_image_feature = selected_image_feature[:, 1:]
+ elif vision_feature_select_strategy == "full":
+ selected_image_feature = selected_image_feature
+
+ image_features = self.multi_modal_projector(selected_image_feature)
+ image_features = torch.split(image_features, image_num_patches, dim=0)
+
+ # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
+ image_features, feature_lens = self.pack_image_features(
+ image_features,
+ image_sizes,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ image_newline=self.image_newline,
+ )
+ return image_features
+
+ def get_placeholder_mask(
+ self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
+ ):
+ """
+ Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if inputs_embeds[special_image_mask].numel() != image_features.numel():
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {image_features.shape[0]}"
+ )
+ return special_image_mask
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ image_sizes: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, LlavaNextModelOutputWithPast]:
+ r"""
+ vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
+ If `"full"`, the full vision features are used.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ if pixel_values is not None and pixel_values.size(0) > 0:
+ image_features = self.get_image_features(
+ pixel_values,
+ image_sizes,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ )
+ image_features = torch.cat(image_features, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+ special_image_mask = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+ outputs = self.language_model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return LlavaNextModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=image_features if pixel_values is not None else None,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The LLAVA-NeXT model which consists of a vision backbone and a language model.
+ """
+)
+class LlavaNextForConditionalGeneration(LlavaNextPreTrainedModel, GenerationMixin):
+ _checkpoint_conversion_mapping = {
+ "^language_model.model": "model.language_model",
+ "^vision_tower": "model.vision_tower",
+ "^multi_modal_projector": "model.multi_modal_projector",
+ "^image_newline": "model.image_newline",
+ "^language_model.lm_head": "lm_head",
+ }
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: LlavaNextConfig):
+ super().__init__(config)
+ self.model = LlavaNextModel(config)
+ self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ def get_output_embeddings(self) -> nn.Module:
+ return self.lm_head
+
+ def set_decoder(self, decoder):
+ self.model.set_decoder(decoder)
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None):
+ return self.model.pack_image_features(
+ image_features=image_features,
+ image_sizes=image_sizes,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ image_newline=image_newline,
+ )
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ ):
+ return self.model.get_image_features(
+ pixel_values=pixel_values,
+ image_sizes=image_sizes,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ )
+
+ # Make modules available through conditional class for BC
+ @property
+ def language_model(self):
+ return self.model.language_model
+
+ @property
+ def vision_tower(self):
+ return self.model.vision_tower
+
+ @property
+ def multi_modal_projector(self):
+ return self.model.multi_modal_projector
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ image_sizes: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, LlavaNextCausalLMOutputWithPast]:
+ r"""
+ vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
+ If `"full"`, the full vision features are used.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import AutoProcessor, LlavaNextForConditionalGeneration
+
+ >>> model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
+ >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
+
+ >>> prompt = "[INST] \nWhat is shown in this image? [/INST]"
+ >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(**inputs, max_length=30)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "[INST] \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot (...)"
+ ```"""
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ outputs = self.model(
+ input_ids,
+ pixel_values=pixel_values,
+ image_sizes=image_sizes,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return LlavaNextCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ inputs_embeds=None,
+ pixel_values=None,
+ image_sizes=None,
+ attention_mask=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ if cache_position[0] == 0:
+ model_inputs["pixel_values"] = pixel_values
+ model_inputs["image_sizes"] = image_sizes
+
+ return model_inputs
+
+ @staticmethod
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+
+__all__ = ["LlavaNextForConditionalGeneration", "LlavaNextPreTrainedModel", "LlavaNextModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/processing_llava_next.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/processing_llava_next.py
new file mode 100644
index 0000000000000000000000000000000000000000..92d8d56618ed5587c44d53c877f262953ff328af
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next/processing_llava_next.py
@@ -0,0 +1,289 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for LLaVa-NeXT.
+"""
+
+from typing import Union
+
+import numpy as np
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_processing_utils import select_best_resolution
+from ...image_utils import ImageInput, get_image_size, to_numpy_array
+from ...processing_utils import (
+ MultiModalData,
+ ProcessingKwargs,
+ ProcessorMixin,
+ Unpack,
+)
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class LlavaNextProcessorKwargs(ProcessingKwargs, total=False):
+ _defaults = {
+ "text_kwargs": {
+ "padding": False,
+ "return_mm_token_type_ids": False,
+ },
+ "images_kwargs": {
+ "do_pad": True,
+ },
+ }
+
+
+class LlavaNextProcessor(ProcessorMixin):
+ r"""
+ Constructs a LLaVa-NeXT processor which wraps a LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor.
+
+ [`LlavaNextProcessor`] offers all the functionalities of [`LlavaNextImageProcessor`] and [`LlamaTokenizerFast`]. See the
+ [`~LlavaNextProcessor.__call__`] and [`~LlavaNextProcessor.decode`] for more information.
+
+ Args:
+ image_processor ([`LlavaNextImageProcessor`], *optional*):
+ The image processor is a required input.
+ tokenizer ([`LlamaTokenizerFast`], *optional*):
+ The tokenizer is a required input.
+ patch_size (`int`, *optional*):
+ Patch size from the vision tower.
+ vision_feature_select_strategy (`str`, *optional*):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Should be same as in model's config
+ chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+ in a chat into a tokenizable string.
+ image_token (`str`, *optional*, defaults to `""`):
+ Special token used to denote image location.
+ num_additional_image_tokens (`int`, *optional*, defaults to 0):
+ Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other
+ extra tokens appended, no need to set this arg.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "AutoImageProcessor"
+ tokenizer_class = "AutoTokenizer"
+
+ def __init__(
+ self,
+ image_processor=None,
+ tokenizer=None,
+ patch_size=None,
+ vision_feature_select_strategy=None,
+ chat_template=None,
+ image_token="", # set the default and let users change if they have peculiar special tokens in rare cases
+ num_additional_image_tokens=0,
+ **kwargs,
+ ):
+ self.patch_size = patch_size
+ self.num_additional_image_tokens = num_additional_image_tokens
+ self.vision_feature_select_strategy = vision_feature_select_strategy
+ self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
+ self.image_token_id = (
+ tokenizer.image_token_id
+ if getattr(tokenizer, "image_token_id", None)
+ else tokenizer.convert_tokens_to_ids(self.image_token)
+ )
+ super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+ def __call__(
+ self,
+ images: ImageInput = None,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+ audio=None,
+ videos=None,
+ **kwargs: Unpack[LlavaNextProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+ and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
+ the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+ LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
+ of the above two methods for more information.
+
+ Args:
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+ `None`).
+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+ """
+ if images is None and text is None:
+ raise ValueError("You have to specify at least images or text.")
+
+ output_kwargs = self._merge_kwargs(
+ LlavaNextProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+ if images is not None:
+ image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+ else:
+ image_inputs = {}
+
+ if isinstance(text, str):
+ text = [text]
+ elif not isinstance(text, list) and not isinstance(text[0], str):
+ raise TypeError("Invalid input text. Please provide a string, or a list of strings")
+
+ prompt_strings = text
+ if image_inputs:
+ image_sizes = iter(image_inputs["image_sizes"])
+ height, width = get_image_size(to_numpy_array(image_inputs["pixel_values"][0][0]))
+ prompt_strings = []
+ for sample in text:
+ while self.image_token in sample:
+ image_size = next(image_sizes)
+ if not isinstance(image_size, (list, tuple)):
+ # cast to list to avoid numerical precision errors when calculating unpadding
+ image_size = image_size.tolist()
+ orig_height, orig_width = image_size
+ num_image_tokens = self._get_number_of_features(orig_height, orig_width, height, width)
+ if self.vision_feature_select_strategy == "default":
+ num_image_tokens -= 1
+ sample = sample.replace(self.image_token, "" * num_image_tokens, 1)
+ prompt_strings.append(sample)
+ prompt_strings = [sample.replace("", self.image_token) for sample in prompt_strings]
+
+ return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+ return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
+ text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
+ self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
+
+ if return_mm_token_type_ids:
+ array_ids = np.array(text_inputs["input_ids"])
+ mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+ mm_token_type_ids[array_ids == self.image_token_id] = 1
+ text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
+ return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+
+ def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
+ image_grid_pinpoints = self.image_processor.image_grid_pinpoints
+
+ height_best_resolution, width_best_resolution = select_best_resolution(
+ [orig_height, orig_width], image_grid_pinpoints
+ )
+ scale_height, scale_width = height_best_resolution // height, width_best_resolution // width
+
+ patches_height = height // self.patch_size
+ patches_width = width // self.patch_size
+ unpadded_features, newline_features = self._get_unpadded_features(
+ orig_height, orig_width, patches_height, patches_width, scale_height, scale_width
+ )
+ # The base patch covers the entire image (+1 for the CLS)
+ base_features = patches_height * patches_width + self.num_additional_image_tokens
+ num_image_tokens = unpadded_features + newline_features + base_features
+ return num_image_tokens
+
+ def _get_unpadded_features(self, height, width, patches_height, patches_width, scale_height, scale_width):
+ """
+ Get number of features for a given image with height/width. LLaVA-NeXT is different from LLaVA
+ because it divided each image into patches depending on its resolution. Therefore we need to calculate how many
+ patches an image is divided into and get the number of features from that.
+ """
+ current_height = patches_height * scale_height
+ current_width = patches_width * scale_width
+
+ original_aspect_ratio = width / height
+ current_aspect_ratio = current_width / current_height
+ if original_aspect_ratio > current_aspect_ratio:
+ new_height = int(round(height * (current_width / width), 7))
+ padding = (current_height - new_height) // 2
+ current_height -= padding * 2
+ else:
+ new_width = int(round(width * (current_height / height), 7))
+ padding = (current_width - new_width) // 2
+ current_width -= padding * 2
+
+ unpadded_features = current_height * current_width
+ newline_features = current_height
+ return (unpadded_features, newline_features)
+
+ def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+ """
+ Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+ Args:
+ image_sizes (list[list[str]], *optional*):
+ The input sizes formatted as (height, width) per each image.
+ Returns:
+ `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+ input modalities, along with other useful data.
+ """
+ vision_data = {}
+ if image_sizes is not None:
+ images_kwargs = LlavaNextProcessorKwargs._defaults.get("images_kwargs", {})
+ images_kwargs.update(kwargs)
+
+ size = images_kwargs.get("size", None) or self.image_processor.size
+ size = (
+ (size["shortest_edge"], size["shortest_edge"])
+ if "shortest_edge" in size
+ else (min(size["height"], size["width"]), min(size["height"], size["width"]))
+ )
+ processed_height, processed_width = size
+
+ batch_num_image_tokens = []
+ num_image_patches = [1] * len(image_sizes) # llava-next doesn't batch pixels as Idefics, thus `1` patch`
+ for image_size in image_sizes:
+ orig_height, orig_width = image_size
+ num_image_tokens = self._get_number_of_features(
+ orig_height, orig_width, processed_height, processed_width
+ )
+ if self.vision_feature_select_strategy == "default":
+ num_image_tokens -= 1
+ batch_num_image_tokens.append(num_image_tokens)
+ vision_data.update({"num_image_tokens": batch_num_image_tokens, "num_image_patches": num_image_patches})
+
+ return MultiModalData(**vision_data)
+
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+__all__ = ["LlavaNextProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3632c7a2a14278d69c129e6431134314cf59ee5
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_llava_next_video import *
+ from .image_processing_llava_next_video import *
+ from .modeling_llava_next_video import *
+ from .processing_llava_next_video import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/configuration_llava_next_video.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/configuration_llava_next_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..1eb1078b6aceb27128001eac79e0161396cc0387
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/configuration_llava_next_video.py
@@ -0,0 +1,166 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/llava_next_video/modular_llava_next_video.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_llava_next_video.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ...configuration_utils import PretrainedConfig
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+class LlavaNextVideoConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LlavaNextVideoForConditionalGeneration`]. It is used to instantiate an
+ Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the [llava-hf/LLaVA-NeXT-Video-7B-hf](https://huggingface.co/llava-hf/LLaVA-NeXT-Video-7B-hf)
+ model.
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`):
+ The config object or dictionary of the vision backbone.
+ text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
+ The config object or dictionary of the text backbone.
+ image_token_index (`int`, *optional*, defaults to 32001):
+ The image token index to encode the image prompt.
+ projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
+ The activation function used by the multimodal projector.
+ multimodal_projector_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use bias in the multimodal projector.
+ vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
+ If `"full"`, the full vision features are used.
+ vision_feature_layer (`Union[int, list[int]]`, *optional*, defaults to -2):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ image_grid_pinpoints (`List`, *optional*, defaults to `[[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]`):
+ A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list
+ of the form `(height, width)`.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether the model's input and output word embeddings should be tied.
+ video_token_index (`int`, *optional*, defaults to 32000):
+ The video token index to encode the image prompt.
+ spatial_pool_mode (`str`, *optional*, defaults to `"average"`):
+ Pooling mode to use for videos. Can be "average", "max" or "conv".
+ spatial_pool_stride (`int`, *optional*, defaults to 2):
+ Stride used in the pooling layer for videos.
+ image_seq_length (`int`, *optional*, defaults to 576):
+ Sequence length of one image embedding.
+ video_seq_length (`int`, *optional*, defaults to 288):
+ Sequence length of one video embedding.
+
+ Example:
+
+ ```python
+ >>> from transformers import LlavaNextVideoForConditionalGeneration, LlavaNextVideoConfig, CLIPVisionConfig, LlamaConfig
+
+ >>> # Initializing a CLIP-vision config
+ >>> vision_config = CLIPVisionConfig()
+
+ >>> # Initializing a Llama config
+ >>> text_config = LlamaConfig()
+
+ >>> configuration = LlavaNextVideoConfig(vision_config, text_config)
+
+ >>> model = LlavaNextVideoForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "llava_next_video"
+ attribute_map = {
+ "image_token_id": "image_token_index",
+ "video_token_id": "video_token_index",
+ }
+ sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
+
+ def __init__(
+ self,
+ vision_config=None,
+ text_config=None,
+ image_token_index=32001,
+ projector_hidden_act="gelu",
+ multimodal_projector_bias=True,
+ vision_feature_select_strategy="default",
+ vision_feature_layer=-2,
+ image_grid_pinpoints=None,
+ tie_word_embeddings=False,
+ video_token_index=32000,
+ spatial_pool_mode="average",
+ spatial_pool_stride=2,
+ image_seq_length=576,
+ video_seq_length=288,
+ **kwargs,
+ ):
+ self.video_token_index = video_token_index
+ self.spatial_pool_mode = spatial_pool_mode
+ self.spatial_pool_stride = spatial_pool_stride
+ self.image_seq_length = image_seq_length
+ self.video_seq_length = video_seq_length
+ self.image_token_index = image_token_index
+ self.projector_hidden_act = projector_hidden_act
+ self.multimodal_projector_bias = multimodal_projector_bias
+
+ if vision_feature_select_strategy not in ["default", "full"]:
+ raise ValueError(
+ "vision_feature_select_strategy should be one of 'default', 'full'."
+ f"Got: {vision_feature_select_strategy}"
+ )
+
+ self.vision_feature_select_strategy = vision_feature_select_strategy
+ self.vision_feature_layer = vision_feature_layer
+ image_grid_pinpoints = (
+ image_grid_pinpoints
+ if image_grid_pinpoints is not None
+ else [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
+ )
+ self.image_grid_pinpoints = image_grid_pinpoints
+
+ if isinstance(vision_config, dict):
+ vision_config["model_type"] = vision_config.get("model_type", "clip_vision_model")
+ vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
+ elif vision_config is None:
+ vision_config = CONFIG_MAPPING["clip_vision_model"](
+ intermediate_size=4096,
+ hidden_size=1024,
+ patch_size=14,
+ image_size=336,
+ num_hidden_layers=24,
+ num_attention_heads=16,
+ vocab_size=32000,
+ projection_dim=768,
+ )
+
+ self.vision_config = vision_config
+
+ if isinstance(text_config, dict):
+ text_config["model_type"] = text_config.get("model_type", "llama")
+ text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+ elif text_config is None:
+ text_config = CONFIG_MAPPING["llama"]()
+
+ self.text_config = text_config
+
+ super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+__all__ = ["LlavaNextVideoConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/convert_llava_next_video_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/convert_llava_next_video_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..265e543cb55730342d9c6f0a4d301bd86cd30b1a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/convert_llava_next_video_weights_to_hf.py
@@ -0,0 +1,274 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Convert LLaVa-NeXT-Video checkpoints from the original repository.
+
+URL: https://github.com/LLaVA-VL/LLaVA-NeXT/tree/inference
+"""
+
+import argparse
+import glob
+import json
+from pathlib import Path
+
+import torch
+from accelerate import init_empty_weights
+from huggingface_hub import hf_hub_download, snapshot_download
+from safetensors import safe_open
+
+from transformers import (
+ AddedToken,
+ AutoConfig,
+ AutoTokenizer,
+ LlavaNextImageProcessor,
+ LlavaNextVideoConfig,
+ LlavaNextVideoForConditionalGeneration,
+ LlavaNextVideoImageProcessor,
+ LlavaNextVideoProcessor,
+)
+
+
+KEYS_TO_MODIFY_MAPPING = {
+ "model.vision_tower.": "",
+ ".vision_resampler": "", # all lmms-lab models do avg pooling, so no vision_resampler
+ "model.mm_projector": "multi_modal_projector",
+ "model": "model.model",
+ "vision_model.model": "vision_model",
+ "lm_head": "language_model.lm_head",
+ "model.model": "language_model.model",
+ "multi_modal_projector.0": "multi_modal_projector.linear_1",
+ "multi_modal_projector.2": "multi_modal_projector.linear_2",
+ "language_model.model.image_newline": "image_newline",
+}
+
+# {{SYSTEM_PROMPT}} USER: \n{{PROMPT}} ASSISTANT:" assistant end with " "
+chat_vicuna = (
+ "{% for message in messages %}"
+ "{% if message['role'] == 'system' %}"
+ "{{ message['content'][0]['text'] }}"
+ "{% else %}"
+ "{{ message['role'].upper() + ': '}}"
+ "{% endif %}"
+ "{# Render all images first #}"
+ "{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}"
+ "{{ '\n' }}"
+ "{% endfor %}"
+ "{# Render all text next #}"
+ "{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}"
+ "{{ content['text'] + ' '}}"
+ "{% endfor %}"
+ "{% endfor %}"
+ "{% if add_generation_prompt %}"
+ "{{ 'ASSISTANT:' }}"
+ "{% endif %}"
+)
+
+# "[INST] \nWhat is shown in this image? [/INST]" assistant end with " "
+chat_mistral = (
+ "{% for message in messages %}"
+ "{% if message['role'] == 'user' %}"
+ "{{ '[INST] ' }}"
+ "{# Render all images first #}"
+ "{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}"
+ "{{ '\n' }}"
+ "{% endfor %}"
+ "{# Render all text next #}"
+ "{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}"
+ "{{ content['text'] }}"
+ "{% endfor %}"
+ "{{' [/INST]' }}"
+ "{% elif message['role'] == 'assistant' %}"
+ r"{{ ' ' + message['content'][0]['text'] + '<\s> '}}"
+ "{% else %}"
+ "{{ raise_exception('Only user and assistant roles are supported!') }}"
+ "{% endif %}"
+ "{% endfor %}"
+)
+
+# "<|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n\nWhat is shown in this image?<|im_end|><|im_start|>assistant\n"
+chat_yi = (
+ "{% for message in messages %}"
+ "{{'<|im_start|>' + message['role'] + '\n'}}"
+ "{# Render all images first #}"
+ "{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}"
+ "{{ '\n' }}"
+ "{% endfor %}"
+ "{# Render all text next #}"
+ "{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}"
+ "{{ content['text'] }}"
+ "{% endfor %}"
+ "{{'<|im_end|>' + '\n'}}"
+ "{% endfor %}"
+ "{% if add_generation_prompt %}"
+ "{{ '<|im_start|>assistant\n' }}"
+ "{% endif %}"
+)
+
+model2template = {
+ "lmms-lab/LLaVA-NeXT-Video-7B-32K": chat_mistral,
+ "lmms-lab/LLaVA-NeXT-Video-7B": chat_vicuna,
+ "lmms-lab/LLaVA-NeXT-Video-7B-DPO": chat_vicuna,
+ "lmms-lab/LLaVA-NeXT-Video-34B": chat_yi,
+ "lmms-lab/LLaVA-NeXT-Video-34B-DPO": chat_yi,
+}
+
+
+def load_original_state_dict(model_id):
+ directory_path = snapshot_download(repo_id=model_id, allow_patterns=["*.safetensors"])
+
+ original_state_dict = {}
+ for path in glob.glob(f"{directory_path}/*"):
+ if path.endswith(".safetensors"):
+ with safe_open(path, framework="pt", device="cpu") as f:
+ for key in f.keys():
+ original_state_dict[key] = f.get_tensor(key)
+
+ return original_state_dict
+
+
+def convert_state_dict_to_hf(state_dict):
+ new_state_dict = {}
+ for key, value in state_dict.items():
+ if key.endswith(".inv_freq"):
+ continue
+ for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
+ if key_to_modify in key:
+ key = key.replace(key_to_modify, new_key)
+
+ new_state_dict[key] = value.to(torch.bfloat16)
+ return new_state_dict
+
+
+def convert_llava_to_hf(model_id, pytorch_dump_folder_path, push_to_hub=False):
+ # load original config
+ filepath = hf_hub_download(repo_id=model_id, filename="config.json", repo_type="model")
+ with open(filepath) as f:
+ data = json.load(f)
+ print(data)
+
+ if model_id == "lmms-lab/LLaVA-NeXT-Video-7B-32K":
+ text_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
+ video_token_id = 32000
+ image_token_id = 32001
+ overwrite_text_config = {}
+ elif model_id in ["lmms-lab/LLaVA-NeXT-Video-7B", "lmms-lab/LLaVA-NeXT-Video-7B-DPO"]:
+ text_model_id = "lmsys/vicuna-7b-v1.5"
+ video_token_id = 32000
+ image_token_id = 32001
+ overwrite_text_config = {"factor": 2.0, "type": "linear"}
+ elif model_id in ["lmms-lab/LLaVA-NeXT-Video-34B", "lmms-lab/LLaVA-NeXT-Video-34B-DPO"]:
+ text_model_id = "NousResearch/Nous-Hermes-2-Yi-34B"
+ video_token_id = 64000
+ image_token_id = 64001
+ overwrite_text_config = {}
+ else:
+ raise ValueError("Incorrect checkpoint referenced. Text model-id not identified!")
+
+ vision_model_id = data["mm_vision_tower"]
+
+ torch.set_default_dtype(torch.bfloat16)
+ text_config = AutoConfig.from_pretrained(text_model_id)
+ text_config = text_config.to_dict()
+ text_config.update(overwrite_text_config)
+
+ tokenizer = AutoTokenizer.from_pretrained(text_model_id, use_fast=True, padding_side="left")
+ tokenizer.add_tokens(AddedToken("", special=True, normalized=False), special_tokens=True)
+ tokenizer.add_tokens(AddedToken("", special=True, normalized=False), special_tokens=True)
+
+ image_processor = LlavaNextImageProcessor.from_pretrained(vision_model_id)
+ video_processor = LlavaNextVideoImageProcessor.from_pretrained(vision_model_id)
+ processor = LlavaNextVideoProcessor(
+ tokenizer=tokenizer,
+ video_processor=video_processor,
+ image_processor=image_processor,
+ chat_template=model2template[model_id],
+ )
+
+ config = LlavaNextVideoConfig(
+ text_config=text_config,
+ image_grid_pinpoints=image_processor.image_grid_pinpoints,
+ use_image_newline_parameter=True,
+ video_token_id=video_token_id,
+ image_token_id=image_token_id,
+ )
+
+ with init_empty_weights():
+ model = LlavaNextVideoForConditionalGeneration(config)
+
+ # load original state dict
+ state_dict = load_original_state_dict(model_id)
+ state_dict = convert_state_dict_to_hf(state_dict)
+ model.load_state_dict(state_dict, assign=True, strict=True)
+
+ # See https://nlp.stanford.edu/~johnhew/vocab-expansion.html for why we get mean/stdev this way to expand embeddings
+ pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data
+ mu = torch.mean(pre_expansion_embeddings, dim=0).float()
+ n = pre_expansion_embeddings.size()[0]
+ sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n
+ dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma)
+
+ # We add an image token so we resize the model
+ # Pad to 64 for performance reasons
+ pad_shape = 64
+ vocab_size = config.text_config.vocab_size
+
+ # this one has 2 additional tokens, namely , and
+ num_tokens = vocab_size + 3
+ model.resize_token_embeddings(num_tokens, pad_to_multiple_of=pad_shape)
+ model.language_model.model.embed_tokens.weight.data[vocab_size:] = torch.stack(
+ tuple(dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[vocab_size:].shape[0])),
+ dim=0,
+ )
+ model.language_model.lm_head.weight.data[vocab_size:] = torch.stack(
+ tuple(dist.sample() for _ in range(model.language_model.lm_head.weight.data[vocab_size:].shape[0])),
+ dim=0,
+ )
+
+ if pytorch_dump_folder_path is not None:
+ print(f"Saving model and processor for {model_id} to {pytorch_dump_folder_path}")
+ Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+ model.save_pretrained(pytorch_dump_folder_path)
+ processor.save_pretrained(pytorch_dump_folder_path)
+
+ if push_to_hub:
+ repo_id = model_id.split("/")[-1]
+ print(f"Pushing model to hub repo: {repo_id}")
+ model.push_to_hub(f"llava-hf/{repo_id}-hf")
+ processor.push_to_hub(f"llava-hf/{repo_id}-hf")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--model_id",
+ help="Hub location of the model to convert",
+ default="lmms-lab/LLaVA-NeXT-Video-7B",
+ choices=[
+ "lmms-lab/LLaVA-NeXT-Video-7B",
+ "lmms-lab/LLaVA-NeXT-Video-7B-DPO",
+ "lmms-lab/LLaVA-NeXT-Video-7B-32K",
+ "lmms-lab/LLaVA-NeXT-Video-34B",
+ "lmms-lab/LLaVA-NeXT-Video-34B-DPO",
+ ],
+ required=False,
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
+ )
+ parser.add_argument(
+ "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
+ )
+ args = parser.parse_args()
+
+ convert_llava_to_hf(args.model_id, args.pytorch_dump_folder_path, args.push_to_hub)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/image_processing_llava_next_video.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/image_processing_llava_next_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bb7b43e677d18dd90632e1757f51f456301675d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/image_processing_llava_next_video.py
@@ -0,0 +1,402 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for LLaVa-NeXT-Video."""
+
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import (
+ convert_to_rgb,
+ get_resize_output_image_size,
+ resize,
+ to_channel_dimension_format,
+)
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_list_of_images,
+ to_numpy_array,
+ validate_preprocess_arguments,
+)
+from ...utils import TensorType, logging
+from ...video_utils import VideoInput, make_batched_videos
+
+
+logger = logging.get_logger(__name__)
+
+
+class LlavaNextVideoImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a LLaVa-NeXT-Video video processor. Based on [`CLIPImageProcessor`] with incorporation of processing each video frame.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
+ `do_resize` in the `preprocess` method.
+ size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
+ Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
+ method.
+ image_grid_pinpoints (`List` *optional*, defaults to `[[672, 336], [336, 672], [672, 672], [336, 1008], [1008, 336]]`):
+ A list of possible resolutions to use for processing high resolution images. The best resolution is selected
+ based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
+ method. Not used for processing videos.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+ Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
+ do_center_crop (`bool`, *optional*, defaults to `True`):
+ Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the
+ `preprocess` method.
+ crop_size (`dict[str, int]` *optional*, defaults to 224):
+ Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
+ method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
+ the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
+ method.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`float` or `list[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
+ Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+ number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+ Can be overridden by the `image_std` parameter in the `preprocess` method.
+ do_convert_rgb (`bool`, *optional*, defaults to `True`):
+ Whether to convert the image to RGB.
+ """
+
+ model_input_names = ["pixel_values_videos"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ image_grid_pinpoints: Optional[list] = None,
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ do_center_crop: bool = True,
+ crop_size: Optional[dict[str, int]] = None,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_convert_rgb: bool = True,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"shortest_edge": 224}
+ size = get_size_dict(size, default_to_square=False)
+ crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
+ crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")
+
+ self.do_resize = do_resize
+ self.size = size
+ self.image_grid_pinpoints = image_grid_pinpoints
+ self.resample = resample
+ self.do_center_crop = do_center_crop
+ self.crop_size = crop_size
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
+ self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+ self.do_convert_rgb = do_convert_rgb
+
+ # Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize with CLIP->LLaVa
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
+ resized to keep the input aspect ratio.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Size of the output image.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ Resampling filter to use when resiizing the image.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ default_to_square = True
+ if "shortest_edge" in size:
+ size = size["shortest_edge"]
+ default_to_square = False
+ elif "height" in size and "width" in size:
+ size = (size["height"], size["width"])
+ else:
+ raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
+ output_size = get_resize_output_image_size(
+ image,
+ size=size,
+ default_to_square=default_to_square,
+ input_data_format=input_data_format,
+ )
+
+ return resize(
+ image,
+ size=output_size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ def _preprocess(
+ self,
+ images: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[int] = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_convert_rgb: Optional[bool] = None,
+ data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> list[np.ndarray]:
+ """
+ Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
+
+ Args:
+ images (`ImageInput`):
+ Batch of frames (one video) to preprocess. Expects a batch of frames with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio.
+ resample (`int`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+ has an effect if `do_resize` is set to `True`.
+ do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
+ Whether to center crop the image.
+ crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`):
+ Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image.
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+ `True`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ images = make_list_of_images(images)
+
+ if do_convert_rgb:
+ images = [convert_to_rgb(image) for image in images]
+
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if do_rescale and is_scaled_image(images[0]):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ all_images = []
+ for image in images:
+ if do_resize:
+ image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+
+ if do_center_crop:
+ image = self.center_crop(image=image, size=crop_size, input_data_format=input_data_format)
+
+ if do_rescale:
+ image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+
+ if do_normalize:
+ image = self.normalize(
+ image=image, mean=image_mean, std=image_std, input_data_format=input_data_format
+ )
+
+ all_images.append(image)
+ images = [
+ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+ for image in all_images
+ ]
+
+ return images
+
+ def preprocess(
+ self,
+ images: VideoInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[int] = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_convert_rgb: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ """
+ Args:
+ images (`VideoInput`):
+ Videos to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the video.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the video after resizing. Shortest edge of the video is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio.
+ resample (`int`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the video. This can be one of the enum `PILImageResampling`. Only
+ has an effect if `do_resize` is set to `True`.
+ do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
+ Whether to center crop the video.
+ crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`):
+ Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the video.
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the video by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the video.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Frame mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Frame standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+ `True`.
+ do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+ Whether to convert the video to RGB.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ size = get_size_dict(size, param_name="size", default_to_square=False)
+ resample = resample if resample is not None else self.resample
+ do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
+ crop_size = crop_size if crop_size is not None else self.crop_size
+ crop_size = get_size_dict(crop_size, param_name="crop_size", default_to_square=True)
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+ images = make_batched_videos(images)
+ logger.warning(
+ "`LlavaNextVideoImageProcessor` is deprecated and will be removed in v5.0. "
+ "We recommend to load an instance of `LlavaNextVideoVideoProcessor` to process videos for the model. "
+ )
+
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ # preprocess each video frame by frame
+ pixel_values = [
+ self._preprocess(
+ frames,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ for frames in images
+ ]
+
+ data = {"pixel_values_videos": pixel_values}
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+
+__all__ = ["LlavaNextVideoImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/modeling_llava_next_video.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/modeling_llava_next_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..16e5fac66820c94bcb18153560c1a389535cbd5c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/modeling_llava_next_video.py
@@ -0,0 +1,987 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/llava_next_video/modular_llava_next_video.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_llava_next_video.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import numpy as np
+import torch
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache
+from ...generation import GenerationMixin
+from ...image_processing_utils import select_best_resolution
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_outputs import BaseModelOutputWithPast, ModelOutput
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ..auto import AutoModel
+from .configuration_llava_next_video import LlavaNextVideoConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Llava outputs, with hidden states and attentions.
+ """
+)
+class LlavaNextVideoModelOutputWithPast(BaseModelOutputWithPast):
+ r"""
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ video_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`.
+ video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+ video_hidden_states: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for LlavaNextVideo causal language model (or autoregressive) outputs.
+ """
+)
+class LlavaNextVideoCausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ video_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`.
+ video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+ video_hidden_states: Optional[torch.FloatTensor] = None
+
+
+class LlavaNextVideoPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ mode = config.spatial_pool_mode
+ stride = config.spatial_pool_stride
+ out_channels = getattr(config, "spatial_pool_out_channels", config.vision_config.hidden_size)
+ self.image_size = (config.vision_config.image_size // config.vision_config.patch_size) ** 2
+
+ if mode == "average":
+ self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride)
+ elif mode == "max":
+ self.pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
+ elif mode == "conv":
+ self.pool = nn.Conv2d(
+ in_channels=config.vision_config.hidden_size,
+ out_channels=out_channels,
+ kernel_size=stride,
+ stride=stride,
+ )
+ else:
+ raise ValueError(f"Unknown pooling mode: {mode}. Has to be one of [`average`, `max`, `conv`]")
+
+ def forward(self, image_features):
+ ori_width = int(math.sqrt(image_features.shape[1] * self.image_size // self.image_size))
+ ori_height = int(ori_width * self.image_size // self.image_size)
+
+ batch_size, _, dim = image_features.shape
+ image_features_spatial = image_features.view(batch_size, ori_height, ori_height, dim).permute(0, 3, 1, 2)
+ image_features_spatial_pool = self.pool(image_features_spatial)
+
+ return image_features_spatial_pool.flatten(2).transpose(1, 2).contiguous()
+
+
+class LlavaNextVideoMultiModalProjector(nn.Module):
+ def __init__(self, config: LlavaNextVideoConfig):
+ super().__init__()
+ # We have hidden_size * the number of vision feature layers
+ num_feature_layers = 1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer)
+ self.linear_1 = nn.Linear(
+ config.vision_config.hidden_size * num_feature_layers,
+ config.text_config.hidden_size,
+ bias=config.multimodal_projector_bias,
+ )
+ self.act = ACT2FN[config.projector_hidden_act]
+ self.linear_2 = nn.Linear(
+ config.text_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
+ )
+
+ def forward(self, image_features):
+ hidden_states = self.linear_1(image_features)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.linear_2(hidden_states)
+ return hidden_states
+
+
+@auto_docstring
+class LlavaNextVideoPreTrainedModel(PreTrainedModel):
+ config: LlavaNextVideoConfig
+ base_model_prefix = ""
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["LlamaDecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+ _supports_flex_attn = True
+ _supports_attention_backend = True
+
+ def _init_weights(self, module):
+ std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range)
+
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, LlavaNextVideoModel):
+ embed_std = 1 / math.sqrt(self.config.text_config.hidden_size)
+ module.image_newline.data.normal_(mean=0.0, std=embed_std)
+
+
+def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
+ """
+ Calculate the shape of the image patch grid after the preprocessing for images of any resolution.
+
+ Args:
+ image_size (`tuple`):
+ The size of the input image in the format (width, height).
+ grid_pinpoints (`List`):
+ A list containing possible resolutions. Each item in the list should be a tuple or list
+ of the form `(height, width)`.
+ patch_size (`int`):
+ The size of each image patch.
+
+ Returns:
+ tuple: The shape of the image patch grid in the format (width, height).
+ """
+ if not isinstance(grid_pinpoints, list):
+ raise TypeError("grid_pinpoints should be a list of tuples or lists")
+
+ # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
+ if not isinstance(image_size, (list, tuple)):
+ if not isinstance(image_size, (torch.Tensor, np.ndarray)):
+ raise TypeError(
+ f"image_size invalid type: {type(image_size)} not valid, should be either list, tuple, np.ndarray or tensor"
+ )
+ image_size = image_size.tolist()
+
+ height, width = select_best_resolution(image_size, grid_pinpoints)
+ return height // patch_size, width // patch_size
+
+
+def image_size_to_num_patches(image_size, grid_pinpoints, patch_size: int):
+ """
+ Calculate the number of patches after the preprocessing for images of any resolution.
+
+ Args:
+ image_size (`torch.LongTensor` or `np.ndarray` or `tuple[int, int]`):
+ The size of the input image in the format (height, width). ?
+ grid_pinpoints (`List`):
+ A list containing possible resolutions. Each item in the list should be a tuple or list
+ of the form `(height, width)`.
+ patch_size (`int`):
+ The size of each image patch.
+
+ Returns:
+ int: the number of patches
+ """
+ if not isinstance(grid_pinpoints, list):
+ raise TypeError("grid_pinpoints should be a list of tuples or lists")
+
+ # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
+ if not isinstance(image_size, (list, tuple)):
+ if not isinstance(image_size, (torch.Tensor, np.ndarray)):
+ raise TypeError(f"image_size invalid type {type(image_size)} with value {image_size}")
+ image_size = image_size.tolist()
+
+ best_resolution = select_best_resolution(image_size, grid_pinpoints)
+ height, width = best_resolution
+ num_patches = 0
+ # consider change to ceil(height/patch_size)*ceil(width/patch_size) + 1
+ for i in range(0, height, patch_size):
+ for j in range(0, width, patch_size):
+ num_patches += 1
+ # add the base patch
+ num_patches += 1
+ return num_patches
+
+
+def unpad_image(tensor, original_size):
+ """
+ Unpads a PyTorch tensor of a padded and resized image.
+
+ Args:
+ tensor (`torch.Tensor`):
+ The image tensor, assumed to be of shape (num_channels, height, width).
+ original_size (`tuple`):
+ The original size of the image (height, width).
+
+ Returns:
+ `torch.Tensor`: The unpadded image tensor.
+ """
+ if not isinstance(original_size, (list, tuple)):
+ if not isinstance(original_size, (torch.Tensor, np.ndarray)):
+ raise TypeError(
+ f"image_size invalid type: {type(original_size)} not valid, should be either list, tuple, np.ndarray or tensor"
+ )
+ original_size = original_size.tolist()
+ original_height, original_width = original_size
+ current_height, current_width = tensor.shape[1:]
+
+ original_aspect_ratio = original_width / original_height
+ current_aspect_ratio = current_width / current_height
+
+ if original_aspect_ratio > current_aspect_ratio:
+ scale_factor = current_width / original_width
+ new_height = int(round(original_height * scale_factor, 7))
+ padding = (current_height - new_height) // 2
+ unpadded_tensor = tensor[:, padding : current_height - padding, :]
+ else:
+ scale_factor = current_height / original_height
+ new_width = int(round(original_width * scale_factor, 7))
+ padding = (current_width - new_width) // 2
+ unpadded_tensor = tensor[:, :, padding : current_width - padding]
+
+ return unpadded_tensor
+
+
+@auto_docstring(
+ custom_intro="""
+ The Llava-Next model which consists of a vision backbone and a language model without language modeling head.
+ """
+)
+class LlavaNextVideoModel(LlavaNextVideoPreTrainedModel):
+ _checkpoint_conversion_mapping = {"language_model.model": "language_model"}
+
+ def __init__(
+ self,
+ config: LlavaNextVideoConfig,
+ ):
+ super().__init__(config)
+ self.vision_tower = AutoModel.from_config(config.vision_config)
+
+ self.multi_modal_projector = LlavaNextVideoMultiModalProjector(config)
+ embed_std = 1 / math.sqrt(config.text_config.hidden_size)
+ self.image_newline = nn.Parameter(torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std)
+
+ self.vocab_size = config.text_config.vocab_size
+ self.language_model = AutoModel.from_config(config.text_config)
+ self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+ self.vision_resampler = LlavaNextVideoPooler(config)
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def set_decoder(self, decoder):
+ self.language_model = decoder
+
+ def get_decoder(self):
+ return self.language_model
+
+ def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None):
+ """
+ Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors.
+
+ Args:
+ image_features (`list[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`)
+ List of image feature tensor, each contains all the visual feature of all patches.
+ image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
+ Actual image size of each images (H, W).
+ vision_feature_select_strategy (`str`)
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ image_newline (`torch.Tensor` of shape `(embed_dim)`)
+ New line embedding vector.
+ Returns:
+ image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`)
+ feature_lens (`list[int]`)
+ token length of each image in image_features
+ """
+ new_image_features = []
+ feature_lens = []
+ for image_idx, image_feature in enumerate(image_features):
+ if image_feature.shape[0] > 1:
+ base_image_feature = image_feature[0]
+ image_feature = image_feature[1:]
+ height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
+
+ num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+ image_sizes[image_idx],
+ self.config.image_grid_pinpoints,
+ self.config.vision_config.image_size,
+ )
+
+ if (
+ np.prod(image_feature.shape) % (num_patch_height * num_patch_width * height * width) != 0
+ and vision_feature_select_strategy == "default"
+ ):
+ logger.warning_once(
+ "Image feature shape does not line up with the provided patch size. "
+ "You may be using the `default` vision_feature_select_strategy with a"
+ " visual encoder that does not have CLS."
+ )
+
+ image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
+ image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
+ image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+ image_feature = unpad_image(image_feature, image_sizes[image_idx])
+ if image_newline is not None:
+ image_feature = torch.cat(
+ (
+ image_feature,
+ image_newline[:, None, None]
+ .expand(*image_feature.shape[:-1], 1)
+ .to(image_feature.device, image_feature.dtype),
+ ),
+ dim=-1,
+ )
+ image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+ image_feature = torch.cat((base_image_feature, image_feature), dim=0)
+ else:
+ image_feature = image_feature[0]
+ if image_newline is not None:
+ image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0)
+ new_image_features.append(image_feature)
+ feature_lens.append(image_feature.size(0))
+ feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device)
+ return new_image_features, feature_lens
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ ):
+ """
+ Obtains image last hidden states from the vision tower and apply multimodal projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`)
+ The tensors corresponding to the input images.
+ image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
+ Actual image size of each images (H, W).
+ vision_feature_layer (`Union[int, list[int]]`, *optional*):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`, *optional*):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ Returns:
+ image_features (list[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches
+ and are of shape `(num_patches, image_length, embed_dim)`).
+ """
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ # ! infer image_num_patches from image_sizes
+ image_num_patches = [
+ image_size_to_num_patches(
+ image_size=imsize,
+ grid_pinpoints=self.config.image_grid_pinpoints,
+ patch_size=self.config.vision_config.image_size,
+ )
+ for imsize in image_sizes
+ ]
+ if pixel_values.dim() == 5:
+ # stacked if input is (batch_size, num_patches, num_channels, height, width)
+ _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)]
+ pixel_values = torch.cat(_pixel_values_list, dim=0)
+ elif pixel_values.dim() != 4:
+ # otherwise has to be stacked from list of (num_patches, num_channels, height, width)
+ raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")
+
+ image_features = self.vision_tower(pixel_values, output_hidden_states=True)
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_image_feature = image_features.hidden_states[vision_feature_layer]
+ else:
+ hs_pool = [image_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ selected_image_feature = torch.cat(hs_pool, dim=-1)
+
+ if vision_feature_select_strategy == "default":
+ selected_image_feature = selected_image_feature[:, 1:]
+ elif vision_feature_select_strategy == "full":
+ selected_image_feature = selected_image_feature
+ image_features = self.multi_modal_projector(selected_image_feature)
+ image_features = torch.split(image_features, image_num_patches, dim=0)
+
+ image_features, feature_lens = self.pack_image_features(
+ image_features,
+ image_sizes,
+ vision_feature_select_strategy,
+ image_newline=self.image_newline,
+ )
+ return image_features
+
+ def get_placeholder_mask(
+ self,
+ input_ids: torch.LongTensor,
+ inputs_embeds: torch.FloatTensor,
+ image_features: torch.FloatTensor = None,
+ video_features: torch.FloatTensor = None,
+ ):
+ """
+ Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ special_video_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_video_mask = special_video_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+ special_video_mask = input_ids == self.config.video_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if image_features is not None and inputs_embeds[special_image_mask].numel() != image_features.numel():
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {image_features.shape[0]}"
+ )
+
+ n_video_tokens = special_video_mask.sum()
+ special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if video_features is not None and inputs_embeds[special_video_mask].numel() != video_features.numel():
+ raise ValueError(
+ f"Videos features and image tokens do not match: tokens: {n_video_tokens}, features {video_features.shape[0]}"
+ )
+
+ return special_image_mask, special_video_mask
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ pixel_values_videos: torch.FloatTensor = None,
+ image_sizes: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, LlavaNextVideoModelOutputWithPast]:
+ r"""
+ vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
+ If `"full"`, the full vision features are used.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ self.vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ self.vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ if pixel_values is not None:
+ image_features = self.get_image_features(
+ pixel_values,
+ image_sizes,
+ vision_feature_layer=self.vision_feature_layer,
+ vision_feature_select_strategy=self.vision_feature_select_strategy,
+ )
+ image_features = torch.cat(image_features, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+ special_image_mask, _ = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+ if pixel_values_videos is not None:
+ video_features = self.get_video_features(
+ pixel_values_videos,
+ vision_feature_layer=self.vision_feature_layer,
+ vision_feature_select_strategy=self.vision_feature_select_strategy,
+ )
+ video_features = [feature.flatten(0, 1) for feature in video_features]
+ video_feature_lens = [feature.size(0) for feature in video_features]
+ video_features = torch.cat(video_features, dim=0)
+ video_feature_lens = torch.tensor(video_feature_lens, dtype=torch.long, device=video_features.device)
+ video_features = video_features.to(inputs_embeds.device, inputs_embeds.dtype)
+
+ _, special_video_mask = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, video_features=video_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_video_mask, video_features)
+
+ outputs = self.language_model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return LlavaNextVideoModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=image_features if pixel_values is not None else None,
+ video_hidden_states=video_features if pixel_values_videos is not None else None,
+ )
+
+ def get_video_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ ):
+ """
+ Obtains video last hidden states from the vision tower and apply multimodal projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`)
+ The tensors corresponding to the input video.
+ vision_feature_layer (`Union[int, list[int]]`, *optiona;*):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`, *optional*):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ Returns:
+ video_features (list[`torch.Tensor`]): List of video feature tensor, each contains all the visual feature of all patches
+ and are of shape `(num_videos, video_length, embed_dim)`).
+ """
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ batch_size, frames, channels, height, width = pixel_values.shape
+ pixel_values = pixel_values.reshape(batch_size * frames, channels, height, width)
+ video_features = self.vision_tower(pixel_values, output_hidden_states=True)
+
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_video_features = video_features.hidden_states[vision_feature_layer]
+ else:
+ hs_pool = [video_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ selected_video_features = torch.cat(hs_pool, dim=-1)
+
+ if vision_feature_select_strategy == "default":
+ selected_video_features = selected_video_features[:, 1:]
+ elif vision_feature_select_strategy == "full":
+ selected_video_features = selected_video_features
+
+ # Same as image features except that video has pooling layer
+ video_features = self.vision_resampler(selected_video_features)
+ video_features = self.multi_modal_projector(video_features)
+ video_features = torch.split(video_features, frames, dim=0)
+ return video_features
+
+
+@auto_docstring(
+ custom_intro="""
+ The LLAVA-NeXT model which consists of a vision backbone and a language model.
+ """
+)
+class LlavaNextVideoForConditionalGeneration(LlavaNextVideoPreTrainedModel, GenerationMixin):
+ _checkpoint_conversion_mapping = {
+ "^language_model.model": "model.language_model",
+ "^vision_tower": "model.vision_tower",
+ "^multi_modal_projector": "model.multi_modal_projector",
+ "^image_newline": "model.image_newline",
+ "^language_model.lm_head": "lm_head",
+ }
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: LlavaNextVideoConfig):
+ super().__init__(config)
+ self.model = LlavaNextVideoModel(config)
+ self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ def get_output_embeddings(self) -> nn.Module:
+ return self.lm_head
+
+ def set_decoder(self, decoder):
+ self.model.set_decoder(decoder)
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None):
+ return self.model.pack_image_features(
+ image_features=image_features,
+ image_sizes=image_sizes,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ image_newline=image_newline,
+ )
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ ):
+ return self.model.get_image_features(
+ pixel_values=pixel_values,
+ image_sizes=image_sizes,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ )
+
+ # Make modules available through conditional class for BC
+ @property
+ def language_model(self):
+ return self.model.language_model
+
+ @property
+ def vision_tower(self):
+ return self.model.vision_tower
+
+ @property
+ def multi_modal_projector(self):
+ return self.model.multi_modal_projector
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ pixel_values_videos: torch.FloatTensor = None,
+ image_sizes: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, LlavaNextVideoCausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> import av
+ >>> from transformers import AutoProcessor, LlavaNextVideoForConditionalGeneration
+
+ >>> def read_video_pyav(container, indices):
+ ... '''
+ ... Decode the video with PyAV decoder.
+ ... Args:
+ ... container (`av.container.input.InputContainer`): PyAV container.
+ ... indices (`list[int]`): List of frame indices to decode.
+ ... Returns:
+ ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
+ ... '''
+ ... frames = []
+ ... container.seek(0)
+ ... start_index = indices[0]
+ ... end_index = indices[-1]
+ ... for i, frame in enumerate(container.decode(video=0)):
+ ... if i > end_index:
+ ... break
+ ... if i >= start_index and i in indices:
+ ... frames.append(frame)
+ ... return np.stack([x.to_ndarray(format="rgb24") for x in frames])
+
+ >>> model = LlavaNextVideoForConditionalGeneration.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf", device_map="auto")
+ >>> processor = AutoProcessor.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf")
+
+ >>> prompt = "USER: \nWhy is this video funny? ASSISTANT:"
+ >>> video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
+ >>> container = av.open(video_path)
+
+ >>> # sample uniformly 8 frames from the video (model was trained with 32 frames per video, but this video is short)
+ >>> total_frames = container.streams.video[0].frames
+ >>> indices = np.arange(0, total_frames, total_frames / 8).astype(int)
+ >>> clip = read_video_pyav(container, indices)
+ >>> inputs_video = processor(text=prompt, videos=clip, return_tensors="pt").to(model.device)
+
+ >>> # load an image to generate from an image
+ >>> prompt = "USER:\nWhat is shown in this image? ASSISTANT:"
+ >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> inputs_image = processor(text=prompt, images=image, return_tensors="pt").to(model.device)
+
+ >>> # Generate from video
+ >>> generate_ids = model.generate(**inputs_video, max_length=50)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "USER:\nWhy is this video funny? ASSISTANT: The humor in this video comes from the unexpected and endearing sight of a baby wearing glasses and (...)"
+
+ >>> # Generate from image
+ >>> generate_ids = model.generate(**inputs_image, max_length=30)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "USER: \nWhat's the content of the image? ASSISTANT: The image shows a red stop sign on a pole, with a traditional Chinese archway (...)"
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ pixel_values_videos=pixel_values_videos,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ image_sizes=image_sizes,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return LlavaNextVideoCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ video_hidden_states=outputs.video_hidden_states,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ inputs_embeds=None,
+ pixel_values=None,
+ pixel_values_videos=None,
+ image_sizes=None,
+ attention_mask=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- extra custom processing
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ if cache_position[0] == 0:
+ model_inputs["pixel_values"] = pixel_values
+ model_inputs["pixel_values_videos"] = pixel_values_videos
+ model_inputs["image_sizes"] = image_sizes
+
+ return model_inputs
+
+ @staticmethod
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+ def get_video_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ ):
+ return self.model.get_video_features(
+ pixel_values=pixel_values,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ )
+
+
+__all__ = ["LlavaNextVideoForConditionalGeneration", "LlavaNextVideoModel", "LlavaNextVideoPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/modular_llava_next_video.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/modular_llava_next_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..15f3ad9141aa15b2085eefc2d6e47edd525a0988
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/modular_llava_next_video.py
@@ -0,0 +1,723 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import Optional, Union
+
+import torch
+from torch import nn
+
+from transformers.models.llava_next.modeling_llava_next import (
+ LlavaNextCausalLMOutputWithPast,
+ LlavaNextForConditionalGeneration,
+ LlavaNextModel,
+ LlavaNextModelOutputWithPast,
+ LlavaNextMultiModalProjector,
+ TransformersKwargs,
+ image_size_to_num_patches,
+)
+
+from ...cache_utils import Cache
+from ...configuration_utils import PretrainedConfig
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...processing_utils import Unpack
+from ...utils import logging
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class LlavaNextVideoConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LlavaNextVideoForConditionalGeneration`]. It is used to instantiate an
+ Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the [llava-hf/LLaVA-NeXT-Video-7B-hf](https://huggingface.co/llava-hf/LLaVA-NeXT-Video-7B-hf)
+ model.
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`):
+ The config object or dictionary of the vision backbone.
+ text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
+ The config object or dictionary of the text backbone.
+ image_token_index (`int`, *optional*, defaults to 32001):
+ The image token index to encode the image prompt.
+ projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
+ The activation function used by the multimodal projector.
+ multimodal_projector_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use bias in the multimodal projector.
+ vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
+ If `"full"`, the full vision features are used.
+ vision_feature_layer (`Union[int, list[int]]`, *optional*, defaults to -2):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ image_grid_pinpoints (`List`, *optional*, defaults to `[[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]`):
+ A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list
+ of the form `(height, width)`.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether the model's input and output word embeddings should be tied.
+ video_token_index (`int`, *optional*, defaults to 32000):
+ The video token index to encode the image prompt.
+ spatial_pool_mode (`str`, *optional*, defaults to `"average"`):
+ Pooling mode to use for videos. Can be "average", "max" or "conv".
+ spatial_pool_stride (`int`, *optional*, defaults to 2):
+ Stride used in the pooling layer for videos.
+ image_seq_length (`int`, *optional*, defaults to 576):
+ Sequence length of one image embedding.
+ video_seq_length (`int`, *optional*, defaults to 288):
+ Sequence length of one video embedding.
+
+ Example:
+
+ ```python
+ >>> from transformers import LlavaNextVideoForConditionalGeneration, LlavaNextVideoConfig, CLIPVisionConfig, LlamaConfig
+
+ >>> # Initializing a CLIP-vision config
+ >>> vision_config = CLIPVisionConfig()
+
+ >>> # Initializing a Llama config
+ >>> text_config = LlamaConfig()
+
+ >>> configuration = LlavaNextVideoConfig(vision_config, text_config)
+
+ >>> model = LlavaNextVideoForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "llava_next_video"
+ attribute_map = {
+ "image_token_id": "image_token_index",
+ "video_token_id": "video_token_index",
+ }
+ sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
+
+ def __init__(
+ self,
+ vision_config=None,
+ text_config=None,
+ image_token_index=32001,
+ projector_hidden_act="gelu",
+ multimodal_projector_bias=True,
+ vision_feature_select_strategy="default",
+ vision_feature_layer=-2,
+ image_grid_pinpoints=None,
+ tie_word_embeddings=False,
+ video_token_index=32000,
+ spatial_pool_mode="average",
+ spatial_pool_stride=2,
+ image_seq_length=576,
+ video_seq_length=288,
+ **kwargs,
+ ):
+ self.video_token_index = video_token_index
+ self.spatial_pool_mode = spatial_pool_mode
+ self.spatial_pool_stride = spatial_pool_stride
+ self.image_seq_length = image_seq_length
+ self.video_seq_length = video_seq_length
+ self.image_token_index = image_token_index
+ self.projector_hidden_act = projector_hidden_act
+ self.multimodal_projector_bias = multimodal_projector_bias
+
+ if vision_feature_select_strategy not in ["default", "full"]:
+ raise ValueError(
+ "vision_feature_select_strategy should be one of 'default', 'full'."
+ f"Got: {vision_feature_select_strategy}"
+ )
+
+ self.vision_feature_select_strategy = vision_feature_select_strategy
+ self.vision_feature_layer = vision_feature_layer
+ image_grid_pinpoints = (
+ image_grid_pinpoints
+ if image_grid_pinpoints is not None
+ else [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
+ )
+ self.image_grid_pinpoints = image_grid_pinpoints
+
+ if isinstance(vision_config, dict):
+ vision_config["model_type"] = vision_config.get("model_type", "clip_vision_model")
+ vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
+ elif vision_config is None:
+ vision_config = CONFIG_MAPPING["clip_vision_model"](
+ intermediate_size=4096,
+ hidden_size=1024,
+ patch_size=14,
+ image_size=336,
+ num_hidden_layers=24,
+ num_attention_heads=16,
+ vocab_size=32000,
+ projection_dim=768,
+ )
+
+ self.vision_config = vision_config
+
+ if isinstance(text_config, dict):
+ text_config["model_type"] = text_config.get("model_type", "llama")
+ text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+ elif text_config is None:
+ text_config = CONFIG_MAPPING["llama"]()
+
+ self.text_config = text_config
+
+ super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+class LlavaNextVideoModelOutputWithPast(LlavaNextModelOutputWithPast):
+ r"""
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ video_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`.
+ video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ video_hidden_states: Optional[torch.FloatTensor] = None
+
+
+class LlavaNextVideoCausalLMOutputWithPast(LlavaNextCausalLMOutputWithPast):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ video_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`.
+ video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ video_hidden_states: Optional[torch.FloatTensor] = None
+
+
+class LlavaNextVideoPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ mode = config.spatial_pool_mode
+ stride = config.spatial_pool_stride
+ out_channels = getattr(config, "spatial_pool_out_channels", config.vision_config.hidden_size)
+ self.image_size = (config.vision_config.image_size // config.vision_config.patch_size) ** 2
+
+ if mode == "average":
+ self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride)
+ elif mode == "max":
+ self.pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
+ elif mode == "conv":
+ self.pool = nn.Conv2d(
+ in_channels=config.vision_config.hidden_size,
+ out_channels=out_channels,
+ kernel_size=stride,
+ stride=stride,
+ )
+ else:
+ raise ValueError(f"Unknown pooling mode: {mode}. Has to be one of [`average`, `max`, `conv`]")
+
+ def forward(self, image_features):
+ ori_width = int(math.sqrt(image_features.shape[1] * self.image_size // self.image_size))
+ ori_height = int(ori_width * self.image_size // self.image_size)
+
+ batch_size, _, dim = image_features.shape
+ image_features_spatial = image_features.view(batch_size, ori_height, ori_height, dim).permute(0, 3, 1, 2)
+ image_features_spatial_pool = self.pool(image_features_spatial)
+
+ return image_features_spatial_pool.flatten(2).transpose(1, 2).contiguous()
+
+
+class LlavaNextVideoMultiModalProjector(LlavaNextMultiModalProjector):
+ pass
+
+
+class LlavaNextVideoModel(LlavaNextModel):
+ def __init__(self, config: LlavaNextVideoConfig, **super_kwargs):
+ super().__init__(config, **super_kwargs)
+ self.vision_resampler = LlavaNextVideoPooler(config)
+ self.post_init()
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ ):
+ """
+ Obtains image last hidden states from the vision tower and apply multimodal projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`)
+ The tensors corresponding to the input images.
+ image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
+ Actual image size of each images (H, W).
+ vision_feature_layer (`Union[int, list[int]]`, *optional*):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`, *optional*):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ Returns:
+ image_features (list[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches
+ and are of shape `(num_patches, image_length, embed_dim)`).
+ """
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ # ! infer image_num_patches from image_sizes
+ image_num_patches = [
+ image_size_to_num_patches(
+ image_size=imsize,
+ grid_pinpoints=self.config.image_grid_pinpoints,
+ patch_size=self.config.vision_config.image_size,
+ )
+ for imsize in image_sizes
+ ]
+ if pixel_values.dim() == 5:
+ # stacked if input is (batch_size, num_patches, num_channels, height, width)
+ _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)]
+ pixel_values = torch.cat(_pixel_values_list, dim=0)
+ elif pixel_values.dim() != 4:
+ # otherwise has to be stacked from list of (num_patches, num_channels, height, width)
+ raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")
+
+ image_features = self.vision_tower(pixel_values, output_hidden_states=True)
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_image_feature = image_features.hidden_states[vision_feature_layer]
+ else:
+ hs_pool = [image_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ selected_image_feature = torch.cat(hs_pool, dim=-1)
+
+ if vision_feature_select_strategy == "default":
+ selected_image_feature = selected_image_feature[:, 1:]
+ elif vision_feature_select_strategy == "full":
+ selected_image_feature = selected_image_feature
+ image_features = self.multi_modal_projector(selected_image_feature)
+ image_features = torch.split(image_features, image_num_patches, dim=0)
+
+ image_features, feature_lens = self.pack_image_features(
+ image_features,
+ image_sizes,
+ vision_feature_select_strategy,
+ image_newline=self.image_newline,
+ )
+ return image_features
+
+ def get_video_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ ):
+ """
+ Obtains video last hidden states from the vision tower and apply multimodal projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`)
+ The tensors corresponding to the input video.
+ vision_feature_layer (`Union[int, list[int]]`, *optiona;*):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`, *optional*):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ Returns:
+ video_features (list[`torch.Tensor`]): List of video feature tensor, each contains all the visual feature of all patches
+ and are of shape `(num_videos, video_length, embed_dim)`).
+ """
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ batch_size, frames, channels, height, width = pixel_values.shape
+ pixel_values = pixel_values.reshape(batch_size * frames, channels, height, width)
+ video_features = self.vision_tower(pixel_values, output_hidden_states=True)
+
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_video_features = video_features.hidden_states[vision_feature_layer]
+ else:
+ hs_pool = [video_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ selected_video_features = torch.cat(hs_pool, dim=-1)
+
+ if vision_feature_select_strategy == "default":
+ selected_video_features = selected_video_features[:, 1:]
+ elif vision_feature_select_strategy == "full":
+ selected_video_features = selected_video_features
+
+ # Same as image features except that video has pooling layer
+ video_features = self.vision_resampler(selected_video_features)
+ video_features = self.multi_modal_projector(video_features)
+ video_features = torch.split(video_features, frames, dim=0)
+ return video_features
+
+ def get_placeholder_mask(
+ self,
+ input_ids: torch.LongTensor,
+ inputs_embeds: torch.FloatTensor,
+ image_features: torch.FloatTensor = None,
+ video_features: torch.FloatTensor = None,
+ ):
+ """
+ Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ special_video_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_video_mask = special_video_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+ special_video_mask = input_ids == self.config.video_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if image_features is not None and inputs_embeds[special_image_mask].numel() != image_features.numel():
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {image_features.shape[0]}"
+ )
+
+ n_video_tokens = special_video_mask.sum()
+ special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if video_features is not None and inputs_embeds[special_video_mask].numel() != video_features.numel():
+ raise ValueError(
+ f"Videos features and image tokens do not match: tokens: {n_video_tokens}, features {video_features.shape[0]}"
+ )
+
+ return special_image_mask, special_video_mask
+
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ pixel_values_videos: torch.FloatTensor = None,
+ image_sizes: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, LlavaNextVideoModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ self.vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ self.vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ if pixel_values is not None:
+ image_features = self.get_image_features(
+ pixel_values,
+ image_sizes,
+ vision_feature_layer=self.vision_feature_layer,
+ vision_feature_select_strategy=self.vision_feature_select_strategy,
+ )
+ image_features = torch.cat(image_features, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+ special_image_mask, _ = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+ if pixel_values_videos is not None:
+ video_features = self.get_video_features(
+ pixel_values_videos,
+ vision_feature_layer=self.vision_feature_layer,
+ vision_feature_select_strategy=self.vision_feature_select_strategy,
+ )
+ video_features = [feature.flatten(0, 1) for feature in video_features]
+ video_feature_lens = [feature.size(0) for feature in video_features]
+ video_features = torch.cat(video_features, dim=0)
+ video_feature_lens = torch.tensor(video_feature_lens, dtype=torch.long, device=video_features.device)
+ video_features = video_features.to(inputs_embeds.device, inputs_embeds.dtype)
+
+ _, special_video_mask = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, video_features=video_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_video_mask, video_features)
+
+ outputs = self.language_model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return LlavaNextVideoModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=image_features if pixel_values is not None else None,
+ video_hidden_states=video_features if pixel_values_videos is not None else None,
+ )
+
+
+class LlavaNextVideoForConditionalGeneration(LlavaNextForConditionalGeneration):
+ def get_video_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ ):
+ return self.model.get_video_features(
+ pixel_values=pixel_values,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ )
+
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ pixel_values_videos: torch.FloatTensor = None,
+ image_sizes: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, LlavaNextVideoCausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> import av
+ >>> from transformers import AutoProcessor, LlavaNextVideoForConditionalGeneration
+
+ >>> def read_video_pyav(container, indices):
+ ... '''
+ ... Decode the video with PyAV decoder.
+ ... Args:
+ ... container (`av.container.input.InputContainer`): PyAV container.
+ ... indices (`list[int]`): List of frame indices to decode.
+ ... Returns:
+ ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
+ ... '''
+ ... frames = []
+ ... container.seek(0)
+ ... start_index = indices[0]
+ ... end_index = indices[-1]
+ ... for i, frame in enumerate(container.decode(video=0)):
+ ... if i > end_index:
+ ... break
+ ... if i >= start_index and i in indices:
+ ... frames.append(frame)
+ ... return np.stack([x.to_ndarray(format="rgb24") for x in frames])
+
+ >>> model = LlavaNextVideoForConditionalGeneration.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf", device_map="auto")
+ >>> processor = AutoProcessor.from_pretrained("llava-hf/LLaVA-NeXT-Video-7B-hf")
+
+ >>> prompt = "USER: \nWhy is this video funny? ASSISTANT:"
+ >>> video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
+ >>> container = av.open(video_path)
+
+ >>> # sample uniformly 8 frames from the video (model was trained with 32 frames per video, but this video is short)
+ >>> total_frames = container.streams.video[0].frames
+ >>> indices = np.arange(0, total_frames, total_frames / 8).astype(int)
+ >>> clip = read_video_pyav(container, indices)
+ >>> inputs_video = processor(text=prompt, videos=clip, return_tensors="pt").to(model.device)
+
+ >>> # load an image to generate from an image
+ >>> prompt = "USER:\nWhat is shown in this image? ASSISTANT:"
+ >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> inputs_image = processor(text=prompt, images=image, return_tensors="pt").to(model.device)
+
+ >>> # Generate from video
+ >>> generate_ids = model.generate(**inputs_video, max_length=50)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "USER:\nWhy is this video funny? ASSISTANT: The humor in this video comes from the unexpected and endearing sight of a baby wearing glasses and (...)"
+
+ >>> # Generate from image
+ >>> generate_ids = model.generate(**inputs_image, max_length=30)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "USER: \nWhat's the content of the image? ASSISTANT: The image shows a red stop sign on a pole, with a traditional Chinese archway (...)"
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ pixel_values_videos=pixel_values_videos,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ image_sizes=image_sizes,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return LlavaNextVideoCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ video_hidden_states=outputs.video_hidden_states,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ inputs_embeds=None,
+ pixel_values=None,
+ pixel_values_videos=None,
+ image_sizes=None,
+ attention_mask=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- extra custom processing
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ if cache_position[0] == 0:
+ model_inputs["pixel_values"] = pixel_values
+ model_inputs["pixel_values_videos"] = pixel_values_videos
+ model_inputs["image_sizes"] = image_sizes
+
+ return model_inputs
+
+
+__all__ = [
+ "LlavaNextVideoConfig",
+ "LlavaNextVideoForConditionalGeneration",
+ "LlavaNextVideoModel",
+ "LlavaNextVideoPreTrainedModel", # noqa: F822
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/processing_llava_next_video.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/processing_llava_next_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..e04c968c193be57af2e08e096fdf481a1d0750bb
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/processing_llava_next_video.py
@@ -0,0 +1,329 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for LLaVa-NeXT-Video.
+"""
+
+from typing import Union
+
+import numpy as np
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_processing_utils import select_best_resolution
+from ...image_utils import ImageInput, get_image_size, to_numpy_array
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import logging
+from ...video_utils import VideoInput
+
+
+logger = logging.get_logger(__name__)
+
+
+class LlavaNextVideoProcessorKwargs(ProcessingKwargs, total=False):
+ # see processing_utils.ProcessingKwargs documentation for usage.
+ _defaults = {
+ "text_kwargs": {
+ "padding": False,
+ },
+ "common_kwargs": {
+ "return_tensors": "pt",
+ },
+ }
+
+
+class LlavaNextVideoProcessor(ProcessorMixin):
+ r"""
+ Constructs a LLaVa-NeXT-Video processor which wraps a LLaVa-NeXT image processor, LLaVa-NeXT-Video video processor and
+ a LLaMa tokenizer into a single processor.
+
+ [`LlavaNextVideoProcessor`] offers all the functionalities of [`LlavaNextImageProcessor`], [`LlavaNextVideoImageProcessor`] and
+ [`LlamaTokenizerFast`]. See the [`~LlavaNextVideoProcessor.__call__`] and [`~LlavaNextVideoProcessor.decode`] for more information.
+
+ Args:
+ video_processor ([`LlavaNextVideoVideoProcessor`], *optional*):
+ The video processor is a required input.
+ image_processor ([`LlavaNextImageProcessor`], *optional*):
+ The image processor is a required input.
+ tokenizer ([`LlamaTokenizerFast`], *optional*):
+ The tokenizer is a required input.
+ chat_template (`str`, *optional*):
+ Jinja chat template that will be used in tokenizer's `apply_chat_template`
+ patch_size (`int`, *optional*):
+ Patch size from the vision tower.
+ vision_feature_select_strategy (`str`, *optional*):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Should be same as in model's config
+ video_token (`str`, *optional*, defaults to `""`):
+ Special token used to denote video location.
+ image_token (`str`, *optional*, defaults to `""`):
+ Special token used to denote image location.
+ num_additional_image_tokens (`int`, *optional*, defaults to 0):
+ Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other
+ extra tokens appended, no need to set this arg.
+ """
+
+ # video and image processor share same args, but have different processing logic
+ # only image processor config is saved in the hub
+ attributes = ["video_processor", "image_processor", "tokenizer"]
+ image_processor_class = ("LlavaNextImageProcessor", "LlavaNextImageProcessorFast")
+ video_processor_class = "AutoVideoProcessor"
+ tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+
+ def __init__(
+ self,
+ video_processor=None,
+ image_processor=None,
+ tokenizer=None,
+ chat_template=None,
+ patch_size=None,
+ vision_feature_select_strategy=None,
+ video_token="",
+ image_token="",
+ num_additional_image_tokens=0,
+ **kwargs,
+ ):
+ self.patch_size = patch_size
+ self.num_additional_image_tokens = num_additional_image_tokens
+ self.vision_feature_select_strategy = vision_feature_select_strategy
+ self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
+ self.video_token = tokenizer.video_token if hasattr(tokenizer, "video_token") else video_token
+ self.image_token_id = (
+ tokenizer.image_token_id
+ if getattr(tokenizer, "image_token_id", None)
+ else tokenizer.convert_tokens_to_ids(self.image_token)
+ )
+ self.video_token_id = (
+ tokenizer.video_token_id
+ if getattr(tokenizer, "video_token_id", None)
+ else tokenizer.convert_tokens_to_ids(self.video_token)
+ )
+ super().__init__(video_processor, image_processor, tokenizer, chat_template=chat_template)
+
+ def __call__(
+ self,
+ images: ImageInput = None,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+ audio=None,
+ videos: VideoInput = None,
+ **kwargs: Unpack[LlavaNextVideoProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+ and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
+ the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+ LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. To prepare the video(s),
+ this method forwards the `videos` and `kwrags` arguments to LlavaNextVideoImageProcessor's
+ [`~LlavaNextVideoImageProcessor.__call__`] if `videos` is not `None`. Please refer to the docstring
+ of the above two methods for more information.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ videos (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+ tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors of a particular framework. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return NumPy `np.ndarray` objects.
+ - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+ `None`).
+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+ """
+
+ output_kwargs = self._merge_kwargs(
+ LlavaNextVideoProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+ if images is not None:
+ image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+ else:
+ image_inputs = {}
+
+ if videos is not None:
+ videos_inputs = self.video_processor(videos, **output_kwargs["videos_kwargs"])
+ else:
+ videos_inputs = {}
+
+ if isinstance(text, str):
+ text = [text]
+ elif not isinstance(text, list) and not isinstance(text[0], str):
+ raise TypeError("Invalid input text. Please provide a string, or a list of strings")
+
+ if image_inputs:
+ image_sizes = iter(image_inputs["image_sizes"])
+ height, width = get_image_size(to_numpy_array(image_inputs["pixel_values"][0][0]))
+ prompt_strings = []
+ for sample in text:
+ while self.image_token in sample:
+ image_size = next(image_sizes)
+ if not isinstance(image_size, (list, tuple)):
+ # cast to list to avoid numerical precision errors when calculating unpadding
+ image_size = image_size.tolist()
+ orig_height, orig_width = image_size
+ num_image_tokens = self._get_number_of_features(orig_height, orig_width, height, width)
+ if self.vision_feature_select_strategy == "default":
+ num_image_tokens -= 1
+ sample = sample.replace(self.image_token, "" * num_image_tokens, 1)
+ prompt_strings.append(sample)
+ text = [sample.replace("", self.image_token) for sample in prompt_strings]
+
+ # videos are easier, simply get frames and multiply
+ if videos_inputs:
+ one_video = videos_inputs.get("pixel_values_videos")[0]
+ if isinstance(one_video, (list, tuple)):
+ one_video = np.array(one_video)
+ else:
+ one_video = to_numpy_array(one_video)
+ height, width = get_image_size(one_video[0])
+ num_frames = one_video.shape[0] # frame dim is always after batch dim
+
+ # no `self.num_additional_image_tokens` added because video always has a default feature selection strategy
+ num_image_tokens = (height // self.patch_size) * (width // self.patch_size)
+ num_video_tokens = num_image_tokens // 4 * num_frames # divide by 4 needed for avg pooling layer
+ prompt_strings = []
+ for sample in text:
+ sample = sample.replace(self.video_token, self.video_token * num_video_tokens)
+ prompt_strings.append(sample)
+ text = prompt_strings
+
+ return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+ text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+ self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
+
+ return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+
+ # Copied from transformers.models.llava_next.processing_llava_next.LlavaNextProcessor._get_number_of_features
+ def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
+ image_grid_pinpoints = self.image_processor.image_grid_pinpoints
+
+ height_best_resolution, width_best_resolution = select_best_resolution(
+ [orig_height, orig_width], image_grid_pinpoints
+ )
+ scale_height, scale_width = height_best_resolution // height, width_best_resolution // width
+
+ patches_height = height // self.patch_size
+ patches_width = width // self.patch_size
+ unpadded_features, newline_features = self._get_unpadded_features(
+ orig_height, orig_width, patches_height, patches_width, scale_height, scale_width
+ )
+ # The base patch covers the entire image (+1 for the CLS)
+ base_features = patches_height * patches_width + self.num_additional_image_tokens
+ num_image_tokens = unpadded_features + newline_features + base_features
+ return num_image_tokens
+
+ # Copied from transformers.models.llava_next.processing_llava_next.LlavaNextProcessor._get_unpadded_features
+ def _get_unpadded_features(self, height, width, patches_height, patches_width, scale_height, scale_width):
+ """
+ Get number of features for a given image with height/width. LLaVA-NeXT is different from LLaVA
+ because it divided each image into patches depending on its resolution. Therefore we need to calculate how many
+ patches an image is divided into and get the number of features from that.
+ """
+ current_height = patches_height * scale_height
+ current_width = patches_width * scale_width
+
+ original_aspect_ratio = width / height
+ current_aspect_ratio = current_width / current_height
+ if original_aspect_ratio > current_aspect_ratio:
+ new_height = int(round(height * (current_width / width), 7))
+ padding = (current_height - new_height) // 2
+ current_height -= padding * 2
+ else:
+ new_width = int(round(width * (current_height / height), 7))
+ padding = (current_width - new_width) // 2
+ current_width -= padding * 2
+
+ unpadded_features = current_height * current_width
+ newline_features = current_height
+ return (unpadded_features, newline_features)
+
+ def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+ """
+ Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+ Args:
+ image_sizes (list[list[str]], *optional*):
+ The input sizes formatted as (height, width) per each image.
+ Returns:
+ `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+ input modalities, along with other useful data.
+ """
+ vision_data = {}
+ if image_sizes is not None:
+ images_kwargs = LlavaNextVideoProcessorKwargs._defaults.get("images_kwargs", {})
+ images_kwargs.update(kwargs)
+
+ size = images_kwargs.get("size", None) or self.image_processor.size
+ size = (
+ (size["shortest_edge"], size["shortest_edge"])
+ if "shortest_edge" in size
+ else (min(size["height"], size["width"]), min(size["height"], size["width"]))
+ )
+ processed_height, processed_width = size
+
+ batch_num_image_tokens = []
+ num_image_patches = [1] * len(image_sizes) # llava-next doesn't batch pixels as Idefics, thus `1` patch`
+ for image_size in image_sizes:
+ orig_height, orig_width = image_size
+ num_image_tokens = self._get_number_of_features(
+ orig_height, orig_width, processed_height, processed_width
+ )
+ if self.vision_feature_select_strategy == "default":
+ num_image_tokens -= 1
+ batch_num_image_tokens.append(num_image_tokens)
+ vision_data.update({"num_image_tokens": batch_num_image_tokens, "num_image_patches": num_image_patches})
+
+ return MultiModalData(**vision_data)
+
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+__all__ = ["LlavaNextVideoProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/video_processing_llava_next_video.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/video_processing_llava_next_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..95cd79da655112d554cece4857fd76fa8fed0bae
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_next_video/video_processing_llava_next_video.py
@@ -0,0 +1,57 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Video processor class for LLaVa-NeXT-Video."""
+
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+)
+from ...processing_utils import Unpack, VideosKwargs
+from ...utils import is_vision_available
+from ...utils.import_utils import requires
+from ...video_processing_utils import (
+ BaseVideoProcessor,
+)
+
+
+if is_vision_available():
+ from ...image_utils import PILImageResampling
+
+
+class LlavaNextVideoFastVideoProcessorInitKwargs(VideosKwargs): ...
+
+
+@requires(backends=("torchvision",))
+class LlavaNextVideoVideoProcessor(BaseVideoProcessor):
+ resample = PILImageResampling.BICUBIC
+ image_mean = OPENAI_CLIP_MEAN
+ image_std = OPENAI_CLIP_STD
+ size = {"shortest_edge": 224}
+ default_to_square = False
+ crop_size = {"height": 224, "width": 224}
+ do_resize = True
+ do_center_crop = True
+ do_rescale = True
+ do_normalize = True
+ do_convert_rgb = True
+ do_sample_frames = False # Set to False for BC, recommended to set `True` in new models
+ valid_kwargs = LlavaNextVideoFastVideoProcessorInitKwargs
+ model_input_names = ["pixel_values_videos"]
+
+ def __init__(self, **kwargs: Unpack[LlavaNextVideoFastVideoProcessorInitKwargs]):
+ super().__init__(**kwargs)
+
+
+__all__ = ["LlavaNextVideoVideoProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b24d99815e0122c9b3e07c86547d074777e07b8
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/__init__.py
@@ -0,0 +1,31 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_llava_onevision import *
+ from .image_processing_llava_onevision import *
+ from .image_processing_llava_onevision_fast import *
+ from .modeling_llava_onevision import *
+ from .processing_llava_onevision import *
+ from .video_processing_llava_onevision import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/configuration_llava_onevision.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/configuration_llava_onevision.py
new file mode 100644
index 0000000000000000000000000000000000000000..21ead3df17061f3c3545a9d997396babe6d7fbd6
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/configuration_llava_onevision.py
@@ -0,0 +1,194 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import (
+ logging,
+)
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class LlavaOnevisionConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LlavaOnevisionForConditionalGeneration`]. It is used to instantiate an
+ Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the [llava-hf/llava-onevision-qwen2-7b-ov-hf](https://huggingface.co/llava-hf/llava-onevision-qwen2-7b-ov-hf)
+ model.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `SiglipVisionConfig`):
+ The config object or dictionary of the vision backbone.
+ text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Qwen2Config`):
+ The config object or dictionary of the text backbone.
+ image_token_index (`int`, *optional*, defaults to 151646):
+ The image token index to encode the image prompt.
+ video_token_index (`int`, *optional*, defaults to 151647):
+ The video token index to encode the video prompt.
+ projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
+ The activation function used by the multimodal projector.
+ vision_feature_select_strategy (`str`, *optional*, defaults to `"full"`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
+ If `"full"`, the full vision features are used.
+ vision_feature_layer (`Union[int, list[int]]`, *optional*, defaults to -1):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`):
+ Aspect ratio used when processong image features. The default value is "anyres_max_9".
+ image_grid_pinpoints (`List`, *optional*):
+ A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list
+ of the form `(height, width)`.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether the model's input and output word embeddings should be tied.
+ multimodal_projector_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use bias in the multimodal projector.
+
+ Example:
+
+ ```python
+ >>> from transformers import LlavaOnevisionForConditionalGeneration, LlavaOnevisionConfig, SiglipVisionConfig, Qwen2Config
+
+ >>> # Initializing a CLIP-vision config
+ >>> vision_config = SiglipVisionConfig()
+
+ >>> # Initializing a Llama config
+ >>> text_config = Qwen2Config()
+
+ >>> # Initializing a Llava-Next llava-hf/llava-onevision-qwen2-7b-ov-hf style configuration
+ >>> configuration = LlavaOnevisionConfig(vision_config, text_config)
+
+ >>> # Initializing a model from the llava-hf/llava-onevision-qwen2-7b-ov-hf style configuration
+ >>> model = LlavaOnevisionForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "llava_onevision"
+ attribute_map = {
+ "image_token_id": "image_token_index",
+ "video_token_id": "video_token_index",
+ }
+ sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
+
+ def __init__(
+ self,
+ vision_config=None,
+ text_config=None,
+ image_token_index=151646,
+ video_token_index=151647,
+ projector_hidden_act="gelu",
+ vision_feature_select_strategy="full",
+ vision_feature_layer=-1,
+ vision_aspect_ratio="anyres_max_9",
+ image_grid_pinpoints=None,
+ tie_word_embeddings=False,
+ multimodal_projector_bias=True,
+ **kwargs,
+ ):
+ self.image_token_index = image_token_index
+ self.video_token_index = video_token_index
+ self.projector_hidden_act = projector_hidden_act
+ self.multimodal_projector_bias = multimodal_projector_bias
+
+ if vision_feature_select_strategy not in ["default", "full"]:
+ raise ValueError(
+ "vision_feature_select_strategy should be one of 'default', 'full'."
+ f"Got: {vision_feature_select_strategy}"
+ )
+
+ self.vision_feature_select_strategy = vision_feature_select_strategy
+ self.vision_feature_layer = vision_feature_layer
+ self.vision_aspect_ratio = vision_aspect_ratio
+ image_grid_pinpoints = (
+ image_grid_pinpoints
+ if image_grid_pinpoints is not None
+ else [
+ [384, 384],
+ [384, 768],
+ [384, 1152],
+ [384, 1536],
+ [384, 1920],
+ [384, 2304],
+ [768, 384],
+ [768, 768],
+ [768, 1152],
+ [768, 1536],
+ [768, 1920],
+ [768, 2304],
+ [1152, 384],
+ [1152, 768],
+ [1152, 1152],
+ [1152, 1536],
+ [1152, 1920],
+ [1152, 2304],
+ [1536, 384],
+ [1536, 768],
+ [1536, 1152],
+ [1536, 1536],
+ [1536, 1920],
+ [1536, 2304],
+ [1920, 384],
+ [1920, 768],
+ [1920, 1152],
+ [1920, 1536],
+ [1920, 1920],
+ [1920, 2304],
+ [2304, 384],
+ [2304, 768],
+ [2304, 1152],
+ [2304, 1536],
+ [2304, 1920],
+ [2304, 2304],
+ ]
+ )
+ self.image_grid_pinpoints = image_grid_pinpoints
+
+ if isinstance(vision_config, dict):
+ vision_config["model_type"] = vision_config.get("model_type", "siglip_vision_model")
+ vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
+ elif vision_config is None:
+ vision_config = CONFIG_MAPPING["siglip_vision_model"](
+ hidden_size=1152,
+ intermediate_size=4304,
+ patch_size=14,
+ image_size=384,
+ num_hidden_layers=26,
+ num_attention_heads=16,
+ vision_use_head=False,
+ )
+
+ self.vision_config = vision_config
+
+ if isinstance(text_config, dict):
+ text_config["model_type"] = text_config.get("model_type", "qwen2")
+ text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+ elif text_config is None:
+ text_config = CONFIG_MAPPING["qwen2"]()
+
+ self.text_config = text_config
+
+ super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+__all__ = ["LlavaOnevisionConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/convert_llava_onevision_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/convert_llava_onevision_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..79bcad09ce13534fac130b1bd383fb6923795548
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/convert_llava_onevision_weights_to_hf.py
@@ -0,0 +1,386 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Convert LLaVa-Onevision checkpoints from the original repository.
+
+URL: https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main
+
+"""
+
+import argparse
+import gc
+import glob
+import json
+from pathlib import Path
+
+import requests
+import torch
+from accelerate import init_empty_weights
+from huggingface_hub import hf_hub_download, snapshot_download
+from PIL import Image
+from safetensors import safe_open
+
+from transformers import (
+ AddedToken,
+ AutoConfig,
+ AutoTokenizer,
+ LlavaOnevisionConfig,
+ LlavaOnevisionForConditionalGeneration,
+ LlavaOnevisionImageProcessor,
+ LlavaOnevisionProcessor,
+ LlavaOnevisionVideoProcessor,
+ SiglipVisionConfig,
+)
+
+
+KEYS_TO_MODIFY_MAPPING = {
+ "model.vision_tower.": "",
+ "model.mm_projector": "multi_modal_projector",
+ "model": "model.model",
+ "vision_model.model": "vision_model",
+ "lm_head": "language_model.lm_head",
+ "model.model": "language_model.model",
+ "multi_modal_projector.0": "multi_modal_projector.linear_1",
+ "multi_modal_projector.2": "multi_modal_projector.linear_2",
+ "language_model.model.image_newline": "image_newline",
+}
+
+chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n'}}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '\n' }}{% endfor %}{# Render all video then #}{% for content in message['content'] | selectattr('type', 'equalto', 'video') %}{{ '\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] }}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] }}{% endgeneration %}{% endfor %}{% endif %}{{'<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
+
+
+def load_original_state_dict(model_id):
+ directory_path = snapshot_download(repo_id=model_id, allow_patterns=["*.safetensors"])
+
+ original_state_dict = {}
+ for path in glob.glob(f"{directory_path}/*"):
+ if path.endswith(".safetensors"):
+ with safe_open(path, framework="pt", device="cpu") as f:
+ for key in f.keys():
+ original_state_dict[key] = f.get_tensor(key)
+
+ # tied weights so lm.head is not saved. Let's clone to load state dict
+ if "lm_head.weight" not in original_state_dict:
+ original_state_dict["lm_head.weight"] = original_state_dict["model.embed_tokens.weight"].clone()
+
+ return original_state_dict
+
+
+def convert_state_dict_to_hf(state_dict):
+ new_state_dict = {}
+ for key, value in state_dict.items():
+ if key.endswith(".inv_freq"):
+ continue
+ for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
+ if key_to_modify in key:
+ key = key.replace(key_to_modify, new_key)
+
+ new_state_dict[key] = value.to(torch.float16)
+ return new_state_dict
+
+
+def load_image():
+ url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
+ image = Image.open(requests.get(url, stream=True).raw)
+ return image
+
+
+def convert_llava_to_hf(model_id, pytorch_dump_folder_path, push_to_hub=False):
+ # load original config
+ filepath = hf_hub_download(repo_id=model_id, filename="config.json", repo_type="model")
+ # read json
+ with open(filepath) as f:
+ data = json.load(f)
+ print(data)
+
+ if model_id in ["lmms-lab/llava-onevision-qwen2-0.5b-ov", "lmms-lab/llava-onevision-qwen2-0.5b-si"]:
+ text_model_id = "Qwen/Qwen2-0.5B-Instruct"
+ elif model_id in [
+ "lmms-lab/llava-onevision-qwen2-7b-ov",
+ "lmms-lab/llava-onevision-qwen2-7b-si",
+ "lmms-lab/llava-onevision-qwen2-7b-ov-chat",
+ ]:
+ text_model_id = "Qwen/Qwen2-7B-Instruct"
+ elif model_id in [
+ "lmms-lab/llava-onevision-qwen2-72b-ov",
+ "lmms-lab/llava-onevision-qwen2-72b-si",
+ "lmms-lab/llava-onevision-qwen2-72b-ov-chat",
+ ]:
+ text_model_id = "Qwen/Qwen2-72B-Instruct"
+
+ vision_model_id = data["mm_vision_tower"]
+ torch.set_default_dtype(torch.float16)
+ text_config = AutoConfig.from_pretrained(text_model_id)
+
+ tokenizer = AutoTokenizer.from_pretrained(text_model_id, use_fast=True)
+ tokenizer.add_tokens(AddedToken("", special=True, normalized=False), special_tokens=True)
+ tokenizer.add_tokens(AddedToken("", special=True, normalized=False), special_tokens=True)
+
+ image_processor = LlavaOnevisionImageProcessor.from_pretrained(vision_model_id)
+ video_processor = LlavaOnevisionVideoProcessor.from_pretrained(vision_model_id)
+ processor = LlavaOnevisionProcessor(
+ tokenizer=tokenizer,
+ video_processor=video_processor,
+ image_processor=image_processor,
+ num_image_tokens=729,
+ vision_feature_select_strategy="full",
+ chat_template=chat_template,
+ )
+
+ vision_config = SiglipVisionConfig(
+ hidden_size=1152,
+ image_size=384,
+ intermediate_size=4304,
+ num_attention_heads=16,
+ num_hidden_layers=26, # drop the last layer
+ patch_size=14,
+ vision_use_head=False, # no head
+ ).to_dict()
+
+ config = LlavaOnevisionConfig(
+ text_config=text_config.to_dict(),
+ vision_config=vision_config,
+ use_image_newline_parameter=True,
+ )
+
+ with init_empty_weights():
+ model = LlavaOnevisionForConditionalGeneration(config)
+
+ # load original state dict
+ state_dict = load_original_state_dict(model_id)
+ state_dict = convert_state_dict_to_hf(state_dict)
+ model.load_state_dict(state_dict, assign=True)
+ model.eval()
+
+ pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data
+ mu = torch.mean(pre_expansion_embeddings, dim=0).float()
+ n = pre_expansion_embeddings.size()[0]
+ sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n
+ dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma)
+
+ # We add an image token so we resize the model
+ # Pad to 64 for performance reasons
+ # Qwen-based models have extra unused space in the vocab size already, so no need to resize
+ pad_shape = 64
+ vocab_size = config.text_config.vocab_size
+ num_tokens = vocab_size + 2
+ model.resize_token_embeddings(num_tokens, pad_to_multiple_of=pad_shape)
+ model.language_model.model.embed_tokens.weight.data[vocab_size:] = torch.stack(
+ tuple(dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[vocab_size:].shape[0])),
+ dim=0,
+ )
+ model.language_model.lm_head.weight.data[vocab_size:] = torch.stack(
+ tuple(dist.sample() for _ in range(model.language_model.lm_head.weight.data[vocab_size:].shape[0])),
+ dim=0,
+ )
+
+ print(f"Saving model and processor for {model_id} to {pytorch_dump_folder_path}")
+ Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+ model.save_pretrained(pytorch_dump_folder_path)
+ processor.save_pretrained(pytorch_dump_folder_path)
+
+ # Make space so we can load the model properly now.
+ del state_dict
+ gc.collect()
+
+ # Load everything back for inference tests in float32 because prev script was written as that
+ # Though it's mostly loaded in fp16 as original weights are in fp16
+ model = LlavaOnevisionForConditionalGeneration.from_pretrained(
+ pytorch_dump_folder_path, torch_dtype="float16", device_map="auto"
+ )
+ processor = LlavaOnevisionProcessor.from_pretrained(pytorch_dump_folder_path)
+ device = model.device
+
+ # prepare inputs
+ image = load_image()
+ prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n\nWhat is shown in this image?<|im_end|>\n<|im_start|>assistant\n"
+ inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch.float16)
+
+ # verify inputs
+ filepath = hf_hub_download(
+ repo_id="RaushanTurganbay/test-image", filename="llava_onevision_pixel_values.pt", repo_type="dataset"
+ )
+ original_pixel_values = torch.load(filepath, map_location="cpu", weights_only=True)
+ assert torch.allclose(original_pixel_values, inputs.pixel_values.half())
+
+ image_sizes = torch.tensor([[899, 1024]])
+ assert image_sizes[0].tolist() == inputs.image_sizes[0].tolist()
+
+ # verify single forward pass
+ print("Single forward pass")
+ with torch.inference_mode():
+ inputs = inputs.to(device)
+ outputs = model(**inputs)
+ print("Shape of logits:", outputs.logits.shape)
+ print("First values of logits:", outputs.logits[0, :3, :3])
+
+ if model_id == "lmms-lab/llava-onevision-qwen2-0.5b-si":
+ # Not yet checked against reference
+ expected_slice = torch.tensor(
+ [[-12.1953, -14.6797, -12.7891], [0.5840, -0.8467, 1.3799], [3.6055, 4.5430, 9.9062]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "lmms-lab/llava-onevision-qwen2-0.5b-ov":
+ # Not yet checked against reference
+ expected_slice = torch.tensor(
+ [[-12.0234, -14.3828, -12.7500], [2.3594, 1.0000, 3.9336], [3.6582, 4.7148, 9.1172]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "lmms-lab/llava-onevision-qwen2-7b-si":
+ # Not yet checked against reference
+ expected_slice = torch.tensor(
+ [[1.7656, 3.3418, 1.4033], [0.0757, 0.7427, 3.5098], [6.7109, 5.6797, 9.3828]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "lmms-lab/llava-onevision-qwen2-7b-ov":
+ # Not yet checked against reference
+ expected_slice = torch.tensor(
+ [[1.8496, 3.4219, 1.3135], [3.0996, 3.0117, 3.1484], [4.2422, 4.7109, 9.9688]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "lmms-lab/llava-onevision-qwen2-72b-si":
+ # Not yet checked against reference
+ expected_slice = torch.tensor(
+ [[4.1875, 4.4883, 2.7910], [1.2949, 5.1328, 3.1582], [0.9390, 6.4531, 8.4375]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "lmms-lab/llava-onevision-qwen2-72b-ov":
+ # Not yet checked against reference
+ expected_slice = torch.tensor(
+ [[4.2930, 4.7305, 2.7363], [1.7529, 5.0742, 3.9590], [1.3936, 6.3438, 9.3984]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "lmms-lab/llava-onevision-qwen2-7b-ov-chat":
+ # Not yet checked against reference
+ expected_slice = torch.tensor(
+ [[1.8662, 3.4316, 1.3174], [2.7109, 2.5488, 3.0117], [4.4648, 4.9648, 10.3359]],
+ dtype=torch.float32,
+ device=device,
+ )
+ elif model_id == "lmms-lab/llava-onevision-qwen2-72b-ov-chat":
+ # Not yet checked against reference
+ expected_slice = torch.tensor(
+ [[4.3086, 4.7344, 2.6953], [1.7090, 5.1719, 4.0234], [1.3057, 6.3438, 9.5469]],
+ dtype=torch.float32,
+ device=device,
+ )
+ else:
+ raise ValueError(f"Model {model_id} not supported")
+
+ assert torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4)
+ print("Logits are ok!")
+
+ # verify generation
+ output_ids = model.generate(
+ **inputs,
+ max_new_tokens=100,
+ use_cache=True,
+ )
+
+ generated_text = processor.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
+
+ print("Generated text:", repr(generated_text))
+
+ if model_id == "lmms-lab/llava-onevision-qwen2-0.5b-si":
+ expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image is a radar chart that shows the performance of different algorithms or models in a specific domain, such as image classification or natural language processing. The chart is color-coded to represent different algorithms, with each color corresponding to a specific algorithm. The algorithms are labeled as BLIP-2, InstructBLIP, Owen-VL-Chat, and LLaVA-1.5. The chart also includes a legend at the bottom that explains the color coding and the algorithms represented."
+ elif model_id == "lmms-lab/llava-onevision-qwen2-0.5b-ov":
+ expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related to natural language processing or machine learning. The chart is divided into different categories, each represented by a different color and labeled with the name of the model or technique used. The models are evaluated based on their performance metrics, such as BLEU-2, InstructBLIP, Qwen-VL-Chat, and LLaVA-1.5. The radar chart helps to visualize the relative"
+ elif model_id == "lmms-lab/llava-onevision-qwen2-7b-si":
+ expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThis image is a radar chart that compares the performance of different models on various metrics. The models being compared are BLIP-2, InstructBLIP, and Qwen-VL-Chat. The metrics being compared are VQA, QA, GQA, VQA-av2, and VQA-av2. The chart shows that BLIP-2 performs the best on all metrics, followed by InstructBLIP and Qwen-VL-Chat."
+ elif model_id == "lmms-lab/llava-onevision-qwen2-7b-ov":
+ expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image shows a radar chart, also known as a spider chart or a star chart, which is used to compare multiple quantitative variables. Each axis represents a different variable, and the chart is filled with data points that represent the performance or values of different entities across these variables.\n\nIn this particular radar chart, the variables are represented on the axes, and the performance of different models or systems is shown by the lines connecting the data points. The models or systems are labeled along the bottom of the chart,"
+ elif model_id == "lmms-lab/llava-onevision-qwen2-72b-si":
+ expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image shows a radar chart, which is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. The chart is used to compare the performance of different models or systems across various benchmarks or metrics.\n\nIn this specific radar chart, there are multiple axes, each representing a different benchmark or metric, such as VQA2, GQA, TextVQA, and others. The chart includes several colored lines"
+ elif model_id == "lmms-lab/llava-onevision-qwen2-72b-ov":
+ expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image is a radar chart comparing the performance of different models on various multimodal benchmarks. The models compared are BLIP-2, InstructBLIP, POPE, QWen-VL-Chat, and LLava-1.5. The benchmarks include VQAv2, GQA, TextVQA, SQA-IMG, VizWiz, MM-IMDb, MM-VQA, MM-IMDb-CN, MM-IMDb-EN, MM-"
+ elif model_id == "lmms-lab/llava-onevision-qwen2-7b-ov-chat":
+ expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image shows a radar chart, also known as a spider chart or a star chart, which is used to display multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. Each axis represents a different variable, and the values are plotted along these axes.\n\nIn this particular radar chart, there are multiple lines representing different models or systems, each distinguished by a different color and labeled with a name such as BLIP-2, In"
+ elif model_id == "lmms-lab/llava-onevision-qwen2-72b-ov-chat":
+ expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image is a radar chart comparing the performance of different models on various multimodal benchmarks. The models compared are BLIP-2, InstructBLIP, POPE, QWen-VL-Chat, and LLava-1.5. The benchmarks include VQAv2, GQA, TextVQA, SQA-IMG, VizWiz, MM-IMDb, MM-VQA, MM-IMDb-CN, MM-IMDb-EN, MM-"
+ else:
+ raise ValueError(f"Model {model_id} not supported")
+
+ assert generated_text == expected_text
+ print("Generated text is ok!")
+
+ # verify batched generation
+ print("Batched generation...")
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ cats_image = Image.open(requests.get(url, stream=True).raw)
+
+ inputs = processor(
+ images=[image, cats_image],
+ text=[prompt, prompt],
+ padding=True,
+ return_tensors="pt",
+ ).to(device, torch.float16)
+
+ for k, v in inputs.items():
+ print(k, v.shape)
+
+ print("Image sizes:", inputs.image_sizes)
+
+ # make sure image_sizes are the same
+ # as otherwise batched generation doesn't work
+ inputs.image_sizes[1] = inputs.image_sizes[0]
+
+ print("Batched generation...")
+ output_ids = model.generate(
+ **inputs,
+ max_new_tokens=20,
+ use_cache=True,
+ )
+
+ outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+ print(outputs)
+
+ if push_to_hub:
+ checkpoint_name = model_id.split("/")[-1]
+ print(f"Pushing to repo llava-hf/{checkpoint_name}-hf")
+ model.push_to_hub(f"llava-hf/{checkpoint_name}-hf")
+ processor.push_to_hub(f"llava-hf/{checkpoint_name}-hf")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--model_id",
+ help="Hub location of the model to convert",
+ default="lmms-lab/llava-onevision-qwen2-0.5b-ov",
+ choices=[
+ "lmms-lab/llava-onevision-qwen2-0.5b-ov",
+ "lmms-lab/llava-onevision-qwen2-0.5b-si",
+ "lmms-lab/llava-onevision-qwen2-7b-si",
+ "lmms-lab/llava-onevision-qwen2-7b-ov",
+ "lmms-lab/llava-onevision-qwen2-72b-si",
+ "lmms-lab/llava-onevision-qwen2-72b-ov",
+ "lmms-lab/llava-onevision-qwen2-7b-ov-chat",
+ "lmms-lab/llava-onevision-qwen2-72b-ov-chat",
+ ],
+ required=False,
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", type=str, required=True, help="Path to the output PyTorch model directory."
+ )
+ parser.add_argument(
+ "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
+ )
+ args = parser.parse_args()
+
+ convert_llava_to_hf(args.model_id, args.pytorch_dump_folder_path, args.push_to_hub)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/image_processing_llava_onevision.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/image_processing_llava_onevision.py
new file mode 100644
index 0000000000000000000000000000000000000000..8729c7444d299ec52145f379649f528eb3bb28ec
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/image_processing_llava_onevision.py
@@ -0,0 +1,785 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for LLaVa-Onevision."""
+
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import (
+ BaseImageProcessor,
+ BatchFeature,
+ get_patch_output_size,
+ get_size_dict,
+ select_best_resolution,
+)
+from ...image_transforms import (
+ PaddingMode,
+ convert_to_rgb,
+ pad,
+ resize,
+ to_channel_dimension_format,
+)
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ get_image_size,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_flat_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import TensorType, is_vision_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+if is_vision_available():
+ from PIL import Image
+
+
+# Copied from transformers.models.llava_next.image_processing_llava_next.divide_to_patches
+def divide_to_patches(image: np.array, patch_size: int, input_data_format) -> list[np.array]:
+ """
+ Divides an image into patches of a specified size.
+
+ Args:
+ image (`np.array`):
+ The input image.
+ patch_size (`int`):
+ The size of each patch.
+ input_data_format (`ChannelDimension` or `str`):
+ The channel dimension format of the input image.
+
+ Returns:
+ list: A list of np.array representing the patches.
+ """
+ patches = []
+ height, width = get_image_size(image, channel_dim=input_data_format)
+ for i in range(0, height, patch_size):
+ for j in range(0, width, patch_size):
+ if input_data_format == ChannelDimension.LAST:
+ patch = image[i : i + patch_size, j : j + patch_size]
+ else:
+ patch = image[:, i : i + patch_size, j : j + patch_size]
+ patches.append(patch)
+
+ return patches
+
+
+# Copied from transformers.models.llava_next.image_processing_llava_next.expand_to_square
+def expand_to_square(image: np.array, background_color, input_data_format) -> np.array:
+ """
+ Expands an image to a square by adding a background color.
+ """
+
+ height, width = get_image_size(image, channel_dim=input_data_format)
+ if width == height:
+ return image
+ elif width > height:
+ result = np.ones((width, width, image.shape[2]), dtype=image.dtype) * background_color
+ result[(width - height) // 2 : (width - height) // 2 + height, :] = image
+ return result
+ else:
+ result = np.ones((height, height, image.shape[2]), dtype=image.dtype) * background_color
+ result[:, (height - width) // 2 : (height - width) // 2 + width] = image
+ return result
+
+
+class LlavaOnevisionImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a LLaVa-Onevision image processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
+ `do_resize` in the `preprocess` method.
+ size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
+ Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
+ method.
+ image_grid_pinpoints (`List` *optional*, defaults to `[[672, 336], [336, 672], [672, 672], [336, 1008], [1008, 336]]`):
+ A list of possible resolutions to use for processing high resolution images. The best resolution is selected
+ based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
+ method. Not used for processing videos.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+ Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
+ the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
+ method.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`float` or `list[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
+ Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+ number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+ Can be overridden by the `image_std` parameter in the `preprocess` method.
+ do_pad (`bool`, *optional*, defaults to `True`):
+ Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
+ number of patches in the batch. Padding will be applied to the bottom and right with zeros.
+ do_convert_rgb (`bool`, *optional*, defaults to `True`):
+ Whether to convert the image to RGB.
+ """
+
+ model_input_names = ["pixel_values_videos"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ image_grid_pinpoints: Optional[list] = None,
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_pad: Optional[bool] = True,
+ do_convert_rgb: bool = True,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"height": 384, "width": 384}
+ size = get_size_dict(size, default_to_square=False)
+ image_grid_pinpoints = (
+ image_grid_pinpoints
+ if image_grid_pinpoints is not None
+ else [
+ [384, 384],
+ [384, 768],
+ [384, 1152],
+ [384, 1536],
+ [384, 1920],
+ [384, 2304],
+ [768, 384],
+ [768, 768],
+ [768, 1152],
+ [768, 1536],
+ [768, 1920],
+ [768, 2304],
+ [1152, 384],
+ [1152, 768],
+ [1152, 1152],
+ [1152, 1536],
+ [1152, 1920],
+ [1152, 2304],
+ [1536, 384],
+ [1536, 768],
+ [1536, 1152],
+ [1536, 1536],
+ [1536, 1920],
+ [1536, 2304],
+ [1920, 384],
+ [1920, 768],
+ [1920, 1152],
+ [1920, 1536],
+ [1920, 1920],
+ [1920, 2304],
+ [2304, 384],
+ [2304, 768],
+ [2304, 1152],
+ [2304, 1536],
+ [2304, 1920],
+ [2304, 2304],
+ ]
+ )
+
+ self.do_resize = do_resize
+ self.size = size
+ self.image_grid_pinpoints = image_grid_pinpoints
+ self.resample = resample
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
+ self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+ self.do_pad = do_pad
+ self.do_convert_rgb = do_convert_rgb
+
+ # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor.pad
+ def pad(
+ self,
+ image: np.ndarray,
+ padding: Union[int, tuple[int, int], Iterable[tuple[int, int]]],
+ mode: PaddingMode = PaddingMode.CONSTANT,
+ constant_values: Union[float, Iterable[float]] = 0.0,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Pads the `image` with the specified `padding` and `mode`. Padding can be in the (`height`, `width`)
+ dimension of in the (`num_patches`) dimension. In the second case an iterable if tuples is expected
+ as input.
+
+ Args:
+ image (`np.ndarray`):
+ The image to pad.
+ padding (`int` or `tuple[int, int]` or `Iterable[tuple[int, int]]`):
+ Padding to apply to the edges of the height, width axes. Can be one of three formats:
+ - `((before_height, after_height), (before_width, after_width))` unique pad widths for each axis.
+ - `((before, after),)` yields same before and after pad for height and width.
+ - `(pad,)` or int is a shortcut for before = after = pad width for all axes.
+ mode (`PaddingMode`):
+ The padding mode to use. Can be one of:
+ - `"constant"`: pads with a constant value.
+ - `"reflect"`: pads with the reflection of the vector mirrored on the first and last values of the
+ vector along each axis.
+ - `"replicate"`: pads with the replication of the last value on the edge of the array along each axis.
+ - `"symmetric"`: pads with the reflection of the vector mirrored along the edge of the array.
+ constant_values (`float` or `Iterable[float]`, *optional*):
+ The value to use for the padding if `mode` is `"constant"`.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use same as the input image.
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use the inferred format of the input image.
+
+ Returns:
+ `np.ndarray`: The padded image.
+
+ """
+
+ # call the general `pad` if padding on `height/width`, otherwise it's the `num_patched` dim
+ if isinstance(padding, int) or len(padding) != 4:
+ return pad(image, padding, mode, constant_values, data_format, input_data_format)
+
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(image)
+ if mode == PaddingMode.CONSTANT:
+ image = np.pad(image, padding, mode="constant", constant_values=constant_values)
+ elif mode == PaddingMode.REFLECT:
+ image = np.pad(image, padding, mode="reflect")
+ elif mode == PaddingMode.REPLICATE:
+ image = np.pad(image, padding, mode="edge")
+ elif mode == PaddingMode.SYMMETRIC:
+ image = np.pad(image, padding, mode="symmetric")
+ else:
+ raise ValueError(f"Invalid padding mode: {mode}")
+ image = (
+ to_channel_dimension_format(image, data_format, input_data_format) if data_format is not None else image
+ )
+ return image
+
+ # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._resize_for_patching
+ def _resize_for_patching(
+ self, image: np.array, target_resolution: tuple, resample, input_data_format: ChannelDimension
+ ) -> np.array:
+ """
+ Resizes an image to a target resolution while maintaining aspect ratio.
+
+ Args:
+ image (np.array):
+ The input image.
+ target_resolution (tuple):
+ The target resolution (height, width) of the image.
+ resample (`PILImageResampling`):
+ Resampling filter to use if resizing the image.
+ input_data_format (`ChannelDimension` or `str`):
+ The channel dimension format of the input image.
+
+ Returns:
+ np.array: The resized and padded image.
+ """
+ new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
+
+ # Resize the image
+ resized_image = resize(image, (new_height, new_width), resample=resample, input_data_format=input_data_format)
+
+ return resized_image
+
+ # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._get_padding_size
+ def _get_padding_size(self, original_resolution: tuple, target_resolution: tuple):
+ original_height, original_width = original_resolution
+ target_height, target_width = target_resolution
+ paste_x, r_x = divmod(target_width - original_width, 2)
+ paste_y, r_y = divmod(target_height - original_height, 2)
+ return (paste_y, paste_y + r_y), (paste_x, paste_x + r_x)
+
+ # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._pad_for_patching
+ def _pad_for_patching(
+ self, image: np.array, target_resolution: tuple, input_data_format: ChannelDimension
+ ) -> np.array:
+ """
+ Pad an image to a target resolution while maintaining aspect ratio.
+ """
+ new_resolution = get_patch_output_size(image, target_resolution, input_data_format)
+ padding = self._get_padding_size(new_resolution, target_resolution)
+
+ padded_image = self.pad(image, padding=padding)
+
+ return padded_image
+
+ # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor.get_image_patches
+ def get_image_patches(
+ self,
+ image: np.array,
+ grid_pinpoints,
+ size: tuple,
+ patch_size: int,
+ resample: PILImageResampling,
+ data_format: ChannelDimension,
+ input_data_format: ChannelDimension,
+ ) -> list[np.array]:
+ """
+ Process an image with variable resolutions by dividing it into patches.
+
+ Args:
+ image (np.array):
+ The input image to be processed.
+ grid_pinpoints (List):
+ A string representation of a list of possible resolutions.
+ size (`tuple`):
+ Size to resize the original image to.
+ patch_size (`int`):
+ Size of the patches to divide the image into.
+ resample (`PILImageResampling`):
+ Resampling filter to use if resizing the image.
+ data_format (`ChannelDimension` or `str`):
+ The channel dimension format for the output image.
+ input_data_format (`ChannelDimension` or `str`):
+ The channel dimension format of the input image.
+
+ Returns:
+ list[np.array]: A list of NumPy arrays containing the processed image patches.
+ """
+ if not isinstance(grid_pinpoints, list):
+ raise TypeError("grid_pinpoints must be a list of possible resolutions.")
+
+ possible_resolutions = grid_pinpoints
+
+ image_size = get_image_size(image, channel_dim=input_data_format)
+ best_resolution = select_best_resolution(image_size, possible_resolutions)
+ resized_image = self._resize_for_patching(
+ image, best_resolution, resample=resample, input_data_format=input_data_format
+ )
+ padded_image = self._pad_for_patching(resized_image, best_resolution, input_data_format=input_data_format)
+
+ patches = divide_to_patches(padded_image, patch_size=patch_size, input_data_format=input_data_format)
+
+ # make sure that all patches are in the input data format
+ patches = [
+ to_channel_dimension_format(patch, channel_dim=data_format, input_channel_dim=input_data_format)
+ for patch in patches
+ ]
+
+ resized_original_image = resize(
+ image,
+ size=size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+
+ image_patches = [resized_original_image] + patches
+
+ return image_patches
+
+ # Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._pad_for_batching
+ def _pad_for_batching(
+ self,
+ pixel_values: list[np.ndarray],
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ """
+ Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
+
+ Args:
+ pixel_values (`list[np.ndarray]`):
+ An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`)
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use same as the input image.
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use the inferred format of the input image.
+
+ Returns:
+ list[`np.ndarray`]: The padded images.
+ """
+ max_patch = max(len(x) for x in pixel_values)
+ pixel_values = [
+ self.pad(
+ image,
+ padding=((0, max_patch - image.shape[0]), (0, 0), (0, 0), (0, 0)),
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ for image in pixel_values
+ ]
+
+ return pixel_values
+
+ # Copied from transformers.models.llava.image_processing_llava.LlavaImageProcessor.pad_to_square
+ def pad_to_square(
+ self,
+ image: np.ndarray,
+ background_color: Union[int, tuple[int, int, int]] = 0,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.array:
+ """
+ Pads an image to a square based on the longest edge.
+
+ Args:
+ image (`np.ndarray`):
+ The image to pad.
+ background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+ The color to use for the padding. Can be an integer for single channel or a
+ tuple of integers representing for multi-channel images. If passed as integer
+ in mutli-channel mode, it will default to `0` in subsequent channels.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use same as the input image.
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ If unset, will use the inferred format of the input image.
+
+ Returns:
+ `np.ndarray`: The padded image.
+ """
+ height, width = get_image_size(image, input_data_format)
+ num_channels = image.shape[0] if input_data_format == ChannelDimension.FIRST else image.shape[-1]
+
+ if height == width:
+ image = (
+ to_channel_dimension_format(image, data_format, input_data_format)
+ if data_format is not None
+ else image
+ )
+ return image
+
+ max_dim = max(height, width)
+
+ # Ensure background_color is the correct shape
+ if isinstance(background_color, int):
+ background_color = [background_color]
+ elif len(background_color) != num_channels:
+ raise ValueError(
+ f"background_color must have no more than {num_channels} elements to match the number of channels"
+ )
+
+ if input_data_format == ChannelDimension.FIRST:
+ result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype)
+ for i, color in enumerate(background_color):
+ result[i, :, :] = color
+ if width > height:
+ start = (max_dim - height) // 2
+ result[:, start : start + height, :] = image
+ else:
+ start = (max_dim - width) // 2
+ result[:, :, start : start + width] = image
+ else:
+ result = np.zeros((max_dim, max_dim, num_channels), dtype=image.dtype)
+ for i, color in enumerate(background_color):
+ result[:, :, i] = color
+ if width > height:
+ start = (max_dim - height) // 2
+ result[start : start + height, :, :] = image
+ else:
+ start = (max_dim - width) // 2
+ result[:, start : start + width, :] = image
+
+ image = (
+ to_channel_dimension_format(result, data_format, input_data_format) if data_format is not None else result
+ )
+ return image
+
+ def _preprocess(
+ self,
+ images: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_convert_rgb: Optional[bool] = None,
+ data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> Image.Image:
+ """
+ Args:
+ images (`ImageInput`):
+ Batch of frames (one video) to preprocess. Expects a batch of frames with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio.
+ resample (`int`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+ has an effect if `do_resize` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image.
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+ `True`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ if do_resize:
+ images = [
+ resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ if do_rescale:
+ images = [
+ self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ if do_normalize:
+ images = [
+ self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ images = [
+ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
+ ]
+
+ return images
+
+ def preprocess(
+ self,
+ images: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ image_grid_pinpoints: Optional[list] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_pad: Optional[bool] = None,
+ do_convert_rgb: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ """
+ Args:
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio.
+ image_grid_pinpoints (`List` *optional*, defaults to `self.image_grid_pinpoints`):
+ A list of possible resolutions to use for processing high resolution images. The best resolution is
+ selected based on the original size of the image.
+ resample (`int`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+ has an effect if `do_resize` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image.
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+ `True`.
+ do_pad (`bool`, *optional*, defaults to `self.do_pad`):
+ Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
+ number of patches in the batch. Padding will be applied to the bottom and right with zeros.
+ do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+ Whether to convert the image to RGB.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+
+ """
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ size = get_size_dict(size, default_to_square=False)
+ image_grid_pinpoints = image_grid_pinpoints if image_grid_pinpoints is not None else self.image_grid_pinpoints
+ resample = resample if resample is not None else self.resample
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ do_pad = do_pad if do_pad is not None else self.do_pad
+ do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+ if isinstance(images, (tuple, list)) and isinstance(images[0], (tuple, list)):
+ # if the first element is a list, we assume that all elements are lists
+ images = [x for x in images if x] # handle text-only case
+ batch_num_images = [len(x) for x in images]
+ elif isinstance(images, (tuple, list)):
+ # treat this as a single-image case for backward compatibility
+ batch_num_images = [1] * len(images)
+ else:
+ batch_num_images = [1]
+ # only single image patching is supported
+ need_patching = [n == 1 for n in batch_num_images for _ in range(n)]
+
+ images = make_flat_list_of_images(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ if do_convert_rgb:
+ images = [convert_to_rgb(image) for image in images]
+
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if do_rescale and is_scaled_image(images[0]):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ size_tuple = (
+ (size["height"], size["width"])
+ if "height" in size and "width" in size
+ else (size["shortest_edge"], size["shortest_edge"])
+ )
+
+ new_images = []
+ image_sizes = [get_image_size(image, channel_dim=input_data_format) for image in images]
+ for i, image in enumerate(images):
+ if need_patching[i]:
+ # convert image into a list of patches
+ # we intentionally use the same data format as the input data format
+ image_patches = self.get_image_patches(
+ image,
+ image_grid_pinpoints,
+ size=size_tuple,
+ patch_size=size_tuple[0],
+ resample=resample,
+ data_format=input_data_format,
+ input_data_format=input_data_format,
+ )
+ else:
+ padded_image = self.pad_to_square(
+ image=image,
+ background_color=tuple(int(x * 255) for x in self.image_mean),
+ input_data_format=input_data_format,
+ )
+ image_patches = [padded_image]
+
+ # preprocess patches
+ pixel_values = self._preprocess(
+ image_patches,
+ do_resize=do_resize,
+ size=size_tuple,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ pixel_values = np.array(pixel_values)
+ new_images.append(pixel_values)
+
+ if do_pad:
+ processed_images = self._pad_for_batching(new_images)
+
+ return BatchFeature(
+ data={"pixel_values": processed_images, "image_sizes": image_sizes, "batch_num_images": batch_num_images},
+ tensor_type=return_tensors,
+ )
+
+
+__all__ = ["LlavaOnevisionImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..bff75696d6c87923179e939bd5c90c8e6cbd7ba3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
@@ -0,0 +1,355 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/llava_onevision/modular_llava_onevision.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_llava_onevision.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Optional, Union
+
+import torch
+
+from ...image_processing_utils import BatchFeature, get_patch_output_size, select_best_resolution
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ divide_to_patches,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ SizeDict,
+ get_image_size,
+)
+from ...processing_utils import Unpack
+from ...utils import TensorType, auto_docstring, is_torchvision_v2_available
+
+
+if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+else:
+ from torchvision.transforms import functional as F
+
+
+class LlavaOnevisionFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ """
+ image_grid_pinpoints (`list[list[int]]`, *optional*):
+ A list of possible resolutions to use for processing high resolution images. The best resolution is selected
+ based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
+ method.
+ do_pad (`bool`, *optional*):
+ Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
+ number of patches in the batch. Padding will be applied to the bottom and right with zeros.
+ """
+
+ image_grid_pinpoints: Optional[list[list[int]]]
+ do_pad: Optional[bool]
+
+
+@auto_docstring
+class LlavaOnevisionImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BICUBIC
+ image_mean = OPENAI_CLIP_MEAN
+ image_std = OPENAI_CLIP_STD
+ size = {"height": 384, "width": 384}
+ default_to_square = False
+ crop_size = None
+ do_resize = True
+ do_center_crop = None
+ do_rescale = True
+ do_normalize = True
+ do_convert_rgb = True
+ do_pad = True
+ image_grid_pinpoints = [[384, 384], [384, 768], [384, 1152], [384, 1536], [384, 1920], [384, 2304], [768, 384], [768, 768], [768, 1152], [768, 1536], [768, 1920], [768, 2304], [1152, 384], [1152, 768], [1152, 1152], [1152, 1536], [1152, 1920], [1152, 2304], [1536, 384], [1536, 768], [1536, 1152], [1536, 1536], [1536, 1920], [1536, 2304], [1920, 384], [1920, 768], [1920, 1152], [1920, 1536], [1920, 1920], [1920, 2304], [2304, 384], [2304, 768], [2304, 1152], [2304, 1536], [2304, 1920], [2304, 2304]] # fmt: skip
+ valid_kwargs = LlavaOnevisionFastImageProcessorKwargs
+ model_input_names = ["pixel_values_videos"]
+
+ def __init__(self, **kwargs: Unpack[LlavaOnevisionFastImageProcessorKwargs]):
+ super().__init__(**kwargs)
+
+ @auto_docstring
+ def preprocess(self, images: ImageInput, **kwargs: Unpack[LlavaOnevisionFastImageProcessorKwargs]) -> BatchFeature:
+ if isinstance(images, (tuple, list)) and isinstance(images[0], (tuple, list)):
+ # if the first element is a list, we assume that all elements are lists
+ batch_num_images = [len(x) for x in images]
+ elif isinstance(images, (tuple, list)):
+ # treat this as a single-image case for backward compatibility
+ batch_num_images = [1] * len(images)
+ else:
+ batch_num_images = [1]
+ kwargs["batch_num_images"] = batch_num_images
+ return super().preprocess(images, **kwargs)
+
+ def _resize_for_patching(
+ self,
+ image: "torch.Tensor",
+ target_resolution: tuple,
+ interpolation: "F.InterpolationMode",
+ input_data_format: ChannelDimension,
+ ) -> "torch.Tensor":
+ """
+ Resizes an image to a target resolution while maintaining aspect ratio.
+
+ Args:
+ image ("torch.Tensor"):
+ The input image.
+ target_resolution (tuple):
+ The target resolution (height, width) of the image.
+ interpolation (`InterpolationMode`):
+ Resampling filter to use if resizing the image.
+ input_data_format (`ChannelDimension` or `str`):
+ The channel dimension format of the input image.
+
+ Returns:
+ "torch.Tensor": The resized and padded image.
+ """
+ new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
+
+ # Resize the image
+ resized_image = self.resize(
+ image=image,
+ size=SizeDict(height=new_height, width=new_width),
+ interpolation=interpolation,
+ )
+
+ return resized_image
+
+ def _get_padding_size(self, original_resolution: tuple, target_resolution: tuple):
+ original_height, original_width = original_resolution
+ target_height, target_width = target_resolution
+ paste_x, r_x = divmod(target_width - original_width, 2)
+ paste_y, r_y = divmod(target_height - original_height, 2)
+ return [paste_x, paste_y, paste_x + r_x, paste_y + r_y]
+
+ def _pad_for_patching(
+ self, image: "torch.Tensor", target_resolution: tuple, input_data_format: ChannelDimension
+ ) -> "torch.Tensor":
+ """
+ Pad an image to a target resolution while maintaining aspect ratio.
+ """
+ new_resolution = get_patch_output_size(image, target_resolution, input_data_format)
+ padding = self._get_padding_size(new_resolution, target_resolution)
+
+ padded_image = F.pad(image, padding=padding)
+
+ return padded_image
+
+ def _get_image_patches(
+ self,
+ image: "torch.Tensor",
+ grid_pinpoints,
+ size: tuple,
+ patch_size: int,
+ interpolation: "F.InterpolationMode",
+ ) -> list["torch.Tensor"]:
+ """
+ Process an image with variable resolutions by dividing it into patches.
+
+ Args:
+ image ("torch.Tensor"):
+ The input image to be processed.
+ grid_pinpoints (List):
+ A string representation of a list of possible resolutions.
+ size (`tuple`):
+ Size to resize the original image to.
+ patch_size (`int`):
+ Size of the patches to divide the image into.
+ interpolation (`"InterpolationMode"`):
+ Resampling filter to use if resizing the image.
+
+ Returns:
+ list["torch.Tensor"]: A list of NumPy arrays containing the processed image patches.
+ """
+ if not isinstance(grid_pinpoints, list):
+ raise TypeError("grid_pinpoints must be a list of possible resolutions.")
+
+ possible_resolutions = grid_pinpoints
+
+ image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST)
+ best_resolution = select_best_resolution(image_size, possible_resolutions)
+ resized_image = self._resize_for_patching(
+ image, best_resolution, interpolation=interpolation, input_data_format=ChannelDimension.FIRST
+ )
+ padded_image = self._pad_for_patching(resized_image, best_resolution, input_data_format=ChannelDimension.FIRST)
+ patches = divide_to_patches(padded_image, patch_size=patch_size)
+ resized_original_image = F.resize(image, size=size, interpolation=interpolation)
+
+ image_patches = [resized_original_image] + patches
+
+ return image_patches
+
+ def _pad_for_batching(
+ self,
+ pixel_values: list["torch.Tensor"],
+ ) -> list["torch.Tensor"]:
+ """
+ Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
+
+ Args:
+ pixel_values (`list[torch.Tensor]`):
+ An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`)
+
+ Returns:
+ list[`torch.Tensor`]: The padded images.
+ """
+ max_patch = max(len(x) for x in pixel_values)
+ pixel_values = [
+ torch.nn.functional.pad(image, pad=[0, 0, 0, 0, 0, 0, 0, max_patch - image.shape[0]])
+ for image in pixel_values
+ ]
+
+ return pixel_values
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ do_resize: bool,
+ size: SizeDict,
+ image_grid_pinpoints: list[list[int]],
+ interpolation: Optional["F.InterpolationMode"],
+ do_center_crop: bool,
+ crop_size: SizeDict,
+ do_rescale: bool,
+ rescale_factor: float,
+ do_normalize: bool,
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ do_pad: bool,
+ batch_num_images: list[int],
+ disable_grouping: Optional[bool],
+ return_tensors: Optional[Union[str, TensorType]],
+ **kwargs,
+ ) -> BatchFeature:
+ processed_images = []
+ image_sizes = []
+
+ # only single image patching is supported
+ need_patching = [n == 1 for n in batch_num_images for _ in range(n)]
+
+ # Determine the size tuple
+ if size and size.height and size.width:
+ size_tuple = (size.height, size.width)
+ else:
+ size_tuple = (size.shortest_edge, size.shortest_edge)
+
+ # Determine the patch size
+ if crop_size and crop_size.height:
+ patch_size = crop_size.height
+ elif size and size.height:
+ patch_size = size.height
+ else:
+ patch_size = size.shortest_edge
+
+ for i, image in enumerate(images):
+ if need_patching[i]:
+ image_patches = self._get_image_patches(
+ image,
+ image_grid_pinpoints,
+ size=size_tuple,
+ patch_size=patch_size,
+ interpolation=interpolation,
+ )
+ else:
+ padded_image = self.pad_to_square(
+ images=image, background_color=tuple(int(x * 255) for x in self.image_mean)
+ )
+ image_patches = [padded_image]
+
+ # Group images by size for batched processing
+ processed_image_patches_grouped = {}
+ grouped_image_patches, grouped_image_patches_index = group_images_by_shape(
+ image_patches, disable_grouping=disable_grouping
+ )
+ for shape, stacked_image_patches in grouped_image_patches.items():
+ if do_resize:
+ stacked_image_patches = self.resize(
+ image=stacked_image_patches,
+ size=size,
+ interpolation=interpolation,
+ )
+ if do_center_crop:
+ stacked_image_patches = self.center_crop(stacked_image_patches, crop_size)
+ # Fused rescale and normalize
+ stacked_image_patches = self.rescale_and_normalize(
+ stacked_image_patches, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ processed_image_patches_grouped[shape] = stacked_image_patches
+ processed_image_patches = reorder_images(processed_image_patches_grouped, grouped_image_patches_index)
+ processed_image_patches = (
+ torch.stack(processed_image_patches, dim=0) if return_tensors else processed_image_patches
+ )
+ processed_images.append(processed_image_patches)
+ image_sizes.append(get_image_size(image, ChannelDimension.FIRST))
+
+ if do_pad:
+ processed_images = self._pad_for_batching(processed_images)
+ processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+ return BatchFeature(
+ data={"pixel_values": processed_images, "image_sizes": image_sizes, "batch_num_images": batch_num_images},
+ tensor_type=return_tensors,
+ )
+
+ # Copied from transformers.models.llava.image_processing_llava_fast.LlavaImageProcessorFast.pad_to_square
+ def pad_to_square(
+ self,
+ images: "torch.Tensor",
+ background_color: Union[int, tuple[int, int, int]] = 0,
+ ) -> "torch.Tensor":
+ """
+ Pads an image to a square based on the longest edge.
+
+ Args:
+ images (`np.ndarray`):
+ The images to pad.
+ background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+ The color to use for the padding. Can be an integer for single channel or a
+ tuple of integers representing for multi-channel images. If passed as integer
+ in mutli-channel mode, it will default to `0` in subsequent channels.
+ Returns:
+ `torch.Tensor`: The padded images.
+ """
+ height, width = get_image_size(images, ChannelDimension.FIRST)
+
+ if height == width:
+ return images
+
+ num_channels = images.shape[1] if len(images.shape) == 4 else images.shape[0]
+ if isinstance(background_color, int):
+ background_color = [background_color] + [0] * (num_channels - 1)
+ elif len(background_color) != num_channels:
+ raise ValueError(
+ f"background_color must have no more than {num_channels} elements to match the number of channels"
+ )
+
+ max_dim = max(height, width)
+ paste_x_left = (max_dim - width) // 2
+ paste_y_left = (max_dim - height) // 2
+ paste_x_right = max_dim - width - paste_x_left
+ paste_y_right = max_dim - height - paste_y_left
+ padded_images = F.pad(
+ images, padding=[paste_x_left, paste_y_left, paste_x_right, paste_y_right], fill=background_color
+ )
+
+ return padded_images
+
+
+__all__ = ["LlavaOnevisionImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/modeling_llava_onevision.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/modeling_llava_onevision.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9086226a76aaad2999de6d8fe036ed7d6ec2940
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/modeling_llava_onevision.py
@@ -0,0 +1,1008 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/llava_onevision/modular_llava_onevision.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_llava_onevision.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import numpy as np
+import torch
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache
+from ...generation import GenerationMixin
+from ...image_processing_utils import select_best_resolution
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_outputs import BaseModelOutputWithPast, ModelOutput
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import (
+ TransformersKwargs,
+ auto_docstring,
+ can_return_tuple,
+ logging,
+)
+from ..auto import AutoModel
+from .configuration_llava_onevision import LlavaOnevisionConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Llava outputs, with hidden states and attentions.
+ """
+)
+class LlavaOnevisionModelOutputWithPast(BaseModelOutputWithPast):
+ r"""
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ video_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`.
+ video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+ video_hidden_states: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for LlavaOnevision causal language model (or autoregressive) outputs.
+ """
+)
+class LlavaOnevisionCausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ video_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size * num_frames, num_videos, sequence_length, hidden_size)`.
+ video_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+ video_hidden_states: Optional[torch.FloatTensor] = None
+
+
+class LlavaOnevisionPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ mode = config.spatial_pool_mode
+ stride = config.spatial_pool_stride
+ out_channels = getattr(config, "spatial_pool_out_channels", config.vision_config.hidden_size)
+ self.image_size = (config.vision_config.image_size // config.vision_config.patch_size) ** 2
+
+ if mode == "average":
+ self.pool = nn.AvgPool2d(kernel_size=stride, stride=stride)
+ elif mode == "max":
+ self.pool = nn.MaxPool2d(kernel_size=stride, stride=stride)
+ elif mode == "conv":
+ self.pool = nn.Conv2d(
+ in_channels=config.vision_config.hidden_size,
+ out_channels=out_channels,
+ kernel_size=stride,
+ stride=stride,
+ )
+ else:
+ raise ValueError(f"Unknown pooling mode: {mode}. Has to be one of [`average`, `max`, `conv`]")
+
+ def forward(self, image_features):
+ ori_width = int(math.sqrt(image_features.shape[1] * self.image_size // self.image_size))
+ ori_height = int(ori_width * self.image_size // self.image_size)
+
+ batch_size, _, dim = image_features.shape
+ image_features_spatial = image_features.view(batch_size, ori_height, ori_height, dim).permute(0, 3, 1, 2)
+ image_features_spatial_pool = self.pool(image_features_spatial)
+
+ return image_features_spatial_pool.flatten(2).transpose(1, 2).contiguous()
+
+
+class LlavaOnevisionMultiModalProjector(nn.Module):
+ def __init__(self, config: LlavaOnevisionConfig):
+ super().__init__()
+ # We have hidden_size * the number of vision feature layers
+ num_feature_layers = 1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer)
+ self.linear_1 = nn.Linear(
+ config.vision_config.hidden_size * num_feature_layers,
+ config.text_config.hidden_size,
+ bias=config.multimodal_projector_bias,
+ )
+ self.act = ACT2FN[config.projector_hidden_act]
+ self.linear_2 = nn.Linear(
+ config.text_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
+ )
+
+ def forward(self, image_features):
+ hidden_states = self.linear_1(image_features)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.linear_2(hidden_states)
+ return hidden_states
+
+
+def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
+ """
+ Calculate the shape of the image patch grid after the preprocessing for images of any resolution.
+
+ Args:
+ image_size (`tuple`):
+ The size of the input image in the format (width, height).
+ grid_pinpoints (`List`):
+ A list containing possible resolutions. Each item in the list should be a tuple or list
+ of the form `(height, width)`.
+ patch_size (`int`):
+ The size of each image patch.
+
+ Returns:
+ tuple: The shape of the image patch grid in the format (width, height).
+ """
+ if not isinstance(grid_pinpoints, list):
+ raise TypeError("grid_pinpoints should be a list of tuples or lists")
+
+ # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
+ if not isinstance(image_size, (list, tuple)):
+ if not isinstance(image_size, (torch.Tensor, np.ndarray)):
+ raise TypeError(
+ f"image_size invalid type: {type(image_size)} not valid, should be either list, tuple, np.ndarray or tensor"
+ )
+ image_size = image_size.tolist()
+
+ height, width = select_best_resolution(image_size, grid_pinpoints)
+ return height // patch_size, width // patch_size
+
+
+def image_size_to_num_patches(image_size, grid_pinpoints, patch_size: int):
+ """
+ Calculate the number of patches after the preprocessing for images of any resolution.
+
+ Args:
+ image_size (`torch.LongTensor` or `np.ndarray` or `tuple[int, int]`):
+ The size of the input image in the format (height, width). ?
+ grid_pinpoints (`List`):
+ A list containing possible resolutions. Each item in the list should be a tuple or list
+ of the form `(height, width)`.
+ patch_size (`int`):
+ The size of each image patch.
+
+ Returns:
+ int: the number of patches
+ """
+ if not isinstance(grid_pinpoints, list):
+ raise TypeError("grid_pinpoints should be a list of tuples or lists")
+
+ # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
+ if not isinstance(image_size, (list, tuple)):
+ if not isinstance(image_size, (torch.Tensor, np.ndarray)):
+ raise TypeError(f"image_size invalid type {type(image_size)} with value {image_size}")
+ image_size = image_size.tolist()
+
+ best_resolution = select_best_resolution(image_size, grid_pinpoints)
+ height, width = best_resolution
+ num_patches = 0
+ # consider change to ceil(height/patch_size)*ceil(width/patch_size) + 1
+ for i in range(0, height, patch_size):
+ for j in range(0, width, patch_size):
+ num_patches += 1
+ # add the base patch
+ num_patches += 1
+ return num_patches
+
+
+def unpad_image(tensor, original_size):
+ """
+ Unpads a PyTorch tensor of a padded and resized image.
+
+ Args:
+ tensor (`torch.Tensor`):
+ The image tensor, assumed to be of shape (num_channels, height, width).
+ original_size (`tuple`):
+ The original size of the image (height, width).
+
+ Returns:
+ `torch.Tensor`: The unpadded image tensor.
+ """
+ if not isinstance(original_size, (list, tuple)):
+ if not isinstance(original_size, (torch.Tensor, np.ndarray)):
+ raise TypeError(
+ f"image_size invalid type: {type(original_size)} not valid, should be either list, tuple, np.ndarray or tensor"
+ )
+ original_size = original_size.tolist()
+ original_height, original_width = original_size
+ current_height, current_width = tensor.shape[1:]
+
+ original_aspect_ratio = original_width / original_height
+ current_aspect_ratio = current_width / current_height
+
+ if original_aspect_ratio > current_aspect_ratio:
+ scale_factor = current_width / original_width
+ new_height = int(round(original_height * scale_factor, 7))
+ padding = (current_height - new_height) // 2
+ unpadded_tensor = tensor[:, padding : current_height - padding, :]
+ else:
+ scale_factor = current_height / original_height
+ new_width = int(round(original_width * scale_factor, 7))
+ padding = (current_width - new_width) // 2
+ unpadded_tensor = tensor[:, :, padding : current_width - padding]
+
+ return unpadded_tensor
+
+
+@auto_docstring
+class LlavaOnevisionPreTrainedModel(PreTrainedModel):
+ config: LlavaOnevisionConfig
+ base_model_prefix = ""
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["LlamaDecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+ _supports_flex_attn = True
+ _supports_attention_backend = True
+
+ def _init_weights(self, module):
+ std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range)
+
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, LlavaOnevisionModel):
+ embed_std = 1 / math.sqrt(self.config.text_config.hidden_size)
+ module.image_newline.data.normal_(mean=0.0, std=embed_std)
+
+
+@auto_docstring(
+ custom_intro="""
+ The Llava-Next model which consists of a vision backbone and a language model without language modeling head.
+ """
+)
+class LlavaOnevisionModel(LlavaOnevisionPreTrainedModel):
+ _checkpoint_conversion_mapping = {"language_model.model": "language_model"}
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.vision_tower = AutoModel.from_config(config.vision_config)
+
+ self.multi_modal_projector = LlavaOnevisionMultiModalProjector(config)
+ embed_std = 1 / math.sqrt(config.text_config.hidden_size)
+ self.image_newline = nn.Parameter(torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std)
+
+ self.vocab_size = config.text_config.vocab_size
+ self.language_model = AutoModel.from_config(config.text_config)
+ self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def set_decoder(self, decoder):
+ self.language_model = decoder
+
+ def get_decoder(self):
+ return self.language_model
+
+ def pack_image_features(self, image_features, image_sizes, image_newline=None, vision_aspect_ratio="anyres_max_9"):
+ """
+ Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors.
+
+ Args:
+ image_features (`list[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`)
+ List of image feature tensor, each contains all the visual feature of all patches.
+ image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
+ Actual image size of each images (H, W).
+ image_newline (`torch.Tensor` of shape `(embed_dim)`)
+ New line embedding vector.
+ vision_aspect_ratio (`str`, *optional*, "anyres_max_9"):
+ Aspect ratio used when processong image features. The default value is "anyres_max_9".
+ Returns:
+ image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`)
+ feature_lens (`list[int]`)
+ token length of each image in image_features
+ """
+ new_image_features = []
+ feature_lens = []
+ for image_idx, image_feature in enumerate(image_features):
+ if image_feature.shape[0] > 1:
+ base_image_feature = image_feature[0]
+ image_feature = image_feature[1:]
+ height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
+ if height * width != base_image_feature.shape[0]:
+ raise ValueError("The number of patches is not consistent with the image size.")
+ num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+ image_sizes[image_idx],
+ self.config.image_grid_pinpoints,
+ self.config.vision_config.image_size,
+ )
+ image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
+ image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
+ image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+ image_feature = unpad_image(image_feature, image_sizes[image_idx])
+ max_num_patches = int(vision_aspect_ratio.strip("anyres_max_"))
+ channels, curr_height, curr_width = image_feature.shape
+ ratio = math.sqrt(curr_height * curr_width / (max_num_patches * height**2))
+ if ratio > 1.1:
+ image_feature = image_feature[None]
+ image_feature = nn.functional.interpolate(
+ image_feature, [int(curr_height // ratio), int(curr_width // ratio)], mode="bilinear"
+ )[0]
+ if image_newline is not None:
+ image_feature = torch.cat(
+ (
+ image_feature,
+ image_newline[:, None, None]
+ .expand(*image_feature.shape[:-1], 1)
+ .to(image_feature.device, image_feature.dtype),
+ ),
+ dim=-1,
+ )
+ image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+ image_feature = torch.cat((base_image_feature, image_feature), dim=0)
+ else:
+ image_feature = image_feature[0]
+ if image_newline is not None:
+ image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0)
+ new_image_features.append(image_feature)
+ feature_lens.append(image_feature.size(0))
+ feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device)
+ return new_image_features, feature_lens
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ vision_aspect_ratio: Optional[str] = None,
+ batch_num_images: Optional[torch.LongTensor] = None,
+ ):
+ """
+ Obtains image last hidden states from the vision tower and apply multimodal projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`)
+ The tensors corresponding to the input images.
+ image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
+ Actual image size of each images (H, W).
+ vision_feature_layer (`Union[int, list[int]]`):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ batch_num_images (`torch.LongTensor`, *optional*):
+ Number of images in each sample.
+ Returns:
+ image_features (list[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches
+ and are of shape `(num_patches, image_length, embed_dim)`).
+ """
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+ vision_aspect_ratio = (
+ vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
+ )
+
+ # ! infer image_num_patches from image_sizes
+ if batch_num_images is None:
+ # treat this as a single-image case for backward compatibility
+ need_patching = [True] * len(image_sizes)
+ else:
+ need_patching = [n == 1 for n in batch_num_images for _ in range(n)]
+ image_num_patches = [
+ image_size_to_num_patches(
+ image_size=imsize,
+ grid_pinpoints=self.config.image_grid_pinpoints,
+ patch_size=self.config.vision_config.image_size,
+ )
+ if should_patch
+ else 1
+ for imsize, should_patch in zip(image_sizes, need_patching)
+ ]
+ if pixel_values.dim() == 5:
+ # stacked if input is (batch_size, num_patches, num_channels, height, width)
+ _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)]
+ pixel_values = torch.cat(_pixel_values_list, dim=0)
+ elif pixel_values.dim() != 4:
+ # otherwise has to be stacked from list of (num_patches, num_channels, height, width)
+ raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")
+
+ image_features = self.vision_tower(pixel_values, output_hidden_states=True)
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_image_feature = image_features.hidden_states[vision_feature_layer]
+ else:
+ hs_pool = [image_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ selected_image_feature = torch.cat(hs_pool, dim=-1)
+
+ if vision_feature_select_strategy == "default":
+ selected_image_feature = selected_image_feature[:, 1:]
+ elif vision_feature_select_strategy == "full":
+ selected_image_feature = selected_image_feature
+ image_features = self.multi_modal_projector(selected_image_feature)
+ image_features = torch.split(image_features, image_num_patches, dim=0)
+
+ image_features, feature_lens = self.pack_image_features(
+ image_features,
+ image_sizes,
+ image_newline=self.image_newline,
+ vision_aspect_ratio=vision_aspect_ratio,
+ )
+ return image_features
+
+ def get_placeholder_mask(
+ self,
+ input_ids: torch.LongTensor,
+ inputs_embeds: torch.FloatTensor,
+ image_features: torch.FloatTensor = None,
+ video_features: torch.FloatTensor = None,
+ ):
+ """
+ Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ special_video_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_video_mask = special_video_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+ special_video_mask = input_ids == self.config.video_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if image_features is not None and inputs_embeds[special_image_mask].numel() != image_features.numel():
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {image_features.shape[0]}"
+ )
+
+ n_video_tokens = special_video_mask.sum()
+ special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ if video_features is not None and inputs_embeds[special_video_mask].numel() != video_features.numel():
+ raise ValueError(
+ f"Videos features and image tokens do not match: tokens: {n_video_tokens}, features {video_features.shape[0]}"
+ )
+
+ return special_image_mask, special_video_mask
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ image_sizes: Optional[torch.LongTensor] = None,
+ pixel_values_videos: torch.FloatTensor = None,
+ image_sizes_videos: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ vision_aspect_ratio: Optional[str] = None,
+ batch_num_images: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, LlavaOnevisionModelOutputWithPast]:
+ r"""
+ image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*):
+ The sizes of the videos in the batch, being (height, width) for each frame in the video.
+ vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`):
+ Aspect ratio used when processong image features. The default value is "anyres_max_9".
+ batch_num_images (`torch.LongTensor`, *optional*):
+ Number of images in each sample.
+ """
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+ vision_aspect_ratio = (
+ vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
+ )
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ # Images are processed with Anyres
+ if pixel_values is not None:
+ image_features = self.get_image_features(
+ pixel_values,
+ image_sizes,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ batch_num_images=batch_num_images,
+ )
+ image_features = torch.cat(image_features, dim=0)
+ image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+ special_image_mask, _ = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+ # Video are simply embedded and further pooled to decrease seq len
+ if pixel_values_videos is not None:
+ video_features = self.get_video_features(
+ pixel_values_videos,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ )
+ image_newline = (
+ self.image_newline[None, None, :].repeat(video_features.shape[0], 1, 1).to(video_features.device)
+ )
+ video_features = torch.cat((video_features, image_newline), dim=1)
+ video_features = video_features.flatten(0, 1).to(inputs_embeds.device, inputs_embeds.dtype)
+ _, special_video_mask = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, video_features=video_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_video_mask, video_features)
+
+ outputs = self.language_model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return LlavaOnevisionModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=image_features if pixel_values is not None else None,
+ video_hidden_states=video_features if pixel_values_videos is not None else None,
+ )
+
+ def get_video_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ vision_feature_layer: Union[int, list[int]],
+ vision_feature_select_strategy: str,
+ ):
+ """
+ Obtains video last hidden states from the vision tower, apply multimodal projection and pooling.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`)
+ The tensors corresponding to the input video.
+ vision_feature_layer (`Union[int, list[int]], *optional*, defaults to -2`):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ Returns:
+ video_features (list[`torch.Tensor`]): List of video feature tensor, each contains all the visual feature of all patches
+ and are of shape `(num_videos, video_length, embed_dim)`).
+ """
+ batch_size, frames, channels, height, width = pixel_values.shape
+ pixel_values = pixel_values.view(batch_size * frames, channels, height, width)
+ video_features = self.vision_tower(pixel_values, output_hidden_states=True)
+
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_video_feature = video_features.hidden_states[vision_feature_layer]
+ else:
+ hs_pool = [video_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ selected_video_feature = torch.cat(hs_pool, dim=-1)
+
+ if vision_feature_select_strategy == "default":
+ selected_video_feature = selected_video_feature[:, 1:]
+ elif vision_feature_select_strategy == "full":
+ selected_video_feature = selected_video_feature
+ video_features = self.multi_modal_projector(selected_video_feature)
+
+ video_features = self.apply_pooling(video_features)
+ video_features = video_features.reshape(batch_size, frames * video_features.shape[1], -1)
+
+ return video_features
+
+ def apply_pooling(self, image_features):
+ height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
+ batch_frames, seq_len, dim = image_features.shape
+ image_features = image_features.view(batch_frames, height, width, -1)
+ image_features = image_features.permute(0, 3, 1, 2).contiguous()
+
+ height, width = image_features.shape[2:]
+ scaled_shape = [math.ceil(height / 2), math.ceil(width / 2)]
+ image_features = nn.functional.interpolate(image_features, size=scaled_shape, mode="bilinear")
+
+ image_features = image_features.permute(0, 2, 3, 1)
+ image_features = image_features.view(batch_frames, -1, dim)
+ return image_features
+
+
+@auto_docstring(
+ custom_intro="""
+ The LLAVA-NeXT model which consists of a vision backbone and a language model.
+ """
+)
+class LlavaOnevisionForConditionalGeneration(LlavaOnevisionPreTrainedModel, GenerationMixin):
+ _checkpoint_conversion_mapping = {
+ "^language_model.model": "model.language_model",
+ "^vision_tower": "model.vision_tower",
+ "^multi_modal_projector": "model.multi_modal_projector",
+ "^image_newline": "model.image_newline",
+ "^language_model.lm_head": "lm_head",
+ }
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: LlavaOnevisionConfig):
+ super().__init__(config)
+ self.model = LlavaOnevisionModel(config)
+ self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ def get_output_embeddings(self) -> nn.Module:
+ return self.lm_head
+
+ def set_decoder(self, decoder):
+ self.model.set_decoder(decoder)
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None):
+ return self.model.pack_image_features(
+ image_features=image_features,
+ image_sizes=image_sizes,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ image_newline=image_newline,
+ )
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ ):
+ return self.model.get_image_features(
+ pixel_values=pixel_values,
+ image_sizes=image_sizes,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ )
+
+ # Make modules available through conditional class for BC
+ @property
+ def language_model(self):
+ return self.model.language_model
+
+ @property
+ def vision_tower(self):
+ return self.model.vision_tower
+
+ @property
+ def multi_modal_projector(self):
+ return self.model.multi_modal_projector
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ image_sizes: Optional[torch.LongTensor] = None,
+ pixel_values_videos: torch.FloatTensor = None,
+ image_sizes_videos: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ vision_aspect_ratio: Optional[str] = None,
+ batch_num_images: Optional[torch.LongTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, LlavaOnevisionCausalLMOutputWithPast]:
+ r"""
+ image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*):
+ The sizes of the videos in the batch, being (height, width) for each frame in the video.
+ vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`):
+ Aspect ratio used when processong image features. The default value is "anyres_max_9".
+ batch_num_images (`torch.LongTensor`, *optional*):
+ Number of images in each sample.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> import torch
+ >>> from transformers import LlavaOnevisionProcessor, LlavaOnevisionForConditionalGeneration
+
+ >>> model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype="float16", device_map="cuda:0")
+ >>> processor = LlavaOnevisionProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")
+
+ >>> conversation = [
+ ... {
+ ... "role": "user",
+ ... "content": [
+ ... {"type": "text", "text": "What is shown in this image?"},
+ ... {"type": "image"},
+ ... ],
+ ... },
+ ... ]
+ >>> prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+
+ >>> image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> raw_image = Image.open(requests.get(image_file, stream=True).raw)
+ >>> inputs = processor(text=prompt, images=raw_image, return_tensors='pt').to(0, torch.float16)
+
+ >>> output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+ >>> processor.batch_decode(output, skip_special_tokens=True)[0]
+ "user\n\nWhat is shown in this image?\nassistant\ncat"
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+ vision_aspect_ratio = (
+ vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
+ )
+
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ pixel_values_videos=pixel_values_videos,
+ image_sizes=image_sizes,
+ image_sizes_videos=image_sizes_videos,
+ vision_aspect_ratio=vision_aspect_ratio,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ batch_num_images=batch_num_images,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return LlavaOnevisionCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ video_hidden_states=outputs.video_hidden_states,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ inputs_embeds=None,
+ pixel_values=None,
+ image_sizes=None,
+ pixel_values_videos=None,
+ image_sizes_videos=None,
+ attention_mask=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ if cache_position[0] == 0:
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ model_inputs["pixel_values"] = pixel_values
+ model_inputs["image_sizes"] = image_sizes
+ model_inputs["pixel_values_videos"] = pixel_values_videos
+ model_inputs["image_sizes_videos"] = image_sizes_videos
+
+ return model_inputs
+
+ @staticmethod
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+ def get_video_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ ):
+ return self.model.get_video_features(
+ pixel_values=pixel_values,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ )
+
+
+__all__ = ["LlavaOnevisionModel", "LlavaOnevisionForConditionalGeneration", "LlavaOnevisionPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/modular_llava_onevision.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/modular_llava_onevision.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef32f4b8625fcbc56961139c06e56f43a8222880
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/modular_llava_onevision.py
@@ -0,0 +1,760 @@
+# coding=utf-8
+# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import Optional, Union
+
+import torch
+from torch import nn
+
+from transformers.models.llava_next.image_processing_llava_next_fast import LlavaNextImageProcessorFast
+from transformers.models.llava_next_video.modeling_llava_next_video import (
+ LlavaNextVideoCausalLMOutputWithPast,
+ LlavaNextVideoForConditionalGeneration,
+ LlavaNextVideoModel,
+ LlavaNextVideoModelOutputWithPast,
+ LlavaNextVideoPreTrainedModel,
+ TransformersKwargs,
+ get_anyres_image_grid_shape,
+ image_size_to_num_patches,
+ unpad_image,
+)
+
+from ...cache_utils import Cache
+from ...image_processing_utils import BatchFeature
+from ...image_processing_utils_fast import DefaultFastImageProcessorKwargs, group_images_by_shape, reorder_images
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ SizeDict,
+ get_image_size,
+)
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ can_return_tuple,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+ logging,
+)
+
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+logger = logging.get_logger(__name__)
+
+
+class LlavaOnevisionFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ """
+ image_grid_pinpoints (`list[list[int]]`, *optional*):
+ A list of possible resolutions to use for processing high resolution images. The best resolution is selected
+ based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
+ method.
+ do_pad (`bool`, *optional*):
+ Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
+ number of patches in the batch. Padding will be applied to the bottom and right with zeros.
+ """
+
+ image_grid_pinpoints: Optional[list[list[int]]]
+ do_pad: Optional[bool]
+
+
+class LlavaOnevisionImageProcessorFast(LlavaNextImageProcessorFast):
+ resample = PILImageResampling.BICUBIC
+ image_mean = OPENAI_CLIP_MEAN
+ image_std = OPENAI_CLIP_STD
+ size = {"height": 384, "width": 384}
+ crop_size = None
+ default_to_square = False
+ do_resize = True
+ do_center_crop = None
+ do_rescale = True
+ do_normalize = True
+ do_convert_rgb = True
+ do_pad = True
+ image_grid_pinpoints = [[384, 384], [384, 768], [384, 1152], [384, 1536], [384, 1920], [384, 2304], [768, 384], [768, 768], [768, 1152], [768, 1536], [768, 1920], [768, 2304], [1152, 384], [1152, 768], [1152, 1152], [1152, 1536], [1152, 1920], [1152, 2304], [1536, 384], [1536, 768], [1536, 1152], [1536, 1536], [1536, 1920], [1536, 2304], [1920, 384], [1920, 768], [1920, 1152], [1920, 1536], [1920, 1920], [1920, 2304], [2304, 384], [2304, 768], [2304, 1152], [2304, 1536], [2304, 1920], [2304, 2304]] # fmt: skip
+ model_input_names = ["pixel_values_videos"]
+
+ # Copied from transformers.models.llava.image_processing_llava_fast.LlavaImageProcessorFast.pad_to_square
+ def pad_to_square(
+ self,
+ images: "torch.Tensor",
+ background_color: Union[int, tuple[int, int, int]] = 0,
+ ) -> "torch.Tensor":
+ """
+ Pads an image to a square based on the longest edge.
+
+ Args:
+ images (`np.ndarray`):
+ The images to pad.
+ background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+ The color to use for the padding. Can be an integer for single channel or a
+ tuple of integers representing for multi-channel images. If passed as integer
+ in mutli-channel mode, it will default to `0` in subsequent channels.
+ Returns:
+ `torch.Tensor`: The padded images.
+ """
+ height, width = get_image_size(images, ChannelDimension.FIRST)
+
+ if height == width:
+ return images
+
+ num_channels = images.shape[1] if len(images.shape) == 4 else images.shape[0]
+ if isinstance(background_color, int):
+ background_color = [background_color] + [0] * (num_channels - 1)
+ elif len(background_color) != num_channels:
+ raise ValueError(
+ f"background_color must have no more than {num_channels} elements to match the number of channels"
+ )
+
+ max_dim = max(height, width)
+ paste_x_left = (max_dim - width) // 2
+ paste_y_left = (max_dim - height) // 2
+ paste_x_right = max_dim - width - paste_x_left
+ paste_y_right = max_dim - height - paste_y_left
+ padded_images = F.pad(
+ images, padding=[paste_x_left, paste_y_left, paste_x_right, paste_y_right], fill=background_color
+ )
+
+ return padded_images
+
+ @auto_docstring
+ def preprocess(self, images: ImageInput, **kwargs: Unpack[LlavaOnevisionFastImageProcessorKwargs]) -> BatchFeature:
+ if isinstance(images, (tuple, list)) and isinstance(images[0], (tuple, list)):
+ # if the first element is a list, we assume that all elements are lists
+ batch_num_images = [len(x) for x in images]
+ elif isinstance(images, (tuple, list)):
+ # treat this as a single-image case for backward compatibility
+ batch_num_images = [1] * len(images)
+ else:
+ batch_num_images = [1]
+ kwargs["batch_num_images"] = batch_num_images
+ return super().preprocess(images, **kwargs)
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ do_resize: bool,
+ size: SizeDict,
+ image_grid_pinpoints: list[list[int]],
+ interpolation: Optional["F.InterpolationMode"],
+ do_center_crop: bool,
+ crop_size: SizeDict,
+ do_rescale: bool,
+ rescale_factor: float,
+ do_normalize: bool,
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ do_pad: bool,
+ batch_num_images: list[int],
+ disable_grouping: Optional[bool],
+ return_tensors: Optional[Union[str, TensorType]],
+ **kwargs,
+ ) -> BatchFeature:
+ processed_images = []
+ image_sizes = []
+
+ # only single image patching is supported
+ need_patching = [n == 1 for n in batch_num_images for _ in range(n)]
+
+ # Determine the size tuple
+ if size and size.height and size.width:
+ size_tuple = (size.height, size.width)
+ else:
+ size_tuple = (size.shortest_edge, size.shortest_edge)
+
+ # Determine the patch size
+ if crop_size and crop_size.height:
+ patch_size = crop_size.height
+ elif size and size.height:
+ patch_size = size.height
+ else:
+ patch_size = size.shortest_edge
+
+ for i, image in enumerate(images):
+ if need_patching[i]:
+ image_patches = self._get_image_patches(
+ image,
+ image_grid_pinpoints,
+ size=size_tuple,
+ patch_size=patch_size,
+ interpolation=interpolation,
+ )
+ else:
+ padded_image = self.pad_to_square(
+ images=image, background_color=tuple(int(x * 255) for x in self.image_mean)
+ )
+ image_patches = [padded_image]
+
+ # Group images by size for batched processing
+ processed_image_patches_grouped = {}
+ grouped_image_patches, grouped_image_patches_index = group_images_by_shape(
+ image_patches, disable_grouping=disable_grouping
+ )
+ for shape, stacked_image_patches in grouped_image_patches.items():
+ if do_resize:
+ stacked_image_patches = self.resize(
+ image=stacked_image_patches,
+ size=size,
+ interpolation=interpolation,
+ )
+ if do_center_crop:
+ stacked_image_patches = self.center_crop(stacked_image_patches, crop_size)
+ # Fused rescale and normalize
+ stacked_image_patches = self.rescale_and_normalize(
+ stacked_image_patches, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ processed_image_patches_grouped[shape] = stacked_image_patches
+ processed_image_patches = reorder_images(processed_image_patches_grouped, grouped_image_patches_index)
+ processed_image_patches = (
+ torch.stack(processed_image_patches, dim=0) if return_tensors else processed_image_patches
+ )
+ processed_images.append(processed_image_patches)
+ image_sizes.append(get_image_size(image, ChannelDimension.FIRST))
+
+ if do_pad:
+ processed_images = self._pad_for_batching(processed_images)
+ processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+ return BatchFeature(
+ data={"pixel_values": processed_images, "image_sizes": image_sizes, "batch_num_images": batch_num_images},
+ tensor_type=return_tensors,
+ )
+
+
+class LlavaOnevisionModelOutputWithPast(LlavaNextVideoModelOutputWithPast):
+ pass
+
+
+class LlavaOnevisionCausalLMOutputWithPast(LlavaNextVideoCausalLMOutputWithPast):
+ pass
+
+
+class LlavaOnevisionPreTrainedModel(LlavaNextVideoPreTrainedModel):
+ pass
+
+
+class LlavaOnevisionModel(LlavaNextVideoModel):
+ def __init__(self, config):
+ super().__init__(config)
+ del self.vision_resampler
+
+ def pack_image_features(self, image_features, image_sizes, image_newline=None, vision_aspect_ratio="anyres_max_9"):
+ """
+ Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors.
+
+ Args:
+ image_features (`list[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`)
+ List of image feature tensor, each contains all the visual feature of all patches.
+ image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
+ Actual image size of each images (H, W).
+ image_newline (`torch.Tensor` of shape `(embed_dim)`)
+ New line embedding vector.
+ vision_aspect_ratio (`str`, *optional*, "anyres_max_9"):
+ Aspect ratio used when processong image features. The default value is "anyres_max_9".
+ Returns:
+ image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`)
+ feature_lens (`list[int]`)
+ token length of each image in image_features
+ """
+ new_image_features = []
+ feature_lens = []
+ for image_idx, image_feature in enumerate(image_features):
+ if image_feature.shape[0] > 1:
+ base_image_feature = image_feature[0]
+ image_feature = image_feature[1:]
+ height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
+ if height * width != base_image_feature.shape[0]:
+ raise ValueError("The number of patches is not consistent with the image size.")
+ num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+ image_sizes[image_idx],
+ self.config.image_grid_pinpoints,
+ self.config.vision_config.image_size,
+ )
+ image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
+ image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
+ image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+ image_feature = unpad_image(image_feature, image_sizes[image_idx])
+ max_num_patches = int(vision_aspect_ratio.strip("anyres_max_"))
+ channels, curr_height, curr_width = image_feature.shape
+ ratio = math.sqrt(curr_height * curr_width / (max_num_patches * height**2))
+ if ratio > 1.1:
+ image_feature = image_feature[None]
+ image_feature = nn.functional.interpolate(
+ image_feature, [int(curr_height // ratio), int(curr_width // ratio)], mode="bilinear"
+ )[0]
+ if image_newline is not None:
+ image_feature = torch.cat(
+ (
+ image_feature,
+ image_newline[:, None, None]
+ .expand(*image_feature.shape[:-1], 1)
+ .to(image_feature.device, image_feature.dtype),
+ ),
+ dim=-1,
+ )
+ image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+ image_feature = torch.cat((base_image_feature, image_feature), dim=0)
+ else:
+ image_feature = image_feature[0]
+ if image_newline is not None:
+ image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0)
+ new_image_features.append(image_feature)
+ feature_lens.append(image_feature.size(0))
+ feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device)
+ return new_image_features, feature_lens
+
+ def apply_pooling(self, image_features):
+ height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
+ batch_frames, seq_len, dim = image_features.shape
+ image_features = image_features.view(batch_frames, height, width, -1)
+ image_features = image_features.permute(0, 3, 1, 2).contiguous()
+
+ height, width = image_features.shape[2:]
+ scaled_shape = [math.ceil(height / 2), math.ceil(width / 2)]
+ image_features = nn.functional.interpolate(image_features, size=scaled_shape, mode="bilinear")
+
+ image_features = image_features.permute(0, 2, 3, 1)
+ image_features = image_features.view(batch_frames, -1, dim)
+ return image_features
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ vision_aspect_ratio: Optional[str] = None,
+ batch_num_images: Optional[torch.LongTensor] = None,
+ ):
+ """
+ Obtains image last hidden states from the vision tower and apply multimodal projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`)
+ The tensors corresponding to the input images.
+ image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
+ Actual image size of each images (H, W).
+ vision_feature_layer (`Union[int, list[int]]`):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ batch_num_images (`torch.LongTensor`, *optional*):
+ Number of images in each sample.
+ Returns:
+ image_features (list[`torch.Tensor`]): List of image feature tensor, each contains all the visual feature of all patches
+ and are of shape `(num_patches, image_length, embed_dim)`).
+ """
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+ vision_aspect_ratio = (
+ vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
+ )
+
+ # ! infer image_num_patches from image_sizes
+ if batch_num_images is None:
+ # treat this as a single-image case for backward compatibility
+ need_patching = [True] * len(image_sizes)
+ else:
+ need_patching = [n == 1 for n in batch_num_images for _ in range(n)]
+ image_num_patches = [
+ image_size_to_num_patches(
+ image_size=imsize,
+ grid_pinpoints=self.config.image_grid_pinpoints,
+ patch_size=self.config.vision_config.image_size,
+ )
+ if should_patch
+ else 1
+ for imsize, should_patch in zip(image_sizes, need_patching)
+ ]
+ if pixel_values.dim() == 5:
+ # stacked if input is (batch_size, num_patches, num_channels, height, width)
+ _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)]
+ pixel_values = torch.cat(_pixel_values_list, dim=0)
+ elif pixel_values.dim() != 4:
+ # otherwise has to be stacked from list of (num_patches, num_channels, height, width)
+ raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")
+
+ image_features = self.vision_tower(pixel_values, output_hidden_states=True)
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_image_feature = image_features.hidden_states[vision_feature_layer]
+ else:
+ hs_pool = [image_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ selected_image_feature = torch.cat(hs_pool, dim=-1)
+
+ if vision_feature_select_strategy == "default":
+ selected_image_feature = selected_image_feature[:, 1:]
+ elif vision_feature_select_strategy == "full":
+ selected_image_feature = selected_image_feature
+ image_features = self.multi_modal_projector(selected_image_feature)
+ image_features = torch.split(image_features, image_num_patches, dim=0)
+
+ image_features, feature_lens = self.pack_image_features(
+ image_features,
+ image_sizes,
+ image_newline=self.image_newline,
+ vision_aspect_ratio=vision_aspect_ratio,
+ )
+ return image_features
+
+ def get_video_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ vision_feature_layer: Union[int, list[int]],
+ vision_feature_select_strategy: str,
+ ):
+ """
+ Obtains video last hidden states from the vision tower, apply multimodal projection and pooling.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`)
+ The tensors corresponding to the input video.
+ vision_feature_layer (`Union[int, list[int]], *optional*, defaults to -2`):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ Returns:
+ video_features (list[`torch.Tensor`]): List of video feature tensor, each contains all the visual feature of all patches
+ and are of shape `(num_videos, video_length, embed_dim)`).
+ """
+ batch_size, frames, channels, height, width = pixel_values.shape
+ pixel_values = pixel_values.view(batch_size * frames, channels, height, width)
+ video_features = self.vision_tower(pixel_values, output_hidden_states=True)
+
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_video_feature = video_features.hidden_states[vision_feature_layer]
+ else:
+ hs_pool = [video_features.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ selected_video_feature = torch.cat(hs_pool, dim=-1)
+
+ if vision_feature_select_strategy == "default":
+ selected_video_feature = selected_video_feature[:, 1:]
+ elif vision_feature_select_strategy == "full":
+ selected_video_feature = selected_video_feature
+ video_features = self.multi_modal_projector(selected_video_feature)
+
+ video_features = self.apply_pooling(video_features)
+ video_features = video_features.reshape(batch_size, frames * video_features.shape[1], -1)
+
+ return video_features
+
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ image_sizes: Optional[torch.LongTensor] = None,
+ pixel_values_videos: torch.FloatTensor = None,
+ image_sizes_videos: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ vision_aspect_ratio: Optional[str] = None,
+ batch_num_images: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, LlavaOnevisionModelOutputWithPast]:
+ r"""
+ image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*):
+ The sizes of the videos in the batch, being (height, width) for each frame in the video.
+ vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`):
+ Aspect ratio used when processong image features. The default value is "anyres_max_9".
+ batch_num_images (`torch.LongTensor`, *optional*):
+ Number of images in each sample.
+ """
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+ vision_aspect_ratio = (
+ vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
+ )
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ # Images are processed with Anyres
+ if pixel_values is not None:
+ image_features = self.get_image_features(
+ pixel_values,
+ image_sizes,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ batch_num_images=batch_num_images,
+ )
+ image_features = torch.cat(image_features, dim=0)
+ image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+ special_image_mask, _ = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+ # Video are simply embedded and further pooled to decrease seq len
+ if pixel_values_videos is not None:
+ video_features = self.get_video_features(
+ pixel_values_videos,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ )
+ image_newline = (
+ self.image_newline[None, None, :].repeat(video_features.shape[0], 1, 1).to(video_features.device)
+ )
+ video_features = torch.cat((video_features, image_newline), dim=1)
+ video_features = video_features.flatten(0, 1).to(inputs_embeds.device, inputs_embeds.dtype)
+ _, special_video_mask = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, video_features=video_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_video_mask, video_features)
+
+ outputs = self.language_model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return LlavaOnevisionModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=image_features if pixel_values is not None else None,
+ video_hidden_states=video_features if pixel_values_videos is not None else None,
+ )
+
+
+class LlavaOnevisionForConditionalGeneration(LlavaNextVideoForConditionalGeneration):
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ image_sizes: Optional[torch.LongTensor] = None,
+ pixel_values_videos: torch.FloatTensor = None,
+ image_sizes_videos: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ vision_feature_select_strategy: Optional[str] = None,
+ vision_aspect_ratio: Optional[str] = None,
+ batch_num_images: Optional[torch.LongTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, LlavaOnevisionCausalLMOutputWithPast]:
+ r"""
+ image_sizes_videos (`torch.LongTensor` of shape `(batch_size, frames, 2)`, *optional*):
+ The sizes of the videos in the batch, being (height, width) for each frame in the video.
+ vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`):
+ Aspect ratio used when processong image features. The default value is "anyres_max_9".
+ batch_num_images (`torch.LongTensor`, *optional*):
+ Number of images in each sample.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> import torch
+ >>> from transformers import LlavaOnevisionProcessor, LlavaOnevisionForConditionalGeneration
+
+ >>> model = LlavaOnevisionForConditionalGeneration.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf", torch_dtype="float16", device_map="cuda:0")
+ >>> processor = LlavaOnevisionProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")
+
+ >>> conversation = [
+ ... {
+ ... "role": "user",
+ ... "content": [
+ ... {"type": "text", "text": "What is shown in this image?"},
+ ... {"type": "image"},
+ ... ],
+ ... },
+ ... ]
+ >>> prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+
+ >>> image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> raw_image = Image.open(requests.get(image_file, stream=True).raw)
+ >>> inputs = processor(text=prompt, images=raw_image, return_tensors='pt').to(0, torch.float16)
+
+ >>> output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+ >>> processor.batch_decode(output, skip_special_tokens=True)[0]
+ "user\n\nWhat is shown in this image?\nassistant\ncat"
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+ vision_feature_select_strategy = (
+ vision_feature_select_strategy
+ if vision_feature_select_strategy is not None
+ else self.config.vision_feature_select_strategy
+ )
+ vision_aspect_ratio = (
+ vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
+ )
+
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ pixel_values_videos=pixel_values_videos,
+ image_sizes=image_sizes,
+ image_sizes_videos=image_sizes_videos,
+ vision_aspect_ratio=vision_aspect_ratio,
+ vision_feature_layer=vision_feature_layer,
+ vision_feature_select_strategy=vision_feature_select_strategy,
+ batch_num_images=batch_num_images,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return LlavaOnevisionCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ video_hidden_states=outputs.video_hidden_states,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ inputs_embeds=None,
+ pixel_values=None,
+ image_sizes=None,
+ pixel_values_videos=None,
+ image_sizes_videos=None,
+ attention_mask=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ if cache_position[0] == 0:
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ model_inputs["pixel_values"] = pixel_values
+ model_inputs["image_sizes"] = image_sizes
+ model_inputs["pixel_values_videos"] = pixel_values_videos
+ model_inputs["image_sizes_videos"] = image_sizes_videos
+
+ return model_inputs
+
+
+__all__ = [
+ "LlavaOnevisionImageProcessorFast",
+ "LlavaOnevisionModel",
+ "LlavaOnevisionForConditionalGeneration",
+ "LlavaOnevisionPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/processing_llava_onevision.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/processing_llava_onevision.py
new file mode 100644
index 0000000000000000000000000000000000000000..7deadb9131b19a1460dc8506bfe81460b932a685
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/processing_llava_onevision.py
@@ -0,0 +1,356 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for LLaVa-Onevision.
+"""
+
+import math
+from collections.abc import Iterable
+from typing import Union
+
+import numpy as np
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_processing_utils import select_best_resolution
+from ...image_utils import ImageInput, get_image_size, to_numpy_array
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import logging
+from ...video_utils import VideoInput
+
+
+logger = logging.get_logger(__name__)
+
+
+class LlavaOnevisionProcessorKwargs(ProcessingKwargs, total=False):
+ # see processing_utils.ProcessingKwargs documentation for usage.
+ _defaults = {
+ "text_kwargs": {
+ "padding": False,
+ "return_mm_token_type_ids": False,
+ },
+ "image_kwargs": {},
+ "videos_kwargs": {},
+ }
+
+
+class LlavaOnevisionProcessor(ProcessorMixin):
+ r"""
+ Constructs a LLaVa-Onevision processor which wraps a LLaVa-Onevision video processor, LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor.
+
+ [`LlavaNextProcessor`] offers all the functionalities of [`LlavaOnevisionVideoProcessor`], [`LlavaOnevisionImageProcessor`] and [`LlamaTokenizerFast`]. See the
+ [`~LlavaOnevisionVideoProcessor.__call__`], [`~LlavaNextProcessor.__call__`] and [`~LlavaNextProcessor.decode`] for more information.
+
+ Args:
+ image_processor ([`LlavaOnevisionImageProcessor`], *optional*):
+ The image processor is a required input.
+ tokenizer ([`LlamaTokenizerFast`], *optional*):
+ The tokenizer is a required input.
+ video_processor ([`LlavaOnevisionVideoProcessor`], *optional*):
+ The video processor is a required input.
+ num_image_tokens (`int`, *optional*):
+ Number of image tokens for one imagethat will be returned by vision tower.
+ vision_feature_select_strategy (`str`, *optional*):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Should be same as in model's config
+ chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+ in a chat into a tokenizable string.
+ image_token (`str`, *optional*, defaults to `""`):
+ Special token used to denote image location.
+ video_token (`str`, *optional*, defaults to `""`):
+ Special token used to denote video location.
+ vision_aspect_ratio (`str`, *optional*, defaults to `"anyres_max_9"`):
+ Aspect ratio used when processong image features. The default value is "anyres_max_9".
+ """
+
+ attributes = ["image_processor", "tokenizer", "video_processor"]
+ image_processor_class = "AutoImageProcessor"
+ tokenizer_class = "AutoTokenizer"
+ video_processor_class = "AutoVideoProcessor"
+
+ def __init__(
+ self,
+ image_processor=None,
+ tokenizer=None,
+ video_processor=None,
+ num_image_tokens=None,
+ vision_feature_select_strategy=None,
+ chat_template=None,
+ image_token="",
+ video_token="",
+ vision_aspect_ratio="anyres_max_9",
+ **kwargs,
+ ):
+ self.num_image_tokens = num_image_tokens
+ self.vision_feature_select_strategy = vision_feature_select_strategy
+ self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
+ self.video_token = tokenizer.video_token if hasattr(tokenizer, "video_token") else video_token
+ self.image_token_id = (
+ tokenizer.image_token_id
+ if getattr(tokenizer, "image_token_id", None)
+ else tokenizer.convert_tokens_to_ids(self.image_token)
+ )
+ self.video_token_id = (
+ tokenizer.video_token_id
+ if getattr(tokenizer, "video_token_id", None)
+ else tokenizer.convert_tokens_to_ids(self.video_token)
+ )
+ self.vision_aspect_ratio = vision_aspect_ratio
+ super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template)
+
+ def __call__(
+ self,
+ images: ImageInput = None,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+ audio=None,
+ videos: VideoInput = None,
+ **kwargs: Unpack[LlavaOnevisionProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+ and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
+ the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+ LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
+ of the above two methods for more information.
+
+ Args:
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+ videos (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+ `None`).
+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+ - **pixel_values_videos** -- Pixel values of a video input to be fed to a model. Returned when `videos` is not `None`.
+ - **image_sizes** -- Size of each image that will be used to unpad an image. Returned when `images` is not `None`.
+ """
+
+ output_kwargs = self._merge_kwargs(
+ LlavaOnevisionProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+
+ if isinstance(text, str):
+ text = [text]
+ elif not isinstance(text, list) and not isinstance(text[0], str):
+ raise TypeError("Invalid input text. Please provide a string, or a list of strings")
+
+ image_inputs = video_inputs = {}
+
+ if images is not None:
+ image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+
+ batch_num_images = iter(image_inputs["batch_num_images"])
+ image_sizes = iter(image_inputs["image_sizes"])
+ height, width = get_image_size(
+ to_numpy_array(image_inputs["pixel_values"][0][0]),
+ channel_dim=output_kwargs["images_kwargs"].get("data_format"),
+ )
+ text, num_image_tokens = self._expand_image_tokens(
+ text, image_sizes, height, width, self.image_token, batch_num_images
+ )
+
+ if videos is not None:
+ video_inputs = self.video_processor(videos, **output_kwargs["videos_kwargs"])
+
+ one_video = video_inputs.get("pixel_values_videos")[0]
+ if isinstance(video_inputs.get("pixel_values_videos")[0], (list, tuple)):
+ one_video = np.array(one_video)
+ else:
+ one_video = to_numpy_array(one_video)
+ height, width = get_image_size(one_video[0], channel_dim=output_kwargs["images_kwargs"].get("data_format"))
+ num_frames = one_video.shape[0] # frame dim is always after batch dim
+ patches_height_width = int(math.sqrt(self.num_image_tokens))
+ pooled_height_width = math.ceil(patches_height_width / 2)
+ num_video_tokens = (num_frames * pooled_height_width * pooled_height_width) + 1 # +1 for newline token
+ text = [sample.replace(self.video_token, self.video_token * num_video_tokens) for sample in text]
+
+ return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+ return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
+ text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+ self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
+
+ if return_mm_token_type_ids:
+ array_ids = np.array(text_inputs["input_ids"])
+ mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+ mm_token_type_ids[array_ids == self.image_token_id] = 1
+ text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
+ return BatchFeature(data={**text_inputs, **image_inputs, **video_inputs}, tensor_type=return_tensors)
+
+ def _expand_image_tokens(
+ self,
+ text: list[TextInput],
+ image_sizes: Iterable[Union[list[int], int]],
+ height: int,
+ width: int,
+ special_token: str,
+ batch_num_images: Iterable[int],
+ ):
+ prompt_strings = []
+ max_num_vision_tokens = 0
+ for sample in text:
+ if special_token in sample:
+ num_images = next(batch_num_images) # should consume iterable
+ is_multi_image = num_images != 1
+ else:
+ is_multi_image = False
+ while special_token in sample:
+ original_size = next(image_sizes) # should consume iterable
+ if is_multi_image:
+ num_image_tokens = self.num_image_tokens + 1 # one for image_newline
+ else:
+ if not isinstance(original_size, (list, tuple)):
+ # cast to list to avoid numerical precision errors when calculating unpadding
+ original_size = original_size.tolist()
+ orig_height, orig_width = original_size
+ num_image_tokens = self._get_number_of_features(orig_height, orig_width, height, width)
+ max_num_vision_tokens = max(max_num_vision_tokens, num_image_tokens)
+ if self.vision_feature_select_strategy == "default":
+ num_image_tokens -= 1
+ sample = sample.replace(special_token, "" * num_image_tokens, 1)
+ prompt_strings.append(sample)
+ text = [sample.replace("", special_token) for sample in prompt_strings]
+ return text, max_num_vision_tokens
+
+ def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
+ image_grid_pinpoints = self.image_processor.image_grid_pinpoints
+
+ height_best_resolution, width_best_resolution = select_best_resolution(
+ [orig_height, orig_width], image_grid_pinpoints
+ )
+ scale_height, scale_width = height_best_resolution // height, width_best_resolution // width
+
+ patches_height = patches_width = int(math.sqrt(self.num_image_tokens))
+ unpadded_features, newline_features = self._get_unpadded_features(
+ orig_height, orig_width, patches_height, patches_width, scale_height, scale_width
+ )
+
+ # The base patch covers the entire image (no CLS for SigLIP)
+ base_features = self.num_image_tokens
+ num_image_tokens = unpadded_features + newline_features + base_features
+ return num_image_tokens
+
+ # Adapted from transformers.models.llava_next.processing_llava_next.LlavaNextProcessor._get_unpadded_features
+ def _get_unpadded_features(self, height, width, patches_height, patches_width, scale_height, scale_width):
+ """
+ Get number of features for a given image with height/width. LLaVA-NeXT is different from LLaVA
+ because it divided each image into patches depending on its resolution. Therefore we need to calculate how many
+ patches an image is divided into and get the number of features from that.
+ """
+ current_height = patches_height * scale_height
+ current_width = patches_width * scale_width
+
+ original_aspect_ratio = width / height
+ current_aspect_ratio = current_width / current_height
+ if original_aspect_ratio > current_aspect_ratio:
+ new_height = int(round(height * (current_width / width), 7))
+ padding = (current_height - new_height) // 2
+ current_height -= padding * 2
+ else:
+ new_width = int(round(width * (current_height / height), 7))
+ padding = (current_width - new_width) // 2
+ current_width -= padding * 2
+
+ unpadded_features = current_height * current_width
+ newline_features = current_height
+
+ max_num_patches = int(self.vision_aspect_ratio.strip("anyres_max_"))
+ ratio = math.sqrt(current_height * current_width / (max_num_patches * patches_height**2))
+ if ratio > 1.1:
+ unpadded_features = int(current_height // ratio) * int(current_width // ratio)
+ newline_features = int(current_height // ratio)
+
+ return (unpadded_features, newline_features)
+
+ def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
+ """
+ Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+ Args:
+ image_sizes (list[list[str]], *optional*):
+ The input sizes formatted as (height, width) per each image.
+ video_sizes (list[list[str]], *optional*):
+ The input sizes formatted as (num_frames, height, width) per each video.
+ audio_lengths (list[int], *optional*):
+ The input length formatted as per each audio.
+ Returns:
+ dict[str, list[int]]: A dictionary mapping each modality ("image", "video", "audio")
+ to a list containing the number of placeholder tokens required. If the model doesn't accept
+ a certain modality or no input sizes are provided, the dict value is set to an empty list.
+ """
+ vision_data = {}
+ if image_sizes is not None:
+ images_kwargs = LlavaOnevisionProcessorKwargs._defaults.get("images_kwargs", {})
+ images_kwargs.update(kwargs)
+
+ size = images_kwargs.get("size", None) or self.image_processor.size
+ size = (
+ (size["shortest_edge"], size["shortest_edge"])
+ if "shortest_edge" in size
+ else (min(size["height"], size["width"]), min(size["height"], size["width"]))
+ )
+ processed_height, processed_width = size
+
+ batch_num_image_tokens = []
+ num_image_patches = [1] * len(image_sizes) # llava-ov doesn't batch pixels as Idefics, thus `1` patch`
+ for image_size in image_sizes:
+ orig_height, orig_width = image_size
+ num_image_tokens = self._get_number_of_features(
+ orig_height, orig_width, processed_height, processed_width
+ )
+ if self.vision_feature_select_strategy == "default":
+ num_image_tokens -= 1
+ batch_num_image_tokens.append(num_image_tokens)
+ vision_data.update({"num_image_tokens": batch_num_image_tokens, "num_image_patches": num_image_patches})
+
+ return MultiModalData(**vision_data)
+
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+__all__ = ["LlavaOnevisionProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/video_processing_llava_onevision.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/video_processing_llava_onevision.py
new file mode 100644
index 0000000000000000000000000000000000000000..3972f424a94fbb220b0f2e0f7c0f75c120cf661b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/llava_onevision/video_processing_llava_onevision.py
@@ -0,0 +1,58 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Video processor class for LLaVa-Onevision."""
+
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+)
+from ...processing_utils import Unpack, VideosKwargs
+from ...utils import is_vision_available
+from ...utils.import_utils import requires
+from ...video_processing_utils import (
+ BaseVideoProcessor,
+)
+
+
+if is_vision_available():
+ from ...image_utils import PILImageResampling
+
+
+class LlavaOnevisionFastVideoProcessorInitKwargs(VideosKwargs): ...
+
+
+@requires(backends=("torchvision",))
+class LlavaOnevisionVideoProcessor(BaseVideoProcessor):
+ resample = PILImageResampling.BICUBIC
+ image_mean = OPENAI_CLIP_MEAN
+ image_std = OPENAI_CLIP_STD
+ size = {"height": 384, "width": 384}
+ rescale_factor = 1 / 255
+ default_to_square = False
+ crop_size = None
+ do_resize = True
+ do_center_crop = None
+ do_rescale = True
+ do_normalize = True
+ do_convert_rgb = True
+ do_sample_frames = False # Set to False for BC, recommended to set `True` in new models
+ valid_kwargs = LlavaOnevisionFastVideoProcessorInitKwargs
+ model_input_names = ["pixel_values_videos"]
+
+ def __init__(self, **kwargs: Unpack[LlavaOnevisionFastVideoProcessorInitKwargs]):
+ super().__init__(**kwargs)
+
+
+__all__ = ["LlavaOnevisionVideoProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..87f53105424b76e5c18bd740ecfdd37a5b29d0d4
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_longformer import *
+ from .modeling_longformer import *
+ from .modeling_tf_longformer import *
+ from .tokenization_longformer import *
+ from .tokenization_longformer_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/configuration_longformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/configuration_longformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..207cc18394796352a0d42bf28082baf04d11ff8e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/configuration_longformer.py
@@ -0,0 +1,207 @@
+# coding=utf-8
+# Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Longformer configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
+from ...utils import TensorType, logging
+
+
+if TYPE_CHECKING:
+ from ...onnx.config import PatchingSpec
+ from ...tokenization_utils_base import PreTrainedTokenizerBase
+
+
+logger = logging.get_logger(__name__)
+
+
+class LongformerConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LongformerModel`] or a [`TFLongformerModel`]. It
+ is used to instantiate a Longformer model according to the specified arguments, defining the model architecture.
+
+ This is the configuration class to store the configuration of a [`LongformerModel`]. It is used to instantiate an
+ Longformer model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the LongFormer
+ [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) architecture with a sequence
+ length 4,096.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 30522):
+ Vocabulary size of the Longformer model. Defines the number of different tokens that can be represented by
+ the `inputs_ids` passed when calling [`LongformerModel`] or [`TFLongformerModel`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimensionality of the encoder layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 2):
+ The vocabulary size of the `token_type_ids` passed when calling [`LongformerModel`] or
+ [`TFLongformerModel`].
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+ attention_window (`int` or `list[int]`, *optional*, defaults to 512):
+ Size of an attention window around each token. If an `int`, use the same size for all layers. To specify a
+ different window size for each layer, use a `list[int]` where `len(attention_window) == num_hidden_layers`.
+
+ Example:
+
+ ```python
+ >>> from transformers import LongformerConfig, LongformerModel
+
+ >>> # Initializing a Longformer configuration
+ >>> configuration = LongformerConfig()
+
+ >>> # Initializing a model from the configuration
+ >>> model = LongformerModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "longformer"
+
+ def __init__(
+ self,
+ attention_window: Union[list[int], int] = 512,
+ sep_token_id: int = 2,
+ pad_token_id: int = 1,
+ bos_token_id: int = 0,
+ eos_token_id: int = 2,
+ vocab_size: int = 30522,
+ hidden_size: int = 768,
+ num_hidden_layers: int = 12,
+ num_attention_heads: int = 12,
+ intermediate_size: int = 3072,
+ hidden_act: str = "gelu",
+ hidden_dropout_prob: float = 0.1,
+ attention_probs_dropout_prob: float = 0.1,
+ max_position_embeddings: int = 512,
+ type_vocab_size: int = 2,
+ initializer_range: float = 0.02,
+ layer_norm_eps: float = 1e-12,
+ onnx_export: bool = False,
+ **kwargs,
+ ):
+ """Constructs LongformerConfig."""
+ super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+ self.attention_window = attention_window
+ self.sep_token_id = sep_token_id
+ self.bos_token_id = bos_token_id
+ self.eos_token_id = eos_token_id
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.hidden_act = hidden_act
+ self.intermediate_size = intermediate_size
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.max_position_embeddings = max_position_embeddings
+ self.type_vocab_size = type_vocab_size
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.onnx_export = onnx_export
+
+
+class LongformerOnnxConfig(OnnxConfig):
+ def __init__(
+ self, config: "PretrainedConfig", task: str = "default", patching_specs: "Optional[list[PatchingSpec]]" = None
+ ):
+ super().__init__(config, task, patching_specs)
+ config.onnx_export = True
+
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ if self.task == "multiple-choice":
+ dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+ else:
+ dynamic_axis = {0: "batch", 1: "sequence"}
+ return OrderedDict(
+ [
+ ("input_ids", dynamic_axis),
+ ("attention_mask", dynamic_axis),
+ ("global_attention_mask", dynamic_axis),
+ ]
+ )
+
+ @property
+ def outputs(self) -> Mapping[str, Mapping[int, str]]:
+ outputs = super().outputs
+ if self.task == "default":
+ outputs["pooler_output"] = {0: "batch"}
+ return outputs
+
+ @property
+ def atol_for_validation(self) -> float:
+ """
+ What absolute tolerance value to use during model conversion validation.
+
+ Returns:
+ Float absolute tolerance value.
+ """
+ return 1e-4
+
+ @property
+ def default_onnx_opset(self) -> int:
+ # needs to be >= 14 to support tril operator
+ return max(super().default_onnx_opset, 14)
+
+ def generate_dummy_inputs(
+ self,
+ tokenizer: "PreTrainedTokenizerBase",
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ inputs = super().generate_dummy_inputs(
+ preprocessor=tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+ )
+ import torch
+
+ # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
+ # makes the export fail randomly
+ inputs["global_attention_mask"] = torch.zeros_like(inputs["input_ids"])
+ # make every second token global
+ inputs["global_attention_mask"][:, ::2] = 1
+
+ return inputs
+
+
+__all__ = ["LongformerConfig", "LongformerOnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/convert_longformer_original_pytorch_lightning_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/convert_longformer_original_pytorch_lightning_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbd7600e96398a5d595f7df318506edd11db3376
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/convert_longformer_original_pytorch_lightning_to_pytorch.py
@@ -0,0 +1,85 @@
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert RoBERTa checkpoint."""
+
+import argparse
+
+import pytorch_lightning as pl
+import torch
+from torch import nn
+
+from transformers import LongformerForQuestionAnswering, LongformerModel
+
+
+class LightningModel(pl.LightningModule):
+ def __init__(self, model):
+ super().__init__()
+ self.model = model
+ self.num_labels = 2
+ self.qa_outputs = nn.Linear(self.model.config.hidden_size, self.num_labels)
+
+ # implement only because lightning requires to do so
+ def forward(self):
+ pass
+
+
+def convert_longformer_qa_checkpoint_to_pytorch(
+ longformer_model: str, longformer_question_answering_ckpt_path: str, pytorch_dump_folder_path: str
+):
+ # load longformer model from model identifier
+ longformer = LongformerModel.from_pretrained(longformer_model)
+ lightning_model = LightningModel(longformer)
+
+ ckpt = torch.load(longformer_question_answering_ckpt_path, map_location=torch.device("cpu"), weights_only=True)
+ lightning_model.load_state_dict(ckpt["state_dict"])
+
+ # init longformer question answering model
+ longformer_for_qa = LongformerForQuestionAnswering.from_pretrained(longformer_model)
+
+ # transfer weights
+ longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
+ longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
+ longformer_for_qa.eval()
+
+ # save model
+ longformer_for_qa.save_pretrained(pytorch_dump_folder_path)
+
+ print(f"Conversion successful. Model saved under {pytorch_dump_folder_path}")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--longformer_model",
+ default=None,
+ type=str,
+ required=True,
+ help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
+ )
+ parser.add_argument(
+ "--longformer_question_answering_ckpt_path",
+ default=None,
+ type=str,
+ required=True,
+ help="Path the official PyTorch Lightning Checkpoint.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
+ )
+ args = parser.parse_args()
+ convert_longformer_qa_checkpoint_to_pytorch(
+ args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/modeling_longformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/modeling_longformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f181217cd1017a932c7d88f1e4a7817909a42626
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/modeling_longformer.py
@@ -0,0 +1,2223 @@
+# coding=utf-8
+# Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Longformer model."""
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN, gelu
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import ModelOutput, auto_docstring, logging
+from .configuration_longformer import LongformerConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Longformer's outputs, with potential hidden states, local and global attentions.
+ """
+)
+class LongformerBaseModelOutput(ModelOutput):
+ r"""
+ attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ last_hidden_state: torch.FloatTensor
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Longformer's outputs that also contains a pooling of the last hidden states.
+ """
+)
+class LongformerBaseModelOutputWithPooling(ModelOutput):
+ r"""
+ pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
+ Last layer hidden-state of the first token of the sequence (classification token) further processed by a
+ Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
+ prediction (classification) objective during pretraining.
+ attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ last_hidden_state: torch.FloatTensor
+ pooler_output: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for masked language models outputs.
+ """
+)
+class LongformerMaskedLMOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Masked language modeling (MLM) loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of question answering Longformer models.
+ """
+)
+class LongformerQuestionAnsweringModelOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
+ attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ start_logits: Optional[torch.FloatTensor] = None
+ end_logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of sentence classification models.
+ """
+)
+class LongformerSequenceClassifierOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Classification (or regression if config.num_labels==1) loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+ Classification (or regression if config.num_labels==1) scores (before SoftMax).
+ attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of multiple choice Longformer models.
+ """
+)
+class LongformerMultipleChoiceModelOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
+ Classification loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
+ *num_choices* is the second dimension of the input tensors. (see *input_ids* above).
+
+ Classification scores (before SoftMax).
+ attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of token classification models.
+ """
+)
+class LongformerTokenClassifierOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Classification loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
+ Classification scores (before SoftMax).
+ attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`,
+ where `x` is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ global_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+def _get_question_end_index(input_ids, sep_token_id):
+ """
+ Computes the index of the first occurrence of `sep_token_id`.
+ """
+
+ sep_token_indices = (input_ids == sep_token_id).nonzero()
+ batch_size = input_ids.shape[0]
+
+ assert sep_token_indices.shape[1] == 2, "`input_ids` should have two dimensions"
+ assert sep_token_indices.shape[0] == 3 * batch_size, (
+ f"There should be exactly three separator tokens: {sep_token_id} in every sample for questions answering. You"
+ " might also consider to set `global_attention_mask` manually in the forward function to avoid this error."
+ )
+ return sep_token_indices.view(batch_size, 3, 2)[:, 0, 1]
+
+
+def _compute_global_attention_mask(input_ids, sep_token_id, before_sep_token=True):
+ """
+ Computes global attention mask by putting attention on all tokens before `sep_token_id` if `before_sep_token is
+ True` else after `sep_token_id`.
+ """
+ question_end_index = _get_question_end_index(input_ids, sep_token_id)
+ question_end_index = question_end_index.unsqueeze(dim=1) # size: batch_size x 1
+ # bool attention mask with True in locations of global attention
+ attention_mask = torch.arange(input_ids.shape[1], device=input_ids.device)
+ if before_sep_token is True:
+ attention_mask = (attention_mask.expand_as(input_ids) < question_end_index).to(torch.bool)
+ else:
+ # last token is separation token and should not be counted and in the middle are two separation tokens
+ attention_mask = (attention_mask.expand_as(input_ids) > (question_end_index + 1)).to(torch.bool) * (
+ attention_mask.expand_as(input_ids) < input_ids.shape[-1]
+ ).to(torch.bool)
+
+ return attention_mask
+
+
+def create_position_ids_from_input_ids(input_ids, padding_idx):
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+ are ignored. This is modified from fairseq's `utils.make_positions`.
+
+ Args:
+ x: torch.Tensor x:
+
+ Returns: torch.Tensor
+ """
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+ mask = input_ids.ne(padding_idx).int()
+ incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
+ return incremental_indices.long() + padding_idx
+
+
+class LongformerEmbeddings(nn.Module):
+ """
+ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ self.padding_idx = config.pad_token_id
+ self.position_embeddings = nn.Embedding(
+ config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
+ )
+
+ def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
+ if position_ids is None:
+ if input_ids is not None:
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
+ position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx).to(input_ids.device)
+ else:
+ position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
+
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=position_ids.device)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+ position_embeddings = self.position_embeddings(position_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = inputs_embeds + position_embeddings + token_type_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+ """
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+
+ Args:
+ inputs_embeds: torch.Tensor inputs_embeds:
+
+ Returns: torch.Tensor
+ """
+ input_shape = inputs_embeds.size()[:-1]
+ sequence_length = input_shape[1]
+
+ position_ids = torch.arange(
+ self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+ )
+ return position_ids.unsqueeze(0).expand(input_shape)
+
+
+class LongformerSelfAttention(nn.Module):
+ def __init__(self, config, layer_id):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+ self.num_heads = config.num_attention_heads
+ self.head_dim = int(config.hidden_size / config.num_attention_heads)
+ self.embed_dim = config.hidden_size
+
+ self.query = nn.Linear(config.hidden_size, self.embed_dim)
+ self.key = nn.Linear(config.hidden_size, self.embed_dim)
+ self.value = nn.Linear(config.hidden_size, self.embed_dim)
+
+ # separate projection layers for tokens with global attention
+ self.query_global = nn.Linear(config.hidden_size, self.embed_dim)
+ self.key_global = nn.Linear(config.hidden_size, self.embed_dim)
+ self.value_global = nn.Linear(config.hidden_size, self.embed_dim)
+
+ self.dropout = config.attention_probs_dropout_prob
+
+ self.layer_id = layer_id
+ attention_window = config.attention_window[self.layer_id]
+ assert attention_window % 2 == 0, (
+ f"`attention_window` for layer {self.layer_id} has to be an even value. Given {attention_window}"
+ )
+ assert attention_window > 0, (
+ f"`attention_window` for layer {self.layer_id} has to be positive. Given {attention_window}"
+ )
+
+ self.one_sided_attn_window_size = attention_window // 2
+
+ self.config = config
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ layer_head_mask=None,
+ is_index_masked=None,
+ is_index_global_attn=None,
+ is_global_attn=None,
+ output_attentions=False,
+ ):
+ """
+ [`LongformerSelfAttention`] expects *len(hidden_states)* to be multiple of *attention_window*. Padding to
+ *attention_window* happens in [`LongformerModel.forward`] to avoid redoing the padding on each layer.
+
+ The *attention_mask* is changed in [`LongformerModel.forward`] from 0, 1, 2 to:
+
+ - -10000: no attention
+ - 0: local attention
+ - +10000: global attention
+ """
+ hidden_states = hidden_states.transpose(0, 1)
+
+ # project hidden states
+ query_vectors = self.query(hidden_states)
+ key_vectors = self.key(hidden_states)
+ value_vectors = self.value(hidden_states)
+
+ seq_len, batch_size, embed_dim = hidden_states.size()
+ assert embed_dim == self.embed_dim, (
+ f"hidden_states should have embed_dim = {self.embed_dim}, but has {embed_dim}"
+ )
+
+ # normalize query
+ query_vectors /= math.sqrt(self.head_dim)
+
+ query_vectors = query_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
+ key_vectors = key_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
+
+ attn_scores = self._sliding_chunks_query_key_matmul(
+ query_vectors, key_vectors, self.one_sided_attn_window_size
+ )
+
+ # values to pad for attention probs
+ remove_from_windowed_attention_mask = (attention_mask != 0)[:, :, None, None]
+
+ # cast to fp32/fp16 then replace 1's with -inf
+ float_mask = remove_from_windowed_attention_mask.type_as(query_vectors).masked_fill(
+ remove_from_windowed_attention_mask, torch.finfo(query_vectors.dtype).min
+ )
+ # diagonal mask with zeros everywhere and -inf inplace of padding
+ diagonal_mask = self._sliding_chunks_query_key_matmul(
+ float_mask.new_ones(size=float_mask.size()), float_mask, self.one_sided_attn_window_size
+ )
+
+ # pad local attention probs
+ attn_scores += diagonal_mask
+
+ assert list(attn_scores.size()) == [
+ batch_size,
+ seq_len,
+ self.num_heads,
+ self.one_sided_attn_window_size * 2 + 1,
+ ], (
+ f"local_attn_probs should be of size ({batch_size}, {seq_len}, {self.num_heads},"
+ f" {self.one_sided_attn_window_size * 2 + 1}), but is of size {attn_scores.size()}"
+ )
+
+ # compute local attention probs from global attention keys and contact over window dim
+ if is_global_attn:
+ # compute global attn indices required through out forward fn
+ (
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ ) = self._get_global_attn_indices(is_index_global_attn)
+ # calculate global attn probs from global key
+
+ global_key_attn_scores = self._concat_with_global_key_attn_probs(
+ query_vectors=query_vectors,
+ key_vectors=key_vectors,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
+ )
+ # concat to local_attn_probs
+ # (batch_size, seq_len, num_heads, extra attention count + 2*window+1)
+ attn_scores = torch.cat((global_key_attn_scores, attn_scores), dim=-1)
+
+ # free memory
+ del global_key_attn_scores
+
+ attn_probs = nn.functional.softmax(
+ attn_scores, dim=-1, dtype=torch.float32
+ ) # use fp32 for numerical stability
+
+ if layer_head_mask is not None:
+ assert layer_head_mask.size() == (self.num_heads,), (
+ f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+ )
+ attn_probs = layer_head_mask.view(1, 1, -1, 1) * attn_probs
+
+ # softmax sometimes inserts NaN if all positions are masked, replace them with 0
+ attn_probs = torch.masked_fill(attn_probs, is_index_masked[:, :, None, None], 0.0)
+ attn_probs = attn_probs.type_as(attn_scores)
+
+ # free memory
+ del attn_scores
+
+ # apply dropout
+ attn_probs = nn.functional.dropout(attn_probs, p=self.dropout, training=self.training)
+
+ value_vectors = value_vectors.view(seq_len, batch_size, self.num_heads, self.head_dim).transpose(0, 1)
+
+ # compute local attention output with global attention value and add
+ if is_global_attn:
+ # compute sum of global and local attn
+ attn_output = self._compute_attn_output_with_global_indices(
+ value_vectors=value_vectors,
+ attn_probs=attn_probs,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ )
+ else:
+ # compute local attn only
+ attn_output = self._sliding_chunks_matmul_attn_probs_value(
+ attn_probs, value_vectors, self.one_sided_attn_window_size
+ )
+
+ assert attn_output.size() == (batch_size, seq_len, self.num_heads, self.head_dim), "Unexpected size"
+ attn_output = attn_output.transpose(0, 1).reshape(seq_len, batch_size, embed_dim).contiguous()
+
+ # compute value for global attention and overwrite to attention output
+ # TODO: remove the redundant computation
+ if is_global_attn:
+ global_attn_output, global_attn_probs = self._compute_global_attn_output_from_hidden(
+ hidden_states=hidden_states,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ layer_head_mask=layer_head_mask,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
+ is_index_masked=is_index_masked,
+ )
+
+ # get only non zero global attn output
+ nonzero_global_attn_output = global_attn_output[
+ is_local_index_global_attn_nonzero[0], :, is_local_index_global_attn_nonzero[1]
+ ]
+
+ # overwrite values with global attention
+ attn_output[is_index_global_attn_nonzero[::-1]] = nonzero_global_attn_output.view(
+ len(is_local_index_global_attn_nonzero[0]), -1
+ )
+ # The attention weights for tokens with global attention are
+ # just filler values, they were never used to compute the output.
+ # Fill with 0 now, the correct values are in 'global_attn_probs'.
+ attn_probs[is_index_global_attn_nonzero] = 0
+
+ outputs = (attn_output.transpose(0, 1),)
+
+ if output_attentions:
+ outputs += (attn_probs,)
+
+ return outputs + (global_attn_probs,) if (is_global_attn and output_attentions) else outputs
+
+ @staticmethod
+ def _pad_and_transpose_last_two_dims(hidden_states_padded, padding):
+ """pads rows and then flips rows and columns"""
+ hidden_states_padded = nn.functional.pad(
+ hidden_states_padded, padding
+ ) # padding value is not important because it will be overwritten
+ hidden_states_padded = hidden_states_padded.view(
+ *hidden_states_padded.size()[:-2], hidden_states_padded.size(-1), hidden_states_padded.size(-2)
+ )
+ return hidden_states_padded
+
+ @staticmethod
+ def _pad_and_diagonalize(chunked_hidden_states):
+ """
+ shift every row 1 step right, converting columns into diagonals.
+
+ Example:
+
+ ```python
+ chunked_hidden_states: [
+ 0.4983,
+ 2.6918,
+ -0.0071,
+ 1.0492,
+ -1.8348,
+ 0.7672,
+ 0.2986,
+ 0.0285,
+ -0.7584,
+ 0.4206,
+ -0.0405,
+ 0.1599,
+ 2.0514,
+ -1.1600,
+ 0.5372,
+ 0.2629,
+ ]
+ window_overlap = num_rows = 4
+ ```
+
+ (pad & diagonalize) => [ 0.4983, 2.6918, -0.0071, 1.0492, 0.0000, 0.0000, 0.0000
+ 0.0000, -1.8348, 0.7672, 0.2986, 0.0285, 0.0000, 0.0000 0.0000, 0.0000, -0.7584, 0.4206,
+ -0.0405, 0.1599, 0.0000 0.0000, 0.0000, 0.0000, 2.0514, -1.1600, 0.5372, 0.2629 ]
+ """
+ total_num_heads, num_chunks, window_overlap, hidden_dim = chunked_hidden_states.size()
+ chunked_hidden_states = nn.functional.pad(
+ chunked_hidden_states, (0, window_overlap + 1)
+ ) # total_num_heads x num_chunks x window_overlap x (hidden_dim+window_overlap+1). Padding value is not important because it'll be overwritten
+ chunked_hidden_states = chunked_hidden_states.view(
+ total_num_heads, num_chunks, -1
+ ) # total_num_heads x num_chunks x window_overlap*window_overlap+window_overlap
+ chunked_hidden_states = chunked_hidden_states[
+ :, :, :-window_overlap
+ ] # total_num_heads x num_chunks x window_overlap*window_overlap
+ chunked_hidden_states = chunked_hidden_states.view(
+ total_num_heads, num_chunks, window_overlap, window_overlap + hidden_dim
+ )
+ chunked_hidden_states = chunked_hidden_states[:, :, :, :-1]
+ return chunked_hidden_states
+
+ @staticmethod
+ def _chunk(hidden_states, window_overlap, onnx_export: bool = False):
+ """convert into overlapping chunks. Chunk size = 2w, overlap size = w"""
+ if not onnx_export:
+ # non-overlapping chunks of size = 2w
+ hidden_states = hidden_states.view(
+ hidden_states.size(0),
+ torch.div(hidden_states.size(1), (window_overlap * 2), rounding_mode="trunc"),
+ window_overlap * 2,
+ hidden_states.size(2),
+ )
+ # use `as_strided` to make the chunks overlap with an overlap size = window_overlap
+ chunk_size = list(hidden_states.size())
+ chunk_size[1] = chunk_size[1] * 2 - 1
+
+ chunk_stride = list(hidden_states.stride())
+ chunk_stride[1] = chunk_stride[1] // 2
+ return hidden_states.as_strided(size=chunk_size, stride=chunk_stride)
+
+ # When exporting to ONNX, use this separate logic
+ # have to use slow implementation since as_strided, unfold and 2d-tensor indexing aren't supported (yet) in ONNX export
+
+ # TODO replace this with
+ # > return hidden_states.unfold(dimension=1, size=window_overlap * 2, step=window_overlap).transpose(2, 3)
+ # once `unfold` is supported
+ # the case hidden_states.size(1) == window_overlap * 2 can also simply return hidden_states.unsqueeze(1), but that's control flow
+
+ chunk_size = [
+ hidden_states.size(0),
+ torch.div(hidden_states.size(1), window_overlap, rounding_mode="trunc") - 1,
+ window_overlap * 2,
+ hidden_states.size(2),
+ ]
+
+ overlapping_chunks = torch.empty(chunk_size, device=hidden_states.device)
+ for chunk in range(chunk_size[1]):
+ overlapping_chunks[:, chunk, :, :] = hidden_states[
+ :, chunk * window_overlap : chunk * window_overlap + 2 * window_overlap, :
+ ]
+ return overlapping_chunks
+
+ @staticmethod
+ def _mask_invalid_locations(input_tensor, affected_seq_len) -> torch.Tensor:
+ beginning_mask_2d = input_tensor.new_ones(affected_seq_len, affected_seq_len + 1).tril().flip(dims=[0])
+ beginning_mask = beginning_mask_2d[None, :, None, :]
+ ending_mask = beginning_mask.flip(dims=(1, 3))
+ beginning_input = input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1]
+ beginning_mask = beginning_mask.expand(beginning_input.size())
+ input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1] = torch.full_like(
+ beginning_input, -float("inf")
+ ).where(beginning_mask.bool(), beginning_input)
+ ending_input = input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :]
+ ending_mask = ending_mask.expand(ending_input.size())
+ input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :] = torch.full_like(
+ ending_input, -float("inf")
+ ).where(ending_mask.bool(), ending_input)
+
+ def _sliding_chunks_query_key_matmul(self, query: torch.Tensor, key: torch.Tensor, window_overlap: int):
+ """
+ Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
+ implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer) with an
+ overlap of size window_overlap
+ """
+ batch_size, seq_len, num_heads, head_dim = query.size()
+ assert seq_len % (window_overlap * 2) == 0, (
+ f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}"
+ )
+ assert query.size() == key.size()
+
+ chunks_count = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
+
+ # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size window_overlap * 2
+ query = query.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
+ key = key.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
+
+ query = self._chunk(query, window_overlap, getattr(self.config, "onnx_export", False))
+ key = self._chunk(key, window_overlap, getattr(self.config, "onnx_export", False))
+
+ # matrix multiplication
+ # bcxd: batch_size * num_heads x chunks x 2window_overlap x head_dim
+ # bcyd: batch_size * num_heads x chunks x 2window_overlap x head_dim
+ # bcxy: batch_size * num_heads x chunks x 2window_overlap x 2window_overlap
+ diagonal_chunked_attention_scores = torch.einsum("bcxd,bcyd->bcxy", (query, key)) # multiply
+
+ # convert diagonals into columns
+ diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(
+ diagonal_chunked_attention_scores, padding=(0, 0, 0, 1)
+ )
+
+ # allocate space for the overall attention matrix where the chunks are combined. The last dimension
+ # has (window_overlap * 2 + 1) columns. The first (window_overlap) columns are the window_overlap lower triangles (attention from a word to
+ # window_overlap previous words). The following column is attention score from each word to itself, then
+ # followed by window_overlap columns for the upper triangle.
+
+ diagonal_attention_scores = diagonal_chunked_attention_scores.new_zeros(
+ (batch_size * num_heads, chunks_count + 1, window_overlap, window_overlap * 2 + 1)
+ )
+
+ # copy parts from diagonal_chunked_attention_scores into the combined matrix of attentions
+ # - copying the main diagonal and the upper triangle
+ diagonal_attention_scores[:, :-1, :, window_overlap:] = diagonal_chunked_attention_scores[
+ :, :, :window_overlap, : window_overlap + 1
+ ]
+ diagonal_attention_scores[:, -1, :, window_overlap:] = diagonal_chunked_attention_scores[
+ :, -1, window_overlap:, : window_overlap + 1
+ ]
+ # - copying the lower triangle
+ diagonal_attention_scores[:, 1:, :, :window_overlap] = diagonal_chunked_attention_scores[
+ :, :, -(window_overlap + 1) : -1, window_overlap + 1 :
+ ]
+
+ diagonal_attention_scores[:, 0, 1:window_overlap, 1:window_overlap] = diagonal_chunked_attention_scores[
+ :, 0, : window_overlap - 1, 1 - window_overlap :
+ ]
+
+ # separate batch_size and num_heads dimensions again
+ diagonal_attention_scores = diagonal_attention_scores.view(
+ batch_size, num_heads, seq_len, 2 * window_overlap + 1
+ ).transpose(2, 1)
+
+ self._mask_invalid_locations(diagonal_attention_scores, window_overlap)
+ return diagonal_attention_scores
+
+ def _sliding_chunks_matmul_attn_probs_value(
+ self, attn_probs: torch.Tensor, value: torch.Tensor, window_overlap: int
+ ):
+ """
+ Same as _sliding_chunks_query_key_matmul but for attn_probs and value tensors. Returned tensor will be of the
+ same shape as `attn_probs`
+ """
+ batch_size, seq_len, num_heads, head_dim = value.size()
+
+ assert seq_len % (window_overlap * 2) == 0
+ assert attn_probs.size()[:3] == value.size()[:3]
+ assert attn_probs.size(3) == 2 * window_overlap + 1
+ chunks_count = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
+ # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size 2 window overlap
+
+ chunked_attn_probs = attn_probs.transpose(1, 2).reshape(
+ batch_size * num_heads,
+ torch.div(seq_len, window_overlap, rounding_mode="trunc"),
+ window_overlap,
+ 2 * window_overlap + 1,
+ )
+
+ # group batch_size and num_heads dimensions into one
+ value = value.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)
+
+ # pad seq_len with w at the beginning of the sequence and another window overlap at the end
+ padded_value = nn.functional.pad(value, (0, 0, window_overlap, window_overlap), value=-1)
+
+ # chunk padded_value into chunks of size 3 window overlap and an overlap of size window overlap
+ chunked_value_size = (batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim)
+ chunked_value_stride = padded_value.stride()
+ chunked_value_stride = (
+ chunked_value_stride[0],
+ window_overlap * chunked_value_stride[1],
+ chunked_value_stride[1],
+ chunked_value_stride[2],
+ )
+ chunked_value = padded_value.as_strided(size=chunked_value_size, stride=chunked_value_stride)
+
+ chunked_attn_probs = self._pad_and_diagonalize(chunked_attn_probs)
+
+ context = torch.einsum("bcwd,bcdh->bcwh", (chunked_attn_probs, chunked_value))
+ return context.view(batch_size, num_heads, seq_len, head_dim).transpose(1, 2)
+
+ @staticmethod
+ def _get_global_attn_indices(is_index_global_attn):
+ """compute global attn indices required throughout forward pass"""
+ # helper variable
+ num_global_attn_indices = is_index_global_attn.long().sum(dim=1)
+
+ # max number of global attn indices in batch
+ max_num_global_attn_indices = num_global_attn_indices.max()
+
+ # indices of global attn
+ is_index_global_attn_nonzero = is_index_global_attn.nonzero(as_tuple=True)
+
+ # helper variable
+ is_local_index_global_attn = torch.arange(
+ max_num_global_attn_indices, device=is_index_global_attn.device
+ ) < num_global_attn_indices.unsqueeze(dim=-1)
+
+ # location of the non-padding values within global attention indices
+ is_local_index_global_attn_nonzero = is_local_index_global_attn.nonzero(as_tuple=True)
+
+ # location of the padding values within global attention indices
+ is_local_index_no_global_attn_nonzero = (is_local_index_global_attn == 0).nonzero(as_tuple=True)
+ return (
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ )
+
+ def _concat_with_global_key_attn_probs(
+ self,
+ key_vectors,
+ query_vectors,
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ ):
+ batch_size = key_vectors.shape[0]
+
+ # create only global key vectors
+ key_vectors_only_global = key_vectors.new_zeros(
+ batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim
+ )
+
+ key_vectors_only_global[is_local_index_global_attn_nonzero] = key_vectors[is_index_global_attn_nonzero]
+
+ # (batch_size, seq_len, num_heads, max_num_global_attn_indices)
+ attn_probs_from_global_key = torch.einsum("blhd,bshd->blhs", (query_vectors, key_vectors_only_global))
+
+ # need to transpose since ONNX export only supports consecutive indexing: https://pytorch.org/docs/stable/onnx.html#writes-sets
+ attn_probs_from_global_key = attn_probs_from_global_key.transpose(1, 3)
+ attn_probs_from_global_key[
+ is_local_index_no_global_attn_nonzero[0], is_local_index_no_global_attn_nonzero[1], :, :
+ ] = torch.finfo(attn_probs_from_global_key.dtype).min
+ attn_probs_from_global_key = attn_probs_from_global_key.transpose(1, 3)
+
+ return attn_probs_from_global_key
+
+ def _compute_attn_output_with_global_indices(
+ self,
+ value_vectors,
+ attn_probs,
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ ):
+ batch_size = attn_probs.shape[0]
+
+ # cut local attn probs to global only
+ attn_probs_only_global = attn_probs.narrow(-1, 0, max_num_global_attn_indices)
+ # get value vectors for global only
+ value_vectors_only_global = value_vectors.new_zeros(
+ batch_size, max_num_global_attn_indices, self.num_heads, self.head_dim
+ )
+ value_vectors_only_global[is_local_index_global_attn_nonzero] = value_vectors[is_index_global_attn_nonzero]
+
+ # use `matmul` because `einsum` crashes sometimes with fp16
+ # attn = torch.einsum('blhs,bshd->blhd', (selected_attn_probs, selected_v))
+ # compute attn output only global
+ attn_output_only_global = torch.matmul(
+ attn_probs_only_global.transpose(1, 2).clone(), value_vectors_only_global.transpose(1, 2).clone()
+ ).transpose(1, 2)
+
+ # reshape attn probs
+ attn_probs_without_global = attn_probs.narrow(
+ -1, max_num_global_attn_indices, attn_probs.size(-1) - max_num_global_attn_indices
+ ).contiguous()
+
+ # compute attn output with global
+ attn_output_without_global = self._sliding_chunks_matmul_attn_probs_value(
+ attn_probs_without_global, value_vectors, self.one_sided_attn_window_size
+ )
+ return attn_output_only_global + attn_output_without_global
+
+ def _compute_global_attn_output_from_hidden(
+ self,
+ hidden_states,
+ max_num_global_attn_indices,
+ layer_head_mask,
+ is_local_index_global_attn_nonzero,
+ is_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ is_index_masked,
+ ):
+ seq_len, batch_size = hidden_states.shape[:2]
+
+ # prepare global hidden states
+ global_attn_hidden_states = hidden_states.new_zeros(max_num_global_attn_indices, batch_size, self.embed_dim)
+ global_attn_hidden_states[is_local_index_global_attn_nonzero[::-1]] = hidden_states[
+ is_index_global_attn_nonzero[::-1]
+ ]
+
+ # global key, query, value
+ global_query_vectors_only_global = self.query_global(global_attn_hidden_states)
+ global_key_vectors = self.key_global(hidden_states)
+ global_value_vectors = self.value_global(hidden_states)
+
+ # normalize
+ global_query_vectors_only_global /= math.sqrt(self.head_dim)
+
+ # reshape
+ global_query_vectors_only_global = (
+ global_query_vectors_only_global.contiguous()
+ .view(max_num_global_attn_indices, batch_size * self.num_heads, self.head_dim)
+ .transpose(0, 1)
+ ) # (batch_size * self.num_heads, max_num_global_attn_indices, head_dim)
+ global_key_vectors = (
+ global_key_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
+ ) # batch_size * self.num_heads, seq_len, head_dim)
+ global_value_vectors = (
+ global_value_vectors.contiguous().view(-1, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
+ ) # batch_size * self.num_heads, seq_len, head_dim)
+
+ # compute attn scores
+ global_attn_scores = torch.bmm(global_query_vectors_only_global, global_key_vectors.transpose(1, 2))
+
+ assert list(global_attn_scores.size()) == [
+ batch_size * self.num_heads,
+ max_num_global_attn_indices,
+ seq_len,
+ ], (
+ "global_attn_scores have the wrong size. Size should be"
+ f" {(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)}, but is"
+ f" {global_attn_scores.size()}."
+ )
+
+ global_attn_scores = global_attn_scores.view(batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
+
+ # need to transpose since ONNX export only supports consecutive indexing: https://pytorch.org/docs/stable/onnx.html#writes-sets
+ global_attn_scores = global_attn_scores.transpose(1, 2)
+ global_attn_scores[
+ is_local_index_no_global_attn_nonzero[0], is_local_index_no_global_attn_nonzero[1], :, :
+ ] = torch.finfo(global_attn_scores.dtype).min
+ global_attn_scores = global_attn_scores.transpose(1, 2)
+
+ global_attn_scores = global_attn_scores.masked_fill(
+ is_index_masked[:, None, None, :],
+ torch.finfo(global_attn_scores.dtype).min,
+ )
+
+ global_attn_scores = global_attn_scores.view(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)
+
+ # compute global attn probs
+ global_attn_probs_float = nn.functional.softmax(
+ global_attn_scores, dim=-1, dtype=torch.float32
+ ) # use fp32 for numerical stability
+
+ # apply layer head masking
+ if layer_head_mask is not None:
+ assert layer_head_mask.size() == (self.num_heads,), (
+ f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
+ )
+ global_attn_probs_float = layer_head_mask.view(1, -1, 1, 1) * global_attn_probs_float.view(
+ batch_size, self.num_heads, max_num_global_attn_indices, seq_len
+ )
+ global_attn_probs_float = global_attn_probs_float.view(
+ batch_size * self.num_heads, max_num_global_attn_indices, seq_len
+ )
+
+ global_attn_probs = nn.functional.dropout(
+ global_attn_probs_float.type_as(global_attn_scores), p=self.dropout, training=self.training
+ )
+
+ # global attn output
+ global_attn_output = torch.bmm(global_attn_probs, global_value_vectors)
+
+ assert list(global_attn_output.size()) == [
+ batch_size * self.num_heads,
+ max_num_global_attn_indices,
+ self.head_dim,
+ ], (
+ "global_attn_output tensor has the wrong size. Size should be"
+ f" {(batch_size * self.num_heads, max_num_global_attn_indices, self.head_dim)}, but is"
+ f" {global_attn_output.size()}."
+ )
+
+ global_attn_probs = global_attn_probs.view(batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
+ global_attn_output = global_attn_output.view(
+ batch_size, self.num_heads, max_num_global_attn_indices, self.head_dim
+ )
+ return global_attn_output, global_attn_probs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
+class LongformerSelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class LongformerAttention(nn.Module):
+ def __init__(self, config, layer_id=0):
+ super().__init__()
+ self.self = LongformerSelfAttention(config, layer_id)
+ self.output = LongformerSelfOutput(config)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+ )
+
+ # Prune linear layers
+ self.self.query = prune_linear_layer(self.self.query, index)
+ self.self.key = prune_linear_layer(self.self.key, index)
+ self.self.value = prune_linear_layer(self.self.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+ self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ layer_head_mask=None,
+ is_index_masked=None,
+ is_index_global_attn=None,
+ is_global_attn=None,
+ output_attentions=False,
+ ):
+ self_outputs = self.self(
+ hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ is_index_masked=is_index_masked,
+ is_index_global_attn=is_index_global_attn,
+ is_global_attn=is_global_attn,
+ output_attentions=output_attentions,
+ )
+ attn_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attn_output,) + self_outputs[1:]
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate
+class LongformerIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOutput
+class LongformerOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class LongformerLayer(GradientCheckpointingLayer):
+ def __init__(self, config, layer_id=0):
+ super().__init__()
+ self.attention = LongformerAttention(config, layer_id)
+ self.intermediate = LongformerIntermediate(config)
+ self.output = LongformerOutput(config)
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ layer_head_mask=None,
+ is_index_masked=None,
+ is_index_global_attn=None,
+ is_global_attn=None,
+ output_attentions=False,
+ ):
+ self_attn_outputs = self.attention(
+ hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ is_index_masked=is_index_masked,
+ is_index_global_attn=is_index_global_attn,
+ is_global_attn=is_global_attn,
+ output_attentions=output_attentions,
+ )
+ attn_output = self_attn_outputs[0]
+ outputs = self_attn_outputs[1:]
+
+ layer_output = apply_chunking_to_forward(
+ self.ff_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attn_output
+ )
+ outputs = (layer_output,) + outputs
+ return outputs
+
+ def ff_chunk(self, attn_output):
+ intermediate_output = self.intermediate(attn_output)
+ layer_output = self.output(intermediate_output, attn_output)
+ return layer_output
+
+
+class LongformerEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList([LongformerLayer(config, layer_id=i) for i in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ padding_len=0,
+ output_attentions=False,
+ output_hidden_states=False,
+ return_dict=True,
+ ):
+ is_index_masked = attention_mask < 0
+ is_index_global_attn = attention_mask > 0
+
+ # Record `is_global_attn == True` to enable ONNX export
+ is_global_attn = is_index_global_attn.flatten().any().item()
+
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None # All local attentions.
+ all_global_attentions = () if (output_attentions and is_global_attn) else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ assert head_mask.size()[0] == (len(self.layer)), (
+ f"The head_mask should be specified for {len(self.layer)} layers, but it is for {head_mask.size()[0]}."
+ )
+ for idx, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=head_mask[idx] if head_mask is not None else None,
+ is_index_masked=is_index_masked,
+ is_index_global_attn=is_index_global_attn,
+ is_global_attn=is_global_attn,
+ output_attentions=output_attentions,
+ )
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ # bzs x seq_len x num_attn_heads x (num_global_attn + attention_window_len + 1) => bzs x num_attn_heads x seq_len x (num_global_attn + attention_window_len + 1)
+ all_attentions = all_attentions + (layer_outputs[1].transpose(1, 2),)
+
+ if is_global_attn:
+ # bzs x num_attn_heads x num_global_attn x seq_len => bzs x num_attn_heads x seq_len x num_global_attn
+ all_global_attentions = all_global_attentions + (layer_outputs[2].transpose(2, 3),)
+
+ # Add last layer
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ # undo padding if necessary
+ # unpad `hidden_states` because the calling function is expecting a length == input_ids.size(1)
+ hidden_states = hidden_states[:, : hidden_states.shape[1] - padding_len]
+ if output_hidden_states:
+ all_hidden_states = tuple(state[:, : state.shape[1] - padding_len] for state in all_hidden_states)
+
+ if output_attentions:
+ all_attentions = tuple(state[:, :, : state.shape[2] - padding_len, :] for state in all_attentions)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, all_hidden_states, all_attentions, all_global_attentions] if v is not None
+ )
+ return LongformerBaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_attentions,
+ global_attentions=all_global_attentions,
+ )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler
+class LongformerPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+# Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead with Roberta->Longformer
+class LongformerLMHead(nn.Module):
+ """Longformer Head for masked language modeling."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+ self.decoder.bias = self.bias
+
+ def forward(self, features, **kwargs):
+ x = self.dense(features)
+ x = gelu(x)
+ x = self.layer_norm(x)
+
+ # project back to size of vocabulary with bias
+ x = self.decoder(x)
+
+ return x
+
+ def _tie_weights(self):
+ # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
+ # For accelerate compatibility and to not break backward compatibility
+ if self.decoder.bias.device.type == "meta":
+ self.decoder.bias = self.bias
+ else:
+ self.bias = self.decoder.bias
+
+
+@auto_docstring
+class LongformerPreTrainedModel(PreTrainedModel):
+ config: LongformerConfig
+ base_model_prefix = "longformer"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["LongformerSelfAttention"]
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, nn.Linear):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class LongformerModel(LongformerPreTrainedModel):
+ """
+ This class copied code from [`RobertaModel`] and overwrote standard self-attention with longformer self-attention
+ to provide the ability to process long sequences following the self-attention approach described in [Longformer:
+ the Long-Document Transformer](https://huggingface.co/papers/2004.05150) by Iz Beltagy, Matthew E. Peters, and Arman Cohan.
+ Longformer self-attention combines a local (sliding window) and global attention to extend to long documents
+ without the O(n^2) increase in memory and compute.
+
+ The self-attention module `LongformerSelfAttention` implemented here supports the combination of local and global
+ attention but it lacks support for autoregressive attention and dilated attention. Autoregressive and dilated
+ attention are more relevant for autoregressive language modeling than finetuning on downstream tasks. Future
+ release will add support for autoregressive attention, but the support for dilated attention requires a custom CUDA
+ kernel to be memory and compute efficient.
+
+ """
+
+ def __init__(self, config, add_pooling_layer=True):
+ r"""
+ add_pooling_layer (bool, *optional*, defaults to `True`):
+ Whether to add a pooling layer
+ """
+ super().__init__(config)
+ self.config = config
+
+ if isinstance(config.attention_window, int):
+ assert config.attention_window % 2 == 0, "`config.attention_window` has to be an even value"
+ assert config.attention_window > 0, "`config.attention_window` has to be positive"
+ config.attention_window = [config.attention_window] * config.num_hidden_layers # one value per layer
+ else:
+ assert len(config.attention_window) == config.num_hidden_layers, (
+ "`len(config.attention_window)` should equal `config.num_hidden_layers`. "
+ f"Expected {config.num_hidden_layers}, given {len(config.attention_window)}"
+ )
+
+ self.embeddings = LongformerEmbeddings(config)
+ self.encoder = LongformerEncoder(config)
+ self.pooler = LongformerPooler(config) if add_pooling_layer else None
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ def _pad_to_window_size(
+ self,
+ input_ids: torch.Tensor,
+ attention_mask: torch.Tensor,
+ token_type_ids: torch.Tensor,
+ position_ids: torch.Tensor,
+ inputs_embeds: torch.Tensor,
+ pad_token_id: int,
+ ):
+ """A helper function to pad tokens and mask to work with implementation of Longformer self-attention."""
+ # padding
+ attention_window = (
+ self.config.attention_window
+ if isinstance(self.config.attention_window, int)
+ else max(self.config.attention_window)
+ )
+
+ assert attention_window % 2 == 0, f"`attention_window` should be an even value. Given {attention_window}"
+ input_shape = input_ids.shape if input_ids is not None else inputs_embeds.shape
+ batch_size, seq_len = input_shape[:2]
+
+ padding_len = (attention_window - seq_len % attention_window) % attention_window
+
+ # this path should be recorded in the ONNX export, it is fine with padding_len == 0 as well
+ if padding_len > 0:
+ logger.warning_once(
+ f"Input ids are automatically padded to be a multiple of `config.attention_window`: {attention_window}"
+ )
+ if input_ids is not None:
+ input_ids = nn.functional.pad(input_ids, (0, padding_len), value=pad_token_id)
+ if position_ids is not None:
+ # pad with position_id = pad_token_id as in modeling_roberta.RobertaEmbeddings
+ position_ids = nn.functional.pad(position_ids, (0, padding_len), value=pad_token_id)
+ if inputs_embeds is not None:
+ input_ids_padding = inputs_embeds.new_full(
+ (batch_size, padding_len),
+ self.config.pad_token_id,
+ dtype=torch.long,
+ )
+ inputs_embeds_padding = self.embeddings(input_ids_padding)
+ inputs_embeds = torch.cat([inputs_embeds, inputs_embeds_padding], dim=-2)
+
+ attention_mask = nn.functional.pad(
+ attention_mask, (0, padding_len), value=0
+ ) # no attention on the padding tokens
+ token_type_ids = nn.functional.pad(token_type_ids, (0, padding_len), value=0) # pad with token_type_id = 0
+
+ return padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds
+
+ def _merge_to_attention_mask(self, attention_mask: torch.Tensor, global_attention_mask: torch.Tensor):
+ # longformer self attention expects attention mask to have 0 (no attn), 1 (local attn), 2 (global attn)
+ # (global_attention_mask + 1) => 1 for local attention, 2 for global attention
+ # => final attention_mask => 0 for no attention, 1 for local attention 2 for global attention
+ if attention_mask is not None:
+ attention_mask = attention_mask * (global_attention_mask + 1)
+ else:
+ # simply use `global_attention_mask` as `attention_mask`
+ # if no `attention_mask` is given
+ attention_mask = global_attention_mask + 1
+ return attention_mask
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ global_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LongformerBaseModelOutputWithPooling]:
+ r"""
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention. Tokens with global
+ attention attends to all other tokens, and all other tokens attend to them. This is important for
+ task-specific finetuning because it makes the model more flexible at representing the task. For example,
+ for classification, the token should be given global attention. For QA, all question tokens should also
+ have global attention. Please refer to the [Longformer paper](https://huggingface.co/papers/2004.05150) for more
+ details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+
+ Examples:
+
+ ```python
+ >>> import torch
+ >>> from transformers import LongformerModel, AutoTokenizer
+
+ >>> model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
+ >>> tokenizer = AutoTokenizer.from_pretrained("allenai/longformer-base-4096")
+
+ >>> SAMPLE_TEXT = " ".join(["Hello world! "] * 1000) # long input document
+ >>> input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0) # batch of size 1
+
+ >>> attention_mask = torch.ones(
+ ... input_ids.shape, dtype=torch.long, device=input_ids.device
+ ... ) # initialize to local attention
+ >>> global_attention_mask = torch.zeros(
+ ... input_ids.shape, dtype=torch.long, device=input_ids.device
+ ... ) # initialize to global attention to be deactivated for all tokens
+ >>> global_attention_mask[
+ ... :,
+ ... [
+ ... 1,
+ ... 4,
+ ... 21,
+ ... ],
+ ... ] = 1 # Set global attention to random tokens for the sake of this example
+ >>> # Usually, set global attention based on the task. For example,
+ >>> # classification: the token
+ >>> # QA: question tokens
+ >>> # LM: potentially on the beginning of sentences and paragraphs
+ >>> outputs = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)
+ >>> sequence_output = outputs.last_hidden_state
+ >>> pooled_output = outputs.pooler_output
+ ```
+ """
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones(input_shape, device=device)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ # merge `global_attention_mask` and `attention_mask`
+ if global_attention_mask is not None:
+ attention_mask = self._merge_to_attention_mask(attention_mask, global_attention_mask)
+
+ padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds = self._pad_to_window_size(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ pad_token_id=self.config.pad_token_id,
+ )
+
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)[
+ :, 0, 0, :
+ ]
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
+ )
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ padding_len=padding_len,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ return LongformerBaseModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ global_attentions=encoder_outputs.global_attentions,
+ )
+
+
+@auto_docstring
+class LongformerForMaskedLM(LongformerPreTrainedModel):
+ _tied_weights_keys = ["lm_head.decoder"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.longformer = LongformerModel(config, add_pooling_layer=False)
+ self.lm_head = LongformerLMHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.lm_head.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head.decoder = new_embeddings
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ global_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LongformerMaskedLMOutput]:
+ r"""
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention. Tokens with global
+ attention attends to all other tokens, and all other tokens attend to them. This is important for
+ task-specific finetuning because it makes the model more flexible at representing the task. For example,
+ for classification, the token should be given global attention. For QA, all question tokens should also
+ have global attention. Please refer to the [Longformer paper](https://huggingface.co/papers/2004.05150) for more
+ details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+
+ Example Mask filling:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LongformerForMaskedLM
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("allenai/longformer-base-4096")
+ >>> model = LongformerForMaskedLM.from_pretrained("allenai/longformer-base-4096")
+ ```
+
+ Let's try a very long input.
+
+ ```python
+ >>> TXT = (
+ ... "My friends are but they eat too many carbs."
+ ... + " That's why I decide not to eat with them." * 300
+ ... )
+ >>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
+ >>> logits = model(input_ids).logits
+
+ >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
+ >>> probs = logits[0, masked_index].softmax(dim=0)
+ >>> values, predictions = probs.topk(5)
+
+ >>> tokenizer.decode(predictions).split()
+ ['healthy', 'skinny', 'thin', 'good', 'vegetarian']
+ ```
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.longformer(
+ input_ids,
+ attention_mask=attention_mask,
+ global_attention_mask=global_attention_mask,
+ head_mask=head_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = outputs[0]
+ prediction_scores = self.lm_head(sequence_output)
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+
+ labels = labels.to(prediction_scores.device)
+ masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return LongformerMaskedLMOutput(
+ loss=masked_lm_loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ global_attentions=outputs.global_attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ Longformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+ pooled output) e.g. for GLUE tasks.
+ """
+)
+class LongformerForSequenceClassification(LongformerPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.config = config
+
+ self.longformer = LongformerModel(config, add_pooling_layer=False)
+ self.classifier = LongformerClassificationHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ global_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LongformerSequenceClassifierOutput]:
+ r"""
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention. Tokens with global
+ attention attends to all other tokens, and all other tokens attend to them. This is important for
+ task-specific finetuning because it makes the model more flexible at representing the task. For example,
+ for classification, the token should be given global attention. For QA, all question tokens should also
+ have global attention. Please refer to the [Longformer paper](https://huggingface.co/papers/2004.05150) for more
+ details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if global_attention_mask is None:
+ logger.warning_once("Initializing global attention on CLS token...")
+ global_attention_mask = torch.zeros_like(input_ids)
+ # global attention on cls token
+ global_attention_mask[:, 0] = 1
+
+ outputs = self.longformer(
+ input_ids,
+ attention_mask=attention_mask,
+ global_attention_mask=global_attention_mask,
+ head_mask=head_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = outputs[0]
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return LongformerSequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ global_attentions=outputs.global_attentions,
+ )
+
+
+class LongformerClassificationHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+ def forward(self, hidden_states, **kwargs):
+ hidden_states = hidden_states[:, 0, :] # take token (equiv. to [CLS])
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.dense(hidden_states)
+ hidden_states = torch.tanh(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ output = self.out_proj(hidden_states)
+ return output
+
+
+@auto_docstring
+class LongformerForQuestionAnswering(LongformerPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.longformer = LongformerModel(config, add_pooling_layer=False)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ global_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ start_positions: Optional[torch.Tensor] = None,
+ end_positions: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LongformerQuestionAnsweringModelOutput]:
+ r"""
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention. Tokens with global
+ attention attends to all other tokens, and all other tokens attend to them. This is important for
+ task-specific finetuning because it makes the model more flexible at representing the task. For example,
+ for classification, the token should be given global attention. For QA, all question tokens should also
+ have global attention. Please refer to the [Longformer paper](https://huggingface.co/papers/2004.05150) for more
+ details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LongformerForQuestionAnswering
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")
+ >>> model = LongformerForQuestionAnswering.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")
+
+ >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
+ >>> encoding = tokenizer(question, text, return_tensors="pt")
+ >>> input_ids = encoding["input_ids"]
+
+ >>> # default is local attention everywhere
+ >>> # the forward method will automatically set global attention on question tokens
+ >>> attention_mask = encoding["attention_mask"]
+
+ >>> outputs = model(input_ids, attention_mask=attention_mask)
+ >>> start_logits = outputs.start_logits
+ >>> end_logits = outputs.end_logits
+ >>> all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
+
+ >>> answer_tokens = all_tokens[torch.argmax(start_logits) : torch.argmax(end_logits) + 1]
+ >>> answer = tokenizer.decode(
+ ... tokenizer.convert_tokens_to_ids(answer_tokens)
+ ... ) # remove space prepending space token
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if global_attention_mask is None:
+ if input_ids is None:
+ logger.warning(
+ "It is not possible to automatically generate the `global_attention_mask` because input_ids is"
+ " None. Please make sure that it is correctly set."
+ )
+ else:
+ # set global attention on question tokens automatically
+ global_attention_mask = _compute_global_attention_mask(input_ids, self.config.sep_token_id)
+
+ outputs = self.longformer(
+ input_ids,
+ attention_mask=attention_mask,
+ global_attention_mask=global_attention_mask,
+ head_mask=head_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return LongformerQuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ global_attentions=outputs.global_attentions,
+ )
+
+
+@auto_docstring
+class LongformerForTokenClassification(LongformerPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.longformer = LongformerModel(config, add_pooling_layer=False)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ global_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LongformerTokenClassifierOutput]:
+ r"""
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention. Tokens with global
+ attention attends to all other tokens, and all other tokens attend to them. This is important for
+ task-specific finetuning because it makes the model more flexible at representing the task. For example,
+ for classification, the token should be given global attention. For QA, all question tokens should also
+ have global attention. Please refer to the [Longformer paper](https://huggingface.co/papers/2004.05150) for more
+ details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.longformer(
+ input_ids,
+ attention_mask=attention_mask,
+ global_attention_mask=global_attention_mask,
+ head_mask=head_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+
+ labels = labels.to(logits.device)
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return LongformerTokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ global_attentions=outputs.global_attentions,
+ )
+
+
+@auto_docstring
+class LongformerForMultipleChoice(LongformerPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.longformer = LongformerModel(config)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, 1)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ global_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LongformerMultipleChoiceModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ global_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention. Tokens with global
+ attention attends to all other tokens, and all other tokens attend to them. This is important for
+ task-specific finetuning because it makes the model more flexible at representing the task. For example,
+ for classification, the token should be given global attention. For QA, all question tokens should also
+ have global attention. Please refer to the [Longformer paper](https://huggingface.co/papers/2004.05150) for more
+ details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+ num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+ `input_ids` above)
+ position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ """
+ num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # set global attention on question tokens
+ if global_attention_mask is None and input_ids is not None:
+ logger.warning_once("Initializing global attention on multiple choice...")
+ # put global attention on all tokens after `config.sep_token_id`
+ global_attention_mask = torch.stack(
+ [
+ _compute_global_attention_mask(input_ids[:, i], self.config.sep_token_id, before_sep_token=False)
+ for i in range(num_choices)
+ ],
+ dim=1,
+ )
+
+ flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+ flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+ flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+ flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+ flat_global_attention_mask = (
+ global_attention_mask.view(-1, global_attention_mask.size(-1))
+ if global_attention_mask is not None
+ else None
+ )
+ flat_inputs_embeds = (
+ inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+ if inputs_embeds is not None
+ else None
+ )
+
+ outputs = self.longformer(
+ flat_input_ids,
+ position_ids=flat_position_ids,
+ token_type_ids=flat_token_type_ids,
+ attention_mask=flat_attention_mask,
+ global_attention_mask=flat_global_attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=flat_inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ pooled_output = outputs[1]
+
+ pooled_output = self.dropout(pooled_output)
+ logits = self.classifier(pooled_output)
+ reshaped_logits = logits.view(-1, num_choices)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+
+ labels = labels.to(reshaped_logits.device)
+ loss = loss_fct(reshaped_logits, labels)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return LongformerMultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ global_attentions=outputs.global_attentions,
+ )
+
+
+__all__ = [
+ "LongformerForMaskedLM",
+ "LongformerForMultipleChoice",
+ "LongformerForQuestionAnswering",
+ "LongformerForSequenceClassification",
+ "LongformerForTokenClassification",
+ "LongformerModel",
+ "LongformerPreTrainedModel",
+ "LongformerSelfAttention",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/modeling_tf_longformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/modeling_tf_longformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..891f5d76c95ce1f572ea33baf6db51cf21278a80
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/modeling_tf_longformer.py
@@ -0,0 +1,2783 @@
+# coding=utf-8
+# Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tensorflow Longformer model."""
+
+from __future__ import annotations
+
+import warnings
+from dataclasses import dataclass
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_utils import (
+ TFMaskedLanguageModelingLoss,
+ TFModelInputType,
+ TFMultipleChoiceLoss,
+ TFPreTrainedModel,
+ TFQuestionAnsweringLoss,
+ TFSequenceClassificationLoss,
+ TFTokenClassificationLoss,
+ get_initializer,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
+from ...utils import (
+ ModelOutput,
+ add_code_sample_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+)
+from .configuration_longformer import LongformerConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "allenai/longformer-base-4096"
+_CONFIG_FOR_DOC = "LongformerConfig"
+
+LARGE_NEGATIVE = -1e8
+
+
+@dataclass
+class TFLongformerBaseModelOutput(ModelOutput):
+ """
+ Base class for Longformer's outputs, with potential hidden states, local and global attentions.
+
+ Args:
+ last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the model.
+ hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+ attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`, where `x`
+ is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ last_hidden_state: tf.Tensor | None = None
+ hidden_states: tuple[tf.Tensor, ...] | None = None
+ attentions: tuple[tf.Tensor, ...] | None = None
+ global_attentions: tuple[tf.Tensor, ...] | None = None
+
+
+@dataclass
+class TFLongformerBaseModelOutputWithPooling(ModelOutput):
+ """
+ Base class for Longformer's outputs that also contains a pooling of the last hidden states.
+
+ Args:
+ last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the model.
+ pooler_output (`tf.Tensor` of shape `(batch_size, hidden_size)`):
+ Last layer hidden-state of the first token of the sequence (classification token) further processed by a
+ Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
+ prediction (classification) objective during pretraining.
+ hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+ attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`, where `x`
+ is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ last_hidden_state: tf.Tensor | None = None
+ pooler_output: tf.Tensor | None = None
+ hidden_states: tuple[tf.Tensor, ...] | None = None
+ attentions: tuple[tf.Tensor, ...] | None = None
+ global_attentions: tuple[tf.Tensor, ...] | None = None
+
+
+@dataclass
+class TFLongformerMaskedLMOutput(ModelOutput):
+ """
+ Base class for masked language models outputs.
+
+ Args:
+ loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Masked language modeling (MLM) loss.
+ logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+ attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`, where `x`
+ is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: tf.Tensor | None = None
+ logits: tf.Tensor | None = None
+ hidden_states: tuple[tf.Tensor, ...] | None = None
+ attentions: tuple[tf.Tensor, ...] | None = None
+ global_attentions: tuple[tf.Tensor, ...] | None = None
+
+
+@dataclass
+class TFLongformerQuestionAnsweringModelOutput(ModelOutput):
+ """
+ Base class for outputs of question answering Longformer models.
+
+ Args:
+ loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
+ start_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Span-start scores (before SoftMax).
+ end_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Span-end scores (before SoftMax).
+ hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+ attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`, where `x`
+ is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: tf.Tensor | None = None
+ start_logits: tf.Tensor | None = None
+ end_logits: tf.Tensor | None = None
+ hidden_states: tuple[tf.Tensor, ...] | None = None
+ attentions: tuple[tf.Tensor, ...] | None = None
+ global_attentions: tuple[tf.Tensor, ...] | None = None
+
+
+@dataclass
+class TFLongformerSequenceClassifierOutput(ModelOutput):
+ """
+ Base class for outputs of sentence classification models.
+
+ Args:
+ loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Classification (or regression if config.num_labels==1) loss.
+ logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
+ Classification (or regression if config.num_labels==1) scores (before SoftMax).
+ hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+ attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`, where `x`
+ is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: tf.Tensor | None = None
+ logits: tf.Tensor | None = None
+ hidden_states: tuple[tf.Tensor, ...] | None = None
+ attentions: tuple[tf.Tensor, ...] | None = None
+ global_attentions: tuple[tf.Tensor, ...] | None = None
+
+
+@dataclass
+class TFLongformerMultipleChoiceModelOutput(ModelOutput):
+ """
+ Base class for outputs of multiple choice models.
+
+ Args:
+ loss (`tf.Tensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
+ Classification loss.
+ logits (`tf.Tensor` of shape `(batch_size, num_choices)`):
+ *num_choices* is the second dimension of the input tensors. (see *input_ids* above).
+
+ Classification scores (before SoftMax).
+ hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+ attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`, where `x`
+ is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: tf.Tensor | None = None
+ logits: tf.Tensor | None = None
+ hidden_states: tuple[tf.Tensor, ...] | None = None
+ attentions: tuple[tf.Tensor, ...] | None = None
+ global_attentions: tuple[tf.Tensor, ...] | None = None
+
+
+@dataclass
+class TFLongformerTokenClassifierOutput(ModelOutput):
+ """
+ Base class for outputs of token classification models.
+
+ Args:
+ loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
+ Classification loss.
+ logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.num_labels)`):
+ Classification scores (before SoftMax).
+ hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+ attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x +
+ attention_window + 1)`, where `x` is the number of tokens with global attention mask.
+
+ Local attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token in the sequence to every token with
+ global attention (first `x` values) and to every token in the attention window (remaining `attention_window
+ + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
+ remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
+ token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
+ (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
+ If the attention window contains a token with global attention, the attention weight at the corresponding
+ index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
+ attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
+ accessed from `global_attentions`.
+ global_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, x)`, where `x`
+ is the number of tokens with global attention mask.
+
+ Global attentions weights after the attention softmax, used to compute the weighted average in the
+ self-attention heads. Those are the attention weights from every token with global attention to every token
+ in the sequence.
+ """
+
+ loss: tf.Tensor | None = None
+ logits: tf.Tensor | None = None
+ hidden_states: tuple[tf.Tensor, ...] | None = None
+ attentions: tuple[tf.Tensor, ...] | None = None
+ global_attentions: tuple[tf.Tensor, ...] | None = None
+
+
+def _compute_global_attention_mask(input_ids_shape, sep_token_indices, before_sep_token=True):
+ """
+ Computes global attention mask by putting attention on all tokens before `sep_token_id` if `before_sep_token is
+ True` else after `sep_token_id`.
+ """
+ assert shape_list(sep_token_indices)[1] == 2, "`input_ids` should have two dimensions"
+ question_end_index = tf.reshape(sep_token_indices, (input_ids_shape[0], 3, 2))[:, 0, 1][:, None]
+ # bool attention mask with True in locations of global attention
+ attention_mask = tf.expand_dims(tf.range(input_ids_shape[1], dtype=tf.int64), axis=0)
+ attention_mask = tf.tile(attention_mask, (input_ids_shape[0], 1))
+ if before_sep_token is True:
+ question_end_index = tf.tile(question_end_index, (1, input_ids_shape[1]))
+ attention_mask = tf.cast(attention_mask < question_end_index, dtype=question_end_index.dtype)
+ else:
+ # last token is separation token and should not be counted and in the middle are two separation tokens
+ question_end_index = tf.tile(question_end_index + 1, (1, input_ids_shape[1]))
+ attention_mask = tf.cast(
+ attention_mask > question_end_index,
+ dtype=question_end_index.dtype,
+ ) * tf.cast(attention_mask < input_ids_shape[-1], dtype=question_end_index.dtype)
+
+ return attention_mask
+
+
+# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaLMHead with Roberta->Longformer
+class TFLongformerLMHead(keras.layers.Layer):
+ """Longformer Head for masked language modeling."""
+
+ def __init__(self, config, input_embeddings, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.dense = keras.layers.Dense(
+ config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
+ self.act = get_tf_activation("gelu")
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = input_embeddings
+
+ def build(self, input_shape=None):
+ self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "layer_norm", None) is not None:
+ with tf.name_scope(self.layer_norm.name):
+ self.layer_norm.build([None, None, self.config.hidden_size])
+
+ def get_output_embeddings(self):
+ return self.decoder
+
+ def set_output_embeddings(self, value):
+ self.decoder.weight = value
+ self.decoder.vocab_size = shape_list(value)[0]
+
+ def get_bias(self):
+ return {"bias": self.bias}
+
+ def set_bias(self, value):
+ self.bias = value["bias"]
+ self.config.vocab_size = shape_list(value["bias"])[0]
+
+ def call(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.layer_norm(hidden_states)
+
+ # project back to size of vocabulary with bias
+ seq_length = shape_list(tensor=hidden_states)[1]
+ hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
+ hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True)
+ hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size])
+ hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
+
+ return hidden_states
+
+
+class TFLongformerEmbeddings(keras.layers.Layer):
+ """
+ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing and some extra casting.
+ """
+
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.padding_idx = 1
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.max_position_embeddings = config.max_position_embeddings
+ self.initializer_range = config.initializer_range
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+
+ def build(self, input_shape=None):
+ with tf.name_scope("word_embeddings"):
+ self.weight = self.add_weight(
+ name="weight",
+ shape=[self.config.vocab_size, self.hidden_size],
+ initializer=get_initializer(self.initializer_range),
+ )
+
+ with tf.name_scope("token_type_embeddings"):
+ self.token_type_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.config.type_vocab_size, self.hidden_size],
+ initializer=get_initializer(self.initializer_range),
+ )
+
+ with tf.name_scope("position_embeddings"):
+ self.position_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.max_position_embeddings, self.hidden_size],
+ initializer=get_initializer(self.initializer_range),
+ )
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+ def create_position_ids_from_input_ids(self, input_ids, past_key_values_length=0):
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
+ symbols are ignored. This is modified from fairseq's `utils.make_positions`.
+
+ Args:
+ input_ids: tf.Tensor
+ Returns: tf.Tensor
+ """
+ mask = tf.cast(tf.math.not_equal(input_ids, self.padding_idx), dtype=input_ids.dtype)
+ incremental_indices = (tf.math.cumsum(mask, axis=1) + past_key_values_length) * mask
+
+ return incremental_indices + self.padding_idx
+
+ def call(
+ self,
+ input_ids=None,
+ position_ids=None,
+ token_type_ids=None,
+ inputs_embeds=None,
+ past_key_values_length=0,
+ training=False,
+ ):
+ """
+ Applies embedding based on inputs tensor.
+
+ Returns:
+ final_embeddings (`tf.Tensor`): output embedding tensor.
+ """
+ assert not (input_ids is None and inputs_embeds is None)
+
+ if input_ids is not None:
+ check_embeddings_within_bounds(input_ids, self.config.vocab_size)
+ inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
+
+ input_shape = shape_list(inputs_embeds)[:-1]
+
+ if token_type_ids is None:
+ token_type_ids = tf.cast(tf.fill(dims=input_shape, value=0), tf.int64)
+
+ if position_ids is None:
+ if input_ids is not None:
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
+ position_ids = self.create_position_ids_from_input_ids(
+ input_ids=input_ids, past_key_values_length=past_key_values_length
+ )
+ else:
+ position_ids = tf.expand_dims(
+ tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1, dtype=tf.int64),
+ axis=0,
+ )
+
+ position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
+ token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
+ final_embeddings = inputs_embeds + position_embeds + token_type_embeds
+ final_embeddings = self.LayerNorm(inputs=final_embeddings)
+ final_embeddings = self.dropout(inputs=final_embeddings, training=training)
+
+ return final_embeddings
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Longformer
+class TFLongformerIntermediate(keras.layers.Layer):
+ def __init__(self, config: LongformerConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = get_tf_activation(config.hidden_act)
+ else:
+ self.intermediate_act_fn = config.hidden_act
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Longformer
+class TFLongformerOutput(keras.layers.Layer):
+ def __init__(self, config: LongformerConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.dropout(inputs=hidden_states, training=training)
+ hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.intermediate_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Longformer
+class TFLongformerPooler(keras.layers.Layer):
+ def __init__(self, config: LongformerConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ activation="tanh",
+ name="dense",
+ )
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(inputs=first_token_tensor)
+
+ return pooled_output
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Longformer
+class TFLongformerSelfOutput(keras.layers.Layer):
+ def __init__(self, config: LongformerConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.dropout(inputs=hidden_states, training=training)
+ hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+class TFLongformerSelfAttention(keras.layers.Layer):
+ def __init__(self, config, layer_id, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads}"
+ )
+
+ self.num_heads = config.num_attention_heads
+ self.head_dim = int(config.hidden_size / config.num_attention_heads)
+ self.embed_dim = config.hidden_size
+ self.query = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="query",
+ )
+ self.key = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="key",
+ )
+ self.value = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="value",
+ )
+
+ # separate projection layers for tokens with global attention
+ self.query_global = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="query_global",
+ )
+ self.key_global = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="key_global",
+ )
+ self.value_global = keras.layers.Dense(
+ self.embed_dim,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="value_global",
+ )
+ self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob)
+ self.global_dropout = keras.layers.Dropout(config.attention_probs_dropout_prob)
+ self.layer_id = layer_id
+ attention_window = config.attention_window[self.layer_id]
+
+ assert attention_window % 2 == 0, (
+ f"`attention_window` for layer {self.layer_id} has to be an even value. Given {attention_window}"
+ )
+ assert attention_window > 0, (
+ f"`attention_window` for layer {self.layer_id} has to be positive. Given {attention_window}"
+ )
+
+ self.one_sided_attn_window_size = attention_window // 2
+
+ def build(self, input_shape=None):
+ if not self.built:
+ with tf.name_scope("query_global"):
+ self.query_global.build((self.config.hidden_size,))
+ with tf.name_scope("key_global"):
+ self.key_global.build((self.config.hidden_size,))
+ with tf.name_scope("value_global"):
+ self.value_global.build((self.config.hidden_size,))
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "query", None) is not None:
+ with tf.name_scope(self.query.name):
+ self.query.build([None, None, self.config.hidden_size])
+ if getattr(self, "key", None) is not None:
+ with tf.name_scope(self.key.name):
+ self.key.build([None, None, self.config.hidden_size])
+ if getattr(self, "value", None) is not None:
+ with tf.name_scope(self.value.name):
+ self.value.build([None, None, self.config.hidden_size])
+ if getattr(self, "query_global", None) is not None:
+ with tf.name_scope(self.query_global.name):
+ self.query_global.build([None, None, self.config.hidden_size])
+ if getattr(self, "key_global", None) is not None:
+ with tf.name_scope(self.key_global.name):
+ self.key_global.build([None, None, self.config.hidden_size])
+ if getattr(self, "value_global", None) is not None:
+ with tf.name_scope(self.value_global.name):
+ self.value_global.build([None, None, self.config.hidden_size])
+
+ def call(
+ self,
+ inputs,
+ training=False,
+ ):
+ """
+ LongformerSelfAttention expects *len(hidden_states)* to be multiple of *attention_window*. Padding to
+ *attention_window* happens in LongformerModel.forward to avoid redoing the padding on each layer.
+
+ The *attention_mask* is changed in [`LongformerModel.forward`] from 0, 1, 2 to:
+
+ - -10000: no attention
+ - 0: local attention
+ - +10000: global attention
+ """
+ # retrieve input args
+ (
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ is_index_masked,
+ is_index_global_attn,
+ is_global_attn,
+ ) = inputs
+
+ # project hidden states
+ query_vectors = self.query(hidden_states)
+ key_vectors = self.key(hidden_states)
+ value_vectors = self.value(hidden_states)
+ batch_size, seq_len, embed_dim = shape_list(hidden_states)
+
+ tf.debugging.assert_equal(
+ embed_dim,
+ self.embed_dim,
+ message=f"hidden_states should have embed_dim = {self.embed_dim}, but has {embed_dim}",
+ )
+
+ # normalize query
+ query_vectors /= tf.math.sqrt(tf.cast(self.head_dim, dtype=query_vectors.dtype))
+ query_vectors = tf.reshape(query_vectors, (batch_size, seq_len, self.num_heads, self.head_dim))
+ key_vectors = tf.reshape(key_vectors, (batch_size, seq_len, self.num_heads, self.head_dim))
+
+ # attn_probs = (batch_size, seq_len, num_heads, window*2+1)
+ attn_scores = self._sliding_chunks_query_key_matmul(
+ query_vectors, key_vectors, self.one_sided_attn_window_size
+ )
+
+ # values to pad for attention probs
+ remove_from_windowed_attention_mask = attention_mask != 0
+ # cast to fp32/fp16 then replace 1's with -inf
+ float_mask = tf.cast(remove_from_windowed_attention_mask, dtype=query_vectors.dtype) * LARGE_NEGATIVE
+
+ # diagonal mask with zeros everywhere and -inf inplace of padding
+ diagonal_mask = self._sliding_chunks_query_key_matmul(
+ tf.ones(shape_list(attention_mask)),
+ float_mask,
+ self.one_sided_attn_window_size,
+ )
+
+ # pad local attention probs
+ attn_scores += diagonal_mask
+
+ tf.debugging.assert_equal(
+ shape_list(attn_scores),
+ [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + 1],
+ message=(
+ f"attn_probs should be of size ({batch_size}, {seq_len}, {self.num_heads},"
+ f" {self.one_sided_attn_window_size * 2 + 1}), but is of size {shape_list(attn_scores)}"
+ ),
+ )
+
+ # compute global attn indices required through out forward fn
+ (
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ ) = self._get_global_attn_indices(is_index_global_attn)
+
+ # this function is only relevant for global attention
+ if is_global_attn:
+ attn_scores = self._concat_with_global_key_attn_probs(
+ attn_scores=attn_scores,
+ query_vectors=query_vectors,
+ key_vectors=key_vectors,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
+ )
+
+ attn_probs = stable_softmax(attn_scores, axis=-1)
+
+ # softmax sometimes inserts NaN if all positions are masked, replace them with 0
+ # Make sure to create a mask with the proper shape:
+ # if is_global_attn==True => [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + max_num_global_attn_indices + 1]
+ # if is_global_attn==False => [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + 1]
+ if is_global_attn:
+ masked_index = tf.tile(
+ is_index_masked[:, :, None, None],
+ (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + max_num_global_attn_indices + 1),
+ )
+ else:
+ masked_index = tf.tile(
+ is_index_masked[:, :, None, None],
+ (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + 1),
+ )
+ attn_probs = tf.where(
+ masked_index,
+ tf.zeros(shape_list(masked_index), dtype=attn_probs.dtype),
+ attn_probs,
+ )
+
+ if layer_head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(layer_head_mask),
+ [self.num_heads],
+ message=(
+ f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
+ f" {shape_list(layer_head_mask)}"
+ ),
+ )
+
+ attn_probs = tf.reshape(layer_head_mask, (1, 1, -1, 1)) * attn_probs
+
+ # apply dropout
+ attn_probs = self.dropout(attn_probs, training=training)
+ value_vectors = tf.reshape(value_vectors, (batch_size, seq_len, self.num_heads, self.head_dim))
+
+ # if global attention, compute sum of global and local attn
+
+ if is_global_attn:
+ attn_output = self._compute_attn_output_with_global_indices(
+ value_vectors=value_vectors,
+ attn_probs=attn_probs,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ )
+ else:
+ attn_output = self._sliding_chunks_matmul_attn_probs_value(
+ attn_probs, value_vectors, self.one_sided_attn_window_size
+ )
+
+ tf.debugging.assert_equal(
+ shape_list(attn_output), [batch_size, seq_len, self.num_heads, self.head_dim], message="Unexpected size"
+ )
+
+ attn_output = tf.reshape(attn_output, (batch_size, seq_len, embed_dim))
+
+ # compute value for global attention and overwrite to attention output
+ if is_global_attn:
+ attn_output, global_attn_probs = self._compute_global_attn_output_from_hidden(
+ attn_output=attn_output,
+ hidden_states=hidden_states,
+ max_num_global_attn_indices=max_num_global_attn_indices,
+ layer_head_mask=layer_head_mask,
+ is_local_index_global_attn_nonzero=is_local_index_global_attn_nonzero,
+ is_index_global_attn_nonzero=is_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero=is_local_index_no_global_attn_nonzero,
+ is_index_masked=is_index_masked,
+ training=training,
+ )
+ else:
+ # Leave attn_output unchanged
+ global_attn_probs = tf.zeros((batch_size, self.num_heads, max_num_global_attn_indices, seq_len))
+
+ # make sure that local attention probabilities are set to 0 for indices of global attn
+ # Make sure to create a mask with the proper shape:
+ # if is_global_attn==True => [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + max_num_global_attn_indices + 1]
+ # if is_global_attn==False => [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + 1]
+ if is_global_attn:
+ masked_global_attn_index = tf.tile(
+ is_index_global_attn[:, :, None, None],
+ (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + max_num_global_attn_indices + 1),
+ )
+ else:
+ masked_global_attn_index = tf.tile(
+ is_index_global_attn[:, :, None, None],
+ (1, 1, self.num_heads, self.one_sided_attn_window_size * 2 + 1),
+ )
+ attn_probs = tf.where(
+ masked_global_attn_index,
+ tf.zeros(shape_list(masked_global_attn_index), dtype=attn_probs.dtype),
+ attn_probs,
+ )
+
+ outputs = (attn_output, attn_probs, global_attn_probs)
+
+ return outputs
+
+ def _sliding_chunks_query_key_matmul(self, query, key, window_overlap):
+ """
+ Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
+ implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer) with an
+ overlap of size window_overlap
+ """
+ batch_size, seq_len, num_heads, head_dim = shape_list(query)
+
+ tf.debugging.assert_equal(
+ seq_len % (window_overlap * 2),
+ 0,
+ message=f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}",
+ )
+ tf.debugging.assert_equal(
+ shape_list(query),
+ shape_list(key),
+ message=(
+ f"Shape of query and key should be equal, but got query: {shape_list(query)} and key:"
+ f" {shape_list(key)}"
+ ),
+ )
+
+ chunks_count = seq_len // window_overlap - 1
+
+ # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size window_overlap * 2
+ query = tf.reshape(
+ tf.transpose(query, (0, 2, 1, 3)),
+ (batch_size * num_heads, seq_len, head_dim),
+ )
+ key = tf.reshape(tf.transpose(key, (0, 2, 1, 3)), (batch_size * num_heads, seq_len, head_dim))
+ chunked_query = self._chunk(query, window_overlap)
+ chunked_key = self._chunk(key, window_overlap)
+
+ # matrix multiplication
+ # bcxd: batch_size * num_heads x chunks x 2window_overlap x head_dim
+ # bcyd: batch_size * num_heads x chunks x 2window_overlap x head_dim
+ # bcxy: batch_size * num_heads x chunks x 2window_overlap x 2window_overlap
+ chunked_query = tf.cast(chunked_query, dtype=chunked_key.dtype)
+ chunked_attention_scores = tf.einsum("bcxd,bcyd->bcxy", chunked_query, chunked_key) # multiply
+
+ # convert diagonals into columns
+ paddings = tf.convert_to_tensor([[0, 0], [0, 0], [0, 1], [0, 0]])
+ diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(chunked_attention_scores, paddings)
+
+ # allocate space for the overall attention matrix where the chunks are combined. The last dimension
+ # has (window_overlap * 2 + 1) columns. The first (window_overlap) columns are the window_overlap lower triangles (attention from a word to
+ # window_overlap previous words). The following column is attention score from each word to itself, then
+ # followed by window_overlap columns for the upper triangle.
+
+ # copy parts from diagonal_chunked_attention_scores into the combined matrix of attentions
+ # - copying the main diagonal and the upper triangle
+ # TODO: This code is most likely not very efficient and should be improved
+ diagonal_attn_scores_up_triang = tf.concat(
+ [
+ diagonal_chunked_attention_scores[:, :, :window_overlap, : window_overlap + 1],
+ diagonal_chunked_attention_scores[:, -1:, window_overlap:, : window_overlap + 1],
+ ],
+ axis=1,
+ )
+
+ # - copying the lower triangle
+ diagonal_attn_scores_low_triang = tf.concat(
+ [
+ tf.zeros(
+ (batch_size * num_heads, 1, window_overlap, window_overlap),
+ dtype=diagonal_chunked_attention_scores.dtype,
+ ),
+ diagonal_chunked_attention_scores[:, :, -(window_overlap + 1) : -1, window_overlap + 1 :],
+ ],
+ axis=1,
+ )
+ diagonal_attn_scores_first_chunk = tf.concat(
+ [
+ tf.roll(
+ diagonal_chunked_attention_scores,
+ shift=[1, window_overlap],
+ axis=[2, 3],
+ )[:, :, :window_overlap, :window_overlap],
+ tf.zeros(
+ (batch_size * num_heads, 1, window_overlap, window_overlap),
+ dtype=diagonal_chunked_attention_scores.dtype,
+ ),
+ ],
+ axis=1,
+ )
+ first_chunk_mask = (
+ tf.tile(
+ tf.range(chunks_count + 1, dtype=tf.int64)[None, :, None, None],
+ (batch_size * num_heads, 1, window_overlap, window_overlap),
+ )
+ < 1
+ )
+ diagonal_attn_scores_low_triang = tf.where(
+ first_chunk_mask,
+ diagonal_attn_scores_first_chunk,
+ diagonal_attn_scores_low_triang,
+ )
+
+ # merging upper and lower triangle
+ diagonal_attention_scores = tf.concat(
+ [diagonal_attn_scores_low_triang, diagonal_attn_scores_up_triang], axis=-1
+ )
+
+ # separate batch_size and num_heads dimensions again
+ diagonal_attention_scores = tf.transpose(
+ tf.reshape(
+ diagonal_attention_scores,
+ (batch_size, num_heads, seq_len, 2 * window_overlap + 1),
+ ),
+ (0, 2, 1, 3),
+ )
+
+ diagonal_attention_scores = self._mask_invalid_locations(diagonal_attention_scores, window_overlap)
+
+ return diagonal_attention_scores
+
+ @staticmethod
+ def _mask_invalid_locations(input_tensor, window_overlap):
+ # create correct upper triangle bool mask
+ mask_2d_upper = tf.reverse(
+ tf.linalg.band_part(tf.ones(shape=(window_overlap, window_overlap + 1)), -1, 0),
+ axis=[0],
+ )
+
+ # pad to full matrix
+ padding = tf.convert_to_tensor(
+ [[0, shape_list(input_tensor)[1] - window_overlap], [0, shape_list(input_tensor)[3] - window_overlap - 1]]
+ )
+
+ # create lower mask
+ mask_2d = tf.pad(mask_2d_upper, padding)
+
+ # combine with upper mask
+ mask_2d = mask_2d + tf.reverse(mask_2d, axis=[0, 1])
+
+ # broadcast to full matrix
+ mask_4d = tf.tile(mask_2d[None, :, None, :], (shape_list(input_tensor)[0], 1, 1, 1))
+
+ # inf tensor used for masking
+ inf_tensor = -float("inf") * tf.ones_like(input_tensor)
+
+ # mask
+ input_tensor = tf.where(tf.math.greater(mask_4d, 0), inf_tensor, input_tensor)
+
+ return input_tensor
+
+ def _sliding_chunks_matmul_attn_probs_value(self, attn_probs, value, window_overlap):
+ """
+ Same as _sliding_chunks_query_key_matmul but for attn_probs and value tensors. Returned tensor will be of the
+ same shape as `attn_probs`
+ """
+
+ batch_size, seq_len, num_heads, head_dim = shape_list(value)
+
+ tf.debugging.assert_equal(
+ seq_len % (window_overlap * 2), 0, message="Seq_len has to be multiple of 2 * window_overlap"
+ )
+ tf.debugging.assert_equal(
+ shape_list(attn_probs)[:3],
+ shape_list(value)[:3],
+ message="value and attn_probs must have same dims (except head_dim)",
+ )
+ tf.debugging.assert_equal(
+ shape_list(attn_probs)[3],
+ 2 * window_overlap + 1,
+ message="attn_probs last dim has to be 2 * window_overlap + 1",
+ )
+
+ chunks_count = seq_len // window_overlap - 1
+
+ # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size 2 window overlap
+ chunked_attn_probs = tf.reshape(
+ tf.transpose(attn_probs, (0, 2, 1, 3)),
+ (
+ batch_size * num_heads,
+ seq_len // window_overlap,
+ window_overlap,
+ 2 * window_overlap + 1,
+ ),
+ )
+
+ # group batch_size and num_heads dimensions into one
+ value = tf.reshape(
+ tf.transpose(value, (0, 2, 1, 3)),
+ (batch_size * num_heads, seq_len, head_dim),
+ )
+
+ # pad seq_len with w at the beginning of the sequence and another window overlap at the end
+ paddings = tf.convert_to_tensor([[0, 0], [window_overlap, window_overlap], [0, 0]])
+ padded_value = tf.pad(value, paddings, constant_values=-1)
+
+ # chunk padded_value into chunks of size 3 window overlap and an overlap of size window overlap
+ frame_size = 3 * window_overlap * head_dim
+ frame_hop_size = (shape_list(padded_value)[1] * head_dim - frame_size) // chunks_count
+ chunked_value = tf.signal.frame(
+ tf.reshape(padded_value, (batch_size * num_heads, -1)),
+ frame_size,
+ frame_hop_size,
+ )
+ chunked_value = tf.reshape(
+ chunked_value,
+ (batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim),
+ )
+
+ tf.debugging.assert_equal(
+ shape_list(chunked_value),
+ [batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim],
+ message="Chunked value has the wrong shape",
+ )
+
+ chunked_attn_probs = self._pad_and_diagonalize(chunked_attn_probs)
+ context = tf.einsum("bcwd,bcdh->bcwh", chunked_attn_probs, chunked_value)
+ context = tf.transpose(
+ tf.reshape(context, (batch_size, num_heads, seq_len, head_dim)),
+ (0, 2, 1, 3),
+ )
+
+ return context
+
+ @staticmethod
+ def _pad_and_transpose_last_two_dims(hidden_states_padded, paddings):
+ """pads rows and then flips rows and columns"""
+ hidden_states_padded = tf.pad(
+ hidden_states_padded, paddings
+ ) # padding value is not important because it will be overwritten
+ batch_size, chunk_size, seq_length, hidden_dim = shape_list(hidden_states_padded)
+ hidden_states_padded = tf.reshape(hidden_states_padded, (batch_size, chunk_size, hidden_dim, seq_length))
+
+ return hidden_states_padded
+
+ @staticmethod
+ def _pad_and_diagonalize(chunked_hidden_states):
+ """
+ shift every row 1 step right, converting columns into diagonals.
+
+ Example:
+
+ ```python
+ chunked_hidden_states: [
+ 0.4983,
+ 2.6918,
+ -0.0071,
+ 1.0492,
+ -1.8348,
+ 0.7672,
+ 0.2986,
+ 0.0285,
+ -0.7584,
+ 0.4206,
+ -0.0405,
+ 0.1599,
+ 2.0514,
+ -1.1600,
+ 0.5372,
+ 0.2629,
+ ]
+ window_overlap = num_rows = 4
+ ```
+
+ (pad & diagonalize) => [ 0.4983, 2.6918, -0.0071, 1.0492, 0.0000, 0.0000, 0.0000
+ 0.0000, -1.8348, 0.7672, 0.2986, 0.0285, 0.0000, 0.0000 0.0000, 0.0000, -0.7584, 0.4206,
+ -0.0405, 0.1599, 0.0000 0.0000, 0.0000, 0.0000, 2.0514, -1.1600, 0.5372, 0.2629 ]
+ """
+ total_num_heads, num_chunks, window_overlap, hidden_dim = shape_list(chunked_hidden_states)
+ paddings = tf.convert_to_tensor([[0, 0], [0, 0], [0, 0], [0, window_overlap + 1]])
+ chunked_hidden_states = tf.pad(
+ chunked_hidden_states, paddings
+ ) # total_num_heads x num_chunks x window_overlap x (hidden_dim+window_overlap+1). Padding value is not important because it'll be overwritten
+ chunked_hidden_states = tf.reshape(
+ chunked_hidden_states, (total_num_heads, num_chunks, -1)
+ ) # total_num_heads x num_chunks x window_overlapL+window_overlapwindow_overlap+window_overlap
+ chunked_hidden_states = chunked_hidden_states[
+ :, :, :-window_overlap
+ ] # total_num_heads x num_chunks x window_overlapL+window_overlapwindow_overlap
+ chunked_hidden_states = tf.reshape(
+ chunked_hidden_states,
+ (total_num_heads, num_chunks, window_overlap, window_overlap + hidden_dim),
+ ) # total_num_heads x num_chunks, window_overlap x hidden_dim+window_overlap
+ chunked_hidden_states = chunked_hidden_states[:, :, :, :-1]
+
+ return chunked_hidden_states
+
+ @staticmethod
+ def _chunk(hidden_states, window_overlap):
+ """convert into overlapping chunks. Chunk size = 2w, overlap size = w"""
+ batch_size, seq_length, hidden_dim = shape_list(hidden_states)
+ num_output_chunks = 2 * (seq_length // (2 * window_overlap)) - 1
+
+ # define frame size and frame stride (similar to convolution)
+ frame_hop_size = window_overlap * hidden_dim
+ frame_size = 2 * frame_hop_size
+ hidden_states = tf.reshape(hidden_states, (batch_size, seq_length * hidden_dim))
+
+ # chunk with overlap
+ chunked_hidden_states = tf.signal.frame(hidden_states, frame_size, frame_hop_size)
+
+ tf.debugging.assert_equal(
+ shape_list(chunked_hidden_states),
+ [batch_size, num_output_chunks, frame_size],
+ message=(
+ "Make sure chunking is correctly applied. `Chunked hidden states should have output dimension"
+ f" {[batch_size, frame_size, num_output_chunks]}, but got {shape_list(chunked_hidden_states)}."
+ ),
+ )
+
+ chunked_hidden_states = tf.reshape(
+ chunked_hidden_states,
+ (batch_size, num_output_chunks, 2 * window_overlap, hidden_dim),
+ )
+
+ return chunked_hidden_states
+
+ @staticmethod
+ def _get_global_attn_indices(is_index_global_attn):
+ """compute global attn indices required throughout forward pass"""
+ # helper variable
+ num_global_attn_indices = tf.math.count_nonzero(is_index_global_attn, axis=1)
+ num_global_attn_indices = tf.cast(num_global_attn_indices, dtype=tf.constant(1).dtype)
+
+ # max number of global attn indices in batch
+ max_num_global_attn_indices = tf.reduce_max(num_global_attn_indices)
+
+ # indices of global attn
+ is_index_global_attn_nonzero = tf.where(is_index_global_attn)
+
+ # helper variable
+ is_local_index_global_attn = tf.range(max_num_global_attn_indices) < tf.expand_dims(
+ num_global_attn_indices, axis=-1
+ )
+
+ # location of the non-padding values within global attention indices
+ is_local_index_global_attn_nonzero = tf.where(is_local_index_global_attn)
+
+ # location of the padding values within global attention indices
+ is_local_index_no_global_attn_nonzero = tf.where(tf.math.logical_not(is_local_index_global_attn))
+
+ return (
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ )
+
+ def _concat_with_global_key_attn_probs(
+ self,
+ attn_scores,
+ key_vectors,
+ query_vectors,
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ ):
+ batch_size = shape_list(key_vectors)[0]
+
+ # select global key vectors
+ global_key_vectors = tf.gather_nd(key_vectors, is_index_global_attn_nonzero)
+
+ # create only global key vectors
+ key_vectors_only_global = tf.scatter_nd(
+ is_local_index_global_attn_nonzero,
+ global_key_vectors,
+ shape=(
+ batch_size,
+ max_num_global_attn_indices,
+ self.num_heads,
+ self.head_dim,
+ ),
+ )
+
+ # (batch_size, seq_len, num_heads, max_num_global_attn_indices)
+ attn_probs_from_global_key = tf.einsum("blhd,bshd->blhs", query_vectors, key_vectors_only_global)
+
+ # (batch_size, max_num_global_attn_indices, seq_len, num_heads)
+ attn_probs_from_global_key_trans = tf.transpose(attn_probs_from_global_key, (0, 3, 1, 2))
+ mask_shape = (shape_list(is_local_index_no_global_attn_nonzero)[0],) + tuple(
+ shape_list(attn_probs_from_global_key_trans)[-2:]
+ )
+ mask = tf.ones(mask_shape) * -10000.0
+ mask = tf.cast(mask, dtype=attn_probs_from_global_key_trans.dtype)
+
+ # scatter mask
+ attn_probs_from_global_key_trans = tf.tensor_scatter_nd_update(
+ attn_probs_from_global_key_trans,
+ is_local_index_no_global_attn_nonzero,
+ mask,
+ )
+
+ # (batch_size, seq_len, num_heads, max_num_global_attn_indices)
+ attn_probs_from_global_key = tf.transpose(attn_probs_from_global_key_trans, (0, 2, 3, 1))
+
+ # concat to attn_probs
+ # (batch_size, seq_len, num_heads, extra attention count + 2*window+1)
+ attn_scores = tf.concat((attn_probs_from_global_key, attn_scores), axis=-1)
+
+ return attn_scores
+
+ def _compute_attn_output_with_global_indices(
+ self,
+ value_vectors,
+ attn_probs,
+ max_num_global_attn_indices,
+ is_index_global_attn_nonzero,
+ is_local_index_global_attn_nonzero,
+ ):
+ batch_size = shape_list(attn_probs)[0]
+
+ # cut local attn probs to global only
+ attn_probs_only_global = attn_probs[:, :, :, :max_num_global_attn_indices]
+
+ # select global value vectors
+ global_value_vectors = tf.gather_nd(value_vectors, is_index_global_attn_nonzero)
+
+ # create only global value vectors
+ value_vectors_only_global = tf.scatter_nd(
+ is_local_index_global_attn_nonzero,
+ global_value_vectors,
+ shape=(
+ batch_size,
+ max_num_global_attn_indices,
+ self.num_heads,
+ self.head_dim,
+ ),
+ )
+
+ # compute attn output only global
+ attn_output_only_global = tf.einsum("blhs,bshd->blhd", attn_probs_only_global, value_vectors_only_global)
+
+ # reshape attn probs
+ attn_probs_without_global = attn_probs[:, :, :, max_num_global_attn_indices:]
+
+ # compute attn output with global
+ attn_output_without_global = self._sliding_chunks_matmul_attn_probs_value(
+ attn_probs_without_global, value_vectors, self.one_sided_attn_window_size
+ )
+
+ return attn_output_only_global + attn_output_without_global
+
+ def _compute_global_attn_output_from_hidden(
+ self,
+ attn_output,
+ hidden_states,
+ max_num_global_attn_indices,
+ layer_head_mask,
+ is_local_index_global_attn_nonzero,
+ is_index_global_attn_nonzero,
+ is_local_index_no_global_attn_nonzero,
+ is_index_masked,
+ training,
+ ):
+ batch_size, seq_len = shape_list(hidden_states)[:2]
+
+ # prepare global hidden states
+ global_attn_hidden_states = tf.gather_nd(hidden_states, is_index_global_attn_nonzero)
+ global_attn_hidden_states = tf.scatter_nd(
+ is_local_index_global_attn_nonzero,
+ global_attn_hidden_states,
+ shape=(batch_size, max_num_global_attn_indices, self.embed_dim),
+ )
+
+ # global key, query, value
+ global_query_vectors_only_global = self.query_global(global_attn_hidden_states)
+ global_key_vectors = self.key_global(hidden_states)
+ global_value_vectors = self.value_global(hidden_states)
+
+ # normalize
+ global_query_vectors_only_global /= tf.math.sqrt(
+ tf.cast(self.head_dim, dtype=global_query_vectors_only_global.dtype)
+ )
+ global_query_vectors_only_global = self.reshape_and_transpose(global_query_vectors_only_global, batch_size)
+ global_key_vectors = self.reshape_and_transpose(global_key_vectors, batch_size)
+ global_value_vectors = self.reshape_and_transpose(global_value_vectors, batch_size)
+
+ # compute attn scores
+ global_attn_scores = tf.matmul(global_query_vectors_only_global, global_key_vectors, transpose_b=True)
+
+ tf.debugging.assert_equal(
+ shape_list(global_attn_scores),
+ [batch_size * self.num_heads, max_num_global_attn_indices, seq_len],
+ message=(
+ "global_attn_scores have the wrong size. Size should be"
+ f" {(batch_size * self.num_heads, max_num_global_attn_indices, seq_len)}, but is"
+ f" {shape_list(global_attn_scores)}."
+ ),
+ )
+
+ global_attn_scores = tf.reshape(
+ global_attn_scores,
+ (batch_size, self.num_heads, max_num_global_attn_indices, seq_len),
+ )
+ global_attn_scores_trans = tf.transpose(global_attn_scores, (0, 2, 1, 3))
+ mask_shape = (shape_list(is_local_index_no_global_attn_nonzero)[0],) + tuple(
+ shape_list(global_attn_scores_trans)[-2:]
+ )
+ global_attn_mask = tf.ones(mask_shape) * -10000.0
+ global_attn_mask = tf.cast(global_attn_mask, dtype=global_attn_scores_trans.dtype)
+
+ # scatter mask
+ global_attn_scores_trans = tf.tensor_scatter_nd_update(
+ global_attn_scores_trans,
+ is_local_index_no_global_attn_nonzero,
+ global_attn_mask,
+ )
+ global_attn_scores = tf.transpose(global_attn_scores_trans, (0, 2, 1, 3))
+
+ # mask global attn scores
+ attn_mask = tf.tile(is_index_masked[:, None, None, :], (1, shape_list(global_attn_scores)[1], 1, 1))
+ global_attn_scores = tf.where(attn_mask, -10000.0, global_attn_scores)
+ global_attn_scores = tf.reshape(
+ global_attn_scores,
+ (batch_size * self.num_heads, max_num_global_attn_indices, seq_len),
+ )
+
+ # compute global attn probs
+ global_attn_probs_float = stable_softmax(global_attn_scores, axis=-1)
+
+ # apply layer head masking
+ if layer_head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(layer_head_mask),
+ [self.num_heads],
+ message=(
+ f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
+ f" {shape_list(layer_head_mask)}"
+ ),
+ )
+ global_attn_probs_float = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
+ global_attn_probs_float, (batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
+ )
+ global_attn_probs_float = tf.reshape(
+ global_attn_probs_float, (batch_size * self.num_heads, max_num_global_attn_indices, seq_len)
+ )
+
+ # dropout
+ global_attn_probs = self.global_dropout(global_attn_probs_float, training=training)
+
+ # global attn output
+ global_attn_output = tf.matmul(global_attn_probs, global_value_vectors)
+
+ tf.debugging.assert_equal(
+ shape_list(global_attn_output),
+ [batch_size * self.num_heads, max_num_global_attn_indices, self.head_dim],
+ message=(
+ "global_attn_output tensor has the wrong size. Size should be"
+ f" {(batch_size * self.num_heads, max_num_global_attn_indices, self.head_dim)}, but is"
+ f" {shape_list(global_attn_output)}."
+ ),
+ )
+
+ global_attn_output = tf.reshape(
+ global_attn_output,
+ (batch_size, self.num_heads, max_num_global_attn_indices, self.head_dim),
+ )
+
+ # get only non zero global attn output
+ nonzero_global_attn_output = tf.gather_nd(
+ tf.transpose(global_attn_output, (0, 2, 1, 3)),
+ is_local_index_global_attn_nonzero,
+ )
+ nonzero_global_attn_output = tf.reshape(
+ nonzero_global_attn_output,
+ (shape_list(is_local_index_global_attn_nonzero)[0], -1),
+ )
+
+ # overwrite values with global attention
+ attn_output = tf.tensor_scatter_nd_update(
+ attn_output, is_index_global_attn_nonzero, nonzero_global_attn_output
+ )
+
+ global_attn_probs = tf.reshape(
+ global_attn_probs, (batch_size, self.num_heads, max_num_global_attn_indices, seq_len)
+ )
+
+ return attn_output, global_attn_probs
+
+ def reshape_and_transpose(self, vector, batch_size):
+ return tf.reshape(
+ tf.transpose(
+ tf.reshape(vector, (batch_size, -1, self.num_heads, self.head_dim)),
+ (0, 2, 1, 3),
+ ),
+ (batch_size * self.num_heads, -1, self.head_dim),
+ )
+
+
+class TFLongformerAttention(keras.layers.Layer):
+ def __init__(self, config, layer_id=0, **kwargs):
+ super().__init__(**kwargs)
+
+ self.self_attention = TFLongformerSelfAttention(config, layer_id, name="self")
+ self.dense_output = TFLongformerSelfOutput(config, name="output")
+
+ def prune_heads(self, heads):
+ raise NotImplementedError
+
+ def call(self, inputs, training=False):
+ (
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ is_index_masked,
+ is_index_global_attn,
+ is_global_attn,
+ ) = inputs
+
+ self_outputs = self.self_attention(
+ [hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn],
+ training=training,
+ )
+ attention_output = self.dense_output(self_outputs[0], hidden_states, training=training)
+ outputs = (attention_output,) + self_outputs[1:]
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self_attention", None) is not None:
+ with tf.name_scope(self.self_attention.name):
+ self.self_attention.build(None)
+ if getattr(self, "dense_output", None) is not None:
+ with tf.name_scope(self.dense_output.name):
+ self.dense_output.build(None)
+
+
+class TFLongformerLayer(keras.layers.Layer):
+ def __init__(self, config, layer_id=0, **kwargs):
+ super().__init__(**kwargs)
+
+ self.attention = TFLongformerAttention(config, layer_id, name="attention")
+ self.intermediate = TFLongformerIntermediate(config, name="intermediate")
+ self.longformer_output = TFLongformerOutput(config, name="output")
+
+ def call(self, inputs, training=False):
+ (
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ is_index_masked,
+ is_index_global_attn,
+ is_global_attn,
+ ) = inputs
+
+ attention_outputs = self.attention(
+ [hidden_states, attention_mask, layer_head_mask, is_index_masked, is_index_global_attn, is_global_attn],
+ training=training,
+ )
+ attention_output = attention_outputs[0]
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.longformer_output(intermediate_output, attention_output, training=training)
+ outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "attention", None) is not None:
+ with tf.name_scope(self.attention.name):
+ self.attention.build(None)
+ if getattr(self, "intermediate", None) is not None:
+ with tf.name_scope(self.intermediate.name):
+ self.intermediate.build(None)
+ if getattr(self, "longformer_output", None) is not None:
+ with tf.name_scope(self.longformer_output.name):
+ self.longformer_output.build(None)
+
+
+class TFLongformerEncoder(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.output_hidden_states = config.output_hidden_states
+ self.output_attentions = config.output_attentions
+ self.layer = [TFLongformerLayer(config, i, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
+
+ def call(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ padding_len=0,
+ is_index_masked=None,
+ is_index_global_attn=None,
+ is_global_attn=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ training=False,
+ ):
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = all_global_attentions = () if output_attentions else None
+
+ for idx, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ hidden_states_to_add = hidden_states[:, :-padding_len] if padding_len > 0 else hidden_states
+ all_hidden_states = all_hidden_states + (hidden_states_to_add,)
+
+ layer_outputs = layer_module(
+ [
+ hidden_states,
+ attention_mask,
+ head_mask[idx] if head_mask is not None else None,
+ is_index_masked,
+ is_index_global_attn,
+ is_global_attn,
+ ],
+ training=training,
+ )
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ # bzs x seq_len x num_attn_heads x (num_global_attn + attention_window_len + 1) => bzs x num_attn_heads x seq_len x (num_global_attn + attention_window_len + 1)
+ all_attentions = all_attentions + (tf.transpose(layer_outputs[1], (0, 2, 1, 3)),)
+
+ # bzs x num_attn_heads x num_global_attn x seq_len => bzs x num_attn_heads x seq_len x num_global_attn
+ all_global_attentions = all_global_attentions + (tf.transpose(layer_outputs[2], (0, 1, 3, 2)),)
+
+ # Add last layer
+ if output_hidden_states:
+ hidden_states_to_add = hidden_states[:, :-padding_len] if padding_len > 0 else hidden_states
+ all_hidden_states = all_hidden_states + (hidden_states_to_add,)
+
+ # undo padding
+ # unpad `hidden_states` because the calling function is expecting a length == input_ids.size(1)
+ hidden_states = hidden_states[:, :-padding_len] if padding_len > 0 else hidden_states
+ if output_attentions:
+ all_attentions = (
+ tuple(state[:, :, :-padding_len, :] for state in all_attentions) if padding_len > 0 else all_attentions
+ )
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, all_hidden_states, all_attentions, all_global_attentions] if v is not None
+ )
+
+ return TFLongformerBaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_attentions,
+ global_attentions=all_global_attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layer", None) is not None:
+ for layer in self.layer:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+@keras_serializable
+class TFLongformerMainLayer(keras.layers.Layer):
+ config_class = LongformerConfig
+
+ def __init__(self, config, add_pooling_layer=True, **kwargs):
+ super().__init__(**kwargs)
+
+ if isinstance(config.attention_window, int):
+ assert config.attention_window % 2 == 0, "`config.attention_window` has to be an even value"
+ assert config.attention_window > 0, "`config.attention_window` has to be positive"
+ config.attention_window = [config.attention_window] * config.num_hidden_layers # one value per layer
+ else:
+ assert len(config.attention_window) == config.num_hidden_layers, (
+ "`len(config.attention_window)` should equal `config.num_hidden_layers`. "
+ f"Expected {config.num_hidden_layers}, given {len(config.attention_window)}"
+ )
+
+ self.config = config
+ self.num_hidden_layers = config.num_hidden_layers
+ self.initializer_range = config.initializer_range
+ self.output_attentions = config.output_attentions
+ self.output_hidden_states = config.output_hidden_states
+ self.return_dict = config.use_return_dict
+ self.pad_token_id = config.pad_token_id
+ self.attention_window = config.attention_window
+ self.embeddings = TFLongformerEmbeddings(config, name="embeddings")
+ self.encoder = TFLongformerEncoder(config, name="encoder")
+ self.pooler = TFLongformerPooler(config, name="pooler") if add_pooling_layer else None
+
+ def get_input_embeddings(self):
+ return self.embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.weight = value
+ self.embeddings.vocab_size = shape_list(value)[0]
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ raise NotImplementedError
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ head_mask=None,
+ global_attention_mask=None,
+ token_type_ids=None,
+ position_ids=None,
+ inputs_embeds=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ training=False,
+ ):
+ if input_ids is not None and not isinstance(input_ids, tf.Tensor):
+ input_ids = tf.convert_to_tensor(input_ids, dtype=tf.int64)
+ elif input_ids is not None:
+ input_ids = tf.cast(input_ids, tf.int64)
+
+ if attention_mask is not None and not isinstance(attention_mask, tf.Tensor):
+ attention_mask = tf.convert_to_tensor(attention_mask, dtype=tf.int64)
+ elif attention_mask is not None:
+ attention_mask = tf.cast(attention_mask, tf.int64)
+
+ if global_attention_mask is not None and not isinstance(global_attention_mask, tf.Tensor):
+ global_attention_mask = tf.convert_to_tensor(global_attention_mask, dtype=tf.int64)
+ elif global_attention_mask is not None:
+ global_attention_mask = tf.cast(global_attention_mask, tf.int64)
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if attention_mask is None:
+ attention_mask = tf.cast(tf.fill(input_shape, 1), tf.int64)
+
+ if token_type_ids is None:
+ token_type_ids = tf.cast(tf.fill(input_shape, 0), tf.int64)
+
+ # merge `global_attention_mask` and `attention_mask`
+ if global_attention_mask is not None:
+ attention_mask = self._merge_to_attention_mask(attention_mask, global_attention_mask)
+
+ (
+ padding_len,
+ input_ids,
+ attention_mask,
+ token_type_ids,
+ position_ids,
+ inputs_embeds,
+ ) = self._pad_to_window_size(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ pad_token_id=self.pad_token_id,
+ )
+
+ # is index masked or global attention
+ is_index_masked = tf.math.less(attention_mask, 1)
+ is_index_global_attn = tf.math.greater(attention_mask, 1)
+ is_global_attn = tf.math.reduce_any(is_index_global_attn)
+
+ # We create a 3D attention mask from a 2D tensor mask.
+ # Sizes are [batch_size, to_seq_length, 1, 1]
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+ # this attention mask is more simple than the triangular masking of causal attention
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+ attention_mask_shape = shape_list(attention_mask)
+ extended_attention_mask = tf.reshape(attention_mask, (attention_mask_shape[0], attention_mask_shape[1], 1, 1))
+
+ # Since attention_mask is 1.0 for positions we want to attend locally and 0.0 for
+ # masked and global attn positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and -10000.0 for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+ extended_attention_mask = tf.cast(tf.math.abs(1 - extended_attention_mask), tf.dtypes.float32) * -10000.0
+ embedding_output = self.embeddings(
+ input_ids,
+ position_ids,
+ token_type_ids,
+ inputs_embeds,
+ training=training,
+ )
+ encoder_outputs = self.encoder(
+ embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ padding_len=padding_len,
+ is_index_masked=is_index_masked,
+ is_index_global_attn=is_index_global_attn,
+ is_global_attn=is_global_attn,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+ if not return_dict:
+ return (
+ sequence_output,
+ pooled_output,
+ ) + encoder_outputs[1:]
+
+ return TFLongformerBaseModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ global_attentions=encoder_outputs.global_attentions,
+ )
+
+ def _pad_to_window_size(
+ self,
+ input_ids,
+ attention_mask,
+ token_type_ids,
+ position_ids,
+ inputs_embeds,
+ pad_token_id,
+ ):
+ """A helper function to pad tokens and mask to work with implementation of Longformer selfattention."""
+ # padding
+ attention_window = (
+ self.attention_window if isinstance(self.attention_window, int) else max(self.attention_window)
+ )
+
+ assert attention_window % 2 == 0, f"`attention_window` should be an even value. Given {attention_window}"
+
+ input_shape = shape_list(input_ids) if input_ids is not None else shape_list(inputs_embeds)
+ batch_size, seq_len = input_shape[:2]
+ padding_len = (attention_window - seq_len % attention_window) % attention_window
+
+ paddings = tf.convert_to_tensor([[0, 0], [0, padding_len]])
+
+ if input_ids is not None:
+ input_ids = tf.pad(input_ids, paddings, constant_values=pad_token_id)
+
+ if position_ids is not None:
+ # pad with position_id = pad_token_id as in modeling_roberta.RobertaEmbeddings
+ position_ids = tf.pad(position_ids, paddings, constant_values=pad_token_id)
+
+ if inputs_embeds is not None:
+ if padding_len > 0:
+ input_ids_padding = tf.cast(tf.fill((batch_size, padding_len), self.pad_token_id), tf.int64)
+ inputs_embeds_padding = self.embeddings(input_ids_padding)
+ inputs_embeds = tf.concat([inputs_embeds, inputs_embeds_padding], axis=-2)
+
+ attention_mask = tf.pad(attention_mask, paddings, constant_values=False) # no attention on the padding tokens
+ token_type_ids = tf.pad(token_type_ids, paddings, constant_values=0) # pad with token_type_id = 0
+
+ return (
+ padding_len,
+ input_ids,
+ attention_mask,
+ token_type_ids,
+ position_ids,
+ inputs_embeds,
+ )
+
+ @staticmethod
+ def _merge_to_attention_mask(attention_mask: tf.Tensor, global_attention_mask: tf.Tensor):
+ # longformer self attention expects attention mask to have 0 (no attn), 1 (local attn), 2 (global attn)
+ # (global_attention_mask + 1) => 1 for local attention, 2 for global attention
+ # => final attention_mask => 0 for no attention, 1 for local attention 2 for global attention
+ if attention_mask is not None:
+ attention_mask = attention_mask * (global_attention_mask + 1)
+ else:
+ # simply use `global_attention_mask` as `attention_mask`
+ # if no `attention_mask` is given
+ attention_mask = global_attention_mask + 1
+
+ return attention_mask
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embeddings", None) is not None:
+ with tf.name_scope(self.embeddings.name):
+ self.embeddings.build(None)
+ if getattr(self, "encoder", None) is not None:
+ with tf.name_scope(self.encoder.name):
+ self.encoder.build(None)
+ if getattr(self, "pooler", None) is not None:
+ with tf.name_scope(self.pooler.name):
+ self.pooler.build(None)
+
+
+class TFLongformerPreTrainedModel(TFPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = LongformerConfig
+ base_model_prefix = "longformer"
+
+ @property
+ def input_signature(self):
+ sig = super().input_signature
+ sig["global_attention_mask"] = tf.TensorSpec((None, None), tf.int32, name="global_attention_mask")
+ return sig
+
+
+LONGFORMER_START_DOCSTRING = r"""
+
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Parameters:
+ config ([`LongformerConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+LONGFORMER_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
+ [`PreTrainedTokenizer.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`np.ndarray` or `tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ global_attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
+ Mask to decide the attention given on each token, local attention or global attention. Tokens with global
+ attention attends to all other tokens, and all other tokens attend to them. This is important for
+ task-specific finetuning because it makes the model more flexible at representing the task. For example,
+ for classification, the token should be given global attention. For QA, all question tokens should also
+ have global attention. Please refer to the [Longformer paper](https://huggingface.co/papers/2004.05150) for more
+ details. Mask values selected in `[0, 1]`:
+
+ - 0 for local attention (a sliding window attention),
+ - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
+
+ token_type_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+ config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+ used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+ eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+"""
+
+
+@add_start_docstrings(
+ "The bare Longformer Model outputting raw hidden-states without any specific head on top.",
+ LONGFORMER_START_DOCSTRING,
+)
+class TFLongformerModel(TFLongformerPreTrainedModel):
+ """
+
+ This class copies code from [`TFRobertaModel`] and overwrites standard self-attention with longformer
+ self-attention to provide the ability to process long sequences following the self-attention approach described in
+ [Longformer: the Long-Document Transformer](https://huggingface.co/papers/2004.05150) by Iz Beltagy, Matthew E. Peters, and
+ Arman Cohan. Longformer self-attention combines a local (sliding window) and global attention to extend to long
+ documents without the O(n^2) increase in memory and compute.
+
+ The self-attention module `TFLongformerSelfAttention` implemented here supports the combination of local and global
+ attention but it lacks support for autoregressive attention and dilated attention. Autoregressive and dilated
+ attention are more relevant for autoregressive language modeling than finetuning on downstream tasks. Future
+ release will add support for autoregressive attention, but the support for dilated attention requires a custom CUDA
+ kernel to be memory and compute efficient.
+
+ """
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.longformer = TFLongformerMainLayer(config, name="longformer")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ global_attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ ) -> TFLongformerBaseModelOutputWithPooling | tuple[tf.Tensor]:
+ outputs = self.longformer(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ global_attention_mask=global_attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "longformer", None) is not None:
+ with tf.name_scope(self.longformer.name):
+ self.longformer.build(None)
+
+
+@add_start_docstrings(
+ """Longformer Model with a `language modeling` head on top.""",
+ LONGFORMER_START_DOCSTRING,
+)
+class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModelingLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.longformer = TFLongformerMainLayer(config, add_pooling_layer=False, name="longformer")
+ self.lm_head = TFLongformerLMHead(config, self.longformer.embeddings, name="lm_head")
+
+ def get_lm_head(self):
+ return self.lm_head
+
+ def get_prefix_bias_name(self):
+ warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
+ return self.name + "/" + self.lm_head.name
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint="allenai/longformer-base-4096",
+ output_type=TFLongformerMaskedLMOutput,
+ config_class=_CONFIG_FOR_DOC,
+ mask="",
+ expected_output="' Paris'",
+ expected_loss=0.44,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ global_attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> TFLongformerMaskedLMOutput | tuple[tf.Tensor]:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ """
+
+ outputs = self.longformer(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ global_attention_mask=global_attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+ prediction_scores = self.lm_head(sequence_output, training=training)
+ loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores)
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+
+ return ((loss,) + output) if loss is not None else output
+
+ return TFLongformerMaskedLMOutput(
+ loss=loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ global_attentions=outputs.global_attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "longformer", None) is not None:
+ with tf.name_scope(self.longformer.name):
+ self.longformer.build(None)
+ if getattr(self, "lm_head", None) is not None:
+ with tf.name_scope(self.lm_head.name):
+ self.lm_head.build(None)
+
+
+@add_start_docstrings(
+ """
+ Longformer Model with a span classification head on top for extractive question-answering tasks like SQuAD /
+ TriviaQA (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
+ """,
+ LONGFORMER_START_DOCSTRING,
+)
+class TFLongformerForQuestionAnswering(TFLongformerPreTrainedModel, TFQuestionAnsweringLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.num_labels = config.num_labels
+ self.longformer = TFLongformerMainLayer(config, add_pooling_layer=False, name="longformer")
+ self.qa_outputs = keras.layers.Dense(
+ config.num_labels,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="qa_outputs",
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint="allenai/longformer-large-4096-finetuned-triviaqa",
+ output_type=TFLongformerQuestionAnsweringModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ expected_output="' puppet'",
+ expected_loss=0.96,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ global_attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ start_positions: np.ndarray | tf.Tensor | None = None,
+ end_positions: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> TFLongformerQuestionAnsweringModelOutput | tuple[tf.Tensor]:
+ r"""
+ start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the start of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
+ are not taken into account for computing the loss.
+ end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the end of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
+ are not taken into account for computing the loss.
+ """
+
+ if input_ids is not None and not isinstance(input_ids, tf.Tensor):
+ input_ids = tf.convert_to_tensor(input_ids, dtype=tf.int64)
+ elif input_ids is not None:
+ input_ids = tf.cast(input_ids, tf.int64)
+
+ if attention_mask is not None and not isinstance(attention_mask, tf.Tensor):
+ attention_mask = tf.convert_to_tensor(attention_mask, dtype=tf.int64)
+ elif attention_mask is not None:
+ attention_mask = tf.cast(attention_mask, tf.int64)
+
+ if global_attention_mask is not None and not isinstance(global_attention_mask, tf.Tensor):
+ global_attention_mask = tf.convert_to_tensor(global_attention_mask, dtype=tf.int64)
+ elif global_attention_mask is not None:
+ global_attention_mask = tf.cast(global_attention_mask, tf.int64)
+
+ # set global attention on question tokens
+ if global_attention_mask is None and input_ids is not None:
+ if shape_list(tf.where(input_ids == self.config.sep_token_id))[0] != 3 * shape_list(input_ids)[0]:
+ logger.warning(
+ f"There should be exactly three separator tokens: {self.config.sep_token_id} in every sample for"
+ " questions answering. You might also consider to set `global_attention_mask` manually in the"
+ " forward function to avoid this. This is most likely an error. The global attention is disabled"
+ " for this forward pass."
+ )
+ global_attention_mask = tf.cast(tf.fill(shape_list(input_ids), value=0), tf.int64)
+ else:
+ logger.warning_once("Initializing global attention on question tokens...")
+ # put global attention on all tokens until `config.sep_token_id` is reached
+ sep_token_indices = tf.where(input_ids == self.config.sep_token_id)
+ sep_token_indices = tf.cast(sep_token_indices, dtype=tf.int64)
+ global_attention_mask = _compute_global_attention_mask(shape_list(input_ids), sep_token_indices)
+
+ outputs = self.longformer(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ global_attention_mask=global_attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = tf.split(logits, 2, axis=-1)
+ start_logits = tf.squeeze(start_logits, axis=-1)
+ end_logits = tf.squeeze(end_logits, axis=-1)
+ loss = None
+
+ if start_positions is not None and end_positions is not None:
+ labels = {"start_position": start_positions}
+ labels["end_position"] = end_positions
+ loss = self.hf_compute_loss(labels, (start_logits, end_logits))
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+
+ return ((loss,) + output) if loss is not None else output
+
+ return TFLongformerQuestionAnsweringModelOutput(
+ loss=loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ global_attentions=outputs.global_attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "longformer", None) is not None:
+ with tf.name_scope(self.longformer.name):
+ self.longformer.build(None)
+ if getattr(self, "qa_outputs", None) is not None:
+ with tf.name_scope(self.qa_outputs.name):
+ self.qa_outputs.build([None, None, self.config.hidden_size])
+
+
+class TFLongformerClassificationHead(keras.layers.Layer):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(
+ config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ activation="tanh",
+ name="dense",
+ )
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.out_proj = keras.layers.Dense(
+ config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
+ )
+ self.config = config
+
+ def call(self, hidden_states, training=False):
+ hidden_states = hidden_states[:, 0, :] # take token (equiv. to [CLS])
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+ output = self.out_proj(hidden_states)
+ return output
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "out_proj", None) is not None:
+ with tf.name_scope(self.out_proj.name):
+ self.out_proj.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ Longformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+ pooled output) e.g. for GLUE tasks.
+ """,
+ LONGFORMER_START_DOCSTRING,
+)
+class TFLongformerForSequenceClassification(TFLongformerPreTrainedModel, TFSequenceClassificationLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.num_labels = config.num_labels
+
+ self.longformer = TFLongformerMainLayer(config, add_pooling_layer=False, name="longformer")
+ self.classifier = TFLongformerClassificationHead(config, name="classifier")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFLongformerSequenceClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ global_attention_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> TFLongformerSequenceClassifierOutput | tuple[tf.Tensor]:
+ if input_ids is not None and not isinstance(input_ids, tf.Tensor):
+ input_ids = tf.convert_to_tensor(input_ids, dtype=tf.int64)
+ elif input_ids is not None:
+ input_ids = tf.cast(input_ids, tf.int64)
+
+ if attention_mask is not None and not isinstance(attention_mask, tf.Tensor):
+ attention_mask = tf.convert_to_tensor(attention_mask, dtype=tf.int64)
+ elif attention_mask is not None:
+ attention_mask = tf.cast(attention_mask, tf.int64)
+
+ if global_attention_mask is not None and not isinstance(global_attention_mask, tf.Tensor):
+ global_attention_mask = tf.convert_to_tensor(global_attention_mask, dtype=tf.int64)
+ elif global_attention_mask is not None:
+ global_attention_mask = tf.cast(global_attention_mask, tf.int64)
+
+ if global_attention_mask is None and input_ids is not None:
+ logger.warning_once("Initializing global attention on CLS token...")
+ # global attention on cls token
+ global_attention_mask = tf.zeros_like(input_ids)
+ updates = tf.ones(shape_list(input_ids)[0], dtype=tf.int64)
+ indices = tf.pad(
+ tensor=tf.expand_dims(tf.range(shape_list(input_ids)[0], dtype=tf.int64), axis=1),
+ paddings=[[0, 0], [0, 1]],
+ constant_values=0,
+ )
+ global_attention_mask = tf.tensor_scatter_nd_update(
+ global_attention_mask,
+ indices,
+ updates,
+ )
+
+ outputs = self.longformer(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ global_attention_mask=global_attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+ logits = self.classifier(sequence_output)
+
+ loss = None if labels is None else self.hf_compute_loss(labels, logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFLongformerSequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ global_attentions=outputs.global_attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "longformer", None) is not None:
+ with tf.name_scope(self.longformer.name):
+ self.longformer.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build(None)
+
+
+@add_start_docstrings(
+ """
+ Longformer Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
+ a softmax) e.g. for RocStories/SWAG tasks.
+ """,
+ LONGFORMER_START_DOCSTRING,
+)
+class TFLongformerForMultipleChoice(TFLongformerPreTrainedModel, TFMultipleChoiceLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_missing = [r"dropout"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.longformer = TFLongformerMainLayer(config, name="longformer")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.classifier = keras.layers.Dense(
+ 1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
+ )
+ self.config = config
+
+ @property
+ def input_signature(self):
+ return {
+ "input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
+ "attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
+ "global_attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="global_attention_mask"),
+ }
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(
+ LONGFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
+ )
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFLongformerMultipleChoiceModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ global_attention_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> TFLongformerMultipleChoiceModelOutput | tuple[tf.Tensor]:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
+ where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)
+ """
+
+ if input_ids is not None:
+ num_choices = shape_list(input_ids)[1]
+ seq_length = shape_list(input_ids)[2]
+ else:
+ num_choices = shape_list(inputs_embeds)[1]
+ seq_length = shape_list(inputs_embeds)[2]
+
+ flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
+ flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
+ flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
+ flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
+ flat_global_attention_mask = (
+ tf.reshape(global_attention_mask, (-1, shape_list(global_attention_mask)[-1]))
+ if global_attention_mask is not None
+ else None
+ )
+ flat_inputs_embeds = (
+ tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
+ if inputs_embeds is not None
+ else None
+ )
+
+ outputs = self.longformer(
+ flat_input_ids,
+ position_ids=flat_position_ids,
+ token_type_ids=flat_token_type_ids,
+ attention_mask=flat_attention_mask,
+ head_mask=head_mask,
+ global_attention_mask=flat_global_attention_mask,
+ inputs_embeds=flat_inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ pooled_output = outputs[1]
+
+ pooled_output = self.dropout(pooled_output)
+ logits = self.classifier(pooled_output)
+ reshaped_logits = tf.reshape(logits, (-1, num_choices))
+
+ loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFLongformerMultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ global_attentions=outputs.global_attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "longformer", None) is not None:
+ with tf.name_scope(self.longformer.name):
+ self.longformer.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ Longformer Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
+ for Named-Entity-Recognition (NER) tasks.
+ """,
+ LONGFORMER_START_DOCSTRING,
+)
+class TFLongformerForTokenClassification(TFLongformerPreTrainedModel, TFTokenClassificationLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [r"pooler"]
+ _keys_to_ignore_on_load_missing = [r"dropout"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.num_labels = config.num_labels
+ self.longformer = TFLongformerMainLayer(config=config, add_pooling_layer=False, name="longformer")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.classifier = keras.layers.Dense(
+ config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFLongformerTokenClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ global_attention_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.array | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> TFLongformerTokenClassifierOutput | tuple[tf.Tensor]:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+
+ outputs = self.longformer(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ global_attention_mask=global_attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+ loss = None if labels is None else self.hf_compute_loss(labels, logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFLongformerTokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ global_attentions=outputs.global_attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "longformer", None) is not None:
+ with tf.name_scope(self.longformer.name):
+ self.longformer.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build([None, None, self.config.hidden_size])
+
+
+__all__ = [
+ "TFLongformerForMaskedLM",
+ "TFLongformerForMultipleChoice",
+ "TFLongformerForQuestionAnswering",
+ "TFLongformerForSequenceClassification",
+ "TFLongformerForTokenClassification",
+ "TFLongformerModel",
+ "TFLongformerPreTrainedModel",
+ "TFLongformerSelfAttention",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/tokenization_longformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/tokenization_longformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..104bdd7a9b99f5a4809557dcee97cda4873cea12
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/tokenization_longformer.py
@@ -0,0 +1,402 @@
+# coding=utf-8
+# Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from functools import lru_cache
+from typing import Optional
+
+import regex as re
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
+
+
+@lru_cache
+# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
+def bytes_to_unicode():
+ """
+ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
+ characters the bpe code barfs on.
+
+ The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
+ if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
+ decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
+ tables between utf-8 bytes and unicode strings.
+ """
+ bs = (
+ list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
+ )
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8 + n)
+ n += 1
+ cs = [chr(n) for n in cs]
+ return dict(zip(bs, cs))
+
+
+# Copied from transformers.models.roberta.tokenization_roberta.get_pairs
+def get_pairs(word):
+ """
+ Return set of symbol pairs in a word.
+
+ Word is represented as tuple of symbols (symbols being variable-length strings).
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer with FacebookAI/roberta-base->allenai/longformer-base-4096, RoBERTa->Longformer all-casing, RobertaTokenizer->LongformerTokenizer
+class LongformerTokenizer(PreTrainedTokenizer):
+ """
+ Constructs a Longformer tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
+
+ This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
+ be encoded differently whether it is at the beginning of the sentence (without space) or not:
+
+ ```python
+ >>> from transformers import LongformerTokenizer
+
+ >>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
+ >>> tokenizer("Hello world")["input_ids"]
+ [0, 31414, 232, 2]
+
+ >>> tokenizer(" Hello world")["input_ids"]
+ [0, 20920, 232, 2]
+ ```
+
+ You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
+ call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
+
+
+
+ When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
+
+
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ errors (`str`, *optional*, defaults to `"replace"`):
+ Paradigm to follow when decoding bytes to UTF-8. See
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ add_prefix_space (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. (Longformer tokenizer detect beginning of words by the preceding space).
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ def __init__(
+ self,
+ vocab_file,
+ merges_file,
+ errors="replace",
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ add_prefix_space=False,
+ **kwargs,
+ ):
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+ sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
+ cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
+
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = (
+ AddedToken(mask_token, lstrip=True, rstrip=False, normalized=False)
+ if isinstance(mask_token, str)
+ else mask_token
+ )
+
+ # these special tokens are not part of the vocab.json, let's add them in the correct order
+
+ with open(vocab_file, encoding="utf-8") as vocab_handle:
+ self.encoder = json.load(vocab_handle)
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ self.errors = errors # how to handle errors in decoding
+ self.byte_encoder = bytes_to_unicode()
+ self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+ with open(merges_file, encoding="utf-8") as merges_handle:
+ bpe_merges = merges_handle.read().split("\n")[1:-1]
+ bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
+ self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+ self.cache = {}
+ self.add_prefix_space = add_prefix_space
+
+ # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
+ self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
+
+ super().__init__(
+ errors=errors,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ add_prefix_space=add_prefix_space,
+ **kwargs,
+ )
+
+ @property
+ def vocab_size(self):
+ return len(self.encoder)
+
+ def get_vocab(self):
+ vocab = dict(self.encoder).copy()
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ def bpe(self, token):
+ if token in self.cache:
+ return self.cache[token]
+ word = tuple(token)
+ pairs = get_pairs(word)
+
+ if not pairs:
+ return token
+
+ while True:
+ bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+ if bigram not in self.bpe_ranks:
+ break
+ first, second = bigram
+ new_word = []
+ i = 0
+ while i < len(word):
+ try:
+ j = word.index(first, i)
+ except ValueError:
+ new_word.extend(word[i:])
+ break
+ else:
+ new_word.extend(word[i:j])
+ i = j
+
+ if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+ new_word.append(first + second)
+ i += 2
+ else:
+ new_word.append(word[i])
+ i += 1
+ new_word = tuple(new_word)
+ word = new_word
+ if len(word) == 1:
+ break
+ else:
+ pairs = get_pairs(word)
+ word = " ".join(word)
+ self.cache[token] = word
+ return word
+
+ def _tokenize(self, text):
+ """Tokenize a string."""
+ bpe_tokens = []
+ for token in re.findall(self.pat, text):
+ token = "".join(
+ self.byte_encoder[b] for b in token.encode("utf-8")
+ ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
+ bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
+ return bpe_tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.decoder.get(index)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ text = "".join(tokens)
+ text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
+ return text
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ merge_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
+ )
+
+ with open(vocab_file, "w", encoding="utf-8") as f:
+ f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+ index = 0
+ with open(merge_file, "w", encoding="utf-8") as writer:
+ writer.write("#version: 0.2\n")
+ for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
+ " Please check that the tokenizer is not corrupted!"
+ )
+ index = token_index
+ writer.write(" ".join(bpe_tokens) + "\n")
+ index += 1
+
+ return vocab_file, merge_file
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A Longformer sequence has the following format:
+
+ - single sequence: ` X `
+ - pair of sequences: ` A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is None:
+ return [1] + ([0] * len(token_ids_0)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. Longformer does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
+ add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
+ if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
+ text = " " + text
+ return (text, kwargs)
+
+
+__all__ = ["LongformerTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/tokenization_longformer_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/tokenization_longformer_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..bde6bb55fec609ae3550c5cf01b43f0dd3bc866c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longformer/tokenization_longformer_fast.py
@@ -0,0 +1,265 @@
+# coding=utf-8
+# Copyright 2020 The Allen Institute for AI team and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Tokenization classes for Longformer."""
+
+import json
+from typing import Optional
+
+from tokenizers import processors
+
+from ...tokenization_utils_base import AddedToken, BatchEncoding
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import logging
+from .tokenization_longformer import LongformerTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
+
+
+# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast with FacebookAI/roberta-base->allenai/longformer-base-4096, RoBERTa->Longformer all-casing, Roberta->Longformer
+class LongformerTokenizerFast(PreTrainedTokenizerFast):
+ """
+ Construct a "fast" Longformer tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2
+ tokenizer, using byte-level Byte-Pair-Encoding.
+
+ This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
+ be encoded differently whether it is at the beginning of the sentence (without space) or not:
+
+ ```python
+ >>> from transformers import LongformerTokenizerFast
+
+ >>> tokenizer = LongformerTokenizerFast.from_pretrained("allenai/longformer-base-4096")
+ >>> tokenizer("Hello world")["input_ids"]
+ [0, 31414, 232, 2]
+
+ >>> tokenizer(" Hello world")["input_ids"]
+ [0, 20920, 232, 2]
+ ```
+
+ You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
+ call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
+
+
+
+ When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
+
+
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ errors (`str`, *optional*, defaults to `"replace"`):
+ Paradigm to follow when decoding bytes to UTF-8. See
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ add_prefix_space (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. (Longformer tokenizer detect beginning of words by the preceding space).
+ trim_offsets (`bool`, *optional*, defaults to `True`):
+ Whether the post processing step should trim offsets to avoid including whitespaces.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+ slow_tokenizer_class = LongformerTokenizer
+
+ def __init__(
+ self,
+ vocab_file=None,
+ merges_file=None,
+ tokenizer_file=None,
+ errors="replace",
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ add_prefix_space=False,
+ trim_offsets=True,
+ **kwargs,
+ ):
+ mask_token = (
+ AddedToken(mask_token, lstrip=True, rstrip=False, normalized=False)
+ if isinstance(mask_token, str)
+ else mask_token
+ )
+ super().__init__(
+ vocab_file,
+ merges_file,
+ tokenizer_file=tokenizer_file,
+ errors=errors,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ add_prefix_space=add_prefix_space,
+ trim_offsets=trim_offsets,
+ **kwargs,
+ )
+
+ tokenizer_component = "post_processor"
+ tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
+ if tokenizer_component_instance:
+ state = json.loads(tokenizer_component_instance.__getstate__())
+
+ # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
+ if "sep" in state:
+ state["sep"] = tuple(state["sep"])
+ if "cls" in state:
+ state["cls"] = tuple(state["cls"])
+
+ changes_to_apply = False
+
+ if state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
+ state["add_prefix_space"] = add_prefix_space
+ changes_to_apply = True
+
+ if state.get("trim_offsets", trim_offsets) != trim_offsets:
+ state["trim_offsets"] = trim_offsets
+ changes_to_apply = True
+
+ if changes_to_apply:
+ component_class = getattr(processors, state.pop("type"))
+ new_value = component_class(**state)
+ setattr(self.backend_tokenizer, tokenizer_component, new_value)
+
+ @property
+ def mask_token(self) -> str:
+ """
+ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
+ having been set.
+
+ Longformer tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
+ comprise the space before the **.
+ """
+ if self._mask_token is None:
+ if self.verbose:
+ logger.error("Using mask_token, but it is not set yet.")
+ return None
+ return str(self._mask_token)
+
+ @mask_token.setter
+ def mask_token(self, value):
+ """
+ Overriding the default behavior of the mask token to have it eat the space before it.
+
+ This is needed to preserve backward compatibility with all the previously used models based on Longformer.
+ """
+ # Mask token behave like a normal word, i.e. include the space before it
+ # So we set lstrip to True
+ value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value
+ self._mask_token = value
+
+ def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
+ is_split_into_words = kwargs.get("is_split_into_words", False)
+ assert self.add_prefix_space or not is_split_into_words, (
+ f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
+ "to use it with pretokenized inputs."
+ )
+
+ return super()._batch_encode_plus(*args, **kwargs)
+
+ def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
+ is_split_into_words = kwargs.get("is_split_into_words", False)
+
+ assert self.add_prefix_space or not is_split_into_words, (
+ f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
+ "to use it with pretokenized inputs."
+ )
+
+ return super()._encode_plus(*args, **kwargs)
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+ return tuple(files)
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
+ if token_ids_1 is None:
+ return output
+
+ return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. Longformer does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+
+__all__ = ["LongformerTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2716e62cd7b28ac6121300395a470d855e144a42
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_longt5 import *
+ from .modeling_flax_longt5 import *
+ from .modeling_longt5 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/configuration_longt5.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/configuration_longt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..245e9948a1aece66a9912ca5f895251e785a4783
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/configuration_longt5.py
@@ -0,0 +1,180 @@
+# coding=utf-8
+# Copyright 2022, The LongT5 Authors and HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LongT5 model configuration"""
+
+from collections.abc import Mapping
+
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxSeq2SeqConfigWithPast
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class LongT5Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LongT5Model`] or a [`FlaxLongT5Model`]. It is
+ used to instantiate a LongT5 model according to the specified arguments, defining the model architecture.
+ Instantiating a configuration with the defaults will yield a similar configuration to that of the LongT5
+ [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Arguments:
+ vocab_size (`int`, *optional*, defaults to 32128):
+ Vocabulary size of the LongT5 model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`LongT5Model`].
+ d_model (`int`, *optional*, defaults to 512):
+ Size of the encoder layers and the pooler layer.
+ d_kv (`int`, *optional*, defaults to 64):
+ Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model //
+ num_heads`.
+ d_ff (`int`, *optional*, defaults to 2048):
+ Size of the intermediate feed forward layer in each `LongT5Block`.
+ num_layers (`int`, *optional*, defaults to 6):
+ Number of hidden layers in the Transformer encoder.
+ num_decoder_layers (`int`, *optional*):
+ Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
+ num_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ local_radius (`int`, *optional*, defaults to 127)
+ Number of tokens to the left/right for each token to locally self-attend in a local attention mechanism.
+ global_block_size (`int`, *optional*, defaults to 16)
+ Length of blocks an input sequence is divided into for a global token representation. Used only for
+ `encoder_attention_type = "transient-global"`.
+ relative_attention_num_buckets (`int`, *optional*, defaults to 32):
+ The number of buckets to use for each attention layer.
+ relative_attention_max_distance (`int`, *optional*, defaults to 128):
+ The maximum distance of the longer sequences for the bucket separation.
+ dropout_rate (`float`, *optional*, defaults to 0.1):
+ The ratio for all dropout layers.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+ The epsilon used by the layer normalization layers.
+ initializer_factor (`float`, *optional*, defaults to 1):
+ A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+ testing).
+ feed_forward_proj (`string`, *optional*, defaults to `"relu"`):
+ Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. LongT5v1.1 uses the
+ `"gated-gelu"` feed forward projection. Original LongT5 implementation uses `"gated-gelu"`.
+ encoder_attention_type (`string`, *optional*, defaults to `"local"`):
+ Type of encoder attention to be used. Should be one of `"local"` or `"transient-global"`, which are
+ supported by LongT5 implementation.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models).
+ """
+
+ model_type = "longt5"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {
+ "hidden_size": "d_model",
+ "num_attention_heads": "num_heads",
+ "num_hidden_layers": "num_layers",
+ "head_dim": "d_kv",
+ }
+
+ def __init__(
+ self,
+ vocab_size=32128,
+ d_model=512,
+ d_kv=64,
+ d_ff=2048,
+ num_layers=6,
+ num_decoder_layers=None,
+ num_heads=8,
+ local_radius=127,
+ global_block_size=16,
+ relative_attention_num_buckets=32,
+ relative_attention_max_distance=128,
+ dropout_rate=0.1,
+ layer_norm_epsilon=1e-6,
+ initializer_factor=1.0,
+ feed_forward_proj="relu",
+ is_encoder_decoder=True,
+ encoder_attention_type="local",
+ use_cache=True,
+ pad_token_id=0,
+ eos_token_id=1,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.d_model = d_model
+ self.d_kv = d_kv
+ self.d_ff = d_ff
+ self.num_layers = num_layers
+ # default = symmetry
+ self.num_decoder_layers = num_decoder_layers if num_decoder_layers is not None else self.num_layers
+ self.num_heads = num_heads
+ self.local_radius = local_radius
+ self.global_block_size = global_block_size
+ self.relative_attention_num_buckets = relative_attention_num_buckets
+ self.relative_attention_max_distance = relative_attention_max_distance
+ self.dropout_rate = dropout_rate
+ self.layer_norm_epsilon = layer_norm_epsilon
+ self.initializer_factor = initializer_factor
+ self.feed_forward_proj = feed_forward_proj
+ self.encoder_attention_type = encoder_attention_type
+ self.use_cache = use_cache
+
+ act_info = self.feed_forward_proj.split("-")
+ self.dense_act_fn = act_info[-1]
+ self.is_gated_act = act_info[0] == "gated"
+
+ if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2:
+ raise ValueError(
+ f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer. "
+ "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
+ "'gated-gelu' or 'relu'"
+ )
+
+ # for backwards compatibility
+ if feed_forward_proj == "gated-gelu":
+ self.dense_act_fn = "gelu_new"
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ eos_token_id=eos_token_id,
+ is_encoder_decoder=is_encoder_decoder,
+ **kwargs,
+ )
+
+
+class LongT5OnnxConfig(OnnxSeq2SeqConfigWithPast):
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ common_inputs = {
+ "input_ids": {0: "batch", 1: "encoder_sequence"},
+ "attention_mask": {0: "batch", 1: "encoder_sequence"},
+ }
+ if self.use_past:
+ common_inputs["attention_mask"][1] = "past_encoder_sequence + sequence"
+ common_inputs["decoder_input_ids"] = {0: "batch"}
+ common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
+ else:
+ common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
+ common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
+
+ if self.use_past:
+ self.fill_with_past_key_values_(common_inputs, direction="inputs")
+
+ return common_inputs
+
+ @property
+ def default_onnx_opset(self) -> int:
+ return 13
+
+
+__all__ = ["LongT5Config", "LongT5OnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/convert_longt5x_checkpoint_to_flax.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/convert_longt5x_checkpoint_to_flax.py
new file mode 100644
index 0000000000000000000000000000000000000000..d99797107363d2d0175d3b29033fb415a8073a25
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/convert_longt5x_checkpoint_to_flax.py
@@ -0,0 +1,215 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Convert T5/LongT5X checkpoints from the original repository to JAX/FLAX model. This script is an extension of
+'src/transformers/models/t5/convert_t5x_checkpoint_to_flax.
+"""
+
+import argparse
+
+from t5x import checkpoints
+
+from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM
+
+
+def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path):
+ config = AutoConfig.from_pretrained(config_name)
+ flax_model = FlaxAutoModelForSeq2SeqLM.from_config(config=config)
+ t5x_model = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path)
+
+ split_mlp_wi = "wi_0" in t5x_model["target"]["encoder"]["layers_0"]["mlp"]
+
+ if config.model_type == "t5":
+ encoder_attn_name = "SelfAttention"
+ if config.model_type == "longt5" and config.encoder_attention_type == "local":
+ encoder_attn_name = "LocalSelfAttention"
+ elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
+ encoder_attn_name = "TransientGlobalSelfAttention"
+ else:
+ raise ValueError(
+ "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
+ " attribute with a value from ['local', 'transient-global]."
+ )
+
+ # Encoder
+ for layer_index in range(config.num_layers):
+ layer_name = f"layers_{str(layer_index)}"
+
+ # Self-Attention
+ t5x_attention_key = t5x_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
+ t5x_attention_out = t5x_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
+ t5x_attention_query = t5x_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
+ t5x_attention_value = t5x_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]
+
+ # Global input layer norm
+ if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
+ t5x_global_layer_norm = t5x_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]
+
+ # Layer Normalization
+ t5x_attention_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]
+
+ if split_mlp_wi:
+ t5x_mlp_wi_0 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
+ t5x_mlp_wi_1 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
+ else:
+ t5x_mlp_wi = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]
+
+ t5x_mlp_wo = t5x_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]
+
+ # Layer Normalization
+ t5x_mlp_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]
+
+ # Assigning
+ flax_model_encoder_layer_block = flax_model.params["encoder"]["block"][str(layer_index)]["layer"]
+ flax_model_encoder_layer_block["0"][encoder_attn_name]["k"]["kernel"] = t5x_attention_key
+ flax_model_encoder_layer_block["0"][encoder_attn_name]["o"]["kernel"] = t5x_attention_out
+ flax_model_encoder_layer_block["0"][encoder_attn_name]["q"]["kernel"] = t5x_attention_query
+ flax_model_encoder_layer_block["0"][encoder_attn_name]["v"]["kernel"] = t5x_attention_value
+
+ flax_model_encoder_layer_block["0"]["layer_norm"]["weight"] = t5x_attention_layer_norm
+
+ # Global input layer norm
+ if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
+ flax_model_encoder_layer_block["0"][encoder_attn_name]["global_input_layer_norm"]["weight"] = (
+ t5x_global_layer_norm
+ )
+
+ if split_mlp_wi:
+ flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0
+ flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1
+ else:
+ flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi
+
+ flax_model_encoder_layer_block["1"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo
+ flax_model_encoder_layer_block["1"]["layer_norm"]["weight"] = t5x_mlp_layer_norm
+
+ flax_model.params["encoder"]["block"][str(layer_index)]["layer"] = flax_model_encoder_layer_block
+
+ # Only for layer 0:
+ t5x_encoder_rel_embedding = t5x_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
+ flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["relative_attention_bias"][
+ "embedding"
+ ] = t5x_encoder_rel_embedding
+
+ # Side/global relative position_bias + layer norm
+ if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
+ t5x_encoder_global_rel_embedding = t5x_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
+ flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["global_relative_attention_bias"][
+ "embedding"
+ ] = t5x_encoder_global_rel_embedding
+
+ # Assigning
+ t5x_encoder_norm = t5x_model["target"]["encoder"]["encoder_norm"]["scale"]
+ flax_model.params["encoder"]["final_layer_norm"]["weight"] = t5x_encoder_norm
+
+ # Decoder
+ for layer_index in range(config.num_layers):
+ layer_name = f"layers_{str(layer_index)}"
+
+ # Self-Attention
+ t5x_attention_key = t5x_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
+ t5x_attention_out = t5x_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
+ t5x_attention_query = t5x_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
+ t5x_attention_value = t5x_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]
+
+ # Layer Normalization
+ t5x_pre_attention_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
+ "scale"
+ ]
+
+ # Encoder-Decoder-Attention
+ t5x_enc_dec_attention_module = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
+ t5x_enc_dec_attention_key = t5x_enc_dec_attention_module["key"]["kernel"]
+ t5x_enc_dec_attention_out = t5x_enc_dec_attention_module["out"]["kernel"]
+ t5x_enc_dec_attention_query = t5x_enc_dec_attention_module["query"]["kernel"]
+ t5x_enc_dec_attention_value = t5x_enc_dec_attention_module["value"]["kernel"]
+
+ # Layer Normalization
+ t5x_cross_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]
+
+ # MLP
+ if split_mlp_wi:
+ t5x_mlp_wi_0 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
+ t5x_mlp_wi_1 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
+ else:
+ t5x_mlp_wi = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]
+
+ t5x_mlp_wo = t5x_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]
+
+ # Layer Normalization
+ tx5_mlp_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]
+
+ # Assigning
+ flax_model_decoder_layer_block = flax_model.params["decoder"]["block"][str(layer_index)]["layer"]
+ flax_model_decoder_layer_block["0"]["SelfAttention"]["k"]["kernel"] = t5x_attention_key
+ flax_model_decoder_layer_block["0"]["SelfAttention"]["o"]["kernel"] = t5x_attention_out
+ flax_model_decoder_layer_block["0"]["SelfAttention"]["q"]["kernel"] = t5x_attention_query
+ flax_model_decoder_layer_block["0"]["SelfAttention"]["v"]["kernel"] = t5x_attention_value
+
+ flax_model_decoder_layer_block["0"]["layer_norm"]["weight"] = t5x_pre_attention_layer_norm
+
+ flax_model_decoder_layer_block["1"]["EncDecAttention"]["k"]["kernel"] = t5x_enc_dec_attention_key
+ flax_model_decoder_layer_block["1"]["EncDecAttention"]["o"]["kernel"] = t5x_enc_dec_attention_out
+ flax_model_decoder_layer_block["1"]["EncDecAttention"]["q"]["kernel"] = t5x_enc_dec_attention_query
+ flax_model_decoder_layer_block["1"]["EncDecAttention"]["v"]["kernel"] = t5x_enc_dec_attention_value
+
+ flax_model_decoder_layer_block["1"]["layer_norm"]["weight"] = t5x_cross_layer_norm
+
+ if split_mlp_wi:
+ flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0
+ flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1
+ else:
+ flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi
+
+ flax_model_decoder_layer_block["2"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo
+
+ flax_model_decoder_layer_block["2"]["layer_norm"]["weight"] = tx5_mlp_layer_norm
+
+ flax_model.params["decoder"]["block"][str(layer_index)]["layer"] = flax_model_decoder_layer_block
+
+ # Decoder Normalization
+ tx5_decoder_norm = t5x_model["target"]["decoder"]["decoder_norm"]["scale"]
+ flax_model.params["decoder"]["final_layer_norm"]["weight"] = tx5_decoder_norm
+
+ # Only for layer 0:
+ t5x_decoder_rel_embedding = t5x_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
+ flax_model.params["decoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"][
+ "embedding"
+ ] = t5x_decoder_rel_embedding
+
+ # Token Embeddings
+ tx5_token_embeddings = t5x_model["target"]["token_embedder"]["embedding"]
+ flax_model.params["shared"]["embedding"] = tx5_token_embeddings
+
+ # LM Head (only in v1.1 and LongT5 checkpoints)
+ if "logits_dense" in t5x_model["target"]["decoder"]:
+ flax_model.params["lm_head"]["kernel"] = t5x_model["target"]["decoder"]["logits_dense"]["kernel"]
+
+ flax_model.save_pretrained(flax_dump_folder_path)
+ print("T5X Model was successfully converted!")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
+ )
+ parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
+ parser.add_argument(
+ "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
+ )
+ args = parser.parse_args()
+ convert_t5x_checkpoint_to_flax(args.t5x_checkpoint_path, args.config_name, args.flax_dump_folder_path)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/modeling_flax_longt5.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/modeling_flax_longt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..dee4afeadf724b8f48db3a2071c005cd201741d9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/modeling_flax_longt5.py
@@ -0,0 +1,2449 @@
+# coding=utf-8
+# Copyright 2022 LongT5 Authors and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Flax LongT5 model."""
+
+import copy
+from typing import Any, Callable, Optional
+
+import flax.linen as nn
+import jax
+import jax.numpy as jnp
+import numpy as np
+from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
+from flax.linen import combine_masks, make_causal_mask
+from flax.linen import partitioning as nn_partitioning
+from flax.linen.attention import dot_product_attention_weights
+from flax.traverse_util import flatten_dict, unflatten_dict
+from jax.random import PRNGKey
+
+from ...modeling_flax_outputs import (
+ FlaxBaseModelOutput,
+ FlaxBaseModelOutputWithPastAndCrossAttentions,
+ FlaxCausalLMOutputWithCrossAttentions,
+ FlaxSeq2SeqLMOutput,
+ FlaxSeq2SeqModelOutput,
+)
+from ...modeling_flax_utils import (
+ ACT2FN,
+ FlaxPreTrainedModel,
+ append_call_sample_docstring,
+ append_replace_return_docstrings,
+ overwrite_call_docstring,
+)
+from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from .configuration_longt5 import LongT5Config
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "google/long-t5-local-base"
+_CONFIG_FOR_DOC = "LongT5Config"
+
+remat = nn_partitioning.remat
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right
+def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray:
+ """
+ Shift input ids one token to the right.
+ """
+ shifted_input_ids = jnp.zeros_like(input_ids)
+ shifted_input_ids = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1])
+ shifted_input_ids = shifted_input_ids.at[:, 0].set(decoder_start_token_id)
+
+ shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
+ return shifted_input_ids
+
+
+def _pad_to_multiple(x: jnp.ndarray, block_len: int, axis: int, pad_value: int = 0) -> jnp.ndarray:
+ """Pad an array so that a sequence length will be a multiple of `block_len`"""
+ pad_len = -x.shape[axis] % block_len
+ pad = [(0, 0)] * x.ndim
+ pad[axis] = (0, pad_len)
+ x = jnp.pad(x, pad_width=pad, mode="constant", constant_values=pad_value)
+ return x
+
+
+def _split_into_blocks(x: jnp.ndarray, block_len: int, axis: int) -> jnp.ndarray:
+ """Split an input array into blocks of a given `block_len` along the given `axis`. If the dimension length
+ is not a multiple of `block_len`, it will be padded first with selected `pad_value`.
+ """
+ # pad tensor to multiple of block_len
+ if x.shape[axis] % block_len != 0:
+ x = _pad_to_multiple(x, block_len, axis, pad_value=0)
+ num_blocks = x.shape[axis] // block_len
+ output_shape = x.shape[:axis] + (num_blocks, block_len) + x.shape[(axis + 1) :]
+ return x.reshape(output_shape)
+
+
+def _concatenate_3_blocks(x: jnp.ndarray, block_axis: int, sequence_axis: int, pad_value: int = 0) -> jnp.ndarray:
+ """Concatenate three consecutive blocks for each input block for local attentiont.
+ For more information, see: https://huggingface.co/papers/2112.07916.
+ """
+ num_blocks = x.shape[block_axis]
+
+ pad = [(0, 0)] * x.ndim
+ pad[block_axis] = (1, 1)
+ # [batch_size, num_blocks, block_len] -> [batch_size, num_blocks + 2, block_len]
+ x = jnp.pad(x, pad_width=pad, mode="constant", constant_values=pad_value)
+
+ blocks_list: list[np.array] = []
+ for i in range(3):
+ # We use indexing approach here:
+ # https://numpy.org/doc/stable/user/basics.indexing.html#dealing-with-variable-numbers-of-indices-within-programs
+ indices = [slice(0, None)] * x.ndim
+ indices[block_axis] = slice(i, i + num_blocks)
+ indices = tuple(indices)
+ blocks_list.append(x[indices])
+ return jnp.concatenate(blocks_list, axis=sequence_axis) # [batch_size, num_blocks, 3 * block_len, ...]
+
+
+def _make_3block_relative_position_ids(block_len: int) -> jnp.ndarray:
+ """Makes 3-blocked relative position ids for local attention."""
+ position_ids = jnp.arange(3 * block_len, dtype=jnp.int32)
+ center_position_ids = position_ids[block_len:-block_len]
+ relative_position_ids = position_ids[None, :] - center_position_ids[:, None] # [block_len, 3 * block_len]
+ return relative_position_ids
+
+
+def _mask_local_attention_mask(local_attention_mask: np.ndarray, block_len: int) -> jnp.ndarray:
+ """Mask local attention mask to enforce that tokens are not allowed to attend tokens farther than ``local_radius."""
+ relative_position_ids = _make_3block_relative_position_ids(block_len)
+ locality_mask = jnp.abs(relative_position_ids) < block_len
+ locality_mask = locality_mask[None, None, :, :]
+ return jnp.logical_and(local_attention_mask, locality_mask)
+
+
+def _get_local_attention_mask(attention_mask: np.ndarray, block_len: int) -> jnp.ndarray:
+ """Prepare attention mask to be applied for a local attention."""
+ # [batch_size, num_blocks, block_len]
+ _blocked_attention_mask = _split_into_blocks(attention_mask, block_len, axis=1)
+ # [batch_size, num_block, 3 * block_len]
+ _3blocked_attention_mask = _concatenate_3_blocks(_blocked_attention_mask, block_axis=1, sequence_axis=2)
+
+ _blocked_attention_mask = _blocked_attention_mask[..., None]
+ _3blocked_attention_mask = _3blocked_attention_mask[..., None, :]
+ # [batch_size, num_block, block_len, 3 * block_len]
+ local_attention_mask = jnp.logical_and(_blocked_attention_mask, _3blocked_attention_mask)
+ local_attention_mask = _mask_local_attention_mask(local_attention_mask, block_len)
+ # [batch_size, 1, num_block, block_len, 3 * block_len]
+ return local_attention_mask[:, None, ...]
+
+
+def _make_global_fixed_block_ids(attention_mask: np.ndarray, global_block_size: int) -> tuple[jnp.ndarray, np.ndarray]:
+ """Obtain the "fixed block" global id corresponding to each input token.
+
+ This implementation is a simplified version of the original Flaxformr implementation adopted from:
+ https://github.com/google/flaxformer/blob/main/flaxformer/architectures/longt5/long_attention.py.
+
+ In our scenario, as we use this strategy only for a decoder, orphan tokens, i.e. those tokens which do not make for
+ the whole fixed block, are assigned to the preceding block.
+
+ Padding tokens from the original sequence are represented by -1.
+ """
+ batch_size, seq_len = attention_mask.shape[:2]
+
+ def handle_orphan_tokens(block_ids: np.ndarray) -> jnp.ndarray:
+ block_ends = (jnp.arange(seq_len) % global_block_size) == global_block_size - 1
+ true_block_ends = jnp.logical_and(block_ends, block_ids >= 0)
+ full_blocks = true_block_ends.sum(-1)[..., None]
+ block_ids = jnp.minimum(block_ids, full_blocks - 1)
+ return block_ids
+
+ fixed_block_mask = jnp.ones_like(attention_mask) / global_block_size
+ fixed_block_mask = jnp.cumsum(fixed_block_mask, axis=1) - fixed_block_mask
+ mask = jnp.where(attention_mask != 0.0, 1.0, -1000.0)
+ global_block_ids = jnp.maximum(
+ jnp.floor(mask + fixed_block_mask - 1.0), jnp.array(-1.0, dtype=attention_mask.dtype)
+ )
+ # set padding tokens to -1
+ global_block_ids = (global_block_ids * attention_mask) + (attention_mask - 1)
+ # [batch_size, seq_len]
+ global_block_ids = handle_orphan_tokens(global_block_ids)
+ num_globals = seq_len // global_block_size
+
+ # [batch_size, seq_len // global_block_size]
+ if num_globals > 0:
+ _sequence_block_ids_max = jnp.repeat(global_block_ids.max(axis=-1)[:, None], repeats=num_globals, axis=1)
+ else:
+ _sequence_block_ids_max = jnp.zeros((batch_size, 0), dtype=global_block_ids.dtype)
+ global_segment_ids = jnp.cumsum(jnp.ones((batch_size, num_globals)), axis=-1) - 1
+ global_segment_ids = jnp.where(global_segment_ids <= _sequence_block_ids_max, 1, 0)
+ return global_block_ids, global_segment_ids
+
+
+def _make_side_relative_position_ids(attention_mask: np.ndarray, global_block_size: int) -> np.ndarray:
+ """Create the relative position tensor for local -> global attention."""
+ block_ids, global_segment_ids = _make_global_fixed_block_ids(attention_mask, global_block_size)
+ global_seq_len = global_segment_ids.shape[-1]
+ global_positions = jnp.arange(global_seq_len)
+ side_relative_position = global_positions - block_ids[..., None]
+ return side_relative_position
+
+
+def _create_global_aggregates(hidden_states: np.ndarray, block_ids: np.ndarray, global_seq_len: int) -> np.ndarray:
+ """Compute individual block aggregates by summing over individual blocks."""
+ # (batch..., seq_len, global_seq_len))
+ one_hot_block_ids = jax.nn.one_hot(block_ids, global_seq_len)
+ return jnp.einsum("...nd,...ng->...gd", hidden_states, one_hot_block_ids)
+
+
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5LayerNorm with T5->LongT5
+class FlaxLongT5LayerNorm(nn.Module):
+ hidden_size: int
+ dtype: jnp.dtype = jnp.float32
+ eps: float = 1e-6
+ weight_init: Callable[..., np.ndarray] = jax.nn.initializers.ones
+
+ def setup(self):
+ self.weight = self.param("weight", self.weight_init, (self.hidden_size,))
+
+ def __call__(self, hidden_states):
+ """
+ Construct a layernorm module in the LongT5 style; No bias and no subtraction of mean.
+ """
+ # layer norm should always be calculated in float32
+ variance = jnp.power(hidden_states.astype("f4"), 2).mean(axis=-1, keepdims=True)
+ hidden_states = hidden_states / jnp.sqrt(variance + self.eps)
+
+ return self.weight * hidden_states
+
+
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5DenseActDense with T5->LongT5
+class FlaxLongT5DenseActDense(nn.Module):
+ config: LongT5Config
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ wi_init_std = self.config.initializer_factor * (self.config.d_model**-0.5)
+ wo_init_std = self.config.initializer_factor * (self.config.d_ff**-0.5)
+
+ self.wi = nn.Dense(
+ self.config.d_ff,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(wi_init_std),
+ dtype=self.dtype,
+ )
+ self.wo = nn.Dense(
+ self.config.d_model,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(wo_init_std),
+ dtype=self.dtype,
+ )
+ self.dropout = nn.Dropout(self.config.dropout_rate)
+ self.act = ACT2FN[self.config.dense_act_fn]
+
+ def __call__(self, hidden_states, deterministic=True):
+ hidden_states = self.wi(hidden_states)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.dropout(hidden_states, deterministic=deterministic)
+ hidden_states = self.wo(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5DenseGatedActDense with T5->LongT5
+class FlaxLongT5DenseGatedActDense(nn.Module):
+ config: LongT5Config
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ wi_init_std = self.config.initializer_factor * (self.config.d_model**-0.5)
+ wo_init_std = self.config.initializer_factor * (self.config.d_ff**-0.5)
+
+ self.wi_0 = nn.Dense(
+ self.config.d_ff,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(wi_init_std),
+ dtype=self.dtype,
+ )
+ self.wi_1 = nn.Dense(
+ self.config.d_ff,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(wi_init_std),
+ dtype=self.dtype,
+ )
+ self.wo = nn.Dense(
+ self.config.d_model,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(wo_init_std),
+ dtype=self.dtype,
+ )
+ self.dropout = nn.Dropout(self.config.dropout_rate)
+ self.act = ACT2FN[self.config.dense_act_fn]
+
+ def __call__(self, hidden_states, deterministic):
+ hidden_gelu = self.act(self.wi_0(hidden_states))
+ hidden_linear = self.wi_1(hidden_states)
+ hidden_states = hidden_gelu * hidden_linear
+ hidden_states = self.dropout(hidden_states, deterministic=deterministic)
+ hidden_states = self.wo(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5LayerFF with T5->LongT5
+class FlaxLongT5LayerFF(nn.Module):
+ config: LongT5Config
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ if self.config.is_gated_act:
+ self.DenseReluDense = FlaxLongT5DenseGatedActDense(self.config, dtype=self.dtype)
+ else:
+ self.DenseReluDense = FlaxLongT5DenseActDense(self.config, dtype=self.dtype)
+
+ self.layer_norm = FlaxLongT5LayerNorm(
+ self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype
+ )
+ self.dropout = nn.Dropout(self.config.dropout_rate)
+
+ def __call__(self, hidden_states, deterministic=True):
+ forwarded_states = self.layer_norm(hidden_states)
+ forwarded_states = self.DenseReluDense(forwarded_states, deterministic=deterministic)
+ hidden_states = hidden_states + self.dropout(forwarded_states, deterministic=deterministic)
+ return hidden_states
+
+
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5Attention with T5->LongT5
+class FlaxLongT5Attention(nn.Module):
+ config: LongT5Config
+ has_relative_attention_bias: bool = False
+ causal: bool = False
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.relative_attention_num_buckets = self.config.relative_attention_num_buckets
+ self.relative_attention_max_distance = self.config.relative_attention_max_distance
+ self.d_model = self.config.d_model
+ self.key_value_proj_dim = self.config.d_kv
+ self.n_heads = self.config.num_heads
+ self.dropout = self.config.dropout_rate
+ self.inner_dim = self.n_heads * self.key_value_proj_dim
+
+ q_init_std = self.config.initializer_factor * ((self.inner_dim * self.key_value_proj_dim) ** -0.5)
+ kv_init_std = self.config.initializer_factor * (self.inner_dim**-0.5)
+ o_init_std = self.config.initializer_factor * (self.inner_dim**-0.5)
+
+ self.q = nn.Dense(
+ self.inner_dim,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(q_init_std),
+ dtype=self.dtype,
+ )
+ self.k = nn.Dense(
+ self.inner_dim,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(kv_init_std),
+ dtype=self.dtype,
+ )
+ self.v = nn.Dense(
+ self.inner_dim,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(kv_init_std),
+ dtype=self.dtype,
+ )
+ self.o = nn.Dense(
+ self.d_model,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(o_init_std),
+ dtype=self.dtype,
+ )
+
+ if self.has_relative_attention_bias:
+ self.relative_attention_bias = nn.Embed(
+ self.relative_attention_num_buckets,
+ self.n_heads,
+ embedding_init=jax.nn.initializers.normal(kv_init_std),
+ dtype=self.dtype,
+ )
+
+ @staticmethod
+ def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+ """
+ Adapted from Mesh Tensorflow:
+ https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+ Translate relative position to a bucket number for relative attention. The relative position is defined as
+ memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+ position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+ small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+ positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+ This should allow for more graceful generalization to longer sequences than the model has been trained on
+ """
+ relative_buckets = 0
+ if bidirectional:
+ num_buckets //= 2
+ relative_buckets += (relative_position > 0) * num_buckets
+ relative_position = jnp.abs(relative_position)
+ else:
+ relative_position = -jnp.clip(relative_position, a_max=0)
+ # now relative_position is in the range [0, inf)
+
+ # half of the buckets are for exact increments in positions
+ max_exact = num_buckets // 2
+ is_small = relative_position < max_exact
+
+ # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ relative_position_if_large = max_exact + (
+ jnp.log(relative_position / max_exact) / jnp.log(max_distance / max_exact) * (num_buckets - max_exact)
+ )
+ relative_position_if_large = jnp.clip(relative_position_if_large, a_max=num_buckets - 1)
+
+ relative_buckets += jnp.where(is_small, relative_position, relative_position_if_large)
+
+ return relative_buckets.astype("i4")
+
+ def compute_bias(self, query_length, key_length):
+ """Compute binned relative position bias"""
+ context_position = jnp.arange(query_length, dtype="i4")[:, None]
+ memory_position = jnp.arange(key_length, dtype="i4")[None, :]
+
+ relative_position = memory_position - context_position
+ relative_position_bucket = self._relative_position_bucket(
+ relative_position,
+ bidirectional=(not self.causal),
+ num_buckets=self.relative_attention_num_buckets,
+ max_distance=self.relative_attention_max_distance,
+ )
+
+ values = self.relative_attention_bias(relative_position_bucket)
+ values = values.transpose((2, 0, 1))[None, :, :, :]
+ return values
+
+ def _split_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.n_heads, self.key_value_proj_dim))
+
+ def _merge_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.inner_dim,))
+
+ @nn.compact
+ def _concatenate_to_cache(self, key, value, query, attention_mask):
+ """
+ This function takes projected key, value states from a single input token and concatenates the states to cached
+ states from previous steps. This function is slightly adapted from the official Flax repository:
+ https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
+ """
+ # detect if we're initializing by absence of existing cache data.
+ is_initialized = self.has_variable("cache", "cached_key")
+ cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
+ cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
+ cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
+
+ if is_initialized:
+ *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
+ # update key, value caches with our new 1d spatial slices
+ cur_index = cache_index.value
+ indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
+ key = jax.lax.dynamic_update_slice(cached_key.value, key, indices)
+ value = jax.lax.dynamic_update_slice(cached_value.value, value, indices)
+ cached_key.value = key
+ cached_value.value = value
+ num_updated_cache_vectors = query.shape[1]
+ cache_index.value = cache_index.value + num_updated_cache_vectors
+ # causal mask for cached decoder self-attention: our single query position should only attend to those key positions
+ # that have already been generated and cached, not the remaining zero elements.
+ pad_mask = jnp.broadcast_to(
+ jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
+ tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
+ )
+ attention_mask = combine_masks(pad_mask, attention_mask)
+ return key, value, attention_mask
+
+ def _create_position_bias(
+ self, key_states, query_states, attention_mask, init_cache, seq_length, causal_attention_mask_shift
+ ):
+ cache_is_filled = self.causal and self.has_variable("cache", "cached_key") and (not init_cache)
+ key_length = key_states.shape[1]
+ query_length = key_length if cache_is_filled else query_states.shape[1]
+
+ if self.has_relative_attention_bias:
+ position_bias = self.compute_bias(query_length, key_length)
+ elif attention_mask is not None:
+ position_bias = jnp.zeros_like(attention_mask)
+ else:
+ position_bias = jnp.zeros((1, self.n_heads, query_length, key_length), dtype=self.dtype)
+
+ # if key and values are already calculated, only the last query position bias should be taken
+ if cache_is_filled:
+ max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
+ position_bias = jax.lax.dynamic_slice(
+ position_bias,
+ (0, 0, causal_attention_mask_shift, 0),
+ (1, self.n_heads, seq_length, max_decoder_length),
+ )
+ return position_bias
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ key_value_states=None,
+ position_bias=None,
+ use_cache=False,
+ output_attentions=False,
+ deterministic=True,
+ init_cache=False,
+ ):
+ """
+ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
+ """
+ batch_size, seq_length = hidden_states.shape[:2]
+
+ # q, k, v projections
+ query_states = self.q(hidden_states) # (batch_size, n_heads, seq_length, dim_per_head)
+ key_states = self.k(hidden_states) if key_value_states is None else self.k(key_value_states)
+ value_states = self.v(hidden_states) if key_value_states is None else self.v(key_value_states)
+
+ # reshape to (batch_size, seq_length, n_heads, head_dim)
+ query_states = self._split_heads(query_states)
+ key_states = self._split_heads(key_states)
+ value_states = self._split_heads(value_states)
+
+ # counter-act scaling in dot_product_attention_weights function
+ query_states *= jnp.sqrt(query_states.shape[-1])
+
+ # for fast decoding causal attention mask should be shifted
+ causal_attention_mask_shift = (
+ self.variables["cache"]["cache_index"] if (self.has_variable("cache", "cached_key") and self.causal) else 0
+ )
+ # create causal attention_mask; attention_mask has to be defined when model is causal
+ if self.causal:
+ causal_attention_mask = make_causal_mask(attention_mask, dtype="bool")
+
+ # fast decoding for generate requires special attention_mask
+ if self.has_variable("cache", "cached_key"):
+ max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
+ causal_attention_mask = jax.lax.dynamic_slice(
+ causal_attention_mask,
+ (0, 0, causal_attention_mask_shift, 0),
+ (1, 1, seq_length, max_decoder_length),
+ )
+
+ # broadcast causal attention mask & attention mask to fit for merge
+ causal_attention_mask = jnp.broadcast_to(
+ causal_attention_mask, (batch_size,) + causal_attention_mask.shape[1:]
+ )
+ attention_mask = jnp.broadcast_to(
+ jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_attention_mask.shape
+ )
+ attention_mask = combine_masks(attention_mask, causal_attention_mask)
+ elif attention_mask is not None:
+ attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
+
+ # During fast autoregressive decoding, we feed one position at a time,
+ # and cache the keys and values step by step.
+ if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
+ key_states, value_states, attention_mask = self._concatenate_to_cache(
+ key_states, value_states, query_states, attention_mask
+ )
+
+ # replace masked positions with -10_000
+ if attention_mask is not None:
+ mask_value = jnp.finfo(self.dtype).min
+ attention_mask = jax.lax.select(
+ attention_mask > 0,
+ jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
+ jnp.full(attention_mask.shape, mask_value).astype(self.dtype),
+ )
+
+ if position_bias is None:
+ # compute position bias (only for first layer)
+ position_bias = self._create_position_bias(
+ key_states, query_states, attention_mask, init_cache, seq_length, causal_attention_mask_shift
+ )
+
+ if attention_mask is not None:
+ position_bias = position_bias + attention_mask
+
+ # create dropout rng
+ dropout_rng = None
+ if not deterministic and self.dropout > 0.0:
+ dropout_rng = self.make_rng("dropout")
+
+ # Softmax(QK^T)
+ attn_weights = dot_product_attention_weights(
+ query_states,
+ key_states,
+ bias=position_bias,
+ dropout_rng=dropout_rng,
+ dropout_rate=self.dropout,
+ broadcast_dropout=True,
+ deterministic=deterministic,
+ dtype=self.dtype,
+ )
+
+ # multiply with value states
+ attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
+
+ # bring back to (batch_size, seq_length, d_model)
+ attn_output = self._merge_heads(attn_output)
+
+ # apply output matrix
+ attn_output = self.o(attn_output)
+
+ outputs = (attn_output, position_bias)
+
+ if output_attentions:
+ outputs = outputs + (attn_weights,)
+
+ return outputs
+
+
+class FlaxLongT5LocalAttention(nn.Module):
+ config: LongT5Config
+ has_relative_attention_bias: bool = False
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.relative_attention_num_buckets = self.config.relative_attention_num_buckets
+ self.relative_attention_max_distance = self.config.relative_attention_max_distance
+ self.d_model = self.config.d_model
+ self.key_value_proj_dim = self.config.d_kv
+ self.n_heads = self.config.num_heads
+ self.local_radius = self.config.local_radius
+ self.block_len = self.local_radius + 1
+ self.dropout = self.config.dropout_rate
+ self.inner_dim = self.n_heads * self.key_value_proj_dim
+
+ q_init_std = self.config.initializer_factor * ((self.inner_dim * self.key_value_proj_dim) ** -0.5)
+ kv_init_std = self.config.initializer_factor * (self.inner_dim**-0.5)
+ o_init_std = self.config.initializer_factor * (self.inner_dim**-0.5)
+
+ self.q = nn.Dense(
+ self.inner_dim,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(q_init_std),
+ dtype=self.dtype,
+ )
+ self.k = nn.Dense(
+ self.inner_dim,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(kv_init_std),
+ dtype=self.dtype,
+ )
+ self.v = nn.Dense(
+ self.inner_dim,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(kv_init_std),
+ dtype=self.dtype,
+ )
+ self.o = nn.Dense(
+ self.d_model,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(o_init_std),
+ dtype=self.dtype,
+ )
+
+ if self.has_relative_attention_bias:
+ self.relative_attention_bias = nn.Embed(
+ self.relative_attention_num_buckets,
+ self.n_heads,
+ embedding_init=jax.nn.initializers.normal(kv_init_std),
+ )
+
+ @staticmethod
+ # Copied from transformers.models.t5.modeling_flax_t5.FlaxT5Attention._relative_position_bucket
+ def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+ """
+ Adapted from Mesh Tensorflow:
+ https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+ Translate relative position to a bucket number for relative attention. The relative position is defined as
+ memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+ position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+ small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+ positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+ This should allow for more graceful generalization to longer sequences than the model has been trained on
+ """
+ relative_buckets = 0
+ if bidirectional:
+ num_buckets //= 2
+ relative_buckets += (relative_position > 0) * num_buckets
+ relative_position = jnp.abs(relative_position)
+ else:
+ relative_position = -jnp.clip(relative_position, a_max=0)
+ # now relative_position is in the range [0, inf)
+
+ # half of the buckets are for exact increments in positions
+ max_exact = num_buckets // 2
+ is_small = relative_position < max_exact
+
+ # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ relative_position_if_large = max_exact + (
+ jnp.log(relative_position / max_exact) / jnp.log(max_distance / max_exact) * (num_buckets - max_exact)
+ )
+ relative_position_if_large = jnp.clip(relative_position_if_large, a_max=num_buckets - 1)
+
+ relative_buckets += jnp.where(is_small, relative_position, relative_position_if_large)
+
+ return relative_buckets.astype("i4")
+
+ def compute_bias(self, block_length: int):
+ """Compute binned relative position bias"""
+ memory_position = jnp.arange(3 * block_length, dtype="i4")
+ context_position = memory_position[block_length:-block_length]
+
+ relative_position = memory_position[None, :] - context_position[:, None]
+ relative_position_bucket = self._relative_position_bucket(
+ relative_position,
+ bidirectional=True,
+ num_buckets=self.relative_attention_num_buckets,
+ max_distance=self.relative_attention_max_distance,
+ )
+
+ values = self.relative_attention_bias(relative_position_bucket)
+ values = values.transpose((2, 0, 1))[None, None, :, :, :]
+ return values
+
+ def _split_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.n_heads, self.key_value_proj_dim))
+
+ def _merge_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[0], -1, self.inner_dim)
+
+ def _create_position_bias(self, block_len: int, attention_mask: Optional[np.ndarray]) -> np.ndarray:
+ # position_bias shape: # (1, 1, n_heads, block_len, 3 * block_len)
+ if self.has_relative_attention_bias:
+ position_bias = self.compute_bias(block_len)
+ elif attention_mask is not None:
+ position_bias = jnp.zeros_like(attention_mask)
+ else:
+ position_bias = jnp.zeros((1, 1, self.n_heads, block_len, 3 * block_len), dtype=self.dtype)
+
+ return position_bias
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ key_value_states=None,
+ position_bias=None,
+ output_attentions=False,
+ deterministic=True,
+ ):
+ """
+ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
+ """
+ batch_size, seq_length = hidden_states.shape[:2]
+
+ # q, k, v projections
+ query_states = self.q(hidden_states) # (batch_size, n_heads, seq_length, dim_per_head)
+ key_states = self.k(hidden_states) if key_value_states is None else self.k(key_value_states)
+ value_states = self.v(hidden_states) if key_value_states is None else self.v(key_value_states)
+
+ # reshape to (batch_size, seq_length, n_heads, head_dim)
+ query_states = self._split_heads(query_states)
+ key_states = self._split_heads(key_states)
+ value_states = self._split_heads(value_states)
+
+ # Split into blocks -> (batch_size, num_blocks, block_len, n_heads, head_dim)
+ query_states = _split_into_blocks(query_states, self.block_len, axis=1)
+ key_states = _split_into_blocks(key_states, self.block_len, axis=1)
+ value_states = _split_into_blocks(value_states, self.block_len, axis=1)
+
+ # Concatenate 3 blocks for keys and values -> (batch_size, num_blocks, 3 * block_len, n_heads, dim_per_head)
+ key_states = _concatenate_3_blocks(key_states, block_axis=1, sequence_axis=2)
+ value_states = _concatenate_3_blocks(value_states, block_axis=1, sequence_axis=2)
+
+ # counter-act scaling in dot_product_attention_weights function
+ query_states *= jnp.sqrt(query_states.shape[-1])
+
+ if attention_mask is not None:
+ attention_mask = _get_local_attention_mask(attention_mask, self.block_len)
+
+ # replace masked positions with -10_000
+ attention_mask = jax.lax.select(
+ attention_mask > 0,
+ jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
+ jnp.full(attention_mask.shape, -1e10).astype(self.dtype),
+ )
+
+ if position_bias is None:
+ # compute position bias (only for first layer)
+ position_bias = self._create_position_bias(self.block_len, attention_mask)
+
+ if attention_mask is not None:
+ position_bias = position_bias + attention_mask.swapaxes(1, 2)
+
+ # create dropout rng
+ dropout_rng = None
+ if not deterministic and self.dropout > 0.0:
+ dropout_rng = self.make_rng("dropout")
+
+ # Softmax(QK^T)
+ attn_weights = dot_product_attention_weights(
+ query_states,
+ key_states,
+ bias=position_bias,
+ dropout_rng=dropout_rng,
+ dropout_rate=self.dropout,
+ broadcast_dropout=True,
+ deterministic=deterministic,
+ dtype=self.dtype,
+ )
+
+ # multiply with value states
+ attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
+
+ # bring back to (batch_size, seq_length, d_model)
+ attn_output = self._merge_heads(attn_output)
+ attn_output = attn_output[:, :seq_length, :]
+
+ # apply output matrix
+ attn_output = self.o(attn_output)
+
+ outputs = (attn_output, position_bias)
+
+ if output_attentions:
+ outputs = outputs + (attn_weights,)
+
+ return outputs
+
+
+class FlaxLongT5TransientGlobalAttention(nn.Module):
+ config: LongT5Config
+ has_relative_attention_bias: bool = False
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.relative_attention_num_buckets = self.config.relative_attention_num_buckets
+ self.relative_attention_max_distance = self.config.relative_attention_max_distance
+ self.d_model = self.config.d_model
+ self.key_value_proj_dim = self.config.d_kv
+ self.n_heads = self.config.num_heads
+ self.local_radius = self.config.local_radius
+ self.block_len = self.local_radius + 1
+ self.global_block_size = self.config.global_block_size
+ self.dropout = self.config.dropout_rate
+ self.inner_dim = self.n_heads * self.key_value_proj_dim
+
+ q_init_std = self.config.initializer_factor * ((self.inner_dim * self.key_value_proj_dim) ** -0.5)
+ kv_init_std = self.config.initializer_factor * (self.inner_dim**-0.5)
+ o_init_std = self.config.initializer_factor * (self.inner_dim**-0.5)
+
+ self.q = nn.Dense(
+ self.inner_dim,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(q_init_std),
+ dtype=self.dtype,
+ )
+ self.k = nn.Dense(
+ self.inner_dim,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(kv_init_std),
+ dtype=self.dtype,
+ )
+ self.v = nn.Dense(
+ self.inner_dim,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(kv_init_std),
+ dtype=self.dtype,
+ )
+ self.o = nn.Dense(
+ self.d_model,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(o_init_std),
+ dtype=self.dtype,
+ )
+
+ if self.has_relative_attention_bias:
+ self.relative_attention_bias = nn.Embed(
+ self.relative_attention_num_buckets,
+ self.n_heads,
+ embedding_init=jax.nn.initializers.normal(kv_init_std),
+ )
+
+ # Relativen attention bias & Layer norm for global attention
+ if self.has_relative_attention_bias:
+ self.global_relative_attention_bias = nn.Embed(
+ self.relative_attention_num_buckets,
+ self.n_heads,
+ embedding_init=jax.nn.initializers.normal(kv_init_std),
+ )
+ self.global_input_layer_norm = FlaxLongT5LayerNorm(
+ self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype
+ )
+
+ @staticmethod
+ # Copied from transformers.models.t5.modeling_flax_t5.FlaxT5Attention._relative_position_bucket
+ def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+ """
+ Adapted from Mesh Tensorflow:
+ https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+ Translate relative position to a bucket number for relative attention. The relative position is defined as
+ memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+ position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+ small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+ positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+ This should allow for more graceful generalization to longer sequences than the model has been trained on
+ """
+ relative_buckets = 0
+ if bidirectional:
+ num_buckets //= 2
+ relative_buckets += (relative_position > 0) * num_buckets
+ relative_position = jnp.abs(relative_position)
+ else:
+ relative_position = -jnp.clip(relative_position, a_max=0)
+ # now relative_position is in the range [0, inf)
+
+ # half of the buckets are for exact increments in positions
+ max_exact = num_buckets // 2
+ is_small = relative_position < max_exact
+
+ # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ relative_position_if_large = max_exact + (
+ jnp.log(relative_position / max_exact) / jnp.log(max_distance / max_exact) * (num_buckets - max_exact)
+ )
+ relative_position_if_large = jnp.clip(relative_position_if_large, a_max=num_buckets - 1)
+
+ relative_buckets += jnp.where(is_small, relative_position, relative_position_if_large)
+
+ return relative_buckets.astype("i4")
+
+ def compute_bias(self, block_length: int):
+ """Compute binned relative position bias"""
+ memory_position = jnp.arange(3 * block_length, dtype="i4")
+ context_position = memory_position[block_length:-block_length]
+
+ relative_position = memory_position[None, :] - context_position[:, None]
+ relative_position_bucket = self._relative_position_bucket(
+ relative_position,
+ bidirectional=True,
+ num_buckets=self.relative_attention_num_buckets,
+ max_distance=self.relative_attention_max_distance,
+ )
+
+ values = self.relative_attention_bias(relative_position_bucket)
+ values = values.transpose((2, 0, 1))[None, None, :, :, :]
+ return values
+
+ def compute_side_bias(self, attention_mask: np.ndarray, global_segment_ids: np.ndarray) -> np.ndarray:
+ # (batch_size, 1, 1, seq_len, global_seq_len)
+ side_attention_mask = jnp.equal(attention_mask[..., None], global_segment_ids[:, None, :])[:, None, ...]
+ attention_side_bias = jax.lax.select(
+ side_attention_mask > 0,
+ jnp.full(side_attention_mask.shape, 0.0).astype(self.dtype),
+ jnp.full(side_attention_mask.shape, -1e10).astype(self.dtype),
+ )
+ # (batch_size, seq_len, global_seq_len)
+ side_relative_position = _make_side_relative_position_ids(attention_mask, self.global_block_size)
+ side_relative_position_bucket = self._relative_position_bucket(
+ side_relative_position,
+ bidirectional=True,
+ num_buckets=self.relative_attention_num_buckets,
+ max_distance=self.relative_attention_max_distance,
+ )
+ # (batch_size, seq_len, global_seq_len, num_heads)
+ side_bias = self.global_relative_attention_bias(side_relative_position_bucket)
+
+ # (batch_size, 1, num_heads, seq_len, global_seq_len)
+ side_bias = jnp.transpose(side_bias, (0, 3, 1, 2))
+ # (batch_size, num_heads, seq_len, global_seq_len)
+ attention_side_bias = attention_side_bias + side_bias
+ return attention_side_bias
+
+ def _split_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.n_heads, self.key_value_proj_dim))
+
+ def _merge_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[0], -1, self.inner_dim)
+
+ def _create_position_bias(self, block_len: int, attention_mask: Optional[np.ndarray]) -> np.ndarray:
+ # position_bias shape: # (1, 1, n_heads, block_len, 3 * block_len)
+ if self.has_relative_attention_bias:
+ position_bias = self.compute_bias(block_len)
+ elif attention_mask is not None:
+ position_bias = jnp.zeros_like(attention_mask)
+ else:
+ position_bias = jnp.zeros((1, 1, self.n_heads, block_len, 3 * block_len), dtype=self.dtype)
+
+ return position_bias
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ key_value_states=None,
+ position_bias=None,
+ output_attentions=False,
+ deterministic=True,
+ ):
+ """
+ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
+ """
+ batch_size, seq_length = hidden_states.shape[:2]
+
+ # Prepare components for transient-global attention
+ # Obtain block_ids and global_segment_ids
+ # global_seq_len := seq_len // self.global_block_size
+ # shapes: (batch_size, seq_len) & (batch_size, global_seq_len)
+ block_ids, global_segment_ids = _make_global_fixed_block_ids(
+ attention_mask if attention_mask is not None else jnp.ones((batch_size, seq_length)),
+ self.global_block_size,
+ )
+ # Create global inputs
+ _global_seq_len = global_segment_ids.shape[-1]
+ global_inputs = _create_global_aggregates(hidden_states, block_ids, _global_seq_len)
+ global_inputs = self.global_input_layer_norm(global_inputs)
+
+ # q, k, v projections
+ query_states = self.q(hidden_states) # (batch_size, n_heads, seq_length, dim_per_head)
+ key_states = self.k(hidden_states) if key_value_states is None else self.k(key_value_states)
+ value_states = self.v(hidden_states) if key_value_states is None else self.v(key_value_states)
+
+ # reshape to (batch_size, seq_length, n_heads, head_dim)
+ query_states = self._split_heads(query_states)
+ key_states = self._split_heads(key_states)
+ value_states = self._split_heads(value_states)
+
+ # Get global/side key/value_states
+ side_key_states = self.k(global_inputs)
+ side_value_states = self.v(global_inputs)
+
+ # reshape to (batch_size, global_seq_len, n_heads, head_dim)
+ side_key_states = self._split_heads(side_key_states)
+ side_value_states = self._split_heads(side_value_states)
+
+ # Split into blocks -> (batch_size, num_blocks, block_len, n_heads, head_dim)
+ query_states = _split_into_blocks(query_states, self.block_len, axis=1)
+ key_states = _split_into_blocks(key_states, self.block_len, axis=1)
+ value_states = _split_into_blocks(value_states, self.block_len, axis=1)
+
+ # Concatenate 3 blocks for keys and values -> (batch_size, num_blocks, 3 * block_len, n_heads, dim_per_head)
+ key_states = _concatenate_3_blocks(key_states, block_axis=1, sequence_axis=2)
+ value_states = _concatenate_3_blocks(value_states, block_axis=1, sequence_axis=2)
+
+ # Tile side inputs across local key/value blocks
+ # New shape: (batch_size, num_blocks, global_seq_len, n_heads, dim_per_head)
+ reps = [1] * (side_key_states.ndim + 1)
+ reps[1] = key_states.shape[1]
+ side_key_states = jnp.tile(side_key_states[:, None, ...], reps)
+ side_value_states = jnp.tile(side_value_states[:, None, ...], reps)
+
+ # Concatenate "local" and "side"/"global" key/value states to allow each token to attend global aggregated ones
+ # New shape: (batch_size, num_blocks, 3 * block_len + global_seq_len, n_heads, dim_per_head)
+ key_states = jnp.concatenate((key_states, side_key_states), axis=2)
+ value_states = jnp.concatenate((value_states, side_value_states), axis=2)
+
+ # counter-act scaling in dot_product_attention_weights function
+ query_states *= jnp.sqrt(query_states.shape[-1])
+
+ if attention_mask is not None:
+ local_attention_mask = _get_local_attention_mask(attention_mask, self.block_len)
+ local_attention_mask = jax.lax.select(
+ local_attention_mask > 0,
+ jnp.full(local_attention_mask.shape, 0.0).astype(self.dtype),
+ jnp.full(local_attention_mask.shape, -1e10).astype(self.dtype),
+ )
+ else:
+ local_attention_mask = None
+
+ if position_bias is None:
+ # compute position bias (only for first layer)
+ position_bias = self._create_position_bias(self.block_len, attention_mask)
+ if local_attention_mask is not None:
+ position_bias = position_bias + local_attention_mask.swapaxes(1, 2)
+
+ # Calculate global/side bias - shape: # (batch_size, num_heads, seq_len, global_seq_len)
+ if attention_mask is None:
+ attention_mask = jnp.ones((batch_size, seq_length))
+ side_position_bias = self.compute_side_bias(attention_mask, global_segment_ids)
+ side_position_bias = _split_into_blocks(side_position_bias, self.block_len, axis=-2)
+ side_position_bias = jnp.swapaxes(side_position_bias, 1, 2)
+ position_bias = jnp.concatenate((position_bias, side_position_bias), axis=-1)
+
+ # create dropout rng
+ dropout_rng = None
+ if not deterministic and self.dropout > 0.0:
+ dropout_rng = self.make_rng("dropout")
+
+ # Softmax(QK^T)
+ attn_weights = dot_product_attention_weights(
+ query_states,
+ key_states,
+ bias=position_bias,
+ dropout_rng=dropout_rng,
+ dropout_rate=self.dropout,
+ broadcast_dropout=True,
+ deterministic=deterministic,
+ dtype=self.dtype,
+ )
+
+ # multiply with value states
+ attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
+
+ # bring back to (batch_size, seq_length, d_model)
+ attn_output = self._merge_heads(attn_output)
+ attn_output = attn_output[:, :seq_length, :]
+
+ # apply output matrix
+ attn_output = self.o(attn_output)
+
+ outputs = (attn_output, position_bias)
+
+ if output_attentions:
+ outputs = outputs + (attn_weights,)
+
+ return outputs
+
+
+class FlaxLongT5LayerLocalSelfAttention(nn.Module):
+ """Local self attention used in encoder"""
+
+ config: LongT5Config
+ has_relative_attention_bias: bool = False
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.LocalSelfAttention = FlaxLongT5LocalAttention(
+ self.config, has_relative_attention_bias=self.has_relative_attention_bias, dtype=self.dtype
+ )
+ self.layer_norm = FlaxLongT5LayerNorm(
+ self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype
+ )
+ self.dropout = nn.Dropout(self.config.dropout_rate)
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_bias=None,
+ output_attentions=False,
+ deterministic=True,
+ **kwargs: Any, # to accept init_cache kwargs
+ ):
+ normed_hidden_states = self.layer_norm(hidden_states)
+ attention_output = self.LocalSelfAttention(
+ normed_hidden_states,
+ attention_mask=attention_mask,
+ position_bias=position_bias,
+ output_attentions=output_attentions,
+ deterministic=deterministic,
+ )
+ hidden_states = hidden_states + self.dropout(attention_output[0], deterministic=deterministic)
+ outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
+ return outputs
+
+
+class FlaxLongT5LayerTransientGlobalSelfAttention(nn.Module):
+ """Transient-Global self attention used in encoder"""
+
+ config: LongT5Config
+ has_relative_attention_bias: bool = False
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.TransientGlobalSelfAttention = FlaxLongT5TransientGlobalAttention(
+ self.config, has_relative_attention_bias=self.has_relative_attention_bias, dtype=self.dtype
+ )
+ self.layer_norm = FlaxLongT5LayerNorm(
+ self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype
+ )
+ self.dropout = nn.Dropout(self.config.dropout_rate)
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_bias=None,
+ output_attentions=False,
+ deterministic=True,
+ **kwargs: Any, # to accept init_cache kwargs
+ ):
+ normed_hidden_states = self.layer_norm(hidden_states)
+ attention_output = self.TransientGlobalSelfAttention(
+ normed_hidden_states,
+ attention_mask=attention_mask,
+ position_bias=position_bias,
+ output_attentions=output_attentions,
+ deterministic=deterministic,
+ )
+ hidden_states = hidden_states + self.dropout(attention_output[0], deterministic=deterministic)
+ outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5LayerSelfAttention with T5->LongT5
+class FlaxLongT5LayerSelfAttention(nn.Module):
+ config: LongT5Config
+ has_relative_attention_bias: bool = False
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.SelfAttention = FlaxLongT5Attention(
+ self.config,
+ has_relative_attention_bias=self.has_relative_attention_bias,
+ causal=self.config.causal,
+ dtype=self.dtype,
+ )
+ self.layer_norm = FlaxLongT5LayerNorm(
+ self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype
+ )
+ self.dropout = nn.Dropout(self.config.dropout_rate)
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_bias=None,
+ output_attentions=False,
+ deterministic=True,
+ init_cache=False,
+ ):
+ normed_hidden_states = self.layer_norm(hidden_states)
+ attention_output = self.SelfAttention(
+ normed_hidden_states,
+ attention_mask=attention_mask,
+ position_bias=position_bias,
+ output_attentions=output_attentions,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ )
+ hidden_states = hidden_states + self.dropout(attention_output[0], deterministic=deterministic)
+ outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5LayerCrossAttention with T5->LongT5
+class FlaxLongT5LayerCrossAttention(nn.Module):
+ config: LongT5Config
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.EncDecAttention = FlaxLongT5Attention(
+ self.config, has_relative_attention_bias=False, causal=False, dtype=self.dtype
+ )
+ self.layer_norm = FlaxLongT5LayerNorm(
+ self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype
+ )
+ self.dropout = nn.Dropout(self.config.dropout_rate)
+
+ def __call__(
+ self,
+ hidden_states,
+ key_value_states,
+ attention_mask=None,
+ position_bias=None,
+ output_attentions=False,
+ deterministic=True,
+ ):
+ normed_hidden_states = self.layer_norm(hidden_states)
+ attention_output = self.EncDecAttention(
+ normed_hidden_states,
+ attention_mask=attention_mask,
+ key_value_states=key_value_states,
+ position_bias=position_bias,
+ output_attentions=output_attentions,
+ )
+ hidden_states = hidden_states + self.dropout(attention_output[0], deterministic=deterministic)
+ outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
+ return outputs
+
+
+class FlaxLongT5Block(nn.Module):
+ config: LongT5Config
+ has_relative_attention_bias: bool = False
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.causal = self.config.causal
+ if self.causal:
+ attention_layer = FlaxLongT5LayerSelfAttention
+ elif self.config.encoder_attention_type == "local":
+ attention_layer = FlaxLongT5LayerLocalSelfAttention
+ elif self.config.encoder_attention_type == "transient-global":
+ attention_layer = FlaxLongT5LayerTransientGlobalSelfAttention
+ else:
+ raise ValueError(
+ "For encoder attention mechanism, either `local` or `transient-global` attention type is expected, "
+ f"but got {self.config.encoder_attention_type}."
+ )
+ self.layer = (
+ attention_layer(
+ self.config,
+ has_relative_attention_bias=self.has_relative_attention_bias,
+ name=str(0),
+ dtype=self.dtype,
+ ),
+ )
+ feed_forward_index = 1
+ if self.causal:
+ self.layer += (FlaxLongT5LayerCrossAttention(self.config, name=str(1), dtype=self.dtype),)
+ feed_forward_index += 1
+
+ self.layer += (FlaxLongT5LayerFF(self.config, name=str(feed_forward_index), dtype=self.dtype),)
+
+ # Copied from transformers.models.t5.modeling_flax_t5.FlaxT5Block.__call__ with T5->LongT5
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_bias=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ encoder_decoder_position_bias=None,
+ output_attentions=False,
+ return_dict=True,
+ deterministic=True,
+ init_cache=False,
+ ):
+ self_attention_outputs = self.layer[0](
+ hidden_states,
+ attention_mask=attention_mask,
+ position_bias=position_bias,
+ output_attentions=output_attentions,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ )
+ hidden_states = self_attention_outputs[0]
+ attention_outputs = self_attention_outputs[1:] # Keep self-attention outputs and relative position weights
+
+ do_cross_attention = self.causal and encoder_hidden_states is not None
+ if do_cross_attention:
+ cross_attention_outputs = self.layer[1](
+ hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ position_bias=encoder_decoder_position_bias,
+ output_attentions=output_attentions,
+ deterministic=deterministic,
+ )
+ hidden_states = cross_attention_outputs[0]
+
+ # Keep cross-attention outputs and relative position weights
+ attention_outputs = attention_outputs + cross_attention_outputs[1:]
+
+ # Apply Feed Forward layer
+ hidden_states = self.layer[-1](hidden_states, deterministic=deterministic)
+
+ outputs = (hidden_states,)
+
+ outputs = outputs + attention_outputs
+
+ # returns hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights),
+ # (cross-attention position bias), (cross-attention weights)
+ return outputs
+
+
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5LayerCollection with T5->LongT5
+class FlaxLongT5LayerCollection(nn.Module):
+ config: LongT5Config
+ has_relative_attention_bias: bool
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.layer = FlaxLongT5Block(
+ self.config, has_relative_attention_bias=self.has_relative_attention_bias, dtype=self.dtype
+ )
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_bias=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ encoder_decoder_position_bias=None,
+ output_attentions=False,
+ deterministic=True,
+ init_cache=False,
+ ):
+ return self.layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_bias=position_bias,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ encoder_decoder_position_bias=encoder_decoder_position_bias,
+ output_attentions=output_attentions,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ )
+
+
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5BlockCollection with T5->LongT5
+class FlaxLongT5BlockCollection(nn.Module):
+ config: LongT5Config
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+ gradient_checkpointing: bool = False
+
+ def setup(self):
+ self.causal = self.config.causal
+ if self.gradient_checkpointing:
+ FlaxLongT5CheckpointLayer = remat(FlaxLongT5LayerCollection, static_argnums=(6, 7, 8))
+ self.blocks = [
+ FlaxLongT5CheckpointLayer(
+ self.config,
+ has_relative_attention_bias=(i == 0),
+ dtype=self.dtype,
+ name=str(i),
+ )
+ for i in range(self.config.num_layers)
+ ]
+ else:
+ self.blocks = [
+ FlaxLongT5LayerCollection(
+ self.config,
+ has_relative_attention_bias=(i == 0),
+ dtype=self.dtype,
+ name=str(i),
+ )
+ for i in range(self.config.num_layers)
+ ]
+
+ def __call__(
+ self,
+ hidden_states=None,
+ attention_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ ):
+ # Prepare head mask if needed
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+ all_cross_attentions = () if (output_attentions and self.causal) else None
+ position_bias = None
+ encoder_decoder_position_bias = None
+
+ for i, layer_module in enumerate(self.blocks):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask,
+ position_bias,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ encoder_decoder_position_bias,
+ output_attentions,
+ deterministic,
+ init_cache,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ # We share the position biases between the layers - the first layer store them
+ # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
+ # (cross-attention position bias), (cross-attention weights)
+ position_bias = layer_outputs[1]
+
+ if self.causal and encoder_hidden_states is not None:
+ encoder_decoder_position_bias = layer_outputs[3 if output_attentions else 2]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[2],)
+ if self.causal:
+ all_cross_attentions = all_cross_attentions + (layer_outputs[4],)
+
+ return FlaxBaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_attentions,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5Stack with T5->LongT5
+class FlaxLongT5Stack(nn.Module):
+ config: LongT5Config
+ embed_tokens: nn.Embed
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+ gradient_checkpointing: bool = False
+
+ def setup(self):
+ self.causal = self.config.causal
+
+ self.block = FlaxLongT5BlockCollection(
+ self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
+ )
+ self.final_layer_norm = FlaxLongT5LayerNorm(
+ self.config.d_model, eps=self.config.layer_norm_epsilon, dtype=self.dtype
+ )
+ self.dropout = nn.Dropout(self.config.dropout_rate)
+
+ def __call__(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ ):
+ hidden_states = self.embed_tokens(input_ids)
+ hidden_states = self.dropout(hidden_states, deterministic=deterministic)
+
+ outputs = self.block(
+ hidden_states,
+ attention_mask=attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ )
+
+ hidden_states = outputs[0]
+
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.dropout(hidden_states, deterministic=deterministic)
+
+ # Add last layer
+ all_hidden_states = None
+
+ if output_hidden_states:
+ all_hidden_states = outputs.hidden_states
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ if output_hidden_states:
+ return (
+ hidden_states,
+ all_hidden_states,
+ ) + outputs[2:]
+ return (hidden_states,) + outputs[1:]
+
+ return FlaxBaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=outputs.attentions,
+ cross_attentions=outputs.cross_attentions,
+ )
+
+
+LONGT5_ENCODE_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so
+ you should be able to pad the inputs on both the right and the left.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for detail.
+
+ To know more on how to prepare `input_ids` for pretraining take a look a [LONGT5
+ Training](./longt5#training).
+ attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+LONGT5_DECODE_INPUTS_DOCSTRING = r"""
+ Args:
+ decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ For training, `decoder_input_ids` should be provided.
+ encoder_outputs (`tuple(tuple(jnp.ndarray)`):
+ Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
+ `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
+ hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
+ encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
+ paper](https://huggingface.co/papers/1910.13461) for more information on the default strategy.
+ past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
+ Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
+ auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+LONGT5_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so
+ you should be able to pad the inputs on both the right and the left.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for detail.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ To know more on how to prepare `input_ids` for pretraining take a look a [LONGT5
+ Training](./longt5#training).
+ attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ LONGT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ To know more on how to prepare `decoder_input_ids` for pretraining take a look at [LONGT5
+ Training](./longt5#training).
+ decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ encoder_outputs (`tuple(tuple(jnp.ndarray)`, *optional*):
+ Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
+ `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
+ the output of the last layer of the encoder. Used in the cross-attention of the decoder.
+ past_key_values (`tuple(tuple(jnp.ndarray))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+ Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+
+
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+class FlaxLongT5PreTrainedModel(FlaxPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = LongT5Config
+ base_model_prefix = "transformer"
+ module_class: nn.Module = None
+
+ def __init__(
+ self,
+ config: LongT5Config,
+ input_shape: tuple[int] = (1, 1),
+ seed: int = 0,
+ dtype: jnp.dtype = jnp.float32,
+ _do_init: bool = True,
+ **kwargs,
+ ):
+ module = self.module_class(config=config, dtype=dtype, **kwargs)
+ super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
+
+ def enable_gradient_checkpointing(self):
+ self._module = self.module_class(
+ config=self.config,
+ dtype=self.dtype,
+ gradient_checkpointing=True,
+ )
+
+ def init_weights(self, rng: jax.random.PRNGKey, input_shape: tuple, params: FrozenDict = None) -> FrozenDict:
+ # init input tensors
+ input_ids = jnp.zeros(input_shape, dtype="i4")
+
+ attention_mask = jnp.ones_like(input_ids)
+ decoder_input_ids = jnp.ones_like(input_ids)
+ decoder_attention_mask = jnp.ones_like(input_ids)
+
+ params_rng, dropout_rng = jax.random.split(rng)
+ rngs = {"params": params_rng, "dropout": dropout_rng}
+
+ random_params = self.module.init(
+ rngs,
+ input_ids,
+ attention_mask,
+ decoder_input_ids,
+ decoder_attention_mask,
+ )["params"]
+
+ if params is not None:
+ random_params = flatten_dict(unfreeze(random_params))
+ params = flatten_dict(unfreeze(params))
+ for missing_key in self._missing_keys:
+ params[missing_key] = random_params[missing_key]
+ self._missing_keys = set()
+ return freeze(unflatten_dict(params))
+ else:
+ return random_params
+
+ @add_start_docstrings_to_model_forward(LONGT5_INPUTS_DOCSTRING)
+ def __call__(
+ self,
+ input_ids: jnp.ndarray,
+ attention_mask: Optional[jnp.ndarray] = None,
+ decoder_input_ids: jnp.ndarray = None,
+ decoder_attention_mask: Optional[jnp.ndarray] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ if decoder_input_ids is None:
+ raise ValueError(
+ "Make sure to provide both `input_ids` and `decoder_input_ids`. `decoder_input_ids` is not passed"
+ " here."
+ )
+
+ # prepare encoder inputs
+ if attention_mask is None:
+ attention_mask = jnp.ones_like(input_ids)
+
+ # prepare decoder inputs
+ if decoder_attention_mask is None:
+ decoder_attention_mask = jnp.ones_like(decoder_input_ids)
+
+ # Handle any PRNG if needed
+ rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
+
+ return self.module.apply(
+ {"params": params or self.params},
+ input_ids=jnp.array(input_ids, dtype="i4"),
+ attention_mask=jnp.array(attention_mask, dtype="i4"),
+ decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+ decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ )
+
+ def init_cache(self, batch_size, max_length, encoder_outputs):
+ r"""
+ Args:
+ batch_size (`int`):
+ batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
+ max_length (`int`):
+ maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
+ cache.
+ encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
+ `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
+ `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*)
+ is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
+ cross-attention of the decoder.
+ """
+ # init input variables to retrieve cache
+ decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
+ decoder_attention_mask = jnp.ones_like(decoder_input_ids)
+
+ def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, **kwargs):
+ decoder_module = module._get_decoder_module()
+ return decoder_module(
+ decoder_input_ids,
+ decoder_attention_mask,
+ **kwargs,
+ )
+
+ init_variables = self.module.init(
+ jax.random.PRNGKey(0),
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ encoder_hidden_states=encoder_outputs[0],
+ init_cache=True,
+ method=_decoder_forward, # we only need to call the decoder to init the cache
+ )
+ return unfreeze(init_variables["cache"])
+
+ @add_start_docstrings(LONGT5_ENCODE_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=LongT5Config)
+ def encode(
+ self,
+ input_ids: jnp.ndarray,
+ attention_mask: Optional[jnp.ndarray] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ r"""
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxLongT5ForConditionalGeneration
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
+ >>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/long-t5-local-base")
+
+ >>> text = "My friends are cool but they eat too many carbs."
+ >>> inputs = tokenizer(text, return_tensors="np")
+ >>> encoder_outputs = model.encode(**inputs)
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ if attention_mask is None:
+ attention_mask = jnp.ones_like(input_ids)
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ def _encoder_forward(module, input_ids, attention_mask, **kwargs):
+ encode_module = module._get_encoder_module()
+ return encode_module(input_ids, attention_mask, **kwargs)
+
+ return self.module.apply(
+ {"params": params or self.params},
+ input_ids=jnp.array(input_ids, dtype="i4"),
+ attention_mask=jnp.array(attention_mask, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ method=_encoder_forward,
+ )
+
+ @add_start_docstrings(LONGT5_DECODE_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=LongT5Config)
+ def decode(
+ self,
+ decoder_input_ids,
+ encoder_outputs,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ decoder_attention_mask: Optional[jnp.ndarray] = None,
+ past_key_values: Optional[dict] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ r"""
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxLongT5ForConditionalGeneration
+ >>> import jax.numpy as jnp
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
+ >>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/long-t5-local-base")
+
+ >>> text = "My friends are cool but they eat too many carbs."
+ >>> inputs = tokenizer(text, return_tensors="np")
+ >>> encoder_outputs = model.encode(**inputs)
+
+ >>> decoder_start_token_id = model.config.decoder_start_token_id
+ >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
+
+ >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
+ >>> logits = outputs.logits
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ encoder_hidden_states = encoder_outputs[0]
+ if encoder_attention_mask is None:
+ batch_size, sequence_length = encoder_hidden_states.shape[:2]
+ encoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ batch_size, sequence_length = decoder_input_ids.shape
+ if decoder_attention_mask is None:
+ decoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ inputs = {"params": params or self.params}
+
+ # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
+ # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
+ # it can be changed by FlaxLongT5Attention module
+ if past_key_values:
+ inputs["cache"] = past_key_values
+ mutable = ["cache"]
+ else:
+ mutable = False
+
+ def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, **kwargs):
+ decoder_module = module._get_decoder_module()
+ return decoder_module(
+ decoder_input_ids,
+ decoder_attention_mask,
+ **kwargs,
+ )
+
+ outputs = self.module.apply(
+ inputs,
+ decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+ decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ mutable=mutable,
+ method=_decoder_forward,
+ )
+
+ # add updated cache to model output
+ if past_key_values is not None and return_dict:
+ outputs, past = outputs
+ outputs["past_key_values"] = unfreeze(past["cache"])
+ return outputs
+ elif past_key_values is not None and not return_dict:
+ outputs, past = outputs
+ outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
+
+ return outputs
+
+
+LONGT5_START_DOCSTRING = r"""
+ The LongT5 model was proposed in [LongT5: Efficient Text-To-Text Transformer for Long
+ Sequences](https://huggingface.co/papers/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo
+ Ni, Yun-Hsuan Sung and Yinfei Yang. It's an encoder-decoder transformer pre-trained in a text-to-text denoising
+ generative setting. LongT5 model is an extension of T5 model, and it enables using one of the two different
+ efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention.
+
+ This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a Flax Linen
+ [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
+ regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
+
+ Finally, this model supports inherent JAX features such as:
+
+ - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
+ - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
+ - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
+ - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
+
+ Parameters:
+ config ([`LongT5Config`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
+ dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
+ The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
+ `jax.numpy.bfloat16` (on TPUs).
+
+ This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
+ specified all the computation will be performed with the given `dtype`.
+
+ **Note that this only specifies the dtype of the computation and does not influence the dtype of model
+ parameters.**
+
+ If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
+ [`~FlaxPreTrainedModel.to_bf16`].
+"""
+
+
+@add_start_docstrings(
+ "The bare LONGT5 Model transformer outputting raw hidden-stateswithout any specific head on top.",
+ LONGT5_START_DOCSTRING,
+)
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5Module with T5->LongT5
+class FlaxLongT5Module(nn.Module):
+ config: LongT5Config
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+ gradient_checkpointing: bool = False
+
+ def _get_encoder_module(self):
+ return self.encoder
+
+ def _get_decoder_module(self):
+ return self.decoder
+
+ def setup(self):
+ self.shared = nn.Embed(
+ self.config.vocab_size,
+ self.config.d_model,
+ embedding_init=jax.nn.initializers.normal(self.config.initializer_factor * 1.0),
+ dtype=self.dtype,
+ )
+
+ encoder_config = copy.deepcopy(self.config)
+ encoder_config.causal = False
+ self.encoder = FlaxLongT5Stack(
+ encoder_config,
+ embed_tokens=self.shared,
+ dtype=self.dtype,
+ gradient_checkpointing=self.gradient_checkpointing,
+ )
+
+ decoder_config = copy.deepcopy(self.config)
+ decoder_config.causal = True
+ decoder_config.num_layers = self.config.num_decoder_layers
+ self.decoder = FlaxLongT5Stack(
+ decoder_config,
+ embed_tokens=self.shared,
+ dtype=self.dtype,
+ gradient_checkpointing=self.gradient_checkpointing,
+ )
+
+ def __call__(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ decoder_input_ids=None,
+ decoder_attention_mask=None,
+ encoder_outputs=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ deterministic: bool = True,
+ ):
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # Encode if needed (training, first prediction pass)
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ # Decode
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return FlaxSeq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5Model with T5->LongT5
+class FlaxLongT5Model(FlaxLongT5PreTrainedModel):
+ module_class = FlaxLongT5Module
+
+
+append_call_sample_docstring(FlaxLongT5Model, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)
+
+FLAX_LONGT5_MODEL_DOCSTRING = """
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxLongT5Model
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
+ >>> model = FlaxLongT5Model.from_pretrained("google/long-t5-local-base")
+
+ >>> input_ids = tokenizer(
+ ... "Studies have been shown that owning a dog is good for you", return_tensors="np"
+ ... ).input_ids
+ >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="np").input_ids
+
+ >>> # forward pass
+ >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
+ >>> last_hidden_states = outputs.last_hidden_state
+ ```
+"""
+
+
+overwrite_call_docstring(FlaxLongT5Model, LONGT5_INPUTS_DOCSTRING + FLAX_LONGT5_MODEL_DOCSTRING)
+append_replace_return_docstrings(FlaxLongT5Model, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
+
+
+@add_start_docstrings("""LONGT5 Model with a `language modeling` head on top.""", LONGT5_START_DOCSTRING)
+# Copied from transformers.models.t5.modeling_flax_t5.FlaxT5ForConditionalGenerationModule with T5->LongT5
+class FlaxLongT5ForConditionalGenerationModule(nn.Module):
+ config: LongT5Config
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+ gradient_checkpointing: bool = False
+
+ def _get_encoder_module(self):
+ return self.encoder
+
+ def _get_decoder_module(self):
+ return self.decoder
+
+ def setup(self):
+ self.model_dim = self.config.d_model
+
+ self.shared = nn.Embed(
+ self.config.vocab_size,
+ self.config.d_model,
+ embedding_init=jax.nn.initializers.normal(self.config.initializer_factor),
+ dtype=self.dtype,
+ )
+
+ encoder_config = copy.deepcopy(self.config)
+ encoder_config.causal = False
+ encoder_config.use_cache = False
+ encoder_config.is_encoder_decoder = False
+ self.encoder = FlaxLongT5Stack(
+ encoder_config, self.shared, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
+ )
+
+ decoder_config = copy.deepcopy(self.config)
+ decoder_config.causal = True
+ decoder_config.is_encoder_decoder = False
+ decoder_config.num_layers = self.config.num_decoder_layers
+ self.decoder = FlaxLongT5Stack(
+ decoder_config, self.shared, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
+ )
+
+ self.lm_head = nn.Dense(
+ self.config.vocab_size,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(self.config.initializer_factor),
+ dtype=self.dtype,
+ )
+
+ def __call__(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ decoder_input_ids=None,
+ decoder_attention_mask=None,
+ encoder_outputs=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ deterministic: bool = True,
+ ):
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # Encode
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ hidden_states = encoder_outputs[0]
+
+ # Decode
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_hidden_states=hidden_states,
+ encoder_attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ sequence_output = decoder_outputs[0]
+
+ if self.config.tie_word_embeddings:
+ # Rescale output before projecting on vocab
+ # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
+ sequence_output = sequence_output * (self.model_dim**-0.5)
+
+ if self.config.tie_word_embeddings:
+ shared_embedding = self.shared.variables["params"]["embedding"]
+ lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, sequence_output)
+ else:
+ lm_logits = self.lm_head(sequence_output)
+
+ if not return_dict:
+ return (lm_logits,) + decoder_outputs[1:] + encoder_outputs
+
+ return FlaxSeq2SeqLMOutput(
+ logits=lm_logits,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+class FlaxLongT5ForConditionalGeneration(FlaxLongT5PreTrainedModel):
+ module_class = FlaxLongT5ForConditionalGenerationModule
+
+ @add_start_docstrings(LONGT5_DECODE_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=LongT5Config)
+ def decode(
+ self,
+ decoder_input_ids,
+ encoder_outputs,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ decoder_attention_mask: Optional[jnp.ndarray] = None,
+ past_key_values: Optional[dict] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ r"""
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxLongT5ForConditionalGeneration
+ >>> import jax.numpy as jnp
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
+ >>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/long-t5-local-base")
+
+ >>> text = "summarize: My friends are cool but they eat too many carbs."
+ >>> inputs = tokenizer(text, return_tensors="np")
+ >>> encoder_outputs = model.encode(**inputs)
+
+ >>> decoder_start_token_id = model.config.decoder_start_token_id
+ >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
+
+ >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
+ >>> logits = outputs.logits
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ encoder_hidden_states = encoder_outputs[0]
+ if encoder_attention_mask is None:
+ batch_size, sequence_length = encoder_hidden_states.shape[:2]
+ encoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ batch_size, sequence_length = decoder_input_ids.shape
+ if decoder_attention_mask is None:
+ decoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ inputs = {"params": params or self.params}
+
+ # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
+ # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
+ # it can be changed by FlaxLongT5Attention module
+ if past_key_values:
+ inputs["cache"] = past_key_values
+ mutable = ["cache"]
+ else:
+ mutable = False
+
+ def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, **kwargs):
+ decoder_module = module._get_decoder_module()
+ decoder_outputs = decoder_module(
+ decoder_input_ids,
+ decoder_attention_mask,
+ **kwargs,
+ )
+
+ sequence_output = decoder_outputs[0]
+
+ if self.config.tie_word_embeddings:
+ # Rescale output before projecting on vocab
+ # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
+ sequence_output = sequence_output * (self.config.d_model**-0.5)
+
+ if self.config.tie_word_embeddings:
+ shared_embedding = module.shared.variables["params"]["embedding"]
+ lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, sequence_output)
+ else:
+ lm_logits = module.lm_head(sequence_output)
+
+ return lm_logits, decoder_outputs
+
+ outputs = self.module.apply(
+ inputs,
+ decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+ decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ mutable=mutable,
+ method=_decoder_forward,
+ )
+
+ if past_key_values is None:
+ lm_logits, decoder_outputs = outputs
+ else:
+ (lm_logits, decoder_outputs), past = outputs
+
+ if return_dict:
+ outputs = FlaxCausalLMOutputWithCrossAttentions(
+ logits=lm_logits,
+ hidden_states=decoder_outputs.hidden_states,
+ attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ )
+ else:
+ outputs = (lm_logits,) + decoder_outputs[1:]
+
+ # add updated cache to model output
+ if past_key_values is not None and return_dict:
+ outputs["past_key_values"] = unfreeze(past["cache"])
+ return outputs
+ elif past_key_values is not None and not return_dict:
+ outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
+
+ return outputs
+
+ def prepare_inputs_for_generation(
+ self,
+ decoder_input_ids,
+ max_length,
+ attention_mask: Optional[jax.Array] = None,
+ decoder_attention_mask: Optional[jax.Array] = None,
+ encoder_outputs=None,
+ **kwargs,
+ ):
+ # initializing the cache
+ batch_size, seq_length = decoder_input_ids.shape
+
+ past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
+ # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
+ # But since the decoder uses a causal mask, those positions are masked anyways.
+ # Thus we can create a single static attention_mask here, which is more efficient for compilation
+ extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
+ if decoder_attention_mask is not None:
+ extended_attention_mask = jax.lax.dynamic_update_slice(
+ extended_attention_mask, decoder_attention_mask, (0, 0)
+ )
+
+ return {
+ "past_key_values": past_key_values,
+ "encoder_outputs": encoder_outputs,
+ "encoder_attention_mask": attention_mask,
+ "decoder_attention_mask": extended_attention_mask,
+ }
+
+ def update_inputs_for_generation(self, model_outputs, model_kwargs):
+ model_kwargs["past_key_values"] = model_outputs.past_key_values
+ return model_kwargs
+
+
+FLAX_LONGT5_CONDITIONAL_GENERATION_DOCSTRING = """
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxLongT5ForConditionalGeneration
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
+ >>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/long-t5-local-base")
+
+ >>> ARTICLE_TO_SUMMARIZE = "summarize: My friends are cool but they eat too many carbs."
+ >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], return_tensors="np")
+
+ >>> # Generate Summary
+ >>> summary_ids = model.generate(inputs["input_ids"]).sequences
+ >>> print(tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False))
+ ```
+"""
+
+
+overwrite_call_docstring(
+ FlaxLongT5ForConditionalGeneration, LONGT5_INPUTS_DOCSTRING + FLAX_LONGT5_CONDITIONAL_GENERATION_DOCSTRING
+)
+append_replace_return_docstrings(
+ FlaxLongT5ForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
+)
+
+
+__all__ = ["FlaxLongT5ForConditionalGeneration", "FlaxLongT5Model", "FlaxLongT5PreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/modeling_longt5.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/modeling_longt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d89feab0567bd5229d954bc2d50ac6a5070b6f9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/longt5/modeling_longt5.py
@@ -0,0 +1,2201 @@
+# coding=utf-8
+# Copyright 2022 Google LLC., LongT5 Authors and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch LongT5 model."""
+
+import copy
+import math
+import warnings
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import AttentionMaskConverter
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPastAndCrossAttentions,
+ Seq2SeqLMOutput,
+ Seq2SeqModelOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import (
+ DUMMY_INPUTS,
+ DUMMY_MASK,
+ auto_docstring,
+ is_torch_flex_attn_available,
+ is_torch_fx_proxy,
+ is_torchdynamo_compiling,
+ logging,
+)
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_longt5 import LongT5Config
+
+
+if is_torch_flex_attn_available():
+ from torch.nn.attention.flex_attention import BlockMask
+
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+
+logger = logging.get_logger(__name__)
+
+
+# TODO: Update before the merge
+
+
+def _pad_to_multiple(x: torch.Tensor, block_len: int, dim: int, pad_value: int = 0) -> torch.Tensor:
+ """Pad a tensor so that a sequence length will be a multiple of `block_len`"""
+ pad_len = -x.shape[dim] % block_len
+ # Handle cases when an empty input sequence is given
+ if not all(x.shape):
+ new_shape = list(x.shape)
+ new_shape[dim] += pad_len
+ return torch.zeros(new_shape, dtype=x.dtype)
+
+ pad = [(0, 0)] * x.ndim
+ pad[dim] = (0, pad_len)
+ pad = sum(pad[::-1], ())
+ x = nn.functional.pad(x, pad=pad, mode="constant", value=pad_value)
+ return x
+
+
+def _split_into_blocks(x: torch.Tensor, block_len: int, dim: int) -> torch.Tensor:
+ """Split an input tensor into blocks of a given `block_len` along the given `dim`. If the dimension length
+ is not a multiple of `block_len`, it will be padded first with selected `pad_value`.
+ """
+ # pad tensor to multiple of block_len
+ if x.shape[dim] % block_len != 0:
+ x = _pad_to_multiple(x, block_len, dim, pad_value=0)
+ num_blocks = x.shape[dim] // block_len
+ output_shape = x.shape[:dim] + (num_blocks, block_len) + x.shape[(dim + 1) :]
+ # If 0 is in output_shape, we cannot apply reshape because of incompatibility with ONNX conversion
+ if 0 in output_shape:
+ return torch.empty(output_shape, dtype=x.dtype, device=x.device)
+ return x.reshape(output_shape)
+
+
+def _concatenate_3_blocks(x: torch.Tensor, block_dim: int, sequence_dim: int, pad_value: int = 0) -> torch.Tensor:
+ """Concatenate three consecutive blocks for each input block for local attentiont.
+
+ For more information, see: https://huggingface.co/papers/2112.07916.
+ """
+ num_blocks = x.shape[block_dim]
+
+ pad = [(0, 0)] * x.ndim
+ pad[block_dim] = (1, 1)
+ pad = sum(pad[::-1], ())
+ # [batch_size, num_blocks, block_len] -> [batch_size, num_blocks + 2, block_len]
+ x = nn.functional.pad(x, pad=pad, mode="constant", value=pad_value)
+
+ blocks_list: list[torch.Tensor] = []
+ for i in range(3):
+ # We use indexing approach here:
+ # https://numpy.org/doc/stable/user/basics.indexing.html#dealing-with-variable-numbers-of-indices-within-programs
+ indices = [slice(0, None)] * x.ndim
+ indices[block_dim] = slice(i, i + num_blocks)
+ indices = tuple(indices)
+ blocks_list.append(x[indices])
+ # [batch_size, num_blocks, 3 * block_len, ...]
+ return torch.cat(blocks_list, dim=sequence_dim)
+
+
+def _make_3block_relative_position_ids(block_len: int) -> torch.Tensor:
+ """Makes 3-blocked relative position ids for local attention."""
+ position_ids = torch.arange(3 * block_len, dtype=torch.int32)
+ center_position_ids = position_ids[block_len:-block_len]
+ # [block_len, 3 * block_len]
+ relative_position_ids = position_ids.unsqueeze(0) - center_position_ids.unsqueeze(1)
+ return relative_position_ids
+
+
+def _mask_local_attention_mask(local_attention_mask: torch.Tensor, block_len: int) -> torch.Tensor:
+ """Mask local attention mask to enforce that tokens are not allowed to attend tokens farther than ``local_radius."""
+ relative_position_ids = _make_3block_relative_position_ids(block_len)
+ locality_mask = torch.abs(relative_position_ids) < block_len
+ locality_mask = locality_mask[None, None, :, :]
+ locality_mask = locality_mask.to(local_attention_mask.device)
+ return torch.logical_and(local_attention_mask, locality_mask)
+
+
+def _get_local_attention_mask(attention_mask: torch.Tensor, block_len: int, device: torch.device) -> torch.Tensor:
+ """Prepare attention mask to be applied for a local attention."""
+ # [batch_size, num_blocks, block_len]
+ _blocked_attention_mask = _split_into_blocks(attention_mask, block_len, dim=1)
+ # [batch_size, num_block, 3 * block_len]
+ _3blocked_attention_mask = _concatenate_3_blocks(_blocked_attention_mask, block_dim=1, sequence_dim=2)
+
+ _blocked_attention_mask = _blocked_attention_mask.unsqueeze(-1)
+ _3blocked_attention_mask = _3blocked_attention_mask.unsqueeze(-2)
+ # [batch_size, num_block, block_len, 3 * block_len]
+ local_attention_mask = torch.logical_and(_blocked_attention_mask, _3blocked_attention_mask)
+ local_attention_mask = _mask_local_attention_mask(local_attention_mask, block_len)
+ # [batch_size, 1, num_block, block_len, 3 * block_len]
+ return local_attention_mask.unsqueeze(1).to(device)
+
+
+def _make_global_fixed_block_ids(
+ attention_mask: torch.Tensor, global_block_size: int
+) -> tuple[torch.Tensor, torch.Tensor]:
+ """Obtain the "fixed block" global id corresponding to each input token.
+
+ This implementation is a simplified version of the original Flaxformr implementation adopted from:
+ https://github.com/google/flaxformer/blob/main/flaxformer/architectures/longt5/long_attention.py.
+
+ In our scenario, as we use this strategy only for a decoder, orphan tokens, i.e. those tokens which do not make for
+ the whole fixed block, are assigned to the preceding block.
+
+ Padding tokens from the original sequence are represented by -1.
+ """
+ batch_size, seq_len = attention_mask.shape[:2]
+
+ def handle_orphan_tokens(block_ids: torch.Tensor) -> torch.Tensor:
+ block_ends = (torch.arange(seq_len) % global_block_size) == global_block_size - 1
+ block_ends = block_ends.to(block_ids.device)
+ true_block_ends = torch.logical_and(block_ends, block_ids >= 0)
+ full_blocks = true_block_ends.sum(-1).unsqueeze(-1).type(block_ids.dtype) - 1
+ block_ids = torch.where(block_ids < full_blocks, block_ids, full_blocks)
+ return block_ids
+
+ fixed_block_mask = torch.ones_like(attention_mask, device=attention_mask.device) / global_block_size
+ fixed_block_mask = torch.cumsum(fixed_block_mask, axis=1) - fixed_block_mask
+ mask = torch.where(attention_mask != 0.0, 1.0, -1000.0).type(attention_mask.dtype)
+ global_block_ids = torch.floor(mask + fixed_block_mask - 1.0).type(attention_mask.dtype)
+ _global_block_ids_lower_bound = torch.tensor(-1, dtype=global_block_ids.dtype, device=global_block_ids.device)
+ global_block_ids = torch.where(
+ global_block_ids > _global_block_ids_lower_bound, global_block_ids, _global_block_ids_lower_bound
+ )
+ # set padding tokens to -1
+ global_block_ids = (global_block_ids * attention_mask) + (attention_mask - 1)
+ # [batch_size, seq_len]
+ global_block_ids = handle_orphan_tokens(global_block_ids)
+ num_globals = seq_len // global_block_size
+ # [batch_size, seq_len // global_block_size]
+ if num_globals > 0:
+ _sequence_block_ids_max = torch.max(global_block_ids, dim=-1).values.repeat(num_globals, 1).transpose(0, 1)
+ else:
+ _sequence_block_ids_max = torch.zeros(
+ batch_size, 0, dtype=global_block_ids.dtype, device=global_block_ids.device
+ )
+ global_segment_ids = torch.cumsum(torch.ones(batch_size, num_globals), dim=-1) - 1
+ global_segment_ids = global_segment_ids.to(attention_mask.device)
+ global_segment_ids = torch.where(global_segment_ids <= _sequence_block_ids_max, 1, 0)
+ return global_block_ids.type(torch.int), global_segment_ids.type(torch.int)
+
+
+def _make_side_relative_position_ids(attention_mask: torch.Tensor, global_block_size: int) -> torch.Tensor:
+ """Create the relative position tensor for local -> global attention."""
+ block_ids, global_segment_ids = _make_global_fixed_block_ids(attention_mask, global_block_size)
+ global_seq_len = global_segment_ids.shape[-1]
+ global_positions = torch.arange(global_seq_len, device=block_ids.device)
+ side_relative_position = global_positions - block_ids[..., None]
+ return side_relative_position.type(torch.int64)
+
+
+def _create_global_aggregates(
+ hidden_states: torch.Tensor, block_ids: torch.Tensor, global_seq_len: int
+) -> torch.Tensor:
+ """Compute individual block aggregates by summing over individual blocks."""
+ # (batch..., seq_len, global_seq_len))
+ block_ids = block_ids.where(
+ block_ids >= 0, torch.tensor(global_seq_len, dtype=block_ids.dtype, device=block_ids.device)
+ )
+ one_hot_block_ids = nn.functional.one_hot(block_ids.type(torch.int64), global_seq_len + 1)[:, :, :-1]
+ return torch.einsum("...nd,...ng->...gd", hidden_states, one_hot_block_ids.type(hidden_states.dtype))
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->LongT5
+class LongT5LayerNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ Construct a layernorm module in the LongT5 style. No bias and no subtraction of mean.
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ # LongT5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
+ # Square Layer Normalization https://huggingface.co/papers/1910.07467 thus variance is calculated
+ # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
+ # half-precision inputs is done in fp32
+
+ variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+ # convert into half-precision if necessary
+ if self.weight.dtype in [torch.float16, torch.bfloat16]:
+ hidden_states = hidden_states.to(self.weight.dtype)
+
+ return self.weight * hidden_states
+
+
+try:
+ from apex.normalization import FusedRMSNorm
+
+ LongT5LayerNorm = FusedRMSNorm # noqa
+
+ logger.info("Discovered apex.normalization.FusedRMSNorm - will use it instead of LongT5LayerNorm")
+except ImportError:
+ # using the normal LongT5LayerNorm
+ pass
+except Exception:
+ logger.warning("discovered apex but it failed to load, falling back to LongT5LayerNorm")
+ pass
+
+
+# Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->LongT5
+class LongT5DenseActDense(nn.Module):
+ def __init__(self, config: LongT5Config):
+ super().__init__()
+ self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
+ self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
+ self.dropout = nn.Dropout(config.dropout_rate)
+ self.act = ACT2FN[config.dense_act_fn]
+
+ def forward(self, hidden_states):
+ hidden_states = self.wi(hidden_states)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ if (
+ isinstance(self.wo.weight, torch.Tensor)
+ and hidden_states.dtype != self.wo.weight.dtype
+ and self.wo.weight.dtype != torch.int8
+ ):
+ hidden_states = hidden_states.to(self.wo.weight.dtype)
+ hidden_states = self.wo(hidden_states)
+ return hidden_states
+
+
+class LongT5DenseGatedActDense(nn.Module):
+ def __init__(self, config: LongT5Config):
+ super().__init__()
+ self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
+ self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
+ self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
+ self.dropout = nn.Dropout(config.dropout_rate)
+ self.act = ACT2FN[config.dense_act_fn]
+
+ def forward(self, hidden_states):
+ hidden_gelu = self.act(self.wi_0(hidden_states))
+ hidden_linear = self.wi_1(hidden_states)
+ hidden_states = hidden_gelu * hidden_linear
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.wo(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerFF with T5->LongT5
+class LongT5LayerFF(nn.Module):
+ def __init__(self, config: LongT5Config):
+ super().__init__()
+ if config.is_gated_act:
+ self.DenseReluDense = LongT5DenseGatedActDense(config)
+ else:
+ self.DenseReluDense = LongT5DenseActDense(config)
+
+ self.layer_norm = LongT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+ self.dropout = nn.Dropout(config.dropout_rate)
+
+ def forward(self, hidden_states):
+ forwarded_states = self.layer_norm(hidden_states)
+ forwarded_states = self.DenseReluDense(forwarded_states)
+ hidden_states = hidden_states + self.dropout(forwarded_states)
+ return hidden_states
+
+
+# Copied from transformers.models.t5.modeling_t5.T5Attention with T5->LongT5
+class LongT5Attention(nn.Module):
+ def __init__(
+ self,
+ config: LongT5Config,
+ has_relative_attention_bias=False,
+ layer_idx: Optional[int] = None,
+ ):
+ super().__init__()
+ self.is_decoder = config.is_decoder
+ self.has_relative_attention_bias = has_relative_attention_bias
+ self.relative_attention_num_buckets = config.relative_attention_num_buckets
+ self.relative_attention_max_distance = config.relative_attention_max_distance
+ self.d_model = config.d_model
+ self.key_value_proj_dim = config.d_kv
+ self.n_heads = config.num_heads
+ self.dropout = config.dropout_rate
+ self.inner_dim = self.n_heads * self.key_value_proj_dim
+ self.layer_idx = layer_idx
+ if layer_idx is None and self.is_decoder:
+ logger.warning_once(
+ f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
+ "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ # Mesh TensorFlow initialization to avoid scaling before softmax
+ self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
+
+ if self.has_relative_attention_bias:
+ self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
+ self.pruned_heads = set()
+ self.gradient_checkpointing = False
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
+ )
+ # Prune linear layers
+ self.q = prune_linear_layer(self.q, index)
+ self.k = prune_linear_layer(self.k, index)
+ self.v = prune_linear_layer(self.v, index)
+ self.o = prune_linear_layer(self.o, index, dim=1)
+ # Update hyper params
+ self.n_heads = self.n_heads - len(heads)
+ self.inner_dim = self.key_value_proj_dim * self.n_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ @staticmethod
+ def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+ """
+ Adapted from Mesh Tensorflow:
+ https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+ Translate relative position to a bucket number for relative attention. The relative position is defined as
+ memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+ position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+ small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+ positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+ This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+ Args:
+ relative_position: an int32 Tensor
+ bidirectional: a boolean - whether the attention is bidirectional
+ num_buckets: an integer
+ max_distance: an integer
+
+ Returns:
+ a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+ """
+ relative_buckets = 0
+ if bidirectional:
+ num_buckets //= 2
+ relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
+ relative_position = torch.abs(relative_position)
+ else:
+ relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
+ # now relative_position is in the range [0, inf)
+
+ # half of the buckets are for exact increments in positions
+ max_exact = num_buckets // 2
+ is_small = relative_position < max_exact
+
+ # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ relative_position_if_large = max_exact + (
+ torch.log(relative_position.float() / max_exact)
+ / math.log(max_distance / max_exact)
+ * (num_buckets - max_exact)
+ ).to(torch.long)
+ relative_position_if_large = torch.min(
+ relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
+ )
+
+ relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
+ return relative_buckets
+
+ def compute_bias(self, query_length, key_length, device=None, cache_position=None):
+ """Compute binned relative position bias"""
+ if device is None:
+ device = self.relative_attention_bias.weight.device
+ if cache_position is None:
+ context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
+ else:
+ context_position = cache_position[:, None].to(device)
+ memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
+ relative_position = memory_position - context_position # shape (query_length, key_length)
+ relative_position_bucket = self._relative_position_bucket(
+ relative_position, # shape (query_length, key_length)
+ bidirectional=(not self.is_decoder),
+ num_buckets=self.relative_attention_num_buckets,
+ max_distance=self.relative_attention_max_distance,
+ )
+ values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
+ values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
+ return values
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states,
+ mask=None,
+ key_value_states=None,
+ position_bias=None,
+ past_key_values=None,
+ layer_head_mask=None,
+ query_length=None,
+ use_cache=False,
+ output_attentions=False,
+ cache_position=None,
+ ):
+ """
+ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
+ """
+ # Input is (batch_size, seq_length, dim)
+ # Mask is (batch_size, 1, 1, key_length) (non-causal encoder) or (batch_size, 1, seq_length, key_length) (causal decoder)
+ batch_size, seq_length = hidden_states.shape[:2]
+
+ # if key_value_states are provided this layer is used as a cross-attention layer for the decoder
+ is_cross_attention = key_value_states is not None
+
+ query_states = self.q(hidden_states)
+ query_states = query_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
+
+ # Check is encoder-decoder model is being used. Otherwise we'll get `DynamicCache`
+ if past_key_values is not None and isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_states from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = key_value_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_states = curr_past_key_value.layers[self.layer_idx].keys
+ value_states = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_states = self.k(current_states)
+ value_states = self.v(current_states)
+ key_states = key_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
+ value_states = value_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_states, value_states = curr_past_key_value.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ # compute scores, equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
+ scores = torch.matmul(query_states, key_states.transpose(3, 2))
+
+ if position_bias is None:
+ key_length = key_states.shape[-2]
+ # cache position is 0-indexed so we add 1 to get the real length of queries (aka with past)
+ real_seq_length = query_length if query_length is not None else cache_position[-1] + 1
+ if not self.has_relative_attention_bias:
+ position_bias = torch.zeros(
+ (1, self.n_heads, seq_length, key_length), device=scores.device, dtype=scores.dtype
+ )
+ if self.gradient_checkpointing and self.training:
+ position_bias.requires_grad = True
+ else:
+ position_bias = self.compute_bias(
+ real_seq_length, key_length, device=scores.device, cache_position=cache_position
+ )
+ position_bias = position_bias[:, :, -seq_length:, :]
+
+ if mask is not None:
+ causal_mask = mask[:, :, :, : key_states.shape[-2]]
+ position_bias = position_bias + causal_mask
+
+ if self.pruned_heads:
+ mask = torch.ones(position_bias.shape[1])
+ mask[list(self.pruned_heads)] = 0
+ position_bias_masked = position_bias[:, mask.bool()]
+ else:
+ position_bias_masked = position_bias
+
+ scores += position_bias_masked
+
+ # (batch_size, n_heads, seq_length, key_length)
+ attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+ # Mask heads if we want to
+ if layer_head_mask is not None:
+ attn_weights = attn_weights * layer_head_mask
+
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.view(batch_size, -1, self.inner_dim)
+ attn_output = self.o(attn_output)
+
+ outputs = (attn_output, position_bias)
+
+ if output_attentions:
+ outputs = outputs + (attn_weights,)
+ return outputs
+
+
+class LongT5LocalAttention(nn.Module):
+ def __init__(self, config: LongT5Config, has_relative_attention_bias: bool = False) -> None:
+ super().__init__()
+ self.is_decoder = config.is_decoder
+ self.has_relative_attention_bias = has_relative_attention_bias
+ self.relative_attention_num_buckets = config.relative_attention_num_buckets
+ self.relative_attention_max_distance = config.relative_attention_max_distance
+ self.d_model = config.d_model
+ self.key_value_proj_dim = config.d_kv
+ self.n_heads = config.num_heads
+ self.local_radius = config.local_radius
+ self.block_len = self.local_radius + 1
+ self.dropout = config.dropout_rate
+ self.inner_dim = self.n_heads * self.key_value_proj_dim
+
+ # Mesh TensorFlow initialization to avoid scaling before softmax
+ self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
+
+ if self.has_relative_attention_bias:
+ self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
+ self.pruned_heads = set()
+ self.gradient_checkpointing = False
+
+ # Copied from transformers.models.t5.modeling_t5.T5Attention.prune_heads
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
+ )
+ # Prune linear layers
+ self.q = prune_linear_layer(self.q, index)
+ self.k = prune_linear_layer(self.k, index)
+ self.v = prune_linear_layer(self.v, index)
+ self.o = prune_linear_layer(self.o, index, dim=1)
+ # Update hyper params
+ self.n_heads = self.n_heads - len(heads)
+ self.inner_dim = self.key_value_proj_dim * self.n_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ @staticmethod
+ # Copied from transformers.models.t5.modeling_t5.T5Attention._relative_position_bucket
+ def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+ """
+ Adapted from Mesh Tensorflow:
+ https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+ Translate relative position to a bucket number for relative attention. The relative position is defined as
+ memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+ position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+ small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+ positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+ This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+ Args:
+ relative_position: an int32 Tensor
+ bidirectional: a boolean - whether the attention is bidirectional
+ num_buckets: an integer
+ max_distance: an integer
+
+ Returns:
+ a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+ """
+ relative_buckets = 0
+ if bidirectional:
+ num_buckets //= 2
+ relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
+ relative_position = torch.abs(relative_position)
+ else:
+ relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
+ # now relative_position is in the range [0, inf)
+
+ # half of the buckets are for exact increments in positions
+ max_exact = num_buckets // 2
+ is_small = relative_position < max_exact
+
+ # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ relative_position_if_large = max_exact + (
+ torch.log(relative_position.float() / max_exact)
+ / math.log(max_distance / max_exact)
+ * (num_buckets - max_exact)
+ ).to(torch.long)
+ relative_position_if_large = torch.min(
+ relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
+ )
+
+ relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
+ return relative_buckets
+
+ def compute_bias(self, block_length: int):
+ """Compute binned relative position bias"""
+ target_device = (
+ self.relative_attention_bias.weight.device
+ if self.relative_attention_bias.weight.device.type != "meta"
+ else None
+ )
+ memory_position = torch.arange(3 * block_length, dtype=torch.long, device=target_device)
+ context_position = memory_position[block_length:-block_length]
+
+ # (block_length, 3 * block_length)
+ relative_position = memory_position[None, :] - context_position[:, None]
+ relative_position_bucket = self._relative_position_bucket(
+ relative_position, # (block_length, 3 * block_length)
+ bidirectional=(not self.is_decoder),
+ num_buckets=self.relative_attention_num_buckets,
+ max_distance=self.relative_attention_max_distance,
+ )
+ # (block_length, 3 * block_length, num_heads)
+ values = self.relative_attention_bias(relative_position_bucket)
+ # (1, 1, num_heads, block_length, 3 * block_length)
+ values = values.permute([2, 0, 1]).unsqueeze(0).unsqueeze(0)
+ return values
+
+ def forward(
+ self,
+ hidden_states,
+ mask=None,
+ position_bias=None,
+ layer_head_mask=None,
+ output_attentions=False,
+ ):
+ batch_size, seq_length = hidden_states.shape[:2]
+
+ def shape(states):
+ """projection"""
+ return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim)
+
+ def unshape(states):
+ """reshape"""
+ return states.contiguous().view(batch_size, -1, self.inner_dim)
+
+ # get query/key/value states -> (batch_size, seq_length, n_heads, dim_per_head)
+ query_states = shape(self.q(hidden_states))
+ key_states = shape(self.k(hidden_states))
+ value_states = shape(self.v(hidden_states))
+
+ # Split into blocks -> (batch_size, num_blocks, block_len, n_heads, dim_per_head)
+ query_states = _split_into_blocks(query_states, self.block_len, dim=1)
+ key_states = _split_into_blocks(key_states, self.block_len, dim=1)
+ value_states = _split_into_blocks(value_states, self.block_len, dim=1)
+
+ # Concatenate 3 blocks for keys and values -> (batch_size, num_blocks, 3 * block_len, n_heads, dim_per_head)
+ key_states = _concatenate_3_blocks(key_states, block_dim=1, sequence_dim=2)
+ value_states = _concatenate_3_blocks(value_states, block_dim=1, sequence_dim=2)
+
+ # Compute scores
+ scores = torch.einsum(
+ "...qhd,...khd->...hqk", query_states, key_states
+ ) # (batch_size, num_block, n_heads, block_len, 3 * block_len)
+
+ if position_bias is None:
+ # position_bias shape: # (1, 1, n_heads, block_len, 3 * block_len)
+ if not self.has_relative_attention_bias:
+ position_bias = torch.zeros(
+ (1, 1, self.n_heads, self.block_len, 3 * self.block_len), device=scores.device, dtype=scores.dtype
+ )
+ if self.gradient_checkpointing and self.training:
+ position_bias.requires_grad = True
+ else:
+ position_bias = self.compute_bias(self.block_len)
+
+ if mask is not None:
+ # Replace masked positions with -1e10 (according to the original implementation)
+ mask = torch.where(mask > 0, 0.0, -1e10)
+ # We need to adjust position bias shape to be sum with mask
+ position_bias = position_bias + mask.transpose(1, 2)
+
+ scores += position_bias
+ # (batch_size, num_blocks, n_heads, block_len, 3 * block_len)
+ attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
+ # (batch_size, num_blocks, n_heads, block_len, 3 * block_len)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+ # Mask heads if we want to
+ if layer_head_mask is not None:
+ attn_weights = attn_weights * layer_head_mask
+ attn_weights = attn_weights.type(value_states.dtype)
+ attn_output = unshape(torch.einsum("...hqk,...khd->...qhd", attn_weights, value_states))
+ attn_output = attn_output[:, :seq_length, :]
+ attn_output = self.o(attn_output)
+
+ outputs = (
+ attn_output,
+ position_bias,
+ )
+
+ if output_attentions:
+ outputs = outputs + (attn_weights,)
+ return outputs
+
+
+class LongT5TransientGlobalAttention(nn.Module):
+ def __init__(self, config: LongT5Config, has_relative_attention_bias: bool = False) -> None:
+ super().__init__()
+ self.is_decoder = config.is_decoder
+ self.has_relative_attention_bias = has_relative_attention_bias
+ self.relative_attention_num_buckets = config.relative_attention_num_buckets
+ self.relative_attention_max_distance = config.relative_attention_max_distance
+ self.d_model = config.d_model
+ self.key_value_proj_dim = config.d_kv
+ self.n_heads = config.num_heads
+ self.local_radius = config.local_radius
+ self.block_len = self.local_radius + 1
+ self.global_block_size = config.global_block_size
+ self.dropout = config.dropout_rate
+ self.inner_dim = self.n_heads * self.key_value_proj_dim
+
+ # Mesh TensorFlow initialization to avoid scaling before softmax
+ self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
+
+ if self.has_relative_attention_bias:
+ self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
+ self.pruned_heads = set()
+
+ # Relativen attention bias & Layer norm for global attention
+ if self.has_relative_attention_bias:
+ self.global_relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
+ self.global_input_layer_norm = LongT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+
+ # Copied from transformers.models.t5.modeling_t5.T5Attention.prune_heads
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
+ )
+ # Prune linear layers
+ self.q = prune_linear_layer(self.q, index)
+ self.k = prune_linear_layer(self.k, index)
+ self.v = prune_linear_layer(self.v, index)
+ self.o = prune_linear_layer(self.o, index, dim=1)
+ # Update hyper params
+ self.n_heads = self.n_heads - len(heads)
+ self.inner_dim = self.key_value_proj_dim * self.n_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ @staticmethod
+ # Copied from transformers.models.t5.modeling_t5.T5Attention._relative_position_bucket
+ def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+ """
+ Adapted from Mesh Tensorflow:
+ https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+ Translate relative position to a bucket number for relative attention. The relative position is defined as
+ memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+ position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+ small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+ positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+ This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+ Args:
+ relative_position: an int32 Tensor
+ bidirectional: a boolean - whether the attention is bidirectional
+ num_buckets: an integer
+ max_distance: an integer
+
+ Returns:
+ a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+ """
+ relative_buckets = 0
+ if bidirectional:
+ num_buckets //= 2
+ relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
+ relative_position = torch.abs(relative_position)
+ else:
+ relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
+ # now relative_position is in the range [0, inf)
+
+ # half of the buckets are for exact increments in positions
+ max_exact = num_buckets // 2
+ is_small = relative_position < max_exact
+
+ # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ relative_position_if_large = max_exact + (
+ torch.log(relative_position.float() / max_exact)
+ / math.log(max_distance / max_exact)
+ * (num_buckets - max_exact)
+ ).to(torch.long)
+ relative_position_if_large = torch.min(
+ relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
+ )
+
+ relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
+ return relative_buckets
+
+ def compute_bias(self, block_length: int):
+ """Compute binned relative position bias"""
+ target_device = (
+ self.relative_attention_bias.weight.device
+ if self.relative_attention_bias.weight.device.type != "meta"
+ else None
+ )
+ memory_position = torch.arange(3 * block_length, dtype=torch.long, device=target_device)
+ context_position = memory_position[block_length:-block_length]
+
+ # (block_length, 3 * block_length)
+ relative_position = memory_position[None, :] - context_position[:, None]
+ relative_position_bucket = self._relative_position_bucket(
+ relative_position, # (block_length, 3 * block_length)
+ bidirectional=(not self.is_decoder),
+ num_buckets=self.relative_attention_num_buckets,
+ max_distance=self.relative_attention_max_distance,
+ )
+ # (block_length, 3 * block_length, num_heads)
+ values = self.relative_attention_bias(relative_position_bucket)
+ # (1, 1, num_heads, block_length, 3 * block_length)
+ values = values.permute([2, 0, 1]).unsqueeze(0).unsqueeze(0)
+ return values
+
+ def compute_side_bias(self, mask: torch.Tensor, global_segment_ids: torch.Tensor) -> torch.Tensor:
+ # (batch_size, 1, seq_len, global_seq_len)
+ side_attention_mask = torch.eq(mask[..., None], global_segment_ids[:, None, :])[:, None, ...]
+ attention_side_bias = torch.where(side_attention_mask > 0, 0.0, -1e10)
+ # (batch_size, seq_len, global_seq_len)
+ side_relative_position = _make_side_relative_position_ids(mask, self.global_block_size)
+ side_relative_position_bucket = self._relative_position_bucket(
+ side_relative_position,
+ bidirectional=(not self.is_decoder),
+ num_buckets=self.relative_attention_num_buckets,
+ max_distance=self.relative_attention_max_distance,
+ )
+ # (batch_size, seq_len, global_seq_len, num_heads)
+ side_bias = self.global_relative_attention_bias(side_relative_position_bucket)
+
+ # (batch_size, num_heads, seq_len, global_seq_len)
+ side_bias = side_bias.permute([0, 3, 1, 2])
+ # (batch_size, num_heads, seq_len, global_seq_len)
+ attention_side_bias = attention_side_bias + side_bias
+ return attention_side_bias
+
+ def forward(
+ self,
+ hidden_states,
+ mask=None,
+ position_bias=None,
+ layer_head_mask=None,
+ output_attentions=False,
+ ):
+ batch_size, seq_length = hidden_states.shape[:2]
+
+ def shape(states):
+ """projection"""
+ return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim)
+
+ def unshape(states):
+ """reshape"""
+ return states.contiguous().view(batch_size, -1, self.inner_dim)
+
+ # Prepare components for transient-global attention
+ # Obtain block_ids and global_segment_ids
+ # global_seq_len := seq_len // self.global_block_size
+ # shapes: (batch_size, seq_len) & (batch_size, global_seq_len)
+ block_ids, global_segment_ids = _make_global_fixed_block_ids(
+ mask if mask is not None else torch.ones(hidden_states.shape[:-1]),
+ self.global_block_size,
+ )
+ # Create global inputs
+ _global_seq_len = global_segment_ids.shape[-1]
+ global_inputs = _create_global_aggregates(hidden_states, block_ids, _global_seq_len)
+ global_inputs = self.global_input_layer_norm(global_inputs)
+
+ # get query states -> (batch_size, seq_length, n_heads, dim_per_head)
+ query_states = shape(self.q(hidden_states))
+ key_states = shape(self.k(hidden_states))
+ value_states = shape(self.v(hidden_states))
+ # Get global/side key/value states shape: (batch_size, global_seq_len, n_heads, dim_per_head)
+ side_key_states = shape(self.k(global_inputs))
+ side_value_states = shape(self.v(global_inputs))
+
+ # Split into blocks -> (batch_size, num_blocks, block_len, n_heads, dim_per_head)
+ query_states = _split_into_blocks(query_states, self.block_len, dim=1)
+ key_states = _split_into_blocks(key_states, self.block_len, dim=1)
+ value_states = _split_into_blocks(value_states, self.block_len, dim=1)
+
+ # Concatenate 3 blocks for keys and values -> (batch_size, num_blocks, 3 * block_len, n_heads, dim_per_head)
+ key_states = _concatenate_3_blocks(key_states, block_dim=1, sequence_dim=2)
+ value_states = _concatenate_3_blocks(value_states, block_dim=1, sequence_dim=2)
+
+ # Tile side inputs across local key/value blocks
+ # New shape: (batch_size, num_blocks, global_seq_len, n_heads, dim_per_head)
+ reps = [1] * (side_key_states.ndim + 1)
+ reps[1] = key_states.shape[1]
+ side_key_states = side_key_states.unsqueeze(1).repeat(reps)
+ side_value_states = side_value_states.unsqueeze(1).repeat(reps)
+
+ # Concatenate "local" and "side"/"global" key/value states to allow each token to attend global aggregated ones
+ # New shape: (batch_size, num_blocks, 3 * block_len + global_seq_len, n_heads, dim_per_head)
+ key_states = torch.cat([key_states, side_key_states], dim=2)
+ value_states = torch.cat([value_states, side_value_states], dim=2)
+
+ # Compute scores -> (batch_size, num_block, n_heads, block_len, 3 * block_len + global_seq_len)
+ scores = torch.einsum("...qhd,...khd->...hqk", query_states, key_states)
+
+ if mask is not None:
+ # We need to adjust position bias shape to be sum with mask
+ local_attention_mask = _get_local_attention_mask(mask, self.block_len, hidden_states.device)
+ # Replace masked positions with -10_000 (according to the original implementation)
+ local_attention_mask = torch.where(local_attention_mask > 0, 0.0, -1e10)
+ else:
+ local_attention_mask = None
+
+ if position_bias is None:
+ # position_bias shape: # (1, 1, n_heads, block_len, 3 * block_len)
+ if not self.has_relative_attention_bias:
+ position_bias = torch.zeros(
+ (1, 1, self.n_heads, self.block_len, 3 * self.block_len),
+ device=scores.device,
+ dtype=scores.dtype,
+ )
+ if self.gradient_checkpointing and self.training:
+ position_bias.requires_grad = True
+ else:
+ position_bias = self.compute_bias(self.block_len)
+
+ if local_attention_mask is not None:
+ # (batch_size, 1, n_heads, block_len, 3 * block_len)
+ position_bias = position_bias + local_attention_mask.transpose(1, 2)
+ position_bias = position_bias.type(scores.dtype)
+
+ # Calculate global/side bias - shape: # (batch_size, num_heads, seq_len, global_seq_len)
+ if mask is None:
+ mask = torch.ones(batch_size, seq_length)
+ # (batch_size, num_heads, seq_len, global_seq_len)
+ side_position_bias = self.compute_side_bias(mask, global_segment_ids)
+ # (batch_size, num_blocks, num_heads, block_len, global_seq_len)
+ side_position_bias = _split_into_blocks(side_position_bias, self.block_len, dim=-2).transpose(1, 2)
+ side_position_bias = side_position_bias.type(scores.dtype).to(scores.device)
+ # (batch_size, num_blocks, num_heads, block_len, 3 * block_len + global_seq_len)
+ position_bias = torch.cat([position_bias, side_position_bias], dim=-1)
+
+ scores += position_bias
+ # (batch_size, num_blocks, n_heads, block_len, 3 * block_len + global_seq_len)
+ attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+ # Mask heads if we want to
+ if layer_head_mask is not None:
+ attn_weights = attn_weights * layer_head_mask
+ attn_weights = attn_weights.type(value_states.dtype)
+ attn_output = unshape(torch.einsum("...hqk,...khd->...qhd", attn_weights, value_states))
+ attn_output = attn_output[:, :seq_length, :]
+ attn_output = self.o(attn_output)
+
+ outputs = (attn_output, position_bias)
+
+ if output_attentions:
+ outputs = outputs + (attn_weights,)
+ return outputs
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->LongT5
+class LongT5LayerSelfAttention(nn.Module):
+ def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.SelfAttention = LongT5Attention(
+ config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
+ )
+ self.layer_norm = LongT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+ self.dropout = nn.Dropout(config.dropout_rate)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_bias=None,
+ layer_head_mask=None,
+ past_key_values=None,
+ use_cache=False,
+ output_attentions=False,
+ cache_position=None,
+ ):
+ normed_hidden_states = self.layer_norm(hidden_states)
+ attention_output = self.SelfAttention(
+ normed_hidden_states,
+ mask=attention_mask,
+ position_bias=position_bias,
+ layer_head_mask=layer_head_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = hidden_states + self.dropout(attention_output[0])
+ outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
+ return outputs
+
+
+class LongT5LayerLocalSelfAttention(nn.Module):
+ """Local self attention used in encoder"""
+
+ def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.LocalSelfAttention = LongT5LocalAttention(config, has_relative_attention_bias=has_relative_attention_bias)
+ self.layer_norm = LongT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+ self.dropout = nn.Dropout(config.dropout_rate)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_bias=None,
+ layer_head_mask=None,
+ output_attentions=False,
+ **kwargs: Any, # to accept past_key_values and use_cache kwargs
+ ):
+ normed_hidden_states = self.layer_norm(hidden_states)
+ attention_output = self.LocalSelfAttention(
+ normed_hidden_states,
+ mask=attention_mask,
+ position_bias=position_bias,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states = hidden_states + self.dropout(attention_output[0])
+ outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
+ return outputs
+
+
+class LongT5LayerTransientGlobalSelfAttention(nn.Module):
+ """Transient-Global self attention used in encoder"""
+
+ def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.TransientGlobalSelfAttention = LongT5TransientGlobalAttention(
+ config, has_relative_attention_bias=has_relative_attention_bias
+ )
+ self.layer_norm = LongT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+ self.dropout = nn.Dropout(config.dropout_rate)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_bias=None,
+ layer_head_mask=None,
+ output_attentions=False,
+ **kwargs: Any, # to accept past_key_values and use_cache kwargs
+ ):
+ normed_hidden_states = self.layer_norm(hidden_states)
+ attention_output = self.TransientGlobalSelfAttention(
+ normed_hidden_states,
+ mask=attention_mask,
+ position_bias=position_bias,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states = hidden_states + self.dropout(attention_output[0])
+ outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5->LongT5
+class LongT5LayerCrossAttention(nn.Module):
+ def __init__(self, config, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.EncDecAttention = LongT5Attention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
+ self.layer_norm = LongT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+ self.dropout = nn.Dropout(config.dropout_rate)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states,
+ key_value_states,
+ attention_mask=None,
+ position_bias=None,
+ layer_head_mask=None,
+ past_key_values=None,
+ use_cache=False,
+ query_length=None,
+ output_attentions=False,
+ cache_position=None,
+ ):
+ normed_hidden_states = self.layer_norm(hidden_states)
+ attention_output = self.EncDecAttention(
+ normed_hidden_states,
+ mask=attention_mask,
+ key_value_states=key_value_states,
+ position_bias=position_bias,
+ layer_head_mask=layer_head_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ query_length=query_length,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ layer_output = hidden_states + self.dropout(attention_output[0])
+ outputs = (layer_output,) + attention_output[1:] # add attentions if we output them
+ return outputs
+
+
+class LongT5Block(GradientCheckpointingLayer):
+ def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.is_decoder = config.is_decoder
+ if config.is_decoder:
+ attention_layer = LongT5LayerSelfAttention
+ elif config.encoder_attention_type == "local":
+ attention_layer = LongT5LayerLocalSelfAttention
+ elif config.encoder_attention_type == "transient-global":
+ attention_layer = LongT5LayerTransientGlobalSelfAttention
+ else:
+ raise ValueError(
+ "For encoder attention mechanism, either `local` or `transient-global` attention type is expected, "
+ f"but got {config.encoder_attention_type}."
+ )
+ self.layer = nn.ModuleList()
+ self.layer.append(
+ attention_layer(config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx)
+ )
+ if self.is_decoder:
+ self.layer.append(LongT5LayerCrossAttention(config, layer_idx=layer_idx))
+
+ self.layer.append(LongT5LayerFF(config))
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_bias=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ encoder_decoder_position_bias=None,
+ layer_head_mask=None,
+ cross_attn_layer_head_mask=None,
+ past_key_values=None,
+ use_cache=False,
+ output_attentions=False,
+ return_dict=True,
+ cache_position=None,
+ ):
+ self_attention_outputs = self.layer[0](
+ hidden_states,
+ attention_mask=attention_mask,
+ position_bias=position_bias,
+ layer_head_mask=layer_head_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = self_attention_outputs[0]
+ attention_outputs = self_attention_outputs[1:] # Keep self-attention outputs and relative position weights
+
+ # clamp inf values to enable fp16 inference - check https://github.com/huggingface/transformers/pull/19229/
+ if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ do_cross_attention = self.is_decoder and encoder_hidden_states is not None
+ if do_cross_attention:
+ cross_attention_outputs = self.layer[1](
+ hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ position_bias=encoder_decoder_position_bias,
+ layer_head_mask=cross_attn_layer_head_mask,
+ past_key_values=past_key_values,
+ query_length=cache_position[-1] + 1,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = cross_attention_outputs[0]
+
+ # clamp inf values to enable fp16 inference - check https://github.com/huggingface/transformers/pull/19229/
+ if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ # Keep cross-attention outputs and relative position weights
+ attention_outputs = attention_outputs + cross_attention_outputs[1:]
+
+ # Apply Feed Forward layer
+ hidden_states = self.layer[-1](hidden_states)
+
+ # clamp inf values to enable fp16 inference - check https://github.com/huggingface/transformers/pull/19229/
+ if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ return (
+ (hidden_states,) + attention_outputs
+ ) # hidden-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
+
+
+@auto_docstring
+class LongT5PreTrainedModel(PreTrainedModel):
+ config: LongT5Config
+ base_model_prefix = "transformer"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["LongT5Block"]
+
+ _can_compile_fullgraph = False # TODO: @raushan more involved due to local/global attn
+
+ @property
+ # Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel.dummy_inputs
+ def dummy_inputs(self):
+ input_ids = torch.tensor(DUMMY_INPUTS)
+ input_mask = torch.tensor(DUMMY_MASK)
+ dummy_inputs = {
+ "decoder_input_ids": input_ids,
+ "input_ids": input_ids,
+ "decoder_attention_mask": input_mask,
+ }
+ return dummy_inputs
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ factor = self.config.initializer_factor # Used for testing weights initialization
+ if isinstance(module, LongT5LayerNorm):
+ module.weight.data.fill_(factor * 1.0)
+ elif isinstance(module, (LongT5Model, LongT5ForConditionalGeneration, LongT5EncoderModel)):
+ # Mesh TensorFlow embeddings initialization
+ # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
+ module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
+ if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
+ module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0)
+ elif isinstance(module, LongT5DenseActDense):
+ # Mesh TensorFlow FF initialization
+ # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
+ # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
+ module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+ if hasattr(module.wi, "bias") and module.wi.bias is not None:
+ module.wi.bias.data.zero_()
+ module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
+ if hasattr(module.wo, "bias") and module.wo.bias is not None:
+ module.wo.bias.data.zero_()
+ elif isinstance(module, LongT5DenseGatedActDense):
+ module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+ if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
+ module.wi_0.bias.data.zero_()
+ module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+ if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None:
+ module.wi_1.bias.data.zero_()
+ module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
+ if hasattr(module.wo, "bias") and module.wo.bias is not None:
+ module.wo.bias.data.zero_()
+ elif isinstance(module, (LongT5Attention, LongT5LocalAttention, LongT5TransientGlobalAttention)):
+ # Mesh TensorFlow attention initialization to avoid scaling before softmax
+ # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
+ d_model = self.config.d_model
+ key_value_proj_dim = self.config.d_kv
+ n_heads = self.config.num_heads
+ module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
+ module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
+ module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
+ module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
+ if module.has_relative_attention_bias:
+ module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
+ if isinstance(module, LongT5TransientGlobalAttention):
+ module.global_relative_attention_bias.weight.data.normal_(
+ mean=0.0, std=factor * ((d_model) ** -0.5)
+ )
+
+ # Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel._shift_right with T5->LongT5
+ def _shift_right(self, input_ids):
+ decoder_start_token_id = self.config.decoder_start_token_id
+ pad_token_id = self.config.pad_token_id
+
+ if decoder_start_token_id is None:
+ raise ValueError(
+ "self.model.config.decoder_start_token_id has to be defined. In LongT5 it is usually set to the pad_token_id. "
+ "See LongT5 docs for more information."
+ )
+
+ # shift inputs to the right
+ if is_torch_fx_proxy(input_ids):
+ # Item assignment is not supported natively for proxies.
+ shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id)
+ shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
+ else:
+ shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+ shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
+ shifted_input_ids[..., 0] = decoder_start_token_id
+
+ if pad_token_id is None:
+ raise ValueError("self.model.config.pad_token_id has to be defined.")
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+ return shifted_input_ids
+
+
+class LongT5Stack(LongT5PreTrainedModel):
+ def __init__(self, config, embed_tokens=None):
+ super().__init__(config)
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model)
+ if embed_tokens is not None:
+ self.embed_tokens.weight = embed_tokens.weight
+ self.is_decoder = config.is_decoder
+
+ self.local_radius = config.local_radius
+ self.block_len = self.local_radius + 1
+
+ self.block = nn.ModuleList(
+ [
+ LongT5Block(config, has_relative_attention_bias=bool(i == 0), layer_idx=i)
+ for i in range(config.num_layers)
+ ]
+ )
+ self.final_layer_norm = LongT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+ self.dropout = nn.Dropout(config.dropout_rate)
+
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ # Copied from transformers.models.t5.modeling_t5.T5Stack.set_input_embeddings
+ def set_input_embeddings(self, new_embeddings):
+ self.embed_tokens = new_embeddings
+
+ def forward(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ inputs_embeds=None,
+ head_mask=None,
+ cross_attn_head_mask=None,
+ past_key_values=None,
+ use_cache=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ cache_position=None,
+ ):
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ err_msg_prefix = "decoder_" if self.is_decoder else ""
+ raise ValueError(
+ f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
+ )
+ elif input_ids is not None:
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ err_msg_prefix = "decoder_" if self.is_decoder else ""
+ raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ if inputs_embeds is None:
+ assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ batch_size, seq_length = input_shape
+
+ if self.is_decoder:
+ if use_cache and past_key_values is None:
+ if self.config.is_encoder_decoder:
+ past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
+ else:
+ past_key_values = DynamicCache()
+ elif not self.is_decoder:
+ # do not pass cache object down the line for encoder stack
+ # it messes indexing later in decoder-stack because cache object is modified in-place
+ past_key_values = None
+
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+ if cache_position is None:
+ cache_position = torch.arange(
+ past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
+ )
+
+ if attention_mask is None and not is_torchdynamo_compiling():
+ # required mask seq length can be calculated via length of past
+ mask_seq_length = past_key_values_length + seq_length
+ attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
+
+ if self.is_decoder:
+ causal_mask = self._update_causal_mask(
+ attention_mask,
+ inputs_embeds,
+ cache_position,
+ past_key_values.self_attention_cache
+ if isinstance(past_key_values, EncoderDecoderCache)
+ else past_key_values,
+ output_attentions,
+ )
+ # We use local attention in encoder self-attention, otherwise standard self & cross attentions are used
+ elif self.config.encoder_attention_type == "local":
+ causal_mask = _get_local_attention_mask(attention_mask, self.block_len, inputs_embeds.device)
+ else: # we need to use both local attention mask and standard extended mask for transient-global attention
+ causal_mask = attention_mask
+
+ # If a 2D or 3D attention mask is provided for the cross-attention
+ # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+ if self.is_decoder and encoder_hidden_states is not None:
+ encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+ encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+ if encoder_attention_mask is None:
+ encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device)
+ encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+ else:
+ encoder_extended_attention_mask = None
+
+ # Prepare head mask if needed
+ head_mask = self.get_head_mask(head_mask, self.config.num_layers)
+ cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+ all_cross_attentions = () if (output_attentions and self.is_decoder) else None
+ position_bias = None
+ encoder_decoder_position_bias = None
+
+ hidden_states = self.dropout(inputs_embeds)
+
+ for i, layer_module in enumerate(self.block):
+ layer_head_mask = head_mask[i]
+ cross_attn_layer_head_mask = cross_attn_head_mask[i]
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = layer_module(
+ hidden_states,
+ causal_mask,
+ position_bias,
+ encoder_hidden_states,
+ encoder_extended_attention_mask,
+ encoder_decoder_position_bias, # as a positional argument for gradient checkpointing
+ layer_head_mask=layer_head_mask,
+ cross_attn_layer_head_mask=cross_attn_layer_head_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ # layer_outputs is a tuple with:
+ # hidden-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
+
+ hidden_states = layer_outputs[0]
+
+ # We share the position biases between the layers - the first layer store them
+ # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
+ # (cross-attention position bias), (cross-attention weights)
+ position_bias = layer_outputs[1]
+ if self.is_decoder and encoder_hidden_states is not None:
+ encoder_decoder_position_bias = layer_outputs[3 if output_attentions else 2]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[2],)
+ if self.is_decoder:
+ all_cross_attentions = all_cross_attentions + (layer_outputs[4],)
+
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+
+ # Add last layer
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ hidden_states,
+ past_key_values,
+ all_hidden_states,
+ all_attentions,
+ all_cross_attentions,
+ ]
+ if v is not None
+ )
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_attentions,
+ cross_attentions=all_cross_attentions,
+ )
+
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, "BlockMask"],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ output_attentions: bool = False,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and (attention_mask == 0.0).any():
+ return attention_mask
+ return None
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ return attention_mask
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ sequence_length = input_tensor.shape[1]
+ if using_compilable_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ and not output_attentions
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+
+# Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+__HEAD_MASK_WARNING_MSG = """
+The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
+`decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
+If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers,
+num_heads)`.
+"""
+
+
+@auto_docstring
+class LongT5Model(LongT5PreTrainedModel):
+ _keys_to_ignore_on_load_unexpected = [
+ r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
+ ]
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+ def __init__(self, config: LongT5Config):
+ super().__init__(config)
+ self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+ encoder_config = copy.deepcopy(config)
+ encoder_config.is_decoder = False
+ encoder_config.use_cache = False
+ encoder_config.tie_encoder_decoder = False
+ self.encoder = LongT5Stack(encoder_config, self.shared)
+
+ decoder_config = copy.deepcopy(config)
+ decoder_config.is_decoder = True
+ decoder_config.tie_encoder_decoder = False
+ decoder_config.num_layers = config.num_decoder_layers
+ self.decoder = LongT5Stack(decoder_config, self.shared)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, new_embeddings):
+ self.shared = new_embeddings
+ self.encoder.set_input_embeddings(new_embeddings)
+ self.decoder.set_input_embeddings(new_embeddings)
+
+ def _tie_weights(self):
+ if self.config.tie_word_embeddings:
+ self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
+ self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
+
+ def get_encoder(self):
+ return self.encoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.BoolTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ decoder_head_mask: Optional[torch.FloatTensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ decoder_inputs_embeds: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple[torch.FloatTensor], Seq2SeqModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so
+ you should be able to pad the inputs on both the right and the left.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for detail.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ To know more on how to prepare `input_ids` for pretraining take a look a [LONGT5
+ Training](./longt5#training).
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ LONGT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ To know more on how to prepare `decoder_input_ids` for pretraining take a look at [LONGT5
+ Training](./longt5#training).
+ decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
+ `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LongT5Model
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-base")
+ >>> model = LongT5Model.from_pretrained("google/long-t5-local-base")
+
+ >>> # Let's try a very long encoder input.
+ >>> input_ids = tokenizer(
+ ... 100 * "Studies have been shown that owning a dog is good for you", return_tensors="pt"
+ ... ).input_ids # Batch size 1
+
+ >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
+
+ >>> # forward pass
+ >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
+ >>> last_hidden_states = outputs.last_hidden_state
+ ```"""
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+ if head_mask is not None and decoder_head_mask is None:
+ if self.config.num_layers == self.config.num_decoder_layers:
+ warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
+ decoder_head_mask = head_mask
+
+ # Encode if needed (training, first prediction pass)
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+ encoder_outputs = BaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+
+ hidden_states = encoder_outputs[0]
+
+ # Decode
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ inputs_embeds=decoder_inputs_embeds,
+ past_key_values=past_key_values,
+ encoder_hidden_states=hidden_states,
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return Seq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ LONGT5 Model with a `language modeling` head on top.
+ """
+)
+class LongT5ForConditionalGeneration(LongT5PreTrainedModel, GenerationMixin):
+ _keys_to_ignore_on_load_unexpected = [
+ r"decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight",
+ ]
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
+
+ def __init__(self, config: LongT5Config):
+ super().__init__(config)
+ self.model_dim = config.d_model
+
+ self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+ encoder_config = copy.deepcopy(config)
+ encoder_config.is_decoder = False
+ encoder_config.use_cache = False
+ encoder_config.tie_encoder_decoder = False
+ self.encoder = LongT5Stack(encoder_config, self.shared)
+
+ decoder_config = copy.deepcopy(config)
+ decoder_config.is_decoder = True
+ decoder_config.tie_encoder_decoder = False
+ decoder_config.num_layers = config.num_decoder_layers
+ self.decoder = LongT5Stack(decoder_config, self.shared)
+
+ self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, new_embeddings):
+ self.shared = new_embeddings
+ self.encoder.set_input_embeddings(new_embeddings)
+ self.decoder.set_input_embeddings(new_embeddings)
+
+ def _tie_weights(self):
+ if self.config.tie_word_embeddings:
+ self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
+ self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
+
+ def get_encoder(self):
+ return self.encoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.BoolTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ decoder_head_mask: Optional[torch.FloatTensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.Tensor]]] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple[torch.FloatTensor], Seq2SeqLMOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so
+ you should be able to pad the inputs on both the right and the left.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for detail.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ To know more on how to prepare `input_ids` for pretraining take a look a [LONGT5
+ Training](./longt5#training).
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ LONGT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ To know more on how to prepare `decoder_input_ids` for pretraining take a look at [LONGT5
+ Training](./longt5#training).
+ decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
+ `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
+ labels in `[0, ..., config.vocab_size]`
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LongT5ForConditionalGeneration
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps")
+ >>> model = LongT5ForConditionalGeneration.from_pretrained(
+ ... "Stancld/longt5-tglobal-large-16384-pubmed-3k_steps"
+ ... )
+
+ >>> # Let's try a very long input.
+ >>> inputs = tokenizer(100 * "studies have shown that owning a dog is good for you ", return_tensors="pt")
+ >>> input_ids = inputs.input_ids
+
+ >>> outputs = model.generate(input_ids)
+ >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+ abstractthe aim of this article is to provide an overview of the literature on the role of dog
+ ```"""
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+ if head_mask is not None and decoder_head_mask is None:
+ if self.config.num_layers == self.config.num_decoder_layers:
+ warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
+ decoder_head_mask = head_mask
+
+ # Encode if needed (training, first prediction pass)
+ if encoder_outputs is None:
+ # Convert encoder inputs in embeddings if needed
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+ encoder_outputs = BaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+
+ hidden_states = encoder_outputs[0]
+
+ if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
+ # get decoder inputs from shifting lm labels to the right
+ decoder_input_ids = self._shift_right(labels)
+
+ # Decode
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ inputs_embeds=decoder_inputs_embeds,
+ past_key_values=past_key_values,
+ encoder_hidden_states=hidden_states,
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ sequence_output = decoder_outputs[0]
+
+ if self.config.tie_word_embeddings:
+ # Rescale output before projecting on vocab
+ # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
+ sequence_output = sequence_output * (self.model_dim**-0.5)
+
+ lm_logits = self.lm_head(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss(ignore_index=-100)
+
+ labels = labels.to(lm_logits.device)
+ loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
+ # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
+
+ if not return_dict:
+ output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
+ return ((loss,) + output) if loss is not None else output
+
+ return Seq2SeqLMOutput(
+ loss=loss,
+ logits=lm_logits,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+ def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+ return self._shift_right(labels)
+
+
+@auto_docstring
+class LongT5EncoderModel(LongT5PreTrainedModel):
+ _tied_weights_keys = ["encoder.embed_tokens.weight"]
+ _keys_to_ignore_on_load_unexpected = [r"decoder"]
+
+ def __init__(self, config: LongT5Config):
+ super().__init__(config)
+ self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+ encoder_config = copy.deepcopy(config)
+ encoder_config.use_cache = False
+ encoder_config.tie_encoder_decoder = False
+ self.encoder = LongT5Stack(encoder_config, self.shared)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, new_embeddings):
+ self.shared = new_embeddings
+ self.encoder.set_input_embeddings(new_embeddings)
+
+ def _tie_weights(self):
+ if self.config.tie_word_embeddings:
+ self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
+
+ def get_encoder(self):
+ return self.encoder
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.FloatTensor], BaseModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. LongT5 is a model with relative position embeddings so
+ you should be able to pad the inputs on both the right and the left.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for detail.
+
+ To know more on how to prepare `input_ids` for pretraining take a look a [LONGT5
+ Training](./longt5#training).
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LongT5ForConditionalGeneration
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-base")
+ >>> model = LongT5EncoderModel.from_pretrained("google/long-t5-local-base")
+ >>> input_ids = tokenizer(
+ ... 100 * "Studies have been shown that owning a dog is good for you ", return_tensors="pt"
+ ... ).input_ids # Batch size 1
+ >>> outputs = model(input_ids=input_ids)
+ >>> last_hidden_states = outputs.last_hidden_state
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ return encoder_outputs
+
+
+__all__ = ["LongT5EncoderModel", "LongT5ForConditionalGeneration", "LongT5Model", "LongT5PreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..db588fb2da1976194806932bf5d39438f9769e19
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_luke import *
+ from .modeling_luke import *
+ from .tokenization_luke import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/configuration_luke.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/configuration_luke.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbac7c36d7641808f7d6f0acecdd45c43c0a2ddc
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/configuration_luke.py
@@ -0,0 +1,142 @@
+# coding=utf-8
+# Copyright Studio Ousia and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LUKE configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class LukeConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LukeModel`]. It is used to instantiate a LUKE
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the LUKE
+ [studio-ousia/luke-base](https://huggingface.co/studio-ousia/luke-base) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50267):
+ Vocabulary size of the LUKE model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`LukeModel`].
+ entity_vocab_size (`int`, *optional*, defaults to 500000):
+ Entity vocabulary size of the LUKE model. Defines the number of different entities that can be represented
+ by the `entity_ids` passed when calling [`LukeModel`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimensionality of the encoder layers and the pooler layer.
+ entity_emb_size (`int`, *optional*, defaults to 256):
+ The number of dimensions of the entity embedding.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 2):
+ The vocabulary size of the `token_type_ids` passed when calling [`LukeModel`].
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+ use_entity_aware_attention (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should use the entity-aware self-attention mechanism proposed in [LUKE: Deep
+ Contextualized Entity Representations with Entity-aware Self-attention (Yamada et
+ al.)](https://huggingface.co/papers/2010.01057).
+ classifier_dropout (`float`, *optional*):
+ The dropout ratio for the classification head.
+ pad_token_id (`int`, *optional*, defaults to 1):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 0):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ End of stream token id.
+
+ Examples:
+
+ ```python
+ >>> from transformers import LukeConfig, LukeModel
+
+ >>> # Initializing a LUKE configuration
+ >>> configuration = LukeConfig()
+
+ >>> # Initializing a model from the configuration
+ >>> model = LukeModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "luke"
+
+ def __init__(
+ self,
+ vocab_size=50267,
+ entity_vocab_size=500000,
+ hidden_size=768,
+ entity_emb_size=256,
+ num_hidden_layers=12,
+ num_attention_heads=12,
+ intermediate_size=3072,
+ hidden_act="gelu",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ type_vocab_size=2,
+ initializer_range=0.02,
+ layer_norm_eps=1e-12,
+ use_entity_aware_attention=True,
+ classifier_dropout=None,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ **kwargs,
+ ):
+ """Constructs LukeConfig."""
+ super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+ self.vocab_size = vocab_size
+ self.entity_vocab_size = entity_vocab_size
+ self.hidden_size = hidden_size
+ self.entity_emb_size = entity_emb_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.hidden_act = hidden_act
+ self.intermediate_size = intermediate_size
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.max_position_embeddings = max_position_embeddings
+ self.type_vocab_size = type_vocab_size
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.use_entity_aware_attention = use_entity_aware_attention
+ self.classifier_dropout = classifier_dropout
+
+
+__all__ = ["LukeConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/convert_luke_original_pytorch_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/convert_luke_original_pytorch_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e0e461862a8b32be02330e7c5897f030f04a137
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/convert_luke_original_pytorch_checkpoint_to_pytorch.py
@@ -0,0 +1,170 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert LUKE checkpoint."""
+
+import argparse
+import json
+import os
+
+import torch
+
+from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
+from transformers.tokenization_utils_base import AddedToken
+
+
+@torch.no_grad()
+def convert_luke_checkpoint(checkpoint_path, metadata_path, entity_vocab_path, pytorch_dump_folder_path, model_size):
+ # Load configuration defined in the metadata file
+ with open(metadata_path) as metadata_file:
+ metadata = json.load(metadata_file)
+ config = LukeConfig(use_entity_aware_attention=True, **metadata["model_config"])
+
+ # Load in the weights from the checkpoint_path
+ state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
+
+ # Load the entity vocab file
+ entity_vocab = load_entity_vocab(entity_vocab_path)
+
+ tokenizer = RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"])
+
+ # Add special tokens to the token vocabulary for downstream tasks
+ entity_token_1 = AddedToken("", lstrip=False, rstrip=False)
+ entity_token_2 = AddedToken("", lstrip=False, rstrip=False)
+ tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_1, entity_token_2]})
+ config.vocab_size += 2
+
+ print(f"Saving tokenizer to {pytorch_dump_folder_path}")
+ tokenizer.save_pretrained(pytorch_dump_folder_path)
+ with open(os.path.join(pytorch_dump_folder_path, LukeTokenizer.vocab_files_names["entity_vocab_file"]), "w") as f:
+ json.dump(entity_vocab, f)
+
+ tokenizer = LukeTokenizer.from_pretrained(pytorch_dump_folder_path)
+
+ # Initialize the embeddings of the special tokens
+ word_emb = state_dict["embeddings.word_embeddings.weight"]
+ ent_emb = word_emb[tokenizer.convert_tokens_to_ids(["@"])[0]].unsqueeze(0)
+ ent2_emb = word_emb[tokenizer.convert_tokens_to_ids(["#"])[0]].unsqueeze(0)
+ state_dict["embeddings.word_embeddings.weight"] = torch.cat([word_emb, ent_emb, ent2_emb])
+
+ # Initialize the query layers of the entity-aware self-attention mechanism
+ for layer_index in range(config.num_hidden_layers):
+ for matrix_name in ["query.weight", "query.bias"]:
+ prefix = f"encoder.layer.{layer_index}.attention.self."
+ state_dict[prefix + "w2e_" + matrix_name] = state_dict[prefix + matrix_name]
+ state_dict[prefix + "e2w_" + matrix_name] = state_dict[prefix + matrix_name]
+ state_dict[prefix + "e2e_" + matrix_name] = state_dict[prefix + matrix_name]
+
+ # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
+ entity_emb = state_dict["entity_embeddings.entity_embeddings.weight"]
+ entity_emb[entity_vocab["[MASK2]"]] = entity_emb[entity_vocab["[MASK]"]]
+
+ model = LukeModel(config=config).eval()
+
+ missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
+ if not (len(missing_keys) == 1 and missing_keys[0] == "embeddings.position_ids"):
+ raise ValueError(f"Missing keys {', '.join(missing_keys)}. Expected only missing embeddings.position_ids")
+ if not (all(key.startswith("entity_predictions") or key.startswith("lm_head") for key in unexpected_keys)):
+ raise ValueError(
+ "Unexpected keys"
+ f" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions') or key.startswith('lm_head'))])}"
+ )
+
+ # Check outputs
+ tokenizer = LukeTokenizer.from_pretrained(pytorch_dump_folder_path, task="entity_classification")
+
+ text = (
+ "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
+ " new world number one avoid a humiliating second- round exit at Wimbledon ."
+ )
+ span = (39, 42)
+ encoding = tokenizer(text, entity_spans=[span], add_prefix_space=True, return_tensors="pt")
+
+ outputs = model(**encoding)
+
+ # Verify word hidden states
+ if model_size == "large":
+ expected_shape = torch.Size((1, 42, 1024))
+ expected_slice = torch.tensor(
+ [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]
+ )
+ else: # base
+ expected_shape = torch.Size((1, 42, 768))
+ expected_slice = torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]])
+
+ if not (outputs.last_hidden_state.shape == expected_shape):
+ raise ValueError(
+ f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"
+ )
+ if not torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4):
+ raise ValueError
+
+ # Verify entity hidden states
+ if model_size == "large":
+ expected_shape = torch.Size((1, 1, 1024))
+ expected_slice = torch.tensor([[0.0466, -0.0106, -0.0179]])
+ else: # base
+ expected_shape = torch.Size((1, 1, 768))
+ expected_slice = torch.tensor([[0.1457, 0.1044, 0.0174]])
+
+ if not (outputs.entity_last_hidden_state.shape != expected_shape):
+ raise ValueError(
+ f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
+ f" {expected_shape}"
+ )
+ if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, atol=1e-4):
+ raise ValueError
+
+ # Finally, save our PyTorch model and tokenizer
+ print(f"Saving PyTorch model to {pytorch_dump_folder_path}")
+ model.save_pretrained(pytorch_dump_folder_path)
+
+
+def load_entity_vocab(entity_vocab_path):
+ entity_vocab = {}
+ with open(entity_vocab_path, "r", encoding="utf-8") as f:
+ for index, line in enumerate(f):
+ title, _ = line.rstrip().split("\t")
+ entity_vocab[title] = index
+
+ return entity_vocab
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
+ parser.add_argument(
+ "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
+ )
+ parser.add_argument(
+ "--entity_vocab_path",
+ default=None,
+ type=str,
+ help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
+ )
+ parser.add_argument(
+ "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
+ )
+ args = parser.parse_args()
+ convert_luke_checkpoint(
+ args.checkpoint_path,
+ args.metadata_path,
+ args.entity_vocab_path,
+ args.pytorch_dump_folder_path,
+ args.model_size,
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/modeling_luke.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/modeling_luke.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed1f2084c9e82f8e73e850e41f515664fc3bd0a8
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/modeling_luke.py
@@ -0,0 +1,2180 @@
+# coding=utf-8
+# Copyright Studio Ousia and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch LUKE model."""
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN, gelu
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import apply_chunking_to_forward
+from ...utils import ModelOutput, auto_docstring, logging
+from .configuration_luke import LukeConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of the LUKE model.
+ """
+)
+class BaseLukeModelOutputWithPooling(BaseModelOutputWithPooling):
+ r"""
+ pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
+ Last layer hidden-state of the first token of the sequence (classification token) further processed by a
+ Linear layer and a Tanh activation function.
+ entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`):
+ Sequence of entity hidden-states at the output of the last layer of the model.
+ entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
+ layer plus the initial entity embedding outputs.
+ """
+
+ entity_last_hidden_state: Optional[torch.FloatTensor] = None
+ entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for model's outputs, with potential hidden states and attentions.
+ """
+)
+class BaseLukeModelOutput(BaseModelOutput):
+ r"""
+ entity_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, entity_length, hidden_size)`):
+ Sequence of entity hidden-states at the output of the last layer of the model.
+ entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
+ layer plus the initial entity embedding outputs.
+ """
+
+ entity_last_hidden_state: Optional[torch.FloatTensor] = None
+ entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for model's outputs, with potential hidden states and attentions.
+ """
+)
+class LukeMaskedLMOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ The sum of masked language modeling (MLM) loss and entity prediction loss.
+ mlm_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Masked language modeling (MLM) loss.
+ mep_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Masked entity prediction (MEP) loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ entity_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the entity prediction head (scores for each entity vocabulary token before SoftMax).
+ entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
+ layer plus the initial entity embedding outputs.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ mlm_loss: Optional[torch.FloatTensor] = None
+ mep_loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ entity_logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Outputs of entity classification models.
+ """
+)
+class EntityClassificationOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Classification loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+ Classification scores (before SoftMax).
+ entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
+ layer plus the initial entity embedding outputs.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Outputs of entity pair classification models.
+ """
+)
+class EntityPairClassificationOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Classification loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+ Classification scores (before SoftMax).
+ entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
+ layer plus the initial entity embedding outputs.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Outputs of entity span classification models.
+ """
+)
+class EntitySpanClassificationOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Classification loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, entity_length, config.num_labels)`):
+ Classification scores (before SoftMax).
+ entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
+ layer plus the initial entity embedding outputs.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Outputs of sentence classification models.
+ """
+)
+class LukeSequenceClassifierOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Classification (or regression if config.num_labels==1) loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+ Classification (or regression if config.num_labels==1) scores (before SoftMax).
+ entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
+ layer plus the initial entity embedding outputs.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of token classification models.
+ """
+)
+class LukeTokenClassifierOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Classification loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
+ Classification scores (before SoftMax).
+ entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
+ layer plus the initial entity embedding outputs.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Outputs of question answering models.
+ """
+)
+class LukeQuestionAnsweringModelOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
+ entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
+ layer plus the initial entity embedding outputs.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ start_logits: Optional[torch.FloatTensor] = None
+ end_logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Outputs of multiple choice models.
+ """
+)
+class LukeMultipleChoiceModelOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
+ Classification loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
+ *num_choices* is the second dimension of the input tensors. (see *input_ids* above).
+
+ Classification scores (before SoftMax).
+ entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
+ layer plus the initial entity embedding outputs.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+class LukeEmbeddings(nn.Module):
+ """
+ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ # End copy
+ self.padding_idx = config.pad_token_id
+ self.position_embeddings = nn.Embedding(
+ config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
+ )
+
+ def forward(
+ self,
+ input_ids=None,
+ token_type_ids=None,
+ position_ids=None,
+ inputs_embeds=None,
+ ):
+ if position_ids is None:
+ if input_ids is not None:
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
+ position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx).to(input_ids.device)
+ else:
+ position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
+
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+
+ position_embeddings = self.position_embeddings(position_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = inputs_embeds + position_embeddings + token_type_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+ """
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+
+ Args:
+ inputs_embeds: torch.Tensor
+
+ Returns: torch.Tensor
+ """
+ input_shape = inputs_embeds.size()[:-1]
+ sequence_length = input_shape[1]
+
+ position_ids = torch.arange(
+ self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+ )
+ return position_ids.unsqueeze(0).expand(input_shape)
+
+
+class LukeEntityEmbeddings(nn.Module):
+ def __init__(self, config: LukeConfig):
+ super().__init__()
+ self.config = config
+
+ self.entity_embeddings = nn.Embedding(config.entity_vocab_size, config.entity_emb_size, padding_idx=0)
+ if config.entity_emb_size != config.hidden_size:
+ self.entity_embedding_dense = nn.Linear(config.entity_emb_size, config.hidden_size, bias=False)
+
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(
+ self,
+ entity_ids: torch.LongTensor,
+ position_ids: torch.LongTensor,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ ):
+ if token_type_ids is None:
+ token_type_ids = torch.zeros_like(entity_ids)
+
+ entity_embeddings = self.entity_embeddings(entity_ids)
+ if self.config.entity_emb_size != self.config.hidden_size:
+ entity_embeddings = self.entity_embedding_dense(entity_embeddings)
+
+ position_embeddings = self.position_embeddings(position_ids.clamp(min=0))
+ position_embedding_mask = (position_ids != -1).type_as(position_embeddings).unsqueeze(-1)
+ position_embeddings = position_embeddings * position_embedding_mask
+ position_embeddings = torch.sum(position_embeddings, dim=-2)
+ position_embeddings = position_embeddings / position_embedding_mask.sum(dim=-2).clamp(min=1e-7)
+
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = entity_embeddings + position_embeddings + token_type_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+
+ return embeddings
+
+
+class LukeSelfAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
+ f"heads {config.num_attention_heads}."
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+ self.use_entity_aware_attention = config.use_entity_aware_attention
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ if self.use_entity_aware_attention:
+ self.w2e_query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.e2w_query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.e2e_query = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+ def transpose_for_scores(self, x):
+ new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+ x = x.view(*new_x_shape)
+ return x.permute(0, 2, 1, 3)
+
+ def forward(
+ self,
+ word_hidden_states,
+ entity_hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ ):
+ word_size = word_hidden_states.size(1)
+
+ if entity_hidden_states is None:
+ concat_hidden_states = word_hidden_states
+ else:
+ concat_hidden_states = torch.cat([word_hidden_states, entity_hidden_states], dim=1)
+
+ key_layer = self.transpose_for_scores(self.key(concat_hidden_states))
+ value_layer = self.transpose_for_scores(self.value(concat_hidden_states))
+
+ if self.use_entity_aware_attention and entity_hidden_states is not None:
+ # compute query vectors using word-word (w2w), word-entity (w2e), entity-word (e2w), entity-entity (e2e)
+ # query layers
+ w2w_query_layer = self.transpose_for_scores(self.query(word_hidden_states))
+ w2e_query_layer = self.transpose_for_scores(self.w2e_query(word_hidden_states))
+ e2w_query_layer = self.transpose_for_scores(self.e2w_query(entity_hidden_states))
+ e2e_query_layer = self.transpose_for_scores(self.e2e_query(entity_hidden_states))
+
+ # compute w2w, w2e, e2w, and e2e key vectors used with the query vectors computed above
+ w2w_key_layer = key_layer[:, :, :word_size, :]
+ e2w_key_layer = key_layer[:, :, :word_size, :]
+ w2e_key_layer = key_layer[:, :, word_size:, :]
+ e2e_key_layer = key_layer[:, :, word_size:, :]
+
+ # compute attention scores based on the dot product between the query and key vectors
+ w2w_attention_scores = torch.matmul(w2w_query_layer, w2w_key_layer.transpose(-1, -2))
+ w2e_attention_scores = torch.matmul(w2e_query_layer, w2e_key_layer.transpose(-1, -2))
+ e2w_attention_scores = torch.matmul(e2w_query_layer, e2w_key_layer.transpose(-1, -2))
+ e2e_attention_scores = torch.matmul(e2e_query_layer, e2e_key_layer.transpose(-1, -2))
+
+ # combine attention scores to create the final attention score matrix
+ word_attention_scores = torch.cat([w2w_attention_scores, w2e_attention_scores], dim=3)
+ entity_attention_scores = torch.cat([e2w_attention_scores, e2e_attention_scores], dim=3)
+ attention_scores = torch.cat([word_attention_scores, entity_attention_scores], dim=2)
+
+ else:
+ query_layer = self.transpose_for_scores(self.query(concat_hidden_states))
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in LukeModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+
+ output_word_hidden_states = context_layer[:, :word_size, :]
+ if entity_hidden_states is None:
+ output_entity_hidden_states = None
+ else:
+ output_entity_hidden_states = context_layer[:, word_size:, :]
+
+ if output_attentions:
+ outputs = (output_word_hidden_states, output_entity_hidden_states, attention_probs)
+ else:
+ outputs = (output_word_hidden_states, output_entity_hidden_states)
+
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
+class LukeSelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class LukeAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.self = LukeSelfAttention(config)
+ self.output = LukeSelfOutput(config)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ raise NotImplementedError("LUKE does not support the pruning of attention heads")
+
+ def forward(
+ self,
+ word_hidden_states,
+ entity_hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ ):
+ word_size = word_hidden_states.size(1)
+ self_outputs = self.self(
+ word_hidden_states,
+ entity_hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions,
+ )
+ if entity_hidden_states is None:
+ concat_self_outputs = self_outputs[0]
+ concat_hidden_states = word_hidden_states
+ else:
+ concat_self_outputs = torch.cat(self_outputs[:2], dim=1)
+ concat_hidden_states = torch.cat([word_hidden_states, entity_hidden_states], dim=1)
+
+ attention_output = self.output(concat_self_outputs, concat_hidden_states)
+
+ word_attention_output = attention_output[:, :word_size, :]
+ if entity_hidden_states is None:
+ entity_attention_output = None
+ else:
+ entity_attention_output = attention_output[:, word_size:, :]
+
+ # add attentions if we output them
+ outputs = (word_attention_output, entity_attention_output) + self_outputs[2:]
+
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate
+class LukeIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOutput
+class LukeOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class LukeLayer(GradientCheckpointingLayer):
+ def __init__(self, config):
+ super().__init__()
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = LukeAttention(config)
+ self.intermediate = LukeIntermediate(config)
+ self.output = LukeOutput(config)
+
+ def forward(
+ self,
+ word_hidden_states,
+ entity_hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ ):
+ word_size = word_hidden_states.size(1)
+
+ self_attention_outputs = self.attention(
+ word_hidden_states,
+ entity_hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions=output_attentions,
+ )
+ if entity_hidden_states is None:
+ concat_attention_output = self_attention_outputs[0]
+ else:
+ concat_attention_output = torch.cat(self_attention_outputs[:2], dim=1)
+
+ outputs = self_attention_outputs[2:] # add self attentions if we output attention weights
+
+ layer_output = apply_chunking_to_forward(
+ self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, concat_attention_output
+ )
+ word_layer_output = layer_output[:, :word_size, :]
+ if entity_hidden_states is None:
+ entity_layer_output = None
+ else:
+ entity_layer_output = layer_output[:, word_size:, :]
+
+ outputs = (word_layer_output, entity_layer_output) + outputs
+
+ return outputs
+
+ def feed_forward_chunk(self, attention_output):
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+
+class LukeEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList([LukeLayer(config) for _ in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ word_hidden_states,
+ entity_hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_attentions=False,
+ output_hidden_states=False,
+ return_dict=True,
+ ):
+ all_word_hidden_states = () if output_hidden_states else None
+ all_entity_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_word_hidden_states = all_word_hidden_states + (word_hidden_states,)
+ all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)
+
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+ layer_outputs = layer_module(
+ word_hidden_states,
+ entity_hidden_states,
+ attention_mask,
+ layer_head_mask,
+ output_attentions,
+ )
+
+ word_hidden_states = layer_outputs[0]
+
+ if entity_hidden_states is not None:
+ entity_hidden_states = layer_outputs[1]
+
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[2],)
+
+ if output_hidden_states:
+ all_word_hidden_states = all_word_hidden_states + (word_hidden_states,)
+ all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ word_hidden_states,
+ all_word_hidden_states,
+ all_self_attentions,
+ entity_hidden_states,
+ all_entity_hidden_states,
+ ]
+ if v is not None
+ )
+ return BaseLukeModelOutput(
+ last_hidden_state=word_hidden_states,
+ hidden_states=all_word_hidden_states,
+ attentions=all_self_attentions,
+ entity_last_hidden_state=entity_hidden_states,
+ entity_hidden_states=all_entity_hidden_states,
+ )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler
+class LukePooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+class EntityPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.entity_emb_size)
+ if isinstance(config.hidden_act, str):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = nn.LayerNorm(config.entity_emb_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+class EntityPredictionHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.transform = EntityPredictionHeadTransform(config)
+ self.decoder = nn.Linear(config.entity_emb_size, config.entity_vocab_size, bias=False)
+ self.bias = nn.Parameter(torch.zeros(config.entity_vocab_size))
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states) + self.bias
+
+ return hidden_states
+
+
+@auto_docstring
+class LukePreTrainedModel(PreTrainedModel):
+ config: LukeConfig
+ base_model_prefix = "luke"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["LukeAttention", "LukeEntityEmbeddings"]
+
+ def _init_weights(self, module: nn.Module):
+ """Initialize the weights"""
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ if module.embedding_dim == 1: # embedding for bias parameters
+ module.weight.data.zero_()
+ else:
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+@auto_docstring(
+ custom_intro="""
+ The bare LUKE model transformer outputting raw hidden-states for both word tokens and entities without any
+ """
+)
+class LukeModel(LukePreTrainedModel):
+ def __init__(self, config: LukeConfig, add_pooling_layer: bool = True):
+ r"""
+ add_pooling_layer (bool, *optional*, defaults to `True`):
+ Whether to add a pooling layer
+ """
+ super().__init__(config)
+ self.config = config
+
+ self.embeddings = LukeEmbeddings(config)
+ self.entity_embeddings = LukeEntityEmbeddings(config)
+ self.encoder = LukeEncoder(config)
+
+ self.pooler = LukePooler(config) if add_pooling_layer else None
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def get_entity_embeddings(self):
+ return self.entity_embeddings.entity_embeddings
+
+ def set_entity_embeddings(self, value):
+ self.entity_embeddings.entity_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ raise NotImplementedError("LUKE does not support the pruning of attention heads")
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ entity_ids: Optional[torch.LongTensor] = None,
+ entity_attention_mask: Optional[torch.FloatTensor] = None,
+ entity_token_type_ids: Optional[torch.LongTensor] = None,
+ entity_position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseLukeModelOutputWithPooling]:
+ r"""
+ entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
+ Indices of entity tokens in the entity vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+ entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for entity tokens that are **not masked**,
+ - 0 for entity tokens that are **masked**.
+ entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the entity token inputs. Indices are
+ selected in `[0, 1]`:
+
+ - 0 corresponds to a *portion A* entity token,
+ - 1 corresponds to a *portion B* entity token.
+ entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
+ Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LukeModel
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base")
+ >>> model = LukeModel.from_pretrained("studio-ousia/luke-base")
+ # Compute the contextualized entity representation corresponding to the entity mention "Beyoncé"
+
+ >>> text = "Beyoncé lives in Los Angeles."
+ >>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé"
+
+ >>> encoding = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
+ >>> outputs = model(**encoding)
+ >>> word_last_hidden_state = outputs.last_hidden_state
+ >>> entity_last_hidden_state = outputs.entity_last_hidden_state
+ # Input Wikipedia entities to obtain enriched contextualized representations of word tokens
+
+ >>> text = "Beyoncé lives in Los Angeles."
+ >>> entities = [
+ ... "Beyoncé",
+ ... "Los Angeles",
+ ... ] # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles"
+ >>> entity_spans = [
+ ... (0, 7),
+ ... (17, 28),
+ ... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
+
+ >>> encoding = tokenizer(
+ ... text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt"
+ ... )
+ >>> outputs = model(**encoding)
+ >>> word_last_hidden_state = outputs.last_hidden_state
+ >>> entity_last_hidden_state = outputs.entity_last_hidden_state
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ batch_size, seq_length = input_shape
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones((batch_size, seq_length), device=device)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+ if entity_ids is not None:
+ entity_seq_length = entity_ids.size(1)
+ if entity_attention_mask is None:
+ entity_attention_mask = torch.ones((batch_size, entity_seq_length), device=device)
+ if entity_token_type_ids is None:
+ entity_token_type_ids = torch.zeros((batch_size, entity_seq_length), dtype=torch.long, device=device)
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+ # First, compute word embeddings
+ word_embedding_output = self.embeddings(
+ input_ids=input_ids,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ )
+
+ # Second, compute extended attention mask
+ extended_attention_mask = self.get_extended_attention_mask(attention_mask, entity_attention_mask)
+
+ # Third, compute entity embeddings and concatenate with word embeddings
+ if entity_ids is None:
+ entity_embedding_output = None
+ else:
+ entity_embedding_output = self.entity_embeddings(entity_ids, entity_position_ids, entity_token_type_ids)
+
+ # Fourth, send embeddings through the model
+ encoder_outputs = self.encoder(
+ word_embedding_output,
+ entity_embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ # Fifth, get the output. LukeModel outputs the same as BertModel, namely sequence_output of shape (batch_size, seq_len, hidden_size)
+ sequence_output = encoder_outputs[0]
+
+ # Sixth, we compute the pooled_output, word_sequence_output and entity_sequence_output based on the sequence_output
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ return BaseLukeModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ entity_last_hidden_state=encoder_outputs.entity_last_hidden_state,
+ entity_hidden_states=encoder_outputs.entity_hidden_states,
+ )
+
+ def get_extended_attention_mask(
+ self, word_attention_mask: torch.LongTensor, entity_attention_mask: Optional[torch.LongTensor]
+ ):
+ """
+ Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
+
+ Arguments:
+ word_attention_mask (`torch.LongTensor`):
+ Attention mask for word tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
+ entity_attention_mask (`torch.LongTensor`, *optional*):
+ Attention mask for entity tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
+
+ Returns:
+ `torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
+ """
+ attention_mask = word_attention_mask
+ if entity_attention_mask is not None:
+ attention_mask = torch.cat([attention_mask, entity_attention_mask], dim=-1)
+
+ if attention_mask.dim() == 3:
+ extended_attention_mask = attention_mask[:, None, :, :]
+ elif attention_mask.dim() == 2:
+ extended_attention_mask = attention_mask[:, None, None, :]
+ else:
+ raise ValueError(f"Wrong shape for attention_mask (shape {attention_mask.shape})")
+
+ extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
+ extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
+ return extended_attention_mask
+
+
+def create_position_ids_from_input_ids(input_ids, padding_idx):
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+ are ignored. This is modified from fairseq's `utils.make_positions`.
+
+ Args:
+ x: torch.Tensor x:
+
+ Returns: torch.Tensor
+ """
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+ mask = input_ids.ne(padding_idx).int()
+ incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask
+ return incremental_indices.long() + padding_idx
+
+
+# Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead
+class LukeLMHead(nn.Module):
+ """Roberta Head for masked language modeling."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+ self.decoder.bias = self.bias
+
+ def forward(self, features, **kwargs):
+ x = self.dense(features)
+ x = gelu(x)
+ x = self.layer_norm(x)
+
+ # project back to size of vocabulary with bias
+ x = self.decoder(x)
+
+ return x
+
+ def _tie_weights(self):
+ # To tie those two weights if they get disconnected (on TPU or when the bias is resized)
+ # For accelerate compatibility and to not break backward compatibility
+ if self.decoder.bias.device.type == "meta":
+ self.decoder.bias = self.bias
+ else:
+ self.bias = self.decoder.bias
+
+
+@auto_docstring(
+ custom_intro="""
+ The LUKE model with a language modeling head and entity prediction head on top for masked language modeling and
+ masked entity prediction.
+ """
+)
+class LukeForMaskedLM(LukePreTrainedModel):
+ _tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias", "entity_predictions.decoder.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.luke = LukeModel(config)
+
+ self.lm_head = LukeLMHead(config)
+ self.entity_predictions = EntityPredictionHead(config)
+
+ self.loss_fn = nn.CrossEntropyLoss()
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def tie_weights(self):
+ super().tie_weights()
+ self._tie_or_clone_weights(self.entity_predictions.decoder, self.luke.entity_embeddings.entity_embeddings)
+
+ def get_output_embeddings(self):
+ return self.lm_head.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head.decoder = new_embeddings
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ entity_ids: Optional[torch.LongTensor] = None,
+ entity_attention_mask: Optional[torch.LongTensor] = None,
+ entity_token_type_ids: Optional[torch.LongTensor] = None,
+ entity_position_ids: Optional[torch.LongTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ entity_labels: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LukeMaskedLMOutput]:
+ r"""
+ entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
+ Indices of entity tokens in the entity vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+ entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for entity tokens that are **not masked**,
+ - 0 for entity tokens that are **masked**.
+ entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the entity token inputs. Indices are
+ selected in `[0, 1]`:
+
+ - 0 corresponds to a *portion A* entity token,
+ - 1 corresponds to a *portion B* entity token.
+ entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
+ Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ entity_labels (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.luke(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ entity_ids=entity_ids,
+ entity_attention_mask=entity_attention_mask,
+ entity_token_type_ids=entity_token_type_ids,
+ entity_position_ids=entity_position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ loss = None
+
+ mlm_loss = None
+ logits = self.lm_head(outputs.last_hidden_state)
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ mlm_loss = self.loss_fn(logits.view(-1, self.config.vocab_size), labels.view(-1))
+ if loss is None:
+ loss = mlm_loss
+
+ mep_loss = None
+ entity_logits = None
+ if outputs.entity_last_hidden_state is not None:
+ entity_logits = self.entity_predictions(outputs.entity_last_hidden_state)
+ if entity_labels is not None:
+ mep_loss = self.loss_fn(entity_logits.view(-1, self.config.entity_vocab_size), entity_labels.view(-1))
+ if loss is None:
+ loss = mep_loss
+ else:
+ loss = loss + mep_loss
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ loss,
+ mlm_loss,
+ mep_loss,
+ logits,
+ entity_logits,
+ outputs.hidden_states,
+ outputs.entity_hidden_states,
+ outputs.attentions,
+ ]
+ if v is not None
+ )
+
+ return LukeMaskedLMOutput(
+ loss=loss,
+ mlm_loss=mlm_loss,
+ mep_loss=mep_loss,
+ logits=logits,
+ entity_logits=entity_logits,
+ hidden_states=outputs.hidden_states,
+ entity_hidden_states=outputs.entity_hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity
+ token) for entity classification tasks, such as Open Entity.
+ """
+)
+class LukeForEntityClassification(LukePreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.luke = LukeModel(config)
+
+ self.num_labels = config.num_labels
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ entity_ids: Optional[torch.LongTensor] = None,
+ entity_attention_mask: Optional[torch.FloatTensor] = None,
+ entity_token_type_ids: Optional[torch.LongTensor] = None,
+ entity_position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, EntityClassificationOutput]:
+ r"""
+ entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
+ Indices of entity tokens in the entity vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+ entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for entity tokens that are **not masked**,
+ - 0 for entity tokens that are **masked**.
+ entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the entity token inputs. Indices are
+ selected in `[0, 1]`:
+
+ - 0 corresponds to a *portion A* entity token,
+ - 1 corresponds to a *portion B* entity token.
+ entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
+ Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
+ Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
+ used for the single-label classification. In this case, labels should contain the indices that should be in
+ `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
+ loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
+ and 1 indicate false and true, respectively.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LukeForEntityClassification
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
+ >>> model = LukeForEntityClassification.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
+
+ >>> text = "Beyoncé lives in Los Angeles."
+ >>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé"
+ >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
+ >>> outputs = model(**inputs)
+ >>> logits = outputs.logits
+ >>> predicted_class_idx = logits.argmax(-1).item()
+ >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
+ Predicted class: person
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.luke(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ entity_ids=entity_ids,
+ entity_attention_mask=entity_attention_mask,
+ entity_token_type_ids=entity_token_type_ids,
+ entity_position_ids=entity_position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ feature_vector = outputs.entity_last_hidden_state[:, 0, :]
+ feature_vector = self.dropout(feature_vector)
+ logits = self.classifier(feature_vector)
+
+ loss = None
+ if labels is not None:
+ # When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
+ # cross entropy is used otherwise.
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ if labels.ndim == 1:
+ loss = nn.functional.cross_entropy(logits, labels)
+ else:
+ loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
+ if v is not None
+ )
+
+ return EntityClassificationOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ entity_hidden_states=outputs.entity_hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The LUKE model with a classification head on top (a linear layer on top of the hidden states of the two entity
+ tokens) for entity pair classification tasks, such as TACRED.
+ """
+)
+class LukeForEntityPairClassification(LukePreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.luke = LukeModel(config)
+
+ self.num_labels = config.num_labels
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size * 2, config.num_labels, False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ entity_ids: Optional[torch.LongTensor] = None,
+ entity_attention_mask: Optional[torch.FloatTensor] = None,
+ entity_token_type_ids: Optional[torch.LongTensor] = None,
+ entity_position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, EntityPairClassificationOutput]:
+ r"""
+ entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
+ Indices of entity tokens in the entity vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+ entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for entity tokens that are **not masked**,
+ - 0 for entity tokens that are **masked**.
+ entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the entity token inputs. Indices are
+ selected in `[0, 1]`:
+
+ - 0 corresponds to a *portion A* entity token,
+ - 1 corresponds to a *portion B* entity token.
+ entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
+ Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
+ Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
+ used for the single-label classification. In this case, labels should contain the indices that should be in
+ `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
+ loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
+ and 1 indicate false and true, respectively.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LukeForEntityPairClassification
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
+ >>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
+
+ >>> text = "Beyoncé lives in Los Angeles."
+ >>> entity_spans = [
+ ... (0, 7),
+ ... (17, 28),
+ ... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
+ >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
+ >>> outputs = model(**inputs)
+ >>> logits = outputs.logits
+ >>> predicted_class_idx = logits.argmax(-1).item()
+ >>> print("Predicted class:", model.config.id2label[predicted_class_idx])
+ Predicted class: per:cities_of_residence
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.luke(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ entity_ids=entity_ids,
+ entity_attention_mask=entity_attention_mask,
+ entity_token_type_ids=entity_token_type_ids,
+ entity_position_ids=entity_position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ feature_vector = torch.cat(
+ [outputs.entity_last_hidden_state[:, 0, :], outputs.entity_last_hidden_state[:, 1, :]], dim=1
+ )
+ feature_vector = self.dropout(feature_vector)
+ logits = self.classifier(feature_vector)
+
+ loss = None
+ if labels is not None:
+ # When the number of dimension of `labels` is 1, cross entropy is used as the loss function. The binary
+ # cross entropy is used otherwise.
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ if labels.ndim == 1:
+ loss = nn.functional.cross_entropy(logits, labels)
+ else:
+ loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
+ if v is not None
+ )
+
+ return EntityPairClassificationOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ entity_hidden_states=outputs.entity_hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The LUKE model with a span classification head on top (a linear layer on top of the hidden states output) for tasks
+ such as named entity recognition.
+ """
+)
+class LukeForEntitySpanClassification(LukePreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.luke = LukeModel(config)
+
+ self.num_labels = config.num_labels
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size * 3, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ entity_ids: Optional[torch.LongTensor] = None,
+ entity_attention_mask: Optional[torch.LongTensor] = None,
+ entity_token_type_ids: Optional[torch.LongTensor] = None,
+ entity_position_ids: Optional[torch.LongTensor] = None,
+ entity_start_positions: Optional[torch.LongTensor] = None,
+ entity_end_positions: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, EntitySpanClassificationOutput]:
+ r"""
+ entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
+ Indices of entity tokens in the entity vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+ entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for entity tokens that are **not masked**,
+ - 0 for entity tokens that are **masked**.
+ entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the entity token inputs. Indices are
+ selected in `[0, 1]`:
+
+ - 0 corresponds to a *portion A* entity token,
+ - 1 corresponds to a *portion B* entity token.
+ entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
+ Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ entity_start_positions (`torch.LongTensor`):
+ The start positions of entities in the word token sequence.
+ entity_end_positions (`torch.LongTensor`):
+ The end positions of entities in the word token sequence.
+ labels (`torch.LongTensor` of shape `(batch_size, entity_length)` or `(batch_size, entity_length, num_labels)`, *optional*):
+ Labels for computing the classification loss. If the shape is `(batch_size, entity_length)`, the cross
+ entropy loss is used for the single-label classification. In this case, labels should contain the indices
+ that should be in `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, entity_length,
+ num_labels)`, the binary cross entropy loss is used for the multi-label classification. In this case,
+ labels should only contain `[0, 1]`, where 0 and 1 indicate false and true, respectively.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, LukeForEntitySpanClassification
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
+ >>> model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
+
+ >>> text = "Beyoncé lives in Los Angeles"
+ # List all possible entity spans in the text
+
+ >>> word_start_positions = [0, 8, 14, 17, 21] # character-based start positions of word tokens
+ >>> word_end_positions = [7, 13, 16, 20, 28] # character-based end positions of word tokens
+ >>> entity_spans = []
+ >>> for i, start_pos in enumerate(word_start_positions):
+ ... for end_pos in word_end_positions[i:]:
+ ... entity_spans.append((start_pos, end_pos))
+
+ >>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
+ >>> outputs = model(**inputs)
+ >>> logits = outputs.logits
+ >>> predicted_class_indices = logits.argmax(-1).squeeze().tolist()
+ >>> for span, predicted_class_idx in zip(entity_spans, predicted_class_indices):
+ ... if predicted_class_idx != 0:
+ ... print(text[span[0] : span[1]], model.config.id2label[predicted_class_idx])
+ Beyoncé PER
+ Los Angeles LOC
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.luke(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ entity_ids=entity_ids,
+ entity_attention_mask=entity_attention_mask,
+ entity_token_type_ids=entity_token_type_ids,
+ entity_position_ids=entity_position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+ hidden_size = outputs.last_hidden_state.size(-1)
+
+ entity_start_positions = entity_start_positions.unsqueeze(-1).expand(-1, -1, hidden_size)
+ if entity_start_positions.device != outputs.last_hidden_state.device:
+ entity_start_positions = entity_start_positions.to(outputs.last_hidden_state.device)
+ start_states = torch.gather(outputs.last_hidden_state, -2, entity_start_positions)
+
+ entity_end_positions = entity_end_positions.unsqueeze(-1).expand(-1, -1, hidden_size)
+ if entity_end_positions.device != outputs.last_hidden_state.device:
+ entity_end_positions = entity_end_positions.to(outputs.last_hidden_state.device)
+ end_states = torch.gather(outputs.last_hidden_state, -2, entity_end_positions)
+
+ feature_vector = torch.cat([start_states, end_states, outputs.entity_last_hidden_state], dim=2)
+
+ feature_vector = self.dropout(feature_vector)
+ logits = self.classifier(feature_vector)
+
+ loss = None
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ # When the number of dimension of `labels` is 2, cross entropy is used as the loss function. The binary
+ # cross entropy is used otherwise.
+ if labels.ndim == 2:
+ loss = nn.functional.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))
+ else:
+ loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
+ if v is not None
+ )
+
+ return EntitySpanClassificationOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ entity_hidden_states=outputs.entity_hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The LUKE Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+ pooled output) e.g. for GLUE tasks.
+ """
+)
+class LukeForSequenceClassification(LukePreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.luke = LukeModel(config)
+ self.dropout = nn.Dropout(
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ entity_ids: Optional[torch.LongTensor] = None,
+ entity_attention_mask: Optional[torch.FloatTensor] = None,
+ entity_token_type_ids: Optional[torch.LongTensor] = None,
+ entity_position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LukeSequenceClassifierOutput]:
+ r"""
+ entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
+ Indices of entity tokens in the entity vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+ entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for entity tokens that are **not masked**,
+ - 0 for entity tokens that are **masked**.
+ entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the entity token inputs. Indices are
+ selected in `[0, 1]`:
+
+ - 0 corresponds to a *portion A* entity token,
+ - 1 corresponds to a *portion B* entity token.
+ entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
+ Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.luke(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ entity_ids=entity_ids,
+ entity_attention_mask=entity_attention_mask,
+ entity_token_type_ids=entity_token_type_ids,
+ entity_position_ids=entity_position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ pooled_output = outputs.pooler_output
+
+ pooled_output = self.dropout(pooled_output)
+ logits = self.classifier(pooled_output)
+
+ loss = None
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
+ if v is not None
+ )
+
+ return LukeSequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ entity_hidden_states=outputs.entity_hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The LUKE Model with a token classification head on top (a linear layer on top of the hidden-states output). To
+ solve Named-Entity Recognition (NER) task using LUKE, `LukeForEntitySpanClassification` is more suitable than this
+ class.
+ """
+)
+class LukeForTokenClassification(LukePreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.luke = LukeModel(config, add_pooling_layer=False)
+ self.dropout = nn.Dropout(
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ entity_ids: Optional[torch.LongTensor] = None,
+ entity_attention_mask: Optional[torch.FloatTensor] = None,
+ entity_token_type_ids: Optional[torch.LongTensor] = None,
+ entity_position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LukeTokenClassifierOutput]:
+ r"""
+ entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
+ Indices of entity tokens in the entity vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+ entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for entity tokens that are **not masked**,
+ - 0 for entity tokens that are **masked**.
+ entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the entity token inputs. Indices are
+ selected in `[0, 1]`:
+
+ - 0 corresponds to a *portion A* entity token,
+ - 1 corresponds to a *portion B* entity token.
+ entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
+ Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+ num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+ `input_ids` above)
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.luke(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ entity_ids=entity_ids,
+ entity_attention_mask=entity_attention_mask,
+ entity_token_type_ids=entity_token_type_ids,
+ entity_position_ids=entity_position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ sequence_output = outputs.last_hidden_state
+
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions]
+ if v is not None
+ )
+
+ return LukeTokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ entity_hidden_states=outputs.entity_hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class LukeForQuestionAnswering(LukePreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.num_labels = config.num_labels
+
+ self.luke = LukeModel(config, add_pooling_layer=False)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.FloatTensor] = None,
+ entity_ids: Optional[torch.LongTensor] = None,
+ entity_attention_mask: Optional[torch.FloatTensor] = None,
+ entity_token_type_ids: Optional[torch.LongTensor] = None,
+ entity_position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LukeQuestionAnsweringModelOutput]:
+ r"""
+ entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
+ Indices of entity tokens in the entity vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+ entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for entity tokens that are **not masked**,
+ - 0 for entity tokens that are **masked**.
+ entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the entity token inputs. Indices are
+ selected in `[0, 1]`:
+
+ - 0 corresponds to a *portion A* entity token,
+ - 1 corresponds to a *portion B* entity token.
+ entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
+ Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.luke(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ entity_ids=entity_ids,
+ entity_attention_mask=entity_attention_mask,
+ entity_token_type_ids=entity_token_type_ids,
+ entity_position_ids=entity_position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ sequence_output = outputs.last_hidden_state
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1)
+ end_logits = end_logits.squeeze(-1)
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions.clamp_(0, ignored_index)
+ end_positions.clamp_(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ total_loss,
+ start_logits,
+ end_logits,
+ outputs.hidden_states,
+ outputs.entity_hidden_states,
+ outputs.attentions,
+ ]
+ if v is not None
+ )
+
+ return LukeQuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ entity_hidden_states=outputs.entity_hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class LukeForMultipleChoice(LukePreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.luke = LukeModel(config)
+ self.dropout = nn.Dropout(
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.classifier = nn.Linear(config.hidden_size, 1)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ entity_ids: Optional[torch.LongTensor] = None,
+ entity_attention_mask: Optional[torch.FloatTensor] = None,
+ entity_token_type_ids: Optional[torch.LongTensor] = None,
+ entity_position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, LukeMultipleChoiceModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
+ Indices of entity tokens in the entity vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+ entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for entity tokens that are **not masked**,
+ - 0 for entity tokens that are **masked**.
+ entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the entity token inputs. Indices are
+ selected in `[0, 1]`:
+
+ - 0 corresponds to a *portion A* entity token,
+ - 1 corresponds to a *portion B* entity token.
+ entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
+ Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+ num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+ `input_ids` above)
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+ input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+ attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+ token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+ position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+ inputs_embeds = (
+ inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+ if inputs_embeds is not None
+ else None
+ )
+
+ entity_ids = entity_ids.view(-1, entity_ids.size(-1)) if entity_ids is not None else None
+ entity_attention_mask = (
+ entity_attention_mask.view(-1, entity_attention_mask.size(-1))
+ if entity_attention_mask is not None
+ else None
+ )
+ entity_token_type_ids = (
+ entity_token_type_ids.view(-1, entity_token_type_ids.size(-1))
+ if entity_token_type_ids is not None
+ else None
+ )
+ entity_position_ids = (
+ entity_position_ids.view(-1, entity_position_ids.size(-2), entity_position_ids.size(-1))
+ if entity_position_ids is not None
+ else None
+ )
+
+ outputs = self.luke(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ entity_ids=entity_ids,
+ entity_attention_mask=entity_attention_mask,
+ entity_token_type_ids=entity_token_type_ids,
+ entity_position_ids=entity_position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ pooled_output = outputs.pooler_output
+
+ pooled_output = self.dropout(pooled_output)
+ logits = self.classifier(pooled_output)
+ reshaped_logits = logits.view(-1, num_choices)
+
+ loss = None
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(reshaped_logits.device)
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(reshaped_logits, labels)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ loss,
+ reshaped_logits,
+ outputs.hidden_states,
+ outputs.entity_hidden_states,
+ outputs.attentions,
+ ]
+ if v is not None
+ )
+
+ return LukeMultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ entity_hidden_states=outputs.entity_hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "LukeForEntityClassification",
+ "LukeForEntityPairClassification",
+ "LukeForEntitySpanClassification",
+ "LukeForMultipleChoice",
+ "LukeForQuestionAnswering",
+ "LukeForSequenceClassification",
+ "LukeForTokenClassification",
+ "LukeForMaskedLM",
+ "LukeModel",
+ "LukePreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/tokenization_luke.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/tokenization_luke.py
new file mode 100644
index 0000000000000000000000000000000000000000..efbc757e86307c7b70a4d36d7d6930f5557a43e7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/luke/tokenization_luke.py
@@ -0,0 +1,1731 @@
+# coding=utf-8
+# Copyright Studio-Ouisa and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for LUKE."""
+
+import itertools
+import json
+import os
+from collections.abc import Mapping
+from functools import lru_cache
+from typing import Optional, Union
+
+import numpy as np
+import regex as re
+
+from ...tokenization_utils import PreTrainedTokenizer
+from ...tokenization_utils_base import (
+ ENCODE_KWARGS_DOCSTRING,
+ AddedToken,
+ BatchEncoding,
+ EncodedInput,
+ PaddingStrategy,
+ TensorType,
+ TextInput,
+ TextInputPair,
+ TruncationStrategy,
+ to_py_obj,
+)
+from ...utils import add_end_docstrings, is_tf_tensor, is_torch_tensor, logging
+
+
+logger = logging.get_logger(__name__)
+
+EntitySpan = tuple[int, int]
+EntitySpanInput = list[EntitySpan]
+Entity = str
+EntityInput = list[Entity]
+
+VOCAB_FILES_NAMES = {
+ "vocab_file": "vocab.json",
+ "merges_file": "merges.txt",
+ "entity_vocab_file": "entity_vocab.json",
+}
+
+
+ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
+ return_token_type_ids (`bool`, *optional*):
+ Whether to return token type IDs. If left to the default, will return the token type IDs according to
+ the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ return_attention_mask (`bool`, *optional*):
+ Whether to return the attention mask. If left to the default, will return the attention mask according
+ to the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+ [What are attention masks?](../glossary#attention-mask)
+ return_overflowing_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
+ of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
+ of returning overflowing tokens.
+ return_special_tokens_mask (`bool`, *optional*, defaults to `False`):
+ Whether or not to return special tokens mask information.
+ return_offsets_mapping (`bool`, *optional*, defaults to `False`):
+ Whether or not to return `(char_start, char_end)` for each token.
+
+ This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using
+ Python's tokenizer, this method will raise `NotImplementedError`.
+ return_length (`bool`, *optional*, defaults to `False`):
+ Whether or not to return the lengths of the encoded inputs.
+ verbose (`bool`, *optional*, defaults to `True`):
+ Whether or not to print more information and warnings.
+ **kwargs: passed to the `self.tokenize()` method
+
+ Return:
+ [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or
+ if *"token_type_ids"* is in `self.model_input_names`).
+
+ [What are token type IDs?](../glossary#token-type-ids)
+
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ - **entity_ids** -- List of entity ids to be fed to a model.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ - **entity_position_ids** -- List of entity positions in the input sequence to be fed to a model.
+
+ - **entity_token_type_ids** -- List of entity token type ids to be fed to a model (when
+ `return_token_type_ids=True` or if *"entity_token_type_ids"* is in `self.model_input_names`).
+
+ [What are token type IDs?](../glossary#token-type-ids)
+
+ - **entity_attention_mask** -- List of indices specifying which entities should be attended to by the model
+ (when `return_attention_mask=True` or if *"entity_attention_mask"* is in `self.model_input_names`).
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ - **entity_start_positions** -- List of the start positions of entities in the word token sequence (when
+ `task="entity_span_classification"`).
+ - **entity_end_positions** -- List of the end positions of entities in the word token sequence (when
+ `task="entity_span_classification"`).
+ - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
+ `return_overflowing_tokens=True`).
+ - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
+ `return_overflowing_tokens=True`).
+ - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
+ regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
+ - **length** -- The length of the inputs (when `return_length=True`)
+
+"""
+
+
+@lru_cache
+# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
+def bytes_to_unicode():
+ """
+ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
+ characters the bpe code barfs on.
+
+ The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
+ if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
+ decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
+ tables between utf-8 bytes and unicode strings.
+ """
+ bs = (
+ list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
+ )
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8 + n)
+ n += 1
+ cs = [chr(n) for n in cs]
+ return dict(zip(bs, cs))
+
+
+# Copied from transformers.models.roberta.tokenization_roberta.get_pairs
+def get_pairs(word):
+ """
+ Return set of symbol pairs in a word.
+
+ Word is represented as tuple of symbols (symbols being variable-length strings).
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+class LukeTokenizer(PreTrainedTokenizer):
+ """
+ Constructs a LUKE tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
+
+ This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
+ be encoded differently whether it is at the beginning of the sentence (without space) or not:
+
+ ```python
+ >>> from transformers import LukeTokenizer
+
+ >>> tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base")
+ >>> tokenizer("Hello world")["input_ids"]
+ [0, 31414, 232, 2]
+
+ >>> tokenizer(" Hello world")["input_ids"]
+ [0, 20920, 232, 2]
+ ```
+
+ You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
+ call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
+
+
+
+ When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
+
+
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods. It also creates entity sequences, namely
+ `entity_ids`, `entity_attention_mask`, `entity_token_type_ids`, and `entity_position_ids` to be used by the LUKE
+ model.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ entity_vocab_file (`str`):
+ Path to the entity vocabulary file.
+ task (`str`, *optional*):
+ Task for which you want to prepare sequences. One of `"entity_classification"`,
+ `"entity_pair_classification"`, or `"entity_span_classification"`. If you specify this argument, the entity
+ sequence is automatically created based on the given entity span(s).
+ max_entity_length (`int`, *optional*, defaults to 32):
+ The maximum length of `entity_ids`.
+ max_mention_length (`int`, *optional*, defaults to 30):
+ The maximum number of tokens inside an entity span.
+ entity_token_1 (`str`, *optional*, defaults to ``):
+ The special token used to represent an entity span in a word token sequence. This token is only used when
+ `task` is set to `"entity_classification"` or `"entity_pair_classification"`.
+ entity_token_2 (`str`, *optional*, defaults to ``):
+ The special token used to represent an entity span in a word token sequence. This token is only used when
+ `task` is set to `"entity_pair_classification"`.
+ errors (`str`, *optional*, defaults to `"replace"`):
+ Paradigm to follow when decoding bytes to UTF-8. See
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ add_prefix_space (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. (LUKE tokenizer detect beginning of words by the preceding space).
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ def __init__(
+ self,
+ vocab_file,
+ merges_file,
+ entity_vocab_file,
+ task=None,
+ max_entity_length=32,
+ max_mention_length=30,
+ entity_token_1="",
+ entity_token_2="",
+ entity_unk_token="[UNK]",
+ entity_pad_token="[PAD]",
+ entity_mask_token="[MASK]",
+ entity_mask2_token="[MASK2]",
+ errors="replace",
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ add_prefix_space=False,
+ **kwargs,
+ ):
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+ sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
+ cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+ with open(vocab_file, encoding="utf-8") as vocab_handle:
+ self.encoder = json.load(vocab_handle)
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ self.errors = errors # how to handle errors in decoding
+ self.byte_encoder = bytes_to_unicode()
+ self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+ with open(merges_file, encoding="utf-8") as merges_handle:
+ bpe_merges = merges_handle.read().split("\n")[1:-1]
+ bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
+ self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+ self.cache = {}
+ self.add_prefix_space = add_prefix_space
+
+ # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
+ self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
+
+ # we add 2 special tokens for downstream tasks
+ # for more information about lstrip and rstrip, see https://github.com/huggingface/transformers/pull/2778
+ entity_token_1 = (
+ AddedToken(entity_token_1, lstrip=False, rstrip=False)
+ if isinstance(entity_token_1, str)
+ else entity_token_1
+ )
+ entity_token_2 = (
+ AddedToken(entity_token_2, lstrip=False, rstrip=False)
+ if isinstance(entity_token_2, str)
+ else entity_token_2
+ )
+ kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", [])
+ kwargs["additional_special_tokens"] += [entity_token_1, entity_token_2]
+
+ with open(entity_vocab_file, encoding="utf-8") as entity_vocab_handle:
+ self.entity_vocab = json.load(entity_vocab_handle)
+ for entity_special_token in [entity_unk_token, entity_pad_token, entity_mask_token, entity_mask2_token]:
+ if entity_special_token not in self.entity_vocab:
+ raise ValueError(
+ f"Specified entity special token ``{entity_special_token}`` is not found in entity_vocab. "
+ f"Probably an incorrect entity vocab file is loaded: {entity_vocab_file}."
+ )
+ self.entity_unk_token_id = self.entity_vocab[entity_unk_token]
+ self.entity_pad_token_id = self.entity_vocab[entity_pad_token]
+ self.entity_mask_token_id = self.entity_vocab[entity_mask_token]
+ self.entity_mask2_token_id = self.entity_vocab[entity_mask2_token]
+
+ self.task = task
+ if task is None or task == "entity_span_classification":
+ self.max_entity_length = max_entity_length
+ elif task == "entity_classification":
+ self.max_entity_length = 1
+ elif task == "entity_pair_classification":
+ self.max_entity_length = 2
+ else:
+ raise ValueError(
+ f"Task {task} not supported. Select task from ['entity_classification', 'entity_pair_classification',"
+ " 'entity_span_classification'] only."
+ )
+
+ self.max_mention_length = max_mention_length
+
+ super().__init__(
+ errors=errors,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ add_prefix_space=add_prefix_space,
+ task=task,
+ max_entity_length=32,
+ max_mention_length=30,
+ entity_token_1="",
+ entity_token_2="",
+ entity_unk_token=entity_unk_token,
+ entity_pad_token=entity_pad_token,
+ entity_mask_token=entity_mask_token,
+ entity_mask2_token=entity_mask2_token,
+ **kwargs,
+ )
+
+ @property
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Luke, RoBERTa->LUKE
+ def vocab_size(self):
+ return len(self.encoder)
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_vocab with Roberta->Luke, RoBERTa->LUKE
+ def get_vocab(self):
+ vocab = dict(self.encoder).copy()
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.bpe with Roberta->Luke, RoBERTa->LUKE
+ def bpe(self, token):
+ if token in self.cache:
+ return self.cache[token]
+ word = tuple(token)
+ pairs = get_pairs(word)
+
+ if not pairs:
+ return token
+
+ while True:
+ bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+ if bigram not in self.bpe_ranks:
+ break
+ first, second = bigram
+ new_word = []
+ i = 0
+ while i < len(word):
+ try:
+ j = word.index(first, i)
+ except ValueError:
+ new_word.extend(word[i:])
+ break
+ else:
+ new_word.extend(word[i:j])
+ i = j
+
+ if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+ new_word.append(first + second)
+ i += 2
+ else:
+ new_word.append(word[i])
+ i += 1
+ new_word = tuple(new_word)
+ word = new_word
+ if len(word) == 1:
+ break
+ else:
+ pairs = get_pairs(word)
+ word = " ".join(word)
+ self.cache[token] = word
+ return word
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._tokenize with Roberta->Luke, RoBERTa->LUKE
+ def _tokenize(self, text):
+ """Tokenize a string."""
+ bpe_tokens = []
+ for token in re.findall(self.pat, text):
+ token = "".join(
+ self.byte_encoder[b] for b in token.encode("utf-8")
+ ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
+ bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
+ return bpe_tokens
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_token_to_id with Roberta->Luke, RoBERTa->LUKE
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_id_to_token with Roberta->Luke, RoBERTa->LUKE
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.decoder.get(index)
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.convert_tokens_to_string with Roberta->Luke, RoBERTa->LUKE
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ text = "".join(tokens)
+ text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
+ return text
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.build_inputs_with_special_tokens with Roberta->Luke, RoBERTa->LUKE
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A LUKE sequence has the following format:
+
+ - single sequence: ` X `
+ - pair of sequences: ` A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_special_tokens_mask with Roberta->Luke, RoBERTa->LUKE
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is None:
+ return [1] + ([0] * len(token_ids_0)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.create_token_type_ids_from_sequences with Roberta->Luke, RoBERTa->LUKE
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. LUKE does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.prepare_for_tokenization with Roberta->Luke, RoBERTa->LUKE
+ def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
+ add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
+ if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
+ text = " " + text
+ return (text, kwargs)
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def __call__(
+ self,
+ text: Union[TextInput, list[TextInput]],
+ text_pair: Optional[Union[TextInput, list[TextInput]]] = None,
+ entity_spans: Optional[Union[EntitySpanInput, list[EntitySpanInput]]] = None,
+ entity_spans_pair: Optional[Union[EntitySpanInput, list[EntitySpanInput]]] = None,
+ entities: Optional[Union[EntityInput, list[EntityInput]]] = None,
+ entities_pair: Optional[Union[EntityInput, list[EntityInput]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ stride: int = 0,
+ is_split_into_words: Optional[bool] = False,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
+ sequences, depending on the task you want to prepare them for.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
+ tokenizer does not support tokenization based on pretokenized strings.
+ text_pair (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
+ tokenizer does not support tokenization based on pretokenized strings.
+ entity_spans (`list[tuple[int, int]]`, `list[list[tuple[int, int]]]`, *optional*):
+ The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
+ with two integers denoting character-based start and end positions of entities. If you specify
+ `"entity_classification"` or `"entity_pair_classification"` as the `task` argument in the constructor,
+ the length of each sequence must be 1 or 2, respectively. If you specify `entities`, the length of each
+ sequence must be equal to the length of each sequence of `entities`.
+ entity_spans_pair (`list[tuple[int, int]]`, `list[list[tuple[int, int]]]`, *optional*):
+ The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
+ with two integers denoting character-based start and end positions of entities. If you specify the
+ `task` argument in the constructor, this argument is ignored. If you specify `entities_pair`, the
+ length of each sequence must be equal to the length of each sequence of `entities_pair`.
+ entities (`list[str]`, `list[list[str]]`, *optional*):
+ The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
+ representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
+ Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of
+ each sequence must be equal to the length of each sequence of `entity_spans`. If you specify
+ `entity_spans` without specifying this argument, the entity sequence or the batch of entity sequences
+ is automatically constructed by filling it with the [MASK] entity.
+ entities_pair (`list[str]`, `list[list[str]]`, *optional*):
+ The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
+ representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
+ Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of
+ each sequence must be equal to the length of each sequence of `entity_spans_pair`. If you specify
+ `entity_spans_pair` without specifying this argument, the entity sequence or the batch of entity
+ sequences is automatically constructed by filling it with the [MASK] entity.
+ max_entity_length (`int`, *optional*):
+ The maximum length of `entity_ids`.
+ """
+ # Input type checking for clearer error
+ is_valid_single_text = isinstance(text, str)
+ is_valid_batch_text = isinstance(text, (list, tuple)) and (len(text) == 0 or (isinstance(text[0], str)))
+ if not (is_valid_single_text or is_valid_batch_text):
+ raise ValueError("text input must be of type `str` (single example) or `list[str]` (batch).")
+
+ is_valid_single_text_pair = isinstance(text_pair, str)
+ is_valid_batch_text_pair = isinstance(text_pair, (list, tuple)) and (
+ len(text_pair) == 0 or isinstance(text_pair[0], str)
+ )
+ if not (text_pair is None or is_valid_single_text_pair or is_valid_batch_text_pair):
+ raise ValueError("text_pair input must be of type `str` (single example) or `list[str]` (batch).")
+
+ is_batched = bool(isinstance(text, (list, tuple)))
+
+ if is_batched:
+ batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
+ if entities is None:
+ batch_entities_or_entities_pairs = None
+ else:
+ batch_entities_or_entities_pairs = (
+ list(zip(entities, entities_pair)) if entities_pair is not None else entities
+ )
+
+ if entity_spans is None:
+ batch_entity_spans_or_entity_spans_pairs = None
+ else:
+ batch_entity_spans_or_entity_spans_pairs = (
+ list(zip(entity_spans, entity_spans_pair)) if entity_spans_pair is not None else entity_spans
+ )
+
+ return self.batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ batch_entity_spans_or_entity_spans_pairs=batch_entity_spans_or_entity_spans_pairs,
+ batch_entities_or_entities_pairs=batch_entities_or_entities_pairs,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ stride=stride,
+ is_split_into_words=is_split_into_words,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+ else:
+ return self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ entity_spans=entity_spans,
+ entity_spans_pair=entity_spans_pair,
+ entities=entities,
+ entities_pair=entities_pair,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ stride=stride,
+ is_split_into_words=is_split_into_words,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def _encode_plus(
+ self,
+ text: Union[TextInput],
+ text_pair: Optional[Union[TextInput]] = None,
+ entity_spans: Optional[EntitySpanInput] = None,
+ entity_spans_pair: Optional[EntitySpanInput] = None,
+ entities: Optional[EntityInput] = None,
+ entities_pair: Optional[EntityInput] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ stride: int = 0,
+ is_split_into_words: Optional[bool] = False,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast. "
+ "More information on available tokenizers at "
+ "https://github.com/huggingface/transformers/pull/2674"
+ )
+
+ if is_split_into_words:
+ raise NotImplementedError("is_split_into_words is not supported in this tokenizer.")
+
+ (
+ first_ids,
+ second_ids,
+ first_entity_ids,
+ second_entity_ids,
+ first_entity_token_spans,
+ second_entity_token_spans,
+ ) = self._create_input_sequence(
+ text=text,
+ text_pair=text_pair,
+ entities=entities,
+ entities_pair=entities_pair,
+ entity_spans=entity_spans,
+ entity_spans_pair=entity_spans_pair,
+ **kwargs,
+ )
+
+ # prepare_for_model will create the attention_mask and token_type_ids
+ return self.prepare_for_model(
+ first_ids,
+ pair_ids=second_ids,
+ entity_ids=first_entity_ids,
+ pair_entity_ids=second_entity_ids,
+ entity_token_spans=first_entity_token_spans,
+ pair_entity_token_spans=second_entity_token_spans,
+ add_special_tokens=add_special_tokens,
+ padding=padding_strategy.value,
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ prepend_batch_axis=True,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ verbose=verbose,
+ )
+
+ def _batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[list[TextInput], list[TextInputPair]],
+ batch_entity_spans_or_entity_spans_pairs: Optional[
+ Union[list[EntitySpanInput], list[tuple[EntitySpanInput, EntitySpanInput]]]
+ ] = None,
+ batch_entities_or_entities_pairs: Optional[
+ Union[list[EntityInput], list[tuple[EntityInput, EntityInput]]]
+ ] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ stride: int = 0,
+ is_split_into_words: Optional[bool] = False,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast."
+ )
+
+ if is_split_into_words:
+ raise NotImplementedError("is_split_into_words is not supported in this tokenizer.")
+
+ # input_ids is a list of tuples (one for each example in the batch)
+ input_ids = []
+ entity_ids = []
+ entity_token_spans = []
+ for index, text_or_text_pair in enumerate(batch_text_or_text_pairs):
+ if not isinstance(text_or_text_pair, (list, tuple)):
+ text, text_pair = text_or_text_pair, None
+ else:
+ text, text_pair = text_or_text_pair
+
+ entities, entities_pair = None, None
+ if batch_entities_or_entities_pairs is not None:
+ entities_or_entities_pairs = batch_entities_or_entities_pairs[index]
+ if entities_or_entities_pairs:
+ if isinstance(entities_or_entities_pairs[0], str):
+ entities, entities_pair = entities_or_entities_pairs, None
+ else:
+ entities, entities_pair = entities_or_entities_pairs
+
+ entity_spans, entity_spans_pair = None, None
+ if batch_entity_spans_or_entity_spans_pairs is not None:
+ entity_spans_or_entity_spans_pairs = batch_entity_spans_or_entity_spans_pairs[index]
+ if len(entity_spans_or_entity_spans_pairs) > 0 and isinstance(
+ entity_spans_or_entity_spans_pairs[0], list
+ ):
+ entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs
+ else:
+ entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs, None
+
+ (
+ first_ids,
+ second_ids,
+ first_entity_ids,
+ second_entity_ids,
+ first_entity_token_spans,
+ second_entity_token_spans,
+ ) = self._create_input_sequence(
+ text=text,
+ text_pair=text_pair,
+ entities=entities,
+ entities_pair=entities_pair,
+ entity_spans=entity_spans,
+ entity_spans_pair=entity_spans_pair,
+ **kwargs,
+ )
+ input_ids.append((first_ids, second_ids))
+ entity_ids.append((first_entity_ids, second_entity_ids))
+ entity_token_spans.append((first_entity_token_spans, second_entity_token_spans))
+
+ batch_outputs = self._batch_prepare_for_model(
+ input_ids,
+ batch_entity_ids_pairs=entity_ids,
+ batch_entity_token_spans_pairs=entity_token_spans,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=return_tensors,
+ verbose=verbose,
+ )
+
+ return BatchEncoding(batch_outputs)
+
+ def _check_entity_input_format(self, entities: Optional[EntityInput], entity_spans: Optional[EntitySpanInput]):
+ if not isinstance(entity_spans, list):
+ raise TypeError("entity_spans should be given as a list")
+ elif len(entity_spans) > 0 and not isinstance(entity_spans[0], tuple):
+ raise ValueError(
+ "entity_spans should be given as a list of tuples containing the start and end character indices"
+ )
+
+ if entities is not None:
+ if not isinstance(entities, list):
+ raise ValueError("If you specify entities, they should be given as a list")
+
+ if len(entities) > 0 and not isinstance(entities[0], str):
+ raise ValueError("If you specify entities, they should be given as a list of entity names")
+
+ if len(entities) != len(entity_spans):
+ raise ValueError("If you specify entities, entities and entity_spans must be the same length")
+
+ def _create_input_sequence(
+ self,
+ text: Union[TextInput],
+ text_pair: Optional[Union[TextInput]] = None,
+ entities: Optional[EntityInput] = None,
+ entities_pair: Optional[EntityInput] = None,
+ entity_spans: Optional[EntitySpanInput] = None,
+ entity_spans_pair: Optional[EntitySpanInput] = None,
+ **kwargs,
+ ) -> tuple[list, list, list, list, list, list]:
+ def get_input_ids(text):
+ tokens = self.tokenize(text, **kwargs)
+ return self.convert_tokens_to_ids(tokens)
+
+ def get_input_ids_and_entity_token_spans(text, entity_spans):
+ if entity_spans is None:
+ return get_input_ids(text), None
+
+ cur = 0
+ input_ids = []
+ entity_token_spans = [None] * len(entity_spans)
+
+ split_char_positions = sorted(frozenset(itertools.chain(*entity_spans)))
+ char_pos2token_pos = {}
+
+ for split_char_position in split_char_positions:
+ orig_split_char_position = split_char_position
+ if (
+ split_char_position > 0 and text[split_char_position - 1] == " "
+ ): # whitespace should be prepended to the following token
+ split_char_position -= 1
+ if cur != split_char_position:
+ input_ids += get_input_ids(text[cur:split_char_position])
+ cur = split_char_position
+ char_pos2token_pos[orig_split_char_position] = len(input_ids)
+
+ input_ids += get_input_ids(text[cur:])
+
+ entity_token_spans = [
+ (char_pos2token_pos[char_start], char_pos2token_pos[char_end]) for char_start, char_end in entity_spans
+ ]
+
+ return input_ids, entity_token_spans
+
+ first_ids, second_ids = None, None
+ first_entity_ids, second_entity_ids = None, None
+ first_entity_token_spans, second_entity_token_spans = None, None
+
+ if self.task is None:
+ if entity_spans is None:
+ first_ids = get_input_ids(text)
+ else:
+ self._check_entity_input_format(entities, entity_spans)
+
+ first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
+ if entities is None:
+ first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
+ else:
+ first_entity_ids = [self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities]
+
+ if text_pair is not None:
+ if entity_spans_pair is None:
+ second_ids = get_input_ids(text_pair)
+ else:
+ self._check_entity_input_format(entities_pair, entity_spans_pair)
+
+ second_ids, second_entity_token_spans = get_input_ids_and_entity_token_spans(
+ text_pair, entity_spans_pair
+ )
+ if entities_pair is None:
+ second_entity_ids = [self.entity_mask_token_id] * len(entity_spans_pair)
+ else:
+ second_entity_ids = [
+ self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities_pair
+ ]
+
+ elif self.task == "entity_classification":
+ if not (isinstance(entity_spans, list) and len(entity_spans) == 1 and isinstance(entity_spans[0], tuple)):
+ raise ValueError(
+ "Entity spans should be a list containing a single tuple "
+ "containing the start and end character indices of an entity"
+ )
+ first_entity_ids = [self.entity_mask_token_id]
+ first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
+
+ # add special tokens to input ids
+ entity_token_start, entity_token_end = first_entity_token_spans[0]
+ first_ids = (
+ first_ids[:entity_token_end] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_end:]
+ )
+ first_ids = (
+ first_ids[:entity_token_start]
+ + [self.additional_special_tokens_ids[0]]
+ + first_ids[entity_token_start:]
+ )
+ first_entity_token_spans = [(entity_token_start, entity_token_end + 2)]
+
+ elif self.task == "entity_pair_classification":
+ if not (
+ isinstance(entity_spans, list)
+ and len(entity_spans) == 2
+ and isinstance(entity_spans[0], tuple)
+ and isinstance(entity_spans[1], tuple)
+ ):
+ raise ValueError(
+ "Entity spans should be provided as a list of two tuples, "
+ "each tuple containing the start and end character indices of an entity"
+ )
+
+ head_span, tail_span = entity_spans
+ first_entity_ids = [self.entity_mask_token_id, self.entity_mask2_token_id]
+ first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
+
+ head_token_span, tail_token_span = first_entity_token_spans
+ token_span_with_special_token_ids = [
+ (head_token_span, self.additional_special_tokens_ids[0]),
+ (tail_token_span, self.additional_special_tokens_ids[1]),
+ ]
+ if head_token_span[0] < tail_token_span[0]:
+ first_entity_token_spans[0] = (head_token_span[0], head_token_span[1] + 2)
+ first_entity_token_spans[1] = (tail_token_span[0] + 2, tail_token_span[1] + 4)
+ token_span_with_special_token_ids = reversed(token_span_with_special_token_ids)
+ else:
+ first_entity_token_spans[0] = (head_token_span[0] + 2, head_token_span[1] + 4)
+ first_entity_token_spans[1] = (tail_token_span[0], tail_token_span[1] + 2)
+
+ for (entity_token_start, entity_token_end), special_token_id in token_span_with_special_token_ids:
+ first_ids = first_ids[:entity_token_end] + [special_token_id] + first_ids[entity_token_end:]
+ first_ids = first_ids[:entity_token_start] + [special_token_id] + first_ids[entity_token_start:]
+
+ elif self.task == "entity_span_classification":
+ if not (isinstance(entity_spans, list) and len(entity_spans) > 0 and isinstance(entity_spans[0], tuple)):
+ raise ValueError(
+ "Entity spans should be provided as a list of tuples, "
+ "each tuple containing the start and end character indices of an entity"
+ )
+
+ first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
+ first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
+
+ else:
+ raise ValueError(f"Task {self.task} not supported")
+
+ return (
+ first_ids,
+ second_ids,
+ first_entity_ids,
+ second_entity_ids,
+ first_entity_token_spans,
+ second_entity_token_spans,
+ )
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def _batch_prepare_for_model(
+ self,
+ batch_ids_pairs: list[tuple[list[int], None]],
+ batch_entity_ids_pairs: list[tuple[Optional[list[int]], Optional[list[int]]]],
+ batch_entity_token_spans_pairs: list[tuple[Optional[list[tuple[int, int]]], Optional[list[tuple[int, int]]]]],
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
+ adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
+ manages a moving window (with user defined stride) for overflowing tokens
+
+
+ Args:
+ batch_ids_pairs: list of tokenized input ids or input ids pairs
+ batch_entity_ids_pairs: list of entity ids or entity ids pairs
+ batch_entity_token_spans_pairs: list of entity spans or entity spans pairs
+ max_entity_length: The maximum length of the entity sequence.
+ """
+
+ batch_outputs = {}
+ for input_ids, entity_ids, entity_token_span_pairs in zip(
+ batch_ids_pairs, batch_entity_ids_pairs, batch_entity_token_spans_pairs
+ ):
+ first_ids, second_ids = input_ids
+ first_entity_ids, second_entity_ids = entity_ids
+ first_entity_token_spans, second_entity_token_spans = entity_token_span_pairs
+ outputs = self.prepare_for_model(
+ first_ids,
+ second_ids,
+ entity_ids=first_entity_ids,
+ pair_entity_ids=second_entity_ids,
+ entity_token_spans=first_entity_token_spans,
+ pair_entity_token_spans=second_entity_token_spans,
+ add_special_tokens=add_special_tokens,
+ padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ stride=stride,
+ pad_to_multiple_of=None, # we pad in batch afterward
+ padding_side=None, # we pad in batch afterward
+ return_attention_mask=False, # we pad in batch afterward
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=None, # We convert the whole batch to tensors at the end
+ prepend_batch_axis=False,
+ verbose=verbose,
+ )
+
+ for key, value in outputs.items():
+ if key not in batch_outputs:
+ batch_outputs[key] = []
+ batch_outputs[key].append(value)
+
+ batch_outputs = self.pad(
+ batch_outputs,
+ padding=padding_strategy.value,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
+
+ return batch_outputs
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def prepare_for_model(
+ self,
+ ids: list[int],
+ pair_ids: Optional[list[int]] = None,
+ entity_ids: Optional[list[int]] = None,
+ pair_entity_ids: Optional[list[int]] = None,
+ entity_token_spans: Optional[list[tuple[int, int]]] = None,
+ pair_entity_token_spans: Optional[list[tuple[int, int]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ prepend_batch_axis: bool = False,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence of input id, entity id and entity span, or a pair of sequences of inputs ids, entity ids,
+ entity spans so that it can be used by the model. It adds special tokens, truncates sequences if overflowing
+ while taking into account the special tokens and manages a moving window (with user defined stride) for
+ overflowing tokens. Please Note, for *pair_ids* different than `None` and *truncation_strategy = longest_first*
+ or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an
+ error.
+
+ Args:
+ ids (`list[int]`):
+ Tokenized input ids of the first sequence.
+ pair_ids (`list[int]`, *optional*):
+ Tokenized input ids of the second sequence.
+ entity_ids (`list[int]`, *optional*):
+ Entity ids of the first sequence.
+ pair_entity_ids (`list[int]`, *optional*):
+ Entity ids of the second sequence.
+ entity_token_spans (`list[tuple[int, int]]`, *optional*):
+ Entity spans of the first sequence.
+ pair_entity_token_spans (`list[tuple[int, int]]`, *optional*):
+ Entity spans of the second sequence.
+ max_entity_length (`int`, *optional*):
+ The maximum length of the entity sequence.
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ # Compute lengths
+ pair = bool(pair_ids is not None)
+ len_ids = len(ids)
+ len_pair_ids = len(pair_ids) if pair else 0
+
+ if return_token_type_ids and not add_special_tokens:
+ raise ValueError(
+ "Asking to return token_type_ids while setting add_special_tokens to False "
+ "results in an undefined behavior. Please set add_special_tokens to True or "
+ "set return_token_type_ids to None."
+ )
+ if (
+ return_overflowing_tokens
+ and truncation_strategy == TruncationStrategy.LONGEST_FIRST
+ and pair_ids is not None
+ ):
+ raise ValueError(
+ "Not possible to return overflowing tokens for pair of sequences with the "
+ "`longest_first`. Please select another truncation strategy than `longest_first`, "
+ "for instance `only_second` or `only_first`."
+ )
+
+ # Load from model defaults
+ if return_token_type_ids is None:
+ return_token_type_ids = "token_type_ids" in self.model_input_names
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ encoded_inputs = {}
+
+ # Compute the total size of the returned word encodings
+ total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
+
+ # Truncation: Handle max sequence length and max_entity_length
+ overflowing_tokens = []
+ if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
+ # truncate words up to max_length
+ ids, pair_ids, overflowing_tokens = self.truncate_sequences(
+ ids,
+ pair_ids=pair_ids,
+ num_tokens_to_remove=total_len - max_length,
+ truncation_strategy=truncation_strategy,
+ stride=stride,
+ )
+
+ if return_overflowing_tokens:
+ encoded_inputs["overflowing_tokens"] = overflowing_tokens
+ encoded_inputs["num_truncated_tokens"] = total_len - max_length
+
+ # Add special tokens
+ if add_special_tokens:
+ sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
+ token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
+ entity_token_offset = 1 # 1 * token
+ pair_entity_token_offset = len(ids) + 3 # 1 * token & 2 * tokens
+ else:
+ sequence = ids + pair_ids if pair else ids
+ token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
+ entity_token_offset = 0
+ pair_entity_token_offset = len(ids)
+
+ # Build output dictionary
+ encoded_inputs["input_ids"] = sequence
+ if return_token_type_ids:
+ encoded_inputs["token_type_ids"] = token_type_ids
+ if return_special_tokens_mask:
+ if add_special_tokens:
+ encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
+ else:
+ encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
+
+ # Set max entity length
+ if not max_entity_length:
+ max_entity_length = self.max_entity_length
+
+ if entity_ids is not None:
+ total_entity_len = 0
+ num_invalid_entities = 0
+ valid_entity_ids = [ent_id for ent_id, span in zip(entity_ids, entity_token_spans) if span[1] <= len(ids)]
+ valid_entity_token_spans = [span for span in entity_token_spans if span[1] <= len(ids)]
+
+ total_entity_len += len(valid_entity_ids)
+ num_invalid_entities += len(entity_ids) - len(valid_entity_ids)
+
+ valid_pair_entity_ids, valid_pair_entity_token_spans = None, None
+ if pair_entity_ids is not None:
+ valid_pair_entity_ids = [
+ ent_id
+ for ent_id, span in zip(pair_entity_ids, pair_entity_token_spans)
+ if span[1] <= len(pair_ids)
+ ]
+ valid_pair_entity_token_spans = [span for span in pair_entity_token_spans if span[1] <= len(pair_ids)]
+ total_entity_len += len(valid_pair_entity_ids)
+ num_invalid_entities += len(pair_entity_ids) - len(valid_pair_entity_ids)
+
+ if num_invalid_entities != 0:
+ logger.warning(
+ f"{num_invalid_entities} entities are ignored because their entity spans are invalid due to the"
+ " truncation of input tokens"
+ )
+
+ if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and total_entity_len > max_entity_length:
+ # truncate entities up to max_entity_length
+ valid_entity_ids, valid_pair_entity_ids, overflowing_entities = self.truncate_sequences(
+ valid_entity_ids,
+ pair_ids=valid_pair_entity_ids,
+ num_tokens_to_remove=total_entity_len - max_entity_length,
+ truncation_strategy=truncation_strategy,
+ stride=stride,
+ )
+ valid_entity_token_spans = valid_entity_token_spans[: len(valid_entity_ids)]
+ if valid_pair_entity_token_spans is not None:
+ valid_pair_entity_token_spans = valid_pair_entity_token_spans[: len(valid_pair_entity_ids)]
+
+ if return_overflowing_tokens:
+ encoded_inputs["overflowing_entities"] = overflowing_entities
+ encoded_inputs["num_truncated_entities"] = total_entity_len - max_entity_length
+
+ final_entity_ids = valid_entity_ids + valid_pair_entity_ids if valid_pair_entity_ids else valid_entity_ids
+ encoded_inputs["entity_ids"] = list(final_entity_ids)
+ entity_position_ids = []
+ entity_start_positions = []
+ entity_end_positions = []
+ for token_spans, offset in (
+ (valid_entity_token_spans, entity_token_offset),
+ (valid_pair_entity_token_spans, pair_entity_token_offset),
+ ):
+ if token_spans is not None:
+ for start, end in token_spans:
+ start += offset
+ end += offset
+ position_ids = list(range(start, end))[: self.max_mention_length]
+ position_ids += [-1] * (self.max_mention_length - end + start)
+ entity_position_ids.append(position_ids)
+ entity_start_positions.append(start)
+ entity_end_positions.append(end - 1)
+
+ encoded_inputs["entity_position_ids"] = entity_position_ids
+ if self.task == "entity_span_classification":
+ encoded_inputs["entity_start_positions"] = entity_start_positions
+ encoded_inputs["entity_end_positions"] = entity_end_positions
+
+ if return_token_type_ids:
+ encoded_inputs["entity_token_type_ids"] = [0] * len(encoded_inputs["entity_ids"])
+
+ # Check lengths
+ self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
+
+ # Padding
+ if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
+ encoded_inputs = self.pad(
+ encoded_inputs,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ padding=padding_strategy.value,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ if return_length:
+ encoded_inputs["length"] = len(encoded_inputs["input_ids"])
+
+ batch_outputs = BatchEncoding(
+ encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
+ )
+
+ return batch_outputs
+
+ def pad(
+ self,
+ encoded_inputs: Union[
+ BatchEncoding,
+ list[BatchEncoding],
+ dict[str, EncodedInput],
+ dict[str, list[EncodedInput]],
+ list[dict[str, EncodedInput]],
+ ],
+ padding: Union[bool, str, PaddingStrategy] = True,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ verbose: bool = True,
+ ) -> BatchEncoding:
+ """
+ Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
+ in the batch. Padding side (left/right) padding token ids are defined at the tokenizer level (with
+ `self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`) .. note:: If the `encoded_inputs` passed
+ are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the result will use the same type unless
+ you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the
+ specific device of your tensors however.
+
+ Args:
+ encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `dict[str, list[int]]`, `dict[str, list[list[int]]` or `list[dict[str, list[int]]]`):
+ Tokenized inputs. Can represent one input ([`BatchEncoding`] or `dict[str, list[int]]`) or a batch of
+ tokenized inputs (list of [`BatchEncoding`], *dict[str, list[list[int]]]* or *list[dict[str,
+ list[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader
+ collate function. Instead of `list[int]` you can have tensors (numpy arrays, PyTorch tensors or
+ TensorFlow tensors), see the note above for the return type.
+ padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
+ Select a strategy to pad the returned sequences (according to the model's padding side and padding
+ index) among:
+
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ max_length (`int`, *optional*):
+ Maximum length of the returned list and optionally padding length (see above).
+ max_entity_length (`int`, *optional*):
+ The maximum length of the entity sequence.
+ pad_to_multiple_of (`int`, *optional*):
+ If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
+ the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
+ padding_side:
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask (`bool`, *optional*):
+ Whether to return the attention mask. If left to the default, will return the attention mask according
+ to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention
+ masks?](../glossary#attention-mask)
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors instead of list of python integers. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return Numpy `np.ndarray` objects.
+ verbose (`bool`, *optional*, defaults to `True`):
+ Whether or not to print more information and warnings.
+ """
+ # If we have a list of dicts, let's convert it in a dict of lists
+ # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
+ if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], Mapping):
+ encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0]}
+
+ # The model's main input name, usually `input_ids`, has be passed for padding
+ if self.model_input_names[0] not in encoded_inputs:
+ raise ValueError(
+ "You should supply an encoding or a list of encodings to this method "
+ f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}"
+ )
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+
+ if not required_input:
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = []
+ return encoded_inputs
+
+ # If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects
+ # and rebuild them afterwards if no return_tensors is specified
+ # Note that we lose the specific device the tensor may be on for PyTorch
+
+ first_element = required_input[0]
+ if isinstance(first_element, (list, tuple)):
+ # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
+ index = 0
+ while len(required_input[index]) == 0:
+ index += 1
+ if index < len(required_input):
+ first_element = required_input[index][0]
+ # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
+ if not isinstance(first_element, (int, list, tuple)):
+ if is_tf_tensor(first_element):
+ return_tensors = "tf" if return_tensors is None else return_tensors
+ elif is_torch_tensor(first_element):
+ return_tensors = "pt" if return_tensors is None else return_tensors
+ elif isinstance(first_element, np.ndarray):
+ return_tensors = "np" if return_tensors is None else return_tensors
+ else:
+ raise ValueError(
+ f"type of {first_element} unknown: {type(first_element)}. "
+ "Should be one of a python, numpy, pytorch or tensorflow object."
+ )
+
+ for key, value in encoded_inputs.items():
+ encoded_inputs[key] = to_py_obj(value)
+
+ # Convert padding_strategy in PaddingStrategy
+ padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
+ padding=padding, max_length=max_length, verbose=verbose
+ )
+
+ if max_entity_length is None:
+ max_entity_length = self.max_entity_length
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+ if required_input and not isinstance(required_input[0], (list, tuple)):
+ encoded_inputs = self._pad(
+ encoded_inputs,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ padding_strategy=padding_strategy,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+ return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
+
+ batch_size = len(required_input)
+ if any(len(v) != batch_size for v in encoded_inputs.values()):
+ raise ValueError("Some items in the output dictionary have a different batch size than others.")
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = max(len(inputs) for inputs in required_input)
+ max_entity_length = (
+ max(len(inputs) for inputs in encoded_inputs["entity_ids"]) if "entity_ids" in encoded_inputs else 0
+ )
+ padding_strategy = PaddingStrategy.MAX_LENGTH
+
+ batch_outputs = {}
+ for i in range(batch_size):
+ inputs = {k: v[i] for k, v in encoded_inputs.items()}
+ outputs = self._pad(
+ inputs,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ padding_strategy=padding_strategy,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ for key, value in outputs.items():
+ if key not in batch_outputs:
+ batch_outputs[key] = []
+ batch_outputs[key].append(value)
+
+ return BatchEncoding(batch_outputs, tensor_type=return_tensors)
+
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ """
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+
+ Args:
+ encoded_inputs:
+ Dictionary of tokenized inputs (`list[int]`) or batch of tokenized inputs (`list[list[int]]`).
+ max_length: maximum length of the returned list and optionally padding length (see below).
+ Will truncate by taking into account the special tokens.
+ max_entity_length: The maximum length of the entity sequence.
+ padding_strategy: PaddingStrategy to use for padding.
+
+
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
+ The tokenizer padding sides are defined in self.padding_side:
+
+
+ - 'left': pads on the left of the sequences
+ - 'right': pads on the right of the sequences
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta).
+ padding_side:
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask:
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+ """
+ entities_provided = bool("entity_ids" in encoded_inputs)
+
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = len(encoded_inputs["input_ids"])
+ if entities_provided:
+ max_entity_length = len(encoded_inputs["entity_ids"])
+
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ if (
+ entities_provided
+ and max_entity_length is not None
+ and pad_to_multiple_of is not None
+ and (max_entity_length % pad_to_multiple_of != 0)
+ ):
+ max_entity_length = ((max_entity_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and (
+ len(encoded_inputs["input_ids"]) != max_length
+ or (entities_provided and len(encoded_inputs["entity_ids"]) != max_entity_length)
+ )
+
+ # Initialize attention mask if not present.
+ if return_attention_mask and "attention_mask" not in encoded_inputs:
+ encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
+ if entities_provided and return_attention_mask and "entity_attention_mask" not in encoded_inputs:
+ encoded_inputs["entity_attention_mask"] = [1] * len(encoded_inputs["entity_ids"])
+
+ if needs_to_be_padded:
+ difference = max_length - len(encoded_inputs["input_ids"])
+ padding_side = padding_side if padding_side is not None else self.padding_side
+ if entities_provided:
+ entity_difference = max_entity_length - len(encoded_inputs["entity_ids"])
+ if padding_side == "right":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
+ if entities_provided:
+ encoded_inputs["entity_attention_mask"] = (
+ encoded_inputs["entity_attention_mask"] + [0] * entity_difference
+ )
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"] + [0] * difference
+ if entities_provided:
+ encoded_inputs["entity_token_type_ids"] = (
+ encoded_inputs["entity_token_type_ids"] + [0] * entity_difference
+ )
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
+ encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference
+ if entities_provided:
+ encoded_inputs["entity_ids"] = (
+ encoded_inputs["entity_ids"] + [self.entity_pad_token_id] * entity_difference
+ )
+ encoded_inputs["entity_position_ids"] = (
+ encoded_inputs["entity_position_ids"] + [[-1] * self.max_mention_length] * entity_difference
+ )
+ if self.task == "entity_span_classification":
+ encoded_inputs["entity_start_positions"] = (
+ encoded_inputs["entity_start_positions"] + [0] * entity_difference
+ )
+ encoded_inputs["entity_end_positions"] = (
+ encoded_inputs["entity_end_positions"] + [0] * entity_difference
+ )
+
+ elif padding_side == "left":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+ if entities_provided:
+ encoded_inputs["entity_attention_mask"] = [0] * entity_difference + encoded_inputs[
+ "entity_attention_mask"
+ ]
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = [0] * difference + encoded_inputs["token_type_ids"]
+ if entities_provided:
+ encoded_inputs["entity_token_type_ids"] = [0] * entity_difference + encoded_inputs[
+ "entity_token_type_ids"
+ ]
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
+ encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
+ if entities_provided:
+ encoded_inputs["entity_ids"] = [self.entity_pad_token_id] * entity_difference + encoded_inputs[
+ "entity_ids"
+ ]
+ encoded_inputs["entity_position_ids"] = [
+ [-1] * self.max_mention_length
+ ] * entity_difference + encoded_inputs["entity_position_ids"]
+ if self.task == "entity_span_classification":
+ encoded_inputs["entity_start_positions"] = [0] * entity_difference + encoded_inputs[
+ "entity_start_positions"
+ ]
+ encoded_inputs["entity_end_positions"] = [0] * entity_difference + encoded_inputs[
+ "entity_end_positions"
+ ]
+ else:
+ raise ValueError("Invalid padding strategy:" + str(padding_side))
+
+ return encoded_inputs
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ merge_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
+ )
+
+ with open(vocab_file, "w", encoding="utf-8") as f:
+ f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+ index = 0
+ with open(merge_file, "w", encoding="utf-8") as writer:
+ writer.write("#version: 0.2\n")
+ for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
+ " Please check that the tokenizer is not corrupted!"
+ )
+ index = token_index
+ writer.write(" ".join(bpe_tokens) + "\n")
+ index += 1
+
+ entity_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["entity_vocab_file"]
+ )
+
+ with open(entity_vocab_file, "w", encoding="utf-8") as f:
+ f.write(json.dumps(self.entity_vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+ return vocab_file, merge_file, entity_vocab_file
+
+
+__all__ = ["LukeTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ad507465039eed2cf0729b8587a540b03c8691e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_lxmert import *
+ from .modeling_lxmert import *
+ from .modeling_tf_lxmert import *
+ from .tokenization_lxmert import *
+ from .tokenization_lxmert_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/configuration_lxmert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/configuration_lxmert.py
new file mode 100644
index 0000000000000000000000000000000000000000..18d3d2e60d7b41cb9303f7b09f279c34a9b0d134
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/configuration_lxmert.py
@@ -0,0 +1,169 @@
+# coding=utf-8
+# Copyright 2018, Hao Tan, Mohit Bansal
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LXMERT model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class LxmertConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`LxmertModel`] or a [`TFLxmertModel`]. It is used
+ to instantiate a LXMERT model according to the specified arguments, defining the model architecture. Instantiating
+ a configuration with the defaults will yield a similar configuration to that of the Lxmert
+ [unc-nlp/lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 30522):
+ Vocabulary size of the LXMERT model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`LxmertModel`] or [`TFLxmertModel`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimensionality of the encoder layers and the pooler layer.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_qa_labels (`int`, *optional*, defaults to 9500):
+ This represents the total number of different question answering (QA) labels there are. If using more than
+ one dataset with QA, the user will need to account for the total number of labels that all of the datasets
+ have in total.
+ num_object_labels (`int`, *optional*, defaults to 1600):
+ This represents the total number of semantically unique objects that lxmert will be able to classify a
+ pooled-object feature as belonging too.
+ num_attr_labels (`int`, *optional*, defaults to 400):
+ This represents the total number of semantically unique attributes that lxmert will be able to classify a
+ pooled-object feature as possessing.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 2):
+ The vocabulary size of the *token_type_ids* passed into [`BertModel`].
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+ l_layers (`int`, *optional*, defaults to 9):
+ Number of hidden layers in the Transformer language encoder.
+ x_layers (`int`, *optional*, defaults to 5):
+ Number of hidden layers in the Transformer cross modality encoder.
+ r_layers (`int`, *optional*, defaults to 5):
+ Number of hidden layers in the Transformer visual encoder.
+ visual_feat_dim (`int`, *optional*, defaults to 2048):
+ This represents the last dimension of the pooled-object features used as input for the model, representing
+ the size of each object feature itself.
+ visual_pos_dim (`int`, *optional*, defaults to 4):
+ This represents the number of spatial features that are mixed into the visual features. The default is set
+ to 4 because most commonly this will represent the location of a bounding box. i.e., (x, y, width, height)
+ visual_loss_normalizer (`float`, *optional*, defaults to 6.67):
+ This represents the scaling factor in which each visual loss is multiplied by if during pretraining, one
+ decided to train with multiple vision-based loss objectives.
+ task_matched (`bool`, *optional*, defaults to `True`):
+ This task is used for sentence-image matching. If the sentence correctly describes the image the label will
+ be 1. If the sentence does not correctly describe the image, the label will be 0.
+ task_mask_lm (`bool`, *optional*, defaults to `True`):
+ Whether or not to add masked language modeling (as used in pretraining models such as BERT) to the loss
+ objective.
+ task_obj_predict (`bool`, *optional*, defaults to `True`):
+ Whether or not to add object prediction, attribute prediction and feature regression to the loss objective.
+ task_qa (`bool`, *optional*, defaults to `True`):
+ Whether or not to add the question-answering loss to the objective
+ visual_obj_loss (`bool`, *optional*, defaults to `True`):
+ Whether or not to calculate the object-prediction loss objective
+ visual_attr_loss (`bool`, *optional*, defaults to `True`):
+ Whether or not to calculate the attribute-prediction loss objective
+ visual_feat_loss (`bool`, *optional*, defaults to `True`):
+ Whether or not to calculate the feature-regression loss objective
+ """
+
+ model_type = "lxmert"
+ attribute_map = {}
+
+ def __init__(
+ self,
+ vocab_size=30522,
+ hidden_size=768,
+ num_attention_heads=12,
+ num_qa_labels=9500,
+ num_object_labels=1600,
+ num_attr_labels=400,
+ intermediate_size=3072,
+ hidden_act="gelu",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ type_vocab_size=2,
+ initializer_range=0.02,
+ layer_norm_eps=1e-12,
+ l_layers=9,
+ x_layers=5,
+ r_layers=5,
+ visual_feat_dim=2048,
+ visual_pos_dim=4,
+ visual_loss_normalizer=6.67,
+ task_matched=True,
+ task_mask_lm=True,
+ task_obj_predict=True,
+ task_qa=True,
+ visual_obj_loss=True,
+ visual_attr_loss=True,
+ visual_feat_loss=True,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.num_attention_heads = num_attention_heads
+ self.hidden_act = hidden_act
+ self.intermediate_size = intermediate_size
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.max_position_embeddings = max_position_embeddings
+ self.type_vocab_size = type_vocab_size
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.num_qa_labels = num_qa_labels
+ self.num_object_labels = num_object_labels
+ self.num_attr_labels = num_attr_labels
+ self.l_layers = l_layers
+ self.x_layers = x_layers
+ self.r_layers = r_layers
+ self.visual_feat_dim = visual_feat_dim
+ self.visual_pos_dim = visual_pos_dim
+ self.visual_loss_normalizer = visual_loss_normalizer
+ self.task_matched = task_matched
+ self.task_mask_lm = task_mask_lm
+ self.task_obj_predict = task_obj_predict
+ self.task_qa = task_qa
+ self.visual_obj_loss = visual_obj_loss
+ self.visual_attr_loss = visual_attr_loss
+ self.visual_feat_loss = visual_feat_loss
+ self.num_hidden_layers = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
+ super().__init__(**kwargs)
+
+
+__all__ = ["LxmertConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/convert_lxmert_original_tf_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/convert_lxmert_original_tf_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..1dd77bc36f800fce66a2065d194f1b82893b14b1
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/convert_lxmert_original_tf_checkpoint_to_pytorch.py
@@ -0,0 +1,59 @@
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert LXMERT checkpoint."""
+
+import argparse
+
+import torch
+
+from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+
+
+def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
+ # Initialise PyTorch model
+ config = LxmertConfig.from_json_file(config_file)
+ print(f"Building PyTorch model from configuration: {config}")
+ model = LxmertForPreTraining(config)
+
+ # Load weights from tf checkpoint
+ load_tf_weights_in_lxmert(model, config, tf_checkpoint_path)
+
+ # Save pytorch-model
+ print(f"Save PyTorch model to {pytorch_dump_path}")
+ torch.save(model.state_dict(), pytorch_dump_path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
+ )
+ parser.add_argument(
+ "--config_file",
+ default=None,
+ type=str,
+ required=True,
+ help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
+ )
+ args = parser.parse_args()
+ convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/modeling_lxmert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/modeling_lxmert.py
new file mode 100644
index 0000000000000000000000000000000000000000..00243ce123299cff8aea89fb8ed8587aa9bc59b4
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/modeling_lxmert.py
@@ -0,0 +1,1415 @@
+# coding=utf-8
+# Copyright 2018 Hao Tan, Mohit Bansal, and the HuggingFace team
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch LXMERT model."""
+
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss, SmoothL1Loss
+
+from ...activations import ACT2FN, gelu
+from ...modeling_utils import PreTrainedModel
+from ...utils import ModelOutput, auto_docstring, logging
+from .configuration_lxmert import LxmertConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class GeLU(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(self, x):
+ return gelu(x)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Lxmert's outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,
+ visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship"
+ encoder")
+ """
+)
+class LxmertModelOutput(ModelOutput):
+ r"""
+ language_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the language encoder.
+ vision_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the visual encoder.
+ pooled_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
+ Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed
+ by a Linear layer and a Tanh activation function. The Linear
+ language_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
+ shape `(batch_size, sequence_length, hidden_size)`.
+ vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
+ shape `(batch_size, sequence_length, hidden_size)`.
+ language_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ cross_encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ """
+
+ language_output: Optional[torch.FloatTensor] = None
+ vision_output: Optional[torch.FloatTensor] = None
+ pooled_output: Optional[torch.FloatTensor] = None
+ language_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ language_attentions: Optional[tuple[torch.FloatTensor]] = None
+ vision_attentions: Optional[tuple[torch.FloatTensor]] = None
+ cross_encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Output type of [`LxmertForQuestionAnswering`].
+ """
+)
+class LxmertForQuestionAnsweringOutput(ModelOutput):
+ r"""
+ loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
+ Total loss as the sum of the masked language modeling loss and the next sequence prediction
+ (classification) loss.k.
+ question_answering_score (`torch.FloatTensor` of shape `(batch_size, n_qa_answers)`, *optional*):
+ Prediction scores of question answering objective (classification).
+ language_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
+ shape `(batch_size, sequence_length, hidden_size)`.
+ vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
+ shape `(batch_size, sequence_length, hidden_size)`.
+ language_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ cross_encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ question_answering_score: Optional[torch.FloatTensor] = None
+ language_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ language_attentions: Optional[tuple[torch.FloatTensor]] = None
+ vision_attentions: Optional[tuple[torch.FloatTensor]] = None
+ cross_encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Output type of [`LxmertForPreTraining`].
+ """
+)
+class LxmertForPreTrainingOutput(ModelOutput):
+ r"""
+ loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
+ Total loss as the sum of the masked language modeling loss and the next sequence prediction
+ (classification) loss.
+ prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ cross_relationship_score (`torch.FloatTensor` of shape `(batch_size, 2)`):
+ Prediction scores of the textual matching objective (classification) head (scores of True/False
+ continuation before SoftMax).
+ question_answering_score (`torch.FloatTensor` of shape `(batch_size, n_qa_answers)`):
+ Prediction scores of question answering objective (classification).
+ language_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
+ shape `(batch_size, sequence_length, hidden_size)`.
+ vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
+ shape `(batch_size, sequence_length, hidden_size)`.
+ language_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ cross_encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ prediction_logits: Optional[torch.FloatTensor] = None
+ cross_relationship_score: Optional[torch.FloatTensor] = None
+ question_answering_score: Optional[torch.FloatTensor] = None
+ language_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ language_attentions: Optional[tuple[torch.FloatTensor]] = None
+ vision_attentions: Optional[tuple[torch.FloatTensor]] = None
+ cross_encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+def load_tf_weights_in_lxmert(model, config, tf_checkpoint_path):
+ """Load tf checkpoints in a pytorch model."""
+ try:
+ import re
+
+ import numpy as np
+ import tensorflow as tf
+ except ImportError:
+ logger.error(
+ "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
+ "https://www.tensorflow.org/install/ for installation instructions."
+ )
+ raise
+ tf_path = os.path.abspath(tf_checkpoint_path)
+ logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
+ # Load weights from TF model
+ init_vars = tf.train.list_variables(tf_path)
+ names = []
+ arrays = []
+ for name, shape in init_vars:
+ logger.info(f"Loading TF weight {name} with shape {shape}")
+ array = tf.train.load_variable(tf_path, name)
+ names.append(name)
+ arrays.append(array)
+
+ for name, array in zip(names, arrays):
+ name = name.split("/")
+ # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+ # which are not required for using pretrained model
+ if any(
+ n
+ in [
+ "adam_v",
+ "adam_m",
+ "AdamWeightDecayOptimizer",
+ "AdamWeightDecayOptimizer_1",
+ "global_step",
+ ]
+ for n in name
+ ):
+ logger.info(f"Skipping {'/'.join(name)}")
+ continue
+ pointer = model
+ for m_name in name:
+ if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
+ scope_names = re.split(r"_(\d+)", m_name)
+ else:
+ scope_names = [m_name]
+ if scope_names[0] == "kernel" or scope_names[0] == "gamma":
+ pointer = getattr(pointer, "weight")
+ elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
+ pointer = getattr(pointer, "bias")
+ elif scope_names[0] == "output_weights":
+ pointer = getattr(pointer, "weight")
+ elif scope_names[0] == "squad":
+ pointer = getattr(pointer, "classifier")
+ else:
+ try:
+ pointer = getattr(pointer, scope_names[0])
+ except AttributeError:
+ logger.info(f"Skipping {'/'.join(name)}")
+ continue
+ if len(scope_names) >= 2:
+ num = int(scope_names[1])
+ pointer = pointer[num]
+ if m_name[-11:] == "_embeddings":
+ pointer = getattr(pointer, "weight")
+ elif m_name == "kernel":
+ array = np.transpose(array)
+ try:
+ assert pointer.shape == array.shape
+ except AssertionError as e:
+ e.args += (pointer.shape, array.shape)
+ raise
+ logger.info(f"Initialize PyTorch weight {name}")
+ pointer.data = torch.from_numpy(array)
+ return model
+
+
+class LxmertEmbeddings(nn.Module):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0)
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=0)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size, padding_idx=0)
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, input_ids, token_type_ids=None, inputs_embeds=None):
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ device = input_ids.device
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+ device = inputs_embeds.device
+ seq_length = input_shape[1]
+
+ position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
+ position_ids = position_ids.unsqueeze(0).expand(input_shape)
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+ position_embeddings = self.position_embeddings(position_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = inputs_embeds + position_embeddings + token_type_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+class LxmertAttention(nn.Module):
+ def __init__(self, config, ctx_dim=None):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.head_size = self.num_attention_heads * self.attention_head_size
+
+ # visual_dim = 2048
+ if ctx_dim is None:
+ ctx_dim = config.hidden_size
+ self.query = nn.Linear(config.hidden_size, self.head_size)
+ self.key = nn.Linear(ctx_dim, self.head_size)
+ self.value = nn.Linear(ctx_dim, self.head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+ def forward(self, hidden_states, context, attention_mask=None, output_attentions=False):
+ batch_size, seq_length, _ = hidden_states.shape
+ query_layer = (
+ self.query(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ key_layer = (
+ self.key(context).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
+ )
+ value_layer = (
+ self.value(context)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+ if attention_mask is not None:
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.head_size,)
+ context_layer = context_layer.view(new_context_layer_shape)
+
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+ return outputs
+
+
+class LxmertAttentionOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class LxmertCrossAttentionLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.att = LxmertAttention(config)
+ self.output = LxmertAttentionOutput(config)
+
+ def forward(self, input_tensor, ctx_tensor, ctx_att_mask=None, output_attentions=False):
+ output = self.att(input_tensor, ctx_tensor, ctx_att_mask, output_attentions=output_attentions)
+ if output_attentions:
+ attention_probs = output[1]
+ attention_output = self.output(output[0], input_tensor)
+ outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
+ return outputs
+
+
+class LxmertSelfAttentionLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.self = LxmertAttention(config)
+ self.output = LxmertAttentionOutput(config)
+
+ def forward(self, input_tensor, attention_mask, output_attentions=False):
+ # Self attention attends to itself, thus keys and queries are the same (input_tensor).
+ output = self.self(
+ input_tensor,
+ input_tensor,
+ attention_mask,
+ output_attentions=output_attentions,
+ )
+ if output_attentions:
+ attention_probs = output[1]
+ attention_output = self.output(output[0], input_tensor)
+ outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
+ return outputs
+
+
+class LxmertIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+class LxmertOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states, input_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class LxmertLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.attention = LxmertSelfAttentionLayer(config)
+ self.intermediate = LxmertIntermediate(config)
+ self.output = LxmertOutput(config)
+
+ def forward(self, hidden_states, attention_mask=None, output_attentions=False):
+ outputs = self.attention(hidden_states, attention_mask, output_attentions=output_attentions)
+ attention_output = outputs[0]
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ outputs = (layer_output,) + outputs[1:] # add attentions if we output them
+ return outputs
+
+
+class LxmertXLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ # The cross-attention Layer
+ self.visual_attention = LxmertCrossAttentionLayer(config)
+
+ # Self-attention Layers
+ self.lang_self_att = LxmertSelfAttentionLayer(config)
+ self.visn_self_att = LxmertSelfAttentionLayer(config)
+
+ # Intermediate and Output Layers (FFNs)
+ self.lang_inter = LxmertIntermediate(config)
+ self.lang_output = LxmertOutput(config)
+ self.visn_inter = LxmertIntermediate(config)
+ self.visn_output = LxmertOutput(config)
+
+ def cross_att(
+ self,
+ lang_input,
+ lang_attention_mask,
+ visual_input,
+ visual_attention_mask,
+ output_x_attentions=False,
+ ):
+ # Cross Attention
+ lang_att_output = self.visual_attention(
+ lang_input,
+ visual_input,
+ ctx_att_mask=visual_attention_mask,
+ output_attentions=output_x_attentions,
+ )
+ visual_att_output = self.visual_attention(
+ visual_input,
+ lang_input,
+ ctx_att_mask=lang_attention_mask,
+ output_attentions=False,
+ )
+ return lang_att_output, visual_att_output
+
+ def self_att(self, lang_input, lang_attention_mask, visual_input, visual_attention_mask):
+ # Self Attention
+ lang_att_output = self.lang_self_att(lang_input, lang_attention_mask, output_attentions=False)
+ visual_att_output = self.visn_self_att(visual_input, visual_attention_mask, output_attentions=False)
+ return lang_att_output[0], visual_att_output[0]
+
+ def output_fc(self, lang_input, visual_input):
+ # FC layers
+ lang_inter_output = self.lang_inter(lang_input)
+ visual_inter_output = self.visn_inter(visual_input)
+
+ # Layer output
+ lang_output = self.lang_output(lang_inter_output, lang_input)
+ visual_output = self.visn_output(visual_inter_output, visual_input)
+
+ return lang_output, visual_output
+
+ def forward(
+ self,
+ lang_feats,
+ lang_attention_mask,
+ visual_feats,
+ visual_attention_mask,
+ output_attentions=False,
+ ):
+ lang_att_output, visual_att_output = self.cross_att(
+ lang_input=lang_feats,
+ lang_attention_mask=lang_attention_mask,
+ visual_input=visual_feats,
+ visual_attention_mask=visual_attention_mask,
+ output_x_attentions=output_attentions,
+ )
+ attention_probs = lang_att_output[1:]
+ lang_att_output, visual_att_output = self.self_att(
+ lang_att_output[0],
+ lang_attention_mask,
+ visual_att_output[0],
+ visual_attention_mask,
+ )
+
+ lang_output, visual_output = self.output_fc(lang_att_output, visual_att_output)
+ return (
+ (
+ lang_output,
+ visual_output,
+ attention_probs[0],
+ )
+ if output_attentions
+ else (lang_output, visual_output)
+ )
+
+
+class LxmertVisualFeatureEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ feat_dim = config.visual_feat_dim
+ pos_dim = config.visual_pos_dim
+
+ # Object feature encoding
+ self.visn_fc = nn.Linear(feat_dim, config.hidden_size)
+ self.visn_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-12)
+
+ # Box position encoding
+ self.box_fc = nn.Linear(pos_dim, config.hidden_size)
+ self.box_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-12)
+
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, visual_feats, visual_pos):
+ x = self.visn_fc(visual_feats)
+ x = self.visn_layer_norm(x)
+ y = self.box_fc(visual_pos)
+ y = self.box_layer_norm(y)
+ output = (x + y) / 2
+
+ output = self.dropout(output)
+ return output
+
+
+class LxmertEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ # Obj-level image embedding layer
+ self.visn_fc = LxmertVisualFeatureEncoder(config)
+ self.config = config
+
+ # Number of layers
+ self.num_l_layers = config.l_layers
+ self.num_x_layers = config.x_layers
+ self.num_r_layers = config.r_layers
+
+ # Layers
+ # Using self.layer instead of self.l_layer to support loading BERT weights.
+ self.layer = nn.ModuleList([LxmertLayer(config) for _ in range(self.num_l_layers)])
+ self.x_layers = nn.ModuleList([LxmertXLayer(config) for _ in range(self.num_x_layers)])
+ self.r_layers = nn.ModuleList([LxmertLayer(config) for _ in range(self.num_r_layers)])
+
+ def forward(
+ self,
+ lang_feats,
+ lang_attention_mask,
+ visual_feats,
+ visual_pos,
+ visual_attention_mask=None,
+ output_attentions=None,
+ ):
+ vision_hidden_states = ()
+ language_hidden_states = ()
+ vision_attentions = () if output_attentions or self.config.output_attentions else None
+ language_attentions = () if output_attentions or self.config.output_attentions else None
+ cross_encoder_attentions = () if output_attentions or self.config.output_attentions else None
+
+ visual_feats = self.visn_fc(visual_feats, visual_pos)
+
+ # Run language layers
+ for layer_module in self.layer:
+ l_outputs = layer_module(lang_feats, lang_attention_mask, output_attentions=output_attentions)
+ lang_feats = l_outputs[0]
+ language_hidden_states = language_hidden_states + (lang_feats,)
+ if language_attentions is not None:
+ language_attentions = language_attentions + (l_outputs[1],)
+
+ # Run relational layers
+ for layer_module in self.r_layers:
+ v_outputs = layer_module(visual_feats, visual_attention_mask, output_attentions=output_attentions)
+ visual_feats = v_outputs[0]
+ vision_hidden_states = vision_hidden_states + (visual_feats,)
+ if vision_attentions is not None:
+ vision_attentions = vision_attentions + (v_outputs[1],)
+
+ # Run cross-modality layers
+ for layer_module in self.x_layers:
+ x_outputs = layer_module(
+ lang_feats,
+ lang_attention_mask,
+ visual_feats,
+ visual_attention_mask,
+ output_attentions=output_attentions,
+ )
+ lang_feats, visual_feats = x_outputs[:2]
+ vision_hidden_states = vision_hidden_states + (visual_feats,)
+ language_hidden_states = language_hidden_states + (lang_feats,)
+ if cross_encoder_attentions is not None:
+ cross_encoder_attentions = cross_encoder_attentions + (x_outputs[2],)
+ visual_encoder_outputs = (
+ vision_hidden_states,
+ vision_attentions if output_attentions else None,
+ )
+ lang_encoder_outputs = (
+ language_hidden_states,
+ language_attentions if output_attentions else None,
+ )
+ return (
+ visual_encoder_outputs,
+ lang_encoder_outputs,
+ cross_encoder_attentions if output_attentions else None,
+ )
+
+
+class LxmertPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states):
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+class LxmertPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
+
+ def forward(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+class LxmertLMPredictionHead(nn.Module):
+ def __init__(self, config, lxmert_model_embedding_weights):
+ super().__init__()
+ self.transform = LxmertPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(
+ lxmert_model_embedding_weights.size(1),
+ lxmert_model_embedding_weights.size(0),
+ bias=False,
+ )
+ self.decoder.weight = lxmert_model_embedding_weights
+ self.bias = nn.Parameter(torch.zeros(lxmert_model_embedding_weights.size(0)))
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states) + self.bias
+ return hidden_states
+
+
+class LxmertVisualAnswerHead(nn.Module):
+ def __init__(self, config, num_labels):
+ super().__init__()
+ hid_dim = config.hidden_size
+ self.logit_fc = nn.Sequential(
+ nn.Linear(hid_dim, hid_dim * 2),
+ GeLU(),
+ nn.LayerNorm(hid_dim * 2, eps=1e-12),
+ nn.Linear(hid_dim * 2, num_labels),
+ )
+
+ def forward(self, hidden_states):
+ return self.logit_fc(hidden_states)
+
+
+class LxmertVisualObjHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.transform = LxmertPredictionHeadTransform(config)
+ # Decide the use of visual losses
+ visual_losses = {}
+ if config.visual_obj_loss:
+ visual_losses["obj"] = {"shape": (-1,), "num": config.num_object_labels}
+ if config.visual_attr_loss:
+ visual_losses["attr"] = {"shape": (-1,), "num": config.num_attr_labels}
+ if config.visual_feat_loss:
+ visual_losses["feat"] = {
+ "shape": (-1, config.visual_feat_dim),
+ "num": config.visual_feat_dim,
+ }
+ self.visual_losses = visual_losses
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder_dict = nn.ModuleDict(
+ {key: nn.Linear(config.hidden_size, self.visual_losses[key]["num"]) for key in self.visual_losses}
+ )
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ output = {}
+ for key in self.visual_losses:
+ output[key] = self.decoder_dict[key](hidden_states)
+ return output
+
+
+class LxmertPreTrainingHeads(nn.Module):
+ def __init__(self, config, lxmert_model_embedding_weights):
+ super().__init__()
+ self.predictions = LxmertLMPredictionHead(config, lxmert_model_embedding_weights)
+ self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+ def forward(self, sequence_output, pooled_output):
+ prediction_scores = self.predictions(sequence_output)
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return prediction_scores, seq_relationship_score
+
+
+@auto_docstring
+class LxmertPreTrainedModel(PreTrainedModel):
+ config: LxmertConfig
+ load_tf_weights = load_tf_weights_in_lxmert
+ base_model_prefix = "lxmert"
+ _supports_param_buffer_assignment = False
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, nn.Linear):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, LxmertLMPredictionHead):
+ module.bias.data.zero_()
+
+
+@auto_docstring
+class LxmertModel(LxmertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.embeddings = LxmertEmbeddings(config)
+ self.encoder = LxmertEncoder(config)
+ self.pooler = LxmertPooler(config)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, new_embeddings):
+ self.embeddings.word_embeddings = new_embeddings
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ visual_feats: Optional[torch.FloatTensor] = None,
+ visual_pos: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ visual_attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[LxmertModelOutput, tuple[torch.FloatTensor]]:
+ r"""
+ visual_feats (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
+ This input represents visual features. They ROI pooled object features from bounding boxes using a
+ faster-RCNN model)
+
+ These are currently not provided by the transformers library.
+ visual_pos (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_pos_dim)`):
+ This input represents spatial features corresponding to their relative (via index) visual features. The
+ pre-trained LXMERT model expects these spatial features to be normalized bounding boxes on a scale of 0 to
+ 1.
+
+ These are currently not provided by the transformers library.
+ visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if visual_feats is None:
+ raise ValueError("`visual_feats` cannot be `None`")
+ if visual_pos is None:
+ raise ValueError("`visual_pos` cannot be `None`")
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones(input_shape, device=device)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ # We create a 3D attention mask from a 2D tensor mask.
+ # Sizes are [batch_size, 1, 1, to_seq_length]
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+ # this attention mask is more simple than the triangular masking of causal attention
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+ extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and the dtype's smallest value for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+ extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
+ extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
+
+ # Process the visual attention mask
+ if visual_attention_mask is not None:
+ extended_visual_attention_mask = visual_attention_mask.unsqueeze(1).unsqueeze(2)
+ extended_visual_attention_mask = extended_visual_attention_mask.to(dtype=self.dtype)
+ extended_visual_attention_mask = (1.0 - extended_visual_attention_mask) * torch.finfo(self.dtype).min
+ else:
+ extended_visual_attention_mask = None
+
+ # Positional Word Embeddings
+ embedding_output = self.embeddings(input_ids, token_type_ids, inputs_embeds)
+
+ # Run Lxmert encoder
+ encoder_outputs = self.encoder(
+ embedding_output,
+ extended_attention_mask,
+ visual_feats=visual_feats,
+ visual_pos=visual_pos,
+ visual_attention_mask=extended_visual_attention_mask,
+ output_attentions=output_attentions,
+ )
+
+ visual_encoder_outputs, lang_encoder_outputs = encoder_outputs[:2]
+ vision_hidden_states = visual_encoder_outputs[0]
+ language_hidden_states = lang_encoder_outputs[0]
+
+ all_attentions = ()
+ if output_attentions:
+ language_attentions = lang_encoder_outputs[1]
+ vision_attentions = visual_encoder_outputs[1]
+ cross_encoder_attentions = encoder_outputs[2]
+ all_attentions = (
+ language_attentions,
+ vision_attentions,
+ cross_encoder_attentions,
+ )
+
+ hidden_states = (language_hidden_states, vision_hidden_states) if output_hidden_states else ()
+
+ visual_output = vision_hidden_states[-1]
+ lang_output = language_hidden_states[-1]
+ pooled_output = self.pooler(lang_output)
+
+ if not return_dict:
+ return (lang_output, visual_output, pooled_output) + hidden_states + all_attentions
+
+ return LxmertModelOutput(
+ pooled_output=pooled_output,
+ language_output=lang_output,
+ vision_output=visual_output,
+ language_hidden_states=language_hidden_states if output_hidden_states else None,
+ vision_hidden_states=vision_hidden_states if output_hidden_states else None,
+ language_attentions=language_attentions if output_attentions else None,
+ vision_attentions=vision_attentions if output_attentions else None,
+ cross_encoder_attentions=cross_encoder_attentions if output_attentions else None,
+ )
+
+
+@auto_docstring
+class LxmertForPreTraining(LxmertPreTrainedModel):
+ _tied_weights_keys = ["cls.predictions.decoder.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ # Configuration
+ self.config = config
+ self.num_qa_labels = config.num_qa_labels
+ self.visual_loss_normalizer = config.visual_loss_normalizer
+
+ # Use of pretraining tasks
+ self.task_mask_lm = config.task_mask_lm
+ self.task_obj_predict = config.task_obj_predict
+ self.task_matched = config.task_matched
+ self.task_qa = config.task_qa
+
+ # Lxmert backbone
+ self.lxmert = LxmertModel(config)
+
+ # Pre-training heads
+ self.cls = LxmertPreTrainingHeads(config, self.lxmert.embeddings.word_embeddings.weight)
+ if self.task_obj_predict:
+ self.obj_predict_head = LxmertVisualObjHead(config)
+ if self.task_qa:
+ self.answer_head = LxmertVisualAnswerHead(config, self.num_qa_labels)
+
+ # Weight initialization
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ # Loss functions
+ self.loss_fcts = {
+ "l2": SmoothL1Loss(reduction="none"),
+ "visual_ce": CrossEntropyLoss(reduction="none"),
+ "ce": CrossEntropyLoss(),
+ }
+
+ visual_losses = {}
+ if config.visual_obj_loss:
+ visual_losses["obj"] = {
+ "shape": (-1,),
+ "num": config.num_object_labels,
+ "loss": "visual_ce",
+ }
+ if config.visual_attr_loss:
+ visual_losses["attr"] = {
+ "shape": (-1,),
+ "num": config.num_attr_labels,
+ "loss": "visual_ce",
+ }
+ if config.visual_feat_loss:
+ visual_losses["feat"] = {
+ "shape": (-1, config.visual_feat_dim),
+ "num": config.visual_feat_dim,
+ "loss": "l2",
+ }
+ self.visual_losses = visual_losses
+
+ def _tie_weights(self):
+ self.cls.predictions.decoder.weight = self.lxmert.embeddings.word_embeddings.weight
+
+ def resize_token_embeddings(
+ self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+ ) -> nn.Embedding:
+ # Adding the following steps to resize bias to match the shape of resized embeddings
+ new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
+ self.cls.predictions.bias = self._resize_bias(self.cls.predictions.bias, new_num_tokens)
+ return new_embeddings
+
+ def _resize_bias(self, bias, new_num_tokens: int):
+ old_num_tokens = bias.shape[0]
+ if new_num_tokens <= old_num_tokens:
+ new_bias = bias[:new_num_tokens]
+ else:
+ extra_bias = torch.zeros(new_num_tokens - old_num_tokens, device=bias.device)
+ new_bias = torch.cat([bias, extra_bias])
+ new_bias = nn.Parameter(new_bias)
+ return new_bias
+
+ def resize_num_qa_labels(self, num_labels):
+ """
+ Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
+ will add newly initialized weights. Reducing the size will remove weights from the end
+
+ Args:
+ num_labels (`int`, *optional*):
+ New number of labels in the linear layer weight matrix. Increasing the size will add newly initialized
+ weights at the end. Reducing the size will remove weights from the end. If not provided or `None`, just
+ returns a pointer to the qa labels ``torch.nn.Linear``` module of the model without doing anything.
+
+ Return:
+ `torch.nn.Linear`: Pointer to the resized Linear layer or the old Linear layer
+ """
+
+ cur_qa_logit_layer = self.get_qa_logit_layer()
+ if num_labels is None or cur_qa_logit_layer is None:
+ return
+ new_qa_logit_layer = self._resize_qa_labels(num_labels)
+ self.config.num_qa_labels = num_labels
+ self.num_qa_labels = num_labels
+
+ return new_qa_logit_layer
+
+ def _resize_qa_labels(self, num_labels):
+ cur_qa_logit_layer = self.get_qa_logit_layer()
+ new_qa_logit_layer = self._get_resized_qa_labels(cur_qa_logit_layer, num_labels)
+ self._set_qa_logit_layer(new_qa_logit_layer)
+ return self.get_qa_logit_layer()
+
+ def get_qa_logit_layer(self) -> nn.Module:
+ """
+ Returns the linear layer that produces question answering logits.
+
+ Returns:
+ `nn.Module`: A torch module mapping the question answering prediction hidden states or `None` if LXMERT
+ does not have a visual answering head.
+ """
+ if hasattr(self, "answer_head"):
+ return self.answer_head.logit_fc[-1]
+
+ def _set_qa_logit_layer(self, qa_logit_layer):
+ self.answer_head.logit_fc[-1] = qa_logit_layer
+
+ def _get_resized_qa_labels(self, cur_qa_logit_layer, num_labels):
+ if num_labels is None:
+ return cur_qa_logit_layer
+
+ cur_qa_labels, hidden_dim = cur_qa_logit_layer.weight.size()
+ if cur_qa_labels == num_labels:
+ return cur_qa_logit_layer
+
+ # Build new linear output
+ if getattr(cur_qa_logit_layer, "bias", None) is not None:
+ new_qa_logit_layer = nn.Linear(hidden_dim, num_labels)
+ else:
+ new_qa_logit_layer = nn.Linear(hidden_dim, num_labels, bias=False)
+
+ new_qa_logit_layer.to(cur_qa_logit_layer.weight.device)
+
+ # initialize all new labels
+ self._init_weights(new_qa_logit_layer)
+
+ # Copy labels from the previous weights
+ num_labels_to_copy = min(cur_qa_labels, num_labels)
+ new_qa_logit_layer.weight.data[:num_labels_to_copy, :] = cur_qa_logit_layer.weight.data[:num_labels_to_copy, :]
+ if getattr(cur_qa_logit_layer, "bias", None) is not None:
+ new_qa_logit_layer.bias.data[:num_labels_to_copy] = cur_qa_logit_layer.bias.data[:num_labels_to_copy]
+
+ return new_qa_logit_layer
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ visual_feats: Optional[torch.FloatTensor] = None,
+ visual_pos: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ visual_attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ obj_labels: Optional[dict[str, tuple[torch.FloatTensor, torch.FloatTensor]]] = None,
+ matched_label: Optional[torch.LongTensor] = None,
+ ans: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[LxmertForPreTrainingOutput, tuple[torch.FloatTensor]]:
+ r"""
+ visual_feats (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
+ This input represents visual features. They ROI pooled object features from bounding boxes using a
+ faster-RCNN model)
+
+ These are currently not provided by the transformers library.
+ visual_pos (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_pos_dim)`):
+ This input represents spatial features corresponding to their relative (via index) visual features. The
+ pre-trained LXMERT model expects these spatial features to be normalized bounding boxes on a scale of 0 to
+ 1.
+
+ These are currently not provided by the transformers library.
+ visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ obj_labels (`dict[Str: tuple[Torch.FloatTensor, Torch.FloatTensor]]`, *optional*):
+ each key is named after each one of the visual losses and each element of the tuple is of the shape
+ `(batch_size, num_features)` and `(batch_size, num_features, visual_feature_dim)` for each the label id and
+ the label score respectively
+ matched_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the whether or not the text input matches the image (classification) loss. Input
+ should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`:
+
+ - 0 indicates that the sentence does not match the image,
+ - 1 indicates that the sentence does match the image.
+ ans (`Torch.Tensor` of shape `(batch_size)`, *optional*):
+ a one hot representation hof the correct answer *optional*
+ """
+
+ if "masked_lm_labels" in kwargs:
+ warnings.warn(
+ "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels`"
+ " instead.",
+ FutureWarning,
+ )
+ labels = kwargs.pop("masked_lm_labels")
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ lxmert_output = self.lxmert(
+ input_ids=input_ids,
+ visual_feats=visual_feats,
+ visual_pos=visual_pos,
+ token_type_ids=token_type_ids,
+ attention_mask=attention_mask,
+ visual_attention_mask=visual_attention_mask,
+ inputs_embeds=inputs_embeds,
+ output_hidden_states=output_hidden_states,
+ output_attentions=output_attentions,
+ return_dict=return_dict,
+ )
+
+ lang_output, visual_output, pooled_output = (
+ lxmert_output[0],
+ lxmert_output[1],
+ lxmert_output[2],
+ )
+ lang_prediction_scores, cross_relationship_score = self.cls(lang_output, pooled_output)
+ if self.task_qa:
+ answer_score = self.answer_head(pooled_output)
+ else:
+ answer_score = pooled_output[0][0]
+
+ total_loss = (
+ None
+ if (labels is None and matched_label is None and obj_labels is None and ans is None)
+ else torch.tensor(0.0, device=device)
+ )
+ if labels is not None and self.task_mask_lm:
+ masked_lm_loss = self.loss_fcts["ce"](
+ lang_prediction_scores.view(-1, self.config.vocab_size),
+ labels.view(-1),
+ )
+ total_loss += masked_lm_loss
+ if matched_label is not None and self.task_matched:
+ matched_loss = self.loss_fcts["ce"](cross_relationship_score.view(-1, 2), matched_label.view(-1))
+ total_loss += matched_loss
+ if obj_labels is not None and self.task_obj_predict:
+ total_visual_loss = torch.tensor(0.0, device=input_ids.device)
+ visual_prediction_scores_dict = self.obj_predict_head(visual_output)
+ for key, key_info in self.visual_losses.items():
+ label, mask_conf = obj_labels[key]
+ output_dim = key_info["num"]
+ loss_fct_name = key_info["loss"]
+ label_shape = key_info["shape"]
+ weight = self.visual_loss_normalizer
+ visual_loss_fct = self.loss_fcts[loss_fct_name]
+ visual_prediction_scores = visual_prediction_scores_dict[key]
+ visual_loss = visual_loss_fct(
+ visual_prediction_scores.view(-1, output_dim),
+ label.view(label_shape),
+ )
+ if visual_loss.dim() > 1: # Regression Losses
+ visual_loss = visual_loss.mean(1)
+ visual_loss = (visual_loss * mask_conf.view(-1)).mean() * weight
+ total_visual_loss += visual_loss
+ total_loss += total_visual_loss
+ if ans is not None and self.task_qa:
+ answer_loss = self.loss_fcts["ce"](answer_score.view(-1, self.num_qa_labels), ans.view(-1))
+ total_loss += answer_loss
+
+ if not return_dict:
+ output = (
+ lang_prediction_scores,
+ cross_relationship_score,
+ answer_score,
+ ) + lxmert_output[3:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return LxmertForPreTrainingOutput(
+ loss=total_loss,
+ prediction_logits=lang_prediction_scores,
+ cross_relationship_score=cross_relationship_score,
+ question_answering_score=answer_score,
+ language_hidden_states=lxmert_output.language_hidden_states,
+ vision_hidden_states=lxmert_output.vision_hidden_states,
+ language_attentions=lxmert_output.language_attentions,
+ vision_attentions=lxmert_output.vision_attentions,
+ cross_encoder_attentions=lxmert_output.cross_encoder_attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ Lxmert Model with a visual-answering head on top for downstream QA tasks
+ """
+)
+class LxmertForQuestionAnswering(LxmertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ # Configuration
+ self.config = config
+ self.num_qa_labels = config.num_qa_labels
+ self.visual_loss_normalizer = config.visual_loss_normalizer
+
+ # Lxmert backbone
+ self.lxmert = LxmertModel(config)
+
+ self.answer_head = LxmertVisualAnswerHead(config, self.num_qa_labels)
+
+ # Weight initialization
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ # Loss function
+ self.loss = CrossEntropyLoss()
+
+ def resize_num_qa_labels(self, num_labels):
+ """
+ Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
+ will add newly initialized weights. Reducing the size will remove weights from the end
+
+ Args:
+ num_labels (`int`, *optional*):
+ New number of labels in the linear layer weight matrix. Increasing the size will add newly initialized
+ weights at the end. Reducing the size will remove weights from the end. If not provided or `None`, just
+ returns a pointer to the qa labels ``torch.nn.Linear``` module of the model without doing anything.
+
+ Return:
+ `torch.nn.Linear`: Pointer to the resized Linear layer or the old Linear layer
+ """
+
+ cur_qa_logit_layer = self.get_qa_logit_layer()
+ if num_labels is None or cur_qa_logit_layer is None:
+ return
+ new_qa_logit_layer = self._resize_qa_labels(num_labels)
+ self.config.num_qa_labels = num_labels
+ self.num_qa_labels = num_labels
+
+ return new_qa_logit_layer
+
+ def _resize_qa_labels(self, num_labels):
+ cur_qa_logit_layer = self.get_qa_logit_layer()
+ new_qa_logit_layer = self._get_resized_qa_labels(cur_qa_logit_layer, num_labels)
+ self._set_qa_logit_layer(new_qa_logit_layer)
+ return self.get_qa_logit_layer()
+
+ def get_qa_logit_layer(self) -> nn.Module:
+ """
+ Returns the linear layer that produces question answering logits
+
+ Returns:
+ `nn.Module`: A torch module mapping the question answering prediction hidden states. `None`: A NoneType
+ object if Lxmert does not have the visual answering head.
+ """
+
+ if hasattr(self, "answer_head"):
+ return self.answer_head.logit_fc[-1]
+
+ def _set_qa_logit_layer(self, qa_logit_layer):
+ self.answer_head.logit_fc[-1] = qa_logit_layer
+
+ def _get_resized_qa_labels(self, cur_qa_logit_layer, num_labels):
+ if num_labels is None:
+ return cur_qa_logit_layer
+
+ cur_qa_labels, hidden_dim = cur_qa_logit_layer.weight.size()
+ if cur_qa_labels == num_labels:
+ return cur_qa_logit_layer
+
+ # Build new linear output
+ if getattr(cur_qa_logit_layer, "bias", None) is not None:
+ new_qa_logit_layer = nn.Linear(hidden_dim, num_labels)
+ else:
+ new_qa_logit_layer = nn.Linear(hidden_dim, num_labels, bias=False)
+
+ new_qa_logit_layer.to(cur_qa_logit_layer.weight.device)
+
+ # initialize all new labels
+ self._init_weights(new_qa_logit_layer)
+
+ # Copy labels from the previous weights
+ num_labels_to_copy = min(cur_qa_labels, num_labels)
+ new_qa_logit_layer.weight.data[:num_labels_to_copy, :] = cur_qa_logit_layer.weight.data[:num_labels_to_copy, :]
+ if getattr(cur_qa_logit_layer, "bias", None) is not None:
+ new_qa_logit_layer.bias.data[:num_labels_to_copy] = cur_qa_logit_layer.bias.data[:num_labels_to_copy]
+
+ return new_qa_logit_layer
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ visual_feats: Optional[torch.FloatTensor] = None,
+ visual_pos: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ visual_attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[LxmertForQuestionAnsweringOutput, tuple[torch.FloatTensor]]:
+ r"""
+ visual_feats (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
+ This input represents visual features. They ROI pooled object features from bounding boxes using a
+ faster-RCNN model)
+
+ These are currently not provided by the transformers library.
+ visual_pos (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_pos_dim)`):
+ This input represents spatial features corresponding to their relative (via index) visual features. The
+ pre-trained LXMERT model expects these spatial features to be normalized bounding boxes on a scale of 0 to
+ 1.
+
+ These are currently not provided by the transformers library.
+ visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ labels (`Torch.Tensor` of shape `(batch_size)`, *optional*):
+ A one-hot representation of the correct answer
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ lxmert_output = self.lxmert(
+ input_ids=input_ids,
+ visual_feats=visual_feats,
+ visual_pos=visual_pos,
+ token_type_ids=token_type_ids,
+ attention_mask=attention_mask,
+ visual_attention_mask=visual_attention_mask,
+ inputs_embeds=inputs_embeds,
+ output_hidden_states=output_hidden_states,
+ output_attentions=output_attentions,
+ return_dict=return_dict,
+ )
+
+ pooled_output = lxmert_output[2]
+ answer_score = self.answer_head(pooled_output)
+ loss = None
+ if labels is not None:
+ loss = self.loss(answer_score.view(-1, self.num_qa_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (answer_score,) + lxmert_output[3:]
+ return (loss,) + output if loss is not None else output
+
+ return LxmertForQuestionAnsweringOutput(
+ loss=loss,
+ question_answering_score=answer_score,
+ language_hidden_states=lxmert_output.language_hidden_states,
+ vision_hidden_states=lxmert_output.vision_hidden_states,
+ language_attentions=lxmert_output.language_attentions,
+ vision_attentions=lxmert_output.vision_attentions,
+ cross_encoder_attentions=lxmert_output.cross_encoder_attentions,
+ )
+
+
+__all__ = [
+ "LxmertEncoder",
+ "LxmertForPreTraining",
+ "LxmertForQuestionAnswering",
+ "LxmertModel",
+ "LxmertPreTrainedModel",
+ "LxmertVisualFeatureEncoder",
+ "LxmertXLayer",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/modeling_tf_lxmert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/modeling_tf_lxmert.py
new file mode 100644
index 0000000000000000000000000000000000000000..aee9fb7857967945f03f5c0cfcfaa0e1adb7b50e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/modeling_tf_lxmert.py
@@ -0,0 +1,1660 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors, The HuggingFace Inc. team, and the
+# Lxmert Authors.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TF 2.0 LXMERT model."""
+
+from __future__ import annotations
+
+import warnings
+from dataclasses import dataclass
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_utils import (
+ TFModelInputType,
+ TFPreTrainedModel,
+ get_initializer,
+ keras,
+ keras_serializable,
+ shape_list,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, stable_softmax
+from ...utils import (
+ ModelOutput,
+ add_code_sample_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+ replace_return_docstrings,
+)
+from .configuration_lxmert import LxmertConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "unc-nlp/lxmert-base-uncased"
+_CONFIG_FOR_DOC = "LxmertConfig"
+
+
+@dataclass
+class TFLxmertModelOutput(ModelOutput):
+ """
+ Lxmert's outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,
+ visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship"
+ encoder")
+
+
+ Args:
+ language_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the language encoder.
+ vision_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the visual encoder.
+ pooled_output (`tf.Tensor` of shape `(batch_size, hidden_size)`):
+ Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed
+ by a Linear layer and a Tanh activation function. The Linear
+ language_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+ vision_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+ language_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ vision_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ cross_encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ """
+
+ language_output: tf.Tensor | None = None
+ vision_output: tf.Tensor | None = None
+ pooled_output: tf.Tensor | None = None
+ language_hidden_states: tuple[tf.Tensor] | None = None
+ vision_hidden_states: tuple[tf.Tensor] | None = None
+ language_attentions: tuple[tf.Tensor] | None = None
+ vision_attentions: tuple[tf.Tensor] | None = None
+ cross_encoder_attentions: tuple[tf.Tensor] | None = None
+
+
+@dataclass
+class TFLxmertForPreTrainingOutput(ModelOutput):
+ """
+ Output type of [`LxmertForPreTraining`].
+
+ Args:
+ loss (*optional*, returned when `labels` is provided, `tf.Tensor` of shape `(1,)`):
+ Total loss as the sum of the masked language modeling loss and the next sequence prediction
+ (classification) loss.
+ prediction_logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ cross_relationship_score (`tf.Tensor` of shape `(batch_size, 2)`):
+ Prediction scores of the textual matching objective (classification) head (scores of True/False
+ continuation before SoftMax).
+ question_answering_score (`tf.Tensor` of shape `(batch_size, n_qa_answers)`):
+ Prediction scores of question answering objective (classification).
+ language_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+ vision_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+ language_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ vision_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+ cross_encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads.
+
+ """
+
+ loss: tf.Tensor | None = None
+ prediction_logits: tf.Tensor | None = None
+ cross_relationship_score: tf.Tensor | None = None
+ question_answering_score: tf.Tensor | None = None
+ language_hidden_states: tuple[tf.Tensor] | None = None
+ vision_hidden_states: tuple[tf.Tensor] | None = None
+ language_attentions: tuple[tf.Tensor] | None = None
+ vision_attentions: tuple[tf.Tensor] | None = None
+ cross_encoder_attentions: tuple[tf.Tensor] | None = None
+
+
+class TFLxmertVisualFeatureEncoder(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ # Object feature encoding
+ self.visn_fc = keras.layers.Dense(
+ config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="visn_fc",
+ )
+ self.visn_layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="visn_layer_norm")
+
+ # Box position encoding
+ self.box_fc = keras.layers.Dense(
+ config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="box_fc",
+ )
+ self.box_layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="box_layer_norm")
+
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.feat_dim = config.visual_feat_dim
+ self.pos_dim = config.visual_pos_dim
+ self.config = config
+
+ def call(self, visn_input, training=False):
+ feats, boxes = visn_input
+
+ x = self.visn_fc(feats)
+ x = self.visn_layer_norm(x)
+ y = self.box_fc(boxes)
+ y = self.box_layer_norm(y)
+ output = (x + y) / 2
+
+ output = self.dropout(output, training=training)
+ return output
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "visn_fc", None) is not None:
+ with tf.name_scope(self.visn_fc.name):
+ self.visn_fc.build([None, None, self.feat_dim])
+ if getattr(self, "visn_layer_norm", None) is not None:
+ with tf.name_scope(self.visn_layer_norm.name):
+ self.visn_layer_norm.build([None, None, self.config.hidden_size])
+ if getattr(self, "box_fc", None) is not None:
+ with tf.name_scope(self.box_fc.name):
+ self.box_fc.build([None, None, self.pos_dim])
+ if getattr(self, "box_layer_norm", None) is not None:
+ with tf.name_scope(self.box_layer_norm.name):
+ self.box_layer_norm.build([None, None, self.config.hidden_size])
+
+
+class TFLxmertEmbeddings(keras.layers.Layer):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.max_position_embeddings = config.max_position_embeddings
+ self.initializer_range = config.initializer_range
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+
+ def build(self, input_shape=None):
+ with tf.name_scope("word_embeddings"):
+ self.weight = self.add_weight(
+ name="weight",
+ shape=[self.config.vocab_size, self.hidden_size],
+ initializer=get_initializer(initializer_range=self.initializer_range),
+ )
+
+ with tf.name_scope("token_type_embeddings"):
+ self.token_type_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.config.type_vocab_size, self.hidden_size],
+ initializer=get_initializer(initializer_range=self.initializer_range),
+ )
+
+ with tf.name_scope("position_embeddings"):
+ self.position_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.max_position_embeddings, self.hidden_size],
+ initializer=get_initializer(initializer_range=self.initializer_range),
+ )
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+ def call(self, input_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
+ """
+ Applies embedding based on inputs tensor.
+
+ Returns:
+ final_embeddings (`tf.Tensor`): output embedding tensor.
+ """
+ assert not (input_ids is None and inputs_embeds is None)
+
+ if input_ids is not None:
+ check_embeddings_within_bounds(input_ids, self.config.vocab_size)
+ inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
+
+ input_shape = shape_list(inputs_embeds)[:-1]
+
+ if token_type_ids is None:
+ token_type_ids = tf.fill(dims=input_shape, value=0)
+
+ position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
+ position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
+ token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
+ final_embeddings = inputs_embeds + position_embeds + token_type_embeds
+ final_embeddings = self.LayerNorm(inputs=final_embeddings)
+ final_embeddings = self.dropout(inputs=final_embeddings, training=training)
+
+ return final_embeddings
+
+
+class TFLxmertAttention(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads}"
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ assert config.hidden_size % config.num_attention_heads == 0
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = keras.layers.Dense(
+ self.all_head_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="query",
+ )
+ self.key = keras.layers.Dense(
+ self.all_head_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="key",
+ )
+ self.value = keras.layers.Dense(
+ self.all_head_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="value",
+ )
+
+ self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob)
+ self.ctx_dim = config.hidden_size
+ self.config = config
+
+ def transpose_for_scores(self, x, batch_size):
+ # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
+ x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
+ return tf.transpose(x, perm=[0, 2, 1, 3])
+
+ def call(self, hidden_states, context, attention_mask, output_attentions, training=False):
+ batch_size = shape_list(hidden_states)[0]
+ mixed_query_layer = self.query(hidden_states)
+ mixed_key_layer = self.key(context)
+ mixed_value_layer = self.value(context)
+
+ query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
+ key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
+ value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = tf.matmul(
+ query_layer, key_layer, transpose_b=True
+ ) # (batch size, num_heads, seq_len_q, seq_len_k)
+ dk = tf.cast(shape_list(key_layer)[-1], dtype=attention_scores.dtype) # scale attention_scores
+ attention_scores = attention_scores / tf.math.sqrt(dk)
+
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in TFLxmertModel call() function)
+ attention_mask = tf.cast(attention_mask, dtype=attention_scores.dtype)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = stable_softmax(attention_scores, axis=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs, training=training)
+ context_layer = tf.matmul(attention_probs, value_layer)
+
+ context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
+ context_layer = tf.reshape(
+ context_layer, (batch_size, -1, self.all_head_size)
+ ) # (batch_size, seq_len_q, all_head_size)
+
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "query", None) is not None:
+ with tf.name_scope(self.query.name):
+ self.query.build([None, None, self.config.hidden_size])
+ if getattr(self, "key", None) is not None:
+ with tf.name_scope(self.key.name):
+ self.key.build([None, None, self.ctx_dim])
+ if getattr(self, "value", None) is not None:
+ with tf.name_scope(self.value.name):
+ self.value.build([None, None, self.ctx_dim])
+
+
+class TFLxmertIntermediate(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(
+ config.intermediate_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="dense",
+ )
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = get_tf_activation(config.hidden_act)
+ else:
+ self.intermediate_act_fn = config.hidden_act
+ self.config = config
+
+ def call(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+
+
+class TFLxmertOutput(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(
+ config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="dense",
+ )
+
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.config = config
+
+ def call(self, hidden_states, input_tensor, training=False):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states, training)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.intermediate_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+class TFLxmertAttentionOutput(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(
+ config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="dense",
+ )
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.config = config
+
+ def call(self, hidden_states, input_tensor, training=False):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+class TFLxmertSelfAttentionLayer(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.self = TFLxmertAttention(config, name="self")
+ self.attention_output = TFLxmertAttentionOutput(config, name="output")
+
+ def call(self, input_tensor, attention_mask, output_attentions, training=False):
+ # Self attention attends to itself, thus keys and queries are the same (input_tensor).
+ self_output = self.self(input_tensor, input_tensor, attention_mask, output_attentions)
+ if output_attentions:
+ attention_probs = self_output[1]
+ attention_output = self.attention_output(self_output[0], input_tensor)
+ return (attention_output, attention_probs) if output_attentions else (attention_output,)
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self", None) is not None:
+ with tf.name_scope(self.self.name):
+ self.self.build(None)
+ if getattr(self, "attention_output", None) is not None:
+ with tf.name_scope(self.attention_output.name):
+ self.attention_output.build(None)
+
+
+class TFLxmertCrossAttentionLayer(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.att = TFLxmertAttention(config, name="att")
+ self.attention_output = TFLxmertAttentionOutput(config, name="output")
+
+ def call(
+ self,
+ input_tensor,
+ ctx_tensor,
+ ctx_att_mask,
+ output_attentions=False,
+ training=False,
+ ):
+ output = self.att(input_tensor, ctx_tensor, ctx_att_mask, output_attentions, training=training)
+ if output_attentions:
+ attention_probs = output[1]
+ attention_output = self.attention_output(output[0], input_tensor, training=training)
+ outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "att", None) is not None:
+ with tf.name_scope(self.att.name):
+ self.att.build(None)
+ if getattr(self, "attention_output", None) is not None:
+ with tf.name_scope(self.attention_output.name):
+ self.attention_output.build(None)
+
+
+class TFLxmertLayer(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.attention = TFLxmertSelfAttentionLayer(config, name="attention")
+ self.intermediate = TFLxmertIntermediate(config, name="intermediate")
+ self.transformer_output = TFLxmertOutput(config, name="output")
+
+ def call(self, hidden_states, attention_mask, output_attentions, training=False):
+ attention_outputs = self.attention(hidden_states, attention_mask, output_attentions, training=training)
+ attention_output = attention_outputs[0]
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.transformer_output(intermediate_output, attention_output, training=training)
+ outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "attention", None) is not None:
+ with tf.name_scope(self.attention.name):
+ self.attention.build(None)
+ if getattr(self, "intermediate", None) is not None:
+ with tf.name_scope(self.intermediate.name):
+ self.intermediate.build(None)
+ if getattr(self, "transformer_output", None) is not None:
+ with tf.name_scope(self.transformer_output.name):
+ self.transformer_output.build(None)
+
+
+class TFLxmertXLayer(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.visual_attention = TFLxmertCrossAttentionLayer(config, name="visual_attention")
+
+ # Self-attention Layers
+ self.lang_self_att = TFLxmertSelfAttentionLayer(config, name="lang_self_att")
+ self.visn_self_att = TFLxmertSelfAttentionLayer(config, name="visn_self_att")
+
+ # Intermediate and Output Layers (FFNs)
+ self.lang_inter = TFLxmertIntermediate(config, name="lang_inter")
+ self.lang_output = TFLxmertOutput(config, name="lang_output")
+ self.visn_inter = TFLxmertIntermediate(config, name="visn_inter")
+ self.visn_output = TFLxmertOutput(config, name="visn_output")
+
+ def cross_att(
+ self,
+ lang_input,
+ lang_attention_mask,
+ visn_input,
+ visn_attention_mask,
+ output_attentions,
+ training=False,
+ ):
+ # Cross Attention
+
+ # Keras saving and loading model *does not work* with the same inputs for two layers.
+ lang_attention_lang_input = tf.identity(lang_input)
+ visn_attention_lang_input = tf.identity(lang_input)
+ lang_attention_visn_input = tf.identity(visn_input)
+ visn_attention_visn_input = tf.identity(visn_input)
+
+ lang_att_output = self.visual_attention(
+ lang_attention_lang_input,
+ lang_attention_visn_input,
+ visn_attention_mask,
+ output_attentions=output_attentions,
+ training=training,
+ )
+ visn_att_output = self.visual_attention(
+ visn_attention_visn_input,
+ visn_attention_lang_input,
+ lang_attention_mask,
+ output_attentions=output_attentions,
+ training=training,
+ )
+ return lang_att_output, visn_att_output
+
+ def self_att(
+ self,
+ lang_input,
+ lang_attention_mask,
+ visn_input,
+ visn_attention_mask,
+ training=False,
+ ):
+ # Self Attention
+ output_attentions = False
+ lang_att_output = self.lang_self_att(lang_input, lang_attention_mask, output_attentions, training=training)
+ visn_att_output = self.visn_self_att(visn_input, visn_attention_mask, output_attentions, training=training)
+ return lang_att_output[0], visn_att_output[0]
+
+ def output_fc(self, lang_input, visn_input, training=False):
+ # FC layers
+ lang_inter_output = self.lang_inter(lang_input)
+ visn_inter_output = self.visn_inter(visn_input)
+
+ # Layer output
+ lang_output = self.lang_output(lang_inter_output, lang_input, training)
+ visn_output = self.visn_output(visn_inter_output, visn_input, training)
+ return lang_output, visn_output
+
+ def call(
+ self,
+ lang_feats,
+ lang_attention_mask,
+ visn_feats,
+ visn_attention_mask,
+ output_attentions,
+ training=False,
+ ):
+ lang_att_output = lang_feats
+ visn_att_output = visn_feats
+
+ lang_att_output, visn_att_output = self.cross_att(
+ lang_att_output,
+ lang_attention_mask,
+ visn_att_output,
+ visn_attention_mask,
+ output_attentions,
+ training=training,
+ )
+ attention_probs = lang_att_output[1:]
+ lang_att_output, visn_att_output = self.self_att(
+ lang_att_output[0],
+ lang_attention_mask,
+ visn_att_output[0],
+ visn_attention_mask,
+ training=training,
+ )
+ lang_output, visn_output = self.output_fc(lang_att_output, visn_att_output, training=training)
+
+ return (lang_output, visn_output, attention_probs[0]) if output_attentions else (lang_output, visn_output)
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "visual_attention", None) is not None:
+ with tf.name_scope(self.visual_attention.name):
+ self.visual_attention.build(None)
+ if getattr(self, "lang_self_att", None) is not None:
+ with tf.name_scope(self.lang_self_att.name):
+ self.lang_self_att.build(None)
+ if getattr(self, "visn_self_att", None) is not None:
+ with tf.name_scope(self.visn_self_att.name):
+ self.visn_self_att.build(None)
+ if getattr(self, "lang_inter", None) is not None:
+ with tf.name_scope(self.lang_inter.name):
+ self.lang_inter.build(None)
+ if getattr(self, "lang_output", None) is not None:
+ with tf.name_scope(self.lang_output.name):
+ self.lang_output.build(None)
+ if getattr(self, "visn_inter", None) is not None:
+ with tf.name_scope(self.visn_inter.name):
+ self.visn_inter.build(None)
+ if getattr(self, "visn_output", None) is not None:
+ with tf.name_scope(self.visn_output.name):
+ self.visn_output.build(None)
+
+
+class TFLxmertEncoder(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.visn_fc = TFLxmertVisualFeatureEncoder(config, name="visn_fc")
+
+ # Number of layers
+ self.num_l_layers = config.l_layers
+ self.num_x_layers = config.x_layers
+ self.num_r_layers = config.r_layers
+
+ # Layers
+ # Using self.layer instead of self.l_layer to support loading BERT weights.
+ self.layer = [TFLxmertLayer(config, name=f"layer_._{i}") for i in range(self.num_l_layers)]
+ self.x_layers = [TFLxmertXLayer(config, name=f"x_layers_._{i}") for i in range(self.num_x_layers)]
+ self.r_layers = [TFLxmertLayer(config, name=f"r_layers_._{i}") for i in range(self.num_r_layers)]
+ self.config = config
+
+ def call(
+ self,
+ lang_feats=None,
+ lang_attention_mask=None,
+ visual_feats=None,
+ visual_pos=None,
+ visual_attention_mask=None,
+ output_attentions=None,
+ training=False,
+ ):
+ vision_hidden_states = ()
+ language_hidden_states = ()
+ vision_attentions = () if output_attentions or self.config.output_attentions else None
+ language_attentions = () if output_attentions or self.config.output_attentions else None
+ cross_encoder_attentions = () if output_attentions or self.config.output_attentions else None
+
+ visual_feats = self.visn_fc([visual_feats, visual_pos], training=training)
+
+ # Run language layers
+ for layer_module in self.layer:
+ l_outputs = layer_module(lang_feats, lang_attention_mask, output_attentions, training=training)
+ lang_feats = l_outputs[0]
+ language_hidden_states = language_hidden_states + (lang_feats,)
+ if language_attentions is not None:
+ language_attentions = language_attentions + (l_outputs[1],)
+
+ # Run relational layers
+ for layer_module in self.r_layers:
+ v_outputs = layer_module(
+ visual_feats,
+ visual_attention_mask,
+ output_attentions,
+ training=training,
+ )
+ visual_feats = v_outputs[0]
+ vision_hidden_states = vision_hidden_states + (visual_feats,)
+ if vision_attentions is not None:
+ vision_attentions = vision_attentions + (v_outputs[1],)
+
+ # Run cross-modality layers
+ for layer_module in self.x_layers:
+ x_outputs = layer_module(
+ lang_feats,
+ lang_attention_mask,
+ visual_feats,
+ visual_attention_mask,
+ output_attentions,
+ training=training,
+ )
+ lang_feats, visual_feats = x_outputs[:2]
+ vision_hidden_states = vision_hidden_states + (visual_feats,)
+ language_hidden_states = language_hidden_states + (lang_feats,)
+ if cross_encoder_attentions is not None:
+ cross_encoder_attentions = cross_encoder_attentions + (x_outputs[2],)
+
+ visual_encoder_outputs = (
+ vision_hidden_states,
+ vision_attentions if output_attentions else None,
+ )
+ lang_encoder_outputs = (
+ language_hidden_states,
+ language_attentions if output_attentions else None,
+ )
+
+ return (
+ visual_encoder_outputs,
+ lang_encoder_outputs,
+ cross_encoder_attentions if output_attentions else None,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "visn_fc", None) is not None:
+ with tf.name_scope(self.visn_fc.name):
+ self.visn_fc.build(None)
+ if getattr(self, "layer", None) is not None:
+ for layer in self.layer:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+ if getattr(self, "x_layers", None) is not None:
+ for layer in self.x_layers:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+ if getattr(self, "r_layers", None) is not None:
+ for layer in self.r_layers:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+@keras_serializable
+class TFLxmertMainLayer(keras.layers.Layer):
+ config_class = LxmertConfig
+
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.num_l_layers = config.l_layers
+ self.num_x_layers = config.x_layers
+ self.num_r_layers = config.r_layers
+ self.initializer_range = config.initializer_range
+ self.output_attentions = config.output_attentions
+ self.output_hidden_states = config.output_hidden_states
+ self.return_dict = config.use_return_dict
+ self.embeddings = TFLxmertEmbeddings(config, name="embeddings")
+ self.encoder = TFLxmertEncoder(config, name="encoder")
+ self.pooler = TFLxmertPooler(config, name="pooler")
+ self.config = config
+
+ def get_input_embeddings(self):
+ return self.embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.weight = value
+ self.embeddings.vocab_size = shape_list(value)[0]
+
+ def _prune_heads(self, heads_to_prune):
+ raise NotImplementedError
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids=None,
+ visual_feats=None,
+ visual_pos=None,
+ attention_mask=None,
+ visual_attention_mask=None,
+ token_type_ids=None,
+ inputs_embeds=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ training=False,
+ ):
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+ if visual_pos is None or visual_feats is None:
+ raise ValueError("visual_feats and visual_pos cannot be `None` in LXMERT's `call` method.")
+
+ if attention_mask is None:
+ attention_mask = tf.fill(input_shape, 1)
+
+ if token_type_ids is None:
+ token_type_ids = tf.fill(input_shape, 0)
+
+ # Positional Word Embeddings
+ embedding_output = self.embeddings(input_ids, token_type_ids, inputs_embeds, training)
+
+ # We create a 3D attention mask from a 2D tensor mask.
+ # Sizes are [batch_size, 1, 1, to_seq_length]
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+ # this attention mask is more simple than the triangular masking of causal attention
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+ extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1]))
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and -10000.0 for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+
+ extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
+ one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
+ ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
+ extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
+
+ if visual_attention_mask is not None:
+ extended_visual_attention_mask = tf.reshape(visual_attention_mask, (input_shape[0], 1, 1, input_shape[1]))
+ extended_visual_attention_mask = tf.expand_dims(tf.expand_dims(visual_attention_mask, axis=1), axis=1)
+
+ extended_visual_attention_mask = tf.cast(extended_visual_attention_mask, dtype=embedding_output.dtype)
+ extended_visual_attention_mask = tf.multiply(
+ tf.subtract(one_cst, extended_visual_attention_mask), ten_thousand_cst
+ )
+ else:
+ extended_visual_attention_mask = None
+
+ # Run Lxmert encoder
+ encoder_outputs = self.encoder(
+ embedding_output,
+ extended_attention_mask,
+ visual_feats,
+ visual_pos,
+ extended_visual_attention_mask,
+ output_attentions,
+ training,
+ )
+ visual_encoder_outputs, lang_encoder_outputs = encoder_outputs[:2]
+ vision_hidden_states = visual_encoder_outputs[0]
+ language_hidden_states = lang_encoder_outputs[0]
+
+ all_attentions = ()
+ if output_attentions:
+ language_attentions = lang_encoder_outputs[1]
+ vision_attentions = visual_encoder_outputs[1]
+ cross_encoder_attentions = encoder_outputs[2]
+ all_attentions = (
+ language_attentions,
+ vision_attentions,
+ cross_encoder_attentions,
+ )
+
+ hidden_states = (language_hidden_states, vision_hidden_states) if output_hidden_states else ()
+
+ visual_output = vision_hidden_states[-1]
+ lang_output = language_hidden_states[-1]
+ pooled_output = self.pooler(lang_output)
+
+ if not return_dict:
+ return (lang_output, visual_output, pooled_output) + hidden_states + all_attentions
+
+ return TFLxmertModelOutput(
+ pooled_output=pooled_output,
+ language_output=lang_output,
+ vision_output=visual_output,
+ language_hidden_states=language_hidden_states if output_hidden_states else None,
+ vision_hidden_states=vision_hidden_states if output_hidden_states else None,
+ language_attentions=language_attentions if output_attentions else None,
+ vision_attentions=vision_attentions if output_attentions else None,
+ cross_encoder_attentions=cross_encoder_attentions if output_attentions else None,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embeddings", None) is not None:
+ with tf.name_scope(self.embeddings.name):
+ self.embeddings.build(None)
+ if getattr(self, "encoder", None) is not None:
+ with tf.name_scope(self.encoder.name):
+ self.encoder.build(None)
+ if getattr(self, "pooler", None) is not None:
+ with tf.name_scope(self.pooler.name):
+ self.pooler.build(None)
+
+
+class TFLxmertPreTrainedModel(TFPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = LxmertConfig
+ base_model_prefix = "lxmert"
+
+ @property
+ def dummy_inputs(self):
+ """
+ Dummy inputs to build the network.
+
+ Returns:
+ tf.Tensor with dummy inputs
+ """
+ batch_size = 2
+ num_visual_features = 10
+ input_ids = tf.constant([[3, 5, 6], [2, 3, 4]], dtype=tf.int32)
+ visual_feats = tf.random.uniform((batch_size, num_visual_features, self.config.visual_feat_dim))
+ visual_pos = tf.random.uniform((batch_size, num_visual_features, 4))
+
+ return {
+ "input_ids": input_ids,
+ "visual_feats": visual_feats,
+ "visual_pos": visual_pos,
+ }
+
+ @property
+ def input_signature(self):
+ return {
+ "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
+ "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
+ "visual_feats": tf.TensorSpec((None, None, self.config.visual_feat_dim), tf.float32, name="visual_feats"),
+ "visual_pos": tf.TensorSpec((None, None, 4), tf.float32, name="visual_pos"),
+ "visual_attention_mask": tf.TensorSpec((None, None), tf.int32, name="visual_attention_mask"),
+ "token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
+ }
+
+
+LXMERT_START_DOCSTRING = r"""
+
+ The LXMERT model was proposed in [LXMERT: Learning Cross-Modality Encoder Representations from
+ Transformers](https://huggingface.co/papers/1908.07490) by Hao Tan and Mohit Bansal. It's a vision and language transformer
+ model, pre-trained on a variety of multi-modal datasets comprising of GQA, VQAv2.0, MCSCOCO captions, and Visual
+ genome, using a combination of masked language modeling, region of interest feature regression, cross entropy loss
+ for question answering attribute prediction, and object tag prediction.
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Parameters:
+ config ([`LxmertConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+LXMERT_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
+ [`PreTrainedTokenizer.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ visual_feats (`tf.Tensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
+ This input represents visual features. They ROI pooled object features from bounding boxes using a
+ faster-RCNN model)
+
+ These are currently not provided by the transformers library.
+ visual_pos (`tf.Tensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
+ This input represents spatial features corresponding to their relative (via index) visual features. The
+ pre-trained LXMERT model expects these spatial features to be normalized bounding boxes on a scale of 0 to
+ 1.
+
+ These are currently not provided by the transformers library.
+ attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ visual_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ MMask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ token_type_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+ config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+ used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+ eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+"""
+
+
+@add_start_docstrings(
+ "The bare Lxmert Model transformer outputting raw hidden-states without any specific head on top.",
+ LXMERT_START_DOCSTRING,
+)
+class TFLxmertModel(TFLxmertPreTrainedModel):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.lxmert = TFLxmertMainLayer(config, name="lxmert")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING)
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFLxmertModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ visual_feats: tf.Tensor | None = None,
+ visual_pos: tf.Tensor | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ visual_attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ) -> tuple | TFLxmertModelOutput:
+ outputs = self.lxmert(
+ input_ids,
+ visual_feats,
+ visual_pos,
+ attention_mask,
+ visual_attention_mask,
+ token_type_ids,
+ inputs_embeds,
+ output_attentions,
+ output_hidden_states,
+ return_dict,
+ training,
+ )
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "lxmert", None) is not None:
+ with tf.name_scope(self.lxmert.name):
+ self.lxmert.build(None)
+
+
+class TFLxmertPooler(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(
+ config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ activation="tanh",
+ name="dense",
+ )
+ self.config = config
+
+ def call(self, hidden_states):
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ return pooled_output
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->Lxmert
+class TFLxmertPredictionHeadTransform(keras.layers.Layer):
+ def __init__(self, config: LxmertConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="dense",
+ )
+
+ if isinstance(config.hidden_act, str):
+ self.transform_act_fn = get_tf_activation(config.hidden_act)
+ else:
+ self.transform_act_fn = config.hidden_act
+
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(inputs=hidden_states)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->Lxmert
+class TFLxmertLMPredictionHead(keras.layers.Layer):
+ def __init__(self, config: LxmertConfig, input_embeddings: keras.layers.Layer, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.hidden_size = config.hidden_size
+
+ self.transform = TFLxmertPredictionHeadTransform(config, name="transform")
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.input_embeddings = input_embeddings
+
+ def build(self, input_shape=None):
+ self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "transform", None) is not None:
+ with tf.name_scope(self.transform.name):
+ self.transform.build(None)
+
+ def get_output_embeddings(self) -> keras.layers.Layer:
+ return self.input_embeddings
+
+ def set_output_embeddings(self, value: tf.Variable):
+ self.input_embeddings.weight = value
+ self.input_embeddings.vocab_size = shape_list(value)[0]
+
+ def get_bias(self) -> dict[str, tf.Variable]:
+ return {"bias": self.bias}
+
+ def set_bias(self, value: tf.Variable):
+ self.bias = value["bias"]
+ self.config.vocab_size = shape_list(value["bias"])[0]
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ hidden_states = self.transform(hidden_states=hidden_states)
+ seq_length = shape_list(hidden_states)[1]
+ hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
+ hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
+ hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size])
+ hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
+
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->Lxmert
+class TFLxmertMLMHead(keras.layers.Layer):
+ def __init__(self, config: LxmertConfig, input_embeddings: keras.layers.Layer, **kwargs):
+ super().__init__(**kwargs)
+
+ self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions")
+
+ def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
+ prediction_scores = self.predictions(hidden_states=sequence_output)
+
+ return prediction_scores
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "predictions", None) is not None:
+ with tf.name_scope(self.predictions.name):
+ self.predictions.build(None)
+
+
+class TFLxmertPreTrainingHeads(keras.layers.Layer):
+ def __init__(self, config, input_embeddings, **kwargs):
+ super().__init__(**kwargs)
+ self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions")
+
+ self.seq_relationship = keras.layers.Dense(
+ 2,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="seq_relationship",
+ )
+ self.config = config
+
+ def call(self, sequence_output, pooled_output):
+ prediction_scores = self.predictions(sequence_output)
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return prediction_scores, seq_relationship_score
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "predictions", None) is not None:
+ with tf.name_scope(self.predictions.name):
+ self.predictions.build(None)
+ if getattr(self, "seq_relationship", None) is not None:
+ with tf.name_scope(self.seq_relationship.name):
+ self.seq_relationship.build([None, None, self.config.hidden_size])
+
+
+class TFLxmertVisualAnswerHead(keras.layers.Layer):
+ def __init__(self, config, num_labels, **kwargs):
+ super().__init__(**kwargs)
+ hid_dim = config.hidden_size
+ self.dense = keras.layers.Dense(
+ hid_dim * 2,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="logit_fc_._0",
+ )
+ self.activation = get_tf_activation("gelu")
+ self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="logit_fc_._2")
+ self.dense_1 = keras.layers.Dense(
+ num_labels,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="logit_fc_._3",
+ )
+ self.hid_dim = hid_dim
+
+ def call(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.activation(hidden_states)
+ hidden_states = self.layer_norm(hidden_states)
+ hidden_states = self.dense_1(hidden_states)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.hid_dim])
+ if getattr(self, "layer_norm", None) is not None:
+ with tf.name_scope(self.layer_norm.name):
+ self.layer_norm.build([None, self.hid_dim * 2])
+ if getattr(self, "dense_1", None) is not None:
+ with tf.name_scope(self.dense_1.name):
+ self.dense_1.build([None, None, self.hid_dim * 2])
+
+
+class TFLxmertVisualObjHead(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.transform = TFLxmertPredictionHeadTransform(config, name="transform")
+
+ # Decide the use of visual losses
+ visual_losses = {}
+ if config.visual_obj_loss:
+ visual_losses["obj"] = {"shape": (-1,), "num": config.num_object_labels}
+ if config.visual_attr_loss:
+ visual_losses["attr"] = {"shape": (-1,), "num": config.num_attr_labels}
+ if config.visual_feat_loss:
+ visual_losses["feat"] = {"shape": (-1, 2048), "num": config.visual_feat_dim}
+ self.visual_losses = visual_losses
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder_dict = {
+ key: keras.layers.Dense(
+ self.visual_losses[key]["num"],
+ kernel_initializer=get_initializer(config.initializer_range),
+ name=f"decoder_dict.{key}",
+ )
+ for key in self.visual_losses
+ }
+ self.config = config
+
+ def call(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ output = {}
+ for key in self.visual_losses:
+ output[key] = self.decoder_dict[key](hidden_states)
+ return output
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "transform", None) is not None:
+ with tf.name_scope(self.transform.name):
+ self.transform.build(None)
+ if getattr(self, "decoder_dict", None) is not None:
+ for layer in self.decoder_dict.values():
+ with tf.name_scope(layer.name):
+ layer.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings("""Lxmert Model with a `language modeling` head on top.""", LXMERT_START_DOCSTRING)
+class TFLxmertForPreTraining(TFLxmertPreTrainedModel):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.config = config
+ self.num_qa_labels = config.num_qa_labels
+ self.visual_loss_normalizer = config.visual_loss_normalizer
+
+ # Use of pretraining tasks
+ self.task_mask_lm = config.task_mask_lm
+ self.task_obj_predict = config.task_obj_predict
+ self.task_matched = config.task_matched
+ self.task_qa = config.task_qa
+
+ # Lxmert backbone
+ self.lxmert = TFLxmertMainLayer(config, name="lxmert")
+
+ # Pre-training heads
+ self.cls = TFLxmertPreTrainingHeads(config, self.lxmert.embeddings, name="cls")
+ if self.task_obj_predict:
+ self.obj_predict_head = TFLxmertVisualObjHead(config, name="obj_predict_head")
+ if self.task_qa:
+ self.answer_head = TFLxmertVisualAnswerHead(config, self.num_qa_labels, name="answer_head")
+
+ # Loss functions
+ self.loss_fcts = {
+ "l2": keras.losses.Huber(delta=1.0, name="huber_loss"),
+ "visn_ce": keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+ "ce": keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+ }
+
+ visual_losses = {}
+ if config.visual_obj_loss:
+ visual_losses["obj"] = {
+ "shape": (-1,),
+ "num": config.num_object_labels,
+ "loss": "visn_ce",
+ }
+ if config.visual_attr_loss:
+ visual_losses["attr"] = {
+ "shape": (-1,),
+ "num": config.num_attr_labels,
+ "loss": "visn_ce",
+ }
+ if config.visual_feat_loss:
+ visual_losses["feat"] = {
+ "shape": (-1, config.visual_feat_dim),
+ "num": config.visual_feat_dim,
+ "loss": "l2",
+ }
+ self.visual_losses = visual_losses
+
+ @property
+ def dummy_inputs(self):
+ """
+ Dummy inputs to build the network.
+
+ Returns:
+ tf.Tensor with dummy inputs
+ """
+ batch_size = 2
+ num_visual_features = 10
+ input_ids = tf.constant([[3, 5, 6], [2, 3, 4]], dtype=tf.int32)
+ visual_feats = tf.random.uniform((batch_size, num_visual_features, self.config.visual_feat_dim))
+ visual_pos = tf.random.uniform((batch_size, num_visual_features, 4))
+
+ if self.config.task_obj_predict:
+ obj_labels = {}
+ if self.config.visual_attr_loss and self.config.task_obj_predict:
+ obj_labels["attr"] = (
+ tf.ones([batch_size, num_visual_features]),
+ tf.ones([batch_size, num_visual_features]),
+ )
+ if self.config.visual_feat_loss and self.config.task_obj_predict:
+ obj_labels["feat"] = (
+ tf.ones([batch_size, num_visual_features, self.config.visual_feat_dim]),
+ tf.ones([batch_size, num_visual_features]),
+ )
+ if self.config.visual_obj_loss and self.config.task_obj_predict:
+ obj_labels["obj"] = (
+ tf.ones([batch_size, num_visual_features]),
+ tf.ones([batch_size, num_visual_features]),
+ )
+
+ return {
+ **{
+ "input_ids": input_ids,
+ "visual_feats": visual_feats,
+ "visual_pos": visual_pos,
+ },
+ **({"obj_labels": obj_labels} if self.config.task_obj_predict else {}),
+ }
+
+ def get_lm_head(self):
+ return self.cls.predictions
+
+ def get_prefix_bias_name(self):
+ warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
+ return self.name + "/" + self.cls.name + "/" + self.cls.predictions.name
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=TFLxmertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ visual_feats: tf.Tensor | None = None,
+ visual_pos: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ visual_attention_mask: tf.Tensor | None = None,
+ token_type_ids: tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ masked_lm_labels: tf.Tensor | None = None,
+ obj_labels: dict[str, tuple[tf.Tensor, tf.Tensor]] | None = None,
+ matched_label: tf.Tensor | None = None,
+ ans: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ) -> tuple[tf.Tensor] | TFLxmertForPreTrainingOutput:
+ r"""
+ masked_lm_labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ obj_labels (`dict[Str: tuple[tf.Tensor, tf.Tensor]]`, *optional*, defaults to `None`):
+ each key is named after each one of the visual losses and each element of the tuple is of the shape
+ `(batch_size, num_features)` and `(batch_size, num_features, visual_feature_dim)` for each the label id and
+ the label score respectively
+ matched_label (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the whether or not the text input matches the image (classification) loss. Input
+ should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`:
+
+ - 0 indicates that the sentence does not match the image,
+ - 1 indicates that the sentence does match the image.
+ ans (`tf.Tensor` of shape `(batch_size)`, *optional*, defaults to `None`):
+ a one hot representation hof the correct answer *optional*
+
+ Returns:
+ """
+
+ lxmert_output = self.lxmert(
+ input_ids,
+ visual_feats,
+ visual_pos,
+ attention_mask,
+ visual_attention_mask,
+ token_type_ids,
+ inputs_embeds,
+ output_attentions,
+ output_hidden_states,
+ return_dict,
+ training,
+ )
+
+ lang_output, visual_output, pooled_output = (
+ lxmert_output[0],
+ lxmert_output[1],
+ lxmert_output[2],
+ )
+ lang_prediction_scores, cross_relationship_score = self.cls(lang_output, pooled_output)
+ if self.task_qa:
+ answer_score = self.answer_head(pooled_output)
+ else:
+ answer_score = pooled_output[0][0]
+
+ total_loss = (
+ None
+ if (masked_lm_labels is None and matched_label is None and obj_labels is None and ans is None)
+ else tf.constant(0.0)
+ )
+ losses = ()
+ if masked_lm_labels is not None and self.task_mask_lm:
+ masked_lm_loss = self.loss_fcts["ce"](
+ tf.reshape(masked_lm_labels, [-1]),
+ tf.reshape(lang_prediction_scores, [-1, self.config.vocab_size]),
+ )
+ total_loss += masked_lm_loss
+ losses += (masked_lm_loss,)
+ if matched_label is not None and self.task_matched:
+ matched_loss = self.loss_fcts["ce"](
+ tf.reshape(matched_label, [-1]),
+ tf.reshape(cross_relationship_score, [-1, 2]),
+ )
+ total_loss += matched_loss
+ losses += (matched_loss,)
+ if obj_labels is not None and self.task_obj_predict:
+ total_visn_loss = 0.0
+ visn_prediction_scores_dict = self.obj_predict_head(visual_output)
+ for key, key_info in self.visual_losses.items():
+ label, mask_conf = obj_labels[key]
+ output_dim = key_info["num"]
+ loss_fct_name = key_info["loss"]
+ label_shape = key_info["shape"]
+ weight = self.visual_loss_normalizer
+ visn_loss_fct = self.loss_fcts[loss_fct_name]
+ visn_prediction_scores = visn_prediction_scores_dict[key]
+ visn_loss = visn_loss_fct(
+ tf.reshape(label, label_shape),
+ tf.reshape(visn_prediction_scores, [-1, output_dim]),
+ )
+
+ if visn_loss.ndim > 1: # Regression Losses
+ visn_loss = tf.reduce_mean(visn_loss)
+ visn_loss = tf.reduce_mean(visn_loss * tf.cast(tf.reshape(mask_conf, [-1]), visn_loss.dtype)) * weight
+ total_visn_loss += visn_loss
+ losses += (visn_loss,)
+ total_loss += total_visn_loss
+ if ans is not None and self.task_qa:
+ answer_loss = self.loss_fcts["ce"](
+ tf.reshape(ans, [-1]), tf.reshape(answer_score, [-1, self.num_qa_labels])
+ )
+ # exclude "*2" here to match the effect of QA losses.
+ # Previous: (loss *0) for 6 epochs, (loss *2) for 6 epochs. (Used 10 instead of 6 in EMNLP paper)
+ # Now : (loss *1) for 12 epochs
+ #
+ # * 2 # Multiply by 2 because > half of the data will not have label
+ total_loss += answer_loss
+ losses += (answer_loss,)
+ # return total_loss, tf.stack(losses)[tf.new_axis, ...], answer_score.detach()
+
+ if not return_dict:
+ output = (
+ lang_prediction_scores,
+ cross_relationship_score,
+ answer_score,
+ ) + lxmert_output[3:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return TFLxmertForPreTrainingOutput(
+ loss=total_loss,
+ prediction_logits=lang_prediction_scores,
+ cross_relationship_score=cross_relationship_score,
+ question_answering_score=answer_score,
+ language_hidden_states=lxmert_output.language_hidden_states,
+ vision_hidden_states=lxmert_output.vision_hidden_states,
+ language_attentions=lxmert_output.language_attentions,
+ vision_attentions=lxmert_output.vision_attentions,
+ cross_encoder_attentions=lxmert_output.cross_encoder_attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "lxmert", None) is not None:
+ with tf.name_scope(self.lxmert.name):
+ self.lxmert.build(None)
+ if getattr(self, "cls", None) is not None:
+ with tf.name_scope(self.cls.name):
+ self.cls.build(None)
+ if getattr(self, "obj_predict_head", None) is not None:
+ with tf.name_scope(self.obj_predict_head.name):
+ self.obj_predict_head.build(None)
+ if getattr(self, "answer_head", None) is not None:
+ with tf.name_scope(self.answer_head.name):
+ self.answer_head.build(None)
+
+
+__all__ = [
+ "TFLxmertForPreTraining",
+ "TFLxmertMainLayer",
+ "TFLxmertModel",
+ "TFLxmertPreTrainedModel",
+ "TFLxmertVisualFeatureEncoder",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/tokenization_lxmert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/tokenization_lxmert.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd1d7e205ea5588bec3f66307d5ae6c8412ee4a6
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/tokenization_lxmert.py
@@ -0,0 +1,482 @@
+# coding=utf-8
+# Copyright 2020 The Google AI Team, Stanford University and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import collections
+import os
+import unicodedata
+from typing import Optional
+
+from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+
+# Copied from transformers.models.bert.tokenization_bert.load_vocab
+def load_vocab(vocab_file):
+ """Loads a vocabulary file into a dictionary."""
+ vocab = collections.OrderedDict()
+ with open(vocab_file, "r", encoding="utf-8") as reader:
+ tokens = reader.readlines()
+ for index, token in enumerate(tokens):
+ token = token.rstrip("\n")
+ vocab[token] = index
+ return vocab
+
+
+# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
+def whitespace_tokenize(text):
+ """Runs basic whitespace cleaning and splitting on a piece of text."""
+ text = text.strip()
+ if not text:
+ return []
+ tokens = text.split()
+ return tokens
+
+
+# Copied from transformers.models.bert.tokenization_bert.BertTokenizer with bert-base-cased->unc-nlp/lxmert-base-uncased, BERT->Lxmert, BertTokenizer->LxmertTokenizer
+class LxmertTokenizer(PreTrainedTokenizer):
+ r"""
+ Construct a Lxmert tokenizer. Based on WordPiece.
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ File containing the vocabulary.
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ do_basic_tokenize (`bool`, *optional*, defaults to `True`):
+ Whether or not to do basic tokenization before WordPiece.
+ never_split (`Iterable`, *optional*):
+ Collection of tokens which will never be split during tokenization. Only has an effect when
+ `do_basic_tokenize=True`
+ unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ sep_token (`str`, *optional*, defaults to `"[SEP]"`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ pad_token (`str`, *optional*, defaults to `"[PAD]"`):
+ The token used for padding, for example when batching sequences of different lengths.
+ cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ mask_token (`str`, *optional*, defaults to `"[MASK]"`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters.
+
+ This should likely be deactivated for Japanese (see this
+ [issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original Lxmert).
+ clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
+ Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+ extra spaces.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+
+ def __init__(
+ self,
+ vocab_file,
+ do_lower_case=True,
+ do_basic_tokenize=True,
+ never_split=None,
+ unk_token="[UNK]",
+ sep_token="[SEP]",
+ pad_token="[PAD]",
+ cls_token="[CLS]",
+ mask_token="[MASK]",
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ clean_up_tokenization_spaces=True,
+ **kwargs,
+ ):
+ if not os.path.isfile(vocab_file):
+ raise ValueError(
+ f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
+ " model use `tokenizer = LxmertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+ )
+ self.vocab = load_vocab(vocab_file)
+ self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
+ self.do_basic_tokenize = do_basic_tokenize
+ if do_basic_tokenize:
+ self.basic_tokenizer = BasicTokenizer(
+ do_lower_case=do_lower_case,
+ never_split=never_split,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ )
+
+ self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
+
+ super().__init__(
+ do_lower_case=do_lower_case,
+ do_basic_tokenize=do_basic_tokenize,
+ never_split=never_split,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ pad_token=pad_token,
+ cls_token=cls_token,
+ mask_token=mask_token,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+ **kwargs,
+ )
+
+ @property
+ def do_lower_case(self):
+ return self.basic_tokenizer.do_lower_case
+
+ @property
+ def vocab_size(self):
+ return len(self.vocab)
+
+ def get_vocab(self):
+ return dict(self.vocab, **self.added_tokens_encoder)
+
+ def _tokenize(self, text, split_special_tokens=False):
+ split_tokens = []
+ if self.do_basic_tokenize:
+ for token in self.basic_tokenizer.tokenize(
+ text, never_split=self.all_special_tokens if not split_special_tokens else None
+ ):
+ # If the token is part of the never_split set
+ if token in self.basic_tokenizer.never_split:
+ split_tokens.append(token)
+ else:
+ split_tokens += self.wordpiece_tokenizer.tokenize(token)
+ else:
+ split_tokens = self.wordpiece_tokenizer.tokenize(text)
+ return split_tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.ids_to_tokens.get(index, self.unk_token)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ out_string = " ".join(tokens).replace(" ##", "").strip()
+ return out_string
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A Lxmert sequence has the following format:
+
+ - single sequence: `[CLS] X [SEP]`
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + token_ids_1 + sep
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is not None:
+ return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1]
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ index = 0
+ if os.path.isdir(save_directory):
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ else:
+ vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
+ with open(vocab_file, "w", encoding="utf-8") as writer:
+ for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+ " Please check that the vocabulary is not corrupted!"
+ )
+ index = token_index
+ writer.write(token + "\n")
+ index += 1
+ return (vocab_file,)
+
+
+# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
+class BasicTokenizer:
+ """
+ Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
+
+ Args:
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ never_split (`Iterable`, *optional*):
+ Collection of tokens which will never be split during tokenization. Only has an effect when
+ `do_basic_tokenize=True`
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters.
+
+ This should likely be deactivated for Japanese (see this
+ [issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original BERT).
+ do_split_on_punc (`bool`, *optional*, defaults to `True`):
+ In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
+ the full context of the words, such as contractions.
+ """
+
+ def __init__(
+ self,
+ do_lower_case=True,
+ never_split=None,
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ do_split_on_punc=True,
+ ):
+ if never_split is None:
+ never_split = []
+ self.do_lower_case = do_lower_case
+ self.never_split = set(never_split)
+ self.tokenize_chinese_chars = tokenize_chinese_chars
+ self.strip_accents = strip_accents
+ self.do_split_on_punc = do_split_on_punc
+
+ def tokenize(self, text, never_split=None):
+ """
+ Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
+
+ Args:
+ never_split (`List[str]`, *optional*)
+ Kept for backward compatibility purposes. Now implemented directly at the base class level (see
+ [`PreTrainedTokenizer.tokenize`]) List of token not to split.
+ """
+ # union() returns a new set by concatenating the two sets.
+ never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
+ text = self._clean_text(text)
+
+ # This was added on November 1st, 2018 for the multilingual and Chinese
+ # models. This is also applied to the English models now, but it doesn't
+ # matter since the English models were not trained on any Chinese data
+ # and generally don't have any Chinese data in them (there are Chinese
+ # characters in the vocabulary because Wikipedia does have some Chinese
+ # words in the English Wikipedia.).
+ if self.tokenize_chinese_chars:
+ text = self._tokenize_chinese_chars(text)
+ # prevents treating the same character with different unicode codepoints as different characters
+ unicode_normalized_text = unicodedata.normalize("NFC", text)
+ orig_tokens = whitespace_tokenize(unicode_normalized_text)
+ split_tokens = []
+ for token in orig_tokens:
+ if token not in never_split:
+ if self.do_lower_case:
+ token = token.lower()
+ if self.strip_accents is not False:
+ token = self._run_strip_accents(token)
+ elif self.strip_accents:
+ token = self._run_strip_accents(token)
+ split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+ output_tokens = whitespace_tokenize(" ".join(split_tokens))
+ return output_tokens
+
+ def _run_strip_accents(self, text):
+ """Strips accents from a piece of text."""
+ text = unicodedata.normalize("NFD", text)
+ output = []
+ for char in text:
+ cat = unicodedata.category(char)
+ if cat == "Mn":
+ continue
+ output.append(char)
+ return "".join(output)
+
+ def _run_split_on_punc(self, text, never_split=None):
+ """Splits punctuation on a piece of text."""
+ if not self.do_split_on_punc or (never_split is not None and text in never_split):
+ return [text]
+ chars = list(text)
+ i = 0
+ start_new_word = True
+ output = []
+ while i < len(chars):
+ char = chars[i]
+ if _is_punctuation(char):
+ output.append([char])
+ start_new_word = True
+ else:
+ if start_new_word:
+ output.append([])
+ start_new_word = False
+ output[-1].append(char)
+ i += 1
+
+ return ["".join(x) for x in output]
+
+ def _tokenize_chinese_chars(self, text):
+ """Adds whitespace around any CJK character."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if self._is_chinese_char(cp):
+ output.append(" ")
+ output.append(char)
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+ def _is_chinese_char(self, cp):
+ """Checks whether CP is the codepoint of a CJK character."""
+ # This defines a "chinese character" as anything in the CJK Unicode block:
+ # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+ #
+ # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+ # despite its name. The modern Korean Hangul alphabet is a different block,
+ # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+ # space-separated words, so they are not treated specially and handled
+ # like the all of the other languages.
+ if (
+ (cp >= 0x4E00 and cp <= 0x9FFF)
+ or (cp >= 0x3400 and cp <= 0x4DBF)
+ or (cp >= 0x20000 and cp <= 0x2A6DF)
+ or (cp >= 0x2A700 and cp <= 0x2B73F)
+ or (cp >= 0x2B740 and cp <= 0x2B81F)
+ or (cp >= 0x2B820 and cp <= 0x2CEAF)
+ or (cp >= 0xF900 and cp <= 0xFAFF)
+ or (cp >= 0x2F800 and cp <= 0x2FA1F)
+ ):
+ return True
+
+ return False
+
+ def _clean_text(self, text):
+ """Performs invalid character removal and whitespace cleanup on text."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if cp == 0 or cp == 0xFFFD or _is_control(char):
+ continue
+ if _is_whitespace(char):
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+
+# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
+class WordpieceTokenizer:
+ """Runs WordPiece tokenization."""
+
+ def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+ self.vocab = vocab
+ self.unk_token = unk_token
+ self.max_input_chars_per_word = max_input_chars_per_word
+
+ def tokenize(self, text):
+ """
+ Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
+ tokenization using the given vocabulary.
+
+ For example, `input = "unaffable"` will return as output `["un", "##aff", "##able"]`.
+
+ Args:
+ text: A single token or whitespace separated tokens. This should have
+ already been passed through *BasicTokenizer*.
+
+ Returns:
+ A list of wordpiece tokens.
+ """
+
+ output_tokens = []
+ for token in whitespace_tokenize(text):
+ chars = list(token)
+ if len(chars) > self.max_input_chars_per_word:
+ output_tokens.append(self.unk_token)
+ continue
+
+ is_bad = False
+ start = 0
+ sub_tokens = []
+ while start < len(chars):
+ end = len(chars)
+ cur_substr = None
+ while start < end:
+ substr = "".join(chars[start:end])
+ if start > 0:
+ substr = "##" + substr
+ if substr in self.vocab:
+ cur_substr = substr
+ break
+ end -= 1
+ if cur_substr is None:
+ is_bad = True
+ break
+ sub_tokens.append(cur_substr)
+ start = end
+
+ if is_bad:
+ output_tokens.append(self.unk_token)
+ else:
+ output_tokens.extend(sub_tokens)
+ return output_tokens
+
+
+__all__ = ["LxmertTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/tokenization_lxmert_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/tokenization_lxmert_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcfa3263acdaaae8f59be7d33cea96f5f2a307dd
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/lxmert/tokenization_lxmert_fast.py
@@ -0,0 +1,143 @@
+# coding=utf-8
+# Copyright 2020 The Google AI Team, Stanford University and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+from typing import Optional
+
+from tokenizers import normalizers
+
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from .tokenization_lxmert import LxmertTokenizer
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
+
+
+# Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast with bert-base-cased->unc-nlp/lxmert-base-uncased, BERT->Lxmert, Bert->Lxmert
+class LxmertTokenizerFast(PreTrainedTokenizerFast):
+ r"""
+ Construct a "fast" Lxmert tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ File containing the vocabulary.
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ sep_token (`str`, *optional*, defaults to `"[SEP]"`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ pad_token (`str`, *optional*, defaults to `"[PAD]"`):
+ The token used for padding, for example when batching sequences of different lengths.
+ cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ mask_token (`str`, *optional*, defaults to `"[MASK]"`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ clean_text (`bool`, *optional*, defaults to `True`):
+ Whether or not to clean the text before tokenization by removing any control characters and replacing all
+ whitespaces by the classic one.
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
+ issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original Lxmert).
+ wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
+ The prefix for subwords.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ slow_tokenizer_class = LxmertTokenizer
+
+ def __init__(
+ self,
+ vocab_file=None,
+ tokenizer_file=None,
+ do_lower_case=True,
+ unk_token="[UNK]",
+ sep_token="[SEP]",
+ pad_token="[PAD]",
+ cls_token="[CLS]",
+ mask_token="[MASK]",
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ **kwargs,
+ ):
+ super().__init__(
+ vocab_file,
+ tokenizer_file=tokenizer_file,
+ do_lower_case=do_lower_case,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ pad_token=pad_token,
+ cls_token=cls_token,
+ mask_token=mask_token,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ **kwargs,
+ )
+
+ normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
+ if (
+ normalizer_state.get("lowercase", do_lower_case) != do_lower_case
+ or normalizer_state.get("strip_accents", strip_accents) != strip_accents
+ or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars
+ ):
+ normalizer_class = getattr(normalizers, normalizer_state.pop("type"))
+ normalizer_state["lowercase"] = do_lower_case
+ normalizer_state["strip_accents"] = strip_accents
+ normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars
+ self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state)
+
+ self.do_lower_case = do_lower_case
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A Lxmert sequence has the following format:
+
+ - single sequence: `[CLS] X [SEP]`
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+
+ if token_ids_1 is not None:
+ output += token_ids_1 + [self.sep_token_id]
+
+ return output
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+ return tuple(files)
+
+
+__all__ = ["LxmertTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c456a6b88378a5461e11dadfafb8820698b6b9d0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_m2m_100 import *
+ from .modeling_m2m_100 import *
+ from .tokenization_m2m_100 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/configuration_m2m_100.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/configuration_m2m_100.py
new file mode 100644
index 0000000000000000000000000000000000000000..620641f1cf4e711bc54de79e02eb90328668c219
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/configuration_m2m_100.py
@@ -0,0 +1,284 @@
+# coding=utf-8
+# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""M2M100 model configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+from typing import Any, Optional
+
+from ... import PreTrainedTokenizer
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig, OnnxSeq2SeqConfigWithPast
+from ...onnx.utils import compute_effective_axis_dimension
+from ...utils import TensorType, is_torch_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class M2M100Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`M2M100Model`]. It is used to instantiate an
+ M2M100 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the M2M100
+ [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50265):
+ Vocabulary size of the M2M100 model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`M2M100Model`] or
+ d_model (`int`, *optional*, defaults to 1024):
+ Dimensionality of the layers and the pooler layer.
+ encoder_layers (`int`, *optional*, defaults to 12):
+ Number of encoder layers.
+ decoder_layers (`int`, *optional*, defaults to 12):
+ Number of decoder layers.
+ encoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ decoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ decoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ encoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ classifier_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for classifier.
+ max_position_embeddings (`int`, *optional*, defaults to 1024):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ encoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ decoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models).
+
+ Example:
+
+ ```python
+ >>> from transformers import M2M100Config, M2M100Model
+
+ >>> # Initializing a M2M100 facebook/m2m100_418M style configuration
+ >>> configuration = M2M100Config()
+
+ >>> # Initializing a model (with random weights) from the facebook/m2m100_418M style configuration
+ >>> model = M2M100Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "m2m_100"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
+
+ def __init__(
+ self,
+ vocab_size=128112,
+ max_position_embeddings=1024,
+ encoder_layers=12,
+ encoder_ffn_dim=4096,
+ encoder_attention_heads=16,
+ decoder_layers=12,
+ decoder_ffn_dim=4096,
+ decoder_attention_heads=16,
+ encoder_layerdrop=0.05,
+ decoder_layerdrop=0.05,
+ use_cache=True,
+ is_encoder_decoder=True,
+ activation_function="relu",
+ d_model=1024,
+ dropout=0.1,
+ attention_dropout=0.1,
+ activation_dropout=0.0,
+ init_std=0.02,
+ decoder_start_token_id=2,
+ scale_embedding=True,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.d_model = d_model
+ self.encoder_ffn_dim = encoder_ffn_dim
+ self.encoder_layers = encoder_layers
+ self.encoder_attention_heads = encoder_attention_heads
+ self.decoder_ffn_dim = decoder_ffn_dim
+ self.decoder_layers = decoder_layers
+ self.decoder_attention_heads = decoder_attention_heads
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.activation_function = activation_function
+ self.init_std = init_std
+ self.encoder_layerdrop = encoder_layerdrop
+ self.decoder_layerdrop = decoder_layerdrop
+ self.use_cache = use_cache
+ self.num_hidden_layers = encoder_layers
+ self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ is_encoder_decoder=is_encoder_decoder,
+ decoder_start_token_id=decoder_start_token_id,
+ **kwargs,
+ )
+
+
+class M2M100OnnxConfig(OnnxSeq2SeqConfigWithPast):
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ common_inputs = OrderedDict(
+ [
+ ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+ ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+ ]
+ )
+
+ if self.use_past:
+ common_inputs["decoder_input_ids"] = {0: "batch"}
+ common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
+ else:
+ common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
+ common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
+
+ if self.use_past:
+ self.fill_with_past_key_values_(common_inputs, direction="inputs")
+ return common_inputs
+
+ # Copied from BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering
+ # A better name would be _generate_dummy_inputs_for_encoder_and_decoder because sequence classification and question
+ # answering are not supported for M2M100, but this name is preserved to be able to check that the copy matches what
+ # was done for BART so that it can be updated if need be.
+ def _generate_dummy_inputs_for_sequence_classification_and_question_answering(
+ self,
+ tokenizer: PreTrainedTokenizer,
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ # Copied from OnnxConfig.generate_dummy_inputs
+ # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
+ # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
+ batch_size = compute_effective_axis_dimension(
+ batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
+ )
+
+ # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
+ token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
+ seq_length = compute_effective_axis_dimension(
+ seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
+ )
+
+ # Generate dummy inputs according to compute batch and sequence
+ dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
+ common_inputs = dict(tokenizer(dummy_input, return_tensors=framework))
+ return common_inputs
+
+ # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._generate_dummy_inputs_for_default_and_seq2seq_lm
+ def _generate_dummy_inputs_for_default_and_seq2seq_lm(
+ self,
+ tokenizer: PreTrainedTokenizer,
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+ tokenizer, batch_size, seq_length, is_pair, framework
+ )
+
+ # Generate decoder inputs
+ decoder_seq_length = seq_length if not self.use_past else 1
+ decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+ tokenizer, batch_size, decoder_seq_length, is_pair, framework
+ )
+ decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
+ common_inputs = dict(**encoder_inputs, **decoder_inputs)
+
+ if self.use_past:
+ if not is_torch_available():
+ raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+ else:
+ import torch
+ batch, encoder_seq_length = common_inputs["input_ids"].shape
+ decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
+ num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
+ encoder_shape = (
+ batch,
+ num_encoder_attention_heads,
+ encoder_seq_length,
+ self._config.hidden_size // num_encoder_attention_heads,
+ )
+ decoder_past_length = decoder_seq_length + 3
+ decoder_shape = (
+ batch,
+ num_decoder_attention_heads,
+ decoder_past_length,
+ self._config.hidden_size // num_decoder_attention_heads,
+ )
+
+ common_inputs["decoder_attention_mask"] = torch.cat(
+ [common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
+ )
+
+ common_inputs["past_key_values"] = []
+ # If the number of encoder and decoder layers are present in the model configuration, both are considered
+ num_encoder_layers, num_decoder_layers = self.num_layers
+ min_num_layers = min(num_encoder_layers, num_decoder_layers)
+ max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
+ remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
+
+ for _ in range(min_num_layers):
+ common_inputs["past_key_values"].append(
+ (
+ torch.zeros(decoder_shape),
+ torch.zeros(decoder_shape),
+ torch.zeros(encoder_shape),
+ torch.zeros(encoder_shape),
+ )
+ )
+ # TODO: test this.
+ shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
+ for _ in range(min_num_layers, max_num_layers):
+ common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))
+ return common_inputs
+
+ generate_dummy_inputs = _generate_dummy_inputs_for_default_and_seq2seq_lm
+
+
+__all__ = ["M2M100Config", "M2M100OnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/convert_m2m100_original_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/convert_m2m100_original_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..02e7ef23a085177b7689383c1dbda41002c91a36
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/convert_m2m100_original_checkpoint_to_pytorch.py
@@ -0,0 +1,85 @@
+# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+
+import torch
+from torch import nn
+
+from transformers import M2M100Config, M2M100ForConditionalGeneration
+
+
+def remove_ignore_keys_(state_dict):
+ ignore_keys = [
+ "encoder.version",
+ "decoder.version",
+ "model.encoder.version",
+ "model.decoder.version",
+ "decoder.output_projection.weight",
+ "_float_tensor",
+ "encoder.embed_positions._float_tensor",
+ "decoder.embed_positions._float_tensor",
+ ]
+ for k in ignore_keys:
+ state_dict.pop(k, None)
+
+
+def make_linear_from_emb(emb):
+ vocab_size, emb_size = emb.weight.shape
+ lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
+ lin_layer.weight.data = emb.weight.data
+ return lin_layer
+
+
+def convert_fairseq_m2m100_checkpoint_from_disk(checkpoint_path):
+ m2m_100 = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
+ args = m2m_100["args"] or m2m_100["cfg"]["model"]
+ state_dict = m2m_100["model"]
+ remove_ignore_keys_(state_dict)
+ vocab_size = state_dict["encoder.embed_tokens.weight"].shape[0]
+
+ config = M2M100Config(
+ vocab_size=vocab_size,
+ max_position_embeddings=1024,
+ encoder_layers=args.encoder_layers,
+ decoder_layers=args.decoder_layers,
+ encoder_attention_heads=args.encoder_attention_heads,
+ decoder_attention_heads=args.decoder_attention_heads,
+ encoder_ffn_dim=args.encoder_ffn_embed_dim,
+ decoder_ffn_dim=args.decoder_ffn_embed_dim,
+ d_model=args.encoder_embed_dim,
+ encoder_layerdrop=args.encoder_layerdrop,
+ decoder_layerdrop=args.decoder_layerdrop,
+ dropout=args.dropout,
+ attention_dropout=args.attention_dropout,
+ activation_dropout=args.activation_dropout,
+ activation_function="relu",
+ )
+
+ state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
+ model = M2M100ForConditionalGeneration(config)
+ model.model.load_state_dict(state_dict, strict=False)
+ model.lm_head = make_linear_from_emb(model.model.shared)
+
+ return model
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
+ parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
+ args = parser.parse_args()
+ model = convert_fairseq_m2m100_checkpoint_from_disk(args.fairseq_pathß)
+ model.save_pretrained(args.pytorch_dump_folder_path)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/modeling_m2m_100.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/modeling_m2m_100.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a0a804a844974a3746cc16b3ea17610ff5c3d7b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/modeling_m2m_100.py
@@ -0,0 +1,1449 @@
+# coding=utf-8
+# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch M2M100 model."""
+
+import math
+from typing import Callable, Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import CrossEntropyLoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.fsdp import is_fsdp_managed_module
+from ...modeling_attn_mask_utils import (
+ AttentionMaskConverter,
+ _prepare_4d_attention_mask,
+ _prepare_4d_attention_mask_for_sdpa,
+)
+from ...modeling_flash_attention_utils import (
+ FlashAttentionKwargs,
+)
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPastAndCrossAttentions,
+ Seq2SeqLMOutput,
+ Seq2SeqModelOutput,
+)
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_m2m_100 import M2M100Config
+
+
+if is_torch_flex_attn_available():
+ from ...integrations.flex_attention import BlockMask, make_flex_block_causal_mask
+
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+ """
+ Shift input ids one token to the right.
+ """
+ shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+ shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
+ shifted_input_ids[:, 0] = decoder_start_token_id
+
+ if pad_token_id is None:
+ raise ValueError("self.model.config.pad_token_id has to be defined.")
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+ return shifted_input_ids
+
+
+def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+ are ignored. This is modified from fairseq's `utils.make_positions`.
+ """
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+ mask = input_ids.ne(padding_idx).int()
+ incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
+ return incremental_indices.long() + padding_idx
+
+
+# Copied from transformers.models.bart.modeling_bart.BartScaledWordEmbedding with Bart->M2M100
+class M2M100ScaledWordEmbedding(nn.Embedding):
+ """
+ This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
+ """
+
+ def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
+ super().__init__(num_embeddings, embedding_dim, padding_idx)
+ self.embed_scale = embed_scale
+
+ def forward(self, input_ids: torch.Tensor):
+ return super().forward(input_ids) * self.embed_scale
+
+
+class M2M100SinusoidalPositionalEmbedding(nn.Module):
+ """This module produces sinusoidal positional embeddings of any length."""
+
+ def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
+ super().__init__()
+ self.offset = 2
+ self.embedding_dim = embedding_dim
+ self.padding_idx = padding_idx
+ self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
+
+ def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
+ emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
+ if hasattr(self, "weights"):
+ # in forward put the weights on the correct dtype and device of the param
+ emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
+
+ self.register_buffer("weights", emb_weights, persistent=False)
+
+ @staticmethod
+ def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
+ """
+ Build sinusoidal embeddings.
+
+ This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
+ "Attention Is All You Need".
+ """
+ half_dim = embedding_dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
+ emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
+ if embedding_dim % 2 == 1:
+ # zero pad
+ emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
+ if padding_idx is not None:
+ emb[padding_idx, :] = 0
+
+ return emb.to(torch.get_default_dtype())
+
+ @torch.no_grad()
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ past_key_values_length: int = 0,
+ ):
+ if input_ids is not None:
+ bsz, seq_len = input_ids.size()
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
+ position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to(
+ input_ids.device
+ )
+ else:
+ bsz, seq_len = inputs_embeds.size()[:-1]
+ position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length)
+
+ # expand embeddings if needed
+ max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
+ if max_pos > self.weights.size(0):
+ self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
+
+ return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()
+
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds, past_key_values_length):
+ """
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+
+ Args:
+ inputs_embeds: torch.Tensor
+
+ Returns: torch.Tensor
+ """
+ input_shape = inputs_embeds.size()[:-1]
+ sequence_length = input_shape[1]
+
+ position_ids = torch.arange(
+ self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+ )
+ return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length
+
+
+# Copied from transformers.models.bart.modeling_bart.eager_attention_forward
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: Optional[float] = None,
+ dropout: float = 0.0,
+ head_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+):
+ if scaling is None:
+ scaling = query.size(-1) ** -0.5
+
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ if head_mask is not None:
+ attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
+
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->M2M100
+class M2M100Attention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: float = 0.0,
+ is_decoder: bool = False,
+ bias: bool = True,
+ is_causal: bool = False,
+ config: Optional[M2M100Config] = None,
+ layer_idx: Optional[int] = None,
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+ self.config = config
+
+ if (self.head_dim * num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+ self.is_causal = is_causal
+ self.layer_idx = layer_idx
+ if layer_idx is None and self.is_decoder:
+ logger.warning_once(
+ f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
+ "will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ key_value_states: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ cache_position: Optional[torch.Tensor] = None,
+ # TODO: we need a refactor so that the different attention modules can get their specific kwargs
+ # ATM, we have mixed things encoder, decoder, and encoder-decoder attn
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+
+ # determine input shapes
+ bsz, tgt_len = hidden_states.shape[:-1]
+ src_len = key_value_states.shape[1] if is_cross_attention else tgt_len
+
+ q_input_shape = (bsz, tgt_len, -1, self.head_dim)
+ kv_input_shape = (bsz, src_len, -1, self.head_dim)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states).view(*q_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ if isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_states from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = key_value_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_states = curr_past_key_value.layers[self.layer_idx].keys
+ value_states = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_states = self.k_proj(current_states)
+ value_states = self.v_proj(current_states)
+ key_states = key_states.view(*kv_input_shape).transpose(1, 2)
+ value_states = value_states.view(*kv_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_states, value_states = curr_past_key_value.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=self.scaling,
+ output_attentions=output_attentions,
+ head_mask=layer_head_mask,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->M2M100, MBART->M2M100
+class M2M100EncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: M2M100Config):
+ super().__init__()
+ self.embed_dim = config.d_model
+
+ self.self_attn = M2M100Attention(
+ embed_dim=self.embed_dim,
+ num_heads=config.encoder_attention_heads,
+ dropout=config.attention_dropout,
+ config=config,
+ )
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+ self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
+ self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ layer_head_mask: torch.Tensor,
+ output_attentions: bool = False,
+ ) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ if hidden_states.dtype == torch.float16:
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ return hidden_states, attn_weights
+
+
+# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer with MBart->M2M100, MBART->M2M100
+class M2M100DecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: M2M100Config, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.embed_dim = config.d_model
+
+ self.self_attn = M2M100Attention(
+ embed_dim=self.embed_dim,
+ num_heads=config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ is_causal=True,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.encoder_attn = M2M100Attention(
+ self.embed_dim,
+ config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
+ self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = True,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ encoder_hidden_states (`torch.FloatTensor`):
+ cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
+ encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`.
+ cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
+ size `(decoder_attention_heads,)`.
+ past_key_values (`Tuple(torch.FloatTensor)`): cached past key and value projection states
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
+ cache in the correct position and to infer the complete sequence length.
+ """
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # Cross-Attention Block
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ hidden_states, cross_attn_weights = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ layer_head_mask=cross_attn_layer_head_mask,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ return outputs
+
+
+@auto_docstring
+class M2M100PreTrainedModel(PreTrainedModel):
+ config: M2M100Config
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["M2M100EncoderLayer", "M2M100DecoderLayer"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ # Doesn't support `compile` (dynamic control flow). Can be fixed but low usage model
+ _can_compile_fullgraph = False
+
+ def _init_weights(self, module):
+ std = self.config.init_std
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+
+ # Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_full_mask
+ def _update_full_mask(
+ self,
+ attention_mask: Union[torch.Tensor, None],
+ inputs_embeds: torch.Tensor,
+ ):
+ if attention_mask is not None:
+ if self.config._attn_implementation == "flash_attention_2":
+ attention_mask = attention_mask if 0 in attention_mask else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & head_mask can not be supported when using SDPA, fall back to
+ # the manual implementation that requires a 4D causal mask in all cases.
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, inputs_embeds.dtype)
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask, is_causal=False)
+ else:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
+
+ return attention_mask
+
+ # Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_causal_mask
+ def _update_causal_mask(
+ self,
+ attention_mask: Optional[Union[torch.Tensor, "BlockMask"]],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ ):
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ # Other attention flavors support in-built causal (when `mask is None`)
+ # while we need to create our specific block mask regardless
+ elif attention_mask is None:
+ attention_mask = make_flex_block_causal_mask(
+ torch.ones(
+ size=(input_tensor.shape[0], input_tensor.shape[1]),
+ device=attention_mask.device,
+ )
+ )
+ return attention_mask
+
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and (attention_mask == 0.0).any():
+ return attention_mask
+ return None
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if self.config._attn_implementation == "sdpa" and not using_compilable_cache:
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ sequence_length = input_tensor.shape[1]
+ if using_compilable_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+ # Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_cross_attn_mask
+ def _update_cross_attn_mask(
+ self,
+ encoder_hidden_states: Union[torch.Tensor, None],
+ encoder_attention_mask: Union[torch.Tensor, None],
+ input_shape: torch.Size,
+ inputs_embeds: torch.Tensor,
+ ):
+ # expand encoder attention mask
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ if self.config._attn_implementation == "flash_attention_2":
+ encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
+ encoder_attention_mask,
+ inputs_embeds.dtype,
+ tgt_len=input_shape[-1],
+ )
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(encoder_attention_mask, torch.Tensor):
+ encoder_attention_mask = make_flex_block_causal_mask(
+ encoder_attention_mask,
+ query_length=input_shape[-1],
+ is_causal=False,
+ )
+ else:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask(
+ encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+ )
+
+ return encoder_attention_mask
+
+
+class M2M100Encoder(M2M100PreTrainedModel):
+ """
+ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
+ [`M2M100EncoderLayer`].
+
+ Args:
+ config: M2M100Config
+ embed_tokens (nn.Embedding): output embedding
+ """
+
+ def __init__(self, config: M2M100Config, embed_tokens: Optional[nn.Embedding] = None):
+ super().__init__(config)
+
+ self.dropout = config.dropout
+ self.layerdrop = config.encoder_layerdrop
+
+ embed_dim = config.d_model
+ self.padding_idx = config.pad_token_id
+ self.max_source_positions = config.max_position_embeddings
+ embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
+
+ self.embed_tokens = M2M100ScaledWordEmbedding(
+ config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale
+ )
+
+ if embed_tokens is not None:
+ self.embed_tokens.weight = embed_tokens.weight
+
+ self.embed_positions = M2M100SinusoidalPositionalEmbedding(
+ config.max_position_embeddings,
+ embed_dim,
+ self.padding_idx,
+ )
+ self.layers = nn.ModuleList([M2M100EncoderLayer(config) for _ in range(config.encoder_layers)])
+ self.layer_norm = nn.LayerNorm(config.d_model)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ):
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ embed_pos = self.embed_positions(input_ids, inputs_embeds)
+ embed_pos = embed_pos.to(inputs_embeds.device)
+
+ hidden_states = inputs_embeds + embed_pos
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ attention_mask = self._update_full_mask(
+ attention_mask,
+ inputs_embeds,
+ )
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ if head_mask.size()[0] != len(self.layers):
+ raise ValueError(
+ f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+ synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
+
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = torch.rand([])
+
+ skip_the_layer = self.training and dropout_probability < self.layerdrop
+ if not skip_the_layer or synced_gpus:
+ # under fsdp or deepspeed zero3 all gpus must run in sync
+
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if skip_the_layer:
+ layer_outputs = (None, None)
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ hidden_states = self.layer_norm(hidden_states)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+ )
+
+
+class M2M100Decoder(M2M100PreTrainedModel):
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`M2M100DecoderLayer`]
+
+ Args:
+ config: M2M100Config
+ embed_tokens (nn.Embedding): output embedding
+ """
+
+ def __init__(self, config: M2M100Config, embed_tokens: Optional[nn.Embedding] = None):
+ super().__init__(config)
+ self.dropout = config.dropout
+ self.layerdrop = config.decoder_layerdrop
+ self.padding_idx = config.pad_token_id
+ self.max_target_positions = config.max_position_embeddings
+ embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+
+ self.embed_tokens = M2M100ScaledWordEmbedding(
+ config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale
+ )
+
+ if embed_tokens is not None:
+ self.embed_tokens.weight = embed_tokens.weight
+
+ self.embed_positions = M2M100SinusoidalPositionalEmbedding(
+ config.max_position_embeddings,
+ config.d_model,
+ self.padding_idx,
+ )
+ self.layers = nn.ModuleList([M2M100DecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)])
+ self.layer_norm = nn.LayerNorm(config.d_model)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ):
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
+ cross-attention on hidden heads. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+ shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+ cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+ that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+ all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
+ cache in the correct position and to infer the complete sequence length.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing`. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ # initialize `past_key_values`
+ if use_cache and past_key_values is None:
+ past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
+ if use_cache and isinstance(past_key_values, tuple):
+ logger.warning_once(
+ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
+ "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
+ "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
+ )
+ past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
+
+ batch_size, seq_length = inputs_embeds.size()[:-1]
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+ if cache_position is None:
+ cache_position = torch.arange(
+ past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
+ )
+
+ if attention_mask is None and not is_torchdynamo_compiling():
+ # required mask seq length can be calculated via length of past cache
+ mask_seq_length = past_key_values_length + seq_length
+ attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
+
+ self_attn_cache = (
+ past_key_values.self_attention_cache
+ if isinstance(past_key_values, EncoderDecoderCache)
+ else past_key_values
+ )
+
+ attention_mask = self._update_causal_mask(
+ attention_mask,
+ inputs_embeds,
+ cache_position,
+ self_attn_cache,
+ )
+ encoder_attention_mask = self._update_cross_attn_mask(
+ encoder_hidden_states,
+ encoder_attention_mask,
+ input_shape,
+ inputs_embeds,
+ )
+ # embed positions
+ positions = self.embed_positions(input_ids, inputs_embeds, past_key_values_length)
+ positions = positions.to(inputs_embeds.device)
+
+ hidden_states = inputs_embeds + positions
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attentions = () if output_attentions else None
+
+ # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
+ for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
+ if attn_mask is not None:
+ if attn_mask.size()[0] != len(self.layers):
+ raise ValueError(
+ f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+ synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
+
+ for idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = torch.rand([])
+
+ skip_the_layer = self.training and dropout_probability < self.layerdrop
+ if not skip_the_layer or synced_gpus:
+ # under fsdp or deepspeed zero3 all gpus must run in sync
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask,
+ encoder_hidden_states, # as a positional argument for gradient checkpointing
+ encoder_attention_mask=encoder_attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ cross_attn_layer_head_mask=(
+ cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
+ ),
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if skip_the_layer:
+ continue
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+ all_cross_attentions += (layer_outputs[2],)
+
+ hidden_states = self.layer_norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions]
+ if v is not None
+ )
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+@auto_docstring
+class M2M100Model(M2M100PreTrainedModel):
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+ def __init__(self, config: M2M100Config):
+ super().__init__(config)
+
+ padding_idx, vocab_size = config.pad_token_id, config.vocab_size
+ embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+ self.shared = M2M100ScaledWordEmbedding(vocab_size, config.d_model, padding_idx, embed_scale=embed_scale)
+
+ self.encoder = M2M100Encoder(config, self.shared)
+ self.decoder = M2M100Decoder(config, self.shared)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, value):
+ self.shared = value
+ self.encoder.embed_tokens = self.shared
+ self.decoder.embed_tokens = self.shared
+
+ def _tie_weights(self):
+ if self.config.tie_word_embeddings:
+ self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
+ self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
+
+ def get_encoder(self):
+ return self.encoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple[torch.Tensor], Seq2SeqModelOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ M2M100 uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
+ elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+ encoder_outputs = BaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+
+ # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return Seq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The M2M100 Model with a language modeling head. Can be used for summarization.
+ """
+)
+class M2M100ForConditionalGeneration(M2M100PreTrainedModel, GenerationMixin):
+ base_model_prefix = "model"
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
+
+ def __init__(self, config: M2M100Config):
+ super().__init__(config)
+ self.model = M2M100Model(config)
+ self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_encoder(self):
+ return self.model.get_encoder()
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple[torch.Tensor], Seq2SeqLMOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ M2M100 uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example Translation:
+
+ ```python
+ >>> from transformers import AutoTokenizer, M2M100ForConditionalGeneration
+
+ >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/m2m100_418M")
+
+ >>> text_to_translate = "Life is like a box of chocolates"
+ >>> model_inputs = tokenizer(text_to_translate, return_tensors="pt")
+
+ >>> # translate to French
+ >>> gen_tokens = model.generate(**model_inputs, forced_bos_token_id=tokenizer.get_lang_id("fr"))
+ >>> print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True))
+ ```
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if labels is not None:
+ if decoder_input_ids is None:
+ decoder_input_ids = shift_tokens_right(
+ labels, self.config.pad_token_id, self.config.decoder_start_token_id
+ )
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ encoder_outputs=encoder_outputs,
+ decoder_attention_mask=decoder_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+ lm_logits = self.lm_head(outputs[0])
+
+ masked_lm_loss = None
+ if labels is not None:
+ # move labels to the correct device to enable PP
+ labels = labels.to(lm_logits.device)
+ loss_fct = CrossEntropyLoss()
+ masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return Seq2SeqLMOutput(
+ loss=masked_lm_loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+__all__ = ["M2M100ForConditionalGeneration", "M2M100Model", "M2M100PreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/tokenization_m2m_100.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/tokenization_m2m_100.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccced10f2bacfb30aa03bf6f30f2e8f9343c64d3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/m2m_100/tokenization_m2m_100.py
@@ -0,0 +1,384 @@
+# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for M2M100."""
+
+import json
+import os
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Optional, Union
+
+import sentencepiece
+
+from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
+from ...utils import logging
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+SPIECE_UNDERLINE = "▁"
+
+VOCAB_FILES_NAMES = {
+ "vocab_file": "vocab.json",
+ "spm_file": "sentencepiece.bpe.model",
+ "tokenizer_config_file": "tokenizer_config.json",
+}
+
+
+# fmt: off
+FAIRSEQ_LANGUAGE_CODES = {
+ "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
+ "wmt21": ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
+}
+# fmt: on
+
+
+@requires(backends=("sentencepiece",))
+class M2M100Tokenizer(PreTrainedTokenizer):
+ """
+ Construct an M2M100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ spm_file (`str`):
+ Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
+ contains the vocabulary.
+ src_lang (`str`, *optional*):
+ A string representing the source language.
+ tgt_lang (`str`, *optional*):
+ A string representing the target language.
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ language_codes (`str`, *optional*, defaults to `"m2m100"`):
+ What language codes to use. Should be one of `"m2m100"` or `"wmt21"`.
+ sp_model_kwargs (`dict`, *optional*):
+ Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+ SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+ to set:
+
+ - `enable_sampling`: Enable subword regularization.
+ - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+ - `nbest_size = {0,1}`: No sampling is performed.
+ - `nbest_size > 1`: samples from the nbest_size results.
+ - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+ using forward-filtering-and-backward-sampling algorithm.
+
+ - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+ BPE-dropout.
+
+ Examples:
+
+ ```python
+ >>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
+
+ >>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
+ >>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="ro")
+ >>> src_text = " UN Chief Says There Is No Military Solution in Syria"
+ >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
+ >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
+ >>> outputs = model(**model_inputs) # should work
+ ```"""
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ prefix_tokens: list[int] = []
+ suffix_tokens: list[int] = []
+
+ def __init__(
+ self,
+ vocab_file,
+ spm_file,
+ src_lang=None,
+ tgt_lang=None,
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ pad_token="",
+ unk_token="",
+ language_codes="m2m100",
+ sp_model_kwargs: Optional[dict[str, Any]] = None,
+ num_madeup_words=8,
+ **kwargs,
+ ) -> None:
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+ self.language_codes = language_codes
+ fairseq_language_code = FAIRSEQ_LANGUAGE_CODES[language_codes]
+ self.lang_code_to_token = {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}
+
+ additional_special_tokens = kwargs.pop("additional_special_tokens", [])
+ for lang_code in fairseq_language_code:
+ token = self.get_lang_token(lang_code)
+ if token not in additional_special_tokens and lang_code not in str(token) not in self.added_tokens_encoder:
+ additional_special_tokens.append(token)
+
+ self.vocab_file = vocab_file
+ self.encoder = load_json(vocab_file)
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ self.spm_file = spm_file
+ self.sp_model = load_spm(spm_file, self.sp_model_kwargs)
+
+ self.encoder_size = len(self.encoder)
+
+ self.lang_token_to_id = {
+ self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)
+ }
+ self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)}
+ self.id_to_lang_token = {v: k for k, v in self.lang_token_to_id.items()}
+
+ self._src_lang = src_lang if src_lang is not None else "en"
+ self.tgt_lang = tgt_lang
+ self.cur_lang_id = self.get_lang_id(self._src_lang)
+
+ self.num_madeup_words = num_madeup_words
+
+ super().__init__(
+ src_lang=src_lang,
+ tgt_lang=tgt_lang,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ sep_token=sep_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ language_codes=language_codes,
+ sp_model_kwargs=self.sp_model_kwargs,
+ additional_special_tokens=additional_special_tokens,
+ num_madeup_words=num_madeup_words,
+ **kwargs,
+ )
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ @property
+ def vocab_size(self) -> int:
+ return len(self.encoder)
+
+ def get_vocab(self) -> dict:
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ @property
+ def src_lang(self) -> str:
+ return self._src_lang
+
+ @src_lang.setter
+ def src_lang(self, new_src_lang: str) -> None:
+ self._src_lang = new_src_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ def _tokenize(self, text: str) -> list[str]:
+ return self.sp_model.encode(text, out_type=str)
+
+ def _convert_token_to_id(self, token):
+ if token in self.lang_token_to_id:
+ return self.lang_token_to_id[token]
+ return self.encoder.get(token, self.encoder[self.unk_token])
+
+ def _convert_id_to_token(self, index: int) -> str:
+ """Converts an index (integer) in a token (str) using the decoder."""
+ if index in self.id_to_lang_token:
+ return self.id_to_lang_token[index]
+ return self.decoder.get(index, self.unk_token)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ current_sub_tokens = []
+ out_string = ""
+ for token in tokens:
+ # make sure that special tokens are not decoded using sentencepiece model
+ if token in self.all_special_tokens:
+ out_string += self.sp_model.decode(current_sub_tokens) + token
+ current_sub_tokens = []
+ else:
+ current_sub_tokens.append(token)
+ out_string += self.sp_model.decode(current_sub_tokens)
+ return out_string.strip()
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ prefix_ones = [1] * len(self.prefix_tokens)
+ suffix_ones = [1] * len(self.suffix_tokens)
+ if token_ids_1 is None:
+ return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
+ return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. An MBART sequence has the following format, where `X` represents the sequence:
+
+ - `input_ids` (for encoder) `X [eos, src_lang_code]`
+ - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
+
+ BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
+ separator.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return self.prefix_tokens + token_ids_0 + self.suffix_tokens
+ # We don't expect to process pairs, but leave the pair logic for API consistency
+ return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
+
+ def __getstate__(self) -> dict:
+ state = self.__dict__.copy()
+ state["sp_model"] = None
+ return state
+
+ def __setstate__(self, d: dict) -> None:
+ self.__dict__ = d
+
+ # for backward compatibility
+ if not hasattr(self, "sp_model_kwargs"):
+ self.sp_model_kwargs = {}
+
+ self.sp_model = load_spm(self.spm_file, self.sp_model_kwargs)
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ save_dir = Path(save_directory)
+ if not save_dir.is_dir():
+ raise OSError(f"{save_directory} should be a directory")
+ vocab_save_path = save_dir / (
+ (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
+ )
+ spm_save_path = save_dir / (
+ (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
+ )
+
+ save_json(self.encoder, vocab_save_path)
+
+ if os.path.abspath(self.spm_file) != os.path.abspath(spm_save_path) and os.path.isfile(self.spm_file):
+ copyfile(self.spm_file, spm_save_path)
+ elif not os.path.isfile(self.spm_file):
+ with open(spm_save_path, "wb") as fi:
+ content_spiece_model = self.sp_model.serialized_model_proto()
+ fi.write(content_spiece_model)
+
+ return (str(vocab_save_path), str(spm_save_path))
+
+ def prepare_seq2seq_batch(
+ self,
+ src_texts: list[str],
+ src_lang: str = "en",
+ tgt_texts: Optional[list[str]] = None,
+ tgt_lang: str = "ro",
+ **kwargs,
+ ) -> BatchEncoding:
+ self.src_lang = src_lang
+ self.tgt_lang = tgt_lang
+ self.set_src_lang_special_tokens(self.src_lang)
+ return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
+
+ def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
+ """Used by translation pipeline, to prepare inputs for the generate function"""
+ if src_lang is None or tgt_lang is None:
+ raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+ self.src_lang = src_lang
+ inputs = self(raw_inputs, add_special_tokens=True, **extra_kwargs)
+ tgt_lang_id = self.get_lang_id(tgt_lang)
+ inputs["forced_bos_token_id"] = tgt_lang_id
+ return inputs
+
+ def _switch_to_input_mode(self):
+ self.set_src_lang_special_tokens(self.src_lang)
+
+ def _switch_to_target_mode(self):
+ self.set_tgt_lang_special_tokens(self.tgt_lang)
+
+ def set_src_lang_special_tokens(self, src_lang: str) -> None:
+ """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
+ lang_token = self.get_lang_token(src_lang)
+ self.cur_lang_id = self.lang_token_to_id[lang_token]
+ self.prefix_tokens = [self.cur_lang_id]
+ self.suffix_tokens = [self.eos_token_id]
+
+ def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
+ """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
+ lang_token = self.get_lang_token(tgt_lang)
+ self.cur_lang_id = self.lang_token_to_id[lang_token]
+ self.prefix_tokens = [self.cur_lang_id]
+ self.suffix_tokens = [self.eos_token_id]
+
+ def get_lang_token(self, lang: str) -> str:
+ return self.lang_code_to_token[lang]
+
+ def get_lang_id(self, lang: str) -> int:
+ lang_token = self.get_lang_token(lang)
+ return self.lang_token_to_id[lang_token]
+
+
+def load_spm(path: str, sp_model_kwargs: dict[str, Any]) -> sentencepiece.SentencePieceProcessor:
+ spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs)
+ spm.Load(str(path))
+ return spm
+
+
+def load_json(path: str) -> Union[dict, list]:
+ with open(path, "r") as f:
+ return json.load(f)
+
+
+def save_json(data, path: str) -> None:
+ with open(path, "w") as f:
+ json.dump(data, f, indent=2)
+
+
+__all__ = ["M2M100Tokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8cd57ed3f2bd9ddd52437b42186635279917eeb
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mamba import *
+ from .modeling_mamba import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/configuration_mamba.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/configuration_mamba.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f6c17af01f7e92572064ce6726be3afb7bb6852
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/configuration_mamba.py
@@ -0,0 +1,160 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MAMBA configuration"""
+
+import math
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MambaConfig(PretrainedConfig):
+ """
+ This is the configuration class to store the configuration of a [`MambaModel`]. It is used to instantiate a MAMBA
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the MAMBA
+ [state-spaces/mamba-2.8b](https://huggingface.co/state-spaces/mamba-2.8b) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50280):
+ Vocabulary size of the MAMBA model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MambaModel`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimensionality of the embeddings and hidden states.
+ state_size (`int`, *optional*, defaults to 16): shape of the state space latents.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the model.
+ layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
+ The epsilon to use in the layer normalization layers.
+ pad_token_id (`int`, *optional*, defaults to 0):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 0):
+ The id of the beginning of sentence token in the vocabulary.
+ eos_token_id (`int`, *optional*, defaults to 0):
+ The id of the end of sentence token in the vocabulary.
+ expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size.
+ conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel.
+ use_bias (`bool`, *optional*, defaults to `False`):
+ Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block
+ use_conv_bias (`bool`, *optional*, defaults to `True`):
+ Whether or not to use bias in the convolution layer of the mixer block.
+ hidden_act (`str`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ initializer_range (`float`, *optional*, defaults to 0.1):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ residual_in_fp32 (`bool`, *optional*, defaults to `True`):
+ Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model
+ time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
+ Rank of the discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
+ time_step_scale (`float`, *optional*, defaults to 1.0):
+ Scale used used to scale `dt_proj.bias`.
+ time_step_min (`float`, *optional*, defaults to 0.001):
+ Minimum `time_step` used to bound `dt_proj.bias`.
+ time_step_max (`float`, *optional*, defaults to 0.1):
+ Maximum `time_step` used to bound `dt_proj.bias`.
+ time_step_init_scheme (`float`, *optional*, defaults to `"random"`):
+ Init scheme used for `dt_proj.weight`. Should be one of `["random","uniform"]`
+ time_step_floor (`float`, *optional*, defaults to 0.0001):
+ Minimum clamping value of the `dt_proj.bias` layer initialization.
+ rescale_prenorm_residual (`bool`, *optional*, defaults to `False`):
+ Whether or not to rescale `out_proj` weights when initializing.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the cache should be used.
+ use_mambapy (`bool`, *optional*, defaults to `False`):
+ Determines the fallback strategy during training if the CUDA-based official implementation of Mamba is not available. If `True`, the mamba.py implementation is used. If `False`, the naive and slower implementation is used. Consider switching to the naive version if memory is limited.
+
+
+ Example:
+
+ ```python
+ >>> from transformers import MambaConfig, MambaModel
+
+ >>> # Initializing a Mamba configuration
+ >>> configuration = MambaConfig()
+
+ >>> # Initializing a model (with random weights) from the configuration
+ >>> model = MambaModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mamba"
+
+ def __init__(
+ self,
+ vocab_size=50280,
+ hidden_size=768,
+ state_size=16,
+ num_hidden_layers=32,
+ layer_norm_epsilon=1e-5,
+ pad_token_id=0,
+ bos_token_id=0,
+ eos_token_id=0,
+ expand=2,
+ conv_kernel=4,
+ use_bias=False,
+ use_conv_bias=True,
+ hidden_act="silu",
+ initializer_range=0.1,
+ residual_in_fp32=True,
+ time_step_rank="auto",
+ time_step_scale=1.0,
+ time_step_min=0.001,
+ time_step_max=0.1,
+ time_step_init_scheme="random",
+ time_step_floor=1e-4,
+ rescale_prenorm_residual=False,
+ use_cache=True,
+ use_mambapy=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.state_size = state_size
+ self.num_hidden_layers = num_hidden_layers
+ self.layer_norm_epsilon = layer_norm_epsilon
+ self.conv_kernel = conv_kernel
+ self.expand = expand
+ self.intermediate_size = int(expand * self.hidden_size)
+ self.bos_token_id = bos_token_id
+ self.eos_token_id = eos_token_id
+ self.pad_token_id = pad_token_id
+ self.use_bias = use_bias
+ self.use_conv_bias = use_conv_bias
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.time_step_rank = math.ceil(self.hidden_size / 16) if time_step_rank == "auto" else time_step_rank
+ self.time_step_scale = time_step_scale
+ self.time_step_min = time_step_min
+ self.time_step_max = time_step_max
+ self.time_step_init_scheme = time_step_init_scheme
+ self.time_step_floor = time_step_floor
+ self.rescale_prenorm_residual = rescale_prenorm_residual
+ self.residual_in_fp32 = residual_in_fp32
+ self.use_cache = use_cache
+ self.use_mambapy = use_mambapy
+
+ super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, **kwargs)
+
+
+__all__ = ["MambaConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/convert_mamba_ssm_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/convert_mamba_ssm_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaedafa13fe19ce913069920677b90182eab5b19
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/convert_mamba_ssm_checkpoint_to_pytorch.py
@@ -0,0 +1,152 @@
+# coding=utf-8
+# Copyright 2024 state-spaces/mamba org and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""This script can be used to convert checkpoints provided in the `mamba_ssm` library into the format provided in HuggingFace `transformers`. It depends on the `mamba_ssm` package to be installed."""
+
+import argparse
+import json
+import math
+
+import torch
+
+from transformers import AutoTokenizer, MambaConfig, MambaForCausalLM
+from transformers.utils import logging
+from transformers.utils.import_utils import is_mamba_ssm_available
+
+
+if is_mamba_ssm_available():
+ from mamba_ssm.models.config_mamba import MambaConfig as MambaConfigSSM
+ from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
+
+ def convert_ssm_config_to_hf_config(config_ssm: MambaConfigSSM) -> MambaConfig:
+ """Convert a MambaConfig from mamba_ssm to a MambaConfig from transformers."""
+ hf_config = MambaConfig()
+ # Set config hidden size, num hidden layers, and vocab size directly from the original config
+ hf_config.hidden_size = config_ssm.d_model
+ hf_config.intermediate_size = config_ssm.d_model * 2
+ hf_config.time_step_rank = math.ceil(config_ssm.d_model / 16)
+
+ hf_config.num_hidden_layers = config_ssm.n_layer
+ vocab_size = config_ssm.vocab_size
+ pad_vocab_size_multiple = config_ssm.pad_vocab_size_multiple
+ if (vocab_size % pad_vocab_size_multiple) != 0:
+ vocab_size += pad_vocab_size_multiple - (vocab_size % pad_vocab_size_multiple)
+ hf_config.vocab_size = vocab_size
+ return hf_config
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def convert_mamba_ssm_checkpoint_to_huggingface_model(
+ original_state_dict: dict, original_ssm_config_dict: dict
+) -> tuple[MambaForCausalLM, AutoTokenizer]:
+ if not is_mamba_ssm_available():
+ raise ImportError(
+ "Calling convert_mamba_ssm_checkpoint_to_huggingface_model requires the mamba_ssm library to be installed. Please install it with `pip install mamba_ssm`."
+ )
+ original_ssm_config = MambaConfigSSM(**original_ssm_config_dict)
+
+ # Convert mamba_ssm config to huggingface MambaConfig
+ hf_config = convert_ssm_config_to_hf_config(original_ssm_config)
+
+ # No weights need to be renamed between the two models.
+ converted_state_dict = original_state_dict
+
+ # Load reshaped state dict into a huggingface model.
+ hf_model = MambaForCausalLM(hf_config)
+ tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
+ hf_model.load_state_dict(converted_state_dict)
+ return (hf_model, tokenizer)
+
+
+def validate_converted_model(
+ original_state_dict: dict, original_ssm_config_dict: dict, hf_model: MambaForCausalLM, tokenizer: AutoTokenizer
+) -> None:
+ """Validate the converted model returns the same output as the original model."""
+ torch_device = "cuda"
+
+ original_config = MambaConfigSSM(**original_ssm_config_dict)
+ original_model = MambaLMHeadModel(original_config).to(torch_device)
+ original_model.load_state_dict(original_state_dict)
+
+ hf_model = hf_model.to(torch_device)
+ input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"].to(torch_device)
+ # Assert model logits are close
+ with torch.no_grad():
+ original_model_logits = original_model(input_ids).logits
+ hf_model_logits = hf_model(input_ids).logits
+ if not torch.allclose(original_model_logits, hf_model_logits, atol=1e-3):
+ raise ValueError("The converted model did not return the same logits as the original model.")
+
+ logger.info("Model conversion validated successfully.")
+
+
+def convert_mamba_checkpoint_file_to_huggingface_model_file(
+ mamba_checkpoint_path: str, config_json_file: str, output_dir: str
+) -> None:
+ if not is_mamba_ssm_available():
+ raise ImportError(
+ "Calling convert_mamba_checkpoint_file_to_huggingface_model_file requires the mamba_ssm library to be installed. Please install it with `pip install mamba_ssm`."
+ )
+ if not torch.cuda.is_available():
+ raise ValueError(
+ "This script is to be run with a CUDA device, as the original mamba_ssm model does not support cpu."
+ )
+ logger.info(f"Loading model from {mamba_checkpoint_path} based on config from {config_json_file}")
+ # Load weights and config from paths
+ original_state_dict = torch.load(mamba_checkpoint_path, map_location="cpu", weights_only=True)
+ with open(config_json_file, "r", encoding="utf-8") as json_file:
+ original_ssm_config_dict = json.load(json_file)
+
+ # Convert the model
+ hf_model, tokenizer = convert_mamba_ssm_checkpoint_to_huggingface_model(
+ original_state_dict, original_ssm_config_dict
+ )
+
+ # Validate the conversion
+ validate_converted_model(original_state_dict, original_ssm_config_dict, hf_model, tokenizer)
+
+ logger.info(f"Model converted successfully. Saving model to {output_dir}")
+
+ # Save new model to pytorch_dump_path
+ hf_model.save_pretrained(output_dir)
+ tokenizer.save_pretrained(output_dir)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "-i",
+ "--mamba_checkpoint_file",
+ type=str,
+ required=True,
+ help="Path to a `pytorch_model.bin` mamba_ssm checkpoint file to be converted.",
+ )
+ parser.add_argument(
+ "-c",
+ "--config_json_file",
+ type=str,
+ required=True,
+ help="Path to a `config.json` file corresponding to a MambaConfig of the original mamba_ssm model.",
+ )
+ parser.add_argument(
+ "-o", "--output_dir", type=str, required=True, help="Path to directory to save the converted output model to."
+ )
+ args = parser.parse_args()
+
+ convert_mamba_checkpoint_file_to_huggingface_model_file(
+ args.mamba_checkpoint_file, args.config_json_file, args.output_dir
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/modeling_mamba.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/modeling_mamba.py
new file mode 100644
index 0000000000000000000000000000000000000000..83de5dd6aebc48ef2fc881ce71b35dd60ae1e60a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba/modeling_mamba.py
@@ -0,0 +1,848 @@
+# coding=utf-8
+# Copyright 2024 state-spaces/mamba org and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MAMBA model."""
+
+import math
+from dataclasses import dataclass
+from typing import Any, Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+
+from ...activations import ACT2FN
+from ...configuration_utils import PretrainedConfig
+from ...generation import GenerationMixin
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_utils import PreTrainedModel
+from ...utils import (
+ ModelOutput,
+ auto_docstring,
+ logging,
+)
+from ...utils.import_utils import is_causal_conv1d_available, is_mamba_ssm_available, is_mambapy_available
+from .configuration_mamba import MambaConfig
+
+
+logger = logging.get_logger(__name__)
+
+if is_mambapy_available():
+ from mambapy.pscan import pscan
+else:
+ pscan = None
+
+if is_mamba_ssm_available():
+ from mamba_ssm.ops.selective_scan_interface import mamba_inner_fn, selective_scan_fn
+ from mamba_ssm.ops.triton.selective_state_update import selective_state_update
+else:
+ selective_state_update, selective_scan_fn, mamba_inner_fn = None, None, None
+
+if is_causal_conv1d_available():
+ from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+else:
+ causal_conv1d_update, causal_conv1d_fn = None, None
+
+
+class MambaCache:
+ """
+ Cache for mamba model which does not have attention mechanism and key value states.
+
+ Arguments:
+ config (`PretrainedConfig):
+ The configuration file defining the shape-related attributes required to initialize the static cache.
+ max_batch_size (`int`):
+ The maximum batch size with which the model will be used. Note that a new instance must be instantiated if a smaller batch size is used.
+ dtype (`torch.dtype`, *optional*, defaults to `torch.float16`):
+ The default `dtype` to use when initializing the layer.
+ device (`torch.device` or `str`, *optional*):
+ The device on which the cache should be initialized. Should be the same as the layer.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MambaForCausalLM, MambaCache
+
+ >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf")
+ >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf")
+
+ >>> inputs = tokenizer(text="My name is Mamba", return_tensors="pt")
+
+ >>> # Prepare a cache class and pass it to model's forward
+ >>> past_key_values = MambaCache(config=model.config, max_batch_size=1, device=model.device, dtype=model.dtype)
+ >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+ >>> outputs.past_key_values
+ MambaCache()
+ ```
+ """
+
+ is_compileable = True
+
+ # TODO (joao): add layer_device_map arg and update code in `generate` accordingly
+ def __init__(
+ self,
+ config: PretrainedConfig,
+ max_batch_size: int,
+ dtype: torch.dtype = torch.float16,
+ device: Union[torch.device, str, None] = None,
+ ):
+ self.max_batch_size = max_batch_size
+ self._dtype = dtype
+ self.intermediate_size = config.intermediate_size
+ self.ssm_state_size = config.state_size
+ self.conv_kernel_size = config.conv_kernel
+
+ self.conv_states: list[torch.Tensor] = []
+ self.ssm_states: list[torch.Tensor] = []
+ device = torch.device(device) if device is not None else None
+ for _ in range(config.num_hidden_layers):
+ conv_state: torch.Tensor = torch.zeros(
+ self.max_batch_size,
+ self.intermediate_size,
+ self.conv_kernel_size,
+ device=device,
+ dtype=self._dtype,
+ )
+ ssm_state: torch.Tensor = torch.zeros(
+ self.max_batch_size,
+ self.intermediate_size,
+ self.ssm_state_size,
+ device=device,
+ dtype=self._dtype,
+ )
+
+ torch._dynamo.mark_static_address(conv_state)
+ torch._dynamo.mark_static_address(ssm_state)
+ self.conv_states.append(conv_state)
+ self.ssm_states.append(ssm_state)
+
+ def update_conv_state(
+ self, layer_idx: int, new_conv_state: torch.Tensor, cache_position: torch.LongTensor
+ ) -> torch.Tensor:
+ # This `if` blocks is only reached in multigpu and if `layer_device_map` is not passed. It is used
+ # when the cache is initialized in the forward pass (e.g. Mamba)
+ if self.conv_states[layer_idx].device != new_conv_state.device:
+ self.conv_states[layer_idx] = self.conv_states[layer_idx].to(new_conv_state.device)
+
+ conv_state = self.conv_states[layer_idx]
+ cache_position = cache_position.clamp(0, self.conv_kernel_size - 1)
+
+ conv_state = conv_state.roll(shifts=-1, dims=-1)
+ conv_state[:, :, cache_position] = new_conv_state.to(device=conv_state.device, dtype=conv_state.dtype)
+ self.conv_states[layer_idx].zero_()
+ self.conv_states[layer_idx] += conv_state
+ return self.conv_states[layer_idx]
+
+ def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor):
+ self.ssm_states[layer_idx].zero_()
+ self.ssm_states[layer_idx] += new_ssm_state.to(self.ssm_states[layer_idx].device)
+ return self.ssm_states[layer_idx]
+
+ def reset(self):
+ for layer_idx in range(len(self.conv_states)):
+ # In-place ops prevent breaking the static address
+ self.conv_states[layer_idx].zero_()
+ self.ssm_states[layer_idx].zero_()
+
+
+class MambaMixer(nn.Module):
+ """
+ Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
+ A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
+ ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
+ and is why Mamba is called **selective** state spaces)
+ """
+
+ def __init__(self, config: MambaConfig, layer_idx: int):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.ssm_state_size = config.state_size
+ self.conv_kernel_size = config.conv_kernel
+ self.intermediate_size = config.intermediate_size
+ self.time_step_rank = int(config.time_step_rank)
+ self.layer_idx = layer_idx
+ self.use_conv_bias = config.use_conv_bias
+ self.conv1d = nn.Conv1d(
+ in_channels=self.intermediate_size,
+ out_channels=self.intermediate_size,
+ bias=config.use_conv_bias,
+ kernel_size=config.conv_kernel,
+ groups=self.intermediate_size,
+ padding=config.conv_kernel - 1,
+ )
+
+ self.activation = config.hidden_act
+ self.act = ACT2FN[config.hidden_act]
+
+ self.use_mambapy = config.use_mambapy
+
+ # projection of the input hidden states
+ self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=config.use_bias)
+ # selective projection used to make dt, B and C input dependent
+ self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False)
+ # time step projection (discretization)
+ self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True)
+
+ # S4D real initialization. These are not discretized!
+ # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
+ A = torch.arange(1, self.ssm_state_size + 1, dtype=torch.float32)[None, :]
+ A = A.expand(self.intermediate_size, -1).contiguous()
+
+ self.A_log = nn.Parameter(torch.log(A))
+ self.D = nn.Parameter(torch.ones(self.intermediate_size))
+ self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias)
+ self.use_bias = config.use_bias
+
+ self.warn_slow_implementation()
+
+ def warn_slow_implementation(self):
+ is_fast_path_available = all(
+ (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
+ )
+ if not is_fast_path_available:
+ if self.use_mambapy:
+ if is_mambapy_available():
+ logger.warning_once(
+ "The fast path is not available because one of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)`"
+ " is None. Falling back to the mamba.py backend. To install follow https://github.com/state-spaces/mamba/#installation and"
+ " https://github.com/Dao-AILab/causal-conv1d"
+ )
+ else:
+ raise ImportError(
+ "use_mambapy is set to True but the mambapy package is not installed. To install it follow https://github.com/alxndrTL/mamba.py."
+ )
+ else:
+ logger.warning_once(
+ "The fast path is not available because one of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)`"
+ " is None. Falling back to the sequential implementation of Mamba, as use_mambapy is set to False. To install follow https://github.com/state-spaces/mamba/#installation and"
+ " https://github.com/Dao-AILab/causal-conv1d. For the mamba.py backend, follow https://github.com/alxndrTL/mamba.py."
+ )
+
+ def cuda_kernels_forward(
+ self,
+ hidden_states: torch.Tensor,
+ cache_params: Optional[MambaCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ ):
+ # 1. Gated MLP's linear projection
+ projected_states = self.in_proj(hidden_states).transpose(1, 2)
+
+ if self.training and cache_params is None: # Doesn't support outputting the states -> used for training
+ contextualized_states = mamba_inner_fn(
+ projected_states,
+ self.conv1d.weight,
+ self.conv1d.bias if self.use_conv_bias else None,
+ self.x_proj.weight,
+ self.dt_proj.weight,
+ self.out_proj.weight,
+ self.out_proj.bias.float() if self.use_bias else None,
+ -torch.exp(self.A_log.float()),
+ None, # input-dependent B
+ None, # input-dependent C
+ self.D.float(),
+ delta_bias=self.dt_proj.bias.float(),
+ delta_softplus=True,
+ )
+
+ else:
+ hidden_states, gate = projected_states.chunk(2, dim=1)
+
+ if attention_mask is not None:
+ hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
+ # 2. Convolution sequence transformation
+ conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
+ if cache_params is not None and cache_position[0] > 0:
+ hidden_states = causal_conv1d_update(
+ hidden_states.squeeze(-1),
+ cache_params.conv_states[self.layer_idx],
+ conv_weights,
+ self.conv1d.bias,
+ self.activation,
+ )
+ hidden_states = hidden_states.unsqueeze(-1)
+ else:
+ if cache_params is not None:
+ conv_states = nn.functional.pad(
+ hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0)
+ )
+ cache_params.update_conv_state(self.layer_idx, conv_states, cache_position)
+ hidden_states = causal_conv1d_fn(
+ hidden_states, conv_weights, self.conv1d.bias, activation=self.activation
+ )
+
+ if attention_mask is not None:
+ hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
+ # 3. State Space Model sequence transformation
+ # 3.a. input varying initialization of time_step, B and C
+ ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
+ time_step, B, C = torch.split(
+ ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
+ )
+ discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2)
+
+ A = -torch.exp(self.A_log.float())
+ # 3.c perform the recurrence y ← SSM(A, B, C)(x)
+ time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, "bias") else None
+ if cache_params is not None and cache_position[0] > 0:
+ scan_outputs = selective_state_update(
+ cache_params.ssm_states[self.layer_idx],
+ hidden_states[..., 0],
+ discrete_time_step[..., 0],
+ A,
+ B[:, 0],
+ C[:, 0],
+ self.D,
+ gate[..., 0],
+ time_proj_bias,
+ dt_softplus=True,
+ ).unsqueeze(-1)
+ else:
+ scan_outputs, ssm_state = selective_scan_fn(
+ hidden_states,
+ discrete_time_step,
+ A,
+ B.transpose(1, 2),
+ C.transpose(1, 2),
+ self.D.float(),
+ gate,
+ time_proj_bias,
+ delta_softplus=True,
+ return_last_state=True,
+ )
+ if ssm_state is not None and cache_params is not None:
+ cache_params.update_ssm_state(self.layer_idx, ssm_state)
+
+ # 4. Final linear projection
+ contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
+ return contextualized_states
+
+ # fmt: off
+ def slow_forward(self, input_states, cache_params: Optional[MambaCache]=None, cache_position:Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor] = None):
+ batch_size, seq_len, _ = input_states.shape
+ dtype = input_states.dtype
+ # 1. Gated MLP's linear projection
+ projected_states = self.in_proj(input_states).transpose(1, 2) # [batch, 2 * intermediate_size, seq_len]
+ hidden_states, gate = projected_states.chunk(2, dim=1)
+
+ if attention_mask is not None:
+ hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
+ # 2. Convolution sequence transformation
+ if cache_params is not None:
+ ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+ ssm_state = ssm_state.to(hidden_states.device)
+ # use `cache_position.shape[0]` to check whether we are in prefill
+ # stage, it's equivalent to check `cache_position[0] == 0`, which
+ # breaks dynamo fullgraph constraints
+ if cache_position.shape[0] == self.conv_kernel_size:
+ conv_state = nn.functional.pad(
+ hidden_states,
+ (self.conv_kernel_size - hidden_states.shape[-1], 0)
+ )
+
+ cache_params.update_conv_state(self.layer_idx, conv_state, cache_position)
+ hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len]
+ else:
+ conv_state = cache_params.update_conv_state(self.layer_idx, hidden_states, cache_position)
+ conv_state = conv_state.to(self.conv1d.weight.device)
+ hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
+ if self.use_conv_bias:
+ hidden_states += self.conv1d.bias
+ hidden_states = self.act(hidden_states).to(dtype).unsqueeze(-1) # [batch, intermediate_size, 1] : decoding
+ else:
+ ssm_state = torch.zeros(
+ (batch_size, self.intermediate_size, self.ssm_state_size),
+ device=hidden_states.device, dtype=dtype
+ )
+ hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len]
+
+ if attention_mask is not None:
+ hidden_states = hidden_states * attention_mask.unsqueeze(1)
+
+ # 3. State Space Model sequence transformation
+ # 3.a. Selection: [batch, seq_len, self.time_step_rank + self.ssm_state_size * 2]
+ ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
+ time_step, B, C = torch.split(
+ ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
+ )
+ discrete_time_step = self.dt_proj(time_step) # [batch, seq_len, intermediate_size]
+ discrete_time_step = nn.functional.softplus(discrete_time_step).transpose(1, 2) # [batch, intermediate_size, seq_len]
+
+ # 3.b. Discretization: B and C to [batch, seq_len, intermediate_size, ssm_state_size] (SRAM)
+ A = -torch.exp(self.A_log.float()) # [intermediate_size, ssm_state_size]
+ discrete_A = torch.exp(A[None, :, None, :] * discrete_time_step[:, :, :, None]) # [batch, intermediate_size, seq_len, ssm_state_size]
+ discrete_B = discrete_time_step[:, :, :, None] * B[:, None, :, :].float() # [batch, intermediate_size, seq_len, ssm_state_size]
+ deltaB_u = discrete_B * hidden_states[:, :, :, None].float()
+
+ # 3.c perform the recurrence y ← SSM(A, B, C)(x)
+ if self.use_mambapy and self.training and cache_params is None:
+ hs = pscan(discrete_A.transpose(1, 2), deltaB_u.transpose(1, 2)) # [batch, seq_len, intermediate_size, ssm_state_size]
+
+ scan_output = (hs @ C.unsqueeze(-1)).squeeze(3).transpose(1, 2) # [batch, intermediate_size, seq_len]
+ scan_output = scan_output + hidden_states * self.D[None, :, None]
+ scan_output = scan_output * self.act(gate)
+ else:
+ scan_outputs = []
+ for i in range(seq_len):
+ ssm_state = discrete_A[:, :, i, :] * ssm_state + deltaB_u[:, :, i, :] # [batch, intermediate_size, ssm_state]
+ scan_output = torch.matmul(ssm_state.to(dtype), C[:, i, :].unsqueeze(-1)) # [batch, intermediate_size, 1]
+ scan_outputs.append(scan_output[:, :, 0])
+ scan_output = torch.stack(scan_outputs, dim=-1) # [batch, intermediate_size, seq_len]
+ scan_output = scan_output + (hidden_states * self.D[None, :, None])
+ scan_output = (scan_output * self.act(gate))
+
+ if cache_params is not None:
+ cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+
+ # 4. Final linear projection
+ contextualized_states = self.out_proj(scan_output.transpose(1, 2)) # [batch, seq_len, hidden_size]
+ return contextualized_states
+ # fmt: on
+
+ def forward(
+ self,
+ hidden_states,
+ cache_params: Optional[MambaCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ ):
+ is_fast_path_available = all(
+ (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
+ )
+ if is_fast_path_available and "cuda" in self.x_proj.weight.device.type and not torch._dynamo.is_compiling():
+ return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask)
+ return self.slow_forward(hidden_states, cache_params, cache_position, attention_mask)
+
+
+class MambaRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ MambaRMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{self.weight.shape[0]}, eps={self.variance_epsilon}"
+
+
+class MambaBlock(GradientCheckpointingLayer):
+ def __init__(self, config, layer_idx):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.residual_in_fp32 = config.residual_in_fp32
+ self.norm = MambaRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+ self.mixer = MambaMixer(config, layer_idx=layer_idx)
+
+ def forward(
+ self,
+ hidden_states,
+ cache_params: Optional[MambaCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ ):
+ residual = hidden_states
+ hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype))
+ if self.residual_in_fp32:
+ residual = residual.to(torch.float32)
+
+ hidden_states = self.mixer(
+ hidden_states, cache_params=cache_params, cache_position=cache_position, attention_mask=attention_mask
+ )
+ hidden_states = residual + hidden_states
+ return hidden_states
+
+
+@auto_docstring
+class MambaPreTrainedModel(PreTrainedModel):
+ config: MambaConfig
+ base_model_prefix = "backbone"
+ _no_split_modules = ["MambaBlock", "MambaMixer"]
+ supports_gradient_checkpointing = True
+ _is_stateful = True
+
+ def _init_weights(self, module):
+ """Initialize the weights."""
+ std = self.config.initializer_range
+ if isinstance(module, MambaMixer):
+ # S4D real initialization. These are not discretized!
+ # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
+ A = torch.arange(1, module.ssm_state_size + 1, dtype=torch.float32)[None, :]
+ A = A.expand(module.intermediate_size, -1).contiguous()
+ module.A_log.copy_(torch.log(A))
+ module.A_log._no_weight_decay = True
+ module.D._no_weight_decay = True
+ module.D.data.fill_(1.0)
+
+ dt_init_std = self.config.time_step_rank**-0.5 * self.config.time_step_scale
+ if self.config.time_step_init_scheme == "constant":
+ nn.init.constant_(module.dt_proj.weight, dt_init_std)
+ elif self.config.time_step_init_scheme == "random":
+ nn.init.uniform_(module.dt_proj.weight, -dt_init_std, dt_init_std)
+
+ dt = torch.exp(
+ torch.rand(self.config.intermediate_size)
+ * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
+ + math.log(self.config.time_step_min)
+ ).clamp(min=self.config.time_step_floor)
+ # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
+ inv_dt = dt + torch.log(-torch.expm1(-dt))
+ module.dt_proj.bias.copy_(inv_dt)
+ module.dt_proj.bias._no_reinit = True
+
+ nn.init.kaiming_uniform_(module.conv1d.weight, a=math.sqrt(5))
+ if module.conv1d.bias is not None:
+ if not getattr(module.conv1d.bias, "_no_reinit", False):
+ nn.init.zeros_(module.conv1d.bias)
+ nn.init.kaiming_uniform_(module.out_proj.weight, a=math.sqrt(5))
+
+ if self.config.rescale_prenorm_residual:
+ # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
+ # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
+ # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
+ # > -- GPT-2 :: https://openai.com/blog/better-language-models/
+ #
+ # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
+ # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
+ # Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
+ # We need to reinit p since this code could be called multiple times
+ # Having just p *= scale would repeatedly scale it down
+ p = module.out_proj.weight
+ p /= math.sqrt(self.config.num_hidden_layers)
+
+ if isinstance(module, nn.Linear):
+ if not getattr(module.weight, "_no_reinit", False):
+ nn.init.normal_(module.weight, std=std)
+ if module.bias is not None:
+ if not getattr(module.bias, "_no_reinit", False):
+ nn.init.zeros_(module.bias)
+ elif isinstance(module, MambaRMSNorm):
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, nn.Embedding):
+ nn.init.normal_(module.weight, std=std)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Class for the MAMBA model outputs.
+ """
+)
+class MambaOutput(ModelOutput):
+ r"""
+ cache_params (`MambaCache`):
+ The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
+ avoid providing the old `input_ids`.
+
+ Includes both the State space model state matrices after the selective scan, and the Convolutional states
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ cache_params: Optional[MambaCache] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for causal language model (or autoregressive) outputs.
+ """
+)
+class MambaCausalLMOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ cache_params (`MambaCache`):
+ The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
+ avoid providing the old `input_ids`.
+
+ Includes both the State space model state matrices after the selective scan, and the Convolutional states
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ cache_params: Optional[MambaCache] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+
+
+@auto_docstring
+class MambaModel(MambaPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+ self.layers = nn.ModuleList([MambaBlock(config, layer_idx=idx) for idx in range(config.num_hidden_layers)])
+
+ self.gradient_checkpointing = False
+ self.norm_f = MambaRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+ # Initialize weights and apply final processing
+ self._register_load_state_dict_pre_hook(self.load_hook)
+ self.post_init()
+
+ def load_hook(self, state_dict, prefix, *args):
+ for k in state_dict:
+ if "embedding." in k:
+ state_dict[k.replace("embedding.", "embeddings.")] = state_dict.pop(k)
+ break
+
+ def get_input_embeddings(self):
+ return self.embeddings
+
+ def set_input_embeddings(self, new_embeddings):
+ self.embeddings = new_embeddings
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.LongTensor] = None,
+ cache_params: Optional[MambaCache] = None,
+ use_cache: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, MambaOutput]:
+ r"""
+ cache_params (`MambaCache`, *optional*):
+ If passed along, the model uses the previous state in all the blocks (which will give the output for the
+ `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
+ use_cache (`bool`, *optional*):
+ If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
+ """
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False)
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if (input_ids is None) ^ (inputs_embeds is not None): # ^ is python for xor
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embeddings(input_ids)
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ use_cache = False
+
+ if use_cache:
+ if cache_params is None:
+ cache_params = MambaCache(
+ self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype
+ )
+ cache_position = torch.arange(0, self.config.conv_kernel, device=inputs_embeds.device)
+ elif cache_position is None:
+ # cases when we do manual forward instead of using `model.generate` which will initiate
+ # `cache_position` and makes sure it is not None, throw error here instead of doing some
+ # hack to conjecture the current cache position
+ raise ValueError(
+ "You have to specify the `cache_position` manually when `use_cache=True` and `cache_params` is passed, "
+ "you don't have to pass a `cache_params` if you are in prefilling stage because in that case it will "
+ "be initialized for you automatically"
+ )
+ else:
+ cache_params = None
+
+ hidden_states = inputs_embeds
+ all_hidden_states = () if output_hidden_states else None
+ for mixer_block in self.layers:
+ hidden_states = mixer_block(
+ hidden_states,
+ cache_params=cache_params,
+ cache_position=cache_position,
+ attention_mask=attention_mask,
+ )
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ hidden_states = self.norm_f(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, cache_params, all_hidden_states] if v is not None)
+
+ return MambaOutput(
+ last_hidden_state=hidden_states,
+ cache_params=cache_params if use_cache else None,
+ hidden_states=all_hidden_states,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The MAMBA Model transformer with a language modeling head on top (linear layer with weights tied to the input
+ embeddings).
+ """
+)
+class MambaForCausalLM(MambaPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.backbone = MambaModel(config)
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.backbone.get_input_embeddings()
+
+ def set_input_embeddings(self, new_embeddings):
+ return self.backbone.set_input_embeddings(new_embeddings)
+
+ def _update_model_kwargs_for_generation(
+ self, outputs: ModelOutput, model_kwargs: dict[str, Any], num_new_tokens: int = 1, **kwargs
+ ) -> dict[str, Any]:
+ model_kwargs["cache_params"] = outputs.get("cache_params", None)
+ if (
+ model_kwargs.get("use_cache", True)
+ and "cache_position" in model_kwargs
+ and model_kwargs["cache_position"] is not None
+ ):
+ model_kwargs["cache_position"] = model_kwargs["cache_position"][-1:] + num_new_tokens
+
+ if "attention_mask" in model_kwargs:
+ attention_mask = model_kwargs["attention_mask"]
+ model_kwargs["attention_mask"] = torch.cat(
+ [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+ )
+
+ return model_kwargs
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ inputs_embeds=None,
+ use_cache=None,
+ cache_params: Optional[MambaCache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ):
+ # Overwritten -- uses `cache_params` as opposed to `past_key_values`
+ model_inputs = {"input_ids": input_ids.contiguous()}
+ if use_cache and cache_params is None:
+ # we initialize the `cache_position` to full size of `conv_states` at prefill stage
+ # considering padding will be applied when input length is shorter, and truncation
+ # will be applied when it is longer, so it will be equivalent to always have it match
+ # the length of `cache_params.conv_states`, which is `config.conv_kernel`
+ cache_position = torch.arange(0, self.backbone.config.conv_kernel, device=input_ids.device)
+ if inputs_embeds is not None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ max_batch_size = inputs_embeds.size(0)
+ else:
+ max_batch_size = input_ids.size(0)
+ cache_params = MambaCache(self.backbone.config, max_batch_size, device=self.device, dtype=self.dtype)
+
+ if use_cache and cache_position[0] > 0:
+ model_inputs["input_ids"] = input_ids[:, -1].unsqueeze(-1).contiguous()
+ attention_mask = None
+
+ if not use_cache and inputs_embeds is not None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+
+ model_inputs.update(
+ {
+ "cache_params": cache_params,
+ "use_cache": use_cache,
+ "cache_position": cache_position,
+ "attention_mask": attention_mask,
+ }
+ )
+ return model_inputs
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ cache_params: Optional[MambaCache] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs, # for now we need this for generation
+ ) -> Union[tuple, MambaCausalLMOutput]:
+ r"""
+ cache_params (`MambaCache`, *optional*):
+ If passed along, the model uses the previous state in all the blocks (which will give the output for the
+ `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+ use_cache (`bool`, *optional*):
+ If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ mamba_outputs = self.backbone(
+ input_ids,
+ cache_params=cache_params,
+ inputs_embeds=inputs_embeds,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ attention_mask=attention_mask,
+ )
+ hidden_states = mamba_outputs[0]
+
+ logits = self.lm_head(hidden_states.to(self.lm_head.weight.dtype)).float()
+
+ loss = None
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + mamba_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return MambaCausalLMOutput(
+ loss=loss,
+ logits=logits,
+ cache_params=mamba_outputs.cache_params,
+ hidden_states=mamba_outputs.hidden_states,
+ )
+
+
+__all__ = ["MambaForCausalLM", "MambaModel", "MambaPreTrainedModel", "MambaCache"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1389215e8398512df0285a13766d532e7117d29c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mamba2 import *
+ from .modeling_mamba2 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/configuration_mamba2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/configuration_mamba2.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b1b1177c0a54ff9b5b20634a6f44a2e7928b08e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/configuration_mamba2.py
@@ -0,0 +1,190 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MAMBA2 configuration"""
+
+import math
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Mamba2Config(PretrainedConfig):
+ """
+ This is the configuration class to store the configuration of a [`Mamba2Model`]. It is used to instantiate a MAMBA2
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the MAMBA2
+ [state-spaces/mamba2-2.8b](https://huggingface.co/state-spaces/mamba2-2.8b) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ num_heads (`int`, *optional*, defaults to 128):
+ Number of heads for the evolution matrices of mamba 2.
+ head_dim (`int`, *optional*, defaults to 64):
+ Dimension of each head.
+ vocab_size (`int`, *optional*, defaults to 32768):
+ Vocabulary size of the MAMBA2 model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`Mamba2Model`].
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimensionality of the embeddings and hidden states.
+ state_size (`int`, *optional*, defaults to 128): shape of the state space latents.
+ num_hidden_layers (`int`, *optional*, defaults to 64):
+ Number of hidden layers in the model.
+ layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
+ The epsilon to use in the layer normalization layers.
+ pad_token_id (`int`, *optional*, defaults to 1):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 0):
+ The id of the beginning of sentence token in the vocabulary.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ The id of the end of sentence token in the vocabulary.
+ expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size.
+ conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel.
+ n_groups (`int`, *optional*, defaults to 8):
+ Number of groups for the evolution matrices of mamba 2.
+ use_bias (`bool`, *optional*, defaults to `False`):
+ Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block
+ use_conv_bias (`bool`, *optional*, defaults to `True`):
+ Whether or not to use bias in the convolution layer of the mixer block.
+ hidden_act (`str`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ initializer_range (`float`, *optional*, defaults to 0.1):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ residual_in_fp32 (`bool`, *optional*, defaults to `True`):
+ Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model
+ time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
+ Rank of the discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
+ time_step_min (`float`, *optional*, defaults to 0.001):
+ Minimum `time_step` used to bound `dt_proj.bias`.
+ time_step_max (`float`, *optional*, defaults to 0.1):
+ Maximum `time_step` used to bound `dt_proj.bias`.
+ time_step_floor (`float`, *optional*, defaults to 0.0001):
+ Minimum clamping value of the `dt_proj.bias` layer initialization.
+ time_step_limit (`tuple`, *optional*, defaults to `(0.0, inf)`):
+ Accepted range of time step values.
+ rescale_prenorm_residual (`bool`, *optional*, defaults to `False`):
+ Whether or not to rescale `out_proj` weights when initializing.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the cache should be used.
+ rms_norm (`bool`, *optional*, defaults to `True`):
+ Whether to use RMS norm or not.
+ chunk_size (`int`, *optional*, defaults to 256):
+ Size of the chunks that will comprise the sequence.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie word embeddings or not.
+
+
+ Example:
+
+ ```python
+ >>> from transformers import Mamba2Config, Mamba2Model
+
+ >>> # Initializing a Mamba2 configuration
+ >>> configuration = Mamba2Config()
+
+ >>> # Initializing a model (with random weights) from the configuration
+ >>> model = Mamba2Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mamba2"
+
+ def __init__(
+ self,
+ num_heads=128,
+ head_dim=64,
+ vocab_size=32768,
+ hidden_size=4096,
+ state_size=128,
+ num_hidden_layers=64,
+ layer_norm_epsilon=1e-5,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ expand=2,
+ conv_kernel=4,
+ n_groups=8,
+ use_bias=False,
+ use_conv_bias=True,
+ hidden_act="silu",
+ initializer_range=0.1,
+ residual_in_fp32=True,
+ time_step_rank="auto",
+ time_step_min=0.001,
+ time_step_max=0.1,
+ time_step_floor=1e-4,
+ time_step_limit=(0.0, float("inf")),
+ rescale_prenorm_residual=False,
+ use_cache=True,
+ rms_norm=True,
+ chunk_size=256,
+ tie_word_embeddings=False,
+ **kwargs,
+ ):
+ if (hidden_size * expand) != (num_heads * head_dim):
+ raise ValueError(
+ "Inconsistent configuration: hidden_size * expand "
+ f"({hidden_size * expand}) must equal num_heads * head_dim "
+ f"({num_heads * head_dim})."
+ )
+
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.state_size = state_size
+ self.num_hidden_layers = num_hidden_layers
+ self.layer_norm_epsilon = layer_norm_epsilon
+ self.conv_kernel = conv_kernel
+ self.expand = expand
+
+ self.bos_token_id = bos_token_id
+ self.eos_token_id = eos_token_id
+ self.pad_token_id = pad_token_id
+ self.use_bias = use_bias
+ self.use_conv_bias = use_conv_bias
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.time_step_rank = math.ceil(self.hidden_size / 16) if time_step_rank == "auto" else time_step_rank
+ self.time_step_min = time_step_min
+ self.time_step_max = time_step_max
+ self.time_step_floor = time_step_floor
+ self.rescale_prenorm_residual = rescale_prenorm_residual
+ self.residual_in_fp32 = residual_in_fp32
+ self.use_cache = use_cache
+ self.n_groups = n_groups
+ self.num_heads = num_heads
+ self.head_dim = head_dim
+ self.rms_norm = rms_norm
+ self.state_size = state_size
+ self.chunk_size = chunk_size
+ self.time_step_limit = time_step_limit
+ self.tie_word_embeddings = tie_word_embeddings
+
+ super().__init__(
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ pad_token_id=pad_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+
+__all__ = ["Mamba2Config"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/convert_mamba2_ssm_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/convert_mamba2_ssm_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..482dd539b82d2d1f835685f68573703785233567
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/convert_mamba2_ssm_checkpoint_to_pytorch.py
@@ -0,0 +1,193 @@
+# coding=utf-8
+# Copyright 2024 state-spaces/mamba2 org and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""This script can be used to convert checkpoints provided in the `mamba2_ssm` library into the format provided in HuggingFace `transformers`. It depends on the `mamba2_ssm` package to be installed."""
+
+import argparse
+import json
+from functools import partial
+from os import path
+from typing import Optional
+
+import torch
+from safetensors import safe_open
+from safetensors.torch import save_model
+
+from transformers import GPTNeoXTokenizerFast, LlamaTokenizerFast, Mamba2Config, Mamba2ForCausalLM
+
+
+def load_state_dict_from_safetensors(mamba2_checkpoint_path: str, ckpt_name: str) -> dict[str, torch.Tensor]:
+ # Load weights and config from paths
+ original_state_dict = {}
+ with safe_open(path.join(mamba2_checkpoint_path, ckpt_name), framework="pt") as f:
+ for k in f.keys():
+ newk = k.removeprefix("model.")
+ original_state_dict[newk] = f.get_tensor(k).clone()
+ return original_state_dict
+
+
+def load_state_dict_from_torch(mamba2_checkpoint_path: str, ckpt_name: str) -> dict[str, torch.Tensor]:
+ return torch.load(path.join(mamba2_checkpoint_path, ckpt_name), map_location="cpu", weights_only=True)
+
+
+def convert_ssm_config_to_hf_config(config_ssm: dict, mamba2_model_dict: dict) -> Mamba2Config:
+ """Convert a Mamba2Config from mamba_ssm to a Mamba2Config from here."""
+ hf_config = Mamba2Config()
+
+ # Switch to a different dict depending on model type
+ config_dict = mamba2_model_dict
+
+ # Set important values from config and recalculate other resulting entries
+ hf_config.hidden_size = config_ssm[config_dict["hidden_size"]]
+ hf_config.num_heads = (hf_config.hidden_size * hf_config.expand) // hf_config.head_dim
+ hf_config.num_hidden_layers = config_ssm[config_dict["num_hidden_layers"]]
+ hf_config.n_groups = config_ssm.get(config_dict["n_groups"], 1)
+ hf_config.tie_word_embeddings = config_ssm["tie_embeddings"]
+ hf_config.bos_token_id = config_dict["bos_token_id"]
+ hf_config.pad_token_id = config_dict["pad_token_id"]
+ hf_config.eos_token_id = config_dict["eos_token_id"]
+
+ # Padded vocab size, mostly of 16 but 32 is also very common in different models
+ vocab_size = config_ssm["vocab_size"]
+ pad_vocab_size_multiple = config_ssm["pad_vocab_size_multiple"]
+ if (vocab_size % pad_vocab_size_multiple) != 0:
+ vocab_size += pad_vocab_size_multiple - (vocab_size % pad_vocab_size_multiple)
+ hf_config.vocab_size = vocab_size
+
+ return hf_config
+
+
+def load_and_save_tokenizer(
+ mamba2_model_type: str,
+ output_dir: str,
+ tokenizer_model_path: Optional[str] = None,
+) -> None:
+ tokenizer = None
+
+ # Load tokenizer
+ if tokenizer_model_path is not None and mamba2_model_type == "codestral":
+ tokenizer_class = LlamaTokenizerFast
+ tokenizer = tokenizer_class(tokenizer_model_path, legacy=False, from_slow=True)
+ elif mamba2_model_type == "mamba_ssm":
+ tokenizer = GPTNeoXTokenizerFast.from_pretrained("state-spaces/mamba-130m-hf", padding_side="left")
+
+ # Save tokenizer
+ if tokenizer is not None:
+ tokenizer.save_pretrained(output_dir)
+
+
+_MAMBA2_MODELS_DICT = {
+ "codestral": {
+ "hidden_size": "dim",
+ "num_hidden_layers": "n_layers",
+ "n_groups": "n_groups",
+ "bos_token_id": 0,
+ "pad_token_id": 1,
+ "eos_token_id": 2,
+ "config_name": "params.json",
+ "load_state_dict": partial(load_state_dict_from_safetensors, ckpt_name="consolidated.safetensors"),
+ "load_and_save_tokenizer": partial(load_and_save_tokenizer, "codestral"),
+ },
+ "mamba_ssm": {
+ "hidden_size": "d_model",
+ "num_hidden_layers": "n_layer",
+ "n_groups": "ngroups",
+ "bos_token_id": 0,
+ "pad_token_id": 0,
+ "eos_token_id": 0,
+ "config_name": "config.json",
+ "load_state_dict": partial(load_state_dict_from_torch, ckpt_name="pytorch_model.bin"),
+ "load_and_save_tokenizer": partial(load_and_save_tokenizer, "mamba_ssm"),
+ },
+}
+
+
+def convert_mamba2_checkpoint_file_to_huggingface_model_file(
+ mamba2_checkpoint_path: str,
+ mamba2_model_type: str,
+ precision: str,
+ output_dir: str,
+ tokenizer_model_path: Optional[str] = None,
+) -> None:
+ mamba2_model_dict = _MAMBA2_MODELS_DICT[mamba2_model_type]
+
+ # Load and save config based on name
+ config_path = path.join(mamba2_checkpoint_path, mamba2_model_dict["config_name"])
+ with open(config_path, "r", encoding="utf-8") as json_file:
+ config = json.load(json_file)
+ hf_config = convert_ssm_config_to_hf_config(config_ssm=config, mamba2_model_dict=mamba2_model_dict)
+ hf_config.save_pretrained(output_dir)
+
+ # Load state dict of the original model and transfer to hf model
+ original_state_dict = mamba2_model_dict["load_state_dict"](mamba2_checkpoint_path=mamba2_checkpoint_path)
+ hf_model = Mamba2ForCausalLM(hf_config)
+ hf_model.load_state_dict(original_state_dict)
+
+ # Save new model to pytorch_dump_path
+ dtype = torch.float32 if precision == "fp32" else (torch.bfloat16 if precision == "bf16" else torch.float16)
+ save_model(hf_model.to(dtype), path.join(output_dir, "model.safetensors"), metadata={"format": "pt"})
+
+ # Load and save tokenizer
+ mamba2_model_dict["load_and_save_tokenizer"](output_dir=output_dir, tokenizer_model_path=tokenizer_model_path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "-i",
+ "--mamba2_checkpoint_directory",
+ type=str,
+ required=True,
+ help="Path to a directory containing the `pytorch_model.bin` or `.safetensors` mamba2_ssm checkpoint file to be converted.",
+ )
+ parser.add_argument(
+ "-m",
+ "--mamba2_model_type",
+ type=str,
+ default="mamba_ssm",
+ const="mamba_ssm",
+ required=True,
+ choices=("codestral", "mamba_ssm"),
+ help="The model type the conversion will be performed on. Can choose from either `codestral` or `mamba_ssm`.",
+ )
+ parser.add_argument(
+ "-p",
+ "--precision",
+ type=str,
+ default="fp16",
+ const="fp16",
+ required=True,
+ choices=("fp32", "fp16", "bf16"),
+ help="The precision the model will be saved in. Select from fp32, fp16 or bf16.",
+ )
+ parser.add_argument(
+ "-o", "--output_dir", type=str, required=True, help="Path to directory to save the converted output model to."
+ )
+ parser.add_argument(
+ "-t",
+ "--tokenizer_model_path",
+ type=str,
+ default=None,
+ required=False,
+ help="Path to a `codestral` tokenizer file.",
+ )
+ args = parser.parse_args()
+
+ convert_mamba2_checkpoint_file_to_huggingface_model_file(
+ args.mamba2_checkpoint_directory,
+ args.mamba2_model_type,
+ args.precision,
+ args.output_dir,
+ args.tokenizer_model_path,
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/modeling_mamba2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/modeling_mamba2.py
new file mode 100644
index 0000000000000000000000000000000000000000..7809707d4c844b8563e0e7d1e0d92ad9aa098a57
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mamba2/modeling_mamba2.py
@@ -0,0 +1,1058 @@
+# coding=utf-8
+# Copyright 2024 state-spaces/mamba2 org and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MAMBA2 model."""
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from ...activations import ACT2FN
+from ...generation import GenerationMixin
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_utils import PreTrainedModel
+from ...utils import (
+ ModelOutput,
+ auto_docstring,
+ logging,
+)
+from ...utils.import_utils import is_causal_conv1d_available, is_mamba_2_ssm_available
+from .configuration_mamba2 import Mamba2Config
+
+
+logger = logging.get_logger(__name__)
+
+
+if is_mamba_2_ssm_available():
+ from mamba_ssm.ops.triton.selective_state_update import selective_state_update
+ from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined
+else:
+ mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined, selective_state_update = None, None, None
+
+if is_causal_conv1d_available():
+ from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+else:
+ causal_conv1d_update, causal_conv1d_fn = None, None
+
+is_fast_path_available = all(
+ (
+ selective_state_update,
+ mamba_chunk_scan_combined,
+ mamba_split_conv1d_scan_combined,
+ causal_conv1d_fn,
+ causal_conv1d_update,
+ )
+)
+
+
+# Helper methods for segment sum computation
+
+
+def pad_tensor_by_size(input_tensor: torch.Tensor, pad_size: int):
+ """
+ Padding x tensor with `pad_size` on the seq_len dim (dim=1)
+
+ Assumes that we only have tensors of either size 4 or 3
+ """
+ pad_shape = (0, 0, 0, 0, 0, pad_size, 0, 0) if len(input_tensor.shape) == 4 else (0, 0, 0, pad_size, 0, 0)
+
+ return torch.nn.functional.pad(input_tensor, pad_shape, mode="constant", value=0)
+
+
+def reshape_into_chunks(input_tensor, pad_size, chunk_size):
+ """
+ Padding input_tensor with `pad_size` on the seq_len dim (dim=1) and
+ simultaneously splitting it into chunk sequences.
+
+ Assumes that we only have tensors of either size 4 or 3
+ """
+ # [bsz, seq_len, ...] -> [bsz, seq_len multiple of chunk_size, ...]
+ input_tensor = pad_tensor_by_size(input_tensor, pad_size)
+
+ if len(input_tensor.shape) == 3:
+ # [bsz, seq_len multiple of chunk_size, num_heads] -> [bsz, -1, chunk_size, num_heads]
+ return input_tensor.reshape(input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2])
+ else:
+ # [bsz, seq_len multiple of chunk_size, num_heads, head_dim or state_size] -> [bsz, -1, chunk_size, num_heads, head_dim or state_size]
+ return input_tensor.reshape(
+ input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2], input_tensor.shape[3]
+ )
+
+
+def segment_sum(input_tensor):
+ """
+ More stable segment sum calculation. Uses cumulative sums and masking instead of direct subtractions.
+ """
+ chunk_size = input_tensor.size(-1)
+ # 1. expand input tensor to have an additional dimension and repeat along that dimension
+ # [..., chunk_size] -> [..., chunk_size, chunk_size]
+ input_tensor = input_tensor[..., None].expand(*input_tensor.size(), chunk_size)
+ # 2. create a lower triangular mask with the diagonal set to 0 to 0 out elements above diag
+ mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=-1)
+ input_tensor = input_tensor.masked_fill(~mask, 0)
+ # 3. compute actual cumsum
+ tensor_segsum = torch.cumsum(input_tensor, dim=-2)
+
+ # 4. apply mask to keep only the lower triangular part of the cumulative sum result (incl diagonal this time)
+ mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=0)
+ tensor_segsum = tensor_segsum.masked_fill(~mask, -torch.inf)
+ return tensor_segsum
+
+
+def apply_mask_to_padding_states(hidden_states, attention_mask):
+ """
+ Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66
+ """
+ if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1:
+ dtype = hidden_states.dtype
+ hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+
+ return hidden_states
+
+
+class Mamba2Cache:
+ """
+ Arguments:
+ config: Mamba2Config
+ batch_size: int
+ dtype: torch.dtype
+ device: torch.device
+
+ Attributes:
+ dtype: (`torch.dtype`):
+ The default `dtype` used to initializing the cache.
+ conv_kernel_size: (`int`):
+ Model's convolution kernel size taken from config.
+ n_groups: (`int`):
+ Model's number of groups taken from the config - similar to tensor parallel in Transformer.
+ state_size: (`int`):
+ Model's SSM state size taken from config.
+ num_heads: (`int`):
+ The number of heads used in the linear attention / SSM.
+ head_dim: (`int`):
+ The respective dimension of the heads used in the linear attention / SSM.
+ intermediate_size: (`int`):
+ Model's intermediate_size based on (expand * hidden_dim) from config.
+ conv_states: (`torch.Tensor`):
+ A tensor of shape `[num_layers, batch_size, conv_kernel_size, intermediate_size + 2 * n_groups * state_size]` that holds convolutional states.
+ ssm_states: (`torch.Tensor`):
+ A tensor of shape `[num_layers, batch_size, num_heads, head_dim, state_size]` that holds ssm states.
+ """
+
+ def __init__(
+ self, config: Mamba2Config, batch_size: int, dtype: torch.dtype = torch.float16, device: Optional[str] = None
+ ):
+ self.dtype = dtype
+ self.conv_kernel_size = config.conv_kernel
+ self.n_groups = config.n_groups
+ self.state_size = config.state_size
+ self.num_heads = config.num_heads
+ self.head_dim = config.head_dim
+ self.intermediate_size = int(config.expand * config.hidden_size)
+
+ self.conv_states = torch.zeros(
+ config.num_hidden_layers,
+ batch_size,
+ self.intermediate_size + 2 * self.n_groups * self.state_size,
+ self.conv_kernel_size,
+ device=device,
+ dtype=dtype,
+ )
+ self.ssm_states = torch.zeros(
+ config.num_hidden_layers,
+ batch_size,
+ self.num_heads,
+ self.head_dim,
+ self.state_size,
+ device=device,
+ dtype=dtype,
+ )
+
+ def update_conv_state(
+ self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool = False
+ ) -> torch.Tensor:
+ if cache_init:
+ self.conv_states[layer_idx] = new_conv_state.to(self.conv_states.device)
+ else:
+ self.conv_states[layer_idx] = self.conv_states[layer_idx].roll(shifts=-1, dims=-1)
+ self.conv_states[layer_idx][:, :, -1] = new_conv_state[:, 0, :].to(self.conv_states.device)
+ return self.conv_states[layer_idx]
+
+ def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor):
+ self.ssm_states[layer_idx] = new_ssm_state.to(self.ssm_states.device)
+ return self.ssm_states[layer_idx]
+
+ def reset(self):
+ self.conv_states.zero_()
+ self.ssm_states.zero_()
+
+
+class MambaRMSNormGated(torch.nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states, gate=None):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+
+ if gate is not None:
+ hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+ return self.weight * hidden_states.to(input_dtype)
+
+
+class Mamba2Mixer(nn.Module):
+ """
+ Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
+ A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
+ ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
+ and is why Mamba is called **selective** state spaces)
+ """
+
+ def __init__(self, config: Mamba2Config, layer_idx: int):
+ super().__init__()
+ self.num_heads = config.num_heads
+ self.hidden_size = config.hidden_size
+ self.ssm_state_size = config.state_size
+ self.conv_kernel_size = config.conv_kernel
+ self.intermediate_size = int(config.expand * self.hidden_size)
+ self.time_step_rank = int(config.time_step_rank)
+ self.layer_idx = layer_idx
+ self.use_conv_bias = config.use_conv_bias
+ self.activation = config.hidden_act
+ self.act = ACT2FN[config.hidden_act]
+
+ self.layer_norm_epsilon = config.layer_norm_epsilon
+ self.rms_norm = config.rms_norm
+
+ self.n_groups = config.n_groups
+ self.head_dim = config.head_dim
+ self.chunk_size = config.chunk_size
+
+ self.time_step_limit = config.time_step_limit
+ self.time_step_min = config.time_step_min
+ self.time_step_max = config.time_step_max
+
+ self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size
+ self.conv1d = nn.Conv1d(
+ in_channels=self.conv_dim,
+ out_channels=self.conv_dim,
+ bias=config.use_conv_bias,
+ kernel_size=config.conv_kernel,
+ groups=self.conv_dim,
+ padding=config.conv_kernel - 1,
+ )
+
+ # projection of the input hidden states
+ projection_size = self.intermediate_size + self.conv_dim + self.num_heads
+ self.in_proj = nn.Linear(
+ self.hidden_size,
+ projection_size,
+ bias=config.use_bias,
+ )
+ # selective projection used to make dt, B and C input dependent
+
+ # time step projection (discretization)
+ # instantiate once and copy inv_dt in init_weights of PretrainedModel
+ self.dt_bias = nn.Parameter(torch.ones(self.num_heads))
+
+ # S4D real initialization. These are not discretized!
+ # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
+ A = torch.arange(1, self.num_heads + 1)
+ self.A_log = nn.Parameter(torch.log(A))
+ self.A_log._no_weight_decay = True
+ self.norm = MambaRMSNormGated(self.intermediate_size, eps=self.layer_norm_epsilon)
+ self.D = nn.Parameter(torch.ones(self.num_heads))
+ self.D._no_weight_decay = True
+
+ self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias)
+ self.use_bias = config.use_bias
+
+ if not is_fast_path_available:
+ logger.warning_once(
+ "The fast path is not available because on of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)`"
+ " is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and"
+ " https://github.com/Dao-AILab/causal-conv1d"
+ )
+
+ def cuda_kernels_forward(
+ self,
+ hidden_states: torch.Tensor,
+ cache_params: Optional[Mamba2Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ ):
+ # 1. Gated MLP's linear projection
+ hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
+ projected_states = self.in_proj(hidden_states)
+
+ # Set up dimensions for reshapes later
+ batch_size, seq_len, _ = hidden_states.shape
+ groups_time_state_size = self.n_groups * self.ssm_state_size
+ d_mlp = (
+ projected_states.shape[-1]
+ - 2 * self.intermediate_size
+ - 2 * self.n_groups * self.ssm_state_size
+ - self.num_heads
+ ) // 2
+
+ # Single step calculations via cache
+ if cache_params is not None and cache_position is not None and cache_position[0] > 0:
+ _, _, gate, hidden_states_B_C, dt = projected_states.squeeze(1).split(
+ [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
+ )
+
+ # 2. Convolution sequence transformation
+ hidden_states_B_C = causal_conv1d_update(
+ hidden_states_B_C,
+ cache_params.conv_states[self.layer_idx],
+ self.conv1d.weight.squeeze(1),
+ self.conv1d.bias,
+ self.activation,
+ )
+
+ hidden_states, B, C = torch.split(
+ hidden_states_B_C,
+ [self.intermediate_size, groups_time_state_size, groups_time_state_size],
+ dim=-1,
+ )
+
+ # 3. SSM transformation
+ A = -torch.exp(self.A_log.float()) # (nheads,)
+ A = A[:, None, ...][:, :, None].expand(-1, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
+ dt = dt[:, :, None].expand(-1, -1, self.head_dim)
+ dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim)
+ D = self.D[:, None, ...].expand(-1, self.head_dim)
+ B = B.view(batch_size, self.n_groups, B.shape[1] // self.n_groups)
+ C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
+ hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
+ hidden_states = selective_state_update(
+ cache_params.ssm_states[self.layer_idx],
+ hidden_states_reshaped,
+ dt,
+ A,
+ B,
+ C,
+ D,
+ z=None,
+ dt_bias=dt_bias,
+ dt_softplus=True,
+ )
+ hidden_states = hidden_states.view(batch_size, self.num_heads * self.head_dim)
+ hidden_states = self.norm(hidden_states, gate)
+
+ # 4. Final linear projection
+ out = self.out_proj(hidden_states)[:, None, ...]
+
+ # Fused calculations or step by step if no initialized cache is found
+ else:
+ A = -torch.exp(self.A_log.float()) # (num_heads) or (intermediate_size, state_size)
+ dt_limit_kwargs = {} if self.time_step_limit == (0.0, float("inf")) else {"dt_limit": self.time_step_limit}
+
+ # 2-4. Fused kernel for conv1d, SSM, and the final projection
+ if self.training and cache_params is None:
+ out = mamba_split_conv1d_scan_combined(
+ projected_states,
+ self.conv1d.weight.squeeze(1),
+ self.conv1d.bias,
+ self.dt_bias,
+ A,
+ D=self.D,
+ chunk_size=self.chunk_size,
+ seq_idx=None, # was seq_idx
+ activation=self.activation,
+ rmsnorm_weight=self.norm.weight,
+ rmsnorm_eps=self.norm.variance_epsilon,
+ outproj_weight=self.out_proj.weight,
+ outproj_bias=self.out_proj.bias,
+ headdim=self.head_dim,
+ ngroups=self.n_groups,
+ norm_before_gate=False,
+ return_final_states=False,
+ **dt_limit_kwargs,
+ )
+
+ else:
+ _, _, gate, hidden_states_B_C, dt = projected_states.split(
+ [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
+ )
+
+ # 2. Convolution sequence transformation
+ # Init cache
+ if cache_params is not None:
+ hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
+ conv_states = nn.functional.pad(
+ hidden_states_B_C_transposed,
+ (cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0),
+ )
+ cache_params.update_conv_state(
+ layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True
+ )
+
+ if self.activation not in ["silu", "swish"]:
+ hidden_states_B_C = self.act(
+ self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2)
+ )
+ else:
+ hidden_states_B_C = causal_conv1d_fn(
+ x=hidden_states_B_C.transpose(1, 2),
+ weight=self.conv1d.weight.squeeze(1),
+ bias=self.conv1d.bias,
+ activation=self.activation,
+ ).transpose(1, 2)
+
+ hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
+ hidden_states, B, C = torch.split(
+ hidden_states_B_C,
+ [self.intermediate_size, groups_time_state_size, groups_time_state_size],
+ dim=-1,
+ )
+
+ # 3. SSM transformation
+ scan_output, ssm_state = mamba_chunk_scan_combined(
+ hidden_states.view(batch_size, seq_len, -1, self.head_dim),
+ dt,
+ A,
+ B.view(batch_size, seq_len, self.n_groups, -1),
+ C.view(batch_size, seq_len, self.n_groups, -1),
+ chunk_size=self.chunk_size,
+ D=self.D,
+ z=None,
+ seq_idx=None,
+ return_final_states=True,
+ dt_bias=self.dt_bias,
+ dt_softplus=True,
+ **dt_limit_kwargs,
+ )
+
+ # Init cache
+ if ssm_state is not None and cache_params is not None:
+ cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state)
+
+ scan_output = scan_output.view(batch_size, seq_len, -1)
+ # Multiply "gate" branch and apply extra normalization layer
+ scan_output = self.norm(scan_output, gate)
+
+ # 4. Final linear projection
+ out = self.out_proj(scan_output)
+ return out
+
+ # fmt: off
+ def torch_forward(
+ self,
+ hidden_states: torch.Tensor,
+ cache_params: Optional[Mamba2Cache]=None,
+ cache_position:Optional[torch.LongTensor]=None,
+ attention_mask: Optional[torch.Tensor]=None
+ ):
+ batch_size, seq_len, _ = hidden_states.shape
+ dtype = hidden_states.dtype
+
+ # 1. Gated MLP's linear projection
+ hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
+ projected_states = self.in_proj(hidden_states)
+ d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.ssm_state_size-self.num_heads) // 2
+ _, _, gate, hidden_states_B_C, dt = projected_states.split(
+ [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
+ )
+
+ # 2. Convolution sequence transformation
+ if cache_params is not None and cache_position is not None and cache_position[0] > 0:
+ cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=hidden_states_B_C, cache_init=False)
+
+ # We need to guarantee that anything regarding the cache is on the same device
+ conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device)
+
+ hidden_states_B_C = torch.sum(
+ conv_states * self.conv1d.weight.squeeze(1), dim=-1
+ )
+ if self.use_conv_bias:
+ hidden_states_B_C = hidden_states_B_C + self.conv1d.bias
+ hidden_states_B_C = self.act(hidden_states_B_C)
+ else:
+ # Init cache
+ if cache_params is not None:
+ hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
+ conv_states = nn.functional.pad(
+ hidden_states_B_C_transposed, (cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0)
+ )
+ cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True)
+
+ hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+
+ hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
+ hidden_states, B, C = torch.split(
+ hidden_states_B_C,
+ [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size],
+ dim=-1
+ )
+
+ # 3. SSM transformation
+ A = -torch.exp(self.A_log.float()) # [num_heads]
+ if cache_params is not None and cache_position is not None and cache_position[0] > 0:
+ # We need to guarantee that anything regarding the cache is on the same device
+ cache_device = cache_params.ssm_states.device
+
+ # Note: there is no need to pad parameter matrices here, as there is just one new token
+ # for batched generation
+ dt = dt[:, 0, :][:, None, ...]
+ dt = dt.transpose(1, 2).expand(batch_size, dt.shape[-1], self.head_dim)
+ # [num_heads] -> [num_heads, head_dim]
+ dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim)
+
+ dt = torch.nn.functional.softplus(dt + dt_bias.to(dt.dtype))
+ dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1])
+ A = A[..., None, None].expand(self.num_heads, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
+ # [bsz, num_heads, head_dim, state_size]
+ dA = (torch.exp(dt[..., None] * A)).to(device=cache_device)
+
+ # Discretize B
+ # [bsz, n_groups * state_size] -> [bsz, n_groups, 1, state_size] ->
+ # -> [bsz, n_groups, group to head repetition factor, state_size] -> [bsz, num_heads, state_size]
+ B = B.reshape(batch_size, self.n_groups, -1)[..., None, :]
+ B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous()
+ B = B.reshape(batch_size, -1, B.shape[-1])
+ # [bsz, num_heads, head_dim, state_size]
+ dB = dt[..., None] * B[..., None, :]
+
+ # Discretize x into dB
+ # [bsz, intermediate_size] -> [bsz, num_heads, head_dim]
+ hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim)
+ dBx = (dB * hidden_states[..., None]).to(device=cache_device)
+
+ # State calculation
+ cache_params.update_ssm_state(
+ layer_idx=self.layer_idx,
+ new_ssm_state=cache_params.ssm_states[self.layer_idx] * dA + dBx
+ )
+
+ # Subsequent output
+ # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
+ C = C.reshape(batch_size, self.n_groups, -1)[..., None, :]
+ C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous()
+ C = C.reshape(batch_size, -1, C.shape[-1])
+ # [bsz, num_heads, head_dim]
+
+ ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype) # Shape: [b, h, d, n]
+ # Reshape ssm_states to merge the first two dimensions
+ ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size) # Shape: [b*h, d, n]
+ C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1) # Shape: [b*h, n, 1]
+ y = torch.bmm(ssm_states_reshaped, C_reshaped)
+ y = y.view(batch_size, self.num_heads, self.head_dim)
+
+ # D skip connection
+ # [num_heads] -> [num_heads, head_dim]
+ D = self.D[..., None].expand(self.D.shape[0], self.head_dim)
+ y = (y + hidden_states * D).to(y.dtype)
+
+ # [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size]
+ y = y.reshape(batch_size, -1)[:, None, ...]
+ else:
+ # begin ssd naive implementation without einsums
+ dt = nn.functional.softplus(dt + self.dt_bias)
+ dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1])
+ hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).float()
+ B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
+ C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
+ B = B.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
+ C = C.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
+ pad_size = (self.chunk_size - seq_len % self.chunk_size) % self.chunk_size
+
+ D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size)
+
+ # Discretize x and A
+ hidden_states = hidden_states * dt[..., None]
+ A = A.to(hidden_states.dtype) * dt
+
+ # Rearrange into blocks/chunks
+ hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, A, B, C)]
+
+ # [bsz, -1, chunk_size, num_heads] -> [bsz, num_heads, -1, chunk_size]
+ A = A.permute(0, 3, 1, 2)
+ A_cumsum = torch.cumsum(A, dim=-1)
+
+ # 1. Compute the output for each intra-chunk (diagonal blocks)
+ # This is the analog of a causal mask
+ L = torch.exp(segment_sum(A))
+
+ # Contraction of C and B to get G (attention-weights like)
+ G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, :, :] # shape: (b, c, l, s, h, n)
+ G = G_intermediate.sum(dim=-1) # shape: (b, c, l, s, h)
+
+ # Compute M, equivalent to applying attention mask to weights
+ M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None]
+ M = M_intermediate.sum(dim=-1)
+
+ # Compute Y_diag (apply to values)
+ Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(dim=3)
+
+ # 2. Compute the state for each intra-chunk
+ # (right term of low-rank factorization of off-diagonal blocks; B terms)
+ decay_states = torch.exp(A_cumsum[:, :, :, -1:] - A_cumsum)
+ B_decay = B * decay_states.permute(0, -2, -1, 1)[..., None]
+ states = (B_decay[..., None, :] * hidden_states[..., None]).sum(dim=2)
+
+ # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
+ # (middle term of factorization of off-diag blocks; A terms)
+ if cache_params is not None and cache_position is not None and cache_position[0] > 0:
+ previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device)
+ else:
+ previous_states = torch.zeros_like(states[:, :1])
+ states = torch.cat([previous_states, states], dim=1)
+ decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
+ decay_chunk = decay_chunk.transpose(1, 3)
+ new_states = (decay_chunk[..., None, None] * states[:, :, None, ...]).sum(dim=1)
+ states, ssm_state = new_states[:, :-1], new_states[:, -1]
+
+ # 4. Compute state -> output conversion per chunk
+ # (left term of low-rank factorization of off-diagonal blocks; C terms)
+ state_decay_out = torch.exp(A_cumsum)
+ C_times_states = (C[..., None, :] * states[:, :, None, ...])
+ state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1)
+ Y_off = (C_times_states.sum(-1) * state_decay_out_permuted[..., None])
+
+ # Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks)
+ y = Y_diag + Y_off
+ # [bsz, -1, self.chunk_size, num_heads, head_dim] -> [bsz, (padded) seq_len, num_heads, head_dim]
+ y = y.reshape(batch_size, -1, self.num_heads, self.head_dim)
+
+ y = y + D_residual
+ # Cutting off padded chunks
+ if pad_size > 0:
+ y = y[:, :seq_len, :, :]
+ y = y.reshape(batch_size, seq_len, -1)
+
+ # Init cache
+ if ssm_state is not None and cache_params is not None:
+ cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state)
+
+ scan_output = self.norm(y, gate)
+
+ # end ssd naive
+
+ # 4. Final linear projection
+ contextualized_states = self.out_proj(scan_output.to(dtype)) # [batch, seq_len, hidden_size]
+ return contextualized_states
+ # fmt: on
+
+ def forward(
+ self,
+ hidden_states,
+ cache_params: Optional[Mamba2Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ ):
+ if is_fast_path_available and "cuda" in self.in_proj.weight.device.type:
+ return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask)
+ return self.torch_forward(hidden_states, cache_params, cache_position, attention_mask)
+
+
+class Mamba2RMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ Mamba2RMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+
+class Mamba2Block(GradientCheckpointingLayer):
+ def __init__(self, config, layer_idx):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.residual_in_fp32 = config.residual_in_fp32
+ self.norm = Mamba2RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+ self.mixer = Mamba2Mixer(config, layer_idx=layer_idx)
+
+ def forward(
+ self,
+ hidden_states,
+ cache_params: Optional[Mamba2Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ ):
+ residual = hidden_states
+ hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype))
+ if self.residual_in_fp32:
+ residual = residual.to(torch.float32)
+
+ hidden_states = self.mixer(
+ hidden_states, cache_params=cache_params, cache_position=cache_position, attention_mask=attention_mask
+ )
+ hidden_states = residual + hidden_states
+ return hidden_states
+
+
+@auto_docstring
+class Mamba2PreTrainedModel(PreTrainedModel):
+ config: Mamba2Config
+ base_model_prefix = "backbone"
+ _no_split_modules = ["Mamba2Block"]
+ supports_gradient_checkpointing = True
+ _is_stateful = True
+
+ def _init_weights(self, module):
+ """Initialize the weights."""
+ std = self.config.initializer_range
+ if isinstance(module, Mamba2Mixer):
+ # S4D real initialization. These are not discretized!
+ # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
+ A = torch.arange(1, self.config.num_heads + 1)
+ module.A_log.copy_(torch.log(A))
+ module.A_log._no_weight_decay = True
+ module.D._no_weight_decay = True
+ module.D.data.fill_(1.0)
+
+ dt = torch.exp(
+ torch.rand(self.config.num_heads)
+ * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
+ + math.log(self.config.time_step_min)
+ ).clamp(min=self.config.time_step_floor)
+
+ # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
+ inv_dt = dt + torch.log(-torch.expm1(-dt))
+ module.dt_bias.copy_(inv_dt)
+ module.dt_bias._no_reinit = True
+
+ nn.init.kaiming_uniform_(module.conv1d.weight, a=math.sqrt(5))
+ if module.conv1d.bias is not None:
+ if not getattr(module.conv1d.bias, "_no_reinit", False):
+ nn.init.zeros_(module.conv1d.bias)
+ nn.init.kaiming_uniform_(module.out_proj.weight, a=math.sqrt(5))
+
+ if self.config.rescale_prenorm_residual:
+ # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
+ # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
+ # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
+ # > -- GPT-2 :: https://openai.com/blog/better-language-models/
+ #
+ # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
+ # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
+ # Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
+ # We need to reinit p since this code could be called multiple times
+ # Having just p *= scale would repeatedly scale it down
+ p = module.out_proj.weight
+ p /= math.sqrt(self.config.num_hidden_layers)
+
+ if isinstance(module, nn.Linear):
+ if not getattr(module.weight, "_no_reinit", False):
+ nn.init.normal_(module.weight, std=std)
+ if module.bias is not None:
+ if not getattr(module.bias, "_no_reinit", False):
+ nn.init.zeros_(module.bias)
+ elif isinstance(module, (Mamba2RMSNorm, MambaRMSNormGated)):
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, nn.Embedding):
+ nn.init.normal_(module.weight, std=std)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Class for the MAMBA2 model outputs.
+ """
+)
+# Copied from transformers.models.mamba.modeling_mamba.MambaOutput with MAMBA->MAMBA2,Mamba->Mamba2
+class Mamba2Output(ModelOutput):
+ r"""
+ cache_params (`Mamba2Cache`):
+ The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
+ avoid providing the old `input_ids`.
+
+ Includes both the State space model state matrices after the selective scan, and the Convolutional states
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ cache_params: Optional[Mamba2Cache] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for causal language model (or autoregressive) outputs.
+ """
+)
+# Copied from transformers.models.mamba.modeling_mamba.MambaCausalLMOutput with Mamba->Mamba2
+class Mamba2CausalLMOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ cache_params (`Mamba2Cache`):
+ The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
+ avoid providing the old `input_ids`.
+
+ Includes both the State space model state matrices after the selective scan, and the Convolutional states
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ cache_params: Optional[Mamba2Cache] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+
+
+@auto_docstring
+class Mamba2Model(Mamba2PreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+ self.layers = nn.ModuleList([Mamba2Block(config, layer_idx=idx) for idx in range(config.num_hidden_layers)])
+
+ self.gradient_checkpointing = False
+ self.norm_f = Mamba2RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+ # Initialize weights and apply final processing
+ self._register_load_state_dict_pre_hook(self.load_hook)
+ self.post_init()
+
+ def load_hook(self, state_dict, prefix, *args):
+ for k in state_dict:
+ if "embedding." in k:
+ state_dict[k.replace("embedding.", "embeddings.")] = state_dict.pop(k)
+ break
+
+ def get_input_embeddings(self):
+ return self.embeddings
+
+ def set_input_embeddings(self, new_embeddings):
+ self.embeddings = new_embeddings
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.LongTensor] = None,
+ cache_params: Optional[Mamba2Cache] = None,
+ use_cache: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> Union[tuple, Mamba2Output]:
+ r"""
+ cache_params (`Mamba2Cache`, *optional*):
+ If passed along, the model uses the previous state in all the blocks (which will give the output for the
+ `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
+ use_cache (`bool`, *optional*):
+ If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
+ cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ The position of the current input in the cache. This is used to ensure that the cache is correctly updated.
+ If `cache_params` is passed, `cache_position` should also be passed.
+ """
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False)
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if (input_ids is None) ^ (inputs_embeds is not None): # ^ is python for xor
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embeddings(input_ids)
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ use_cache = False
+
+ if use_cache:
+ if cache_params is None:
+ cache_params = Mamba2Cache(
+ self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype
+ )
+ cache_position = torch.arange(0, self.config.conv_kernel, device=inputs_embeds.device)
+ elif cache_position is None:
+ # cases when we do manual forward instead of using `model.generate` which will initiate
+ # `cache_position` and makes sure it is not None, throw error here instead of doing some
+ # hack to conjecture the current cache position
+ raise ValueError(
+ "You have to specify the `cache_position` manually when `use_cache=True` and `cache_params` is passed, "
+ "you don't have to pass a `cache_params` if you are in prefilling stage because in that case it will "
+ "be initialized for you automatically"
+ )
+ else:
+ cache_params = None
+
+ hidden_states = inputs_embeds
+ all_hidden_states = () if output_hidden_states else None
+ for mixer_block in self.layers:
+ hidden_states = mixer_block(
+ hidden_states,
+ cache_params=cache_params,
+ cache_position=cache_position,
+ attention_mask=attention_mask,
+ )
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ hidden_states = self.norm_f(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, cache_params, all_hidden_states] if v is not None)
+
+ return Mamba2Output(
+ last_hidden_state=hidden_states,
+ cache_params=cache_params if use_cache else None,
+ hidden_states=all_hidden_states,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The MAMBA2 Model transformer with a language modeling head on top (linear layer with weights not tied to the input
+ embeddings).
+ """
+)
+class Mamba2ForCausalLM(Mamba2PreTrainedModel, GenerationMixin):
+ _tied_weights_keys = []
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.backbone = Mamba2Model(config)
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.backbone.get_input_embeddings()
+
+ def set_input_embeddings(self, new_embeddings):
+ return self.backbone.set_input_embeddings(new_embeddings)
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ inputs_embeds=None,
+ use_cache=None,
+ cache_params: Optional[Mamba2Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+ ):
+ # Overwritten -- uses `cache_params` as opposed to `past_key_values`
+ model_inputs = {"input_ids": input_ids.contiguous()}
+ if use_cache and cache_params is None:
+ # we initialize the `cache_position` to full size of `conv_states` at prefill stage
+ # considering padding will be applied when input length is shorter, and truncation
+ # will be applied when it is longer, so it will be equivalent to always have it match
+ # the length of `cache_params.conv_states`, which is `config.conv_kernel`
+ cache_position = torch.arange(0, self.backbone.config.conv_kernel, device=input_ids.device)
+ if inputs_embeds is not None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ max_batch_size = inputs_embeds.size(0)
+ else:
+ max_batch_size = input_ids.size(0)
+ cache_params = Mamba2Cache(self.backbone.config, max_batch_size, device=self.device, dtype=self.dtype)
+
+ if use_cache and cache_position[0] > 0:
+ model_inputs["input_ids"] = input_ids[:, -1].unsqueeze(-1).contiguous()
+ attention_mask = None
+
+ if not use_cache and inputs_embeds is not None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+
+ model_inputs.update(
+ {
+ "cache_params": cache_params,
+ "use_cache": use_cache,
+ "cache_position": cache_position,
+ "attention_mask": attention_mask,
+ }
+ )
+ return model_inputs
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ cache_params: Optional[Mamba2Cache] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs, # for now we need this for generation and loss_function
+ ) -> Union[tuple, Mamba2CausalLMOutput]:
+ r"""
+ cache_params (`Mamba2Cache`, *optional*):
+ If passed along, the model uses the previous state in all the blocks (which will give the output for the
+ `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+ use_cache (`bool`, *optional*):
+ If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
+ cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ The position of the current input in the cache. This is used to ensure that the cache is correctly updated.
+ If `cache_params` is passed, `cache_position` should also be passed.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ mamba2_outputs = self.backbone(
+ input_ids,
+ cache_params=cache_params,
+ inputs_embeds=inputs_embeds,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ attention_mask=attention_mask,
+ )
+ hidden_states = mamba2_outputs[0]
+
+ logits = self.lm_head(hidden_states.to(self.lm_head.weight.dtype)).float()
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+ if not return_dict:
+ output = (logits,) + mamba2_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return Mamba2CausalLMOutput(
+ loss=loss,
+ logits=logits,
+ cache_params=mamba2_outputs.cache_params,
+ hidden_states=mamba2_outputs.hidden_states,
+ )
+
+
+__all__ = ["Mamba2ForCausalLM", "Mamba2Model", "Mamba2PreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cfabc1590f25f9280e2630de381b275e90d85d6
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_marian import *
+ from .modeling_flax_marian import *
+ from .modeling_marian import *
+ from .modeling_tf_marian import *
+ from .tokenization_marian import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/configuration_marian.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/configuration_marian.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e0468c50b5fd4e29b7de632df0c742cc4b003f0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/configuration_marian.py
@@ -0,0 +1,395 @@
+# coding=utf-8
+# Copyright 2021 The Marian Team Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Marian model configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+from typing import Any, Optional
+
+from ... import PreTrainedTokenizer
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
+from ...onnx.utils import compute_effective_axis_dimension
+from ...utils import TensorType, is_torch_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MarianConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MarianModel`]. It is used to instantiate an
+ Marian model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Marian
+ [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 58101):
+ Vocabulary size of the Marian model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MarianModel`] or [`TFMarianModel`].
+ d_model (`int`, *optional*, defaults to 1024):
+ Dimensionality of the layers and the pooler layer.
+ encoder_layers (`int`, *optional*, defaults to 12):
+ Number of encoder layers.
+ decoder_layers (`int`, *optional*, defaults to 12):
+ Number of decoder layers.
+ encoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ decoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ decoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ encoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ max_position_embeddings (`int`, *optional*, defaults to 1024):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ encoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ decoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ scale_embedding (`bool`, *optional*, defaults to `False`):
+ Scale embeddings by diving by sqrt(d_model).
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models)
+ forced_eos_token_id (`int`, *optional*, defaults to 0):
+ The id of the token to force as the last generated token when `max_length` is reached. Usually set to
+ `eos_token_id`.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MarianModel, MarianConfig
+
+ >>> # Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration
+ >>> configuration = MarianConfig()
+
+ >>> # Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration
+ >>> model = MarianModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "marian"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
+
+ def __init__(
+ self,
+ vocab_size=58101,
+ decoder_vocab_size=None,
+ max_position_embeddings=1024,
+ encoder_layers=12,
+ encoder_ffn_dim=4096,
+ encoder_attention_heads=16,
+ decoder_layers=12,
+ decoder_ffn_dim=4096,
+ decoder_attention_heads=16,
+ encoder_layerdrop=0.0,
+ decoder_layerdrop=0.0,
+ use_cache=True,
+ is_encoder_decoder=True,
+ activation_function="gelu",
+ d_model=1024,
+ dropout=0.1,
+ attention_dropout=0.0,
+ activation_dropout=0.0,
+ init_std=0.02,
+ decoder_start_token_id=58100,
+ scale_embedding=False,
+ pad_token_id=58100,
+ eos_token_id=0,
+ forced_eos_token_id=0,
+ share_encoder_decoder_embeddings=True,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.decoder_vocab_size = decoder_vocab_size or vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.d_model = d_model
+ self.encoder_ffn_dim = encoder_ffn_dim
+ self.encoder_layers = encoder_layers
+ self.encoder_attention_heads = encoder_attention_heads
+ self.decoder_ffn_dim = decoder_ffn_dim
+ self.decoder_layers = decoder_layers
+ self.decoder_attention_heads = decoder_attention_heads
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.activation_function = activation_function
+ self.init_std = init_std
+ self.encoder_layerdrop = encoder_layerdrop
+ self.decoder_layerdrop = decoder_layerdrop
+ self.use_cache = use_cache
+ self.num_hidden_layers = encoder_layers
+ self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
+ self.share_encoder_decoder_embeddings = share_encoder_decoder_embeddings
+ super().__init__(
+ pad_token_id=pad_token_id,
+ eos_token_id=eos_token_id,
+ is_encoder_decoder=is_encoder_decoder,
+ decoder_start_token_id=decoder_start_token_id,
+ forced_eos_token_id=forced_eos_token_id,
+ **kwargs,
+ )
+
+
+class MarianOnnxConfig(OnnxSeq2SeqConfigWithPast):
+ @property
+ # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ if self.task in ["default", "seq2seq-lm"]:
+ common_inputs = OrderedDict(
+ [
+ ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+ ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+ ]
+ )
+
+ if self.use_past:
+ common_inputs["decoder_input_ids"] = {0: "batch"}
+ common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
+ else:
+ common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
+ common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
+
+ if self.use_past:
+ self.fill_with_past_key_values_(common_inputs, direction="inputs")
+ elif self.task == "causal-lm":
+ # TODO: figure this case out.
+ common_inputs = OrderedDict(
+ [
+ ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+ ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+ ]
+ )
+ if self.use_past:
+ num_encoder_layers, _ = self.num_layers
+ for i in range(num_encoder_layers):
+ common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
+ common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
+ else:
+ common_inputs = OrderedDict(
+ [
+ ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+ ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+ ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
+ ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
+ ]
+ )
+
+ return common_inputs
+
+ @property
+ # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
+ def outputs(self) -> Mapping[str, Mapping[int, str]]:
+ if self.task in ["default", "seq2seq-lm"]:
+ common_outputs = super().outputs
+ else:
+ common_outputs = super(OnnxConfigWithPast, self).outputs
+ if self.use_past:
+ num_encoder_layers, _ = self.num_layers
+ for i in range(num_encoder_layers):
+ common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
+ common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
+ return common_outputs
+
+ def _generate_dummy_inputs_for_default_and_seq2seq_lm(
+ self,
+ tokenizer: PreTrainedTokenizer,
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ encoder_inputs = self._generate_dummy_inputs_for_encoder_and_decoder(
+ tokenizer, batch_size, seq_length, is_pair, framework
+ )
+
+ # Generate decoder inputs
+ decoder_seq_length = seq_length if not self.use_past else 1
+ decoder_inputs = self._generate_dummy_inputs_for_encoder_and_decoder(
+ tokenizer, batch_size, decoder_seq_length, is_pair, framework
+ )
+ decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
+ common_inputs = dict(**encoder_inputs, **decoder_inputs)
+
+ if self.use_past:
+ if not is_torch_available():
+ raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+ else:
+ import torch
+ batch, encoder_seq_length = common_inputs["input_ids"].shape
+ decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
+ num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
+ encoder_shape = (
+ batch,
+ num_encoder_attention_heads,
+ encoder_seq_length,
+ self._config.hidden_size // num_encoder_attention_heads,
+ )
+ decoder_past_length = decoder_seq_length + 3
+ decoder_shape = (
+ batch,
+ num_decoder_attention_heads,
+ decoder_past_length,
+ self._config.hidden_size // num_decoder_attention_heads,
+ )
+
+ common_inputs["decoder_attention_mask"] = torch.cat(
+ [common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
+ )
+
+ common_inputs["past_key_values"] = []
+ # If the number of encoder and decoder layers are present in the model configuration, both are considered
+ num_encoder_layers, num_decoder_layers = self.num_layers
+ min_num_layers = min(num_encoder_layers, num_decoder_layers)
+ max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
+ remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
+
+ for _ in range(min_num_layers):
+ common_inputs["past_key_values"].append(
+ (
+ torch.zeros(decoder_shape),
+ torch.zeros(decoder_shape),
+ torch.zeros(encoder_shape),
+ torch.zeros(encoder_shape),
+ )
+ )
+ # TODO: test this.
+ shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
+ for _ in range(min_num_layers, max_num_layers):
+ common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))
+ return common_inputs
+
+ def _generate_dummy_inputs_for_causal_lm(
+ self,
+ tokenizer: PreTrainedTokenizer,
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ common_inputs = self._generate_dummy_inputs_for_encoder_and_decoder(
+ tokenizer, batch_size, seq_length, is_pair, framework
+ )
+
+ if self.use_past:
+ if not is_torch_available():
+ raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+ else:
+ import torch
+ batch, seqlen = common_inputs["input_ids"].shape
+ # Not using the same length for past_key_values
+ past_key_values_length = seqlen + 2
+ num_encoder_layers, _ = self.num_layers
+ num_encoder_attention_heads, _ = self.num_attention_heads
+ past_shape = (
+ batch,
+ num_encoder_attention_heads,
+ past_key_values_length,
+ self._config.hidden_size // num_encoder_attention_heads,
+ )
+
+ mask_dtype = common_inputs["attention_mask"].dtype
+ common_inputs["attention_mask"] = torch.cat(
+ [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
+ )
+ common_inputs["past_key_values"] = [
+ (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers)
+ ]
+ return common_inputs
+
+ # Copied from BartOnnxConfig._generate_dummy_inputs_for_sequence_classification_and_question_answering
+ # We renamed this function because Marian models do not have a sequence classification or question answering head
+ def _generate_dummy_inputs_for_encoder_and_decoder(
+ self,
+ tokenizer: PreTrainedTokenizer,
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ # Copied from OnnxConfig.generate_dummy_inputs
+ # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
+ # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
+ batch_size = compute_effective_axis_dimension(
+ batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
+ )
+
+ # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
+ token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
+ seq_length = compute_effective_axis_dimension(
+ seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
+ )
+
+ # Generate dummy inputs according to compute batch and sequence
+ dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
+ common_inputs = dict(tokenizer(dummy_input, return_tensors=framework))
+ return common_inputs
+
+ def generate_dummy_inputs(
+ self,
+ tokenizer: PreTrainedTokenizer,
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ if self.task in ["default", "seq2seq-lm"]:
+ common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm(
+ tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+ )
+
+ else:
+ common_inputs = self._generate_dummy_inputs_for_causal_lm(
+ tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+ )
+
+ return common_inputs
+
+ # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig._flatten_past_key_values_
+ def _flatten_past_key_values_(self, flattened_output, name, idx, t):
+ if self.task in ["default", "seq2seq-lm"]:
+ flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
+ else:
+ flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_(
+ flattened_output, name, idx, t
+ )
+
+ @property
+ def atol_for_validation(self) -> float:
+ return 1e-4
+
+
+__all__ = ["MarianConfig", "MarianOnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..abd1c4768d1618d0aa1e2099c1f02ce91dc28180
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py
@@ -0,0 +1,1326 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import datetime
+import json
+import os
+import re
+from pathlib import Path
+
+import yaml
+from tqdm import tqdm
+
+from transformers.models.marian.convert_marian_to_pytorch import (
+ FRONT_MATTER_TEMPLATE,
+ convert,
+ convert_opus_name_to_hf_name,
+ download_and_unzip,
+ get_system_metadata,
+)
+
+
+DEFAULT_REPO = "Tatoeba-Challenge"
+DEFAULT_MODEL_DIR = os.path.join(DEFAULT_REPO, "models")
+ISO_URL = "https://cdn-datasets.huggingface.co/language_codes/iso-639-3.csv"
+ISO_PATH = "lang_code_data/iso-639-3.csv"
+LANG_CODE_PATH = "lang_code_data/language-codes-3b2.csv"
+TATOEBA_MODELS_URL = "https://object.pouta.csc.fi/Tatoeba-MT-models"
+
+
+class TatoebaConverter:
+ """
+ Convert Tatoeba-Challenge models to huggingface format.
+
+ Steps:
+
+ 1. Convert numpy state dict to hf format (same code as OPUS-MT-Train conversion).
+ 2. Rename opus model to huggingface format. This means replace each alpha3 code with an alpha2 code if a unique
+ one exists. e.g. aav-eng -> aav-en, heb-eng -> he-en
+ 3. Select the best model for a particular pair, parse the yml for it and write a model card. By default the
+ best model is the one listed first in released-model-results, but it's also possible to specify the most
+ recent one.
+ """
+
+ def __init__(self, save_dir="marian_converted"):
+ assert Path(DEFAULT_REPO).exists(), "need git clone git@github.com:Helsinki-NLP/Tatoeba-Challenge.git"
+ self.download_lang_info()
+ self.model_results = json.load(open("Tatoeba-Challenge/models/released-model-results.json"))
+ self.alpha3_to_alpha2 = {}
+ for line in open(ISO_PATH):
+ parts = line.split("\t")
+ if len(parts[0]) == 3 and len(parts[3]) == 2:
+ self.alpha3_to_alpha2[parts[0]] = parts[3]
+ for line in LANG_CODE_PATH:
+ parts = line.split(",")
+ if len(parts[0]) == 3 and len(parts[1]) == 2:
+ self.alpha3_to_alpha2[parts[0]] = parts[1]
+ self.model_card_dir = Path(save_dir)
+ self.tag2name = {}
+ for key, value in GROUP_MEMBERS.items():
+ self.tag2name[key] = value[0]
+
+ def convert_models(self, tatoeba_ids, dry_run=False):
+ models_to_convert = [self.parse_metadata(x) for x in tatoeba_ids]
+ save_dir = Path("marian_ckpt")
+ dest_dir = Path(self.model_card_dir)
+ dest_dir.mkdir(exist_ok=True)
+ for model in tqdm(models_to_convert): # k, prepro, download, test_set_url in tqdm(model_list):
+ if "SentencePiece" not in model["pre-processing"]:
+ print(f"Skipping {model['release']} because it doesn't appear to use SentencePiece")
+ continue
+ if not os.path.exists(save_dir / model["_name"]):
+ download_and_unzip(f"{TATOEBA_MODELS_URL}/{model['release']}", save_dir / model["_name"])
+ # from convert_marian_to_pytorch
+ opus_language_groups_to_hf = convert_opus_name_to_hf_name
+ pair_name = opus_language_groups_to_hf(model["_name"])
+ convert(save_dir / model["_name"], dest_dir / f"opus-mt-{pair_name}")
+ self.write_model_card(model, dry_run=dry_run)
+
+ def expand_group_to_two_letter_codes(self, grp_name):
+ return [self.alpha3_to_alpha2.get(x, x) for x in GROUP_MEMBERS[grp_name][1]]
+
+ def is_group(self, code, name):
+ return "languages" in name or len(GROUP_MEMBERS.get(code, [])) > 1
+
+ def get_tags(self, code, name):
+ if len(code) == 2:
+ assert "languages" not in name, f"{code}: {name}"
+ return [code]
+ elif self.is_group(code, name):
+ group = self.expand_group_to_two_letter_codes(code)
+ group.append(code)
+ return group
+ else: # zho-> zh
+ print(f"Three letter monolingual code: {code}")
+ return [code]
+
+ def resolve_lang_code(self, src, tgt) -> tuple[str, str]:
+ src_tags = self.get_tags(src, self.tag2name[src])
+ tgt_tags = self.get_tags(tgt, self.tag2name[tgt])
+ return src_tags, tgt_tags
+
+ @staticmethod
+ def model_type_info_from_model_name(name):
+ info = {"_has_backtranslated_data": False}
+ if "1m" in name:
+ info["_data_per_pair"] = str(1e6)
+ if "2m" in name:
+ info["_data_per_pair"] = str(2e6)
+ if "4m" in name:
+ info["_data_per_pair"] = str(4e6)
+ if "+bt" in name:
+ info["_has_backtranslated_data"] = True
+ if "tuned4" in name:
+ info["_tuned"] = re.search(r"tuned4[^-]+", name).group()
+ return info
+
+ def write_model_card(self, model_dict, dry_run=False) -> str:
+ """
+ Construct card from data parsed from YAML and the model's name. upload command: aws s3 sync model_card_dir
+ s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
+ """
+ model_dir_url = f"{TATOEBA_MODELS_URL}/{model_dict['release']}"
+ long_pair = model_dict["_name"].split("-")
+ assert len(long_pair) == 2, f"got a translation pair {model_dict['_name']} that doesn't appear to be a pair"
+ short_src = self.alpha3_to_alpha2.get(long_pair[0], long_pair[0])
+ short_tgt = self.alpha3_to_alpha2.get(long_pair[1], long_pair[1])
+ model_dict["_hf_model_id"] = f"opus-mt-{short_src}-{short_tgt}"
+
+ a3_src, a3_tgt = model_dict["_name"].split("-")
+ # opus_src_tags, opus_tgt_tags = a3_src.split("+"), a3_tgt.split("+")
+
+ # This messy part tries to deal with language tags in multilingual models, possibly
+ # not all having three-letter codes
+ resolved_src_tags, resolved_tgt_tags = self.resolve_lang_code(a3_src, a3_tgt)
+ a2_src_tags, a2_tgt_tags = [], []
+ for tag in resolved_src_tags:
+ if tag not in self.alpha3_to_alpha2:
+ a2_src_tags.append(tag)
+ for tag in resolved_tgt_tags:
+ if tag not in self.alpha3_to_alpha2:
+ a2_tgt_tags.append(tag)
+
+ lang_tags = dedup(a2_src_tags + a2_tgt_tags)
+ src_multilingual, tgt_multilingual = (len(a2_src_tags) > 1), (len(a2_tgt_tags) > 1)
+ s, t = ",".join(a2_src_tags), ",".join(a2_tgt_tags)
+
+ metadata = {
+ "hf_name": model_dict["_name"],
+ "source_languages": s,
+ "target_languages": t,
+ "opus_readme_url": f"{model_dir_url}/README.md",
+ "original_repo": "Tatoeba-Challenge",
+ "tags": ["translation"],
+ "languages": lang_tags,
+ }
+ lang_tags = l2front_matter(lang_tags)
+
+ metadata["src_constituents"] = list(GROUP_MEMBERS[a3_src][1])
+ metadata["tgt_constituents"] = list(GROUP_MEMBERS[a3_tgt][1])
+ metadata["src_multilingual"] = src_multilingual
+ metadata["tgt_multilingual"] = tgt_multilingual
+
+ backtranslated_data = ""
+ if model_dict["_has_backtranslated_data"]:
+ backtranslated_data = " with backtranslations"
+
+ multilingual_data = ""
+ if "_data_per_pair" in model_dict:
+ multilingual_data = f"* data per pair in multilingual model: {model_dict['_data_per_pair']}\n"
+
+ tuned = ""
+ if "_tuned" in model_dict:
+ tuned = f"* multilingual model tuned for: {model_dict['_tuned']}\n"
+
+ model_base_filename = model_dict["release"].split("/")[-1]
+ download = f"* download original weights: [{model_base_filename}]({model_dir_url}/{model_dict['release']})\n"
+
+ langtoken = ""
+ if tgt_multilingual:
+ langtoken = (
+ "* a sentence-initial language token is required in the form of >>id<<"
+ "(id = valid, usually three-letter target language ID)\n"
+ )
+
+ metadata.update(get_system_metadata(DEFAULT_REPO))
+
+ scorestable = ""
+ for k, v in model_dict.items():
+ if "scores" in k:
+ this_score_table = f"* {k}\n|Test set|score|\n|---|---|\n"
+ pairs = sorted(v.items(), key=lambda x: x[1], reverse=True)
+ for pair in pairs:
+ this_score_table += f"|{pair[0]}|{pair[1]}|\n"
+ scorestable += this_score_table
+
+ datainfo = ""
+ if "training-data" in model_dict:
+ datainfo += "* Training data: \n"
+ for k, v in model_dict["training-data"].items():
+ datainfo += f" * {str(k)}: {str(v)}\n"
+ if "validation-data" in model_dict:
+ datainfo += "* Validation data: \n"
+ for k, v in model_dict["validation-data"].items():
+ datainfo += f" * {str(k)}: {str(v)}\n"
+ if "test-data" in model_dict:
+ datainfo += "* Test data: \n"
+ for k, v in model_dict["test-data"].items():
+ datainfo += f" * {str(k)}: {str(v)}\n"
+
+ testsetfilename = model_dict["release"].replace(".zip", ".test.txt")
+ testscoresfilename = model_dict["release"].replace(".zip", ".eval.txt")
+ testset = f"* test set translations file: [test.txt]({model_dir_url}/{testsetfilename})\n"
+ testscores = f"* test set scores file: [eval.txt]({model_dir_url}/{testscoresfilename})\n"
+
+ # combine with Tatoeba markdown
+ readme_url = f"{TATOEBA_MODELS_URL}/{model_dict['_name']}/README.md"
+ extra_markdown = f"""
+### {model_dict["_name"]}
+
+* source language name: {self.tag2name[a3_src]}
+* target language name: {self.tag2name[a3_tgt]}
+* OPUS readme: [README.md]({readme_url})
+"""
+
+ content = (
+ f"""
+* model: {model_dict["modeltype"]}
+* source language code{src_multilingual * "s"}: {", ".join(a2_src_tags)}
+* target language code{tgt_multilingual * "s"}: {", ".join(a2_tgt_tags)}
+* dataset: opus {backtranslated_data}
+* release date: {model_dict["release-date"]}
+* pre-processing: {model_dict["pre-processing"]}
+"""
+ + multilingual_data
+ + tuned
+ + download
+ + langtoken
+ + datainfo
+ + testset
+ + testscores
+ + scorestable
+ )
+
+ content = FRONT_MATTER_TEMPLATE.format(lang_tags) + extra_markdown + content
+
+ items = "\n".join([f"* {k}: {v}" for k, v in metadata.items()])
+ sec3 = "\n### System Info: \n" + items
+ content += sec3
+ if dry_run:
+ print("CONTENT:")
+ print(content)
+ print("METADATA:")
+ print(metadata)
+ return
+ sub_dir = self.model_card_dir / model_dict["_hf_model_id"]
+ sub_dir.mkdir(exist_ok=True)
+ dest = sub_dir / "README.md"
+ dest.open("w").write(content)
+ for k, v in metadata.items():
+ if isinstance(v, datetime.date):
+ metadata[k] = datetime.datetime.strftime(v, "%Y-%m-%d")
+ with open(sub_dir / "metadata.json", "w", encoding="utf-8") as writeobj:
+ json.dump(metadata, writeobj)
+
+ def download_lang_info(self):
+ global LANG_CODE_PATH
+ Path(LANG_CODE_PATH).parent.mkdir(exist_ok=True)
+ import wget
+ from huggingface_hub import hf_hub_download
+
+ if not os.path.exists(ISO_PATH):
+ wget.download(ISO_URL, ISO_PATH)
+ if not os.path.exists(LANG_CODE_PATH):
+ LANG_CODE_PATH = hf_hub_download(
+ repo_id="huggingface/language_codes_marianMT", filename="language-codes-3b2.csv", repo_type="dataset"
+ )
+
+ def parse_metadata(self, model_name, repo_path=DEFAULT_MODEL_DIR, method="best"):
+ p = Path(repo_path) / model_name
+
+ def url_to_name(url):
+ return url.split("/")[-1].split(".")[0]
+
+ if model_name not in self.model_results:
+ # This is not a language pair, so model results are ambiguous, go by newest
+ method = "newest"
+
+ if method == "best":
+ # Sort by how early they appear in released-models-results
+ results = [url_to_name(model["download"]) for model in self.model_results[model_name]]
+ ymls = [f for f in os.listdir(p) if f.endswith(".yml") and f[:-4] in results]
+ ymls.sort(key=lambda x: results.index(x[:-4]))
+ metadata = yaml.safe_load(open(p / ymls[0]))
+ metadata.update(self.model_type_info_from_model_name(ymls[0][:-4]))
+ elif method == "newest":
+ ymls = [f for f in os.listdir(p) if f.endswith(".yml")]
+ # Sort by date
+ ymls.sort(
+ key=lambda x: datetime.datetime.strptime(re.search(r"\d\d\d\d-\d\d?-\d\d?", x).group(), "%Y-%m-%d")
+ )
+ metadata = yaml.safe_load(open(p / ymls[-1]))
+ metadata.update(self.model_type_info_from_model_name(ymls[-1][:-4]))
+ else:
+ raise NotImplementedError(f"Don't know argument method='{method}' to parse_metadata()")
+ metadata["_name"] = model_name
+ return metadata
+
+
+GROUP_MEMBERS = {
+ # three letter code -> (group/language name, {constituents...}
+ # if this language is on the target side the constituents can be used as target language codes.
+ # if the language is on the source side they are supported natively without special codes.
+ "aav": ("Austro-Asiatic languages", {"hoc", "hoc_Latn", "kha", "khm", "khm_Latn", "mnw", "vie", "vie_Hani"}),
+ "afa": (
+ "Afro-Asiatic languages",
+ {
+ "acm",
+ "afb",
+ "amh",
+ "apc",
+ "ara",
+ "arq",
+ "ary",
+ "arz",
+ "hau_Latn",
+ "heb",
+ "kab",
+ "mlt",
+ "rif_Latn",
+ "shy_Latn",
+ "som",
+ "thv",
+ "tir",
+ },
+ ),
+ "afr": ("Afrikaans", {"afr"}),
+ "alv": (
+ "Atlantic-Congo languages",
+ {
+ "ewe",
+ "fuc",
+ "fuv",
+ "ibo",
+ "kin",
+ "lin",
+ "lug",
+ "nya",
+ "run",
+ "sag",
+ "sna",
+ "swh",
+ "toi_Latn",
+ "tso",
+ "umb",
+ "wol",
+ "xho",
+ "yor",
+ "zul",
+ },
+ ),
+ "ara": ("Arabic", {"afb", "apc", "apc_Latn", "ara", "ara_Latn", "arq", "arq_Latn", "arz"}),
+ "art": (
+ "Artificial languages",
+ {
+ "afh_Latn",
+ "avk_Latn",
+ "dws_Latn",
+ "epo",
+ "ido",
+ "ido_Latn",
+ "ile_Latn",
+ "ina_Latn",
+ "jbo",
+ "jbo_Cyrl",
+ "jbo_Latn",
+ "ldn_Latn",
+ "lfn_Cyrl",
+ "lfn_Latn",
+ "nov_Latn",
+ "qya",
+ "qya_Latn",
+ "sjn_Latn",
+ "tlh_Latn",
+ "tzl",
+ "tzl_Latn",
+ "vol_Latn",
+ },
+ ),
+ "aze": ("Azerbaijani", {"aze_Latn"}),
+ "bat": ("Baltic languages", {"lit", "lav", "prg_Latn", "ltg", "sgs"}),
+ "bel": ("Belarusian", {"bel", "bel_Latn"}),
+ "ben": ("Bengali", {"ben"}),
+ "bnt": (
+ "Bantu languages",
+ {"kin", "lin", "lug", "nya", "run", "sna", "swh", "toi_Latn", "tso", "umb", "xho", "zul"},
+ ),
+ "bul": ("Bulgarian", {"bul", "bul_Latn"}),
+ "cat": ("Catalan", {"cat"}),
+ "cau": ("Caucasian languages", {"abk", "kat", "che", "ady"}),
+ "ccs": ("South Caucasian languages", {"kat"}),
+ "ceb": ("Cebuano", {"ceb"}),
+ "cel": ("Celtic languages", {"gla", "gle", "bre", "cor", "glv", "cym"}),
+ "ces": ("Czech", {"ces"}),
+ "cpf": ("Creoles and pidgins, French‑based", {"gcf_Latn", "hat", "mfe"}),
+ "cpp": (
+ "Creoles and pidgins, Portuguese-based",
+ {"zsm_Latn", "ind", "pap", "min", "tmw_Latn", "max_Latn", "zlm_Latn"},
+ ),
+ "cus": ("Cushitic languages", {"som"}),
+ "dan": ("Danish", {"dan"}),
+ "deu": ("German", {"deu"}),
+ "dra": ("Dravidian languages", {"tam", "kan", "mal", "tel"}),
+ "ell": ("Modern Greek (1453-)", {"ell"}),
+ "eng": ("English", {"eng"}),
+ "epo": ("Esperanto", {"epo"}),
+ "est": ("Estonian", {"est"}),
+ "euq": ("Basque (family)", {"eus"}),
+ "eus": ("Basque", {"eus"}),
+ "fin": ("Finnish", {"fin"}),
+ "fiu": (
+ "Finno-Ugrian languages",
+ {
+ "est",
+ "fin",
+ "fkv_Latn",
+ "hun",
+ "izh",
+ "kpv",
+ "krl",
+ "liv_Latn",
+ "mdf",
+ "mhr",
+ "myv",
+ "sma",
+ "sme",
+ "udm",
+ "vep",
+ "vro",
+ },
+ ),
+ "fra": ("French", {"fra"}),
+ "gem": (
+ "Germanic languages",
+ {
+ "afr",
+ "ang_Latn",
+ "dan",
+ "deu",
+ "eng",
+ "enm_Latn",
+ "fao",
+ "frr",
+ "fry",
+ "gos",
+ "got_Goth",
+ "gsw",
+ "isl",
+ "ksh",
+ "ltz",
+ "nds",
+ "nld",
+ "nno",
+ "nob",
+ "nob_Hebr",
+ "non_Latn",
+ "pdc",
+ "sco",
+ "stq",
+ "swe",
+ "swg",
+ "yid",
+ },
+ ),
+ "gle": ("Irish", {"gle"}),
+ "glg": ("Galician", {"glg"}),
+ "gmq": ("North Germanic languages", {"dan", "nob", "nob_Hebr", "swe", "isl", "nno", "non_Latn", "fao"}),
+ "gmw": (
+ "West Germanic languages",
+ {
+ "afr",
+ "ang_Latn",
+ "deu",
+ "eng",
+ "enm_Latn",
+ "frr",
+ "fry",
+ "gos",
+ "gsw",
+ "ksh",
+ "ltz",
+ "nds",
+ "nld",
+ "pdc",
+ "sco",
+ "stq",
+ "swg",
+ "yid",
+ },
+ ),
+ "grk": ("Greek languages", {"grc_Grek", "ell"}),
+ "hbs": ("Serbo-Croatian", {"hrv", "srp_Cyrl", "bos_Latn", "srp_Latn"}),
+ "heb": ("Hebrew", {"heb"}),
+ "hin": ("Hindi", {"hin"}),
+ "hun": ("Hungarian", {"hun"}),
+ "hye": ("Armenian", {"hye", "hye_Latn"}),
+ "iir": (
+ "Indo-Iranian languages",
+ {
+ "asm",
+ "awa",
+ "ben",
+ "bho",
+ "gom",
+ "guj",
+ "hif_Latn",
+ "hin",
+ "jdt_Cyrl",
+ "kur_Arab",
+ "kur_Latn",
+ "mai",
+ "mar",
+ "npi",
+ "ori",
+ "oss",
+ "pan_Guru",
+ "pes",
+ "pes_Latn",
+ "pes_Thaa",
+ "pnb",
+ "pus",
+ "rom",
+ "san_Deva",
+ "sin",
+ "snd_Arab",
+ "tgk_Cyrl",
+ "tly_Latn",
+ "urd",
+ "zza",
+ },
+ ),
+ "ilo": ("Iloko", {"ilo"}),
+ "inc": (
+ "Indic languages",
+ {
+ "asm",
+ "awa",
+ "ben",
+ "bho",
+ "gom",
+ "guj",
+ "hif_Latn",
+ "hin",
+ "mai",
+ "mar",
+ "npi",
+ "ori",
+ "pan_Guru",
+ "pnb",
+ "rom",
+ "san_Deva",
+ "sin",
+ "snd_Arab",
+ "urd",
+ },
+ ),
+ "ine": (
+ "Indo-European languages",
+ {
+ "afr",
+ "afr_Arab",
+ "aln",
+ "ang_Latn",
+ "arg",
+ "asm",
+ "ast",
+ "awa",
+ "bel",
+ "bel_Latn",
+ "ben",
+ "bho",
+ "bjn",
+ "bos_Latn",
+ "bre",
+ "bul",
+ "bul_Latn",
+ "cat",
+ "ces",
+ "cor",
+ "cos",
+ "csb_Latn",
+ "cym",
+ "dan",
+ "deu",
+ "dsb",
+ "egl",
+ "ell",
+ "eng",
+ "enm_Latn",
+ "ext",
+ "fao",
+ "fra",
+ "frm_Latn",
+ "frr",
+ "fry",
+ "gcf_Latn",
+ "gla",
+ "gle",
+ "glg",
+ "glv",
+ "gom",
+ "gos",
+ "got_Goth",
+ "grc_Grek",
+ "gsw",
+ "guj",
+ "hat",
+ "hif_Latn",
+ "hin",
+ "hrv",
+ "hsb",
+ "hye",
+ "hye_Latn",
+ "ind",
+ "isl",
+ "ita",
+ "jdt_Cyrl",
+ "ksh",
+ "kur_Arab",
+ "kur_Latn",
+ "lad",
+ "lad_Latn",
+ "lat_Grek",
+ "lat_Latn",
+ "lav",
+ "lij",
+ "lit",
+ "lld_Latn",
+ "lmo",
+ "ltg",
+ "ltz",
+ "mai",
+ "mar",
+ "max_Latn",
+ "mfe",
+ "min",
+ "mkd",
+ "mwl",
+ "nds",
+ "nld",
+ "nno",
+ "nob",
+ "nob_Hebr",
+ "non_Latn",
+ "npi",
+ "oci",
+ "ori",
+ "orv_Cyrl",
+ "oss",
+ "pan_Guru",
+ "pap",
+ "pcd",
+ "pdc",
+ "pes",
+ "pes_Latn",
+ "pes_Thaa",
+ "pms",
+ "pnb",
+ "pol",
+ "por",
+ "prg_Latn",
+ "pus",
+ "roh",
+ "rom",
+ "ron",
+ "rue",
+ "rus",
+ "rus_Latn",
+ "san_Deva",
+ "scn",
+ "sco",
+ "sgs",
+ "sin",
+ "slv",
+ "snd_Arab",
+ "spa",
+ "sqi",
+ "srd",
+ "srp_Cyrl",
+ "srp_Latn",
+ "stq",
+ "swe",
+ "swg",
+ "tgk_Cyrl",
+ "tly_Latn",
+ "tmw_Latn",
+ "ukr",
+ "urd",
+ "vec",
+ "wln",
+ "yid",
+ "zlm_Latn",
+ "zsm_Latn",
+ "zza",
+ },
+ ),
+ "isl": ("Icelandic", {"isl"}),
+ "ita": ("Italian", {"ita"}),
+ "itc": (
+ "Italic languages",
+ {
+ "arg",
+ "ast",
+ "bjn",
+ "cat",
+ "cos",
+ "egl",
+ "ext",
+ "fra",
+ "frm_Latn",
+ "gcf_Latn",
+ "glg",
+ "hat",
+ "ind",
+ "ita",
+ "lad",
+ "lad_Latn",
+ "lat_Grek",
+ "lat_Latn",
+ "lij",
+ "lld_Latn",
+ "lmo",
+ "max_Latn",
+ "mfe",
+ "min",
+ "mwl",
+ "oci",
+ "pap",
+ "pcd",
+ "pms",
+ "por",
+ "roh",
+ "ron",
+ "scn",
+ "spa",
+ "srd",
+ "tmw_Latn",
+ "vec",
+ "wln",
+ "zlm_Latn",
+ "zsm_Latn",
+ },
+ ),
+ "jpn": ("Japanese", {"jpn", "jpn_Bopo", "jpn_Hang", "jpn_Hani", "jpn_Hira", "jpn_Kana", "jpn_Latn", "jpn_Yiii"}),
+ "jpx": ("Japanese (family)", {"jpn"}),
+ "kat": ("Georgian", {"kat"}),
+ "kor": ("Korean", {"kor_Hani", "kor_Hang", "kor_Latn", "kor"}),
+ "lav": ("Latvian", {"lav"}),
+ "lit": ("Lithuanian", {"lit"}),
+ "mkd": ("Macedonian", {"mkd"}),
+ "mkh": ("Mon-Khmer languages", {"vie_Hani", "mnw", "vie", "kha", "khm_Latn", "khm"}),
+ "msa": ("Malay (macrolanguage)", {"zsm_Latn", "ind", "max_Latn", "zlm_Latn", "min"}),
+ "mul": (
+ "Multiple languages",
+ {
+ "abk",
+ "acm",
+ "ady",
+ "afb",
+ "afh_Latn",
+ "afr",
+ "akl_Latn",
+ "aln",
+ "amh",
+ "ang_Latn",
+ "apc",
+ "ara",
+ "arg",
+ "arq",
+ "ary",
+ "arz",
+ "asm",
+ "ast",
+ "avk_Latn",
+ "awa",
+ "aze_Latn",
+ "bak",
+ "bam_Latn",
+ "bel",
+ "bel_Latn",
+ "ben",
+ "bho",
+ "bod",
+ "bos_Latn",
+ "bre",
+ "brx",
+ "brx_Latn",
+ "bul",
+ "bul_Latn",
+ "cat",
+ "ceb",
+ "ces",
+ "cha",
+ "che",
+ "chr",
+ "chv",
+ "cjy_Hans",
+ "cjy_Hant",
+ "cmn",
+ "cmn_Hans",
+ "cmn_Hant",
+ "cor",
+ "cos",
+ "crh",
+ "crh_Latn",
+ "csb_Latn",
+ "cym",
+ "dan",
+ "deu",
+ "dsb",
+ "dtp",
+ "dws_Latn",
+ "egl",
+ "ell",
+ "enm_Latn",
+ "epo",
+ "est",
+ "eus",
+ "ewe",
+ "ext",
+ "fao",
+ "fij",
+ "fin",
+ "fkv_Latn",
+ "fra",
+ "frm_Latn",
+ "frr",
+ "fry",
+ "fuc",
+ "fuv",
+ "gan",
+ "gcf_Latn",
+ "gil",
+ "gla",
+ "gle",
+ "glg",
+ "glv",
+ "gom",
+ "gos",
+ "got_Goth",
+ "grc_Grek",
+ "grn",
+ "gsw",
+ "guj",
+ "hat",
+ "hau_Latn",
+ "haw",
+ "heb",
+ "hif_Latn",
+ "hil",
+ "hin",
+ "hnj_Latn",
+ "hoc",
+ "hoc_Latn",
+ "hrv",
+ "hsb",
+ "hun",
+ "hye",
+ "iba",
+ "ibo",
+ "ido",
+ "ido_Latn",
+ "ike_Latn",
+ "ile_Latn",
+ "ilo",
+ "ina_Latn",
+ "ind",
+ "isl",
+ "ita",
+ "izh",
+ "jav",
+ "jav_Java",
+ "jbo",
+ "jbo_Cyrl",
+ "jbo_Latn",
+ "jdt_Cyrl",
+ "jpn",
+ "kab",
+ "kal",
+ "kan",
+ "kat",
+ "kaz_Cyrl",
+ "kaz_Latn",
+ "kek_Latn",
+ "kha",
+ "khm",
+ "khm_Latn",
+ "kin",
+ "kir_Cyrl",
+ "kjh",
+ "kpv",
+ "krl",
+ "ksh",
+ "kum",
+ "kur_Arab",
+ "kur_Latn",
+ "lad",
+ "lad_Latn",
+ "lao",
+ "lat_Latn",
+ "lav",
+ "ldn_Latn",
+ "lfn_Cyrl",
+ "lfn_Latn",
+ "lij",
+ "lin",
+ "lit",
+ "liv_Latn",
+ "lkt",
+ "lld_Latn",
+ "lmo",
+ "ltg",
+ "ltz",
+ "lug",
+ "lzh",
+ "lzh_Hans",
+ "mad",
+ "mah",
+ "mai",
+ "mal",
+ "mar",
+ "max_Latn",
+ "mdf",
+ "mfe",
+ "mhr",
+ "mic",
+ "min",
+ "mkd",
+ "mlg",
+ "mlt",
+ "mnw",
+ "moh",
+ "mon",
+ "mri",
+ "mwl",
+ "mww",
+ "mya",
+ "myv",
+ "nan",
+ "nau",
+ "nav",
+ "nds",
+ "niu",
+ "nld",
+ "nno",
+ "nob",
+ "nob_Hebr",
+ "nog",
+ "non_Latn",
+ "nov_Latn",
+ "npi",
+ "nya",
+ "oci",
+ "ori",
+ "orv_Cyrl",
+ "oss",
+ "ota_Arab",
+ "ota_Latn",
+ "pag",
+ "pan_Guru",
+ "pap",
+ "pau",
+ "pdc",
+ "pes",
+ "pes_Latn",
+ "pes_Thaa",
+ "pms",
+ "pnb",
+ "pol",
+ "por",
+ "ppl_Latn",
+ "prg_Latn",
+ "pus",
+ "quc",
+ "qya",
+ "qya_Latn",
+ "rap",
+ "rif_Latn",
+ "roh",
+ "rom",
+ "ron",
+ "rue",
+ "run",
+ "rus",
+ "sag",
+ "sah",
+ "san_Deva",
+ "scn",
+ "sco",
+ "sgs",
+ "shs_Latn",
+ "shy_Latn",
+ "sin",
+ "sjn_Latn",
+ "slv",
+ "sma",
+ "sme",
+ "smo",
+ "sna",
+ "snd_Arab",
+ "som",
+ "spa",
+ "sqi",
+ "srp_Cyrl",
+ "srp_Latn",
+ "stq",
+ "sun",
+ "swe",
+ "swg",
+ "swh",
+ "tah",
+ "tam",
+ "tat",
+ "tat_Arab",
+ "tat_Latn",
+ "tel",
+ "tet",
+ "tgk_Cyrl",
+ "tha",
+ "tir",
+ "tlh_Latn",
+ "tly_Latn",
+ "tmw_Latn",
+ "toi_Latn",
+ "ton",
+ "tpw_Latn",
+ "tso",
+ "tuk",
+ "tuk_Latn",
+ "tur",
+ "tvl",
+ "tyv",
+ "tzl",
+ "tzl_Latn",
+ "udm",
+ "uig_Arab",
+ "uig_Cyrl",
+ "ukr",
+ "umb",
+ "urd",
+ "uzb_Cyrl",
+ "uzb_Latn",
+ "vec",
+ "vie",
+ "vie_Hani",
+ "vol_Latn",
+ "vro",
+ "war",
+ "wln",
+ "wol",
+ "wuu",
+ "xal",
+ "xho",
+ "yid",
+ "yor",
+ "yue",
+ "yue_Hans",
+ "yue_Hant",
+ "zho",
+ "zho_Hans",
+ "zho_Hant",
+ "zlm_Latn",
+ "zsm_Latn",
+ "zul",
+ "zza",
+ },
+ ),
+ "nic": (
+ "Niger-Kordofanian languages",
+ {
+ "bam_Latn",
+ "ewe",
+ "fuc",
+ "fuv",
+ "ibo",
+ "kin",
+ "lin",
+ "lug",
+ "nya",
+ "run",
+ "sag",
+ "sna",
+ "swh",
+ "toi_Latn",
+ "tso",
+ "umb",
+ "wol",
+ "xho",
+ "yor",
+ "zul",
+ },
+ ),
+ "nld": ("Dutch", {"nld"}),
+ "nor": ("Norwegian", {"nob", "nno"}),
+ "phi": ("Philippine languages", {"ilo", "akl_Latn", "war", "hil", "pag", "ceb"}),
+ "pol": ("Polish", {"pol"}),
+ "por": ("Portuguese", {"por"}),
+ "pqe": (
+ "Eastern Malayo-Polynesian languages",
+ {"fij", "gil", "haw", "mah", "mri", "nau", "niu", "rap", "smo", "tah", "ton", "tvl"},
+ ),
+ "roa": (
+ "Romance languages",
+ {
+ "arg",
+ "ast",
+ "cat",
+ "cos",
+ "egl",
+ "ext",
+ "fra",
+ "frm_Latn",
+ "gcf_Latn",
+ "glg",
+ "hat",
+ "ind",
+ "ita",
+ "lad",
+ "lad_Latn",
+ "lij",
+ "lld_Latn",
+ "lmo",
+ "max_Latn",
+ "mfe",
+ "min",
+ "mwl",
+ "oci",
+ "pap",
+ "pms",
+ "por",
+ "roh",
+ "ron",
+ "scn",
+ "spa",
+ "tmw_Latn",
+ "vec",
+ "wln",
+ "zlm_Latn",
+ "zsm_Latn",
+ },
+ ),
+ "ron": ("Romanian", {"ron"}),
+ "run": ("Rundi", {"run"}),
+ "rus": ("Russian", {"rus"}),
+ "sal": ("Salishan languages", {"shs_Latn"}),
+ "sem": ("Semitic languages", {"acm", "afb", "amh", "apc", "ara", "arq", "ary", "arz", "heb", "mlt", "tir"}),
+ "sla": (
+ "Slavic languages",
+ {
+ "bel",
+ "bel_Latn",
+ "bos_Latn",
+ "bul",
+ "bul_Latn",
+ "ces",
+ "csb_Latn",
+ "dsb",
+ "hrv",
+ "hsb",
+ "mkd",
+ "orv_Cyrl",
+ "pol",
+ "rue",
+ "rus",
+ "slv",
+ "srp_Cyrl",
+ "srp_Latn",
+ "ukr",
+ },
+ ),
+ "slv": ("Slovenian", {"slv"}),
+ "spa": ("Spanish", {"spa"}),
+ "swe": ("Swedish", {"swe"}),
+ "taw": ("Tai", {"lao", "tha"}),
+ "tgl": ("Tagalog", {"tgl_Latn"}),
+ "tha": ("Thai", {"tha"}),
+ "trk": (
+ "Turkic languages",
+ {
+ "aze_Latn",
+ "bak",
+ "chv",
+ "crh",
+ "crh_Latn",
+ "kaz_Cyrl",
+ "kaz_Latn",
+ "kir_Cyrl",
+ "kjh",
+ "kum",
+ "ota_Arab",
+ "ota_Latn",
+ "sah",
+ "tat",
+ "tat_Arab",
+ "tat_Latn",
+ "tuk",
+ "tuk_Latn",
+ "tur",
+ "tyv",
+ "uig_Arab",
+ "uig_Cyrl",
+ "uzb_Cyrl",
+ "uzb_Latn",
+ },
+ ),
+ "tur": ("Turkish", {"tur"}),
+ "ukr": ("Ukrainian", {"ukr"}),
+ "urd": ("Urdu", {"urd"}),
+ "urj": (
+ "Uralic languages",
+ {
+ "est",
+ "fin",
+ "fkv_Latn",
+ "hun",
+ "izh",
+ "kpv",
+ "krl",
+ "liv_Latn",
+ "mdf",
+ "mhr",
+ "myv",
+ "sma",
+ "sme",
+ "udm",
+ "vep",
+ "vro",
+ },
+ ),
+ "vie": ("Vietnamese", {"vie", "vie_Hani"}),
+ "war": ("Waray (Philippines)", {"war"}),
+ "zho": (
+ "Chinese",
+ {
+ "cjy_Hans",
+ "cjy_Hant",
+ "cmn",
+ "cmn_Bopo",
+ "cmn_Hang",
+ "cmn_Hani",
+ "cmn_Hans",
+ "cmn_Hant",
+ "cmn_Hira",
+ "cmn_Kana",
+ "cmn_Latn",
+ "cmn_Yiii",
+ "gan",
+ "hak_Hani",
+ "lzh",
+ "lzh_Bopo",
+ "lzh_Hang",
+ "lzh_Hani",
+ "lzh_Hans",
+ "lzh_Hira",
+ "lzh_Kana",
+ "lzh_Yiii",
+ "nan",
+ "nan_Hani",
+ "wuu",
+ "wuu_Bopo",
+ "wuu_Hani",
+ "wuu_Latn",
+ "yue",
+ "yue_Bopo",
+ "yue_Hang",
+ "yue_Hani",
+ "yue_Hans",
+ "yue_Hant",
+ "yue_Hira",
+ "yue_Kana",
+ "zho",
+ "zho_Hans",
+ "zho_Hant",
+ },
+ ),
+ "zle": ("East Slavic languages", {"bel", "orv_Cyrl", "bel_Latn", "rus", "ukr", "rue"}),
+ "zls": ("South Slavic languages", {"bos_Latn", "bul", "bul_Latn", "hrv", "mkd", "slv", "srp_Cyrl", "srp_Latn"}),
+ "zlw": ("West Slavic languages", {"csb_Latn", "dsb", "hsb", "pol", "ces"}),
+}
+
+
+def l2front_matter(langs):
+ return "".join(f"- {l}\n" for l in langs)
+
+
+def dedup(lst):
+ """Preservers order"""
+ new_lst = []
+ for item in lst:
+ if not item or item in new_lst:
+ continue
+ else:
+ new_lst.append(item)
+ return new_lst
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "-m", "--models", action="append", help=" Set flag", required=True, nargs="+", dest="models"
+ )
+ parser.add_argument("-save_dir", "--save_dir", default="marian_converted", help="where to save converted models")
+ args = parser.parse_args()
+ resolver = TatoebaConverter(save_dir=args.save_dir)
+ resolver.convert_models(args.models[0])
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/convert_marian_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/convert_marian_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c432ebcdf6f4415dc17ce580942daf50508b701
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/convert_marian_to_pytorch.py
@@ -0,0 +1,717 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import json
+import os
+import socket
+import time
+import warnings
+from pathlib import Path
+from typing import Union
+from zipfile import ZipFile
+
+import numpy as np
+import torch
+from huggingface_hub.hf_api import list_models
+from torch import nn
+from tqdm import tqdm
+
+from transformers import MarianConfig, MarianMTModel, MarianTokenizer
+
+
+def remove_suffix(text: str, suffix: str):
+ if text.endswith(suffix):
+ return text[: -len(suffix)]
+ return text # or whatever
+
+
+def remove_prefix(text: str, prefix: str):
+ if text.startswith(prefix):
+ return text[len(prefix) :]
+ return text # or whatever
+
+
+def convert_encoder_layer(opus_dict, layer_prefix: str, converter: dict):
+ sd = {}
+ for k in opus_dict:
+ if not k.startswith(layer_prefix):
+ continue
+ stripped = remove_prefix(k, layer_prefix)
+ v = opus_dict[k].T # besides embeddings, everything must be transposed.
+ sd[converter[stripped]] = torch.tensor(v).squeeze()
+ return sd
+
+
+def load_layers_(layer_lst: nn.ModuleList, opus_state: dict, converter, is_decoder=False):
+ for i, layer in enumerate(layer_lst):
+ layer_tag = f"decoder_l{i + 1}_" if is_decoder else f"encoder_l{i + 1}_"
+ sd = convert_encoder_layer(opus_state, layer_tag, converter)
+ layer.load_state_dict(sd, strict=False)
+
+
+def find_pretrained_model(src_lang: str, tgt_lang: str) -> list[str]:
+ """Find models that can accept src_lang as input and return tgt_lang as output."""
+ prefix = "Helsinki-NLP/opus-mt-"
+ model_list = list_models()
+ model_ids = [x.id for x in model_list if x.id.startswith("Helsinki-NLP")]
+ src_and_targ = [
+ remove_prefix(m, prefix).lower().split("-") for m in model_ids if "+" not in m
+ ] # + can't be loaded.
+ matching = [f"{prefix}{a}-{b}" for (a, b) in src_and_targ if src_lang in a and tgt_lang in b]
+ return matching
+
+
+def add_emb_entries(wemb, final_bias, n_special_tokens=1):
+ vsize, d_model = wemb.shape
+ embs_to_add = np.zeros((n_special_tokens, d_model))
+ new_embs = np.concatenate([wemb, embs_to_add])
+ bias_to_add = np.zeros((n_special_tokens, 1))
+ new_bias = np.concatenate((final_bias, bias_to_add), axis=1)
+ return new_embs, new_bias
+
+
+def _cast_yaml_str(v):
+ bool_dct = {"true": True, "false": False}
+ if not isinstance(v, str):
+ return v
+ elif v in bool_dct:
+ return bool_dct[v]
+ try:
+ return int(v)
+ except (TypeError, ValueError):
+ return v
+
+
+def cast_marian_config(raw_cfg: dict[str, str]) -> dict:
+ return {k: _cast_yaml_str(v) for k, v in raw_cfg.items()}
+
+
+CONFIG_KEY = "special:model.yml"
+
+
+def load_config_from_state_dict(opus_dict):
+ import yaml
+
+ cfg_str = "".join([chr(x) for x in opus_dict[CONFIG_KEY]])
+ yaml_cfg = yaml.load(cfg_str[:-1], Loader=yaml.BaseLoader)
+ return cast_marian_config(yaml_cfg)
+
+
+def find_model_file(dest_dir): # this one better
+ model_files = list(Path(dest_dir).glob("*.npz"))
+ if len(model_files) != 1:
+ raise ValueError(f"Found more than one model file: {model_files}")
+ model_file = model_files[0]
+ return model_file
+
+
+# Group Names Logic: change long opus model names to something shorter, like opus-mt-en-ROMANCE
+ROM_GROUP = (
+ "fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO+es_EC+es_ES+es_GT"
+ "+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR+pt_PT+gl+lad+an+mwl+it+it_IT+co"
+ "+nap+scn+vec+sc+ro+la"
+)
+GROUPS = [
+ ("cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh", "ZH"),
+ (ROM_GROUP, "ROMANCE"),
+ ("de+nl+fy+af+da+fo+is+no+nb+nn+sv", "NORTH_EU"),
+ ("da+fo+is+no+nb+nn+sv", "SCANDINAVIA"),
+ ("se+sma+smj+smn+sms", "SAMI"),
+ ("nb_NO+nb+nn_NO+nn+nog+no_nb+no", "NORWAY"),
+ ("ga+cy+br+gd+kw+gv", "CELTIC"), # https://en.wikipedia.org/wiki/Insular_Celtic_languages
+]
+GROUP_TO_OPUS_NAME = {
+ "opus-mt-ZH-de": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-de",
+ "opus-mt-ZH-fi": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi",
+ "opus-mt-ZH-sv": "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-sv",
+ "opus-mt-SCANDINAVIA-SCANDINAVIA": "da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv",
+ "opus-mt-NORTH_EU-NORTH_EU": "de+nl+fy+af+da+fo+is+no+nb+nn+sv-de+nl+fy+af+da+fo+is+no+nb+nn+sv",
+ "opus-mt-de-ZH": "de-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
+ "opus-mt-en_el_es_fi-en_el_es_fi": "en+el+es+fi-en+el+es+fi",
+ "opus-mt-en-ROMANCE": (
+ "en-fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO"
+ "+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR"
+ "+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la"
+ ),
+ "opus-mt-en-CELTIC": "en-ga+cy+br+gd+kw+gv",
+ "opus-mt-es-NORWAY": "es-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
+ "opus-mt-fi_nb_no_nn_ru_sv_en-SAMI": "fi+nb+no+nn+ru+sv+en-se+sma+smj+smn+sms",
+ "opus-mt-fi-ZH": "fi-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
+ "opus-mt-fi-NORWAY": "fi-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
+ "opus-mt-ROMANCE-en": (
+ "fr+fr_BE+fr_CA+fr_FR+wa+frp+oc+ca+rm+lld+fur+lij+lmo+es+es_AR+es_CL+es_CO+es_CR+es_DO"
+ "+es_EC+es_ES+es_GT+es_HN+es_MX+es_NI+es_PA+es_PE+es_PR+es_SV+es_UY+es_VE+pt+pt_br+pt_BR"
+ "+pt_PT+gl+lad+an+mwl+it+it_IT+co+nap+scn+vec+sc+ro+la-en"
+ ),
+ "opus-mt-CELTIC-en": "ga+cy+br+gd+kw+gv-en",
+ "opus-mt-sv-ZH": "sv-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh",
+ "opus-mt-sv-NORWAY": "sv-nb_NO+nb+nn_NO+nn+nog+no_nb+no",
+}
+OPUS_GITHUB_URL = "https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/"
+ORG_NAME = "Helsinki-NLP/"
+
+
+def convert_opus_name_to_hf_name(x):
+ """For OPUS-MT-Train/ DEPRECATED"""
+ for substr, grp_name in GROUPS:
+ x = x.replace(substr, grp_name)
+ return x.replace("+", "_")
+
+
+def convert_hf_name_to_opus_name(hf_model_name):
+ """
+ Relies on the assumption that there are no language codes like pt_br in models that are not in GROUP_TO_OPUS_NAME.
+ """
+ hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
+ if hf_model_name in GROUP_TO_OPUS_NAME:
+ opus_w_prefix = GROUP_TO_OPUS_NAME[hf_model_name]
+ else:
+ opus_w_prefix = hf_model_name.replace("_", "+")
+ return remove_prefix(opus_w_prefix, "opus-mt-")
+
+
+def get_system_metadata(repo_root):
+ import git
+
+ return {
+ "helsinki_git_sha": git.Repo(path=repo_root, search_parent_directories=True).head.object.hexsha,
+ "transformers_git_sha": git.Repo(path=".", search_parent_directories=True).head.object.hexsha,
+ "port_machine": socket.gethostname(),
+ "port_time": time.strftime("%Y-%m-%d-%H:%M"),
+ }
+
+
+# docstyle-ignore
+FRONT_MATTER_TEMPLATE = """---
+language:
+{}
+tags:
+- translation
+
+license: apache-2.0
+---
+"""
+DEFAULT_REPO = "Tatoeba-Challenge"
+DEFAULT_MODEL_DIR = os.path.join(DEFAULT_REPO, "models")
+
+
+def write_model_card(
+ hf_model_name: str,
+ repo_root=DEFAULT_REPO,
+ save_dir=Path("marian_converted"),
+ dry_run=False,
+ extra_metadata={},
+) -> str:
+ """
+ Copy the most recent model's readme section from opus, and add metadata. upload command: aws s3 sync model_card_dir
+ s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
+ """
+ import pandas as pd
+
+ hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
+ opus_name: str = convert_hf_name_to_opus_name(hf_model_name)
+ if repo_root not in ("OPUS-MT-train", "Tatoeba-Challenge"):
+ raise ValueError(f"Repos root is {repo_root}. Expected either OPUS-MT-train or Tatoeba-Challenge")
+ opus_readme_path = Path(repo_root).joinpath("models", opus_name, "README.md")
+ if not (opus_readme_path.exists()):
+ raise ValueError(f"Readme file {opus_readme_path} not found")
+
+ opus_src, opus_tgt = [x.split("+") for x in opus_name.split("-")]
+
+ readme_url = f"https://github.com/Helsinki-NLP/{repo_root}/tree/master/models/{opus_name}/README.md"
+
+ s, t = ",".join(opus_src), ",".join(opus_tgt)
+ metadata = {
+ "hf_name": hf_model_name,
+ "source_languages": s,
+ "target_languages": t,
+ "opus_readme_url": readme_url,
+ "original_repo": repo_root,
+ "tags": ["translation"],
+ }
+ metadata.update(extra_metadata)
+ metadata.update(get_system_metadata(repo_root))
+
+ # combine with opus markdown
+
+ extra_markdown = (
+ f"### {hf_model_name}\n\n* source group: {metadata['src_name']} \n* target group: "
+ f"{metadata['tgt_name']} \n* OPUS readme: [{opus_name}]({readme_url})\n"
+ )
+
+ content = opus_readme_path.open().read()
+ content = content.split("\n# ")[-1] # Get the lowest level 1 header in the README -- the most recent model.
+ splat = content.split("*")[2:]
+ print(splat[3])
+ content = "*".join(splat)
+ content = (
+ FRONT_MATTER_TEMPLATE.format(metadata["src_alpha2"])
+ + extra_markdown
+ + "\n* "
+ + content.replace("download", "download original weights")
+ )
+
+ items = "\n\n".join([f"- {k}: {v}" for k, v in metadata.items()])
+ sec3 = "\n### System Info: \n" + items
+ content += sec3
+ if dry_run:
+ return content, metadata
+ sub_dir = save_dir / f"opus-mt-{hf_model_name}"
+ sub_dir.mkdir(exist_ok=True)
+ dest = sub_dir / "README.md"
+ dest.open("w").write(content)
+ pd.Series(metadata).to_json(sub_dir / "metadata.json")
+
+ # if dry_run:
+ return content, metadata
+
+
+def make_registry(repo_path="Opus-MT-train/models"):
+ if not (Path(repo_path) / "fr-en" / "README.md").exists():
+ raise ValueError(
+ f"repo_path:{repo_path} does not exist: "
+ "You must run: git clone git@github.com:Helsinki-NLP/Opus-MT-train.git before calling."
+ )
+ results = {}
+ for p in Path(repo_path).iterdir():
+ n_dash = p.name.count("-")
+ if n_dash == 0:
+ continue
+ else:
+ lns = list(open(p / "README.md").readlines())
+ results[p.name] = _parse_readme(lns)
+ return [(k, v["pre-processing"], v["download"], v["download"][:-4] + ".test.txt") for k, v in results.items()]
+
+
+def convert_all_sentencepiece_models(model_list=None, repo_path=None, dest_dir=Path("marian_converted")):
+ """Requires 300GB"""
+ save_dir = Path("marian_ckpt")
+ dest_dir = Path(dest_dir)
+ dest_dir.mkdir(exist_ok=True)
+ save_paths = []
+ if model_list is None:
+ model_list: list = make_registry(repo_path=repo_path)
+ for k, prepro, download, test_set_url in tqdm(model_list):
+ if "SentencePiece" not in prepro: # dont convert BPE models.
+ continue
+ if not os.path.exists(save_dir / k):
+ download_and_unzip(download, save_dir / k)
+ pair_name = convert_opus_name_to_hf_name(k)
+ convert(save_dir / k, dest_dir / f"opus-mt-{pair_name}")
+
+ save_paths.append(dest_dir / f"opus-mt-{pair_name}")
+ return save_paths
+
+
+def lmap(f, x) -> list:
+ return list(map(f, x))
+
+
+def fetch_test_set(test_set_url):
+ import wget
+
+ fname = wget.download(test_set_url, "opus_test.txt")
+ lns = Path(fname).open().readlines()
+ src = lmap(str.strip, lns[::4])
+ gold = lmap(str.strip, lns[1::4])
+ mar_model = lmap(str.strip, lns[2::4])
+ if not (len(gold) == len(mar_model) == len(src)):
+ raise ValueError(f"Gold, marian and source lengths {len(gold)}, {len(mar_model)}, {len(src)} mismatched")
+ os.remove(fname)
+ return src, mar_model, gold
+
+
+def convert_whole_dir(path=Path("marian_ckpt/")):
+ for subdir in tqdm(list(path.ls())):
+ dest_dir = f"marian_converted/{subdir.name}"
+ if (dest_dir / "pytorch_model.bin").exists():
+ continue
+ convert(source_dir, dest_dir)
+
+
+def _parse_readme(lns):
+ """Get link and metadata from opus model card equivalent."""
+ subres = {}
+ for ln in [x.strip() for x in lns]:
+ if not ln.startswith("*"):
+ continue
+ ln = ln[1:].strip()
+
+ for k in ["download", "dataset", "models", "model", "pre-processing"]:
+ if ln.startswith(k):
+ break
+ else:
+ continue
+ if k in ["dataset", "model", "pre-processing"]:
+ splat = ln.split(":")
+ _, v = splat
+ subres[k] = v
+ elif k == "download":
+ v = ln.split("(")[-1][:-1]
+ subres[k] = v
+ return subres
+
+
+def save_tokenizer_config(dest_dir: Path, separate_vocabs=False):
+ dname = dest_dir.name.split("-")
+ dct = {"target_lang": dname[-1], "source_lang": "-".join(dname[:-1]), "separate_vocabs": separate_vocabs}
+ save_json(dct, dest_dir / "tokenizer_config.json")
+
+
+def add_to_vocab_(vocab: dict[str, int], special_tokens: list[str]):
+ start = max(vocab.values()) + 1
+ added = 0
+ for tok in special_tokens:
+ if tok in vocab:
+ continue
+ vocab[tok] = start + added
+ added += 1
+ return added
+
+
+def find_vocab_file(model_dir):
+ return list(model_dir.glob("*vocab.yml"))[0]
+
+
+def find_src_vocab_file(model_dir):
+ return list(model_dir.glob("*src.vocab.yml"))[0]
+
+
+def find_tgt_vocab_file(model_dir):
+ return list(model_dir.glob("*trg.vocab.yml"))[0]
+
+
+def add_special_tokens_to_vocab(model_dir: Path, separate_vocab=False) -> None:
+ if separate_vocab:
+ vocab = load_yaml(find_src_vocab_file(model_dir))
+ vocab = {k: int(v) for k, v in vocab.items()}
+ num_added = add_to_vocab_(vocab, [""])
+ save_json(vocab, model_dir / "vocab.json")
+
+ vocab = load_yaml(find_tgt_vocab_file(model_dir))
+ vocab = {k: int(v) for k, v in vocab.items()}
+ num_added = add_to_vocab_(vocab, [""])
+ save_json(vocab, model_dir / "target_vocab.json")
+ save_tokenizer_config(model_dir, separate_vocabs=separate_vocab)
+ else:
+ vocab = load_yaml(find_vocab_file(model_dir))
+ vocab = {k: int(v) for k, v in vocab.items()}
+ num_added = add_to_vocab_(vocab, [""])
+ print(f"added {num_added} tokens to vocab")
+ save_json(vocab, model_dir / "vocab.json")
+ save_tokenizer_config(model_dir)
+
+
+def check_equal(marian_cfg, k1, k2):
+ v1, v2 = marian_cfg[k1], marian_cfg[k2]
+ if v1 != v2:
+ raise ValueError(f"hparams {k1},{k2} differ: {v1} != {v2}")
+
+
+def check_marian_cfg_assumptions(marian_cfg):
+ assumed_settings = {
+ "layer-normalization": False,
+ "right-left": False,
+ "transformer-ffn-depth": 2,
+ "transformer-aan-depth": 2,
+ "transformer-no-projection": False,
+ "transformer-postprocess-emb": "d",
+ "transformer-postprocess": "dan", # Dropout, add, normalize
+ "transformer-preprocess": "",
+ "type": "transformer",
+ "ulr-dim-emb": 0,
+ "dec-cell-base-depth": 2,
+ "dec-cell-high-depth": 1,
+ "transformer-aan-nogate": False,
+ }
+ for k, v in assumed_settings.items():
+ actual = marian_cfg[k]
+ if actual != v:
+ raise ValueError(f"Unexpected config value for {k} expected {v} got {actual}")
+
+
+BIAS_KEY = "decoder_ff_logit_out_b"
+BART_CONVERTER = { # for each encoder and decoder layer
+ "self_Wq": "self_attn.q_proj.weight",
+ "self_Wk": "self_attn.k_proj.weight",
+ "self_Wv": "self_attn.v_proj.weight",
+ "self_Wo": "self_attn.out_proj.weight",
+ "self_bq": "self_attn.q_proj.bias",
+ "self_bk": "self_attn.k_proj.bias",
+ "self_bv": "self_attn.v_proj.bias",
+ "self_bo": "self_attn.out_proj.bias",
+ "self_Wo_ln_scale": "self_attn_layer_norm.weight",
+ "self_Wo_ln_bias": "self_attn_layer_norm.bias",
+ "ffn_W1": "fc1.weight",
+ "ffn_b1": "fc1.bias",
+ "ffn_W2": "fc2.weight",
+ "ffn_b2": "fc2.bias",
+ "ffn_ffn_ln_scale": "final_layer_norm.weight",
+ "ffn_ffn_ln_bias": "final_layer_norm.bias",
+ # Decoder Cross Attention
+ "context_Wk": "encoder_attn.k_proj.weight",
+ "context_Wo": "encoder_attn.out_proj.weight",
+ "context_Wq": "encoder_attn.q_proj.weight",
+ "context_Wv": "encoder_attn.v_proj.weight",
+ "context_bk": "encoder_attn.k_proj.bias",
+ "context_bo": "encoder_attn.out_proj.bias",
+ "context_bq": "encoder_attn.q_proj.bias",
+ "context_bv": "encoder_attn.v_proj.bias",
+ "context_Wo_ln_scale": "encoder_attn_layer_norm.weight",
+ "context_Wo_ln_bias": "encoder_attn_layer_norm.bias",
+}
+
+
+class OpusState:
+ def __init__(self, source_dir, eos_token_id=0):
+ npz_path = find_model_file(source_dir)
+ self.state_dict = np.load(npz_path)
+ cfg = load_config_from_state_dict(self.state_dict)
+ if cfg["dim-vocabs"][0] != cfg["dim-vocabs"][1]:
+ raise ValueError
+ if "Wpos" in self.state_dict:
+ raise ValueError("Wpos key in state dictionary")
+ self.state_dict = dict(self.state_dict)
+ if cfg["tied-embeddings-all"]:
+ cfg["tied-embeddings-src"] = True
+ cfg["tied-embeddings"] = True
+ self.share_encoder_decoder_embeddings = cfg["tied-embeddings-src"]
+
+ # create the tokenizer here because we need to know the eos_token_id
+ self.source_dir = source_dir
+ self.tokenizer = self.load_tokenizer()
+ # retrieve EOS token and set correctly
+ tokenizer_has_eos_token_id = (
+ hasattr(self.tokenizer, "eos_token_id") and self.tokenizer.eos_token_id is not None
+ )
+ eos_token_id = self.tokenizer.eos_token_id if tokenizer_has_eos_token_id else 0
+
+ if cfg["tied-embeddings-src"]:
+ self.wemb, self.final_bias = add_emb_entries(self.state_dict["Wemb"], self.state_dict[BIAS_KEY], 1)
+ self.pad_token_id = self.wemb.shape[0] - 1
+ cfg["vocab_size"] = self.pad_token_id + 1
+ else:
+ self.wemb, _ = add_emb_entries(self.state_dict["encoder_Wemb"], self.state_dict[BIAS_KEY], 1)
+ self.dec_wemb, self.final_bias = add_emb_entries(
+ self.state_dict["decoder_Wemb"], self.state_dict[BIAS_KEY], 1
+ )
+ # still assuming that vocab size is same for encoder and decoder
+ self.pad_token_id = self.wemb.shape[0] - 1
+ cfg["vocab_size"] = self.pad_token_id + 1
+ cfg["decoder_vocab_size"] = self.pad_token_id + 1
+
+ if cfg["vocab_size"] != self.tokenizer.vocab_size:
+ raise ValueError(
+ f"Original vocab size {cfg['vocab_size']} and new vocab size {len(self.tokenizer.encoder)} mismatched."
+ )
+
+ # self.state_dict['Wemb'].sha
+ self.state_keys = list(self.state_dict.keys())
+ if "Wtype" in self.state_dict:
+ raise ValueError("Wtype key in state dictionary")
+ self._check_layer_entries()
+ self.cfg = cfg
+ hidden_size, intermediate_shape = self.state_dict["encoder_l1_ffn_W1"].shape
+ if hidden_size != cfg["dim-emb"]:
+ raise ValueError(f"Hidden size {hidden_size} and configured size {cfg['dim_emb']} mismatched")
+
+ # Process decoder.yml
+ decoder_yml = cast_marian_config(load_yaml(source_dir / "decoder.yml"))
+ check_marian_cfg_assumptions(cfg)
+ self.hf_config = MarianConfig(
+ vocab_size=cfg["vocab_size"],
+ decoder_vocab_size=cfg.get("decoder_vocab_size", cfg["vocab_size"]),
+ share_encoder_decoder_embeddings=cfg["tied-embeddings-src"],
+ decoder_layers=cfg["dec-depth"],
+ encoder_layers=cfg["enc-depth"],
+ decoder_attention_heads=cfg["transformer-heads"],
+ encoder_attention_heads=cfg["transformer-heads"],
+ decoder_ffn_dim=cfg["transformer-dim-ffn"],
+ encoder_ffn_dim=cfg["transformer-dim-ffn"],
+ d_model=cfg["dim-emb"],
+ activation_function=cfg["transformer-ffn-activation"],
+ pad_token_id=self.pad_token_id,
+ eos_token_id=eos_token_id,
+ forced_eos_token_id=eos_token_id,
+ bos_token_id=0,
+ max_position_embeddings=cfg["dim-emb"],
+ scale_embedding=True,
+ normalize_embedding="n" in cfg["transformer-preprocess"],
+ static_position_embeddings=not cfg["transformer-train-position-embeddings"],
+ tie_word_embeddings=cfg["tied-embeddings"],
+ dropout=0.1, # see opus-mt-train repo/transformer-dropout param.
+ # default: add_final_layer_norm=False,
+ num_beams=decoder_yml["beam-size"],
+ decoder_start_token_id=self.pad_token_id,
+ bad_words_ids=[[self.pad_token_id]],
+ max_length=512,
+ )
+
+ def _check_layer_entries(self):
+ self.encoder_l1 = self.sub_keys("encoder_l1")
+ self.decoder_l1 = self.sub_keys("decoder_l1")
+ self.decoder_l2 = self.sub_keys("decoder_l2")
+ if len(self.encoder_l1) != 16:
+ warnings.warn(f"Expected 16 keys for each encoder layer, got {len(self.encoder_l1)}")
+ if len(self.decoder_l1) != 26:
+ warnings.warn(f"Expected 26 keys for each decoder layer, got {len(self.decoder_l1)}")
+ if len(self.decoder_l2) != 26:
+ warnings.warn(f"Expected 26 keys for each decoder layer, got {len(self.decoder_l1)}")
+
+ @property
+ def extra_keys(self):
+ extra = []
+ for k in self.state_keys:
+ if (
+ k.startswith("encoder_l")
+ or k.startswith("decoder_l")
+ or k in [CONFIG_KEY, "Wemb", "encoder_Wemb", "decoder_Wemb", "Wpos", "decoder_ff_logit_out_b"]
+ ):
+ continue
+ else:
+ extra.append(k)
+ return extra
+
+ def sub_keys(self, layer_prefix):
+ return [remove_prefix(k, layer_prefix) for k in self.state_dict if k.startswith(layer_prefix)]
+
+ def load_tokenizer(self):
+ # save tokenizer
+ add_special_tokens_to_vocab(self.source_dir, not self.share_encoder_decoder_embeddings)
+ return MarianTokenizer.from_pretrained(str(self.source_dir))
+
+ def load_marian_model(self) -> MarianMTModel:
+ state_dict, cfg = self.state_dict, self.hf_config
+
+ if not cfg.static_position_embeddings:
+ raise ValueError("config.static_position_embeddings should be True")
+ model = MarianMTModel(cfg)
+
+ if "hidden_size" in cfg.to_dict():
+ raise ValueError("hidden_size is in config")
+ load_layers_(
+ model.model.encoder.layers,
+ state_dict,
+ BART_CONVERTER,
+ )
+ load_layers_(model.model.decoder.layers, state_dict, BART_CONVERTER, is_decoder=True)
+
+ # handle tensors not associated with layers
+ if self.cfg["tied-embeddings-src"]:
+ wemb_tensor = nn.Parameter(torch.FloatTensor(self.wemb))
+ bias_tensor = nn.Parameter(torch.FloatTensor(self.final_bias))
+ model.model.shared.weight = wemb_tensor
+ model.model.encoder.embed_tokens = model.model.decoder.embed_tokens = model.model.shared
+ else:
+ wemb_tensor = nn.Parameter(torch.FloatTensor(self.wemb))
+ model.model.encoder.embed_tokens.weight = wemb_tensor
+
+ decoder_wemb_tensor = nn.Parameter(torch.FloatTensor(self.dec_wemb))
+ bias_tensor = nn.Parameter(torch.FloatTensor(self.final_bias))
+ model.model.decoder.embed_tokens.weight = decoder_wemb_tensor
+
+ # handle tied embeddings, otherwise "from_pretrained" loads them incorrectly
+ if self.cfg["tied-embeddings"]:
+ model.lm_head.weight.data = model.model.decoder.embed_tokens.weight.data.clone()
+
+ model.final_logits_bias = bias_tensor
+
+ if "Wpos" in state_dict:
+ print("Unexpected: got Wpos")
+ wpos_tensor = torch.tensor(state_dict["Wpos"])
+ model.model.encoder.embed_positions.weight = wpos_tensor
+ model.model.decoder.embed_positions.weight = wpos_tensor
+
+ if cfg.normalize_embedding:
+ if "encoder_emb_ln_scale_pre" not in state_dict:
+ raise ValueError("encoder_emb_ln_scale_pre is not in state dictionary")
+ raise NotImplementedError("Need to convert layernorm_embedding")
+
+ if self.extra_keys:
+ raise ValueError(f"Failed to convert {self.extra_keys}")
+
+ if model.get_input_embeddings().padding_idx != self.pad_token_id:
+ raise ValueError(
+ f"Padding tokens {model.get_input_embeddings().padding_idx} and {self.pad_token_id} mismatched"
+ )
+ return model
+
+
+def download_and_unzip(url, dest_dir):
+ try:
+ import wget
+ except ImportError:
+ raise ImportError("you must pip install wget")
+
+ filename = wget.download(url)
+ unzip(filename, dest_dir)
+ os.remove(filename)
+
+
+def convert(source_dir: Path, dest_dir):
+ dest_dir = Path(dest_dir)
+ dest_dir.mkdir(exist_ok=True)
+
+ opus_state = OpusState(source_dir)
+
+ # save tokenizer
+ opus_state.tokenizer.save_pretrained(dest_dir)
+
+ # save_json(opus_state.cfg, dest_dir / "marian_original_config.json")
+ # ^^ Uncomment to save human readable marian config for debugging
+
+ model = opus_state.load_marian_model()
+ model = model.half()
+ model.save_pretrained(dest_dir)
+ model.from_pretrained(dest_dir) # sanity check
+
+
+def load_yaml(path):
+ import yaml
+
+ with open(path, encoding="utf-8") as f:
+ return yaml.load(f, Loader=yaml.BaseLoader)
+
+
+def save_json(content: Union[dict, list], path: str) -> None:
+ with open(path, "w") as f:
+ json.dump(content, f)
+
+
+def unzip(zip_path: str, dest_dir: str) -> None:
+ with ZipFile(zip_path, "r") as zipObj:
+ zipObj.extractall(dest_dir)
+
+
+if __name__ == "__main__":
+ """
+ Tatoeba conversion instructions in scripts/tatoeba/README.md
+ """
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--src",
+ type=str,
+ help="path to marian model sub dir. yaml.load will be used to load the configuration file, please be wary of which file you're loading.",
+ default="en-de",
+ )
+ parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model.")
+ args = parser.parse_args()
+
+ source_dir = Path(args.src)
+ if not source_dir.exists():
+ raise ValueError(f"Source directory {source_dir} not found")
+ dest_dir = f"converted-{source_dir.name}" if args.dest is None else args.dest
+ convert(source_dir, dest_dir)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/modeling_flax_marian.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/modeling_flax_marian.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1d9bea4fcdb29fb310fe26f24d6809f592da3e7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/modeling_flax_marian.py
@@ -0,0 +1,1500 @@
+# coding=utf-8
+# Copyright 2021 The Marian Team Authors and The Google Flax Team Authors And The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Flax Marian model."""
+
+import math
+import random
+from functools import partial
+from typing import Callable, Optional
+
+import flax.linen as nn
+import jax
+import jax.numpy as jnp
+import numpy as np
+from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
+from flax.linen import combine_masks, make_causal_mask
+from flax.linen.attention import dot_product_attention_weights
+from flax.traverse_util import flatten_dict, unflatten_dict
+from jax import lax
+from jax.random import PRNGKey
+
+from ...modeling_flax_outputs import (
+ FlaxBaseModelOutput,
+ FlaxBaseModelOutputWithPastAndCrossAttentions,
+ FlaxCausalLMOutputWithCrossAttentions,
+ FlaxSeq2SeqLMOutput,
+ FlaxSeq2SeqModelOutput,
+)
+from ...modeling_flax_utils import (
+ ACT2FN,
+ FlaxPreTrainedModel,
+ append_call_sample_docstring,
+ append_replace_return_docstrings,
+ overwrite_call_docstring,
+)
+from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from .configuration_marian import MarianConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "Helsinki-NLP/opus-mt-en-de"
+_CONFIG_FOR_DOC = "MarianConfig"
+
+
+MARIAN_START_DOCSTRING = r"""
+ This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a Flax Linen
+ [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
+ regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
+
+ Finally, this model supports inherent JAX features such as:
+
+ - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
+ - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
+ - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
+ - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
+
+ Parameters:
+ config ([`MarianConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
+ dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
+ The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
+ `jax.numpy.bfloat16` (on TPUs).
+
+ This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
+ specified all the computation will be performed with the given `dtype`.
+
+ **Note that this only specifies the dtype of the computation and does not influence the dtype of model
+ parameters.**
+
+ If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
+ [`~FlaxPreTrainedModel.to_bf16`].
+"""
+
+MARIAN_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
+ paper](https://huggingface.co/papers/1910.13461) for more information on the default strategy.
+ position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
+ range `[0, config.max_position_embeddings - 1]`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+MARIAN_ENCODE_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+MARIAN_DECODE_INPUTS_DOCSTRING = r"""
+ Args:
+ decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ encoder_outputs (`tuple(tuple(jnp.ndarray)`):
+ Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
+ `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
+ hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
+ encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
+ paper](https://huggingface.co/papers/1910.13461) for more information on the default strategy.
+ decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
+ range `[0, config.max_position_embeddings - 1]`.
+ past_key_values (`dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
+ Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
+ auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+def create_sinusoidal_positions(n_pos, dim):
+ position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
+ sentinel = dim // 2 + dim % 2
+ out = np.zeros_like(position_enc)
+ out[:, 0:sentinel] = np.sin(position_enc[:, 0::2])
+ out[:, sentinel:] = np.cos(position_enc[:, 1::2])
+
+ return jnp.array(out)
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right
+def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray:
+ """
+ Shift input ids one token to the right.
+ """
+ shifted_input_ids = jnp.zeros_like(input_ids)
+ shifted_input_ids = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1])
+ shifted_input_ids = shifted_input_ids.at[:, 0].set(decoder_start_token_id)
+
+ shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
+ return shifted_input_ids
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->Marian
+class FlaxMarianAttention(nn.Module):
+ config: MarianConfig
+ embed_dim: int
+ num_heads: int
+ dropout: float = 0.0
+ causal: bool = False
+ bias: bool = True
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self) -> None:
+ self.head_dim = self.embed_dim // self.num_heads
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ dense = partial(
+ nn.Dense,
+ self.embed_dim,
+ use_bias=self.bias,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+
+ self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
+ self.out_proj = dense()
+
+ self.dropout_layer = nn.Dropout(rate=self.dropout)
+
+ if self.causal:
+ self.causal_mask = make_causal_mask(
+ jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
+ )
+
+ def _split_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
+
+ def _merge_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
+
+ @nn.compact
+ def _concatenate_to_cache(self, key, value, query, attention_mask):
+ """
+ This function takes projected key, value states from a single input token and concatenates the states to cached
+ states from previous steps. This function is slightly adapted from the official Flax repository:
+ https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
+ """
+ # detect if we're initializing by absence of existing cache data.
+ is_initialized = self.has_variable("cache", "cached_key")
+ cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
+ cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
+ cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
+
+ if is_initialized:
+ *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
+ # update key, value caches with our new 1d spatial slices
+ cur_index = cache_index.value
+ indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
+ key = lax.dynamic_update_slice(cached_key.value, key, indices)
+ value = lax.dynamic_update_slice(cached_value.value, value, indices)
+ cached_key.value = key
+ cached_value.value = value
+ num_updated_cache_vectors = query.shape[1]
+ cache_index.value = cache_index.value + num_updated_cache_vectors
+ # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
+ pad_mask = jnp.broadcast_to(
+ jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
+ tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
+ )
+ attention_mask = combine_masks(pad_mask, attention_mask)
+ return key, value, attention_mask
+
+ def __call__(
+ self,
+ hidden_states: jnp.ndarray,
+ key_value_states: Optional[jnp.ndarray] = None,
+ attention_mask: Optional[jnp.ndarray] = None,
+ init_cache: bool = False,
+ deterministic: bool = True,
+ ) -> tuple[jnp.ndarray]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+ batch_size = hidden_states.shape[0]
+
+ # get query proj
+ query_states = self.q_proj(hidden_states)
+ # get key, value proj
+ if is_cross_attention:
+ # cross_attentions
+ key_states = self.k_proj(key_value_states)
+ value_states = self.v_proj(key_value_states)
+ else:
+ # self_attention
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = self._split_heads(query_states)
+ key_states = self._split_heads(key_states)
+ value_states = self._split_heads(value_states)
+
+ # handle cache prepare causal attention mask
+ if self.causal:
+ query_length, key_length = query_states.shape[1], key_states.shape[1]
+ if self.has_variable("cache", "cached_key"):
+ mask_shift = self.variables["cache"]["cache_index"]
+ max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
+ causal_mask = lax.dynamic_slice(
+ self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
+ )
+ else:
+ causal_mask = self.causal_mask[:, :, :query_length, :key_length]
+ causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
+
+ # combine masks if needed
+ if attention_mask is not None and self.causal:
+ attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
+ attention_mask = combine_masks(attention_mask, causal_mask)
+ elif self.causal:
+ attention_mask = causal_mask
+ elif attention_mask is not None:
+ attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
+
+ # During fast autoregressive decoding, we feed one position at a time,
+ # and cache the keys and values step by step.
+ if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
+ key_states, value_states, attention_mask = self._concatenate_to_cache(
+ key_states, value_states, query_states, attention_mask
+ )
+
+ # Convert the boolean attention mask to an attention bias.
+ if attention_mask is not None:
+ # attention mask in the form of attention bias
+ attention_bias = lax.select(
+ attention_mask > 0,
+ jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
+ jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
+ )
+ else:
+ attention_bias = None
+
+ dropout_rng = None
+ if not deterministic and self.dropout > 0.0:
+ dropout_rng = self.make_rng("dropout")
+
+ attn_weights = dot_product_attention_weights(
+ query_states,
+ key_states,
+ bias=attention_bias,
+ dropout_rng=dropout_rng,
+ dropout_rate=self.dropout,
+ broadcast_dropout=True,
+ deterministic=deterministic,
+ dtype=self.dtype,
+ precision=None,
+ )
+
+ attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
+ attn_output = self._merge_heads(attn_output)
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayer with Bart->Marian
+class FlaxMarianEncoderLayer(nn.Module):
+ config: MarianConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self) -> None:
+ self.embed_dim = self.config.d_model
+ self.self_attn = FlaxMarianAttention(
+ config=self.config,
+ embed_dim=self.embed_dim,
+ num_heads=self.config.encoder_attention_heads,
+ dropout=self.config.attention_dropout,
+ dtype=self.dtype,
+ )
+ self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+ self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+ self.activation_fn = ACT2FN[self.config.activation_function]
+ self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
+ self.fc1 = nn.Dense(
+ self.config.encoder_ffn_dim,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+ self.fc2 = nn.Dense(
+ self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
+ )
+ self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+
+ def __call__(
+ self,
+ hidden_states: jnp.ndarray,
+ attention_mask: jnp.ndarray,
+ output_attentions: bool = True,
+ deterministic: bool = True,
+ ) -> tuple[jnp.ndarray]:
+ residual = hidden_states
+ hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask)
+
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayerCollection with Bart->Marian
+class FlaxMarianEncoderLayerCollection(nn.Module):
+ config: MarianConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.layers = [
+ FlaxMarianEncoderLayer(self.config, name=str(i), dtype=self.dtype)
+ for i in range(self.config.encoder_layers)
+ ]
+ self.layerdrop = self.config.encoder_layerdrop
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask,
+ deterministic: bool = True,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ):
+ all_attentions = () if output_attentions else None
+ all_hidden_states = () if output_hidden_states else None
+
+ for encoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = random.uniform(0, 1)
+ if not deterministic and (dropout_probability < self.layerdrop): # skip the layer
+ layer_outputs = (None, None)
+ else:
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ output_attentions,
+ deterministic,
+ )
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ outputs = (hidden_states, all_hidden_states, all_attentions)
+
+ if not return_dict:
+ return tuple(v for v in outputs if v is not None)
+
+ return FlaxBaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
+ )
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayer with Bart->Marian
+class FlaxMarianDecoderLayer(nn.Module):
+ config: MarianConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self) -> None:
+ self.embed_dim = self.config.d_model
+ self.self_attn = FlaxMarianAttention(
+ config=self.config,
+ embed_dim=self.embed_dim,
+ num_heads=self.config.decoder_attention_heads,
+ dropout=self.config.attention_dropout,
+ causal=True,
+ dtype=self.dtype,
+ )
+ self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+ self.activation_fn = ACT2FN[self.config.activation_function]
+ self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
+
+ self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+ self.encoder_attn = FlaxMarianAttention(
+ config=self.config,
+ embed_dim=self.embed_dim,
+ num_heads=self.config.decoder_attention_heads,
+ dropout=self.config.attention_dropout,
+ dtype=self.dtype,
+ )
+ self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+ self.fc1 = nn.Dense(
+ self.config.decoder_ffn_dim,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+ self.fc2 = nn.Dense(
+ self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
+ )
+ self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+
+ def __call__(
+ self,
+ hidden_states: jnp.ndarray,
+ attention_mask: jnp.ndarray,
+ encoder_hidden_states: Optional[jnp.ndarray] = None,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ init_cache: bool = False,
+ output_attentions: bool = True,
+ deterministic: bool = True,
+ ) -> tuple[jnp.ndarray]:
+ residual = hidden_states
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache
+ )
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Cross-Attention Block
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+
+ hidden_states, cross_attn_weights = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ )
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = residual + hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ return outputs
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayerCollection with Bart->Marian
+class FlaxMarianDecoderLayerCollection(nn.Module):
+ config: MarianConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.layers = [
+ FlaxMarianDecoderLayer(self.config, name=str(i), dtype=self.dtype)
+ for i in range(self.config.decoder_layers)
+ ]
+ self.layerdrop = self.config.decoder_layerdrop
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask,
+ encoder_hidden_states: Optional[jnp.ndarray] = None,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ):
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = random.uniform(0, 1)
+ if not deterministic and (dropout_probability < self.layerdrop):
+ layer_outputs = (None, None, None)
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ deterministic=deterministic,
+ )
+
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if encoder_hidden_states is not None:
+ all_cross_attentions += (layer_outputs[2],)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions]
+
+ if not return_dict:
+ return tuple(v for v in outputs if v is not None)
+
+ return FlaxBaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+class FlaxMarianEncoder(nn.Module):
+ config: MarianConfig
+ embed_tokens: nn.Embed
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+
+ embed_dim = self.config.d_model
+ self.max_source_positions = self.config.max_position_embeddings
+ self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0
+
+ self.embed_positions = create_sinusoidal_positions(self.config.max_position_embeddings, embed_dim)
+ self.layers = FlaxMarianEncoderLayerCollection(self.config, self.dtype)
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ position_ids,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ input_shape = input_ids.shape
+ input_ids = input_ids.reshape(-1, input_shape[-1])
+
+ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+ positions = jnp.take(self.embed_positions, position_ids, axis=0)
+ # explicitly cast the positions here, since self.embed_positions are not registered as parameters
+ positions = positions.astype(inputs_embeds.dtype)
+
+ hidden_states = inputs_embeds + positions
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+
+ outputs = self.layers(
+ hidden_states,
+ attention_mask,
+ deterministic=deterministic,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ if not return_dict:
+ return outputs
+
+ return FlaxBaseModelOutput(
+ last_hidden_state=outputs.last_hidden_state,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+class FlaxMarianDecoder(nn.Module):
+ config: MarianConfig
+ embed_tokens: nn.Embed
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+
+ embed_dim = self.config.d_model
+ self.max_target_positions = self.config.max_position_embeddings
+ self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0
+
+ self.embed_positions = create_sinusoidal_positions(self.config.max_position_embeddings, embed_dim)
+ self.layers = FlaxMarianDecoderLayerCollection(self.config, self.dtype)
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ position_ids,
+ encoder_hidden_states: Optional[jnp.ndarray] = None,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ input_shape = input_ids.shape
+ input_ids = input_ids.reshape(-1, input_shape[-1])
+
+ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+ # embed positions
+ positions = jnp.take(self.embed_positions, position_ids, axis=0)
+ # explicitly cast the positions here, since self.embed_positions are not registered as parameters
+ positions = positions.astype(inputs_embeds.dtype)
+
+ hidden_states = inputs_embeds + positions
+
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+
+ outputs = self.layers(
+ hidden_states,
+ attention_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ if not return_dict:
+ return outputs
+
+ return FlaxBaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=outputs.last_hidden_state,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ cross_attentions=outputs.cross_attentions,
+ )
+
+
+class FlaxMarianModule(nn.Module):
+ config: MarianConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.shared = nn.Embed(
+ self.config.vocab_size,
+ self.config.d_model,
+ embedding_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+
+ self.encoder = FlaxMarianEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
+ self.decoder = FlaxMarianDecoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
+
+ def _get_encoder_module(self):
+ return self.encoder
+
+ def _get_decoder_module(self):
+ return self.decoder
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ decoder_input_ids,
+ decoder_attention_mask,
+ position_ids,
+ decoder_position_ids,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ position_ids=decoder_position_ids,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return FlaxSeq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+class FlaxMarianPreTrainedModel(FlaxPreTrainedModel):
+ config_class = MarianConfig
+ base_model_prefix: str = "model"
+ module_class: nn.Module = None
+
+ def __init__(
+ self,
+ config: MarianConfig,
+ input_shape: tuple[int] = (1, 1),
+ seed: int = 0,
+ dtype: jnp.dtype = jnp.float32,
+ _do_init: bool = True,
+ **kwargs,
+ ):
+ module = self.module_class(config=config, dtype=dtype, **kwargs)
+ super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
+
+ def init_weights(self, rng: jax.random.PRNGKey, input_shape: tuple, params: FrozenDict = None) -> FrozenDict:
+ # init input tensors
+ input_ids = jnp.zeros(input_shape, dtype="i4")
+ # make sure initialization pass will work for FlaxMarianForSequenceClassificationModule
+ input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
+ attention_mask = jnp.ones_like(input_ids)
+ decoder_input_ids = input_ids
+ decoder_attention_mask = jnp.ones_like(input_ids)
+
+ batch_size, sequence_length = input_ids.shape
+ position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+ decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+
+ params_rng, dropout_rng = jax.random.split(rng)
+ rngs = {"params": params_rng, "dropout": dropout_rng}
+
+ random_params = self.module.init(
+ rngs,
+ input_ids,
+ attention_mask,
+ decoder_input_ids,
+ decoder_attention_mask,
+ position_ids,
+ decoder_position_ids,
+ )["params"]
+
+ if params is not None:
+ random_params = flatten_dict(unfreeze(random_params))
+ params = flatten_dict(unfreeze(params))
+ for missing_key in self._missing_keys:
+ params[missing_key] = random_params[missing_key]
+ self._missing_keys = set()
+ return freeze(unflatten_dict(params))
+ else:
+ return random_params
+
+ def init_cache(self, batch_size, max_length, encoder_outputs):
+ r"""
+ Args:
+ batch_size (`int`):
+ batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
+ max_length (`int`):
+ maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
+ cache.
+ encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
+ `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
+ `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*)
+ is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
+ cross-attention of the decoder.
+ """
+ # init input variables to retrieve cache
+ decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
+ decoder_attention_mask = jnp.ones_like(decoder_input_ids)
+ decoder_position_ids = jnp.broadcast_to(
+ jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
+ )
+
+ def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
+ decoder_module = module._get_decoder_module()
+ return decoder_module(decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs)
+
+ init_variables = self.module.init(
+ jax.random.PRNGKey(0),
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ decoder_position_ids=decoder_position_ids,
+ encoder_hidden_states=encoder_outputs[0],
+ init_cache=True,
+ method=_decoder_forward, # we only need to call the decoder to init the cache
+ )
+ return unfreeze(init_variables["cache"])
+
+ @add_start_docstrings(MARIAN_ENCODE_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=MarianConfig)
+ def encode(
+ self,
+ input_ids: jnp.ndarray,
+ attention_mask: Optional[jnp.ndarray] = None,
+ position_ids: Optional[jnp.ndarray] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ r"""
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxMarianMTModel
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+ >>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+
+ >>> text = "My friends are cool but they eat too many carbs."
+ >>> inputs = tokenizer(text, max_length=64, return_tensors="jax")
+ >>> encoder_outputs = model.encode(**inputs)
+ ```"""
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ if attention_mask is None:
+ attention_mask = jnp.ones_like(input_ids)
+ if position_ids is None:
+ batch_size, sequence_length = input_ids.shape
+ position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs):
+ encode_module = module._get_encoder_module()
+ return encode_module(input_ids, attention_mask, position_ids, **kwargs)
+
+ return self.module.apply(
+ {"params": params or self.params},
+ input_ids=jnp.array(input_ids, dtype="i4"),
+ attention_mask=jnp.array(attention_mask, dtype="i4"),
+ position_ids=jnp.array(position_ids, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ method=_encoder_forward,
+ )
+
+ @add_start_docstrings(MARIAN_DECODE_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=MarianConfig)
+ def decode(
+ self,
+ decoder_input_ids,
+ encoder_outputs,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ decoder_attention_mask: Optional[jnp.ndarray] = None,
+ decoder_position_ids: Optional[jnp.ndarray] = None,
+ past_key_values: Optional[dict] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ r"""
+ Returns:
+
+ Example:
+
+ ```python
+ >>> import jax.numpy as jnp
+ >>> from transformers import AutoTokenizer, FlaxMarianMTModel
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+ >>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+
+ >>> text = "My friends are cool but they eat too many carbs."
+ >>> inputs = tokenizer(text, max_length=64, return_tensors="jax")
+ >>> encoder_outputs = model.encode(**inputs)
+
+ >>> decoder_start_token_id = model.config.decoder_start_token_id
+ >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
+
+ >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
+ >>> last_decoder_hidden_states = outputs.last_hidden_state
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ encoder_hidden_states = encoder_outputs[0]
+ if encoder_attention_mask is None:
+ batch_size, sequence_length = encoder_hidden_states.shape[:2]
+ encoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ batch_size, sequence_length = decoder_input_ids.shape
+ if decoder_attention_mask is None:
+ decoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ if decoder_position_ids is None:
+ if past_key_values is not None:
+ raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
+
+ decoder_position_ids = jnp.broadcast_to(
+ jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
+ )
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ inputs = {"params": params or self.params}
+
+ # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
+ # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
+ # it can be changed by FlaxMarianAttention module
+ if past_key_values:
+ inputs["cache"] = past_key_values
+ mutable = ["cache"]
+ else:
+ mutable = False
+
+ def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
+ decoder_module = module._get_decoder_module()
+ return decoder_module(
+ decoder_input_ids,
+ decoder_attention_mask,
+ decoder_position_ids,
+ **kwargs,
+ )
+
+ outputs = self.module.apply(
+ inputs,
+ decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+ decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+ decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ mutable=mutable,
+ method=_decoder_forward,
+ )
+
+ # add updated cache to model output
+ if past_key_values is not None and return_dict:
+ outputs, past = outputs
+ outputs["past_key_values"] = unfreeze(past["cache"])
+ return outputs
+ elif past_key_values is not None and not return_dict:
+ outputs, past = outputs
+ outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
+
+ return outputs
+
+ @add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING)
+ def __call__(
+ self,
+ input_ids: jnp.ndarray,
+ attention_mask: Optional[jnp.ndarray] = None,
+ decoder_input_ids: Optional[jnp.ndarray] = None,
+ decoder_attention_mask: Optional[jnp.ndarray] = None,
+ position_ids: Optional[jnp.ndarray] = None,
+ decoder_position_ids: Optional[jnp.ndarray] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ # prepare encoder inputs
+ if attention_mask is None:
+ attention_mask = jnp.ones_like(input_ids)
+ if position_ids is None:
+ batch_size, sequence_length = input_ids.shape
+ position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+
+ # prepare decoder inputs
+ if decoder_input_ids is None:
+ decoder_input_ids = shift_tokens_right(
+ input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id
+ )
+ if decoder_attention_mask is None:
+ decoder_attention_mask = jnp.ones_like(decoder_input_ids)
+ if decoder_position_ids is None:
+ batch_size, sequence_length = decoder_input_ids.shape
+ decoder_position_ids = jnp.broadcast_to(
+ jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
+ )
+
+ # Handle any PRNG if needed
+ rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
+
+ return self.module.apply(
+ {"params": params or self.params},
+ input_ids=jnp.array(input_ids, dtype="i4"),
+ attention_mask=jnp.array(attention_mask, dtype="i4"),
+ position_ids=jnp.array(position_ids, dtype="i4"),
+ decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+ decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+ decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ )
+
+
+@add_start_docstrings(
+ "The bare Marian Model transformer outputting raw hidden-states without any specific head on top.",
+ MARIAN_START_DOCSTRING,
+)
+class FlaxMarianModel(FlaxMarianPreTrainedModel):
+ config: MarianConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+ module_class = FlaxMarianModule
+
+
+append_call_sample_docstring(FlaxMarianModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)
+
+
+class FlaxMarianMTModule(nn.Module):
+ config: MarianConfig
+ dtype: jnp.dtype = jnp.float32
+ bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
+
+ def setup(self):
+ self.model = FlaxMarianModule(config=self.config, dtype=self.dtype)
+ self.lm_head = nn.Dense(
+ self.model.shared.num_embeddings,
+ use_bias=False,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+ self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings))
+
+ def _get_encoder_module(self):
+ return self.model.encoder
+
+ def _get_decoder_module(self):
+ return self.model.decoder
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ decoder_input_ids,
+ decoder_attention_mask,
+ position_ids,
+ decoder_position_ids,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ position_ids=position_ids,
+ decoder_position_ids=decoder_position_ids,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ hidden_states = outputs[0]
+
+ if self.config.tie_word_embeddings:
+ shared_embedding = self.model.variables["params"]["shared"]["embedding"]
+ lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
+ else:
+ lm_logits = self.lm_head(hidden_states)
+
+ lm_logits += self.final_logits_bias.astype(self.dtype)
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return output
+
+ return FlaxSeq2SeqLMOutput(
+ logits=lm_logits,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+@add_start_docstrings(
+ "The MARIAN Model with a language modeling head. Can be used for translation.", MARIAN_START_DOCSTRING
+)
+class FlaxMarianMTModel(FlaxMarianPreTrainedModel):
+ module_class = FlaxMarianMTModule
+ dtype: jnp.dtype = jnp.float32
+
+ @add_start_docstrings(MARIAN_DECODE_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=MarianConfig)
+ def decode(
+ self,
+ decoder_input_ids,
+ encoder_outputs,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ decoder_attention_mask: Optional[jnp.ndarray] = None,
+ decoder_position_ids: Optional[jnp.ndarray] = None,
+ past_key_values: Optional[dict] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ r"""
+ Returns:
+
+ Example:
+
+ ```python
+ >>> import jax.numpy as jnp
+ >>> from transformers import AutoTokenizer, FlaxMarianMTModel
+
+ >>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+ >>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+
+ >>> text = "My friends are cool but they eat too many carbs."
+ >>> inputs = tokenizer(text, max_length=64, return_tensors="jax")
+ >>> encoder_outputs = model.encode(**inputs)
+
+ >>> decoder_start_token_id = model.config.decoder_start_token_id
+ >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
+
+ >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
+ >>> logits = outputs.logits
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ encoder_hidden_states = encoder_outputs[0]
+ if encoder_attention_mask is None:
+ batch_size, sequence_length = encoder_hidden_states.shape[:2]
+ encoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ batch_size, sequence_length = decoder_input_ids.shape
+ if decoder_attention_mask is None:
+ decoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ if decoder_position_ids is None:
+ if past_key_values is not None:
+ raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
+
+ decoder_position_ids = jnp.broadcast_to(
+ jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
+ )
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ inputs = {"params": params or self.params}
+
+ # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
+ # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
+ # it can be changed by FlaxMarianAttention module
+ if past_key_values:
+ inputs["cache"] = past_key_values
+ mutable = ["cache"]
+ else:
+ mutable = False
+
+ def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
+ decoder_module = module._get_decoder_module()
+ outputs = decoder_module(
+ decoder_input_ids,
+ decoder_attention_mask,
+ decoder_position_ids,
+ **kwargs,
+ )
+ hidden_states = outputs[0]
+
+ if self.config.tie_word_embeddings:
+ shared_embedding = module.model.variables["params"]["shared"]["embedding"]
+ lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
+ else:
+ lm_logits = module.lm_head(hidden_states)
+ lm_logits += module.final_logits_bias.astype(self.dtype)
+
+ return lm_logits, outputs
+
+ outputs = self.module.apply(
+ inputs,
+ decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+ decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+ decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ mutable=mutable,
+ method=_decoder_forward,
+ )
+
+ if past_key_values is None:
+ lm_logits, decoder_outputs = outputs
+ else:
+ (lm_logits, decoder_outputs), past = outputs
+
+ if return_dict:
+ outputs = FlaxCausalLMOutputWithCrossAttentions(
+ logits=lm_logits,
+ hidden_states=decoder_outputs.hidden_states,
+ attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ )
+ else:
+ outputs = (lm_logits,) + decoder_outputs[1:]
+
+ # add updated cache to model output
+ if past_key_values is not None and return_dict:
+ outputs["past_key_values"] = unfreeze(past["cache"])
+ return outputs
+ elif past_key_values is not None and not return_dict:
+ outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
+
+ return outputs
+
+ def _adapt_logits_for_beam_search(self, logits):
+ """This function enforces the padding token never to be generated."""
+ logits = logits.at[:, :, self.config.pad_token_id].set(float("-inf"))
+ return logits
+
+ def prepare_inputs_for_generation(
+ self,
+ decoder_input_ids,
+ max_length,
+ attention_mask: Optional[jax.Array] = None,
+ decoder_attention_mask: Optional[jax.Array] = None,
+ encoder_outputs=None,
+ **kwargs,
+ ):
+ # initializing the cache
+ batch_size, seq_length = decoder_input_ids.shape
+
+ past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
+ # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
+ # But since the decoder uses a causal mask, those positions are masked anyways.
+ # Thus we can create a single static attention_mask here, which is more efficient for compilation
+ extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
+ if decoder_attention_mask is not None:
+ position_ids = decoder_attention_mask.cumsum(axis=-1) - 1
+ extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
+ else:
+ position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
+
+ return {
+ "past_key_values": past_key_values,
+ "encoder_outputs": encoder_outputs,
+ "encoder_attention_mask": attention_mask,
+ "decoder_attention_mask": extended_attention_mask,
+ "decoder_position_ids": position_ids,
+ }
+
+ def update_inputs_for_generation(self, model_outputs, model_kwargs):
+ model_kwargs["past_key_values"] = model_outputs.past_key_values
+ model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
+ return model_kwargs
+
+
+FLAX_MARIAN_MT_DOCSTRING = """
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxMarianMTModel
+
+ >>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+ >>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+
+ >>> text = "My friends are cool but they eat too many carbs."
+ >>> input_ids = tokenizer(text, max_length=64, return_tensors="jax").input_ids
+
+ >>> sequences = model.generate(input_ids, max_length=64, num_beams=2).sequences
+
+ >>> outputs = tokenizer.batch_decode(sequences, skip_special_tokens=True)
+ >>> # should give *Meine Freunde sind cool, aber sie essen zu viele Kohlenhydrate.*
+ ```
+"""
+
+overwrite_call_docstring(
+ FlaxMarianMTModel,
+ MARIAN_INPUTS_DOCSTRING + FLAX_MARIAN_MT_DOCSTRING,
+)
+append_replace_return_docstrings(FlaxMarianMTModel, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
+
+
+__all__ = ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/modeling_marian.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/modeling_marian.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ed87c86f31e3f6864a4bdfdc3fd77ff5f86c22b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/modeling_marian.py
@@ -0,0 +1,1707 @@
+# coding=utf-8
+# Copyright 2021 The Marian Team Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MarianMTModel model, ported from the Marian C++ repo."""
+
+import copy
+import math
+from typing import Callable, Optional, Union
+
+import numpy as np
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import (
+ AttentionMaskConverter,
+ _prepare_4d_attention_mask,
+ _prepare_4d_attention_mask_for_sdpa,
+)
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPastAndCrossAttentions,
+ CausalLMOutputWithCrossAttentions,
+ Seq2SeqLMOutput,
+ Seq2SeqModelOutput,
+)
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import (
+ auto_docstring,
+ is_torch_flex_attn_available,
+ is_torchdynamo_compiling,
+ logging,
+)
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_marian import MarianConfig
+
+
+if is_torch_flex_attn_available():
+ from torch.nn.attention.flex_attention import BlockMask
+
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+ """
+ Shift input ids one token to the right.
+ """
+ shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+ shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
+ shifted_input_ids[:, 0] = decoder_start_token_id
+
+ if pad_token_id is None:
+ raise ValueError("self.model.config.pad_token_id has to be defined.")
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+ return shifted_input_ids
+
+
+class MarianSinusoidalPositionalEmbedding(nn.Embedding):
+ """This module produces sinusoidal positional embeddings of any length."""
+
+ def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
+ super().__init__(num_positions, embedding_dim)
+
+ def _init_weight(self):
+ """
+ Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
+ the 2nd half of the vector. [dim // 2:]
+ """
+ n_pos, dim = self.weight.shape
+ position_enc = np.array(
+ [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
+ )
+ out = torch.empty(n_pos, dim, dtype=self.weight.dtype, requires_grad=False)
+ sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1
+ out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
+ out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
+ self.weight = nn.Parameter(out, requires_grad=False)
+
+ @torch.no_grad()
+ def forward(
+ self, input_ids_shape: torch.Size, past_key_values_length: int = 0, position_ids: Optional[torch.Tensor] = None
+ ) -> torch.Tensor:
+ """`input_ids_shape` is expected to be [bsz x seqlen]."""
+ if position_ids is None:
+ bsz, seq_len = input_ids_shape[:2]
+ position_ids = torch.arange(
+ past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
+ )
+ return super().forward(position_ids)
+
+
+# Copied from transformers.models.bart.modeling_bart.eager_attention_forward
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: Optional[float] = None,
+ dropout: float = 0.0,
+ head_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+):
+ if scaling is None:
+ scaling = query.size(-1) ** -0.5
+
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ if head_mask is not None:
+ attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
+
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Marian
+class MarianAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: float = 0.0,
+ is_decoder: bool = False,
+ bias: bool = True,
+ is_causal: bool = False,
+ config: Optional[MarianConfig] = None,
+ layer_idx: Optional[int] = None,
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+ self.config = config
+
+ if (self.head_dim * num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+ self.is_causal = is_causal
+ self.layer_idx = layer_idx
+ if layer_idx is None and self.is_decoder:
+ logger.warning_once(
+ f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
+ "will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ key_value_states: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ cache_position: Optional[torch.Tensor] = None,
+ # TODO: we need a refactor so that the different attention modules can get their specific kwargs
+ # ATM, we have mixed things encoder, decoder, and encoder-decoder attn
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+
+ # determine input shapes
+ bsz, tgt_len = hidden_states.shape[:-1]
+ src_len = key_value_states.shape[1] if is_cross_attention else tgt_len
+
+ q_input_shape = (bsz, tgt_len, -1, self.head_dim)
+ kv_input_shape = (bsz, src_len, -1, self.head_dim)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states).view(*q_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ if isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_states from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = key_value_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_states = curr_past_key_value.layers[self.layer_idx].keys
+ value_states = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_states = self.k_proj(current_states)
+ value_states = self.v_proj(current_states)
+ key_states = key_states.view(*kv_input_shape).transpose(1, 2)
+ value_states = value_states.view(*kv_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_states, value_states = curr_past_key_value.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=self.scaling,
+ output_attentions=output_attentions,
+ head_mask=layer_head_mask,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.bart.modeling_bart.BartEncoderLayer with Bart->Marian, BART->MARIAN
+class MarianEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MarianConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.embed_dim = config.d_model
+
+ self.self_attn = MarianAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.encoder_attention_heads,
+ dropout=config.attention_dropout,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+ self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
+ self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ attention_mask: torch.FloatTensor,
+ layer_head_mask: torch.FloatTensor,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ if hidden_states.dtype == torch.float16 and (
+ torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
+ ):
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs
+
+
+# Copied from transformers.models.bart.modeling_bart.BartDecoderLayer with Bart->Marian, BART->MARIAN
+class MarianDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MarianConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.embed_dim = config.d_model
+
+ self.self_attn = MarianAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ is_causal=True,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.encoder_attn = MarianAttention(
+ self.embed_dim,
+ config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
+ self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = True,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ encoder_hidden_states (`torch.FloatTensor`):
+ cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
+ encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`.
+ cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
+ size `(decoder_attention_heads,)`.
+ past_key_values (`Tuple(torch.FloatTensor)`): cached past key and value projection states
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
+ cache in the correct position and to infer the complete sequence length.
+ """
+ residual = hidden_states
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Cross-Attention Block
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+
+ hidden_states, cross_attn_weights = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ layer_head_mask=cross_attn_layer_head_mask,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ return outputs
+
+
+@auto_docstring
+class MarianPreTrainedModel(PreTrainedModel):
+ config: MarianConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ _can_compile_fullgraph = True
+
+ def _init_weights(self, module: Union[nn.Linear, nn.Embedding, MarianSinusoidalPositionalEmbedding]):
+ std = self.config.init_std
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, MarianSinusoidalPositionalEmbedding):
+ module._init_weight()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+
+ @property
+ def dummy_inputs(self):
+ pad_token = self.config.pad_token_id
+ input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
+ dummy_inputs = {
+ "attention_mask": input_ids.ne(pad_token),
+ "input_ids": input_ids,
+ "decoder_input_ids": input_ids,
+ }
+ return dummy_inputs
+
+ # Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_full_mask
+ def _update_full_mask(
+ self,
+ attention_mask: Union[torch.Tensor, None],
+ inputs_embeds: torch.Tensor,
+ ):
+ if attention_mask is not None:
+ if self.config._attn_implementation == "flash_attention_2":
+ attention_mask = attention_mask if 0 in attention_mask else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & head_mask can not be supported when using SDPA, fall back to
+ # the manual implementation that requires a 4D causal mask in all cases.
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, inputs_embeds.dtype)
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask, is_causal=False)
+ else:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
+
+ return attention_mask
+
+ # Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_causal_mask
+ def _update_causal_mask(
+ self,
+ attention_mask: Optional[Union[torch.Tensor, "BlockMask"]],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ ):
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ # Other attention flavors support in-built causal (when `mask is None`)
+ # while we need to create our specific block mask regardless
+ elif attention_mask is None:
+ attention_mask = make_flex_block_causal_mask(
+ torch.ones(
+ size=(input_tensor.shape[0], input_tensor.shape[1]),
+ device=attention_mask.device,
+ )
+ )
+ return attention_mask
+
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and (attention_mask == 0.0).any():
+ return attention_mask
+ return None
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if self.config._attn_implementation == "sdpa" and not using_compilable_cache:
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ sequence_length = input_tensor.shape[1]
+ if using_compilable_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+ # Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_cross_attn_mask
+ def _update_cross_attn_mask(
+ self,
+ encoder_hidden_states: Union[torch.Tensor, None],
+ encoder_attention_mask: Union[torch.Tensor, None],
+ input_shape: torch.Size,
+ inputs_embeds: torch.Tensor,
+ ):
+ # expand encoder attention mask
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ if self.config._attn_implementation == "flash_attention_2":
+ encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
+ encoder_attention_mask,
+ inputs_embeds.dtype,
+ tgt_len=input_shape[-1],
+ )
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(encoder_attention_mask, torch.Tensor):
+ encoder_attention_mask = make_flex_block_causal_mask(
+ encoder_attention_mask,
+ query_length=input_shape[-1],
+ is_causal=False,
+ )
+ else:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask(
+ encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+ )
+
+ return encoder_attention_mask
+
+
+class MarianEncoder(MarianPreTrainedModel):
+ """
+ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
+ [`MarianEncoderLayer`].
+
+ Args:
+ config: MarianConfig
+ embed_tokens (nn.Embedding): output embedding
+ """
+
+ def __init__(self, config: MarianConfig, embed_tokens: Optional[nn.Embedding] = None):
+ super().__init__(config)
+
+ self.dropout = config.dropout
+ self.layerdrop = config.encoder_layerdrop
+
+ embed_dim = config.d_model
+ self.padding_idx = config.pad_token_id
+ self.max_source_positions = config.max_position_embeddings
+ self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
+
+ if embed_tokens is not None:
+ self.embed_tokens = embed_tokens
+ else:
+ self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
+
+ self.embed_positions = MarianSinusoidalPositionalEmbedding(
+ config.max_position_embeddings, embed_dim, self.padding_idx
+ )
+ self.layers = nn.ModuleList([MarianEncoderLayer(config) for _ in range(config.encoder_layers)])
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], BaseModelOutput]:
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+ embed_pos = self.embed_positions(input_shape)
+
+ hidden_states = inputs_embeds + embed_pos
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ attention_mask = self._update_full_mask(
+ attention_mask,
+ inputs_embeds,
+ )
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ assert head_mask.size()[0] == (len(self.layers)), (
+ f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
+ )
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ to_drop = False
+ if self.training:
+ dropout_probability = torch.rand([])
+ if dropout_probability < self.layerdrop: # skip the layer
+ to_drop = True
+
+ if to_drop:
+ layer_outputs = (None, None)
+ else:
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+ )
+
+
+class MarianDecoder(MarianPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MarianDecoderLayer`]
+
+ Args:
+ config: MarianConfig
+ embed_tokens (nn.Embedding): output embedding
+ """
+
+ def __init__(self, config: MarianConfig, embed_tokens: Optional[nn.Embedding] = None):
+ super().__init__(config)
+ self.dropout = config.dropout
+ self.layerdrop = config.decoder_layerdrop
+ self.padding_idx = config.pad_token_id
+ self.max_target_positions = config.max_position_embeddings
+ self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+
+ if embed_tokens is not None:
+ self.embed_tokens = embed_tokens
+ else:
+ self.embed_tokens = nn.Embedding(config.decoder_vocab_size, config.d_model, self.padding_idx)
+
+ self.embed_positions = MarianSinusoidalPositionalEmbedding(
+ config.max_position_embeddings, config.d_model, self.padding_idx
+ )
+ self.layers = nn.ModuleList([MarianDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)])
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
+ cross-attention on hidden heads. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+ shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+ cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+ that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+ all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
+ cache in the correct position and to infer the complete sequence length.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ # retrieve input_ids and inputs_embeds
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ input = input_ids
+ input_shape = input.shape
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ input = inputs_embeds[:, :, -1]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input)
+
+ # Important to apply outside of the above `if`, in case user passes `embeds`
+ inputs_embeds = inputs_embeds * self.embed_scale
+
+ # initialize `past_key_values`
+ if use_cache and past_key_values is None:
+ past_key_values = (
+ EncoderDecoderCache(DynamicCache(), DynamicCache())
+ if encoder_hidden_states is not None
+ else DynamicCache()
+ )
+ if use_cache and isinstance(past_key_values, tuple):
+ logger.warning_once(
+ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
+ "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
+ "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
+ )
+ past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
+
+ batch_size, seq_length = inputs_embeds.size()[:-1]
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+ if cache_position is None:
+ cache_position = torch.arange(
+ past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
+ )
+
+ if attention_mask is None and not is_torchdynamo_compiling():
+ # required mask seq length can be calculated via length of past cache
+ mask_seq_length = past_key_values_length + seq_length
+ attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
+
+ self_attn_cache = (
+ past_key_values.self_attention_cache
+ if isinstance(past_key_values, EncoderDecoderCache)
+ else past_key_values
+ )
+
+ causal_mask = self._update_causal_mask(
+ attention_mask,
+ inputs_embeds,
+ cache_position,
+ self_attn_cache,
+ )
+ encoder_attention_mask = self._update_cross_attn_mask(
+ encoder_hidden_states,
+ encoder_attention_mask,
+ input_shape,
+ inputs_embeds,
+ )
+
+ # embed positions
+ position_ids = self.embed_positions(
+ (batch_size, seq_length), past_key_values_length, position_ids=cache_position
+ )
+ hidden_states = inputs_embeds + position_ids
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
+
+ # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
+ for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
+ if attn_mask is not None:
+ assert attn_mask.size()[0] == (len(self.layers)), (
+ f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ if self.training:
+ dropout_probability = torch.rand([])
+ if dropout_probability < self.layerdrop:
+ continue
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ causal_mask,
+ encoder_hidden_states, # as a positional argument for gradient checkpointing
+ encoder_attention_mask=encoder_attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if encoder_hidden_states is not None:
+ all_cross_attentions += (layer_outputs[2],)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions]
+ if v is not None
+ )
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+@auto_docstring
+class MarianModel(MarianPreTrainedModel):
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+ def __init__(self, config: MarianConfig):
+ super().__init__(config)
+
+ padding_idx, vocab_size = config.pad_token_id, config.vocab_size
+
+ # We always use self.shared for token embeddings to ensure compatibility with all marian models
+ self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
+ if self.config.share_encoder_decoder_embeddings:
+ encoder_embed_tokens = decoder_embed_tokens = self.shared
+ else:
+ # Since the embeddings are not shared, deepcopy the embeddings here for encoder
+ # and decoder to make sure they are not tied.
+ encoder_embed_tokens = copy.deepcopy(self.shared)
+ decoder_embed_tokens = copy.deepcopy(self.shared)
+ self.shared = None
+
+ self.encoder = MarianEncoder(config, encoder_embed_tokens)
+ self.decoder = MarianDecoder(config, decoder_embed_tokens)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ # This will return shared embeddings if they are shared else specific to encoder.
+ return self.get_encoder().get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ if self.config.share_encoder_decoder_embeddings:
+ self.shared = value
+ self.encoder.embed_tokens = self.shared
+ self.decoder.embed_tokens = self.shared
+ else: # if not shared only set encoder embeedings
+ self.encoder.embed_tokens = value
+
+ def get_decoder_input_embeddings(self):
+ if self.config.share_encoder_decoder_embeddings:
+ raise ValueError(
+ "`get_decoder_input_embeddings` should not be called if `config.share_encoder_decoder_embeddings` "
+ "is `True`. Please use `get_input_embeddings` instead."
+ )
+ return self.get_decoder().get_input_embeddings()
+
+ def set_decoder_input_embeddings(self, value):
+ if self.config.share_encoder_decoder_embeddings:
+ raise ValueError(
+ "`config.share_encoder_decoder_embeddings` is set to `True` meaning the decoder input embeddings "
+ "are shared with the encoder. In order to set the decoder input embeddings, you should simply set "
+ "the encoder input embeddings by calling `set_input_embeddings` with the appropriate embeddings."
+ )
+ self.decoder.embed_tokens = value
+
+ def get_encoder(self):
+ return self.encoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ def resize_decoder_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
+ if self.config.share_encoder_decoder_embeddings:
+ raise ValueError(
+ "`resize_decoder_token_embeddings` should not be called if `config.share_encoder_decoder_embeddings` "
+ "is `True`. Please use `resize_token_embeddings` instead."
+ )
+
+ old_embeddings = self.get_decoder_input_embeddings()
+ new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
+ self.set_decoder_input_embeddings(new_embeddings)
+
+ model_embeds = self.get_decoder_input_embeddings()
+
+ if new_num_tokens is None:
+ return model_embeds
+
+ # Update base model and current model config
+ self.config.decoder_vocab_size = new_num_tokens
+
+ # Tie weights again if needed
+ self.tie_weights()
+
+ return model_embeds
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[Union[tuple[torch.Tensor], BaseModelOutput]] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Seq2SeqModelOutput:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ Marian uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MarianModel
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+ >>> model = MarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+
+ >>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
+ >>> decoder_inputs = tokenizer(
+ ... " Studien haben gezeigt dass es hilfreich ist einen Hund zu besitzen",
+ ... return_tensors="pt",
+ ... add_special_tokens=False,
+ ... )
+ >>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)
+
+ >>> last_hidden_states = outputs.last_hidden_state
+ >>> list(last_hidden_states.shape)
+ [1, 26, 512]
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
+ elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+ encoder_outputs = BaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+
+ # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return Seq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The Marian Model with a language modeling head. Can be used for summarization.
+ """
+)
+class MarianMTModel(MarianPreTrainedModel, GenerationMixin):
+ base_model_prefix = "model"
+ _keys_to_ignore_on_load_missing = [
+ "final_logits_bias",
+ "encoder.embed_positions.weight",
+ "decoder.embed_positions.weight",
+ ]
+ _keys_to_ignore_on_save = ["model.encoder.embed_positions.weight", "model.decoder.embed_positions.weight"]
+ _tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight", "lm_head.weight"]
+
+ def __init__(self, config: MarianConfig):
+ super().__init__(config)
+ self.model = MarianModel(config)
+
+ target_vocab_size = config.vocab_size if config.share_encoder_decoder_embeddings else config.decoder_vocab_size
+ self.register_buffer("final_logits_bias", torch.zeros((1, target_vocab_size)))
+ self.lm_head = nn.Linear(config.d_model, target_vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_encoder(self):
+ return self.model.get_encoder()
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def resize_token_embeddings(
+ self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+ ) -> nn.Embedding:
+ new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
+ if self.config.share_encoder_decoder_embeddings:
+ self._resize_final_logits_bias(new_num_tokens)
+ return new_embeddings
+
+ # NOTE: `_resize_token_embeddings` was rewritten in the base class, *args exists to absorb the extra arg
+ def _resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of=None, *args) -> nn.Embedding:
+ old_embeddings = self.get_input_embeddings()
+ new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens, pad_to_multiple_of)
+ self.set_input_embeddings(new_embeddings)
+
+ new_num_tokens = new_embeddings.weight.shape[0]
+ # update config.decoder_vocab_size if embeddings are tied
+ if self.config.share_encoder_decoder_embeddings:
+ self.config.decoder_vocab_size = new_num_tokens
+
+ # if word embeddings are not tied, make sure that lm head is resized as well
+ if (
+ self.config.share_encoder_decoder_embeddings
+ and self.get_output_embeddings() is not None
+ and not self.config.tie_word_embeddings
+ ):
+ old_lm_head = self.get_output_embeddings()
+ new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
+ self.set_output_embeddings(new_lm_head)
+
+ return self.get_input_embeddings()
+
+ def resize_decoder_token_embeddings(self, new_num_tokens):
+ if self.config.share_encoder_decoder_embeddings:
+ raise ValueError(
+ "`resize_decoder_token_embeddings` should not be called if `config.share_encoder_decoder_embeddings` "
+ "is `True`. Please use `resize_token_embeddings` instead."
+ )
+
+ old_embeddings = self.model.get_decoder_input_embeddings()
+ new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
+ self.model.set_decoder_input_embeddings(new_embeddings)
+
+ # if word embeddings are not tied, make sure that lm head is resized as well
+ if self.get_output_embeddings() is not None and not self.config.tie_word_embeddings:
+ old_lm_head = self.get_output_embeddings()
+ new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
+ self.set_output_embeddings(new_lm_head)
+
+ model_embeds = self.model.get_decoder_input_embeddings()
+
+ if new_num_tokens is None:
+ return model_embeds
+
+ # Update base model and current model config
+ self.config.decoder_vocab_size = new_num_tokens
+
+ # Tie weights again if needed
+ self.tie_weights()
+
+ self._resize_final_logits_bias(new_num_tokens)
+
+ return model_embeds
+
+ def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
+ old_num_tokens = self.final_logits_bias.shape[-1]
+ if new_num_tokens <= old_num_tokens:
+ new_bias = self.final_logits_bias[:, :new_num_tokens]
+ else:
+ extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
+ new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
+ self.register_buffer("final_logits_bias", new_bias)
+
+ def set_output_embeddings(self, new_embeddings: nn.Embedding):
+ self.lm_head = new_embeddings
+
+ def tie_weights(self):
+ """
+ Tie the weights between the input embeddings and the output embeddings.
+
+ If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning the
+ weights instead.
+ """
+ output_embeddings = self.get_output_embeddings()
+ if output_embeddings is not None and getattr(self.config, "tie_word_embeddings", True):
+ # if embeddings are shared this will return shared embeddings otherwise decoder embed_tokens
+ word_embeddings = self.get_decoder().get_input_embeddings()
+ self._tie_or_clone_weights(output_embeddings, word_embeddings)
+
+ if getattr(self.config, "is_encoder_decoder", False) and getattr(self.config, "tie_encoder_decoder", False):
+ if hasattr(self, self.base_model_prefix):
+ self = getattr(self, self.base_model_prefix)
+ tied_weights = self._tie_encoder_decoder_weights(
+ self.encoder, self.decoder, self.base_model_prefix, "encoder"
+ )
+ # Setting a dynamic variable instead of `_tied_weights_keys` because it's a class
+ # attributed not an instance member, therefore modifying it will modify the entire class
+ # Leading to issues on subsequent calls by different tests or subsequent calls.
+ self._dynamic_tied_weights_keys = tied_weights
+
+ for module in self.modules():
+ if hasattr(module, "_tie_weights"):
+ module._tie_weights()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[Union[tuple[torch.Tensor], BaseModelOutput]] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Seq2SeqLMOutput:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ Marian uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MarianMTModel
+
+ >>> src = "fr" # source language
+ >>> trg = "en" # target language
+
+ >>> model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"
+ >>> model = MarianMTModel.from_pretrained(model_name)
+ >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+ >>> sample_text = "où est l'arrêt de bus ?"
+ >>> batch = tokenizer([sample_text], return_tensors="pt")
+
+ >>> generated_ids = model.generate(**batch)
+ >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+ "Where's the bus stop?"
+ ```
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if labels is not None:
+ if use_cache:
+ logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+ use_cache = False
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ decoder_input_ids = shift_tokens_right(
+ labels, self.config.pad_token_id, self.config.decoder_start_token_id
+ )
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ encoder_outputs=encoder_outputs,
+ decoder_attention_mask=decoder_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+ lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.decoder_vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return Seq2SeqLMOutput(
+ loss=masked_lm_loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+ def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+ return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
+
+# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->Marian
+class MarianDecoderWrapper(MarianPreTrainedModel):
+ """
+ This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
+ used in combination with the [`EncoderDecoderModel`] framework.
+ """
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.decoder = MarianDecoder(config)
+
+ def forward(self, *args, **kwargs):
+ return self.decoder(*args, **kwargs)
+
+
+# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->Marian, facebook/bart-base->Helsinki-NLP/opus-mt-fr-en
+class MarianForCausalLM(MarianPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ config.is_decoder = True
+ config.is_encoder_decoder = False
+ super().__init__(config)
+ self.model = MarianDecoderWrapper(config)
+
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.decoder.embed_tokens = value
+
+ def set_decoder(self, decoder):
+ self.model.decoder = decoder
+
+ def get_decoder(self):
+ return self.model.decoder
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
+ r"""
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MarianForCausalLM
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
+ >>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-fr-en", add_cross_attention=False)
+ >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
+ >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+ >>> outputs = model(**inputs)
+
+ >>> logits = outputs.logits
+ >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
+ >>> list(logits.shape) == expected_shape
+ True
+ ```"""
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model.decoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ head_mask=head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ logits = self.lm_head(outputs[0])
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithCrossAttentions(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ cross_attentions=outputs.cross_attentions,
+ )
+
+
+__all__ = ["MarianForCausalLM", "MarianModel", "MarianMTModel", "MarianPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/modeling_tf_marian.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/modeling_tf_marian.py
new file mode 100644
index 0000000000000000000000000000000000000000..c989cfa15f5ac60e7d193dc3474661b47827166d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/modeling_tf_marian.py
@@ -0,0 +1,1558 @@
+# coding=utf-8
+# Copyright 2021 The Marian Team Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TF 2.0 Marian model."""
+
+from __future__ import annotations
+
+import random
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_outputs import (
+ TFBaseModelOutput,
+ TFBaseModelOutputWithPastAndCrossAttentions,
+ TFSeq2SeqLMOutput,
+ TFSeq2SeqModelOutput,
+)
+
+# Public API
+from ...modeling_tf_utils import (
+ TFCausalLanguageModelingLoss,
+ TFPreTrainedModel,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
+from ...utils import (
+ add_code_sample_docstrings,
+ add_end_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+ replace_return_docstrings,
+)
+from .configuration_marian import MarianConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "Helsinki-NLP/opus-mt-en-de"
+_CONFIG_FOR_DOC = "MarianConfig"
+
+
+LARGE_NEGATIVE = -1e8
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right
+def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
+ pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
+ decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
+ start_tokens = tf.fill(
+ (shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype)
+ )
+ shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids = tf.where(
+ shifted_input_ids == -100,
+ tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)),
+ shifted_input_ids,
+ )
+
+ # "Verify that `labels` has only positive values and -100"
+ assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))
+
+ # Make sure the assertion op is called by wrapping the result in an identity no-op
+ with tf.control_dependencies([assert_gte0]):
+ shifted_input_ids = tf.identity(shifted_input_ids)
+
+ return shifted_input_ids
+
+
+# Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask
+def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
+ """
+ Make causal mask used for bi-directional self-attention.
+ """
+ bsz = input_ids_shape[0]
+ tgt_len = input_ids_shape[1]
+ mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
+ mask_cond = tf.range(shape_list(mask)[-1])
+
+ mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
+
+ if past_key_values_length > 0:
+ mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
+
+ return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
+
+
+# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
+def _expand_mask(mask: tf.Tensor, tgt_len: int | None = None):
+ """
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+ """
+ src_len = shape_list(mask)[1]
+ tgt_len = tgt_len if tgt_len is not None else src_len
+ one_cst = tf.constant(1.0)
+ mask = tf.cast(mask, dtype=one_cst.dtype)
+ expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
+
+ return (one_cst - expanded_mask) * LARGE_NEGATIVE
+
+
+class TFMarianSinusoidalPositionalEmbedding(keras.layers.Layer):
+ """This module produces sinusoidal positional embeddings of any length."""
+
+ def __init__(self, num_positions: int, embedding_dim: int, **kwargs):
+ super().__init__(**kwargs)
+
+ if embedding_dim % 2 != 0:
+ raise NotImplementedError(f"odd embedding_dim {embedding_dim} not supported")
+
+ self.embedding_dim = embedding_dim
+ self.num_positions = num_positions
+
+ def build(self, input_shape: tf.TensorShape):
+ """
+ Build shared token embedding layer Shared weights logic adapted from
+ https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
+ """
+
+ weight = self._init_weight(self.num_positions, self.embedding_dim)
+
+ self.weight = self.add_weight(
+ name="embeddings",
+ shape=[self.num_positions, self.embedding_dim],
+ )
+ weight = tf.cast(weight, dtype=self.weight.dtype)
+
+ self.weight.assign(weight)
+
+ super().build(input_shape)
+
+ @staticmethod
+ def _init_weight(n_pos: int, dim: int):
+ """
+ Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
+ the 2nd half of the vector. [dim // 2:]
+ """
+ position_enc = np.array(
+ [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
+ )
+ table = np.zeros_like(position_enc)
+ # index 0 is all zero
+ table[:, 0 : dim // 2] = np.sin(position_enc[:, 0::2])
+ table[:, dim // 2 :] = np.cos(position_enc[:, 1::2])
+ # convert to tensor
+ table = tf.convert_to_tensor(table)
+ tf.stop_gradient(table)
+ return table
+
+ def call(
+ self, input_shape: tf.TensorShape, past_key_values_length: int = 0, position_ids: tf.Tensor | None = None
+ ):
+ """Input is expected to be of size [bsz x seqlen]."""
+ if position_ids is None:
+ seq_len = input_shape[1]
+ position_ids = tf.range(past_key_values_length, seq_len + past_key_values_length, delta=1, name="range")
+ return tf.gather(self.weight, position_ids)
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with Bart->Marian
+class TFMarianAttention(keras.layers.Layer):
+ """Multi-headed attention from "Attention Is All You Need"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: float = 0.0,
+ is_decoder: bool = False,
+ bias: bool = True,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.embed_dim = embed_dim
+
+ self.num_heads = num_heads
+ self.dropout = keras.layers.Dropout(dropout)
+ self.head_dim = embed_dim // num_heads
+ if (self.head_dim * num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+
+ self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
+ self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
+ self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
+ self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
+
+ def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
+ return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ key_value_states: tf.Tensor | None = None,
+ past_key_value: tuple[tuple[tf.Tensor]] | None = None,
+ attention_mask: tf.Tensor | None = None,
+ layer_head_mask: tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple[tf.Tensor, tf.Tensor | None]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+ bsz, tgt_len, embed_dim = shape_list(hidden_states)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states) * self.scaling
+ # get key, value proj
+ if is_cross_attention and past_key_value is not None:
+ # reuse k,v, cross_attentions
+ key_states = past_key_value[0]
+ value_states = past_key_value[1]
+ elif is_cross_attention:
+ # cross_attentions
+ key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
+ value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
+ elif past_key_value is not None:
+ # reuse k, v, self_attention
+ key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+ value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+ key_states = tf.concat([past_key_value[0], key_states], axis=2)
+ value_states = tf.concat([past_key_value[1], value_states], axis=2)
+ else:
+ # self_attention
+ key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+ value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+
+ if self.is_decoder:
+ # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
+ # Further calls to cross_attention layer can then reuse all cross-attention
+ # key/value_states (first "if" case)
+ # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
+ # all previous decoder key/value_states. Further calls to uni-directional self-attention
+ # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+ # if encoder bi-directional self-attention `past_key_value` is always `None`
+ past_key_value = (key_states, value_states)
+
+ proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+ query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
+ key_states = tf.reshape(key_states, proj_shape)
+ value_states = tf.reshape(value_states, proj_shape)
+
+ src_len = shape_list(key_states)[1]
+ attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
+
+ tf.debugging.assert_equal(
+ shape_list(attn_weights),
+ [bsz * self.num_heads, tgt_len, src_len],
+ message=(
+ f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
+ f" {shape_list(attn_weights)}"
+ ),
+ )
+
+ if attention_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(attention_mask),
+ [bsz, 1, tgt_len, src_len],
+ message=(
+ f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
+ f" {shape_list(attention_mask)}"
+ ),
+ )
+
+ attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
+ attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
+ attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
+
+ attn_weights = stable_softmax(attn_weights, axis=-1)
+
+ if layer_head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(layer_head_mask),
+ [self.num_heads],
+ message=(
+ f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
+ f" {shape_list(layer_head_mask)}"
+ ),
+ )
+
+ attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
+ attn_weights, (bsz, self.num_heads, tgt_len, src_len)
+ )
+ attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
+
+ attn_probs = self.dropout(attn_weights, training=training)
+ attn_output = tf.matmul(attn_probs, value_states)
+
+ tf.debugging.assert_equal(
+ shape_list(attn_output),
+ [bsz * self.num_heads, tgt_len, self.head_dim],
+ message=(
+ f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
+ f" {shape_list(attn_output)}"
+ ),
+ )
+
+ attn_output = tf.transpose(
+ tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
+ )
+ attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
+
+ attn_output = self.out_proj(attn_output)
+ attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
+
+ return attn_output, attn_weights, past_key_value
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "k_proj", None) is not None:
+ with tf.name_scope(self.k_proj.name):
+ self.k_proj.build([None, None, self.embed_dim])
+ if getattr(self, "q_proj", None) is not None:
+ with tf.name_scope(self.q_proj.name):
+ self.q_proj.build([None, None, self.embed_dim])
+ if getattr(self, "v_proj", None) is not None:
+ with tf.name_scope(self.v_proj.name):
+ self.v_proj.build([None, None, self.embed_dim])
+ if getattr(self, "out_proj", None) is not None:
+ with tf.name_scope(self.out_proj.name):
+ self.out_proj.build([None, None, self.embed_dim])
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.TFBartEncoderLayer with Bart->Marian
+class TFMarianEncoderLayer(keras.layers.Layer):
+ def __init__(self, config: MarianConfig, **kwargs):
+ super().__init__(**kwargs)
+ self.embed_dim = config.d_model
+ self.self_attn = TFMarianAttention(
+ self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn"
+ )
+ self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
+ self.dropout = keras.layers.Dropout(config.dropout)
+ self.activation_fn = get_tf_activation(config.activation_function)
+ self.activation_dropout = keras.layers.Dropout(config.activation_dropout)
+ self.fc1 = keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
+ self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2")
+ self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+ self.config = config
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: np.ndarray | tf.Tensor | None,
+ layer_head_mask: tf.Tensor | None,
+ training: bool | None = False,
+ ) -> tf.Tensor:
+ """
+ Args:
+ hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`tf.Tensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`
+ """
+ residual = hidden_states
+ hidden_states, self_attn_weights, _ = self.self_attn(
+ hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask
+ )
+
+ tf.debugging.assert_equal(
+ shape_list(hidden_states),
+ shape_list(residual),
+ message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
+ )
+
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = self.activation_dropout(hidden_states, training=training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ return hidden_states, self_attn_weights
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self_attn", None) is not None:
+ with tf.name_scope(self.self_attn.name):
+ self.self_attn.build(None)
+ if getattr(self, "self_attn_layer_norm", None) is not None:
+ with tf.name_scope(self.self_attn_layer_norm.name):
+ self.self_attn_layer_norm.build([None, None, self.embed_dim])
+ if getattr(self, "fc1", None) is not None:
+ with tf.name_scope(self.fc1.name):
+ self.fc1.build([None, None, self.embed_dim])
+ if getattr(self, "fc2", None) is not None:
+ with tf.name_scope(self.fc2.name):
+ self.fc2.build([None, None, self.config.encoder_ffn_dim])
+ if getattr(self, "final_layer_norm", None) is not None:
+ with tf.name_scope(self.final_layer_norm.name):
+ self.final_layer_norm.build([None, None, self.embed_dim])
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.TFBartDecoderLayer with Bart->Marian
+class TFMarianDecoderLayer(keras.layers.Layer):
+ def __init__(self, config: MarianConfig, **kwargs):
+ super().__init__(**kwargs)
+ self.embed_dim = config.d_model
+ self.self_attn = TFMarianAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ name="self_attn",
+ is_decoder=True,
+ )
+ self.dropout = keras.layers.Dropout(config.dropout)
+ self.activation_fn = get_tf_activation(config.activation_function)
+ self.activation_dropout = keras.layers.Dropout(config.activation_dropout)
+
+ self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
+ self.encoder_attn = TFMarianAttention(
+ self.embed_dim,
+ config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ name="encoder_attn",
+ is_decoder=True,
+ )
+ self.encoder_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
+ self.fc1 = keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
+ self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2")
+ self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+ self.config = config
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
+ encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
+ layer_head_mask: tf.Tensor | None = None,
+ cross_attn_layer_head_mask: tf.Tensor | None = None,
+ past_key_value: tuple[tuple[np.ndarray | tf.Tensor]] | None = None,
+ training: bool | None = False,
+ ) -> tuple[tf.Tensor, tf.Tensor, tuple[tuple[tf.Tensor]]]:
+ """
+ Args:
+ hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`tf.Tensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ encoder_hidden_states (`tf.Tensor`):
+ cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
+ encoder_attention_mask (`tf.Tensor`): encoder attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
+ `(decoder_attention_heads,)`
+ cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-attention module.
+ `(decoder_attention_heads,)`
+ past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states
+ """
+ residual = hidden_states
+
+ # Self Attention
+ # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+ self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+ # add present self-attn cache to positions 1,2 of present_key_value tuple
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_value=self_attn_past_key_value,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ )
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Cross-Attention Block
+ cross_attn_present_key_value = None
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+
+ # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
+ cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
+ hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ layer_head_mask=cross_attn_layer_head_mask,
+ past_key_value=cross_attn_past_key_value,
+ )
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ # add cross-attn to positions 3,4 of present_key_value tuple
+ present_key_value = present_key_value + cross_attn_present_key_value
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = self.activation_dropout(hidden_states, training=training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ return (
+ hidden_states,
+ self_attn_weights,
+ cross_attn_weights,
+ present_key_value,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self_attn", None) is not None:
+ with tf.name_scope(self.self_attn.name):
+ self.self_attn.build(None)
+ if getattr(self, "self_attn_layer_norm", None) is not None:
+ with tf.name_scope(self.self_attn_layer_norm.name):
+ self.self_attn_layer_norm.build([None, None, self.embed_dim])
+ if getattr(self, "encoder_attn", None) is not None:
+ with tf.name_scope(self.encoder_attn.name):
+ self.encoder_attn.build(None)
+ if getattr(self, "encoder_attn_layer_norm", None) is not None:
+ with tf.name_scope(self.encoder_attn_layer_norm.name):
+ self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+ if getattr(self, "fc1", None) is not None:
+ with tf.name_scope(self.fc1.name):
+ self.fc1.build([None, None, self.embed_dim])
+ if getattr(self, "fc2", None) is not None:
+ with tf.name_scope(self.fc2.name):
+ self.fc2.build([None, None, self.config.decoder_ffn_dim])
+ if getattr(self, "final_layer_norm", None) is not None:
+ with tf.name_scope(self.final_layer_norm.name):
+ self.final_layer_norm.build([None, None, self.embed_dim])
+
+
+class TFMarianPreTrainedModel(TFPreTrainedModel):
+ config_class = MarianConfig
+ base_model_prefix = "model"
+
+
+MARIAN_START_DOCSTRING = r"""
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Args:
+ config ([`MarianConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+MARIAN_GENERATION_EXAMPLE = r"""
+ TF version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints. Available
+ models are listed [here](https://huggingface.co/models?search=Helsinki-NLP).
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, TFMarianMTModel
+ >>> from typing import List
+
+ >>> src = "fr" # source language
+ >>> trg = "en" # target language
+ >>> sample_text = "où est l'arrêt de bus ?"
+ >>> model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"
+
+ >>> model = TFMarianMTModel.from_pretrained(model_name)
+ >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+ >>> batch = tokenizer([sample_text], return_tensors="tf")
+ >>> gen = model.generate(**batch)
+ >>> tokenizer.batch_decode(gen, skip_special_tokens=True)
+ "Where is the bus stop ?"
+ ```
+"""
+
+MARIAN_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`tf.Tensor` of shape `({0})`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `({0})`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ Marian uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+ decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
+ decoder_position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
+ range `[0, config.max_position_embeddings - 1]`.
+ head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ encoder_outputs (`tf.FloatTensor`, *optional*):
+ hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
+ of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
+ past_key_values (`tuple[tuple[tf.Tensor]]` of length `config.n_layers`)
+ contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`). Set to `False` during training, `True` during generation
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+ config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+ used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+ eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+"""
+
+
+@keras_serializable
+class TFMarianEncoder(keras.layers.Layer):
+ config_class = MarianConfig
+ """
+ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
+ [`TFMarianEncoderLayer`].
+
+ Args:
+ config: MarianConfig
+ """
+
+ def __init__(self, config: MarianConfig, embed_tokens: keras.layers.Embedding | None = None, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.dropout = keras.layers.Dropout(config.dropout)
+ self.layerdrop = config.encoder_layerdrop
+ self.padding_idx = config.pad_token_id
+ self.max_source_positions = config.max_position_embeddings
+ self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
+
+ self.embed_tokens = embed_tokens
+ self.embed_positions = TFMarianSinusoidalPositionalEmbedding(
+ config.max_position_embeddings,
+ config.d_model,
+ name="embed_positions",
+ )
+ self.layers = [TFMarianEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
+
+ def get_embed_tokens(self):
+ return self.embed_tokens
+
+ def set_embed_tokens(self, embed_tokens):
+ self.embed_tokens = embed_tokens
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ):
+ """
+ Args:
+ input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
+ in the config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail. This argument can be used only in eager mode, in graph mode the value in the config
+ will be used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
+ in eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+ """
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
+ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+ embed_pos = self.embed_positions(input_shape)
+ hidden_states = inputs_embeds + embed_pos
+ hidden_states = self.dropout(hidden_states, training=training)
+
+ # check attention mask and invert
+ if attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _expand_mask(attention_mask)
+ else:
+ attention_mask = None
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(head_mask)[0],
+ len(self.layers),
+ message=(
+ f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
+ f" {shape_list(head_mask)[0]}."
+ ),
+ )
+
+ # encoder layers
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = random.uniform(0, 1)
+ if training and (dropout_probability < self.layerdrop): # skip the layer
+ continue
+
+ hidden_states, attn = encoder_layer(
+ hidden_states,
+ attention_mask,
+ head_mask[idx] if head_mask is not None else None,
+ )
+
+ if output_attentions:
+ all_attentions += (attn,)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+ return TFBaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embed_positions", None) is not None:
+ with tf.name_scope(self.embed_positions.name):
+ self.embed_positions.build(None)
+ if getattr(self, "layers", None) is not None:
+ for layer in self.layers:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+@keras_serializable
+class TFMarianDecoder(keras.layers.Layer):
+ config_class = MarianConfig
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFMarianDecoderLayer`]
+
+ Args:
+ config: MarianConfig
+ embed_tokens: output embedding
+ """
+
+ def __init__(self, config: MarianConfig, embed_tokens: keras.layers.Embedding | None = None, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.padding_idx = config.pad_token_id
+ self.embed_tokens = embed_tokens
+ self.layerdrop = config.decoder_layerdrop
+ self.embed_positions = TFMarianSinusoidalPositionalEmbedding(
+ config.max_position_embeddings,
+ config.d_model,
+ name="embed_positions",
+ )
+ self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
+ self.layers = [TFMarianDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
+
+ self.dropout = keras.layers.Dropout(config.dropout)
+
+ def get_embed_tokens(self):
+ return self.embed_tokens
+
+ def set_embed_tokens(self, embed_tokens):
+ self.embed_tokens = embed_tokens
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ position_ids: tf.Tensor | None = None,
+ encoder_hidden_states: tf.Tensor | None = None,
+ encoder_attention_mask: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ cross_attn_head_mask: tf.Tensor | None = None,
+ past_key_values: tuple[tuple[tf.Tensor]] | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ):
+ r"""
+ Args:
+ input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
+ range `[0, config.max_position_embeddings - 1]`.
+ encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`tf.Tensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`tuple[tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+ Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
+ decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+ that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+ all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
+ in the config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail. This argument can be used only in eager mode, in graph mode the value in the config
+ will be used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
+ in eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+ """
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
+
+ # embed positions
+ if position_ids is None:
+ positions = self.embed_positions(input_shape, past_key_values_length)
+ else:
+ positions = self.embed_positions(input_shape, position_ids=position_ids)
+
+ if inputs_embeds is None:
+ check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
+ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+ hidden_states = inputs_embeds
+
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ if input_shape[-1] > 1:
+ combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
+ else:
+ combined_attention_mask = _expand_mask(
+ tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
+ )
+
+ if attention_mask is not None:
+ combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1])
+
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1])
+
+ hidden_states = self.dropout(hidden_states + positions, training=training)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None
+ present_key_values = () if use_cache else None
+
+ # check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired
+ for attn_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]:
+ if attn_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(attn_mask)[0],
+ len(self.layers),
+ message=(
+ f"The {attn_name} should be specified for {len(self.layers)} layers, but it is for"
+ f" {shape_list(attn_mask)[0]}."
+ ),
+ )
+
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ dropout_probability = random.uniform(0, 1)
+
+ if training and (dropout_probability < self.layerdrop):
+ continue
+
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+ hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
+ hidden_states,
+ attention_mask=combined_attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ layer_head_mask=head_mask[idx] if head_mask is not None else None,
+ cross_attn_layer_head_mask=cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
+ past_key_value=past_key_value,
+ )
+
+ if use_cache:
+ present_key_values += (present_key_value,)
+
+ if output_attentions:
+ all_self_attns += (layer_self_attn,)
+
+ if encoder_hidden_states is not None:
+ all_cross_attns += (layer_cross_attn,)
+
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns
+ else:
+ return TFBaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=present_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attns,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embed_positions", None) is not None:
+ with tf.name_scope(self.embed_positions.name):
+ self.embed_positions.build(None)
+ if getattr(self, "layers", None) is not None:
+ for layer in self.layers:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+@keras_serializable
+class TFMarianMainLayer(keras.layers.Layer):
+ config_class = MarianConfig
+
+ def __init__(self, config: MarianConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.shared = keras.layers.Embedding(
+ input_dim=config.vocab_size,
+ output_dim=config.d_model,
+ embeddings_initializer=keras.initializers.TruncatedNormal(stddev=self.config.init_std),
+ name="model.shared",
+ )
+ # Additional attribute to specify the expected name scope of the layer (for loading/storing weights)
+ self.shared.load_weight_prefix = "model.shared"
+
+ self.encoder = TFMarianEncoder(config, self.shared, name="encoder")
+ self.decoder = TFMarianDecoder(config, self.shared, name="decoder")
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, new_embeddings):
+ self.shared = new_embeddings
+ self.encoder.embed_tokens = self.shared
+ self.decoder.embed_tokens = self.shared
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ decoder_input_ids: tf.Tensor | None = None,
+ decoder_attention_mask: tf.Tensor | None = None,
+ decoder_position_ids: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ decoder_head_mask: tf.Tensor | None = None,
+ cross_attn_head_mask: tf.Tensor | None = None,
+ encoder_outputs: tuple | TFBaseModelOutput | None = None,
+ past_key_values: tuple[tuple[tf.Tensor]] | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ decoder_inputs_embeds: tf.Tensor | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ **kwargs,
+ ):
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ use_cache = False
+
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ # If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
+ elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput):
+ encoder_outputs = TFBaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+ # If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
+ elif not return_dict and not isinstance(encoder_outputs, tuple):
+ encoder_outputs = encoder_outputs.to_tuple()
+
+ decoder_outputs = self.decoder(
+ decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ position_ids=decoder_position_ids,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return TFSeq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ # The shared/tied weights expect to be in the model base namespace
+ # Adding "/" to the end (not the start!) of a tf.name_scope puts it in the root namespace rather than
+ # the current one.
+ with tf.name_scope(self.shared.load_weight_prefix + "/" + self.shared.name + "/"):
+ self.shared.build(None)
+ if getattr(self, "encoder", None) is not None:
+ with tf.name_scope(self.encoder.name):
+ self.encoder.build(None)
+ if getattr(self, "decoder", None) is not None:
+ with tf.name_scope(self.decoder.name):
+ self.decoder.build(None)
+
+
+@add_start_docstrings(
+ "The bare MARIAN Model outputting raw hidden-states without any specific head on top.",
+ MARIAN_START_DOCSTRING,
+)
+class TFMarianModel(TFMarianPreTrainedModel):
+ def __init__(self, config: MarianConfig, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.model = TFMarianMainLayer(config, name="model")
+
+ def get_encoder(self):
+ return self.model.encoder
+
+ def get_decoder(self):
+ return self.model.decoder
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFSeq2SeqModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ decoder_input_ids: tf.Tensor | None = None,
+ decoder_attention_mask: tf.Tensor | None = None,
+ decoder_position_ids: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ decoder_head_mask: tf.Tensor | None = None,
+ cross_attn_head_mask: tf.Tensor | None = None,
+ encoder_outputs: tf.Tensor | None = None,
+ past_key_values: tuple[tuple[tf.Tensor]] | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ decoder_inputs_embeds: tf.Tensor | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ **kwargs,
+ ) -> tuple[tf.Tensor] | TFSeq2SeqModelOutput:
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ decoder_position_ids=decoder_position_ids,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ encoder_outputs=encoder_outputs,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ return outputs
+
+ # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
+ def serving_output(self, output):
+ pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
+ dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
+ dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
+ cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
+ enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
+ enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
+
+ return TFSeq2SeqModelOutput(
+ last_hidden_state=output.last_hidden_state,
+ past_key_values=pkv,
+ decoder_hidden_states=dec_hs,
+ decoder_attentions=dec_attns,
+ cross_attentions=cross_attns,
+ encoder_last_hidden_state=output.encoder_last_hidden_state,
+ encoder_hidden_states=enc_hs,
+ encoder_attentions=enc_attns,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "model", None) is not None:
+ with tf.name_scope(self.model.name):
+ self.model.build(None)
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
+class BiasLayer(keras.layers.Layer):
+ """
+ Bias as a layer. It is used for serialization purposes: `keras.Model.save_weights` stores on a per-layer basis,
+ so all weights have to be registered in a layer.
+ """
+
+ def __init__(self, shape, initializer, trainable, name, **kwargs):
+ super().__init__(name=name, **kwargs)
+ # Note: the name of this variable will NOT be scoped when serialized, i.e. it will not be in the format of
+ # "outer_layer/inner_layer/.../name:0". Instead, it will be "name:0". For further details, see:
+ # https://github.com/huggingface/transformers/pull/18833#issuecomment-1233090214
+ self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable)
+
+ def call(self, x):
+ return x + self.bias
+
+
+@add_start_docstrings(
+ "The MARIAN Model with a language modeling head. Can be used for summarization.",
+ MARIAN_START_DOCSTRING,
+)
+class TFMarianMTModel(TFMarianPreTrainedModel, TFCausalLanguageModelingLoss):
+ _keys_to_ignore_on_load_unexpected = [
+ r"model.encoder.embed_tokens.weight",
+ r"model.decoder.embed_tokens.weight",
+ ]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.model = TFMarianMainLayer(config, name="model")
+ self.use_cache = config.use_cache
+ # final_bias_logits is registered as a buffer in pytorch, so not trainable for the sake of consistency.
+ self.bias_layer = BiasLayer(
+ name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
+ )
+
+ def get_decoder(self):
+ return self.model.decoder
+
+ def get_encoder(self):
+ return self.model.encoder
+
+ def get_output_embeddings(self):
+ return self.get_input_embeddings()
+
+ def set_output_embeddings(self, value):
+ self.set_input_embeddings(value)
+
+ def get_bias(self):
+ return {"final_logits_bias": self.bias_layer.bias}
+
+ def set_bias(self, value):
+ # Replaces the existing layers containing bias for correct (de)serialization.
+ vocab_size = value["final_logits_bias"].shape[-1]
+ self.bias_layer = BiasLayer(
+ name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False
+ )
+ self.bias_layer.bias.assign(value["final_logits_bias"])
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
+ @add_end_docstrings(MARIAN_GENERATION_EXAMPLE)
+ def call(
+ self,
+ input_ids: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ decoder_input_ids: tf.Tensor | None = None,
+ decoder_attention_mask: tf.Tensor | None = None,
+ decoder_position_ids: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ decoder_head_mask: tf.Tensor | None = None,
+ cross_attn_head_mask: tf.Tensor | None = None,
+ encoder_outputs: TFBaseModelOutput | None = None,
+ past_key_values: tuple[tuple[tf.Tensor]] | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ decoder_inputs_embeds: tf.Tensor | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> tuple[tf.Tensor] | TFSeq2SeqLMOutput:
+ r"""
+ labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Returns:
+
+ """
+
+ if labels is not None:
+ labels = tf.where(
+ labels == self.config.pad_token_id,
+ tf.fill(shape_list(labels), tf.cast(-100, labels.dtype)),
+ labels,
+ )
+ use_cache = False
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ decoder_input_ids = shift_tokens_right(
+ labels, self.config.pad_token_id, self.config.decoder_start_token_id
+ )
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ encoder_outputs=encoder_outputs,
+ decoder_attention_mask=decoder_attention_mask,
+ decoder_position_ids=decoder_position_ids,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ lm_logits = tf.matmul(outputs[0], self.model.shared.weights, transpose_b=True)
+ lm_logits = self.bias_layer(lm_logits)
+ masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+ return TFSeq2SeqLMOutput(
+ loss=masked_lm_loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values, # index 1 of d outputs
+ decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs
+ decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs
+ cross_attentions=outputs.cross_attentions, # index 4 of d outputs
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs
+ encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out
+ encoder_attentions=outputs.encoder_attentions, # 2 of e out
+ )
+
+ # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
+ def serving_output(self, output):
+ pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
+ dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
+ dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
+ cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
+ enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
+ enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
+
+ return TFSeq2SeqLMOutput(
+ logits=output.logits,
+ past_key_values=pkv,
+ decoder_hidden_states=dec_hs,
+ decoder_attentions=dec_attns,
+ cross_attentions=cross_attns,
+ encoder_last_hidden_state=output.encoder_last_hidden_state,
+ encoder_hidden_states=enc_hs,
+ encoder_attentions=enc_attns,
+ )
+
+ # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.prepare_inputs_for_generation
+ def prepare_inputs_for_generation(
+ self,
+ decoder_input_ids,
+ past_key_values=None,
+ attention_mask=None,
+ decoder_attention_mask=None,
+ head_mask=None,
+ decoder_head_mask=None,
+ cross_attn_head_mask=None,
+ use_cache=None,
+ encoder_outputs=None,
+ **kwargs,
+ ):
+ # cut decoder_input_ids if past_key_values is used
+ if past_key_values is not None:
+ decoder_input_ids = decoder_input_ids[:, -1:]
+
+ if decoder_attention_mask is not None: # xla
+ decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
+ elif past_key_values is not None: # no xla + past_key_values
+ decoder_position_ids = past_key_values[0][0].shape[2]
+ else: # no xla + no past_key_values
+ decoder_position_ids = tf.range(decoder_input_ids.shape[1])
+
+ return {
+ "input_ids": None, # encoder_outputs is defined. input_ids not needed
+ "encoder_outputs": encoder_outputs,
+ "past_key_values": past_key_values,
+ "decoder_input_ids": decoder_input_ids,
+ "attention_mask": attention_mask,
+ "decoder_attention_mask": decoder_attention_mask,
+ "decoder_position_ids": decoder_position_ids,
+ "head_mask": head_mask,
+ "decoder_head_mask": decoder_head_mask,
+ "cross_attn_head_mask": cross_attn_head_mask,
+ "use_cache": use_cache, # change this to avoid caching (presumably for debugging)
+ }
+
+ def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
+ return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "model", None) is not None:
+ with tf.name_scope(self.model.name):
+ self.model.build(None)
+ if getattr(self, "bias_layer", None) is not None:
+ with tf.name_scope(self.bias_layer.name):
+ self.bias_layer.build(None)
+
+
+__all__ = ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/tokenization_marian.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/tokenization_marian.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef8e1537b99dd46e890c5200699cf82f20c5cff0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/marian/tokenization_marian.py
@@ -0,0 +1,396 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import json
+import os
+import warnings
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Optional, Union
+
+import sentencepiece
+
+from ...tokenization_utils import PreTrainedTokenizer
+from ...utils import logging
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {
+ "source_spm": "source.spm",
+ "target_spm": "target.spm",
+ "vocab": "vocab.json",
+ "target_vocab_file": "target_vocab.json",
+ "tokenizer_config_file": "tokenizer_config.json",
+}
+
+
+SPIECE_UNDERLINE = "▁"
+
+# Example URL https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json
+
+
+@requires(backends=("sentencepiece",))
+class MarianTokenizer(PreTrainedTokenizer):
+ r"""
+ Construct a Marian tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ source_spm (`str`):
+ [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
+ contains the vocabulary for the source language.
+ target_spm (`str`):
+ [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
+ contains the vocabulary for the target language.
+ source_lang (`str`, *optional*):
+ A string representing the source language.
+ target_lang (`str`, *optional*):
+ A string representing the target language.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ model_max_length (`int`, *optional*, defaults to 512):
+ The maximum sentence length the model accepts.
+ additional_special_tokens (`list[str]`, *optional*, defaults to `["", ""]`):
+ Additional special tokens used by the tokenizer.
+ sp_model_kwargs (`dict`, *optional*):
+ Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+ SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+ to set:
+
+ - `enable_sampling`: Enable subword regularization.
+ - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+ - `nbest_size = {0,1}`: No sampling is performed.
+ - `nbest_size > 1`: samples from the nbest_size results.
+ - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+ using forward-filtering-and-backward-sampling algorithm.
+
+ - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+ BPE-dropout.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MarianForCausalLM, MarianTokenizer
+
+ >>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+ >>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
+ >>> src_texts = ["I am a small frog.", "Tom asked his teacher for advice."]
+ >>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."] # optional
+ >>> inputs = tokenizer(src_texts, text_target=tgt_texts, return_tensors="pt", padding=True)
+
+ >>> outputs = model(**inputs) # should work
+ ```"""
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ def __init__(
+ self,
+ source_spm,
+ target_spm,
+ vocab,
+ target_vocab_file=None,
+ source_lang=None,
+ target_lang=None,
+ unk_token="",
+ eos_token=" ",
+ pad_token="",
+ model_max_length=512,
+ sp_model_kwargs: Optional[dict[str, Any]] = None,
+ separate_vocabs=False,
+ **kwargs,
+ ) -> None:
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+ assert Path(source_spm).exists(), f"cannot find spm source {source_spm}"
+
+ self.separate_vocabs = separate_vocabs
+ self.encoder = load_json(vocab)
+ if str(unk_token) not in self.encoder:
+ raise KeyError(" token must be in the vocab")
+ assert str(pad_token) in self.encoder
+
+ if separate_vocabs:
+ self.target_encoder = load_json(target_vocab_file)
+ self.decoder = {v: k for k, v in self.target_encoder.items()}
+ self.supported_language_codes = []
+ else:
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ self.supported_language_codes: list = [k for k in self.encoder if k.startswith(">>") and k.endswith("<<")]
+
+ self.source_lang = source_lang
+ self.target_lang = target_lang
+ self.spm_files = [source_spm, target_spm]
+
+ # load SentencePiece model for pre-processing
+ self.spm_source = load_spm(source_spm, self.sp_model_kwargs)
+ self.spm_target = load_spm(target_spm, self.sp_model_kwargs)
+ self.current_spm = self.spm_source
+ self.current_encoder = self.encoder
+
+ # Multilingual target side: default to using first supported language code.
+
+ self._setup_normalizer()
+
+ super().__init__(
+ # bos_token=bos_token, unused. Start decoding with config.decoder_start_token_id
+ source_lang=source_lang,
+ target_lang=target_lang,
+ unk_token=unk_token,
+ eos_token=eos_token,
+ pad_token=pad_token,
+ model_max_length=model_max_length,
+ sp_model_kwargs=self.sp_model_kwargs,
+ target_vocab_file=target_vocab_file,
+ separate_vocabs=separate_vocabs,
+ **kwargs,
+ )
+
+ def _setup_normalizer(self):
+ try:
+ from sacremoses import MosesPunctNormalizer
+
+ self.punc_normalizer = MosesPunctNormalizer(self.source_lang).normalize
+ except (ImportError, FileNotFoundError):
+ warnings.warn("Recommended: pip install sacremoses.")
+ self.punc_normalizer = lambda x: x
+
+ def normalize(self, x: str) -> str:
+ """Cover moses empty string edge case. They return empty list for '' input!"""
+ return self.punc_normalizer(x) if x else ""
+
+ def _convert_token_to_id(self, token):
+ return self.current_encoder.get(token, self.current_encoder[self.unk_token])
+
+ def remove_language_code(self, text: str):
+ """Remove language codes like >>fr<< before sentencepiece"""
+ code = []
+ if text.startswith(">>") and (end_loc := text.find("<<")) != -1:
+ code.append(text[: end_loc + 2])
+ text = text[end_loc + 2 :]
+ return code, text
+
+ def _tokenize(self, text: str) -> list[str]:
+ code, text = self.remove_language_code(text)
+ pieces = self.current_spm.encode(text, out_type=str)
+ return code + pieces
+
+ def _convert_id_to_token(self, index: int) -> str:
+ """Converts an index (integer) in a token (str) using the decoder."""
+ return self.decoder.get(index, self.unk_token)
+
+ def batch_decode(self, sequences, **kwargs):
+ """
+ Convert a list of lists of token ids into a list of strings by calling decode.
+
+ Args:
+ sequences (`Union[list[int], list[list[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
+ List of tokenized input ids. Can be obtained using the `__call__` method.
+ skip_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not to remove special tokens in the decoding.
+ clean_up_tokenization_spaces (`bool`, *optional*):
+ Whether or not to clean up the tokenization spaces. If `None`, will default to
+ `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
+ use_source_tokenizer (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence
+ problems).
+ kwargs (additional keyword arguments, *optional*):
+ Will be passed to the underlying model specific decode method.
+
+ Returns:
+ `list[str]`: The list of decoded sentences.
+ """
+ return super().batch_decode(sequences, **kwargs)
+
+ def decode(self, token_ids, **kwargs):
+ """
+ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
+ tokens and clean up tokenization spaces.
+
+ Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
+
+ Args:
+ token_ids (`Union[int, list[int], np.ndarray, torch.Tensor, tf.Tensor]`):
+ List of tokenized input ids. Can be obtained using the `__call__` method.
+ skip_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not to remove special tokens in the decoding.
+ clean_up_tokenization_spaces (`bool`, *optional*):
+ Whether or not to clean up the tokenization spaces. If `None`, will default to
+ `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
+ use_source_tokenizer (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence
+ problems).
+ kwargs (additional keyword arguments, *optional*):
+ Will be passed to the underlying model specific decode method.
+
+ Returns:
+ `str`: The decoded sentence.
+ """
+ return super().decode(token_ids, **kwargs)
+
+ def convert_tokens_to_string(self, tokens: list[str]) -> str:
+ """Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise"""
+ sp_model = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
+ current_sub_tokens = []
+ out_string = ""
+ for token in tokens:
+ # make sure that special tokens are not decoded using sentencepiece model
+ if token in self.all_special_tokens:
+ out_string += sp_model.decode_pieces(current_sub_tokens) + token + " "
+ current_sub_tokens = []
+ else:
+ current_sub_tokens.append(token)
+ out_string += sp_model.decode_pieces(current_sub_tokens)
+ out_string = out_string.replace(SPIECE_UNDERLINE, " ")
+ return out_string.strip()
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> list[int]:
+ """Build model inputs from a sequence by appending eos_token_id."""
+ if token_ids_1 is None:
+ return token_ids_0 + [self.eos_token_id]
+ # We don't expect to process pairs, but leave the pair logic for API consistency
+ return token_ids_0 + token_ids_1 + [self.eos_token_id]
+
+ def _switch_to_input_mode(self):
+ self.current_spm = self.spm_source
+ self.current_encoder = self.encoder
+
+ def _switch_to_target_mode(self):
+ self.current_spm = self.spm_target
+ if self.separate_vocabs:
+ self.current_encoder = self.target_encoder
+
+ @property
+ def vocab_size(self) -> int:
+ return len(self.encoder)
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ saved_files = []
+
+ if self.separate_vocabs:
+ out_src_vocab_file = os.path.join(
+ save_directory,
+ (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"],
+ )
+ out_tgt_vocab_file = os.path.join(
+ save_directory,
+ (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"],
+ )
+ save_json(self.encoder, out_src_vocab_file)
+ save_json(self.target_encoder, out_tgt_vocab_file)
+ saved_files.append(out_src_vocab_file)
+ saved_files.append(out_tgt_vocab_file)
+ else:
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]
+ )
+ save_json(self.encoder, out_vocab_file)
+ saved_files.append(out_vocab_file)
+
+ for spm_save_filename, spm_orig_path, spm_model in zip(
+ [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]],
+ self.spm_files,
+ [self.spm_source, self.spm_target],
+ ):
+ spm_save_path = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + spm_save_filename
+ )
+ if os.path.abspath(spm_orig_path) != os.path.abspath(spm_save_path) and os.path.isfile(spm_orig_path):
+ copyfile(spm_orig_path, spm_save_path)
+ saved_files.append(spm_save_path)
+ elif not os.path.isfile(spm_orig_path):
+ with open(spm_save_path, "wb") as fi:
+ content_spiece_model = spm_model.serialized_model_proto()
+ fi.write(content_spiece_model)
+ saved_files.append(spm_save_path)
+
+ return tuple(saved_files)
+
+ def get_vocab(self) -> dict:
+ return self.get_src_vocab()
+
+ def get_src_vocab(self):
+ return dict(self.encoder, **self.added_tokens_encoder)
+
+ def get_tgt_vocab(self):
+ return dict(self.target_encoder, **self.added_tokens_decoder)
+
+ def __getstate__(self) -> dict:
+ state = self.__dict__.copy()
+ state.update(
+ dict.fromkeys(["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"])
+ )
+ return state
+
+ def __setstate__(self, d: dict) -> None:
+ self.__dict__ = d
+
+ # for backward compatibility
+ if not hasattr(self, "sp_model_kwargs"):
+ self.sp_model_kwargs = {}
+
+ self.spm_source, self.spm_target = (load_spm(f, self.sp_model_kwargs) for f in self.spm_files)
+ self.current_spm = self.spm_source
+ self._setup_normalizer()
+
+ def num_special_tokens_to_add(self, *args, **kwargs):
+ """Just EOS"""
+ return 1
+
+ def _special_token_mask(self, seq):
+ all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp
+ all_special_ids.remove(self.unk_token_id) # is only sometimes special
+ return [1 if x in all_special_ids else 0 for x in seq]
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list, token_ids_1: Optional[list] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """Get list where entries are [1] if a token is [eos] or [pad] else 0."""
+ if already_has_special_tokens:
+ return self._special_token_mask(token_ids_0)
+ elif token_ids_1 is None:
+ return self._special_token_mask(token_ids_0) + [1]
+ else:
+ return self._special_token_mask(token_ids_0 + token_ids_1) + [1]
+
+
+def load_spm(path: str, sp_model_kwargs: dict[str, Any]) -> sentencepiece.SentencePieceProcessor:
+ spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs)
+ spm.Load(path)
+ return spm
+
+
+def save_json(data, path: str) -> None:
+ with open(path, "w") as f:
+ json.dump(data, f, indent=2)
+
+
+def load_json(path: str) -> Union[dict, list]:
+ with open(path, "r") as f:
+ return json.load(f)
+
+
+__all__ = ["MarianTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b03aa2e625637809438c4b47d035ddf6db26127
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/__init__.py
@@ -0,0 +1,31 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_markuplm import *
+ from .feature_extraction_markuplm import *
+ from .modeling_markuplm import *
+ from .processing_markuplm import *
+ from .tokenization_markuplm import *
+ from .tokenization_markuplm_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/configuration_markuplm.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/configuration_markuplm.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5945cb3307b12e59bdf96a253357baf6e6ee29c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/configuration_markuplm.py
@@ -0,0 +1,170 @@
+# coding=utf-8
+# Copyright 2021, The Microsoft Research Asia MarkupLM Team authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MarkupLM model configuration"""
+
+import warnings
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MarkupLMConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MarkupLMModel`]. It is used to instantiate a
+ MarkupLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the MarkupLM
+ [microsoft/markuplm-base](https://huggingface.co/microsoft/markuplm-base) architecture.
+
+ Configuration objects inherit from [`BertConfig`] and can be used to control the model outputs. Read the
+ documentation from [`BertConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 30522):
+ Vocabulary size of the MarkupLM model. Defines the different tokens that can be represented by the
+ *inputs_ids* passed to the forward method of [`MarkupLMModel`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimensionality of the encoder layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 2):
+ The vocabulary size of the `token_type_ids` passed into [`MarkupLMModel`].
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+ max_tree_id_unit_embeddings (`int`, *optional*, defaults to 1024):
+ The maximum value that the tree id unit embedding might ever use. Typically set this to something large
+ just in case (e.g., 1024).
+ max_xpath_tag_unit_embeddings (`int`, *optional*, defaults to 256):
+ The maximum value that the xpath tag unit embedding might ever use. Typically set this to something large
+ just in case (e.g., 256).
+ max_xpath_subs_unit_embeddings (`int`, *optional*, defaults to 1024):
+ The maximum value that the xpath subscript unit embedding might ever use. Typically set this to something
+ large just in case (e.g., 1024).
+ tag_pad_id (`int`, *optional*, defaults to 216):
+ The id of the padding token in the xpath tags.
+ subs_pad_id (`int`, *optional*, defaults to 1001):
+ The id of the padding token in the xpath subscripts.
+ xpath_tag_unit_hidden_size (`int`, *optional*, defaults to 32):
+ The hidden size of each tree id unit. One complete tree index will have
+ (50*xpath_tag_unit_hidden_size)-dim.
+ max_depth (`int`, *optional*, defaults to 50):
+ The maximum depth in xpath.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MarkupLMModel, MarkupLMConfig
+
+ >>> # Initializing a MarkupLM microsoft/markuplm-base style configuration
+ >>> configuration = MarkupLMConfig()
+
+ >>> # Initializing a model from the microsoft/markuplm-base style configuration
+ >>> model = MarkupLMModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "markuplm"
+
+ def __init__(
+ self,
+ vocab_size=30522,
+ hidden_size=768,
+ num_hidden_layers=12,
+ num_attention_heads=12,
+ intermediate_size=3072,
+ hidden_act="gelu",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ type_vocab_size=2,
+ initializer_range=0.02,
+ layer_norm_eps=1e-12,
+ pad_token_id=0,
+ bos_token_id=0,
+ eos_token_id=2,
+ max_xpath_tag_unit_embeddings=256,
+ max_xpath_subs_unit_embeddings=1024,
+ tag_pad_id=216,
+ subs_pad_id=1001,
+ xpath_unit_hidden_size=32,
+ max_depth=50,
+ position_embedding_type="absolute",
+ use_cache=True,
+ classifier_dropout=None,
+ **kwargs,
+ ):
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ **kwargs,
+ )
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.hidden_act = hidden_act
+ self.intermediate_size = intermediate_size
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.max_position_embeddings = max_position_embeddings
+ self.type_vocab_size = type_vocab_size
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self._position_embedding_type = position_embedding_type
+ self.use_cache = use_cache
+ self.classifier_dropout = classifier_dropout
+ # additional properties
+ self.max_depth = max_depth
+ self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings
+ self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings
+ self.tag_pad_id = tag_pad_id
+ self.subs_pad_id = subs_pad_id
+ self.xpath_unit_hidden_size = xpath_unit_hidden_size
+
+ @property
+ def position_embedding_type(self):
+ warnings.warn(
+ "The `position_embedding_type` attribute is deprecated and will be removed in v4.55.",
+ FutureWarning,
+ )
+ return self._position_embedding_type
+
+ @position_embedding_type.setter
+ def position_embedding_type(self, value):
+ self._position_embedding_type = value
+
+
+__all__ = ["MarkupLMConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/feature_extraction_markuplm.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/feature_extraction_markuplm.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ea35b6da56f12f5f835b4da03e298aef344b297
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/feature_extraction_markuplm.py
@@ -0,0 +1,186 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Feature extractor class for MarkupLM.
+"""
+
+import html
+
+from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
+from ...utils import is_bs4_available, logging, requires_backends
+
+
+if is_bs4_available():
+ import bs4
+ from bs4 import BeautifulSoup
+
+
+logger = logging.get_logger(__name__)
+
+
+class MarkupLMFeatureExtractor(FeatureExtractionMixin):
+ r"""
+ Constructs a MarkupLM feature extractor. This can be used to get a list of nodes and corresponding xpaths from HTML
+ strings.
+
+ This feature extractor inherits from [`~feature_extraction_utils.PreTrainedFeatureExtractor`] which contains most
+ of the main methods. Users should refer to this superclass for more information regarding those methods.
+
+ """
+
+ def __init__(self, **kwargs):
+ requires_backends(self, ["bs4"])
+ super().__init__(**kwargs)
+
+ def xpath_soup(self, element):
+ xpath_tags = []
+ xpath_subscripts = []
+ child = element if element.name else element.parent
+ for parent in child.parents: # type: bs4.element.Tag
+ siblings = parent.find_all(child.name, recursive=False)
+ xpath_tags.append(child.name)
+ xpath_subscripts.append(
+ 0 if 1 == len(siblings) else next(i for i, s in enumerate(siblings, 1) if s is child)
+ )
+ child = parent
+ xpath_tags.reverse()
+ xpath_subscripts.reverse()
+ return xpath_tags, xpath_subscripts
+
+ def get_three_from_single(self, html_string):
+ html_code = BeautifulSoup(html_string, "html.parser")
+
+ all_doc_strings = []
+ string2xtag_seq = []
+ string2xsubs_seq = []
+
+ for element in html_code.descendants:
+ if isinstance(element, bs4.element.NavigableString):
+ if type(element.parent) is not bs4.element.Tag:
+ continue
+
+ text_in_this_tag = html.unescape(element).strip()
+ if not text_in_this_tag:
+ continue
+
+ all_doc_strings.append(text_in_this_tag)
+
+ xpath_tags, xpath_subscripts = self.xpath_soup(element)
+ string2xtag_seq.append(xpath_tags)
+ string2xsubs_seq.append(xpath_subscripts)
+
+ if len(all_doc_strings) != len(string2xtag_seq):
+ raise ValueError("Number of doc strings and xtags does not correspond")
+ if len(all_doc_strings) != len(string2xsubs_seq):
+ raise ValueError("Number of doc strings and xsubs does not correspond")
+
+ return all_doc_strings, string2xtag_seq, string2xsubs_seq
+
+ def construct_xpath(self, xpath_tags, xpath_subscripts):
+ xpath = ""
+ for tagname, subs in zip(xpath_tags, xpath_subscripts):
+ xpath += f"/{tagname}"
+ if subs != 0:
+ xpath += f"[{subs}]"
+ return xpath
+
+ def __call__(self, html_strings) -> BatchFeature:
+ """
+ Main method to prepare for the model one or several HTML strings.
+
+ Args:
+ html_strings (`str`, `list[str]`):
+ The HTML string or batch of HTML strings from which to extract nodes and corresponding xpaths.
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **nodes** -- Nodes.
+ - **xpaths** -- Corresponding xpaths.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MarkupLMFeatureExtractor
+
+ >>> page_name_1 = "page1.html"
+ >>> page_name_2 = "page2.html"
+ >>> page_name_3 = "page3.html"
+
+ >>> with open(page_name_1) as f:
+ ... single_html_string = f.read()
+
+ >>> feature_extractor = MarkupLMFeatureExtractor()
+
+ >>> # single example
+ >>> encoding = feature_extractor(single_html_string)
+ >>> print(encoding.keys())
+ >>> # dict_keys(['nodes', 'xpaths'])
+
+ >>> # batched example
+
+ >>> multi_html_strings = []
+
+ >>> with open(page_name_2) as f:
+ ... multi_html_strings.append(f.read())
+ >>> with open(page_name_3) as f:
+ ... multi_html_strings.append(f.read())
+
+ >>> encoding = feature_extractor(multi_html_strings)
+ >>> print(encoding.keys())
+ >>> # dict_keys(['nodes', 'xpaths'])
+ ```"""
+
+ # Input type checking for clearer error
+ valid_strings = False
+
+ # Check that strings has a valid type
+ if isinstance(html_strings, str):
+ valid_strings = True
+ elif isinstance(html_strings, (list, tuple)):
+ if len(html_strings) == 0 or isinstance(html_strings[0], str):
+ valid_strings = True
+
+ if not valid_strings:
+ raise ValueError(
+ "HTML strings must of type `str`, `list[str]` (batch of examples), "
+ f"but is of type {type(html_strings)}."
+ )
+
+ is_batched = isinstance(html_strings, (list, tuple)) and (isinstance(html_strings[0], str))
+
+ if not is_batched:
+ html_strings = [html_strings]
+
+ # Get nodes + xpaths
+ nodes = []
+ xpaths = []
+ for html_string in html_strings:
+ all_doc_strings, string2xtag_seq, string2xsubs_seq = self.get_three_from_single(html_string)
+ nodes.append(all_doc_strings)
+ xpath_strings = []
+ for node, tag_list, sub_list in zip(all_doc_strings, string2xtag_seq, string2xsubs_seq):
+ xpath_string = self.construct_xpath(tag_list, sub_list)
+ xpath_strings.append(xpath_string)
+ xpaths.append(xpath_strings)
+
+ # return as Dict
+ data = {"nodes": nodes, "xpaths": xpaths}
+ encoded_inputs = BatchFeature(data=data, tensor_type=None)
+
+ return encoded_inputs
+
+
+__all__ = ["MarkupLMFeatureExtractor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/modeling_markuplm.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/modeling_markuplm.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dd845ecff3cff62f6e83dbd2dcaa398916359ab
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/modeling_markuplm.py
@@ -0,0 +1,1057 @@
+# coding=utf-8
+# Copyright 2022 Microsoft Research Asia and the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MarkupLM model."""
+
+import os
+from typing import Callable, Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPooling,
+ MaskedLMOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import (
+ ALL_ATTENTION_FUNCTIONS,
+ PreTrainedModel,
+ apply_chunking_to_forward,
+ find_pruneable_heads_and_indices,
+ prune_linear_layer,
+)
+from ...utils import auto_docstring, can_return_tuple, logging
+from .configuration_markuplm import MarkupLMConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class XPathEmbeddings(nn.Module):
+ """Construct the embeddings from xpath tags and subscripts.
+
+ We drop tree-id in this version, as its info can be covered by xpath.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.max_depth = config.max_depth
+
+ self.xpath_unitseq2_embeddings = nn.Linear(config.xpath_unit_hidden_size * self.max_depth, config.hidden_size)
+
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ self.activation = nn.ReLU()
+ self.xpath_unitseq2_inner = nn.Linear(config.xpath_unit_hidden_size * self.max_depth, 4 * config.hidden_size)
+ self.inner2emb = nn.Linear(4 * config.hidden_size, config.hidden_size)
+
+ self.xpath_tag_sub_embeddings = nn.ModuleList(
+ [
+ nn.Embedding(config.max_xpath_tag_unit_embeddings, config.xpath_unit_hidden_size)
+ for _ in range(self.max_depth)
+ ]
+ )
+
+ self.xpath_subs_sub_embeddings = nn.ModuleList(
+ [
+ nn.Embedding(config.max_xpath_subs_unit_embeddings, config.xpath_unit_hidden_size)
+ for _ in range(self.max_depth)
+ ]
+ )
+
+ def forward(self, xpath_tags_seq=None, xpath_subs_seq=None):
+ xpath_tags_embeddings = []
+ xpath_subs_embeddings = []
+
+ for i in range(self.max_depth):
+ xpath_tags_embeddings.append(self.xpath_tag_sub_embeddings[i](xpath_tags_seq[:, :, i]))
+ xpath_subs_embeddings.append(self.xpath_subs_sub_embeddings[i](xpath_subs_seq[:, :, i]))
+
+ xpath_tags_embeddings = torch.cat(xpath_tags_embeddings, dim=-1)
+ xpath_subs_embeddings = torch.cat(xpath_subs_embeddings, dim=-1)
+
+ xpath_embeddings = xpath_tags_embeddings + xpath_subs_embeddings
+
+ xpath_embeddings = self.inner2emb(self.dropout(self.activation(self.xpath_unitseq2_inner(xpath_embeddings))))
+
+ return xpath_embeddings
+
+
+# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
+def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+ are ignored. This is modified from fairseq's `utils.make_positions`.
+
+ Args:
+ x: torch.Tensor x:
+
+ Returns: torch.Tensor
+ """
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+ mask = input_ids.ne(padding_idx).int()
+ incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
+ return incremental_indices.long() + padding_idx
+
+
+class MarkupLMEmbeddings(nn.Module):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+
+ self.max_depth = config.max_depth
+
+ self.xpath_embeddings = XPathEmbeddings(config)
+
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ self.register_buffer(
+ "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+ )
+
+ self.padding_idx = config.pad_token_id
+ self.position_embeddings = nn.Embedding(
+ config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
+ )
+
+ # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings.create_position_ids_from_inputs_embeds
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+ """
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+
+ Args:
+ inputs_embeds: torch.Tensor
+
+ Returns: torch.Tensor
+ """
+ input_shape = inputs_embeds.size()[:-1]
+ sequence_length = input_shape[1]
+
+ position_ids = torch.arange(
+ self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+ )
+ return position_ids.unsqueeze(0).expand(input_shape)
+
+ def forward(
+ self,
+ input_ids=None,
+ xpath_tags_seq=None,
+ xpath_subs_seq=None,
+ token_type_ids=None,
+ position_ids=None,
+ inputs_embeds=None,
+ past_key_values_length=0,
+ ):
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if position_ids is None:
+ if input_ids is not None:
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
+ position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length)
+ else:
+ position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+
+ # prepare xpath seq
+ if xpath_tags_seq is None:
+ xpath_tags_seq = self.config.tag_pad_id * torch.ones(
+ tuple(list(input_shape) + [self.max_depth]), dtype=torch.long, device=device
+ )
+ if xpath_subs_seq is None:
+ xpath_subs_seq = self.config.subs_pad_id * torch.ones(
+ tuple(list(input_shape) + [self.max_depth]), dtype=torch.long, device=device
+ )
+
+ words_embeddings = inputs_embeds
+ position_embeddings = self.position_embeddings(position_ids)
+
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ xpath_embeddings = self.xpath_embeddings(xpath_tags_seq, xpath_subs_seq)
+ embeddings = words_embeddings + position_embeddings + token_type_embeddings + xpath_embeddings
+
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->MarkupLM
+class MarkupLMSelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate
+class MarkupLMIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->MarkupLM
+class MarkupLMOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler
+class MarkupLMPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->MarkupLM
+class MarkupLMPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ if isinstance(config.hidden_act, str):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->MarkupLM
+class MarkupLMLMPredictionHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.transform = MarkupLMPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+ # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+ self.decoder.bias = self.bias
+
+ def _tie_weights(self):
+ self.decoder.bias = self.bias
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->MarkupLM
+class MarkupLMOnlyMLMHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.predictions = MarkupLMLMPredictionHead(config)
+
+ def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
+ prediction_scores = self.predictions(sequence_output)
+ return prediction_scores
+
+
+# Copied from transformers.models.align.modeling_align.eager_attention_forward
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ head_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+):
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+
+ if head_mask is not None:
+ attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
+
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.align.modeling_align.AlignTextSelfAttention with AlignText->MarkupLM
+class MarkupLMSelfAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+
+ self.config = config
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+ self.attention_dropout = config.attention_probs_dropout_prob
+ self.scaling = self.attention_head_size**-0.5
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ **kwargs,
+ ) -> tuple[torch.Tensor]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.attention_head_size)
+
+ query_states = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
+ key_states = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.value(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ head_mask=head_mask,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
+ return outputs
+
+
+# Copied from transformers.models.align.modeling_align.AlignTextAttention with AlignText->MarkupLM
+class MarkupLMAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.self = MarkupLMSelfAttention(config)
+ self.output = MarkupLMSelfOutput(config)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+ )
+
+ # Prune linear layers
+ self.self.query = prune_linear_layer(self.self.query, index)
+ self.self.key = prune_linear_layer(self.self.key, index)
+ self.self.value = prune_linear_layer(self.self.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+ self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ **kwargs,
+ ) -> tuple[torch.Tensor]:
+ self_outputs = self.self(
+ hidden_states,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ **kwargs,
+ )
+ attention_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.align.modeling_align.AlignTextLayer with AlignText->MarkupLM
+class MarkupLMLayer(GradientCheckpointingLayer):
+ def __init__(self, config):
+ super().__init__()
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = MarkupLMAttention(config)
+ self.intermediate = MarkupLMIntermediate(config)
+ self.output = MarkupLMOutput(config)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ **kwargs,
+ ) -> tuple[torch.Tensor]:
+ self_attention_outputs = self.attention(
+ hidden_states,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ **kwargs,
+ )
+ attention_output = self_attention_outputs[0]
+
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+ layer_output = apply_chunking_to_forward(
+ self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+ )
+ outputs = (layer_output,) + outputs
+
+ return outputs
+
+ def feed_forward_chunk(self, attention_output):
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+
+# Copied from transformers.models.align.modeling_align.AlignTextEncoder with AlignText->MarkupLM
+class MarkupLMEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList([MarkupLMLayer(config) for i in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ @can_return_tuple
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ output_hidden_states: Optional[bool] = False,
+ return_dict: Optional[bool] = True,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], BaseModelOutput]:
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+
+ layer_outputs = layer_module(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ **kwargs,
+ )
+
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+
+
+@auto_docstring
+class MarkupLMPreTrainedModel(PreTrainedModel):
+ config: MarkupLMConfig
+ base_model_prefix = "markuplm"
+
+ # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights with Bert->MarkupLM
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, nn.Linear):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, MarkupLMLMPredictionHead):
+ module.bias.data.zero_()
+
+ @classmethod
+ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
+ return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+
+
+@auto_docstring
+class MarkupLMModel(MarkupLMPreTrainedModel):
+ # Copied from transformers.models.clap.modeling_clap.ClapTextModel.__init__ with ClapText->MarkupLM
+ def __init__(self, config, add_pooling_layer=True):
+ r"""
+ add_pooling_layer (bool, *optional*, defaults to `True`):
+ Whether to add a pooling layer
+ """
+ super().__init__(config)
+ self.config = config
+
+ self.embeddings = MarkupLMEmbeddings(config)
+ self.encoder = MarkupLMEncoder(config)
+
+ self.pooler = MarkupLMPooler(config) if add_pooling_layer else None
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ xpath_tags_seq: Optional[torch.LongTensor] = None,
+ xpath_subs_seq: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPooling]:
+ r"""
+ xpath_tags_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
+ Tag IDs for each token in the input sequence, padded up to config.max_depth.
+ xpath_subs_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
+ Subscript IDs for each token in the input sequence, padded up to config.max_depth.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, MarkupLMModel
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base")
+ >>> model = MarkupLMModel.from_pretrained("microsoft/markuplm-base")
+
+ >>> html_string = " Page Title "
+
+ >>> encoding = processor(html_string, return_tensors="pt")
+
+ >>> outputs = model(**encoding)
+ >>> last_hidden_states = outputs.last_hidden_state
+ >>> list(last_hidden_states.shape)
+ [1, 4, 768]
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones(input_shape, device=device)
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+ extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
+ extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+ if head_mask is not None:
+ if head_mask.dim() == 1:
+ head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+ head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
+ elif head_mask.dim() == 2:
+ head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
+ head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
+ else:
+ head_mask = [None] * self.config.num_hidden_layers
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ xpath_tags_seq=xpath_tags_seq,
+ xpath_subs_seq=xpath_subs_seq,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ )
+ encoder_outputs = self.encoder(
+ embedding_output,
+ extended_attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring
+class MarkupLMForQuestionAnswering(MarkupLMPreTrainedModel):
+ # Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with bert->markuplm, Bert->MarkupLM
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.markuplm = MarkupLMModel(config, add_pooling_layer=False)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ xpath_tags_seq: Optional[torch.Tensor] = None,
+ xpath_subs_seq: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ start_positions: Optional[torch.Tensor] = None,
+ end_positions: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+ r"""
+ xpath_tags_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
+ Tag IDs for each token in the input sequence, padded up to config.max_depth.
+ xpath_subs_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
+ Subscript IDs for each token in the input sequence, padded up to config.max_depth.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, MarkupLMForQuestionAnswering
+ >>> import torch
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base-finetuned-websrc")
+ >>> model = MarkupLMForQuestionAnswering.from_pretrained("microsoft/markuplm-base-finetuned-websrc")
+
+ >>> html_string = " My name is Niels "
+ >>> question = "What's his name?"
+
+ >>> encoding = processor(html_string, questions=question, return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**encoding)
+
+ >>> answer_start_index = outputs.start_logits.argmax()
+ >>> answer_end_index = outputs.end_logits.argmax()
+
+ >>> predict_answer_tokens = encoding.input_ids[0, answer_start_index : answer_end_index + 1]
+ >>> processor.decode(predict_answer_tokens).strip()
+ 'Niels'
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.markuplm(
+ input_ids,
+ xpath_tags_seq=xpath_tags_seq,
+ xpath_subs_seq=xpath_subs_seq,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions.clamp_(0, ignored_index)
+ end_positions.clamp_(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MarkupLM Model with a `token_classification` head on top.
+ """
+)
+class MarkupLMForTokenClassification(MarkupLMPreTrainedModel):
+ # Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with bert->markuplm, Bert->MarkupLM
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.markuplm = MarkupLMModel(config, add_pooling_layer=False)
+ classifier_dropout = (
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.dropout = nn.Dropout(classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ xpath_tags_seq: Optional[torch.Tensor] = None,
+ xpath_subs_seq: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], MaskedLMOutput]:
+ r"""
+ xpath_tags_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
+ Tag IDs for each token in the input sequence, padded up to config.max_depth.
+ xpath_subs_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
+ Subscript IDs for each token in the input sequence, padded up to config.max_depth.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, AutoModelForTokenClassification
+ >>> import torch
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base")
+ >>> processor.parse_html = False
+ >>> model = AutoModelForTokenClassification.from_pretrained("microsoft/markuplm-base", num_labels=7)
+
+ >>> nodes = ["hello", "world"]
+ >>> xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"]
+ >>> node_labels = [1, 2]
+ >>> encoding = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**encoding)
+
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.markuplm(
+ input_ids,
+ xpath_tags_seq=xpath_tags_seq,
+ xpath_subs_seq=xpath_subs_seq,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ sequence_output = outputs[0]
+ prediction_scores = self.classifier(sequence_output) # (batch_size, seq_length, node_type_size)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(
+ prediction_scores.view(-1, self.config.num_labels),
+ labels.view(-1),
+ )
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MarkupLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+ pooled output) e.g. for GLUE tasks.
+ """
+)
+class MarkupLMForSequenceClassification(MarkupLMPreTrainedModel):
+ # Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with bert->markuplm, Bert->MarkupLM
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.config = config
+
+ self.markuplm = MarkupLMModel(config)
+ classifier_dropout = (
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.dropout = nn.Dropout(classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ xpath_tags_seq: Optional[torch.Tensor] = None,
+ xpath_subs_seq: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
+ r"""
+ xpath_tags_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
+ Tag IDs for each token in the input sequence, padded up to config.max_depth.
+ xpath_subs_seq (`torch.LongTensor` of shape `(batch_size, sequence_length, config.max_depth)`, *optional*):
+ Subscript IDs for each token in the input sequence, padded up to config.max_depth.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, AutoModelForSequenceClassification
+ >>> import torch
+
+ >>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base")
+ >>> model = AutoModelForSequenceClassification.from_pretrained("microsoft/markuplm-base", num_labels=7)
+
+ >>> html_string = " Page Title "
+ >>> encoding = processor(html_string, return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**encoding)
+
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.markuplm(
+ input_ids,
+ xpath_tags_seq=xpath_tags_seq,
+ xpath_subs_seq=xpath_subs_seq,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ pooled_output = outputs[1]
+
+ pooled_output = self.dropout(pooled_output)
+ logits = self.classifier(pooled_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "MarkupLMForQuestionAnswering",
+ "MarkupLMForSequenceClassification",
+ "MarkupLMForTokenClassification",
+ "MarkupLMModel",
+ "MarkupLMPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/processing_markuplm.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/processing_markuplm.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e822af6b2daa6f8366509b3c29644411ac44f00
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/processing_markuplm.py
@@ -0,0 +1,150 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for MarkupLM.
+"""
+
+from typing import Optional, Union
+
+from ...file_utils import TensorType
+from ...processing_utils import ProcessorMixin
+from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
+
+
+class MarkupLMProcessor(ProcessorMixin):
+ r"""
+ Constructs a MarkupLM processor which combines a MarkupLM feature extractor and a MarkupLM tokenizer into a single
+ processor.
+
+ [`MarkupLMProcessor`] offers all the functionalities you need to prepare data for the model.
+
+ It first uses [`MarkupLMFeatureExtractor`] to extract nodes and corresponding xpaths from one or more HTML strings.
+ Next, these are provided to [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`], which turns them into token-level
+ `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and `xpath_subs_seq`.
+
+ Args:
+ feature_extractor (`MarkupLMFeatureExtractor`):
+ An instance of [`MarkupLMFeatureExtractor`]. The feature extractor is a required input.
+ tokenizer (`MarkupLMTokenizer` or `MarkupLMTokenizerFast`):
+ An instance of [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`]. The tokenizer is a required input.
+ parse_html (`bool`, *optional*, defaults to `True`):
+ Whether or not to use `MarkupLMFeatureExtractor` to parse HTML strings into nodes and corresponding xpaths.
+ """
+
+ feature_extractor_class = "MarkupLMFeatureExtractor"
+ tokenizer_class = ("MarkupLMTokenizer", "MarkupLMTokenizerFast")
+ parse_html = True
+
+ def __call__(
+ self,
+ html_strings=None,
+ nodes=None,
+ xpaths=None,
+ node_labels=None,
+ questions=None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ This method first forwards the `html_strings` argument to [`~MarkupLMFeatureExtractor.__call__`]. Next, it
+ passes the `nodes` and `xpaths` along with the additional arguments to [`~MarkupLMTokenizer.__call__`] and
+ returns the output.
+
+ Optionally, one can also provide a `text` argument which is passed along as first sequence.
+
+ Please refer to the docstring of the above two methods for more information.
+ """
+ # first, create nodes and xpaths
+ if self.parse_html:
+ if html_strings is None:
+ raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`")
+
+ if nodes is not None or xpaths is not None or node_labels is not None:
+ raise ValueError(
+ "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"
+ )
+
+ features = self.feature_extractor(html_strings)
+ nodes = features["nodes"]
+ xpaths = features["xpaths"]
+ else:
+ if html_strings is not None:
+ raise ValueError("You have passed HTML strings but `parse_html` is set to `False`.")
+ if nodes is None or xpaths is None:
+ raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`")
+
+ # # second, apply the tokenizer
+ if questions is not None and self.parse_html:
+ if isinstance(questions, str):
+ questions = [questions] # add batch dimension (as the feature extractor always adds a batch dimension)
+
+ encoded_inputs = self.tokenizer(
+ text=questions if questions is not None else nodes,
+ text_pair=nodes if questions is not None else None,
+ xpaths=xpaths,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ return_tensors=return_tensors,
+ **kwargs,
+ )
+
+ return encoded_inputs
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to TrOCRTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer
+ to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to TrOCRTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the
+ docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ return tokenizer_input_names
+
+
+__all__ = ["MarkupLMProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/tokenization_markuplm.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/tokenization_markuplm.py
new file mode 100644
index 0000000000000000000000000000000000000000..a090e11ec36dff560134c91ed8053423fe35b7bb
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/tokenization_markuplm.py
@@ -0,0 +1,1470 @@
+# coding=utf-8
+# Copyright Microsoft Research and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization class for MarkupLM."""
+
+import json
+import os
+from functools import lru_cache
+from typing import Optional, Union
+
+import regex as re
+
+from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...tokenization_utils_base import (
+ ENCODE_KWARGS_DOCSTRING,
+ BatchEncoding,
+ EncodedInput,
+ PreTokenizedInput,
+ TextInput,
+ TextInputPair,
+ TruncationStrategy,
+)
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
+
+
+MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
+ add_special_tokens (`bool`, *optional*, defaults to `True`):
+ Whether or not to encode the sequences with the special tokens relative to their model.
+ padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
+ Activates and controls padding. Accepts the following values:
+
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+ Activates and controls truncation. Accepts the following values:
+
+ - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
+ to the maximum acceptable input length for the model if that argument is not provided. This will
+ truncate token by token, removing a token from the longest sequence in the pair if a pair of
+ sequences (or a batch of pairs) is provided.
+ - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
+ greater than the model maximum admissible input size).
+ max_length (`int`, *optional*):
+ Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to
+ `None`, this will use the predefined model maximum length if a maximum length is required by one of the
+ truncation/padding parameters. If the model has no specific maximum input length (like XLNet)
+ truncation/padding to a maximum length will be deactivated.
+ stride (`int`, *optional*, defaults to 0):
+ If set to a number along with `max_length`, the overflowing tokens returned when
+ `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
+ returned to provide some overlap between truncated and overflowing sequences. The value of this
+ argument defines the number of overlapping tokens.
+ pad_to_multiple_of (`int`, *optional*):
+ If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
+ the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
+ return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+ If set, will return tensors instead of list of python integers. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return Numpy `np.ndarray` objects.
+"""
+
+
+@lru_cache
+def bytes_to_unicode():
+ """
+ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
+ characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large #
+ of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset
+ you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe
+ vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+ """
+ bs = (
+ list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
+ )
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8 + n)
+ n += 1
+ cs = [chr(n) for n in cs]
+ return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+ """
+ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length
+ strings).
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+class MarkupLMTokenizer(PreTrainedTokenizer):
+ r"""
+ Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). [`MarkupLMTokenizer`] can be used to
+ turn HTML strings into to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and
+ `xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods.
+ Users should refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ errors (`str`, *optional*, defaults to `"replace"`):
+ Paradigm to follow when decoding bytes to UTF-8. See
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ add_prefix_space (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+
+ def __init__(
+ self,
+ vocab_file,
+ merges_file,
+ tags_dict,
+ errors="replace",
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ add_prefix_space=False,
+ max_depth=50,
+ max_width=1000,
+ pad_width=1001,
+ pad_token_label=-100,
+ only_label_first_subword=True,
+ **kwargs,
+ ):
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+ sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
+ cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+ with open(vocab_file, encoding="utf-8") as vocab_handle:
+ self.encoder = json.load(vocab_handle)
+
+ self.tags_dict = tags_dict
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ self.errors = errors # how to handle errors in decoding
+ self.byte_encoder = bytes_to_unicode()
+ self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+ with open(merges_file, encoding="utf-8") as merges_handle:
+ bpe_merges = merges_handle.read().split("\n")[1:-1]
+ bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
+ self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+ self.cache = {}
+ self.add_prefix_space = add_prefix_space
+
+ # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
+ self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
+
+ # additional properties
+ self.max_depth = max_depth
+ self.max_width = max_width
+ self.pad_width = pad_width
+ self.unk_tag_id = len(self.tags_dict)
+ self.pad_tag_id = self.unk_tag_id + 1
+ self.pad_xpath_tags_seq = [self.pad_tag_id] * self.max_depth
+ self.pad_xpath_subs_seq = [self.pad_width] * self.max_depth
+
+ super().__init__(
+ vocab_file=vocab_file,
+ merges_file=merges_file,
+ tags_dict=tags_dict,
+ errors=errors,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ add_prefix_space=add_prefix_space,
+ max_depth=max_depth,
+ max_width=max_width,
+ pad_width=pad_width,
+ pad_token_label=pad_token_label,
+ only_label_first_subword=only_label_first_subword,
+ **kwargs,
+ )
+
+ self.pad_token_label = pad_token_label
+ self.only_label_first_subword = only_label_first_subword
+
+ def get_xpath_seq(self, xpath):
+ """
+ Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of
+ tag IDs and corresponding subscripts, taking into account max depth.
+ """
+ xpath_tags_list = []
+ xpath_subs_list = []
+
+ xpath_units = xpath.split("/")
+ for unit in xpath_units:
+ if not unit.strip():
+ continue
+ name_subs = unit.strip().split("[")
+ tag_name = name_subs[0]
+ sub = 0 if len(name_subs) == 1 else int(name_subs[1][:-1])
+ xpath_tags_list.append(self.tags_dict.get(tag_name, self.unk_tag_id))
+ xpath_subs_list.append(min(self.max_width, sub))
+
+ xpath_tags_list = xpath_tags_list[: self.max_depth]
+ xpath_subs_list = xpath_subs_list[: self.max_depth]
+ xpath_tags_list += [self.pad_tag_id] * (self.max_depth - len(xpath_tags_list))
+ xpath_subs_list += [self.pad_width] * (self.max_depth - len(xpath_subs_list))
+
+ return xpath_tags_list, xpath_subs_list
+
+ @property
+ def vocab_size(self):
+ return len(self.encoder)
+
+ def get_vocab(self):
+ vocab = self.encoder.copy()
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ def bpe(self, token):
+ if token in self.cache:
+ return self.cache[token]
+ word = tuple(token)
+ pairs = get_pairs(word)
+
+ if not pairs:
+ return token
+
+ while True:
+ bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+ if bigram not in self.bpe_ranks:
+ break
+ first, second = bigram
+ new_word = []
+ i = 0
+ while i < len(word):
+ try:
+ j = word.index(first, i)
+ except ValueError:
+ new_word.extend(word[i:])
+ break
+ else:
+ new_word.extend(word[i:j])
+ i = j
+
+ if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+ new_word.append(first + second)
+ i += 2
+ else:
+ new_word.append(word[i])
+ i += 1
+ new_word = tuple(new_word)
+ word = new_word
+ if len(word) == 1:
+ break
+ else:
+ pairs = get_pairs(word)
+ word = " ".join(word)
+ self.cache[token] = word
+ return word
+
+ def _tokenize(self, text):
+ """Tokenize a string."""
+ bpe_tokens = []
+ for token in re.findall(self.pat, text):
+ token = "".join(
+ self.byte_encoder[b] for b in token.encode("utf-8")
+ ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
+ bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
+ return bpe_tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.decoder.get(index)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ logger.warning(
+ "MarkupLM now does not support generative tasks, decoding is experimental and subject to change."
+ )
+ text = "".join(tokens)
+ text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
+ return text
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ merge_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
+ )
+
+ # save vocab_file
+ with open(vocab_file, "w", encoding="utf-8") as f:
+ f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+ # save merge_file
+ index = 0
+ with open(merge_file, "w", encoding="utf-8") as writer:
+ writer.write("#version: 0.2\n")
+ for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
+ " Please check that the tokenizer is not corrupted!"
+ )
+ index = token_index
+ writer.write(" ".join(bpe_tokens) + "\n")
+ index += 1
+
+ return vocab_file, merge_file
+
+ def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
+ add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
+ if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
+ text = " " + text
+ return (text, kwargs)
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A RoBERTa sequence has the following format:
+ - single sequence: ` X `
+ - pair of sequences: ` A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + token_ids_1 + sep
+
+ def build_xpath_tags_with_special_tokens(
+ self, xpath_tags_0: list[int], xpath_tags_1: Optional[list[int]] = None
+ ) -> list[int]:
+ pad = [self.pad_xpath_tags_seq]
+ if len(xpath_tags_1) == 0:
+ return pad + xpath_tags_0 + pad
+ return pad + xpath_tags_0 + pad + xpath_tags_1 + pad
+
+ def build_xpath_subs_with_special_tokens(
+ self, xpath_subs_0: list[int], xpath_subs_1: Optional[list[int]] = None
+ ) -> list[int]:
+ pad = [self.pad_xpath_subs_seq]
+ if len(xpath_subs_1) == 0:
+ return pad + xpath_subs_0 + pad
+ return pad + xpath_subs_0 + pad + xpath_subs_1 + pad
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Args:
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is None:
+ return [1] + ([0] * len(token_ids_0)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not
+ make use of token type ids, therefore a list of zeros is returned.
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + token_ids_1 + sep) * [0]
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def __call__(
+ self,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]],
+ text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
+ xpaths: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
+ node_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
+ sequences with node-level xpaths and optional labels.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
+ (nodes of a single example or questions of a batch of examples) or a list of list of strings (batch of
+ nodes).
+ text_pair (`list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
+ (pretokenized string).
+ xpaths (`list[list[int]]`, `list[list[list[int]]]`):
+ Node-level xpaths.
+ node_labels (`list[int]`, `list[list[int]]`, *optional*):
+ Node-level integer labels (for token classification tasks).
+ """
+
+ # Input type checking for clearer error
+ def _is_valid_text_input(t):
+ if isinstance(t, str):
+ # Strings are fine
+ return True
+ elif isinstance(t, (list, tuple)):
+ # List are fine as long as they are...
+ if len(t) == 0:
+ # ... empty
+ return True
+ elif isinstance(t[0], str):
+ # ... list of strings
+ return True
+ elif isinstance(t[0], (list, tuple)):
+ # ... list with an empty list or with a list of strings
+ return len(t[0]) == 0 or isinstance(t[0][0], str)
+ else:
+ return False
+ else:
+ return False
+
+ if text_pair is not None:
+ # in case text + text_pair are provided, text = questions, text_pair = nodes
+ if not _is_valid_text_input(text):
+ raise ValueError("text input must of type `str` (single example) or `list[str]` (batch of examples). ")
+ if not isinstance(text_pair, (list, tuple)):
+ raise ValueError(
+ "Nodes must be of type `list[str]` (single pretokenized example), "
+ "or `list[list[str]]` (batch of pretokenized examples)."
+ )
+ else:
+ # in case only text is provided => must be nodes
+ if not isinstance(text, (list, tuple)):
+ raise ValueError(
+ "Nodes must be of type `list[str]` (single pretokenized example), "
+ "or `list[list[str]]` (batch of pretokenized examples)."
+ )
+
+ if text_pair is not None:
+ is_batched = isinstance(text, (list, tuple))
+ else:
+ is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
+
+ nodes = text if text_pair is None else text_pair
+ assert xpaths is not None, "You must provide corresponding xpaths"
+ if is_batched:
+ assert len(nodes) == len(xpaths), "You must provide nodes and xpaths for an equal amount of examples"
+ for nodes_example, xpaths_example in zip(nodes, xpaths):
+ assert len(nodes_example) == len(xpaths_example), "You must provide as many nodes as there are xpaths"
+ else:
+ assert len(nodes) == len(xpaths), "You must provide as many nodes as there are xpaths"
+
+ if is_batched:
+ if text_pair is not None and len(text) != len(text_pair):
+ raise ValueError(
+ f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
+ f" {len(text_pair)}."
+ )
+ batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
+ is_pair = bool(text_pair is not None)
+ return self.batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ xpaths=xpaths,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+ else:
+ return self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ xpaths=xpaths,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ xpaths: Optional[list[list[list[int]]]] = None,
+ node_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ xpaths=xpaths,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def _batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ xpaths: Optional[list[list[list[int]]]] = None,
+ node_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast."
+ )
+
+ batch_outputs = self._batch_prepare_for_model(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ xpaths=xpaths,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=return_tensors,
+ verbose=verbose,
+ )
+
+ return BatchEncoding(batch_outputs)
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def _batch_prepare_for_model(
+ self,
+ batch_text_or_text_pairs,
+ is_pair: Optional[bool] = None,
+ xpaths: Optional[list[list[int]]] = None,
+ node_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
+ adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
+ manages a moving window (with user defined stride) for overflowing tokens.
+
+ Args:
+ batch_ids_pairs: list of tokenized input ids or input ids pairs
+ """
+
+ batch_outputs = {}
+ for idx, example in enumerate(zip(batch_text_or_text_pairs, xpaths)):
+ batch_text_or_text_pair, xpaths_example = example
+ outputs = self.prepare_for_model(
+ batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair,
+ batch_text_or_text_pair[1] if is_pair else None,
+ xpaths_example,
+ node_labels=node_labels[idx] if node_labels is not None else None,
+ add_special_tokens=add_special_tokens,
+ padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=None, # we pad in batch afterward
+ padding_side=None, # we pad in batch afterward
+ return_attention_mask=False, # we pad in batch afterward
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=None, # We convert the whole batch to tensors at the end
+ prepend_batch_axis=False,
+ verbose=verbose,
+ )
+
+ for key, value in outputs.items():
+ if key not in batch_outputs:
+ batch_outputs[key] = []
+ batch_outputs[key].append(value)
+
+ batch_outputs = self.pad(
+ batch_outputs,
+ padding=padding_strategy.value,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
+
+ return batch_outputs
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING)
+ def encode(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ xpaths: Optional[list[list[int]]] = None,
+ node_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> list[int]:
+ encoded_inputs = self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ xpaths=xpaths,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return encoded_inputs["input_ids"]
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ xpaths: Optional[list[list[int]]] = None,
+ node_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
+ `__call__` should be used instead.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
+ text_pair (`list[str]` or `list[int]`, *optional*):
+ Optional second sequence to be encoded. This can be a list of strings (nodes of a single example) or a
+ list of list of strings (nodes of a batch of examples).
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._encode_plus(
+ text=text,
+ xpaths=xpaths,
+ text_pair=text_pair,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def _encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ xpaths: Optional[list[list[int]]] = None,
+ node_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast. "
+ "More information on available tokenizers at "
+ "https://github.com/huggingface/transformers/pull/2674"
+ )
+
+ return self.prepare_for_model(
+ text=text,
+ text_pair=text_pair,
+ xpaths=xpaths,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding_strategy.value,
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ prepend_batch_axis=True,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ verbose=verbose,
+ )
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def prepare_for_model(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ xpaths: Optional[list[list[int]]] = None,
+ node_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ prepend_batch_axis: bool = False,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
+ truncates sequences if overflowing while taking into account the special tokens and manages a moving window
+ (with user defined stride) for overflowing tokens. Please Note, for *text_pair* different than `None` and
+ *truncation_strategy = longest_first* or `True`, it is not possible to return overflowing tokens. Such a
+ combination of arguments will raise an error.
+
+ Node-level `xpaths` are turned into token-level `xpath_tags_seq` and `xpath_subs_seq`. If provided, node-level
+ `node_labels` are turned into token-level `labels`. The node label is used for the first token of the node,
+ while remaining tokens are labeled with -100, such that they will be ignored by the loss function.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
+ text_pair (`list[str]` or `list[int]`, *optional*):
+ Optional second sequence to be encoded. This can be a list of strings (nodes of a single example) or a
+ list of list of strings (nodes of a batch of examples).
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ tokens = []
+ pair_tokens = []
+ xpath_tags_seq = []
+ xpath_subs_seq = []
+ pair_xpath_tags_seq = []
+ pair_xpath_subs_seq = []
+ labels = []
+
+ if text_pair is None:
+ if node_labels is None:
+ # CASE 1: web page classification (training + inference) + CASE 2: token classification (inference)
+ for word, xpath in zip(text, xpaths):
+ if len(word) < 1: # skip empty nodes
+ continue
+ word_tokens = self.tokenize(word)
+ tokens.extend(word_tokens)
+ xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath)
+ xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens))
+ xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens))
+ else:
+ # CASE 2: token classification (training)
+ for word, xpath, label in zip(text, xpaths, node_labels):
+ if len(word) < 1: # skip empty nodes
+ continue
+ word_tokens = self.tokenize(word)
+ tokens.extend(word_tokens)
+ xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath)
+ xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens))
+ xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens))
+ if self.only_label_first_subword:
+ # Use the real label id for the first token of the word, and padding ids for the remaining tokens
+ labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
+ else:
+ labels.extend([label] * len(word_tokens))
+ else:
+ # CASE 3: web page question answering (inference)
+ # text = question
+ # text_pair = nodes
+ tokens = self.tokenize(text)
+ xpath_tags_seq = [self.pad_xpath_tags_seq for _ in range(len(tokens))]
+ xpath_subs_seq = [self.pad_xpath_subs_seq for _ in range(len(tokens))]
+
+ for word, xpath in zip(text_pair, xpaths):
+ if len(word) < 1: # skip empty nodes
+ continue
+ word_tokens = self.tokenize(word)
+ pair_tokens.extend(word_tokens)
+ xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpath)
+ pair_xpath_tags_seq.extend([xpath_tags_list] * len(word_tokens))
+ pair_xpath_subs_seq.extend([xpath_subs_list] * len(word_tokens))
+
+ # Create ids + pair_ids
+ ids = self.convert_tokens_to_ids(tokens)
+ pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None
+
+ if (
+ return_overflowing_tokens
+ and truncation_strategy == TruncationStrategy.LONGEST_FIRST
+ and pair_ids is not None
+ ):
+ raise ValueError(
+ "Not possible to return overflowing tokens for pair of sequences with the "
+ "`longest_first`. Please select another truncation strategy than `longest_first`, "
+ "for instance `only_second` or `only_first`."
+ )
+
+ # Compute the total size of the returned encodings
+ pair = bool(pair_ids is not None)
+ len_ids = len(ids)
+ len_pair_ids = len(pair_ids) if pair else 0
+ total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
+
+ # Truncation: Handle max sequence length
+ overflowing_tokens = []
+ overflowing_xpath_tags_seq = []
+ overflowing_xpath_subs_seq = []
+ overflowing_labels = []
+ if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
+ (
+ ids,
+ xpath_tags_seq,
+ xpath_subs_seq,
+ pair_ids,
+ pair_xpath_tags_seq,
+ pair_xpath_subs_seq,
+ labels,
+ overflowing_tokens,
+ overflowing_xpath_tags_seq,
+ overflowing_xpath_subs_seq,
+ overflowing_labels,
+ ) = self.truncate_sequences(
+ ids,
+ xpath_tags_seq=xpath_tags_seq,
+ xpath_subs_seq=xpath_subs_seq,
+ pair_ids=pair_ids,
+ pair_xpath_tags_seq=pair_xpath_tags_seq,
+ pair_xpath_subs_seq=pair_xpath_subs_seq,
+ labels=labels,
+ num_tokens_to_remove=total_len - max_length,
+ truncation_strategy=truncation_strategy,
+ stride=stride,
+ )
+
+ if return_token_type_ids and not add_special_tokens:
+ raise ValueError(
+ "Asking to return token_type_ids while setting add_special_tokens to False "
+ "results in an undefined behavior. Please set add_special_tokens to True or "
+ "set return_token_type_ids to None."
+ )
+
+ # Load from model defaults
+ if return_token_type_ids is None:
+ return_token_type_ids = "token_type_ids" in self.model_input_names
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ encoded_inputs = {}
+
+ if return_overflowing_tokens:
+ encoded_inputs["overflowing_tokens"] = overflowing_tokens
+ encoded_inputs["overflowing_xpath_tags_seq"] = overflowing_xpath_tags_seq
+ encoded_inputs["overflowing_xpath_subs_seq"] = overflowing_xpath_subs_seq
+ encoded_inputs["overflowing_labels"] = overflowing_labels
+ encoded_inputs["num_truncated_tokens"] = total_len - max_length
+
+ # Add special tokens
+ if add_special_tokens:
+ sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
+ token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
+ xpath_tags_ids = self.build_xpath_tags_with_special_tokens(xpath_tags_seq, pair_xpath_tags_seq)
+ xpath_subs_ids = self.build_xpath_subs_with_special_tokens(xpath_subs_seq, pair_xpath_subs_seq)
+ if labels:
+ labels = [self.pad_token_label] + labels + [self.pad_token_label]
+ else:
+ sequence = ids + pair_ids if pair else ids
+ token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
+ xpath_tags_ids = xpath_tags_seq + pair_xpath_tags_seq if pair else xpath_tags_seq
+ xpath_subs_ids = xpath_subs_seq + pair_xpath_subs_seq if pair else xpath_subs_seq
+
+ # Build output dictionary
+ encoded_inputs["input_ids"] = sequence
+ encoded_inputs["xpath_tags_seq"] = xpath_tags_ids
+ encoded_inputs["xpath_subs_seq"] = xpath_subs_ids
+ if return_token_type_ids:
+ encoded_inputs["token_type_ids"] = token_type_ids
+ if return_special_tokens_mask:
+ if add_special_tokens:
+ encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
+ else:
+ encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
+
+ if labels:
+ encoded_inputs["labels"] = labels
+
+ # Check lengths
+ self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
+
+ # Padding
+ if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
+ encoded_inputs = self.pad(
+ encoded_inputs,
+ max_length=max_length,
+ padding=padding_strategy.value,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ if return_length:
+ encoded_inputs["length"] = len(encoded_inputs["input_ids"])
+
+ batch_outputs = BatchEncoding(
+ encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
+ )
+
+ return batch_outputs
+
+ def truncate_sequences(
+ self,
+ ids: list[int],
+ xpath_tags_seq: list[list[int]],
+ xpath_subs_seq: list[list[int]],
+ pair_ids: Optional[list[int]] = None,
+ pair_xpath_tags_seq: Optional[list[list[int]]] = None,
+ pair_xpath_subs_seq: Optional[list[list[int]]] = None,
+ labels: Optional[list[int]] = None,
+ num_tokens_to_remove: int = 0,
+ truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
+ stride: int = 0,
+ ) -> tuple[list[int], list[int], list[int]]:
+ """
+ Args:
+ Truncates a sequence pair in-place following the strategy.
+ ids (`list[int]`):
+ Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
+ `convert_tokens_to_ids` methods.
+ xpath_tags_seq (`list[list[int]]`):
+ XPath tag IDs of the first sequence.
+ xpath_subs_seq (`list[list[int]]`):
+ XPath sub IDs of the first sequence.
+ pair_ids (`list[int]`, *optional*):
+ Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
+ and `convert_tokens_to_ids` methods.
+ pair_xpath_tags_seq (`list[list[int]]`, *optional*):
+ XPath tag IDs of the second sequence.
+ pair_xpath_subs_seq (`list[list[int]]`, *optional*):
+ XPath sub IDs of the second sequence.
+ num_tokens_to_remove (`int`, *optional*, defaults to 0):
+ Number of tokens to remove using the truncation strategy.
+ truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to
+ `False`):
+ The strategy to follow for truncation. Can be:
+ - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will truncate
+ token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
+ batch of pairs) is provided.
+ - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+ maximum acceptable input length for the model if that argument is not provided. This will only
+ truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+ - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
+ than the model maximum admissible input size).
+ stride (`int`, *optional*, defaults to 0):
+ If set to a positive number, the overflowing tokens returned will contain some tokens from the main
+ sequence returned. The value of this argument defines the number of additional tokens.
+ Returns:
+ `tuple[list[int], list[int], list[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
+ overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair
+ of sequences (or a batch of pairs) is provided.
+ """
+ if num_tokens_to_remove <= 0:
+ return ids, xpath_tags_seq, xpath_subs_seq, pair_ids, pair_xpath_tags_seq, pair_xpath_subs_seq, [], [], []
+
+ if not isinstance(truncation_strategy, TruncationStrategy):
+ truncation_strategy = TruncationStrategy(truncation_strategy)
+
+ overflowing_tokens = []
+ overflowing_xpath_tags_seq = []
+ overflowing_xpath_subs_seq = []
+ overflowing_labels = []
+ if truncation_strategy == TruncationStrategy.ONLY_FIRST or (
+ truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None
+ ):
+ if len(ids) > num_tokens_to_remove:
+ window_len = min(len(ids), stride + num_tokens_to_remove)
+ overflowing_tokens = ids[-window_len:]
+ overflowing_xpath_tags_seq = xpath_tags_seq[-window_len:]
+ overflowing_xpath_subs_seq = xpath_subs_seq[-window_len:]
+ ids = ids[:-num_tokens_to_remove]
+ xpath_tags_seq = xpath_tags_seq[:-num_tokens_to_remove]
+ xpath_subs_seq = xpath_subs_seq[:-num_tokens_to_remove]
+ labels = labels[:-num_tokens_to_remove]
+ else:
+ error_msg = (
+ f"We need to remove {num_tokens_to_remove} to truncate the input "
+ f"but the first sequence has a length {len(ids)}. "
+ )
+ if truncation_strategy == TruncationStrategy.ONLY_FIRST:
+ error_msg = (
+ error_msg + "Please select another truncation strategy than "
+ f"{truncation_strategy}, for instance 'longest_first' or 'only_second'."
+ )
+ logger.error(error_msg)
+ elif truncation_strategy == TruncationStrategy.LONGEST_FIRST:
+ logger.warning(
+ "Be aware, overflowing tokens are not returned for the setting you have chosen,"
+ f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
+ "truncation strategy. So the returned list will always be empty even if some "
+ "tokens have been removed."
+ )
+ for _ in range(num_tokens_to_remove):
+ if pair_ids is None or len(ids) > len(pair_ids):
+ ids = ids[:-1]
+ xpath_tags_seq = xpath_tags_seq[:-1]
+ xpath_subs_seq = xpath_subs_seq[:-1]
+ labels = labels[:-1]
+ else:
+ pair_ids = pair_ids[:-1]
+ pair_xpath_tags_seq = pair_xpath_tags_seq[:-1]
+ pair_xpath_subs_seq = pair_xpath_subs_seq[:-1]
+ elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
+ if len(pair_ids) > num_tokens_to_remove:
+ window_len = min(len(pair_ids), stride + num_tokens_to_remove)
+ overflowing_tokens = pair_ids[-window_len:]
+ overflowing_xpath_tags_seq = pair_xpath_tags_seq[-window_len:]
+ overflowing_xpath_subs_seq = pair_xpath_subs_seq[-window_len:]
+ pair_ids = pair_ids[:-num_tokens_to_remove]
+ pair_xpath_tags_seq = pair_xpath_tags_seq[:-num_tokens_to_remove]
+ pair_xpath_subs_seq = pair_xpath_subs_seq[:-num_tokens_to_remove]
+ else:
+ logger.error(
+ f"We need to remove {num_tokens_to_remove} to truncate the input "
+ f"but the second sequence has a length {len(pair_ids)}. "
+ f"Please select another truncation strategy than {truncation_strategy}, "
+ "for instance 'longest_first' or 'only_first'."
+ )
+
+ return (
+ ids,
+ xpath_tags_seq,
+ xpath_subs_seq,
+ pair_ids,
+ pair_xpath_tags_seq,
+ pair_xpath_subs_seq,
+ labels,
+ overflowing_tokens,
+ overflowing_xpath_tags_seq,
+ overflowing_xpath_subs_seq,
+ overflowing_labels,
+ )
+
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ """
+ Args:
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+ encoded_inputs:
+ Dictionary of tokenized inputs (`list[int]`) or batch of tokenized inputs (`list[list[int]]`).
+ max_length: maximum length of the returned list and optionally padding length (see below).
+ Will truncate by taking into account the special tokens.
+ padding_strategy: PaddingStrategy to use for padding.
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
+ The tokenizer padding sides are defined in self.padding_side:
+ - 'left': pads on the left of the sequences
+ - 'right': pads on the right of the sequences
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta).
+ padding_side:
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask:
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+ """
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = len(required_input)
+
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+ # Initialize attention mask if not present.
+ if return_attention_mask and "attention_mask" not in encoded_inputs:
+ encoded_inputs["attention_mask"] = [1] * len(required_input)
+
+ if needs_to_be_padded:
+ difference = max_length - len(required_input)
+ padding_side = padding_side if padding_side is not None else self.padding_side
+ if padding_side == "right":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = (
+ encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
+ )
+ if "xpath_tags_seq" in encoded_inputs:
+ encoded_inputs["xpath_tags_seq"] = (
+ encoded_inputs["xpath_tags_seq"] + [self.pad_xpath_tags_seq] * difference
+ )
+ if "xpath_subs_seq" in encoded_inputs:
+ encoded_inputs["xpath_subs_seq"] = (
+ encoded_inputs["xpath_subs_seq"] + [self.pad_xpath_subs_seq] * difference
+ )
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
+ encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
+ elif padding_side == "left":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
+ "token_type_ids"
+ ]
+ if "xpath_tags_seq" in encoded_inputs:
+ encoded_inputs["xpath_tags_seq"] = [self.pad_xpath_tags_seq] * difference + encoded_inputs[
+ "xpath_tags_seq"
+ ]
+ if "xpath_subs_seq" in encoded_inputs:
+ encoded_inputs["xpath_subs_seq"] = [self.pad_xpath_subs_seq] * difference + encoded_inputs[
+ "xpath_subs_seq"
+ ]
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+ else:
+ raise ValueError("Invalid padding strategy:" + str(padding_side))
+
+ return encoded_inputs
+
+
+__all__ = ["MarkupLMTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/tokenization_markuplm_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/tokenization_markuplm_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..4033ef319ff8cbec11f6c29d075e59b741710513
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/markuplm/tokenization_markuplm_fast.py
@@ -0,0 +1,929 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fast tokenization class for MarkupLM. It overwrites 2 methods of the slow tokenizer class, namely _batch_encode_plus
+and _encode_plus, in which the Rust tokenizer is used.
+"""
+
+import json
+from functools import lru_cache
+from typing import Optional, Union
+
+from tokenizers import processors
+
+from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
+from ...tokenization_utils_base import (
+ ENCODE_KWARGS_DOCSTRING,
+ AddedToken,
+ BatchEncoding,
+ EncodedInput,
+ PreTokenizedInput,
+ TextInput,
+ TextInputPair,
+ TruncationStrategy,
+)
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import logging
+from .tokenization_markuplm import MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING, MarkupLMTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
+
+
+@lru_cache
+def bytes_to_unicode():
+ """
+ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
+ characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large #
+ of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset
+ you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe
+ vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+ """
+ bs = (
+ list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
+ )
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8 + n)
+ n += 1
+ cs = [chr(n) for n in cs]
+ return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+ """
+ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length
+ strings).
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+class MarkupLMTokenizerFast(PreTrainedTokenizerFast):
+ r"""
+ Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
+
+ [`MarkupLMTokenizerFast`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`,
+ `token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`. This tokenizer inherits from [`PreTrainedTokenizer`] which
+ contains most of the main methods.
+
+ Users should refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ errors (`str`, *optional*, defaults to `"replace"`):
+ Paradigm to follow when decoding bytes to UTF-8. See
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ add_prefix_space (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ slow_tokenizer_class = MarkupLMTokenizer
+
+ def __init__(
+ self,
+ vocab_file,
+ merges_file,
+ tags_dict,
+ tokenizer_file=None,
+ errors="replace",
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ add_prefix_space=False,
+ max_depth=50,
+ max_width=1000,
+ pad_width=1001,
+ pad_token_label=-100,
+ only_label_first_subword=True,
+ trim_offsets=False,
+ **kwargs,
+ ):
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+ sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
+ cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+ super().__init__(
+ vocab_file=vocab_file,
+ merges_file=merges_file,
+ tags_dict=tags_dict,
+ tokenizer_file=tokenizer_file,
+ errors=errors,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ add_prefix_space=add_prefix_space,
+ trim_offsets=trim_offsets,
+ max_depth=max_depth,
+ max_width=max_width,
+ pad_width=pad_width,
+ pad_token_label=pad_token_label,
+ only_label_first_subword=only_label_first_subword,
+ **kwargs,
+ )
+ if trim_offsets:
+ # Not implemented yet, because we need to chain two post processors which is not possible yet
+ # We need to wait for https://github.com/huggingface/tokenizers/pull/1005
+ # With `trim_offsets=False` we don't need to do add `processors.ByteLevel(trim_offsets=False)`
+ # because it's not doing anything
+ raise NotImplementedError(
+ "`trim_offsets=True` is not implemented for MarkupLMTokenizerFast. Please set it to False."
+ )
+
+ self.tags_dict = tags_dict
+
+ tokenizer_component = "post_processor"
+ tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
+ if tokenizer_component_instance:
+ state = json.loads(tokenizer_component_instance.__getstate__())
+
+ # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
+ if "sep" in state:
+ state["sep"] = tuple(state["sep"])
+ if "cls" in state:
+ state["cls"] = tuple(state["cls"])
+
+ changes_to_apply = False
+
+ if state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
+ state["add_prefix_space"] = add_prefix_space
+ changes_to_apply = True
+
+ if changes_to_apply:
+ component_class = getattr(processors, state.pop("type"))
+ new_value = component_class(**state)
+ setattr(self.backend_tokenizer, tokenizer_component, new_value)
+
+ # additional properties
+ self.max_depth = max_depth
+ self.max_width = max_width
+ self.pad_width = pad_width
+ self.unk_tag_id = len(self.tags_dict)
+ self.pad_tag_id = self.unk_tag_id + 1
+ self.pad_xpath_tags_seq = [self.pad_tag_id] * self.max_depth
+ self.pad_xpath_subs_seq = [self.pad_width] * self.max_depth
+ self.pad_token_label = pad_token_label
+ self.only_label_first_subword = only_label_first_subword
+
+ def get_xpath_seq(self, xpath):
+ """
+ Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of
+ tag IDs and corresponding subscripts, taking into account max depth.
+ """
+ xpath_tags_list = []
+ xpath_subs_list = []
+
+ xpath_units = xpath.split("/")
+ for unit in xpath_units:
+ if not unit.strip():
+ continue
+ name_subs = unit.strip().split("[")
+ tag_name = name_subs[0]
+ sub = 0 if len(name_subs) == 1 else int(name_subs[1][:-1])
+ xpath_tags_list.append(self.tags_dict.get(tag_name, self.unk_tag_id))
+ xpath_subs_list.append(min(self.max_width, sub))
+
+ xpath_tags_list = xpath_tags_list[: self.max_depth]
+ xpath_subs_list = xpath_subs_list[: self.max_depth]
+ xpath_tags_list += [self.pad_tag_id] * (self.max_depth - len(xpath_tags_list))
+ xpath_subs_list += [self.pad_width] * (self.max_depth - len(xpath_subs_list))
+
+ return xpath_tags_list, xpath_subs_list
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def __call__(
+ self,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]],
+ text_pair: Optional[Union[PreTokenizedInput, list[PreTokenizedInput]]] = None,
+ xpaths: Optional[Union[list[list[int]], list[list[list[int]]]]] = None,
+ node_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
+ sequences with nodes, xpaths and optional labels.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
+ (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
+ words).
+ text_pair (`list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
+ (pretokenized string).
+ xpaths (`list[list[int]]`, `list[list[list[int]]]`):
+ Node-level xpaths. Each bounding box should be normalized to be on a 0-1000 scale.
+ node_labels (`list[int]`, `list[list[int]]`, *optional*):
+ Node-level integer labels (for token classification tasks).
+ """
+
+ # Input type checking for clearer error
+ def _is_valid_text_input(t):
+ if isinstance(t, str):
+ # Strings are fine
+ return True
+ elif isinstance(t, (list, tuple)):
+ # List are fine as long as they are...
+ if len(t) == 0:
+ # ... empty
+ return True
+ elif isinstance(t[0], str):
+ # ... list of strings
+ return True
+ elif isinstance(t[0], (list, tuple)):
+ # ... list with an empty list or with a list of strings
+ return len(t[0]) == 0 or isinstance(t[0][0], str)
+ else:
+ return False
+ else:
+ return False
+
+ if text_pair is not None:
+ # in case text + text_pair are provided, text = questions, text_pair = nodes
+ if not _is_valid_text_input(text):
+ raise ValueError("text input must of type `str` (single example) or `list[str]` (batch of examples). ")
+ if not isinstance(text_pair, (list, tuple)):
+ raise ValueError(
+ "Nodes must be of type `list[str]` (single pretokenized example), "
+ "or `list[list[str]]` (batch of pretokenized examples)."
+ )
+ else:
+ # in case only text is provided => must be nodes
+ if not isinstance(text, (list, tuple)):
+ raise ValueError(
+ "Nodes must be of type `list[str]` (single pretokenized example), "
+ "or `list[list[str]]` (batch of pretokenized examples)."
+ )
+
+ if text_pair is not None:
+ is_batched = isinstance(text, (list, tuple))
+ else:
+ is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
+
+ nodes = text if text_pair is None else text_pair
+ assert xpaths is not None, "You must provide corresponding xpaths"
+ if is_batched:
+ assert len(nodes) == len(xpaths), "You must provide nodes and xpaths for an equal amount of examples"
+ for nodes_example, xpaths_example in zip(nodes, xpaths):
+ assert len(nodes_example) == len(xpaths_example), "You must provide as many nodes as there are xpaths"
+ else:
+ assert len(nodes) == len(xpaths), "You must provide as many nodes as there are xpaths"
+
+ if is_batched:
+ if text_pair is not None and len(text) != len(text_pair):
+ raise ValueError(
+ f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
+ f" {len(text_pair)}."
+ )
+ batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
+ is_pair = bool(text_pair is not None)
+ return self.batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ xpaths=xpaths,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+ else:
+ return self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ xpaths=xpaths,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ xpaths: Optional[list[list[list[int]]]] = None,
+ node_labels: Optional[Union[list[int], list[list[int]]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ is_pair=is_pair,
+ xpaths=xpaths,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> list[str]:
+ batched_input = [(text, pair)] if pair else [text]
+ encodings = self._tokenizer.encode_batch(
+ batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
+ )
+
+ return encodings[0].tokens
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, MARKUPLM_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ def encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ xpaths: Optional[list[list[int]]] = None,
+ node_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
+ `__call__` should be used instead.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
+ text_pair (`list[str]` or `list[int]`, *optional*):
+ Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
+ list of list of strings (words of a batch of examples).
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ return self._encode_plus(
+ text=text,
+ xpaths=xpaths,
+ text_pair=text_pair,
+ node_labels=node_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ def _batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[
+ list[TextInput],
+ list[TextInputPair],
+ list[PreTokenizedInput],
+ ],
+ is_pair: Optional[bool] = None,
+ xpaths: Optional[list[list[list[int]]]] = None,
+ node_labels: Optional[list[list[int]]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ ) -> BatchEncoding:
+ if not isinstance(batch_text_or_text_pairs, list):
+ raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})")
+
+ # Set the truncation and padding strategy and restore the initial configuration
+ self.set_truncation_and_padding(
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ )
+
+ if is_pair:
+ batch_text_or_text_pairs = [([text], text_pair) for text, text_pair in batch_text_or_text_pairs]
+
+ encodings = self._tokenizer.encode_batch(
+ batch_text_or_text_pairs,
+ add_special_tokens=add_special_tokens,
+ is_pretokenized=True, # we set this to True as MarkupLM always expects pretokenized inputs
+ )
+
+ # Convert encoding to dict
+ # `Tokens` is a tuple of (list[dict[str, list[list[int]]]] or list[dict[str, 2D-Tensor]],
+ # list[EncodingFast]) with nested dimensions corresponding to batch, overflows, sequence length
+ tokens_and_encodings = [
+ self._convert_encoding(
+ encoding=encoding,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=True
+ if node_labels is not None
+ else return_offsets_mapping, # we use offsets to create the labels
+ return_length=return_length,
+ verbose=verbose,
+ )
+ for encoding in encodings
+ ]
+
+ # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
+ # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
+ # (we say ~ because the number of overflow varies with the example in the batch)
+ #
+ # To match each overflowing sample with the original sample in the batch
+ # we add an overflow_to_sample_mapping array (see below)
+ sanitized_tokens = {}
+ for key in tokens_and_encodings[0][0]:
+ stack = [e for item, _ in tokens_and_encodings for e in item[key]]
+ sanitized_tokens[key] = stack
+ sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
+
+ # If returning overflowing tokens, we need to return a mapping
+ # from the batch idx to the original sample
+ if return_overflowing_tokens:
+ overflow_to_sample_mapping = []
+ for i, (toks, _) in enumerate(tokens_and_encodings):
+ overflow_to_sample_mapping += [i] * len(toks["input_ids"])
+ sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
+
+ for input_ids in sanitized_tokens["input_ids"]:
+ self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
+
+ # create the token-level xpaths tags and subscripts
+ xpath_tags_seq = []
+ xpath_subs_seq = []
+ for batch_index in range(len(sanitized_tokens["input_ids"])):
+ if return_overflowing_tokens:
+ original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
+ else:
+ original_index = batch_index
+ xpath_tags_seq_example = []
+ xpath_subs_seq_example = []
+ for id, sequence_id, word_id in zip(
+ sanitized_tokens["input_ids"][batch_index],
+ sanitized_encodings[batch_index].sequence_ids,
+ sanitized_encodings[batch_index].word_ids,
+ ):
+ if word_id is not None:
+ if is_pair and sequence_id == 0:
+ xpath_tags_seq_example.append(self.pad_xpath_tags_seq)
+ xpath_subs_seq_example.append(self.pad_xpath_subs_seq)
+ else:
+ xpath_tags_list, xpath_subs_list = self.get_xpath_seq(xpaths[original_index][word_id])
+ xpath_tags_seq_example.extend([xpath_tags_list])
+ xpath_subs_seq_example.extend([xpath_subs_list])
+ else:
+ if id in [self.cls_token_id, self.sep_token_id, self.pad_token_id]:
+ xpath_tags_seq_example.append(self.pad_xpath_tags_seq)
+ xpath_subs_seq_example.append(self.pad_xpath_subs_seq)
+ else:
+ raise ValueError("Id not recognized")
+ xpath_tags_seq.append(xpath_tags_seq_example)
+ xpath_subs_seq.append(xpath_subs_seq_example)
+
+ sanitized_tokens["xpath_tags_seq"] = xpath_tags_seq
+ sanitized_tokens["xpath_subs_seq"] = xpath_subs_seq
+
+ # optionally, create the labels
+ if node_labels is not None:
+ labels = []
+ for batch_index in range(len(sanitized_tokens["input_ids"])):
+ if return_overflowing_tokens:
+ original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
+ else:
+ original_index = batch_index
+ labels_example = []
+ for id, offset, word_id in zip(
+ sanitized_tokens["input_ids"][batch_index],
+ sanitized_tokens["offset_mapping"][batch_index],
+ sanitized_encodings[batch_index].word_ids,
+ ):
+ if word_id is not None:
+ if self.only_label_first_subword:
+ if offset[0] == 0:
+ # Use the real label id for the first token of the word, and padding ids for the remaining tokens
+ labels_example.append(node_labels[original_index][word_id])
+ else:
+ labels_example.append(self.pad_token_label)
+ else:
+ labels_example.append(node_labels[original_index][word_id])
+ else:
+ labels_example.append(self.pad_token_label)
+ labels.append(labels_example)
+
+ sanitized_tokens["labels"] = labels
+ # finally, remove offsets if the user didn't want them
+ if not return_offsets_mapping:
+ del sanitized_tokens["offset_mapping"]
+
+ return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
+
+ def _encode_plus(
+ self,
+ text: Union[TextInput, PreTokenizedInput],
+ text_pair: Optional[PreTokenizedInput] = None,
+ xpaths: Optional[list[list[int]]] = None,
+ node_labels: Optional[list[int]] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[bool] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ # make it a batched input
+ # 2 options:
+ # 1) only text, in case text must be a list of str
+ # 2) text + text_pair, in which case text = str and text_pair a list of str
+ batched_input = [(text, text_pair)] if text_pair else [text]
+ batched_xpaths = [xpaths]
+ batched_node_labels = [node_labels] if node_labels is not None else None
+ batched_output = self._batch_encode_plus(
+ batched_input,
+ is_pair=bool(text_pair is not None),
+ xpaths=batched_xpaths,
+ node_labels=batched_node_labels,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ # Return tensor is None, then we can remove the leading batch axis
+ # Overflowing tokens are returned as a batch of output so we keep them in this case
+ if return_tensors is None and not return_overflowing_tokens:
+ batched_output = BatchEncoding(
+ {
+ key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
+ for key, value in batched_output.items()
+ },
+ batched_output.encodings,
+ )
+
+ self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
+
+ return batched_output
+
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ """
+ Args:
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+ encoded_inputs:
+ Dictionary of tokenized inputs (`list[int]`) or batch of tokenized inputs (`list[list[int]]`).
+ max_length: maximum length of the returned list and optionally padding length (see below).
+ Will truncate by taking into account the special tokens.
+ padding_strategy: PaddingStrategy to use for padding.
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
+ The tokenizer padding sides are defined in self.padding_side:
+ - 'left': pads on the left of the sequences
+ - 'right': pads on the right of the sequences
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta).
+ padding_side:
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask:
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+ """
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = len(required_input)
+
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+ # Initialize attention mask if not present.
+ if return_attention_mask and "attention_mask" not in encoded_inputs:
+ encoded_inputs["attention_mask"] = [1] * len(required_input)
+
+ if needs_to_be_padded:
+ difference = max_length - len(required_input)
+ padding_side = padding_side if padding_side is not None else self.padding_side
+ if padding_side == "right":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = (
+ encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
+ )
+ if "xpath_tags_seq" in encoded_inputs:
+ encoded_inputs["xpath_tags_seq"] = (
+ encoded_inputs["xpath_tags_seq"] + [self.pad_xpath_tags_seq] * difference
+ )
+ if "xpath_subs_seq" in encoded_inputs:
+ encoded_inputs["xpath_subs_seq"] = (
+ encoded_inputs["xpath_subs_seq"] + [self.pad_xpath_subs_seq] * difference
+ )
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
+ encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
+ elif padding_side == "left":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
+ "token_type_ids"
+ ]
+ if "xpath_tags_seq" in encoded_inputs:
+ encoded_inputs["xpath_tags_seq"] = [self.pad_xpath_tags_seq] * difference + encoded_inputs[
+ "xpath_tags_seq"
+ ]
+ if "xpath_subs_seq" in encoded_inputs:
+ encoded_inputs["xpath_subs_seq"] = [self.pad_xpath_subs_seq] * difference + encoded_inputs[
+ "xpath_subs_seq"
+ ]
+ if "labels" in encoded_inputs:
+ encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+ else:
+ raise ValueError("Invalid padding strategy:" + str(padding_side))
+
+ return encoded_inputs
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A RoBERTa sequence has the following format:
+ - single sequence: ` X `
+ - pair of sequences: ` A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + token_ids_1 + sep
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + token_ids_1 + sep) * [0]
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+ return tuple(files)
+
+
+__all__ = ["MarkupLMTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4281e77dfdc571012bc4b9e1052832966ad3712
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mask2former import *
+ from .image_processing_mask2former import *
+ from .image_processing_mask2former_fast import *
+ from .modeling_mask2former import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/configuration_mask2former.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/configuration_mask2former.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ae93892aebd4a071e3e0d5a17fdbe35d1eb7d2d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/configuration_mask2former.py
@@ -0,0 +1,264 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms, Inc.and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Mask2Former model configuration"""
+
+from typing import Optional
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
+from ..auto import CONFIG_MAPPING
+
+
+logger = logging.get_logger(__name__)
+
+
+class Mask2FormerConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Mask2FormerModel`]. It is used to instantiate a
+ Mask2Former model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the Mask2Former
+ [facebook/mask2former-swin-small-coco-instance](https://huggingface.co/facebook/mask2former-swin-small-coco-instance)
+ architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Currently, Mask2Former only supports the [Swin Transformer](swin) as backbone.
+
+ Args:
+ backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `SwinConfig()`):
+ The configuration of the backbone model. If unset, the configuration corresponding to
+ `swin-base-patch4-window12-384` will be used.
+ backbone (`str`, *optional*):
+ Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
+ will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
+ is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
+ use_pretrained_backbone (`bool`, *optional*, `False`):
+ Whether to use pretrained weights for the backbone.
+ use_timm_backbone (`bool`, *optional*, `False`):
+ Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
+ library.
+ backbone_kwargs (`dict`, *optional*):
+ Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
+ e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
+ feature_size (`int`, *optional*, defaults to 256):
+ The features (channels) of the resulting feature maps.
+ mask_feature_size (`int`, *optional*, defaults to 256):
+ The masks' features size, this value will also be used to specify the Feature Pyramid Network features'
+ size.
+ hidden_dim (`int`, *optional*, defaults to 256):
+ Dimensionality of the encoder layers.
+ encoder_feedforward_dim (`int`, *optional*, defaults to 1024):
+ Dimension of feedforward network for deformable detr encoder used as part of pixel decoder.
+ encoder_layers (`int`, *optional*, defaults to 6):
+ Number of layers in the deformable detr encoder used as part of pixel decoder.
+ decoder_layers (`int`, *optional*, defaults to 10):
+ Number of layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder.
+ dim_feedforward (`int`, *optional*, defaults to 2048):
+ Feature dimension in feedforward network for transformer decoder.
+ pre_norm (`bool`, *optional*, defaults to `False`):
+ Whether to use pre-LayerNorm or not for transformer decoder.
+ enforce_input_projection (`bool`, *optional*, defaults to `False`):
+ Whether to add an input projection 1x1 convolution even if the input channels and hidden dim are identical
+ in the Transformer decoder.
+ common_stride (`int`, *optional*, defaults to 4):
+ Parameter used for determining number of FPN levels used as part of pixel decoder.
+ ignore_value (`int`, *optional*, defaults to 255):
+ Category id to be ignored during training.
+ num_queries (`int`, *optional*, defaults to 100):
+ Number of queries for the decoder.
+ no_object_weight (`int`, *optional*, defaults to 0.1):
+ The weight to apply to the null (no object) class.
+ class_weight (`int`, *optional*, defaults to 2.0):
+ The weight for the cross entropy loss.
+ mask_weight (`int`, *optional*, defaults to 5.0):
+ The weight for the mask loss.
+ dice_weight (`int`, *optional*, defaults to 5.0):
+ The weight for the dice loss.
+ train_num_points (`str` or `function`, *optional*, defaults to 12544):
+ Number of points used for sampling during loss calculation.
+ oversample_ratio (`float`, *optional*, defaults to 3.0):
+ Oversampling parameter used for calculating no. of sampled points
+ importance_sample_ratio (`float`, *optional*, defaults to 0.75):
+ Ratio of points that are sampled via importance sampling.
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ init_xavier_std (`float`, *optional*, defaults to 1.0):
+ The scaling factor used for the Xavier initialization gain in the HM Attention map module.
+ use_auxiliary_loss (`boolean``, *optional*, defaults to `True`):
+ If `True` [`Mask2FormerForUniversalSegmentationOutput`] will contain the auxiliary losses computed using
+ the logits from each decoder's stage.
+ feature_strides (`list[int]`, *optional*, defaults to `[4, 8, 16, 32]`):
+ Feature strides corresponding to features generated from backbone network.
+ output_auxiliary_logits (`bool`, *optional*):
+ Should the model output its `auxiliary_logits` or not.
+
+ Examples:
+
+ ```python
+ >>> from transformers import Mask2FormerConfig, Mask2FormerModel
+
+ >>> # Initializing a Mask2Former facebook/mask2former-swin-small-coco-instance configuration
+ >>> configuration = Mask2FormerConfig()
+
+ >>> # Initializing a model (with random weights) from the facebook/mask2former-swin-small-coco-instance style configuration
+ >>> model = Mask2FormerModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+
+ """
+
+ model_type = "mask2former"
+ backbones_supported = ["swin"]
+ attribute_map = {"hidden_size": "hidden_dim"}
+
+ def __init__(
+ self,
+ backbone_config: Optional[dict] = None,
+ feature_size: int = 256,
+ mask_feature_size: int = 256,
+ hidden_dim: int = 256,
+ encoder_feedforward_dim: int = 1024,
+ activation_function: str = "relu",
+ encoder_layers: int = 6,
+ decoder_layers: int = 10,
+ num_attention_heads: int = 8,
+ dropout: float = 0.0,
+ dim_feedforward: int = 2048,
+ pre_norm: bool = False,
+ enforce_input_projection: bool = False,
+ common_stride: int = 4,
+ ignore_value: int = 255,
+ num_queries: int = 100,
+ no_object_weight: float = 0.1,
+ class_weight: float = 2.0,
+ mask_weight: float = 5.0,
+ dice_weight: float = 5.0,
+ train_num_points: int = 12544,
+ oversample_ratio: float = 3.0,
+ importance_sample_ratio: float = 0.75,
+ init_std: float = 0.02,
+ init_xavier_std: float = 1.0,
+ use_auxiliary_loss: bool = True,
+ feature_strides: list[int] = [4, 8, 16, 32],
+ output_auxiliary_logits: Optional[bool] = None,
+ backbone: Optional[str] = None,
+ use_pretrained_backbone: bool = False,
+ use_timm_backbone: bool = False,
+ backbone_kwargs: Optional[dict] = None,
+ **kwargs,
+ ):
+ if backbone_config is None and backbone is None:
+ logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
+ backbone_config = CONFIG_MAPPING["swin"](
+ image_size=224,
+ num_channels=3,
+ patch_size=4,
+ embed_dim=96,
+ depths=[2, 2, 18, 2],
+ num_heads=[3, 6, 12, 24],
+ window_size=7,
+ drop_path_rate=0.3,
+ use_absolute_embeddings=False,
+ out_features=["stage1", "stage2", "stage3", "stage4"],
+ )
+ elif isinstance(backbone_config, dict):
+ backbone_model_type = backbone_config.pop("model_type")
+ config_class = CONFIG_MAPPING[backbone_model_type]
+ backbone_config = config_class.from_dict(backbone_config)
+
+ verify_backbone_config_arguments(
+ use_timm_backbone=use_timm_backbone,
+ use_pretrained_backbone=use_pretrained_backbone,
+ backbone=backbone,
+ backbone_config=backbone_config,
+ backbone_kwargs=backbone_kwargs,
+ )
+ # verify that the backbone is supported
+ if backbone_config is not None and backbone_config.model_type not in self.backbones_supported:
+ logger.warning_once(
+ f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
+ f"Supported model types: {','.join(self.backbones_supported)}"
+ )
+
+ self.backbone_config = backbone_config
+ self.feature_size = feature_size
+ self.mask_feature_size = mask_feature_size
+ self.hidden_dim = hidden_dim
+ self.encoder_feedforward_dim = encoder_feedforward_dim
+ self.activation_function = activation_function
+ self.encoder_layers = encoder_layers
+ self.decoder_layers = decoder_layers
+ self.num_attention_heads = num_attention_heads
+ self.dropout = dropout
+ self.dim_feedforward = dim_feedforward
+ self.pre_norm = pre_norm
+ self.enforce_input_projection = enforce_input_projection
+ self.common_stride = common_stride
+ self.ignore_value = ignore_value
+ self.num_queries = num_queries
+ self.no_object_weight = no_object_weight
+ self.class_weight = class_weight
+ self.mask_weight = mask_weight
+ self.dice_weight = dice_weight
+ self.train_num_points = train_num_points
+ self.oversample_ratio = oversample_ratio
+ self.importance_sample_ratio = importance_sample_ratio
+ self.init_std = init_std
+ self.init_xavier_std = init_xavier_std
+ self.use_auxiliary_loss = use_auxiliary_loss
+ self.feature_strides = feature_strides
+ self.output_auxiliary_logits = output_auxiliary_logits
+ self.num_hidden_layers = decoder_layers
+ self.backbone = backbone
+ self.use_pretrained_backbone = use_pretrained_backbone
+ self.use_timm_backbone = use_timm_backbone
+ self.backbone_kwargs = backbone_kwargs
+
+ super().__init__(**kwargs)
+
+ @property
+ def sub_configs(self):
+ return (
+ {"backbone_config": type(self.backbone_config)}
+ if getattr(self, "backbone_config", None) is not None
+ else {}
+ )
+
+ @classmethod
+ def from_backbone_config(cls, backbone_config: PretrainedConfig, **kwargs):
+ """Instantiate a [`Mask2FormerConfig`] (or a derived class) from a pre-trained backbone model configuration.
+
+ Args:
+ backbone_config ([`PretrainedConfig`]):
+ The backbone configuration.
+
+ Returns:
+ [`Mask2FormerConfig`]: An instance of a configuration object
+ """
+ return cls(
+ backbone_config=backbone_config,
+ **kwargs,
+ )
+
+
+__all__ = ["Mask2FormerConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/convert_mask2former_original_pytorch_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/convert_mask2former_original_pytorch_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..33cba259eed479df7e84660013219ca15f093b26
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/convert_mask2former_original_pytorch_checkpoint_to_pytorch.py
@@ -0,0 +1,1020 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import json
+import sys
+from argparse import ArgumentParser
+from collections.abc import Iterator
+from dataclasses import dataclass
+from pathlib import Path
+from pprint import pformat
+from typing import Any
+
+import requests
+import torch
+import torchvision.transforms as T
+from detectron2.checkpoint import DetectionCheckpointer
+from detectron2.config import get_cfg
+from detectron2.projects.deeplab import add_deeplab_config
+from huggingface_hub import hf_hub_download
+from PIL import Image
+from torch import Tensor, nn
+
+from transformers import (
+ Mask2FormerConfig,
+ Mask2FormerForUniversalSegmentation,
+ Mask2FormerImageProcessor,
+ Mask2FormerModel,
+ SwinConfig,
+)
+from transformers.models.mask2former.modeling_mask2former import (
+ Mask2FormerForUniversalSegmentationOutput,
+ Mask2FormerModelOutput,
+)
+from transformers.utils import logging
+
+
+StateDict = dict[str, Tensor]
+
+logging.set_verbosity_info()
+logger = logging.get_logger()
+
+torch.manual_seed(0)
+
+
+class TrackedStateDict:
+ def __init__(self, to_track: dict):
+ """This class "tracks" a python dictionary by keeping track of which item is accessed.
+
+ Args:
+ to_track (Dict): The dictionary we wish to track
+ """
+ self.to_track = to_track
+ self._seen: set[str] = set()
+
+ def __getitem__(self, key: str) -> Any:
+ return self.to_track[key]
+
+ def __setitem__(self, key: str, item: Any):
+ self._seen.add(key)
+ self.to_track[key] = item
+
+ def diff(self) -> list[str]:
+ """This method returns a set difference between the keys in the tracked state dict and the one we have access so far.
+ This is an effective method to check if we have update all the keys
+
+ Returns:
+ list[str]: List of keys not yet updated
+ """
+ return set(self.to_track.keys()) - self._seen
+
+ def copy(self) -> dict:
+ # proxy the call to the internal dictionary
+ return self.to_track.copy()
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ img_data = requests.get(url, stream=True).raw
+ im = Image.open(img_data)
+ return im
+
+
+@dataclass
+class Args:
+ """Fake command line arguments needed by mask2former/detectron implementation"""
+
+ config_file: str
+
+
+def setup_cfg(args: Args):
+ # load config from file and command-line arguments
+ cfg = get_cfg()
+ add_deeplab_config(cfg)
+ add_maskformer2_config(cfg)
+ cfg.merge_from_file(args.config_file)
+ cfg.freeze()
+ return cfg
+
+
+class OriginalMask2FormerConfigToOursConverter:
+ def __call__(self, original_config: object) -> Mask2FormerConfig:
+ model = original_config.MODEL
+
+ repo_id = "huggingface/label-files"
+ if model.SEM_SEG_HEAD.NUM_CLASSES == 847:
+ filename = "mask2former-ade20k-full-id2label.json"
+ elif model.SEM_SEG_HEAD.NUM_CLASSES == 150:
+ filename = "ade20k-id2label.json"
+ elif model.SEM_SEG_HEAD.NUM_CLASSES == 80:
+ filename = "coco-detection-mmdet-id2label.json"
+ elif model.SEM_SEG_HEAD.NUM_CLASSES == 171:
+ filename = "mask2former-coco-stuff-id2label.json"
+ elif model.SEM_SEG_HEAD.NUM_CLASSES == 133:
+ filename = "coco-panoptic-id2label.json"
+ elif model.SEM_SEG_HEAD.NUM_CLASSES == 19:
+ filename = "cityscapes-id2label.json"
+ elif model.SEM_SEG_HEAD.NUM_CLASSES == 8:
+ filename = "cityscapes-instance-id2label.json"
+ elif model.SEM_SEG_HEAD.NUM_CLASSES == 65:
+ filename = "mapillary-vistas-id2label.json"
+
+ id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
+ id2label = {int(k): v for k, v in id2label.items()}
+ label2id = {label: idx for idx, label in id2label.items()}
+
+ if model.SWIN.EMBED_DIM == 96:
+ backbone_config = SwinConfig.from_pretrained(
+ "microsoft/swin-tiny-patch4-window7-224", out_features=["stage1", "stage2", "stage3", "stage4"]
+ )
+ elif model.SWIN.EMBED_DIM == 128:
+ backbone_config = SwinConfig(
+ embed_dim=128,
+ window_size=12,
+ depths=(2, 2, 18, 2),
+ num_heads=(4, 8, 16, 32),
+ out_features=["stage1", "stage2", "stage3", "stage4"],
+ )
+
+ elif model.SWIN.EMBED_DIM == 192:
+ backbone_config = SwinConfig.from_pretrained(
+ "microsoft/swin-large-patch4-window12-384", out_features=["stage1", "stage2", "stage3", "stage4"]
+ )
+ else:
+ raise ValueError(f"embed dim {model.SWIN.EMBED_DIM} not supported for Swin!")
+
+ backbone_config.drop_path_rate = model.SWIN.DROP_PATH_RATE
+ backbone_config.attention_probs_dropout_prob = model.SWIN.ATTN_DROP_RATE
+ backbone_config.depths = model.SWIN.DEPTHS
+
+ config: Mask2FormerConfig = Mask2FormerConfig(
+ ignore_value=model.SEM_SEG_HEAD.IGNORE_VALUE,
+ num_labels=model.SEM_SEG_HEAD.NUM_CLASSES,
+ num_queries=model.MASK_FORMER.NUM_OBJECT_QUERIES,
+ no_object_weight=model.MASK_FORMER.NO_OBJECT_WEIGHT,
+ class_weight=model.MASK_FORMER.CLASS_WEIGHT,
+ mask_weight=model.MASK_FORMER.MASK_WEIGHT,
+ dice_weight=model.MASK_FORMER.DICE_WEIGHT,
+ train_num_points=model.MASK_FORMER.TRAIN_NUM_POINTS,
+ oversample_ratio=model.MASK_FORMER.OVERSAMPLE_RATIO,
+ importance_sample_ratio=model.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO,
+ init_std=0.02,
+ init_xavier_std=1.0,
+ use_auxiliary_loss=model.MASK_FORMER.DEEP_SUPERVISION,
+ feature_strides=[4, 8, 16, 32],
+ backbone_config=backbone_config,
+ id2label=id2label,
+ label2id=label2id,
+ feature_size=model.SEM_SEG_HEAD.CONVS_DIM,
+ mask_feature_size=model.SEM_SEG_HEAD.MASK_DIM,
+ hidden_dim=model.MASK_FORMER.HIDDEN_DIM,
+ encoder_layers=model.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS,
+ encoder_feedforward_dim=1024,
+ decoder_layers=model.MASK_FORMER.DEC_LAYERS,
+ num_attention_heads=model.MASK_FORMER.NHEADS,
+ dropout=model.MASK_FORMER.DROPOUT,
+ dim_feedforward=model.MASK_FORMER.DIM_FEEDFORWARD,
+ pre_norm=model.MASK_FORMER.PRE_NORM,
+ enforce_input_proj=model.MASK_FORMER.ENFORCE_INPUT_PROJ,
+ common_stride=model.SEM_SEG_HEAD.COMMON_STRIDE,
+ )
+ return config
+
+
+class OriginalMask2FormerConfigToImageProcessorConverter:
+ def __call__(self, original_config: object) -> Mask2FormerImageProcessor:
+ model = original_config.MODEL
+ model_input = original_config.INPUT
+
+ return Mask2FormerImageProcessor(
+ image_mean=(torch.tensor(model.PIXEL_MEAN) / 255).tolist(),
+ image_std=(torch.tensor(model.PIXEL_STD) / 255).tolist(),
+ size=model_input.MIN_SIZE_TEST,
+ max_size=model_input.MAX_SIZE_TEST,
+ num_labels=model.SEM_SEG_HEAD.NUM_CLASSES,
+ ignore_index=model.SEM_SEG_HEAD.IGNORE_VALUE,
+ size_divisibility=32,
+ )
+
+
+class OriginalMask2FormerCheckpointToOursConverter:
+ def __init__(self, original_model: nn.Module, config: Mask2FormerConfig):
+ self.original_model = original_model
+ self.config = config
+
+ def pop_all(self, renamed_keys: list[tuple[str, str]], dst_state_dict: StateDict, src_state_dict: StateDict):
+ for src_key, dst_key in renamed_keys:
+ dst_state_dict[dst_key] = src_state_dict.pop(src_key)
+
+ def replace_maskformer_swin_backbone(
+ self, dst_state_dict: StateDict, src_state_dict: StateDict, config: Mask2FormerConfig
+ ):
+ dst_prefix: str = "pixel_level_module.encoder"
+ src_prefix: str = "backbone"
+
+ renamed_keys = [
+ (
+ f"{src_prefix}.patch_embed.proj.weight",
+ f"{dst_prefix}.model.embeddings.patch_embeddings.projection.weight",
+ ),
+ (f"{src_prefix}.patch_embed.proj.bias", f"{dst_prefix}.model.embeddings.patch_embeddings.projection.bias"),
+ (f"{src_prefix}.patch_embed.norm.weight", f"{dst_prefix}.model.embeddings.norm.weight"),
+ (f"{src_prefix}.patch_embed.norm.bias", f"{dst_prefix}.model.embeddings.norm.bias"),
+ ]
+ num_layers = len(config.backbone_config.depths)
+ for layer_idx in range(num_layers):
+ for block_idx in range(config.backbone_config.depths[layer_idx]):
+ renamed_keys.extend(
+ [ # src, dst
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.bias",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_bias_table",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_bias_table",
+ ),
+ ]
+ )
+ # now we need to handle the attentions
+ # read in weights + bias of input projection layer of cross-attention
+
+ src_att_weight = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight"]
+ src_att_bias = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias"]
+
+ size = src_att_weight.shape[0]
+ offset = size // 3
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.weight"
+ ] = src_att_weight[:offset, :]
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.bias"
+ ] = src_att_bias[:offset]
+
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.weight"
+ ] = src_att_weight[offset : offset * 2, :]
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.bias"
+ ] = src_att_bias[offset : offset * 2]
+
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.weight"
+ ] = src_att_weight[-offset:, :]
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.bias"
+ ] = src_att_bias[-offset:]
+
+ # let's pop them
+ src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight")
+ src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias")
+ # proj
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.bias",
+ ),
+ ]
+ )
+
+ # second norm
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.bias",
+ ),
+ ]
+ )
+
+ # mlp
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.bias",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.bias",
+ ),
+ ]
+ )
+
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_index",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_index",
+ )
+ ]
+ )
+
+ if layer_idx < num_layers - 1:
+ # patch merging
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.reduction.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.reduction.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.norm.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.norm.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.norm.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.norm.bias",
+ ),
+ ]
+ )
+
+ # hidden states norms
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.norm{layer_idx}.weight",
+ f"{dst_prefix}.hidden_states_norms.{layer_idx}.weight",
+ ),
+ (
+ f"{src_prefix}.norm{layer_idx}.bias",
+ f"{dst_prefix}.hidden_states_norms.{layer_idx}.bias",
+ ),
+ ]
+ )
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ def replace_swin_backbone(self, dst_state_dict: StateDict, src_state_dict: StateDict, config: Mask2FormerConfig):
+ dst_prefix: str = "pixel_level_module.encoder"
+ src_prefix: str = "backbone"
+
+ renamed_keys = [
+ (
+ f"{src_prefix}.patch_embed.proj.weight",
+ f"{dst_prefix}.embeddings.patch_embeddings.projection.weight",
+ ),
+ (f"{src_prefix}.patch_embed.proj.bias", f"{dst_prefix}.embeddings.patch_embeddings.projection.bias"),
+ (f"{src_prefix}.patch_embed.norm.weight", f"{dst_prefix}.embeddings.norm.weight"),
+ (f"{src_prefix}.patch_embed.norm.bias", f"{dst_prefix}.embeddings.norm.bias"),
+ ]
+
+ for layer_idx in range(len(config.backbone_config.depths)):
+ for block_idx in range(config.backbone_config.depths[layer_idx]):
+ renamed_keys.extend(
+ [ # src, dst
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.bias",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_bias_table",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_bias_table",
+ ),
+ ]
+ )
+ # now we need to handle the attentions
+ # read in weights + bias of input projection layer of cross-attention
+
+ src_att_weight = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight"]
+ src_att_bias = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias"]
+
+ size = src_att_weight.shape[0]
+ offset = size // 3
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.weight"
+ ] = src_att_weight[:offset, :]
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.bias"
+ ] = src_att_bias[:offset]
+
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.weight"
+ ] = src_att_weight[offset : offset * 2, :]
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.bias"
+ ] = src_att_bias[offset : offset * 2]
+
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.weight"
+ ] = src_att_weight[-offset:, :]
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.bias"
+ ] = src_att_bias[-offset:]
+
+ # let's pop them
+ src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight")
+ src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias")
+ # proj
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.bias",
+ ),
+ ]
+ )
+
+ # second norm
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.bias",
+ ),
+ ]
+ )
+
+ # mlp
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.bias",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.bias",
+ ),
+ ]
+ )
+
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_index",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_index",
+ )
+ ]
+ )
+
+ if layer_idx < 3:
+ # patch merging
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.reduction.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.downsample.reduction.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.norm.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.downsample.norm.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.norm.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.downsample.norm.bias",
+ ),
+ ]
+ )
+
+ # hidden states norms
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.norm{layer_idx}.weight",
+ f"{dst_prefix}.hidden_states_norms.stage{layer_idx + 1}.weight",
+ ),
+ (
+ f"{src_prefix}.norm{layer_idx}.bias",
+ f"{dst_prefix}.hidden_states_norms.stage{layer_idx + 1}.bias",
+ ),
+ ]
+ )
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ # Backbone + Pixel Decoder
+ def replace_pixel_module(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "pixel_level_module.decoder"
+ src_prefix: str = "sem_seg_head.pixel_decoder"
+
+ self.replace_swin_backbone(dst_state_dict, src_state_dict, self.config)
+
+ def rename_keys_for_weight_bias(src_prefix: str, dst_prefix: str):
+ return [
+ (f"{src_prefix}.weight", f"{dst_prefix}.weight"),
+ (f"{src_prefix}.bias", f"{dst_prefix}.bias"),
+ ]
+
+ def rename_keys_for_self_attn(src_prefix: str, dst_prefix: str):
+ self_attn_keys = []
+ self_attn_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.attention_weights", f"{dst_prefix}.attention_weights")
+ )
+ self_attn_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.output_proj", f"{dst_prefix}.output_proj")
+ )
+ self_attn_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.sampling_offsets", f"{dst_prefix}.sampling_offsets")
+ )
+ self_attn_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.value_proj", f"{dst_prefix}.value_proj"))
+
+ return self_attn_keys
+
+ def rename_keys_for_encoder_layer(src_prefix: str, dst_prefix: str):
+ encoder_keys = []
+ encoder_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.linear1", f"{dst_prefix}.fc1"))
+ encoder_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.linear2", f"{dst_prefix}.fc2"))
+ encoder_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.norm1", f"{dst_prefix}.self_attn_layer_norm")
+ )
+ encoder_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.norm2", f"{dst_prefix}.final_layer_norm"))
+ encoder_keys.extend(rename_keys_for_self_attn(f"{src_prefix}.self_attn", f"{dst_prefix}.self_attn"))
+
+ return encoder_keys
+
+ # convolution layer for final features
+ renamed_keys = [
+ (f"{src_prefix}.adapter_1.weight", f"{dst_prefix}.adapter_1.0.weight"),
+ (f"{src_prefix}.adapter_1.norm.weight", f"{dst_prefix}.adapter_1.1.weight"),
+ (f"{src_prefix}.adapter_1.norm.bias", f"{dst_prefix}.adapter_1.1.bias"),
+ ]
+
+ renamed_keys.extend(
+ [
+ (f"{src_prefix}.layer_1.weight", f"{dst_prefix}.layer_1.0.weight"),
+ (f"{src_prefix}.layer_1.norm.weight", f"{dst_prefix}.layer_1.1.weight"),
+ (f"{src_prefix}.layer_1.norm.bias", f"{dst_prefix}.layer_1.1.bias"),
+ ]
+ )
+
+ # proj layers
+ for i in range(3):
+ for j in range(2):
+ renamed_keys.extend(
+ [
+ (f"{src_prefix}.input_proj.{i}.{j}.weight", f"{dst_prefix}.input_projections.{i}.{j}.weight"),
+ (f"{src_prefix}.input_proj.{i}.{j}.bias", f"{dst_prefix}.input_projections.{i}.{j}.bias"),
+ ]
+ )
+
+ renamed_keys.extend([(f"{src_prefix}.transformer.level_embed", f"{dst_prefix}.level_embed")])
+
+ # layers
+ for layer_idx in range(self.config.encoder_layers):
+ renamed_keys.extend(
+ rename_keys_for_encoder_layer(
+ f"{src_prefix}.transformer.encoder.layers.{layer_idx}", f"{dst_prefix}.encoder.layers.{layer_idx}"
+ )
+ )
+
+ # proj
+ renamed_keys.extend(
+ [
+ (f"{src_prefix}.mask_features.weight", f"{dst_prefix}.mask_projection.weight"),
+ (f"{src_prefix}.mask_features.bias", f"{dst_prefix}.mask_projection.bias"),
+ ]
+ )
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ # Transformer Decoder
+ def rename_keys_in_masked_attention_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "transformer_module.decoder"
+ src_prefix: str = "sem_seg_head.predictor"
+
+ rename_keys = []
+ for i in range(self.config.decoder_layers - 1):
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_self_attention_layers.{i}.self_attn.out_proj.weight",
+ f"{dst_prefix}.layers.{i}.self_attn.out_proj.weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_self_attention_layers.{i}.self_attn.out_proj.bias",
+ f"{dst_prefix}.layers.{i}.self_attn.out_proj.bias",
+ )
+ )
+
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_self_attention_layers.{i}.norm.weight",
+ f"{dst_prefix}.layers.{i}.self_attn_layer_norm.weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_self_attention_layers.{i}.norm.bias",
+ f"{dst_prefix}.layers.{i}.self_attn_layer_norm.bias",
+ )
+ )
+
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_cross_attention_layers.{i}.multihead_attn.in_proj_weight",
+ f"{dst_prefix}.layers.{i}.cross_attn.in_proj_weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_cross_attention_layers.{i}.multihead_attn.in_proj_bias",
+ f"{dst_prefix}.layers.{i}.cross_attn.in_proj_bias",
+ )
+ )
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_cross_attention_layers.{i}.multihead_attn.out_proj.weight",
+ f"{dst_prefix}.layers.{i}.cross_attn.out_proj.weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_cross_attention_layers.{i}.multihead_attn.out_proj.bias",
+ f"{dst_prefix}.layers.{i}.cross_attn.out_proj.bias",
+ )
+ )
+
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_cross_attention_layers.{i}.norm.weight",
+ f"{dst_prefix}.layers.{i}.cross_attn_layer_norm.weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_cross_attention_layers.{i}.norm.bias",
+ f"{dst_prefix}.layers.{i}.cross_attn_layer_norm.bias",
+ )
+ )
+
+ rename_keys.append(
+ (f"{src_prefix}.transformer_ffn_layers.{i}.linear1.weight", f"{dst_prefix}.layers.{i}.fc1.weight")
+ )
+ rename_keys.append(
+ (f"{src_prefix}.transformer_ffn_layers.{i}.linear1.bias", f"{dst_prefix}.layers.{i}.fc1.bias")
+ )
+ rename_keys.append(
+ (f"{src_prefix}.transformer_ffn_layers.{i}.linear2.weight", f"{dst_prefix}.layers.{i}.fc2.weight")
+ )
+ rename_keys.append(
+ (f"{src_prefix}.transformer_ffn_layers.{i}.linear2.bias", f"{dst_prefix}.layers.{i}.fc2.bias")
+ )
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_ffn_layers.{i}.norm.weight",
+ f"{dst_prefix}.layers.{i}.final_layer_norm.weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"{src_prefix}.transformer_ffn_layers.{i}.norm.bias",
+ f"{dst_prefix}.layers.{i}.final_layer_norm.bias",
+ )
+ )
+
+ return rename_keys
+
+ def replace_masked_attention_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "transformer_module.decoder"
+ src_prefix: str = "sem_seg_head.predictor"
+
+ renamed_keys = self.rename_keys_in_masked_attention_decoder(dst_state_dict, src_state_dict)
+
+ # add more
+ renamed_keys.extend(
+ [
+ (f"{src_prefix}.decoder_norm.weight", f"{dst_prefix}.layernorm.weight"),
+ (f"{src_prefix}.decoder_norm.bias", f"{dst_prefix}.layernorm.bias"),
+ ]
+ )
+
+ mlp_len = 3
+ for i in range(mlp_len):
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.mask_embed.layers.{i}.weight",
+ f"{dst_prefix}.mask_predictor.mask_embedder.{i}.0.weight",
+ ),
+ (
+ f"{src_prefix}.mask_embed.layers.{i}.bias",
+ f"{dst_prefix}.mask_predictor.mask_embedder.{i}.0.bias",
+ ),
+ ]
+ )
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ def replace_keys_qkv_transformer_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "transformer_module.decoder.layers"
+ src_prefix: str = "sem_seg_head.predictor"
+ for i in range(self.config.decoder_layers - 1):
+ # read in weights + bias of input projection layer of self-attention
+ in_proj_weight = src_state_dict.pop(
+ f"{src_prefix}.transformer_self_attention_layers.{i}.self_attn.in_proj_weight"
+ )
+ in_proj_bias = src_state_dict.pop(
+ f"{src_prefix}.transformer_self_attention_layers.{i}.self_attn.in_proj_bias"
+ )
+ # next, add query, keys and values (in that order) to the state dict
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
+
+ def replace_transformer_module(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "transformer_module"
+ src_prefix: str = "sem_seg_head.predictor"
+
+ self.replace_masked_attention_decoder(dst_state_dict, src_state_dict)
+
+ renamed_keys = [
+ (f"{src_prefix}.query_embed.weight", f"{dst_prefix}.queries_embedder.weight"),
+ (f"{src_prefix}.query_feat.weight", f"{dst_prefix}.queries_features.weight"),
+ (f"{src_prefix}.level_embed.weight", f"{dst_prefix}.level_embed.weight"),
+ ]
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+ self.replace_keys_qkv_transformer_decoder(dst_state_dict, src_state_dict)
+
+ def replace_universal_segmentation_module(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = ""
+ src_prefix: str = "sem_seg_head.predictor"
+
+ renamed_keys = [
+ (f"{src_prefix}.class_embed.weight", f"{dst_prefix}class_predictor.weight"),
+ (f"{src_prefix}.class_embed.bias", f"{dst_prefix}class_predictor.bias"),
+ ]
+
+ logger.info(f"Replacing keys {pformat(renamed_keys)}")
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ def convert(self, mask2former: Mask2FormerModel) -> Mask2FormerModel:
+ dst_state_dict = TrackedStateDict(mask2former.state_dict())
+ src_state_dict = self.original_model.state_dict()
+
+ self.replace_pixel_module(dst_state_dict, src_state_dict)
+ self.replace_transformer_module(dst_state_dict, src_state_dict)
+
+ logger.info(f"Missed keys are {pformat(dst_state_dict.diff())}")
+ logger.info(f"Not copied keys are {pformat(src_state_dict.keys())}")
+ logger.info("🙌 Done")
+
+ state_dict = {key: dst_state_dict[key] for key in dst_state_dict.to_track}
+ mask2former.load_state_dict(state_dict)
+ return mask2former
+
+ def convert_universal_segmentation(
+ self, mask2former: Mask2FormerForUniversalSegmentation
+ ) -> Mask2FormerForUniversalSegmentation:
+ dst_state_dict = TrackedStateDict(mask2former.state_dict())
+ src_state_dict = self.original_model.state_dict()
+
+ self.replace_universal_segmentation_module(dst_state_dict, src_state_dict)
+
+ state_dict = {key: dst_state_dict[key] for key in dst_state_dict.to_track}
+ mask2former.load_state_dict(state_dict)
+
+ return mask2former
+
+ @staticmethod
+ def using_dirs(checkpoints_dir: Path, config_dir: Path) -> Iterator[tuple[object, Path, Path]]:
+ checkpoints: list[Path] = checkpoints_dir.glob("**/*.pkl")
+
+ for checkpoint in checkpoints:
+ logger.info(f"💪 Converting {checkpoint.stem}")
+ # find associated config file
+
+ # dataset_name e.g 'coco'
+ dataset_name = checkpoint.parents[2].stem
+ if dataset_name == "ade":
+ dataset_name = dataset_name.replace("ade", "ade20k")
+
+ # task type e.g 'instance-segmentation'
+ segmentation_task = checkpoint.parents[1].stem
+
+ # config file corresponding to checkpoint
+ config_file_name = f"{checkpoint.parents[0].stem}.yaml"
+
+ config: Path = config_dir / dataset_name / segmentation_task / "swin" / config_file_name
+ yield config, checkpoint
+
+
+def test(
+ original_model,
+ our_model: Mask2FormerForUniversalSegmentation,
+ image_processor: Mask2FormerImageProcessor,
+ tolerance: float,
+):
+ with torch.no_grad():
+ original_model = original_model.eval()
+ our_model = our_model.eval()
+
+ im = prepare_img()
+ x = image_processor(images=im, return_tensors="pt")["pixel_values"]
+
+ original_model_backbone_features = original_model.backbone(x.clone())
+ our_model_output: Mask2FormerModelOutput = our_model.model(x.clone(), output_hidden_states=True)
+
+ # Test backbone
+ for original_model_feature, our_model_feature in zip(
+ original_model_backbone_features.values(), our_model_output.encoder_hidden_states
+ ):
+ assert torch.allclose(original_model_feature, our_model_feature, atol=tolerance), (
+ "The backbone features are not the same."
+ )
+
+ # Test pixel decoder
+ mask_features, _, multi_scale_features = original_model.sem_seg_head.pixel_decoder.forward_features(
+ original_model_backbone_features
+ )
+
+ for original_model_feature, our_model_feature in zip(
+ multi_scale_features, our_model_output.pixel_decoder_hidden_states
+ ):
+ assert torch.allclose(original_model_feature, our_model_feature, atol=tolerance), (
+ "The pixel decoder feature are not the same"
+ )
+
+ # Let's test the full model
+ tr_complete = T.Compose(
+ [T.Resize((384, 384)), T.ToTensor()],
+ )
+ y = (tr_complete(im) * 255.0).to(torch.int).float()
+
+ # modify original Mask2Former code to return mask and class logits
+ original_class_logits, original_mask_logits = original_model([{"image": y.clone().squeeze(0)}])
+
+ our_model_out: Mask2FormerForUniversalSegmentationOutput = our_model(x.clone())
+ our_mask_logits = our_model_out.masks_queries_logits
+ our_class_logits = our_model_out.class_queries_logits
+
+ assert original_mask_logits.shape == our_mask_logits.shape, "Output masks shapes are not matching."
+ assert original_class_logits.shape == our_class_logits.shape, "Output class logits shapes are not matching."
+ assert torch.allclose(original_class_logits, our_class_logits, atol=tolerance), (
+ "The class logits are not the same."
+ )
+ assert torch.allclose(original_mask_logits, our_mask_logits, atol=tolerance), (
+ "The predicted masks are not the same."
+ )
+
+ logger.info("✅ Test passed!")
+
+
+def get_model_name(checkpoint_file: Path):
+ # model_name_raw is something like maskformer2_swin_small_bs16_50ep
+ model_name_raw: str = checkpoint_file.parents[0].stem
+
+ # `segmentation_task_type` must be one of the following: `instance-segmentation`, `panoptic-segmentation`, `semantic-segmentation`
+ segmentation_task_name: str = checkpoint_file.parents[1].stem
+ if segmentation_task_name not in ["instance-segmentation", "panoptic-segmentation", "semantic-segmentation"]:
+ raise ValueError(
+ f"{segmentation_task_name} must be wrong since acceptable values are: instance-segmentation,"
+ " panoptic-segmentation, semantic-segmentation."
+ )
+
+ # dataset name must be one of the following: `coco`, `ade`, `cityscapes`, `mapillary-vistas`
+ dataset_name: str = checkpoint_file.parents[2].stem
+ if dataset_name not in ["coco", "ade", "cityscapes", "mapillary-vistas"]:
+ raise ValueError(
+ f"{dataset_name} must be wrong since we didn't find 'coco' or 'ade' or 'cityscapes' or 'mapillary-vistas'"
+ " in it "
+ )
+
+ backbone = "swin"
+ backbone_types = ["tiny", "small", "base_IN21k", "base", "large"]
+ backbone_type = list(filter(lambda x: x in model_name_raw, backbone_types))[0].replace("_", "-")
+
+ model_name = f"mask2former-{backbone}-{backbone_type}-{dataset_name}-{segmentation_task_name.split('-')[0]}"
+
+ return model_name
+
+
+if __name__ == "__main__":
+ parser = ArgumentParser(
+ description="Command line to convert the original mask2formers (with swin backbone) to our implementations."
+ )
+
+ parser.add_argument(
+ "--checkpoints_dir",
+ type=Path,
+ help=(
+ "A directory containing the model's checkpoints. The directory has to have the following structure:"
+ " ///.pkl"
+ ),
+ )
+ parser.add_argument(
+ "--configs_dir",
+ type=Path,
+ help=(
+ "A directory containing the model's configs, see detectron2 doc. The directory has to have the following"
+ " structure: ///.yaml"
+ ),
+ )
+ parser.add_argument(
+ "--mask2former_dir",
+ required=True,
+ type=Path,
+ help=(
+ "A path to Mask2Former's original implementation directory. You can download from here:"
+ " https://github.com/facebookresearch/Mask2Former"
+ ),
+ )
+
+ args = parser.parse_args()
+
+ checkpoints_dir: Path = args.checkpoints_dir
+ config_dir: Path = args.configs_dir
+ mask2former_dir: Path = args.mask2former_dir
+ # append the path to the parents to mask2former dir
+ sys.path.append(str(mask2former_dir.parent))
+ # import original Mask2Former config and model from original source code repo
+ from Mask2Former.mask2former.config import add_maskformer2_config
+ from Mask2Former.mask2former.maskformer_model import MaskFormer as OriginalMask2Former
+
+ for config_file, checkpoint_file in OriginalMask2FormerCheckpointToOursConverter.using_dirs(
+ checkpoints_dir, config_dir
+ ):
+ model_name = get_model_name(checkpoint_file)
+ image_processor = OriginalMask2FormerConfigToImageProcessorConverter()(
+ setup_cfg(Args(config_file=config_file))
+ )
+ image_processor.size = {"height": 384, "width": 384}
+
+ original_config = setup_cfg(Args(config_file=config_file))
+ mask2former_kwargs = OriginalMask2Former.from_config(original_config)
+ original_model = OriginalMask2Former(**mask2former_kwargs).eval()
+
+ DetectionCheckpointer(original_model).load(str(checkpoint_file))
+
+ config: Mask2FormerConfig = OriginalMask2FormerConfigToOursConverter()(original_config)
+ mask2former = Mask2FormerModel(config=config).eval()
+
+ converter = OriginalMask2FormerCheckpointToOursConverter(original_model, config)
+ mask2former = converter.convert(mask2former)
+
+ mask2former_for_segmentation = Mask2FormerForUniversalSegmentation(config=config).eval()
+ mask2former_for_segmentation.model = mask2former
+
+ mask2former_for_segmentation = converter.convert_universal_segmentation(mask2former_for_segmentation)
+
+ tolerance = 3e-1
+ high_tolerance_models = [
+ "mask2former-swin-base-IN21k-coco-instance",
+ "mask2former-swin-base-coco-instance",
+ "mask2former-swin-small-cityscapes-semantic",
+ ]
+
+ if model_name in high_tolerance_models:
+ tolerance = 3e-1
+
+ logger.info(f"🪄 Testing {model_name}...")
+ test(original_model, mask2former_for_segmentation, image_processor, tolerance)
+ logger.info(f"🪄 Pushing {model_name} to hub...")
+
+ image_processor.push_to_hub(model_name)
+ mask2former_for_segmentation.push_to_hub(model_name)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/image_processing_mask2former.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/image_processing_mask2former.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ccc3bb67bf71905b037282872e1f089c1083121
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/image_processing_mask2former.py
@@ -0,0 +1,1286 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for Mask2Former."""
+
+import math
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import INIT_SERVICE_KWARGS, BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import (
+ PaddingMode,
+ get_resize_output_image_size,
+ pad,
+ rescale,
+ resize,
+ to_channel_dimension_format,
+)
+from ...image_utils import (
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ get_image_size,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import (
+ IMAGENET_DEFAULT_MEAN,
+ IMAGENET_DEFAULT_STD,
+ TensorType,
+ filter_out_non_signature_kwargs,
+ is_torch_available,
+ is_torch_tensor,
+ logging,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+if is_torch_available():
+ import torch
+ from torch import nn
+
+
+# Copied from transformers.models.detr.image_processing_detr.get_size_with_aspect_ratio
+def get_size_with_aspect_ratio(image_size, size, max_size=None) -> tuple[int, int]:
+ """
+ Computes the output image size given the input image size and the desired output size.
+
+ Args:
+ image_size (`tuple[int, int]`):
+ The input image size.
+ size (`int`):
+ The desired output size.
+ max_size (`int`, *optional*):
+ The maximum allowed output size.
+ """
+ height, width = image_size
+ raw_size = None
+ if max_size is not None:
+ min_original_size = float(min((height, width)))
+ max_original_size = float(max((height, width)))
+ if max_original_size / min_original_size * size > max_size:
+ raw_size = max_size * min_original_size / max_original_size
+ size = int(round(raw_size))
+
+ if (height <= width and height == size) or (width <= height and width == size):
+ oh, ow = height, width
+ elif width < height:
+ ow = size
+ if max_size is not None and raw_size is not None:
+ oh = int(raw_size * height / width)
+ else:
+ oh = int(size * height / width)
+ else:
+ oh = size
+ if max_size is not None and raw_size is not None:
+ ow = int(raw_size * width / height)
+ else:
+ ow = int(size * width / height)
+
+ return (oh, ow)
+
+
+# Copied from transformers.models.detr.image_processing_detr.max_across_indices
+def max_across_indices(values: Iterable[Any]) -> list[Any]:
+ """
+ Return the maximum value across all indices of an iterable of values.
+ """
+ return [max(values_i) for values_i in zip(*values)]
+
+
+# Copied from transformers.models.detr.image_processing_detr.get_max_height_width
+def get_max_height_width(
+ images: list[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None
+) -> list[int]:
+ """
+ Get the maximum height and width across all images in a batch.
+ """
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ if input_data_format == ChannelDimension.FIRST:
+ _, max_height, max_width = max_across_indices([img.shape for img in images])
+ elif input_data_format == ChannelDimension.LAST:
+ max_height, max_width, _ = max_across_indices([img.shape for img in images])
+ else:
+ raise ValueError(f"Invalid channel dimension format: {input_data_format}")
+ return (max_height, max_width)
+
+
+# Copied from transformers.models.detr.image_processing_detr.make_pixel_mask
+def make_pixel_mask(
+ image: np.ndarray, output_size: tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
+) -> np.ndarray:
+ """
+ Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
+
+ Args:
+ image (`np.ndarray`):
+ Image to make the pixel mask for.
+ output_size (`tuple[int, int]`):
+ Output size of the mask.
+ """
+ input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+ mask = np.zeros(output_size, dtype=np.int64)
+ mask[:input_height, :input_width] = 1
+ return mask
+
+
+# Copied from transformers.models.detr.image_processing_detr.binary_mask_to_rle
+def binary_mask_to_rle(mask):
+ """
+ Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format.
+
+ Args:
+ mask (`torch.Tensor` or `numpy.array`):
+ A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
+ segment_id or class_id.
+ Returns:
+ `List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
+ format.
+ """
+ if is_torch_tensor(mask):
+ mask = mask.numpy()
+
+ pixels = mask.flatten()
+ pixels = np.concatenate([[0], pixels, [0]])
+ runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
+ runs[1::2] -= runs[::2]
+ return list(runs)
+
+
+# Copied from transformers.models.detr.image_processing_detr.convert_segmentation_to_rle
+def convert_segmentation_to_rle(segmentation):
+ """
+ Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format.
+
+ Args:
+ segmentation (`torch.Tensor` or `numpy.array`):
+ A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
+ Returns:
+ `list[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
+ """
+ segment_ids = torch.unique(segmentation)
+
+ run_length_encodings = []
+ for idx in segment_ids:
+ mask = torch.where(segmentation == idx, 1, 0)
+ rle = binary_mask_to_rle(mask)
+ run_length_encodings.append(rle)
+
+ return run_length_encodings
+
+
+# Copied from transformers.models.detr.image_processing_detr.remove_low_and_no_objects
+def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
+ """
+ Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
+ `labels`.
+
+ Args:
+ masks (`torch.Tensor`):
+ A tensor of shape `(num_queries, height, width)`.
+ scores (`torch.Tensor`):
+ A tensor of shape `(num_queries)`.
+ labels (`torch.Tensor`):
+ A tensor of shape `(num_queries)`.
+ object_mask_threshold (`float`):
+ A number between 0 and 1 used to binarize the masks.
+ Raises:
+ `ValueError`: Raised when the first dimension doesn't match in all input tensors.
+ Returns:
+ `tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
+ < `object_mask_threshold`.
+ """
+ if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
+ raise ValueError("mask, scores and labels must have the same shape!")
+
+ to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
+
+ return masks[to_keep], scores[to_keep], labels[to_keep]
+
+
+# Copied from transformers.models.detr.image_processing_detr.check_segment_validity
+def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
+ # Get the mask associated with the k class
+ mask_k = mask_labels == k
+ mask_k_area = mask_k.sum()
+
+ # Compute the area of all the stuff in query k
+ original_area = (mask_probs[k] >= mask_threshold).sum()
+ mask_exists = mask_k_area > 0 and original_area > 0
+
+ # Eliminate disconnected tiny segments
+ if mask_exists:
+ area_ratio = mask_k_area / original_area
+ if not area_ratio.item() > overlap_mask_area_threshold:
+ mask_exists = False
+
+ return mask_exists, mask_k
+
+
+# Copied from transformers.models.detr.image_processing_detr.compute_segments
+def compute_segments(
+ mask_probs,
+ pred_scores,
+ pred_labels,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ label_ids_to_fuse: Optional[set[int]] = None,
+ target_size: Optional[tuple[int, int]] = None,
+):
+ height = mask_probs.shape[1] if target_size is None else target_size[0]
+ width = mask_probs.shape[2] if target_size is None else target_size[1]
+
+ segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
+ segments: list[dict] = []
+
+ if target_size is not None:
+ mask_probs = nn.functional.interpolate(
+ mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
+ )[0]
+
+ current_segment_id = 0
+
+ # Weigh each mask by its prediction score
+ mask_probs *= pred_scores.view(-1, 1, 1)
+ mask_labels = mask_probs.argmax(0) # [height, width]
+
+ # Keep track of instances of each class
+ stuff_memory_list: dict[str, int] = {}
+ for k in range(pred_labels.shape[0]):
+ pred_class = pred_labels[k].item()
+ should_fuse = pred_class in label_ids_to_fuse
+
+ # Check if mask exists and large enough to be a segment
+ mask_exists, mask_k = check_segment_validity(
+ mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
+ )
+
+ if mask_exists:
+ if pred_class in stuff_memory_list:
+ current_segment_id = stuff_memory_list[pred_class]
+ else:
+ current_segment_id += 1
+
+ # Add current object segment to final segmentation map
+ segmentation[mask_k] = current_segment_id
+ segment_score = round(pred_scores[k].item(), 6)
+ segments.append(
+ {
+ "id": current_segment_id,
+ "label_id": pred_class,
+ "was_fused": should_fuse,
+ "score": segment_score,
+ }
+ )
+ if should_fuse:
+ stuff_memory_list[pred_class] = current_segment_id
+
+ return segmentation, segments
+
+
+# TODO: (Amy) Move to image_transforms
+# Copied from transformers.models.maskformer.image_processing_maskformer.convert_segmentation_map_to_binary_masks
+def convert_segmentation_map_to_binary_masks(
+ segmentation_map: "np.ndarray",
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+):
+ if do_reduce_labels and ignore_index is None:
+ raise ValueError("If `do_reduce_labels` is True, `ignore_index` must be provided.")
+
+ if do_reduce_labels:
+ segmentation_map = np.where(segmentation_map == 0, ignore_index, segmentation_map - 1)
+
+ # Get unique ids (class or instance ids based on input)
+ all_labels = np.unique(segmentation_map)
+
+ # Drop background label if applicable
+ if ignore_index is not None:
+ all_labels = all_labels[all_labels != ignore_index]
+
+ # Generate a binary mask for each object instance
+ binary_masks = [(segmentation_map == i) for i in all_labels]
+
+ # Stack the binary masks
+ if binary_masks:
+ binary_masks = np.stack(binary_masks, axis=0)
+ else:
+ binary_masks = np.zeros((0, *segmentation_map.shape))
+
+ # Convert instance ids to class ids
+ if instance_id_to_semantic_id is not None:
+ labels = np.zeros(all_labels.shape[0])
+
+ for label in all_labels:
+ class_id = instance_id_to_semantic_id[label + 1 if do_reduce_labels else label]
+ labels[all_labels == label] = class_id - 1 if do_reduce_labels else class_id
+ else:
+ labels = all_labels
+
+ return binary_masks.astype(np.float32), labels.astype(np.int64)
+
+
+# Copied from transformers.models.maskformer.image_processing_maskformer.get_maskformer_resize_output_image_size with maskformer->mask2former
+def get_mask2former_resize_output_image_size(
+ image: np.ndarray,
+ size: Union[int, tuple[int, int], list[int], tuple[int]],
+ max_size: Optional[int] = None,
+ size_divisor: int = 0,
+ default_to_square: bool = True,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+) -> tuple[int, int]:
+ """
+ Computes the output size given the desired size.
+
+ Args:
+ image (`np.ndarray`):
+ The input image.
+ size (`int` or `tuple[int, int]` or `list[int]` or `tuple[int]`):
+ The size of the output image.
+ max_size (`int`, *optional*):
+ The maximum size of the output image.
+ size_divisor (`int`, *optional*, defaults to 0):
+ If `size_divisor` is given, the output image size will be divisible by the number.
+ default_to_square (`bool`, *optional*, defaults to `True`):
+ Whether to default to square if no size is provided.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If unset, will use the inferred format from the input.
+
+ Returns:
+ `tuple[int, int]`: The output size.
+ """
+ output_size = get_resize_output_image_size(
+ input_image=image,
+ size=size,
+ default_to_square=default_to_square,
+ max_size=max_size,
+ input_data_format=input_data_format,
+ )
+
+ if size_divisor > 0:
+ height, width = output_size
+ height = int(math.ceil(height / size_divisor) * size_divisor)
+ width = int(math.ceil(width / size_divisor) * size_divisor)
+ output_size = (height, width)
+
+ return output_size
+
+
+class Mask2FormerImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a Mask2Former image processor. The image processor can be used to prepare image(s) and optional targets
+ for the model.
+
+ This image processor inherits from [`BaseImageProcessor`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the input to a certain `size`.
+ size (`int`, *optional*, defaults to 800):
+ Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
+ sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
+ the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
+ height / width, size)`.
+ size_divisor (`int`, *optional*, defaults to 32):
+ Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
+ Swin Transformer.
+ resample (`int`, *optional*, defaults to `Resampling.BILINEAR`):
+ An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
+ `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
+ `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
+ to `True`.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the input to a certain `scale`.
+ rescale_factor (`float`, *optional*, defaults to `1/ 255`):
+ Rescale the input by the given factor. Only has an effect if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether or not to normalize the input with mean and standard deviation.
+ image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
+ The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
+ image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
+ The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
+ ImageNet std.
+ ignore_index (`int`, *optional*):
+ Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels
+ denoted with 0 (background) will be replaced with `ignore_index`.
+ do_reduce_labels (`bool`, *optional*, defaults to `False`):
+ Whether or not to decrement all label values of segmentation maps by 1. Usually used for datasets where 0
+ is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k).
+ The background label will be replaced by `ignore_index`.
+ num_labels (`int`, *optional*):
+ The number of labels in the segmentation map.
+ pad_size (`Dict[str, int]`, *optional*):
+ The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
+ provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
+ height and width in the batch.
+ """
+
+ model_input_names = ["pixel_values", "pixel_mask"]
+
+ @filter_out_non_signature_kwargs(extra=["max_size", *INIT_SERVICE_KWARGS])
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ size_divisor: int = 32,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ do_rescale: bool = True,
+ rescale_factor: float = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+ num_labels: Optional[int] = None,
+ pad_size: Optional[dict[str, int]] = None,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ # We make max_size a private attribute so we can pass it as a default value in the preprocess method whilst
+ # `size` can still be pass in as an int
+ self._max_size = kwargs.pop("max_size", 1333)
+
+ size = size if size is not None else {"shortest_edge": 800, "longest_edge": self._max_size}
+ size = get_size_dict(size, max_size=self._max_size, default_to_square=False)
+
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.size_divisor = size_divisor
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
+ self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
+ self.ignore_index = ignore_index
+ self.do_reduce_labels = do_reduce_labels
+ self.num_labels = num_labels
+ self.pad_size = pad_size
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.to_dict
+ def to_dict(self) -> dict[str, Any]:
+ """
+ Serializes this instance to a Python dictionary. This method calls the superclass method and then removes the
+ `_max_size` attribute from the dictionary.
+ """
+ image_processor_dict = super().to_dict()
+ image_processor_dict.pop("_max_size", None)
+ return image_processor_dict
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.resize with get_maskformer_resize_output_image_size->get_mask2former_resize_output_image_size
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ size_divisor: int = 0,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ data_format=None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize the image to the given size. Size can be min_size (scalar) or `(height, width)` tuple. If size is an
+ int, smaller edge of the image will be matched to this number.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ The size of the output image.
+ size_divisor (`int`, *optional*, defaults to 0):
+ If `size_divisor` is given, the output image size will be divisible by the number.
+ resample (`PILImageResampling` resampling filter, *optional*, defaults to `PILImageResampling.BILINEAR`):
+ Resampling filter to use when resizing the image.
+ data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the output image. If unset, the channel dimension format of the input
+ image is used.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+
+ # Deprecated, backward compatibility
+ max_size = kwargs.pop("max_size", None)
+
+ size = get_size_dict(size, max_size=max_size, default_to_square=False)
+ if "shortest_edge" in size and "longest_edge" in size:
+ size, max_size = size["shortest_edge"], size["longest_edge"]
+ elif "height" in size and "width" in size:
+ size = (size["height"], size["width"])
+ max_size = None
+ else:
+ raise ValueError(
+ "Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
+ f" {size.keys()}."
+ )
+ size = get_mask2former_resize_output_image_size(
+ image=image,
+ size=size,
+ max_size=max_size,
+ size_divisor=size_divisor,
+ default_to_square=False,
+ input_data_format=input_data_format,
+ )
+ image = resize(
+ image, size=size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs
+ )
+ return image
+
+ # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.rescale
+ def rescale(
+ self,
+ image: np.ndarray,
+ rescale_factor: float,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Rescale the image by the given factor. image = image * rescale_factor.
+
+ Args:
+ image (`np.ndarray`):
+ Image to rescale.
+ rescale_factor (`float`):
+ The value to use for rescaling.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the output image. If unset, the channel dimension format of the input
+ image is used. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the input image. If unset, is inferred from the input image. Can be
+ one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ """
+ return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format)
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.convert_segmentation_map_to_binary_masks
+ def convert_segmentation_map_to_binary_masks(
+ self,
+ segmentation_map: "np.ndarray",
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+ ):
+ do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
+ ignore_index = ignore_index if ignore_index is not None else self.ignore_index
+ return convert_segmentation_map_to_binary_masks(
+ segmentation_map=segmentation_map,
+ instance_id_to_semantic_id=instance_id_to_semantic_id,
+ ignore_index=ignore_index,
+ do_reduce_labels=do_reduce_labels,
+ )
+
+ def __call__(self, images, segmentation_maps=None, **kwargs) -> BatchFeature:
+ return self.preprocess(images, segmentation_maps=segmentation_maps, **kwargs)
+
+ def _preprocess(
+ self,
+ image: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ size_divisor: Optional[int] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ if do_resize:
+ image = self.resize(
+ image, size=size, size_divisor=size_divisor, resample=resample, input_data_format=input_data_format
+ )
+ if do_rescale:
+ image = self.rescale(image, rescale_factor=rescale_factor, input_data_format=input_data_format)
+ if do_normalize:
+ image = self.normalize(image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+ return image
+
+ def _preprocess_image(
+ self,
+ image: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ size_divisor: Optional[int] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """Preprocesses a single image."""
+ # All transformations expect numpy arrays.
+ image = to_numpy_array(image)
+ if do_rescale and is_scaled_image(image):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(image)
+ image = self._preprocess(
+ image=image,
+ do_resize=do_resize,
+ size=size,
+ size_divisor=size_divisor,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ input_data_format=input_data_format,
+ )
+ if data_format is not None:
+ image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+ return image
+
+ def _preprocess_mask(
+ self,
+ segmentation_map: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ size_divisor: int = 0,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """Preprocesses a single mask."""
+ segmentation_map = to_numpy_array(segmentation_map)
+ # Add channel dimension if missing - needed for certain transformations
+ if segmentation_map.ndim == 2:
+ added_channel_dim = True
+ segmentation_map = segmentation_map[None, ...]
+ input_data_format = ChannelDimension.FIRST
+ else:
+ added_channel_dim = False
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(segmentation_map)
+ # TODO: (Amy)
+ # Remork segmentation map processing to include reducing labels and resizing which doesn't
+ # drop segment IDs > 255.
+ segmentation_map = self._preprocess(
+ image=segmentation_map,
+ do_resize=do_resize,
+ resample=PILImageResampling.NEAREST,
+ size=size,
+ size_divisor=size_divisor,
+ do_rescale=False,
+ do_normalize=False,
+ input_data_format=input_data_format,
+ )
+ # Remove extra channel dimension if added for processing
+ if added_channel_dim:
+ segmentation_map = segmentation_map.squeeze(0)
+ return segmentation_map
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ segmentation_maps: Optional[ImageInput] = None,
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ size_divisor: Optional[int] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ pad_size: Optional[dict[str, int]] = None,
+ ) -> BatchFeature:
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ size = get_size_dict(size, default_to_square=False, max_size=self._max_size)
+ size_divisor = size_divisor if size_divisor is not None else self.size_divisor
+ resample = resample if resample is not None else self.resample
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ ignore_index = ignore_index if ignore_index is not None else self.ignore_index
+ do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
+ pad_size = self.pad_size if pad_size is None else pad_size
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ if segmentation_maps is not None and not valid_images(segmentation_maps):
+ raise ValueError(
+ "Invalid segmentation map type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ images = make_list_of_images(images)
+ if segmentation_maps is not None:
+ segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)
+
+ if segmentation_maps is not None and len(images) != len(segmentation_maps):
+ raise ValueError("Images and segmentation maps must have the same length.")
+
+ images = [
+ self._preprocess_image(
+ image,
+ do_resize=do_resize,
+ size=size,
+ size_divisor=size_divisor,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ for image in images
+ ]
+
+ if segmentation_maps is not None:
+ segmentation_maps = [
+ self._preprocess_mask(
+ segmentation_map, do_resize, size, size_divisor, input_data_format=input_data_format
+ )
+ for segmentation_map in segmentation_maps
+ ]
+ encoded_inputs = self.encode_inputs(
+ images,
+ segmentation_maps,
+ instance_id_to_semantic_id,
+ ignore_index,
+ do_reduce_labels,
+ return_tensors,
+ input_data_format=data_format,
+ pad_size=pad_size,
+ )
+ return encoded_inputs
+
+ # Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor._pad_image
+ def _pad_image(
+ self,
+ image: np.ndarray,
+ output_size: tuple[int, int],
+ constant_values: Union[float, Iterable[float]] = 0,
+ data_format: Optional[ChannelDimension] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Pad an image with zeros to the given size.
+ """
+ input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+ output_height, output_width = output_size
+
+ pad_bottom = output_height - input_height
+ pad_right = output_width - input_width
+ padding = ((0, pad_bottom), (0, pad_right))
+ padded_image = pad(
+ image,
+ padding,
+ mode=PaddingMode.CONSTANT,
+ constant_values=constant_values,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ return padded_image
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.pad
+ def pad(
+ self,
+ images: list[np.ndarray],
+ constant_values: Union[float, Iterable[float]] = 0,
+ return_pixel_mask: bool = True,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Optional[ChannelDimension] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ pad_size: Optional[dict[str, int]] = None,
+ ) -> BatchFeature:
+ """
+ Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
+ in the batch and optionally returns their corresponding pixel mask.
+
+ Args:
+ image (`np.ndarray`):
+ Image to pad.
+ constant_values (`float` or `Iterable[float]`, *optional*):
+ The value to use for the padding if `mode` is `"constant"`.
+ return_pixel_mask (`bool`, *optional*, defaults to `True`):
+ Whether to return a pixel mask.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ pad_size (`Dict[str, int]`, *optional*):
+ The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
+ provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
+ height and width in the batch.
+ """
+ pad_size = pad_size if pad_size is not None else self.pad_size
+ if pad_size is not None:
+ padded_size = (pad_size["height"], pad_size["width"])
+ else:
+ padded_size = get_max_height_width(images, input_data_format=input_data_format)
+
+ padded_images = [
+ self._pad_image(
+ image,
+ padded_size,
+ constant_values=constant_values,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ for image in images
+ ]
+ data = {"pixel_values": padded_images}
+
+ if return_pixel_mask:
+ masks = [
+ make_pixel_mask(image=image, output_size=padded_size, input_data_format=input_data_format)
+ for image in images
+ ]
+ data["pixel_mask"] = masks
+
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+ def encode_inputs(
+ self,
+ pixel_values_list: list[ImageInput],
+ segmentation_maps: ImageInput = None,
+ instance_id_to_semantic_id: Optional[Union[list[dict[int, int]], dict[int, int]]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ pad_size: Optional[dict[str, int]] = None,
+ ):
+ """
+ Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
+
+ Mask2Former addresses semantic segmentation with a mask classification paradigm, thus input segmentation maps
+ will be converted to lists of binary masks and their respective labels. Let's see an example, assuming
+ `segmentation_maps = [[2,6,7,9]]`, the output will contain `mask_labels =
+ [[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]` (four binary masks) and `class_labels = [2,6,7,9]`, the labels for
+ each mask.
+
+ Args:
+ pixel_values_list (`list[ImageInput]`):
+ List of images (pixel values) to be padded. Each image should be a tensor of shape `(channels, height,
+ width)`.
+
+ segmentation_maps (`ImageInput`, *optional*):
+ The corresponding semantic segmentation maps with the pixel-wise annotations.
+
+ (`bool`, *optional*, defaults to `True`):
+ Whether or not to pad images up to the largest image in a batch and create a pixel mask.
+
+ If left to the default, will return a pixel mask that is:
+
+ - 1 for pixels that are real (i.e. **not masked**),
+ - 0 for pixels that are padding (i.e. **masked**).
+
+ instance_id_to_semantic_id (`list[dict[int, int]]` or `dict[int, int]`, *optional*):
+ A mapping between object instance ids and class ids. If passed, `segmentation_maps` is treated as an
+ instance segmentation map where each pixel represents an instance id. Can be provided as a single
+ dictionary with a global/dataset-level mapping or as a list of dictionaries (one per image), to map
+ instance ids in each image separately.
+
+ return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+ If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
+ objects.
+
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+
+ pad_size (`Dict[str, int]`, *optional*):
+ The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
+ provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
+ height and width in the batch.
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **pixel_values** -- Pixel values to be fed to a model.
+ - **pixel_mask** -- Pixel mask to be fed to a model (when `=True` or if `pixel_mask` is in
+ `self.model_input_names`).
+ - **mask_labels** -- Optional list of mask labels of shape `(labels, height, width)` to be fed to a model
+ (when `annotations` are provided).
+ - **class_labels** -- Optional list of class labels of shape `(labels)` to be fed to a model (when
+ `annotations` are provided). They identify the labels of `mask_labels`, e.g. the label of
+ `mask_labels[i][j]` if `class_labels[i][j]`.
+ """
+ ignore_index = self.ignore_index if ignore_index is None else ignore_index
+ do_reduce_labels = self.do_reduce_labels if do_reduce_labels is None else do_reduce_labels
+
+ pixel_values_list = [to_numpy_array(pixel_values) for pixel_values in pixel_values_list]
+
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(pixel_values_list[0])
+
+ encoded_inputs = self.pad(
+ pixel_values_list, return_tensors=return_tensors, input_data_format=input_data_format, pad_size=pad_size
+ )
+
+ if segmentation_maps is not None:
+ mask_labels = []
+ class_labels = []
+ pad_size = get_max_height_width(pixel_values_list, input_data_format=input_data_format)
+ # Convert to list of binary masks and labels
+ for idx, segmentation_map in enumerate(segmentation_maps):
+ segmentation_map = to_numpy_array(segmentation_map)
+ if isinstance(instance_id_to_semantic_id, list):
+ instance_id = instance_id_to_semantic_id[idx]
+ else:
+ instance_id = instance_id_to_semantic_id
+ # Use instance2class_id mapping per image
+ masks, classes = self.convert_segmentation_map_to_binary_masks(
+ segmentation_map, instance_id, ignore_index=ignore_index, do_reduce_labels=do_reduce_labels
+ )
+ # We add an axis to make them compatible with the transformations library
+ # this will be removed in the future
+ if masks.shape[0] > 0:
+ masks = [mask[None, ...] for mask in masks]
+ masks = [
+ self._pad_image(image=mask, output_size=pad_size, constant_values=ignore_index)
+ for mask in masks
+ ]
+ masks = np.concatenate(masks, axis=0)
+ else:
+ masks = np.zeros((0, *pad_size), dtype=np.float32)
+ mask_labels.append(torch.from_numpy(masks))
+ class_labels.append(torch.from_numpy(classes))
+
+ # we cannot batch them since they don't share a common class size
+ encoded_inputs["mask_labels"] = mask_labels
+ encoded_inputs["class_labels"] = class_labels
+
+ return encoded_inputs
+
+ def post_process_semantic_segmentation(
+ self, outputs, target_sizes: Optional[list[tuple[int, int]]] = None
+ ) -> "torch.Tensor":
+ """
+ Converts the output of [`Mask2FormerForUniversalSegmentation`] into semantic segmentation maps. Only supports
+ PyTorch.
+
+ Args:
+ outputs ([`Mask2FormerForUniversalSegmentation`]):
+ Raw outputs of the model.
+ target_sizes (`list[tuple[int, int]]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ Returns:
+ `list[torch.Tensor]`:
+ A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
+ corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
+ `torch.Tensor` correspond to a semantic class id.
+ """
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ # Scale back to preprocessed image size - (384, 384) for all models
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits, size=(384, 384), mode="bilinear", align_corners=False
+ )
+
+ # Remove the null class `[..., :-1]`
+ masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
+ masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Semantic segmentation logits of shape (batch_size, num_classes, height, width)
+ segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
+ batch_size = class_queries_logits.shape[0]
+
+ # Resize logits and compute semantic segmentation maps
+ if target_sizes is not None:
+ if batch_size != len(target_sizes):
+ raise ValueError(
+ "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+ )
+
+ semantic_segmentation = []
+ for idx in range(batch_size):
+ resized_logits = torch.nn.functional.interpolate(
+ segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+ )
+ semantic_map = resized_logits[0].argmax(dim=0)
+ semantic_segmentation.append(semantic_map)
+ else:
+ semantic_segmentation = segmentation.argmax(dim=1)
+ semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+ return semantic_segmentation
+
+ def post_process_instance_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ return_coco_annotation: Optional[bool] = False,
+ return_binary_maps: Optional[bool] = False,
+ ) -> list[dict]:
+ """
+ Converts the output of [`Mask2FormerForUniversalSegmentationOutput`] into instance segmentation predictions.
+ Only supports PyTorch. If instances could overlap, set either return_coco_annotation or return_binary_maps
+ to `True` to get the correct segmentation result.
+
+ Args:
+ outputs ([`Mask2FormerForUniversalSegmentation`]):
+ Raw outputs of the model.
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ target_sizes (`list[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ return_coco_annotation (`bool`, *optional*, defaults to `False`):
+ If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE) format.
+ return_binary_maps (`bool`, *optional*, defaults to `False`):
+ If set to `True`, segmentation maps are returned as a concatenated tensor of binary segmentation maps
+ (one per detected instance).
+ Returns:
+ `list[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id`, or
+ `list[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
+ `True`, or a tensor of shape `(num_instances, height, width)` if return_binary_maps is set to `True`.
+ Set to `None` if no mask if found above `threshold`.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- An integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+ if return_coco_annotation and return_binary_maps:
+ raise ValueError("return_coco_annotation and return_binary_maps can not be both set to True.")
+
+ # [batch_size, num_queries, num_classes+1]
+ class_queries_logits = outputs.class_queries_logits
+ # [batch_size, num_queries, height, width]
+ masks_queries_logits = outputs.masks_queries_logits
+
+ # Scale back to preprocessed image size - (384, 384) for all models
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits, size=(384, 384), mode="bilinear", align_corners=False
+ )
+
+ device = masks_queries_logits.device
+ num_classes = class_queries_logits.shape[-1] - 1
+ num_queries = class_queries_logits.shape[-2]
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(class_queries_logits.shape[0]):
+ mask_pred = masks_queries_logits[i]
+ mask_cls = class_queries_logits[i]
+
+ scores = torch.nn.functional.softmax(mask_cls, dim=-1)[:, :-1]
+ labels = torch.arange(num_classes, device=device).unsqueeze(0).repeat(num_queries, 1).flatten(0, 1)
+
+ scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False)
+ labels_per_image = labels[topk_indices]
+
+ topk_indices = torch.div(topk_indices, num_classes, rounding_mode="floor")
+ mask_pred = mask_pred[topk_indices]
+ pred_masks = (mask_pred > 0).float()
+
+ # Calculate average mask prob
+ mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * pred_masks.flatten(1)).sum(1) / (
+ pred_masks.flatten(1).sum(1) + 1e-6
+ )
+ pred_scores = scores_per_image * mask_scores_per_image
+ pred_classes = labels_per_image
+
+ segmentation = torch.zeros((384, 384)) - 1
+ if target_sizes is not None:
+ segmentation = torch.zeros(target_sizes[i]) - 1
+ pred_masks = torch.nn.functional.interpolate(
+ pred_masks.unsqueeze(0), size=target_sizes[i], mode="nearest"
+ )[0]
+
+ instance_maps, segments = [], []
+ current_segment_id = 0
+ for j in range(num_queries):
+ score = pred_scores[j].item()
+
+ if not torch.all(pred_masks[j] == 0) and score >= threshold:
+ segmentation[pred_masks[j] == 1] = current_segment_id
+ segments.append(
+ {
+ "id": current_segment_id,
+ "label_id": pred_classes[j].item(),
+ "was_fused": False,
+ "score": round(score, 6),
+ }
+ )
+ current_segment_id += 1
+ instance_maps.append(pred_masks[j])
+
+ # Return segmentation map in run-length encoding (RLE) format
+ if return_coco_annotation:
+ segmentation = convert_segmentation_to_rle(segmentation)
+
+ # Return a concatenated tensor of binary instance maps
+ if return_binary_maps and len(instance_maps) != 0:
+ segmentation = torch.stack(instance_maps, dim=0)
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+ def post_process_panoptic_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ label_ids_to_fuse: Optional[set[int]] = None,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ ) -> list[dict]:
+ """
+ Converts the output of [`Mask2FormerForUniversalSegmentationOutput`] into image panoptic segmentation
+ predictions. Only supports PyTorch.
+
+ Args:
+ outputs ([`Mask2FormerForUniversalSegmentationOutput`]):
+ The outputs from [`Mask2FormerForUniversalSegmentation`].
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ label_ids_to_fuse (`Set[int]`, *optional*):
+ The labels in this state will have all their instances be fused together. For instance we could say
+ there can only be one sky in an image, but several persons, so the label ID for sky would be in that
+ set, but not the one for person.
+ target_sizes (`list[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction in batch. If left to None, predictions will not be
+ resized.
+
+ Returns:
+ `list[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set
+ to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized
+ to the corresponding `target_sizes` entry.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- an integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
+ Multiple instances of the same class / label were fused and assigned a single `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+
+ if label_ids_to_fuse is None:
+ logger.warning("`label_ids_to_fuse` unset. No instance will be fused.")
+ label_ids_to_fuse = set()
+
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ # Scale back to preprocessed image size - (384, 384) for all models
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits, size=(384, 384), mode="bilinear", align_corners=False
+ )
+
+ batch_size = class_queries_logits.shape[0]
+ num_labels = class_queries_logits.shape[-1] - 1
+
+ mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Predicted label and score of each query (batch_size, num_queries)
+ pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(batch_size):
+ mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
+ mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
+ )
+
+ # No mask found
+ if mask_probs_item.shape[0] <= 0:
+ height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
+ segmentation = torch.zeros((height, width)) - 1
+ results.append({"segmentation": segmentation, "segments_info": []})
+ continue
+
+ # Get segmentation map and segment information of batch item
+ target_size = target_sizes[i] if target_sizes is not None else None
+ segmentation, segments = compute_segments(
+ mask_probs=mask_probs_item,
+ pred_scores=pred_scores_item,
+ pred_labels=pred_labels_item,
+ mask_threshold=mask_threshold,
+ overlap_mask_area_threshold=overlap_mask_area_threshold,
+ label_ids_to_fuse=label_ids_to_fuse,
+ target_size=target_size,
+ )
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+
+__all__ = ["Mask2FormerImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/image_processing_mask2former_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/image_processing_mask2former_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..85d5c19c38243c2b5cb23c7f64a0f83542bb366b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/image_processing_mask2former_fast.py
@@ -0,0 +1,721 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/mask2former/modular_mask2former.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_mask2former.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+from typing import Any, Optional, Union
+
+from ...image_processing_utils import BatchFeature, get_size_dict
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ SizeDict,
+ get_image_size_for_max_height_width,
+ get_max_height_width,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_utils import (
+ IMAGENET_DEFAULT_MEAN,
+ IMAGENET_DEFAULT_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+ logging,
+)
+from .image_processing_mask2former import (
+ compute_segments,
+ convert_segmentation_to_rle,
+ get_size_with_aspect_ratio,
+ remove_low_and_no_objects,
+)
+
+
+if is_torch_available():
+ import torch
+ from torch import nn
+
+
+if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+
+elif is_torchvision_available():
+ from torchvision.transforms import functional as F
+
+
+logger = logging.get_logger(__name__)
+
+
+class Mask2FormerFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ r"""
+ size_divisor (`int`, *optional*, defaults to 32):
+ Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
+ Swin Transformer.
+ ignore_index (`int`, *optional*):
+ Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels
+ denoted with 0 (background) will be replaced with `ignore_index`.
+ do_reduce_labels (`bool`, *optional*, defaults to `False`):
+ Whether or not to decrement all label values of segmentation maps by 1. Usually used for datasets where 0
+ is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k).
+ The background label will be replaced by `ignore_index`.
+ num_labels (`int`, *optional*):
+ The number of labels in the segmentation map.
+ do_pad (`bool`, *optional*, defaults to `True`):
+ Controls whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess`
+ method. If `True`, padding will be applied to the bottom and right of the image with zeros.
+ If `pad_size` is provided, the image will be padded to the specified dimensions.
+ Otherwise, the image will be padded to the maximum height and width of the batch.
+ pad_size (`Dict[str, int]`, *optional*):
+ The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
+ provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
+ height and width in the batch.
+ """
+
+ size_divisor: Optional[int]
+ ignore_index: Optional[int]
+ do_reduce_labels: Optional[bool]
+ num_labels: Optional[int]
+ do_pad: Optional[bool]
+ pad_size: Optional[dict[str, int]]
+
+
+def convert_segmentation_map_to_binary_masks_fast(
+ segmentation_map: "torch.Tensor",
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+):
+ if do_reduce_labels and ignore_index is None:
+ raise ValueError("If `do_reduce_labels` is True, `ignore_index` must be provided.")
+
+ if do_reduce_labels:
+ segmentation_map = torch.where(segmentation_map == 0, ignore_index, segmentation_map - 1)
+
+ all_labels = torch.unique(segmentation_map)
+
+ if ignore_index is not None:
+ all_labels = all_labels[all_labels != ignore_index] # drop background label if applicable
+
+ binary_masks = [(segmentation_map == i) for i in all_labels]
+ if binary_masks:
+ binary_masks = torch.stack(binary_masks, dim=0)
+ else:
+ binary_masks = torch.zeros((0, *segmentation_map.shape), device=segmentation_map.device)
+
+ # Convert instance ids to class ids
+ if instance_id_to_semantic_id is not None:
+ labels = torch.zeros(all_labels.shape[0], device=segmentation_map.device)
+
+ for i, label in enumerate(all_labels):
+ class_id = instance_id_to_semantic_id[(label.item() + 1 if do_reduce_labels else label.item())]
+ labels[i] = class_id - 1 if do_reduce_labels else class_id
+ else:
+ labels = all_labels
+ return binary_masks.float(), labels.long()
+
+
+@auto_docstring
+class Mask2FormerImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BILINEAR
+ image_mean = IMAGENET_DEFAULT_MEAN
+ image_std = IMAGENET_DEFAULT_STD
+ size = {"shortest_edge": 800, "longest_edge": 1333}
+ default_to_square = False
+ do_resize = True
+ do_rescale = True
+ rescale_factor = 1 / 255
+ do_normalize = True
+ do_pad = True
+ model_input_names = ["pixel_values", "pixel_mask"]
+ size_divisor = 32
+ do_reduce_labels = False
+ valid_kwargs = Mask2FormerFastImageProcessorKwargs
+
+ def __init__(self, **kwargs: Unpack[Mask2FormerFastImageProcessorKwargs]) -> None:
+ if "pad_and_return_pixel_mask" in kwargs:
+ kwargs["do_pad"] = kwargs.pop("pad_and_return_pixel_mask")
+
+ size = kwargs.pop("size", None)
+ max_size = kwargs.pop("max_size", None)
+
+ if size is None and max_size is not None:
+ size = self.size
+ size["longest_edge"] = max_size
+ elif size is None:
+ size = self.size
+
+ self.size = get_size_dict(size, max_size=max_size, default_to_square=False)
+
+ super().__init__(**kwargs)
+
+ def to_dict(self) -> dict[str, Any]:
+ """
+ Serializes this instance to a Python dictionary. This method calls the superclass method and then removes the
+ `_max_size` attribute from the dictionary.
+ """
+ image_processor_dict = super().to_dict()
+ image_processor_dict.pop("_max_size", None)
+ return image_processor_dict
+
+ def reduce_label(self, labels: list["torch.Tensor"]):
+ for idx in range(len(labels)):
+ label = labels[idx]
+ label = torch.where(label == 0, torch.tensor(255, dtype=label.dtype), label)
+ label = label - 1
+ label = torch.where(label == 254, torch.tensor(255, dtype=label.dtype), label)
+ labels[idx] = label
+
+ def resize(
+ self,
+ image: torch.Tensor,
+ size: SizeDict,
+ size_divisor: int = 0,
+ interpolation: "F.InterpolationMode" = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ """
+ Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an
+ int, smaller edge of the image will be matched to this number.
+
+ Args:
+ image (`torch.Tensor`):
+ Image to resize.
+ size (`SizeDict`):
+ Size of the image's `(height, width)` dimensions after resizing. Available options are:
+ - `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`.
+ Do NOT keep the aspect ratio.
+ - `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting
+ the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge
+ less or equal to `longest_edge`.
+ - `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the
+ aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to
+ `max_width`.
+ size_divisor (`int`, *optional*, defaults to 0):
+ If `size_divisor` is given, the output image size will be divisible by the number.
+ interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
+ Resampling filter to use if resizing the image.
+ """
+ interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
+ if size.shortest_edge and size.longest_edge:
+ # Resize the image so that the shortest edge or the longest edge is of the given size
+ # while maintaining the aspect ratio of the original image.
+ new_size = get_size_with_aspect_ratio(
+ image.size()[-2:],
+ size["shortest_edge"],
+ size["longest_edge"],
+ )
+ elif size.max_height and size.max_width:
+ new_size = get_image_size_for_max_height_width(image.size()[-2:], size["max_height"], size["max_width"])
+ elif size.height and size.width:
+ new_size = (size["height"], size["width"])
+ else:
+ raise ValueError(
+ "Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
+ f" {size.keys()}."
+ )
+ if size_divisor > 0:
+ height, width = new_size
+ height = int(math.ceil(height / size_divisor) * size_divisor)
+ width = int(math.ceil(width / size_divisor) * size_divisor)
+ new_size = (height, width)
+
+ image = F.resize(
+ image,
+ size=new_size,
+ interpolation=interpolation,
+ **kwargs,
+ )
+ return image
+
+ def pad(
+ self,
+ images: torch.Tensor,
+ padded_size: tuple[int, int],
+ segmentation_maps: Optional[torch.Tensor] = None,
+ fill: int = 0,
+ ignore_index: int = 255,
+ ) -> BatchFeature:
+ original_size = images.size()[-2:]
+ padding_bottom = padded_size[0] - original_size[0]
+ padding_right = padded_size[1] - original_size[1]
+ if padding_bottom < 0 or padding_right < 0:
+ raise ValueError(
+ f"Padding dimensions are negative. Please make sure that the padded size is larger than the "
+ f"original size. Got padded size: {padded_size}, original size: {original_size}."
+ )
+ if original_size != padded_size:
+ padding = [0, 0, padding_right, padding_bottom]
+ images = F.pad(images, padding, fill=fill)
+ if segmentation_maps is not None:
+ segmentation_maps = F.pad(segmentation_maps, padding, fill=ignore_index)
+
+ # Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
+ pixel_mask = torch.zeros((images.shape[0], *padded_size), dtype=torch.int64, device=images.device)
+ pixel_mask[:, : original_size[0], : original_size[1]] = 1
+
+ return images, pixel_mask, segmentation_maps
+
+ @auto_docstring
+ def preprocess(
+ self,
+ images: ImageInput,
+ segmentation_maps: Optional[ImageInput] = None,
+ instance_id_to_semantic_id: Optional[Union[list[dict[int, int]], dict[int, int]]] = None,
+ **kwargs: Unpack[Mask2FormerFastImageProcessorKwargs],
+ ) -> BatchFeature:
+ r"""
+ segmentation_maps (`ImageInput`, *optional*):
+ The segmentation maps.
+ instance_id_to_semantic_id (`Union[list[dict[int, int]], dict[int, int]]`, *optional*):
+ A mapping from instance IDs to semantic IDs.
+ """
+ return super().preprocess(
+ images,
+ segmentation_maps,
+ instance_id_to_semantic_id,
+ **kwargs,
+ )
+
+ def _preprocess_image_like_inputs(
+ self,
+ images: ImageInput,
+ segmentation_maps: ImageInput,
+ instance_id_to_semantic_id: Optional[Union[list[dict[int, int]], dict[int, int]]],
+ do_convert_rgb: bool,
+ input_data_format: ChannelDimension,
+ device: Optional[Union[str, "torch.device"]] = None,
+ **kwargs: Unpack[Mask2FormerFastImageProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Preprocess image-like inputs.
+ To be overridden by subclasses when image-like inputs other than images should be processed.
+ It can be used for segmentation maps, depth maps, etc.
+ """
+ # Prepare input images
+ images = self._prepare_image_like_inputs(
+ images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
+ )
+ if segmentation_maps is not None:
+ segmentation_maps = self._prepare_image_like_inputs(
+ images=segmentation_maps,
+ expected_ndims=2,
+ do_convert_rgb=False,
+ input_data_format=ChannelDimension.FIRST,
+ )
+ return self._preprocess(images, segmentation_maps, instance_id_to_semantic_id, **kwargs)
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ segmentation_maps: Optional["torch.Tensor"],
+ instance_id_to_semantic_id: Optional[dict[int, int]],
+ do_resize: Optional[bool],
+ size: Optional[dict[str, int]],
+ pad_size: Optional[dict[str, int]],
+ size_divisor: Optional[int],
+ interpolation: Optional[Union["PILImageResampling", "F.InterpolationMode"]],
+ do_rescale: Optional[bool],
+ rescale_factor: Optional[float],
+ do_normalize: Optional[bool],
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ ignore_index: Optional[int],
+ do_reduce_labels: Optional[bool],
+ disable_grouping: Optional[bool],
+ return_tensors: Optional[Union[str, TensorType]],
+ **kwargs,
+ ) -> BatchFeature:
+ if segmentation_maps is not None and len(images) != len(segmentation_maps):
+ raise ValueError("Images and segmentation maps must have the same length.")
+
+ # Group images by size for batched resizing
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ resized_images_grouped = {}
+ if segmentation_maps is not None:
+ grouped_segmentation_maps, grouped_segmentation_maps_index = group_images_by_shape(
+ segmentation_maps, disable_grouping=disable_grouping
+ )
+ resized_segmentation_maps_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_resize:
+ stacked_images = self.resize(
+ image=stacked_images, size=size, size_divisor=size_divisor, interpolation=interpolation
+ )
+ if segmentation_maps is not None:
+ stacked_segmentation_maps = self.resize(
+ image=grouped_segmentation_maps[shape],
+ size=size,
+ size_divisor=size_divisor,
+ interpolation=F.InterpolationMode.NEAREST_EXACT
+ if is_torchvision_v2_available()
+ else F.InterpolationMode.NEAREST,
+ )
+ resized_images_grouped[shape] = stacked_images
+ if segmentation_maps is not None:
+ resized_segmentation_maps_grouped[shape] = stacked_segmentation_maps
+ resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+ if segmentation_maps is not None:
+ resized_segmentation_maps = reorder_images(
+ resized_segmentation_maps_grouped, grouped_segmentation_maps_index
+ )
+ if pad_size is not None:
+ padded_size = (pad_size["height"], pad_size["width"])
+ else:
+ padded_size = get_max_height_width(resized_images)
+
+ if segmentation_maps is not None:
+ mask_labels = []
+ class_labels = []
+ # Convert to list of binary masks and labels
+ for idx, segmentation_map in enumerate(resized_segmentation_maps):
+ if isinstance(instance_id_to_semantic_id, list):
+ instance_id = instance_id_to_semantic_id[idx]
+ else:
+ instance_id = instance_id_to_semantic_id
+ # Use instance2class_id mapping per image
+ masks, classes = convert_segmentation_map_to_binary_masks_fast(
+ segmentation_map.squeeze(0),
+ instance_id,
+ ignore_index=ignore_index,
+ do_reduce_labels=do_reduce_labels,
+ )
+ mask_labels.append(masks)
+ class_labels.append(classes)
+
+ grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+ processed_images_grouped = {}
+ processed_pixel_masks_grouped = {}
+ if segmentation_maps is not None:
+ grouped_segmentation_maps, grouped_segmentation_maps_index = group_images_by_shape(
+ mask_labels, disable_grouping=disable_grouping
+ )
+ processed_segmentation_maps_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ # Fused rescale and normalize
+ stacked_images = self.rescale_and_normalize(
+ stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ padded_images, pixel_masks, padded_segmentation_maps = self.pad(
+ images=stacked_images,
+ segmentation_maps=grouped_segmentation_maps[shape] if segmentation_maps is not None else None,
+ padded_size=padded_size,
+ ignore_index=ignore_index,
+ )
+ processed_images_grouped[shape] = padded_images
+ processed_pixel_masks_grouped[shape] = pixel_masks
+ if segmentation_maps is not None:
+ processed_segmentation_maps_grouped[shape] = padded_segmentation_maps.squeeze(1)
+ processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+ processed_pixel_masks = reorder_images(processed_pixel_masks_grouped, grouped_images_index)
+ encoded_inputs = BatchFeature(
+ data={
+ "pixel_values": torch.stack(processed_images, dim=0) if return_tensors else processed_images,
+ "pixel_mask": torch.stack(processed_pixel_masks, dim=0) if return_tensors else processed_pixel_masks,
+ },
+ tensor_type=return_tensors,
+ )
+ if segmentation_maps is not None:
+ mask_labels = reorder_images(processed_segmentation_maps_grouped, grouped_segmentation_maps_index)
+ # we cannot batch them since they don't share a common class size
+ encoded_inputs["mask_labels"] = mask_labels
+ encoded_inputs["class_labels"] = class_labels
+
+ return encoded_inputs
+
+ def post_process_semantic_segmentation(
+ self, outputs, target_sizes: Optional[list[tuple[int, int]]] = None
+ ) -> "torch.Tensor":
+ """
+ Converts the output of [`Mask2FormerForUniversalSegmentation`] into semantic segmentation maps. Only supports
+ PyTorch.
+
+ Args:
+ outputs ([`Mask2FormerForUniversalSegmentation`]):
+ Raw outputs of the model.
+ target_sizes (`List[Tuple[int, int]]`, *optional*):
+ List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ Returns:
+ `List[torch.Tensor]`:
+ A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
+ corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
+ `torch.Tensor` correspond to a semantic class id.
+ """
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ # Scale back to preprocessed image size - (384, 384) for all models
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits, size=(384, 384), mode="bilinear", align_corners=False
+ )
+
+ # Remove the null class `[..., :-1]`
+ masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
+ masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Semantic segmentation logits of shape (batch_size, num_classes, height, width)
+ segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
+ batch_size = class_queries_logits.shape[0]
+
+ # Resize logits and compute semantic segmentation maps
+ if target_sizes is not None:
+ if batch_size != len(target_sizes):
+ raise ValueError(
+ "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+ )
+
+ semantic_segmentation = []
+ for idx in range(batch_size):
+ resized_logits = torch.nn.functional.interpolate(
+ segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+ )
+ semantic_map = resized_logits[0].argmax(dim=0)
+ semantic_segmentation.append(semantic_map)
+ else:
+ semantic_segmentation = segmentation.argmax(dim=1)
+ semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+ return semantic_segmentation
+
+ def post_process_instance_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ return_coco_annotation: Optional[bool] = False,
+ return_binary_maps: Optional[bool] = False,
+ ) -> list[dict]:
+ """
+ Converts the output of [`Mask2FormerForUniversalSegmentationOutput`] into instance segmentation predictions.
+ Only supports PyTorch. If instances could overlap, set either return_coco_annotation or return_binary_maps
+ to `True` to get the correct segmentation result.
+
+ Args:
+ outputs ([`Mask2FormerForUniversalSegmentation`]):
+ Raw outputs of the model.
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ target_sizes (`List[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ return_coco_annotation (`bool`, *optional*, defaults to `False`):
+ If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE) format.
+ return_binary_maps (`bool`, *optional*, defaults to `False`):
+ If set to `True`, segmentation maps are returned as a concatenated tensor of binary segmentation maps
+ (one per detected instance).
+ Returns:
+ `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id`, or
+ `List[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
+ `True`, or a tensor of shape `(num_instances, height, width)` if return_binary_maps is set to `True`.
+ Set to `None` if no mask if found above `threshold`.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- An integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+ if return_coco_annotation and return_binary_maps:
+ raise ValueError("return_coco_annotation and return_binary_maps can not be both set to True.")
+
+ # [batch_size, num_queries, num_classes+1]
+ class_queries_logits = outputs.class_queries_logits
+ # [batch_size, num_queries, height, width]
+ masks_queries_logits = outputs.masks_queries_logits
+
+ # Scale back to preprocessed image size - (384, 384) for all models
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits, size=(384, 384), mode="bilinear", align_corners=False
+ )
+
+ device = masks_queries_logits.device
+ num_classes = class_queries_logits.shape[-1] - 1
+ num_queries = class_queries_logits.shape[-2]
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(class_queries_logits.shape[0]):
+ mask_pred = masks_queries_logits[i]
+ mask_cls = class_queries_logits[i]
+
+ scores = torch.nn.functional.softmax(mask_cls, dim=-1)[:, :-1]
+ labels = torch.arange(num_classes, device=device).unsqueeze(0).repeat(num_queries, 1).flatten(0, 1)
+
+ scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False)
+ labels_per_image = labels[topk_indices]
+
+ topk_indices = torch.div(topk_indices, num_classes, rounding_mode="floor")
+ mask_pred = mask_pred[topk_indices]
+ pred_masks = (mask_pred > 0).float()
+
+ # Calculate average mask prob
+ mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * pred_masks.flatten(1)).sum(1) / (
+ pred_masks.flatten(1).sum(1) + 1e-6
+ )
+ pred_scores = scores_per_image * mask_scores_per_image
+ pred_classes = labels_per_image
+
+ segmentation = torch.zeros((384, 384)) - 1
+ if target_sizes is not None:
+ segmentation = torch.zeros(target_sizes[i]) - 1
+ pred_masks = torch.nn.functional.interpolate(
+ pred_masks.unsqueeze(0), size=target_sizes[i], mode="nearest"
+ )[0]
+
+ instance_maps, segments = [], []
+ current_segment_id = 0
+ for j in range(num_queries):
+ score = pred_scores[j].item()
+
+ if not torch.all(pred_masks[j] == 0) and score >= threshold:
+ segmentation[pred_masks[j] == 1] = current_segment_id
+ segments.append(
+ {
+ "id": current_segment_id,
+ "label_id": pred_classes[j].item(),
+ "was_fused": False,
+ "score": round(score, 6),
+ }
+ )
+ current_segment_id += 1
+ instance_maps.append(pred_masks[j])
+
+ # Return segmentation map in run-length encoding (RLE) format
+ if return_coco_annotation:
+ segmentation = convert_segmentation_to_rle(segmentation)
+
+ # Return a concatenated tensor of binary instance maps
+ if return_binary_maps and len(instance_maps) != 0:
+ segmentation = torch.stack(instance_maps, dim=0)
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+ def post_process_panoptic_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ label_ids_to_fuse: Optional[set[int]] = None,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ ) -> list[dict]:
+ """
+ Converts the output of [`Mask2FormerForUniversalSegmentationOutput`] into image panoptic segmentation
+ predictions. Only supports PyTorch.
+
+ Args:
+ outputs ([`Mask2FormerForUniversalSegmentationOutput`]):
+ The outputs from [`Mask2FormerForUniversalSegmentation`].
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ label_ids_to_fuse (`Set[int]`, *optional*):
+ The labels in this state will have all their instances be fused together. For instance we could say
+ there can only be one sky in an image, but several persons, so the label ID for sky would be in that
+ set, but not the one for person.
+ target_sizes (`List[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction in batch. If left to None, predictions will not be
+ resized.
+
+ Returns:
+ `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set
+ to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized
+ to the corresponding `target_sizes` entry.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- an integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
+ Multiple instances of the same class / label were fused and assigned a single `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+
+ if label_ids_to_fuse is None:
+ logger.warning("`label_ids_to_fuse` unset. No instance will be fused.")
+ label_ids_to_fuse = set()
+
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ # Scale back to preprocessed image size - (384, 384) for all models
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits, size=(384, 384), mode="bilinear", align_corners=False
+ )
+
+ batch_size = class_queries_logits.shape[0]
+ num_labels = class_queries_logits.shape[-1] - 1
+
+ mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Predicted label and score of each query (batch_size, num_queries)
+ pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(batch_size):
+ mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
+ mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
+ )
+
+ # No mask found
+ if mask_probs_item.shape[0] <= 0:
+ height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
+ segmentation = torch.zeros((height, width)) - 1
+ results.append({"segmentation": segmentation, "segments_info": []})
+ continue
+
+ # Get segmentation map and segment information of batch item
+ target_size = target_sizes[i] if target_sizes is not None else None
+ segmentation, segments = compute_segments(
+ mask_probs=mask_probs_item,
+ pred_scores=pred_scores_item,
+ pred_labels=pred_labels_item,
+ mask_threshold=mask_threshold,
+ overlap_mask_area_threshold=overlap_mask_area_threshold,
+ label_ids_to_fuse=label_ids_to_fuse,
+ target_size=target_size,
+ )
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+
+__all__ = ["Mask2FormerImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/modeling_mask2former.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/modeling_mask2former.py
new file mode 100644
index 0000000000000000000000000000000000000000..9899a2d41ed10ef4e16d16cfadff12ec2bfe8a78
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/modeling_mask2former.py
@@ -0,0 +1,2488 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Mask2Former model."""
+
+import math
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import numpy as np
+import torch
+from torch import Tensor, nn
+
+from ...activations import ACT2FN
+from ...file_utils import ModelOutput, is_scipy_available, requires_backends
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import compile_compatible_method_lru_cache
+from ...utils import auto_docstring, is_accelerate_available, logging
+from ...utils.backbone_utils import load_backbone
+from .configuration_mask2former import Mask2FormerConfig
+
+
+if is_scipy_available():
+ from scipy.optimize import linear_sum_assignment
+
+if is_accelerate_available():
+ from accelerate import PartialState
+ from accelerate.utils import reduce
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Mask2Former's pixel decoder module output, practically a Multi-Scale Deformable Attention based decoder. It returns
+ the mask features and the multiscale features.
+ """
+)
+class Mask2FormerPixelDecoderOutput(ModelOutput):
+ r"""
+ multi_scale_features (`tuple(torch.FloatTensor)`):
+ Tuple of multi-scale features of scales [1/8, 1/16, 1/32] and shape `(batch_size, num_channels, height,
+ width)`from the Multi-Scale Deformable Attenntion based Pixel Decoder.
+ mask_features (`torch.FloatTensor`):
+ Tensor of shape `(batch_size, num_channels, height, width)`, 1/4 scale features from the last Pixel Decoder
+ Layer.
+ attentions (`tuple(torch.FloatTensor)`, *optional*):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights from pixel decoder. Returned when `output_attentions=True` is passed
+ or when `config.output_attentions=True`
+ """
+
+ multi_scale_features: tuple[torch.FloatTensor] = None
+ mask_features: Optional[torch.FloatTensor] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of the Transformer decoder. This class adds two attributes to
+ BaseModelOutputWithCrossAttentions for mask predictions logits and a tuple of intermediate decoder activations,
+ i.e. the output of each decoder layer, each of them gone through a layernorm.
+ """
+)
+class Mask2FormerMaskedAttentionDecoderOutput(BaseModelOutputWithCrossAttentions):
+ r"""
+ hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
+ plus the initial embedding outputs. Returned when `output_hidden_states=True`.
+ attentions (`tuple(torch.FloatTensor)`, *optional*):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
+ the self-attention heads. Returned when `output_attentions=True`.
+ masks_queries_logits (`tuple(torch.FloatTensor)` of shape `(batch_size, num_queries, height, width)`):
+ Tuple of mask predictions from all layers of the transformer decoder.
+ intermediate_hidden_states (`tuple(torch.FloatTensor)` of shape `(num_queries, 1, hidden_size)`):
+ Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
+ layernorm.
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[torch.FloatTensor] = None
+ masks_queries_logits: tuple[torch.FloatTensor] = None
+ intermediate_hidden_states: tuple[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Mask2Former's pixel level module output. It returns the output of the encoder (optional) and all hidden states
+ (multi-scale features) from the `decoder`. By default, the `encoder` is a Swin Backbone and the `decoder` is a
+ Multi-Scale Deformable Attention based decoder.
+
+ The `decoder_last_hidden_state` are the **per-pixel embeddings** while `decoder_hidden_states` refer to multi-scale
+ feature maps produced using **multi-scaling strategy** defined in the paper.
+ """
+)
+class Mask2FormerPixelLevelModuleOutput(ModelOutput):
+ r"""
+ encoder_last_hidden_state (`torch.FloatTensor`):
+ Last hidden states (final feature map of shape `(batch_size, num_channels, height, width)`) of the last
+ stage of the encoder.
+ encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+ Tuple of `torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`. Hidden states (also
+ called feature maps) of the model at the output of each stage. Returned if output_hidden_states is set to
+ True.
+ decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)):
+ 1/4 scale features from the last Pixel Decoder Layer.
+ decoder_hidden_states (`tuple(torch.FloatTensor)`):
+ Tuple of `torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`. Hidden states (also
+ called feature maps) of the model at the output of each stage.
+ """
+
+ encoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ decoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ decoder_hidden_states: tuple[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Class for outputs of [`Mask2FormerModel`]. This class returns all the needed hidden states to compute the logits.
+ """
+)
+class Mask2FormerModelOutput(ModelOutput):
+ r"""
+ encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, *optional*):
+ Last hidden states (final feature map) of the last stage of the encoder model (backbone). Returned when
+ `output_hidden_states=True` is passed.
+ pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, *optional*):
+ Last hidden states (final feature map) of the last stage of the pixel decoder model.
+ transformer_decoder_last_hidden_state (`tuple(torch.FloatTensor)`):
+ Final output of the transformer decoder `(batch_size, sequence_length, hidden_size)`.
+ encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
+ model at the output of each stage. Returned when `output_hidden_states=True` is passed.
+ pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, , *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
+ decoder model at the output of each stage. Returned when `output_hidden_states=True` is passed.
+ transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the
+ transformer decoder at the output of each stage. Returned when `output_hidden_states=True` is passed.
+ transformer_decoder_intermediate_states (`tuple(torch.FloatTensor)` of shape `(num_queries, 1, hidden_size)`):
+ Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
+ layernorm.
+ masks_queries_logits (`tuple(torch.FloatTensor)` of shape `(batch_size, num_queries, height, width)`)
+ Mask Predictions from each layer in the transformer decoder.
+ attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed):
+ Tuple of `tuple(torch.FloatTensor)` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Self attentions weights from transformer decoder.
+ """
+
+ encoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ pixel_decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ transformer_decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ transformer_decoder_intermediate_states: tuple[torch.FloatTensor] = None
+ masks_queries_logits: tuple[torch.FloatTensor] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Class for outputs of [`Mask2FormerForUniversalSegmentationOutput`].
+
+ This output can be directly passed to [`~Mask2FormerImageProcessor.post_process_semantic_segmentation`] or
+ [`~Mask2FormerImageProcessor.post_process_instance_segmentation`] or
+ [`~Mask2FormerImageProcessor.post_process_panoptic_segmentation`] to compute final segmentation maps. Please, see
+ [`~Mask2FormerImageProcessor] for details regarding usage.
+ """
+)
+class Mask2FormerForUniversalSegmentationOutput(ModelOutput):
+ r"""
+ loss (`torch.Tensor`, *optional*):
+ The computed loss, returned when labels are present.
+ class_queries_logits (`torch.FloatTensor`):
+ A tensor of shape `(batch_size, num_queries, num_labels + 1)` representing the proposed classes for each
+ query. Note the `+ 1` is needed because we incorporate the null class.
+ masks_queries_logits (`torch.FloatTensor`):
+ A tensor of shape `(batch_size, num_queries, height, width)` representing the proposed masks for each
+ query.
+ auxiliary_logits (`list[Dict(str, torch.FloatTensor)]`, *optional*):
+ List of class and mask predictions from each layer of the transformer decoder.
+ encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+ Last hidden states (final feature map) of the last stage of the encoder model (backbone).
+ pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+ Last hidden states (final feature map) of the last stage of the pixel decoder model.
+ transformer_decoder_last_hidden_state (`tuple(torch.FloatTensor)`):
+ Final output of the transformer decoder `(batch_size, sequence_length, hidden_size)`.
+ encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
+ model at the output of each stage.
+ pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
+ decoder model at the output of each stage.
+ transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the
+ transformer decoder at the output of each stage.
+ attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tuple(torch.FloatTensor)` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Self and Cross Attentions weights from transformer decoder.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ class_queries_logits: Optional[torch.FloatTensor] = None
+ masks_queries_logits: Optional[torch.FloatTensor] = None
+ auxiliary_logits: Optional[list[dict[str, torch.FloatTensor]]] = None
+ encoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ pixel_decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ transformer_decoder_hidden_states: Optional[torch.FloatTensor] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+# Adapted from https://github.com/facebookresearch/detectron2/blob/main/projects/PointRend/point_rend/point_features.py
+def sample_point(
+ input_features: torch.Tensor, point_coordinates: torch.Tensor, add_dim=False, **kwargs
+) -> torch.Tensor:
+ """
+ A wrapper around `torch.nn.functional.grid_sample` to support 3D point_coordinates tensors.
+
+ Args:
+ input_features (`torch.Tensor` of shape (batch_size, channels, height, width)):
+ A tensor that contains features map on a height * width grid
+ point_coordinates (`torch.Tensor` of shape (batch_size, num_points, 2) or (batch_size, grid_height, grid_width,:
+ 2)):
+ A tensor that contains [0, 1] * [0, 1] normalized point coordinates
+ add_dim (`bool`):
+ boolean value to keep track of added dimension
+
+ Returns:
+ point_features (`torch.Tensor` of shape (batch_size, channels, num_points) or (batch_size, channels,
+ height_grid, width_grid):
+ A tensor that contains features for points in `point_coordinates`.
+ """
+ if point_coordinates.dim() == 3:
+ add_dim = True
+ point_coordinates = point_coordinates.unsqueeze(2)
+
+ # use nn.function.grid_sample to get features for points in `point_coordinates` via bilinear interpolation
+ point_features = torch.nn.functional.grid_sample(input_features, 2.0 * point_coordinates - 1.0, **kwargs)
+ if add_dim:
+ point_features = point_features.squeeze(3)
+
+ return point_features
+
+
+# Copied from transformers.models.maskformer.modeling_maskformer.dice_loss
+def dice_loss(inputs: Tensor, labels: Tensor, num_masks: int) -> Tensor:
+ r"""
+ Compute the DICE loss, similar to generalized IOU for masks as follows:
+
+ $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x \cap y }{x \cup y + 1}} $$
+
+ In practice, since `labels` is a binary mask, (only 0s and 1s), dice can be computed as follow
+
+ $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x * y }{x + y + 1}} $$
+
+ Args:
+ inputs (`torch.Tensor`):
+ A tensor representing a mask.
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ num_masks (`int`):
+ The number of masks present in the current batch, used for normalization.
+
+ Returns:
+ `torch.Tensor`: The computed loss.
+ """
+ probs = inputs.sigmoid().flatten(1)
+ numerator = 2 * (probs * labels).sum(-1)
+ denominator = probs.sum(-1) + labels.sum(-1)
+ loss = 1 - (numerator + 1) / (denominator + 1)
+ loss = loss.sum() / num_masks
+ return loss
+
+
+def sigmoid_cross_entropy_loss(inputs: torch.Tensor, labels: torch.Tensor, num_masks: int) -> torch.Tensor:
+ r"""
+ Args:
+ inputs (`torch.Tensor`):
+ A float tensor of arbitrary shape.
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+
+ Returns:
+ loss (`torch.Tensor`): The computed loss.
+ """
+ criterion = nn.BCEWithLogitsLoss(reduction="none")
+ cross_entropy_loss = criterion(inputs, labels)
+
+ loss = cross_entropy_loss.mean(1).sum() / num_masks
+ return loss
+
+
+# Copied from transformers.models.maskformer.modeling_maskformer.pair_wise_dice_loss
+def pair_wise_dice_loss(inputs: Tensor, labels: Tensor) -> Tensor:
+ """
+ A pair wise version of the dice loss, see `dice_loss` for usage.
+
+ Args:
+ inputs (`torch.Tensor`):
+ A tensor representing a mask
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+
+ Returns:
+ `torch.Tensor`: The computed loss between each pairs.
+ """
+ inputs = inputs.sigmoid().flatten(1)
+ numerator = 2 * torch.matmul(inputs, labels.T)
+ # using broadcasting to get a [num_queries, NUM_CLASSES] matrix
+ denominator = inputs.sum(-1)[:, None] + labels.sum(-1)[None, :]
+ loss = 1 - (numerator + 1) / (denominator + 1)
+ return loss
+
+
+def pair_wise_sigmoid_cross_entropy_loss(inputs: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
+ r"""
+ A pair wise version of the cross entropy loss, see `sigmoid_cross_entropy_loss` for usage.
+
+ Args:
+ inputs (`torch.Tensor`):
+ A tensor representing a mask.
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+
+ Returns:
+ loss (`torch.Tensor`): The computed loss between each pairs.
+ """
+
+ height_and_width = inputs.shape[1]
+
+ criterion = nn.BCEWithLogitsLoss(reduction="none")
+ cross_entropy_loss_pos = criterion(inputs, torch.ones_like(inputs))
+ cross_entropy_loss_neg = criterion(inputs, torch.zeros_like(inputs))
+
+ loss_pos = torch.matmul(cross_entropy_loss_pos / height_and_width, labels.T)
+ loss_neg = torch.matmul(cross_entropy_loss_neg / height_and_width, (1 - labels).T)
+ loss = loss_pos + loss_neg
+ return loss
+
+
+# Adapted from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/matcher.py
+class Mask2FormerHungarianMatcher(nn.Module):
+ """This class computes an assignment between the labels and the predictions of the network.
+
+ For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more
+ predictions than labels. In this case, we do a 1-to-1 matching of the best predictions, while the others are
+ un-matched (and thus treated as non-objects).
+ """
+
+ def __init__(
+ self, cost_class: float = 1.0, cost_mask: float = 1.0, cost_dice: float = 1.0, num_points: int = 12544
+ ):
+ """Creates the matcher
+
+ Params:
+ cost_class (`float`, *optional*, defaults to 1.0):
+ Relative weight of the classification error in the matching cost.
+ cost_mask (`float`, *optional*, defaults to 1.0):
+ This is the relative weight of the focal loss of the binary mask in the matching cost.
+ cost_dice (`float`, *optional*, defaults to 1.0):
+ This is the relative weight of the dice loss of the binary mask in the matching cost.
+ num_points (`int`, *optional*, defaults to 12544):
+ No. of points to sample on which the mask loss will be calculated. The same set of K points are
+ uniformly sampled for all prediction and ground truth masks to construct the cost matrix for bipartite
+ matching.
+ """
+ super().__init__()
+ if cost_class == 0 and cost_mask == 0 and cost_dice == 0:
+ raise ValueError("All costs can't be 0")
+
+ self.num_points = num_points
+ self.cost_class = cost_class
+ self.cost_mask = cost_mask
+ self.cost_dice = cost_dice
+
+ @torch.no_grad()
+ def forward(
+ self,
+ masks_queries_logits: torch.Tensor,
+ class_queries_logits: torch.Tensor,
+ mask_labels: torch.Tensor,
+ class_labels: torch.Tensor,
+ ) -> list[tuple[Tensor]]:
+ """
+ Params:
+ masks_queries_logits (`torch.Tensor`):
+ A tensor of dim `batch_size, num_queries, num_labels` with the classification logits.
+ class_queries_logits (`torch.Tensor`):
+ A tensor of dim `batch_size, num_queries, height, width` with the predicted masks.
+ class_labels (`torch.Tensor`):
+ A tensor of dim `num_target_boxes` (where num_target_boxes is the number of ground-truth objects in the
+ target) containing the class labels.
+ mask_labels (`torch.Tensor`):
+ A tensor of dim `num_target_boxes, height, width` containing the target masks.
+
+ Returns:
+ matched_indices (`list[tuple[Tensor]]`): A list of size batch_size, containing tuples of (index_i, index_j)
+ where:
+ - index_i is the indices of the selected predictions (in order)
+ - index_j is the indices of the corresponding selected labels (in order)
+ For each batch element, it holds:
+ len(index_i) = len(index_j) = min(num_queries, num_target_boxes).
+ """
+ indices: list[tuple[np.array]] = []
+
+ # iterate through batch size
+ batch_size = masks_queries_logits.shape[0]
+ for i in range(batch_size):
+ pred_probs = class_queries_logits[i].softmax(-1)
+ pred_mask = masks_queries_logits[i]
+
+ # Compute the classification cost. Contrary to the loss, we don't use the NLL, but approximate it in 1 - proba[target class]. The 1 is a constant that doesn't change the matching, it can be omitted.
+ cost_class = -pred_probs[:, class_labels[i]]
+ target_mask = mask_labels[i].to(pred_mask)
+ target_mask = target_mask[:, None]
+ pred_mask = pred_mask[:, None]
+
+ # Sample ground truth and predicted masks
+ point_coordinates = torch.rand(1, self.num_points, 2, device=pred_mask.device)
+
+ target_coordinates = point_coordinates.repeat(target_mask.shape[0], 1, 1)
+ target_mask = sample_point(target_mask, target_coordinates, align_corners=False).squeeze(1)
+
+ pred_coordinates = point_coordinates.repeat(pred_mask.shape[0], 1, 1)
+ pred_mask = sample_point(pred_mask, pred_coordinates, align_corners=False).squeeze(1)
+
+ # compute the cross entropy loss between each mask pairs -> shape (num_queries, num_labels)
+ cost_mask = pair_wise_sigmoid_cross_entropy_loss(pred_mask, target_mask)
+ # Compute the dice loss between each mask pairs -> shape (num_queries, num_labels)
+ cost_dice = pair_wise_dice_loss(pred_mask, target_mask)
+ # final cost matrix
+ cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice
+ # eliminate infinite values in cost_matrix to avoid the error ``ValueError: cost matrix is infeasible``
+ cost_matrix = torch.minimum(cost_matrix, torch.tensor(1e10))
+ cost_matrix = torch.maximum(cost_matrix, torch.tensor(-1e10))
+ cost_matrix = torch.nan_to_num(cost_matrix, 0)
+ # do the assignment using the hungarian algorithm in scipy
+ assigned_indices: tuple[np.array] = linear_sum_assignment(cost_matrix.cpu())
+ indices.append(assigned_indices)
+
+ # It could be stacked in one tensor
+ matched_indices = [
+ (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices
+ ]
+ return matched_indices
+
+
+# Adapted from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/criterion.py
+class Mask2FormerLoss(nn.Module):
+ def __init__(self, config: Mask2FormerConfig, weight_dict: dict[str, float]):
+ """
+ The Mask2Former Loss. The loss is computed very similar to DETR. The process happens in two steps: 1) we
+ compute hungarian assignment between ground truth masks and the outputs of the model 2) we supervise each pair
+ of matched ground-truth / prediction (supervise class and mask)
+
+ Args:
+ config (`Mask2FormerConfig`):
+ The configuration for Mask2Former model also containing loss calculation specific parameters.
+ weight_dict (`dict[str, float]`):
+ A dictionary of weights to be applied to the different losses.
+ """
+ super().__init__()
+ requires_backends(self, ["scipy"])
+ self.num_labels = config.num_labels
+ self.weight_dict = weight_dict
+
+ # Weight to apply to the null class
+ self.eos_coef = config.no_object_weight
+ empty_weight = torch.ones(self.num_labels + 1)
+ empty_weight[-1] = self.eos_coef
+ self.register_buffer("empty_weight", empty_weight)
+
+ # pointwise mask loss parameters
+ self.num_points = config.train_num_points
+ self.oversample_ratio = config.oversample_ratio
+ self.importance_sample_ratio = config.importance_sample_ratio
+
+ self.matcher = Mask2FormerHungarianMatcher(
+ cost_class=config.class_weight,
+ cost_dice=config.dice_weight,
+ cost_mask=config.mask_weight,
+ num_points=self.num_points,
+ )
+
+ def _max_by_axis(self, sizes: list[list[int]]) -> list[int]:
+ maxes = sizes[0]
+ for sublist in sizes[1:]:
+ for index, item in enumerate(sublist):
+ maxes[index] = max(maxes[index], item)
+ return maxes
+
+ # Adapted from nested_tensor_from_tensor_list() in original implementation
+ def _pad_images_to_max_in_batch(self, tensors: list[Tensor]) -> tuple[Tensor, Tensor]:
+ # get the maximum size in the batch
+ max_size = self._max_by_axis([list(tensor.shape) for tensor in tensors])
+ # compute final size
+ batch_shape = [len(tensors)] + max_size
+ batch_size, _, height, width = batch_shape
+ dtype = tensors[0].dtype
+ device = tensors[0].device
+ padded_tensors = torch.zeros(batch_shape, dtype=dtype, device=device)
+ padding_masks = torch.ones((batch_size, height, width), dtype=torch.bool, device=device)
+ # pad the tensors to the size of the biggest one
+ for tensor, padded_tensor, padding_mask in zip(tensors, padded_tensors, padding_masks):
+ padded_tensor[: tensor.shape[0], : tensor.shape[1], : tensor.shape[2]].copy_(tensor)
+ padding_mask[: tensor.shape[1], : tensor.shape[2]] = False
+
+ return padded_tensors, padding_masks
+
+ def loss_labels(
+ self, class_queries_logits: Tensor, class_labels: list[Tensor], indices: tuple[np.array]
+ ) -> dict[str, Tensor]:
+ """Compute the losses related to the labels using cross entropy.
+
+ Args:
+ class_queries_logits (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, num_labels`
+ class_labels (`list[torch.Tensor]`):
+ List of class labels of shape `(labels)`.
+ indices (`tuple[np.array])`:
+ The indices computed by the Hungarian matcher.
+
+ Returns:
+ `dict[str, Tensor]`: A dict of `torch.Tensor` containing the following key:
+ - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels.
+ """
+ pred_logits = class_queries_logits
+ batch_size, num_queries, _ = pred_logits.shape
+ criterion = nn.CrossEntropyLoss(weight=self.empty_weight)
+ idx = self._get_predictions_permutation_indices(indices) # shape of (batch_size, num_queries)
+ target_classes_o = torch.cat(
+ [target[j] for target, (_, j) in zip(class_labels, indices)]
+ ) # shape of (batch_size, num_queries)
+ target_classes = torch.full(
+ (batch_size, num_queries), fill_value=self.num_labels, dtype=torch.int64, device=pred_logits.device
+ )
+ target_classes[idx] = target_classes_o
+ # Permute target_classes (batch_size, num_queries, num_labels) -> (batch_size, num_labels, num_queries)
+ pred_logits_transposed = pred_logits.transpose(1, 2)
+ loss_ce = criterion(pred_logits_transposed, target_classes)
+ losses = {"loss_cross_entropy": loss_ce}
+ return losses
+
+ def loss_masks(
+ self,
+ masks_queries_logits: torch.Tensor,
+ mask_labels: list[torch.Tensor],
+ indices: tuple[np.array],
+ num_masks: int,
+ ) -> dict[str, torch.Tensor]:
+ """Compute the losses related to the masks using sigmoid_cross_entropy_loss and dice loss.
+
+ Args:
+ masks_queries_logits (`torch.Tensor`):
+ A tensor of shape `(batch_size, num_queries, height, width)`.
+ mask_labels (`torch.Tensor`):
+ List of mask labels of shape `(labels, height, width)`.
+ indices (`tuple[np.array])`:
+ The indices computed by the Hungarian matcher.
+ num_masks (`int)`:
+ The number of masks, used for normalization.
+
+ Returns:
+ losses (`dict[str, Tensor]`): A dict of `torch.Tensor` containing two keys:
+ - **loss_mask** -- The loss computed using sigmoid cross entropy loss on the predicted and ground truth.
+ masks.
+ - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth,
+ masks.
+ """
+ src_idx = self._get_predictions_permutation_indices(indices)
+ tgt_idx = self._get_targets_permutation_indices(indices)
+ # shape (batch_size * num_queries, height, width)
+ pred_masks = masks_queries_logits[src_idx]
+ # shape (batch_size, num_queries, height, width)
+ # pad all and stack the targets to the num_labels dimension
+ target_masks, _ = self._pad_images_to_max_in_batch(mask_labels)
+ target_masks = target_masks[tgt_idx]
+
+ # No need to upsample predictions as we are using normalized coordinates
+ pred_masks = pred_masks[:, None]
+ target_masks = target_masks[:, None]
+
+ # Sample point coordinates
+ with torch.no_grad():
+ point_coordinates = self.sample_points_using_uncertainty(
+ pred_masks,
+ lambda logits: self.calculate_uncertainty(logits),
+ self.num_points,
+ self.oversample_ratio,
+ self.importance_sample_ratio,
+ )
+
+ point_labels = sample_point(target_masks, point_coordinates, align_corners=False).squeeze(1)
+
+ point_logits = sample_point(pred_masks, point_coordinates, align_corners=False).squeeze(1)
+
+ losses = {
+ "loss_mask": sigmoid_cross_entropy_loss(point_logits, point_labels, num_masks),
+ "loss_dice": dice_loss(point_logits, point_labels, num_masks),
+ }
+
+ del pred_masks
+ del target_masks
+ return losses
+
+ def _get_predictions_permutation_indices(self, indices):
+ # Permute predictions following indices
+ batch_indices = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
+ predictions_indices = torch.cat([src for (src, _) in indices])
+ return batch_indices, predictions_indices
+
+ def _get_targets_permutation_indices(self, indices):
+ # Permute labels following indices
+ batch_indices = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
+ target_indices = torch.cat([tgt for (_, tgt) in indices])
+ return batch_indices, target_indices
+
+ def calculate_uncertainty(self, logits: torch.Tensor) -> torch.Tensor:
+ """
+ In Mask2Former paper, uncertainty is estimated as L1 distance between 0.0 and the logit prediction in 'logits'
+ for the foreground class in `classes`.
+
+ Args:
+ logits (`torch.Tensor`):
+ A tensor of shape (R, 1, ...) for class-specific or class-agnostic, where R is the total number of predicted masks in all images and C is:
+ the number of foreground classes. The values are logits.
+
+ Returns:
+ scores (`torch.Tensor`): A tensor of shape (R, 1, ...) that contains uncertainty scores with the most
+ uncertain locations having the highest uncertainty score.
+ """
+ uncertainty_scores = -(torch.abs(logits))
+ return uncertainty_scores
+
+ def sample_points_using_uncertainty(
+ self,
+ logits: torch.Tensor,
+ uncertainty_function,
+ num_points: int,
+ oversample_ratio: int,
+ importance_sample_ratio: float,
+ ) -> torch.Tensor:
+ """
+ This function is meant for sampling points in [0, 1] * [0, 1] coordinate space based on their uncertainty. The
+ uncertainty is calculated for each point using the passed `uncertainty function` that takes points logit
+ prediction as input.
+
+ Args:
+ logits (`float`):
+ Logit predictions for P points.
+ uncertainty_function:
+ A function that takes logit predictions for P points and returns their uncertainties.
+ num_points (`int`):
+ The number of points P to sample.
+ oversample_ratio (`int`):
+ Oversampling parameter.
+ importance_sample_ratio (`float`):
+ Ratio of points that are sampled via importance sampling.
+
+ Returns:
+ point_coordinates (`torch.Tensor`):
+ Coordinates for P sampled points.
+ """
+
+ num_boxes = logits.shape[0]
+ num_points_sampled = int(num_points * oversample_ratio)
+
+ # Get random point coordinates
+ point_coordinates = torch.rand(num_boxes, num_points_sampled, 2, device=logits.device)
+ # Get sampled prediction value for the point coordinates
+ point_logits = sample_point(logits, point_coordinates, align_corners=False)
+ # Calculate the uncertainties based on the sampled prediction values of the points
+ point_uncertainties = uncertainty_function(point_logits)
+
+ num_uncertain_points = int(importance_sample_ratio * num_points)
+ num_random_points = num_points - num_uncertain_points
+
+ idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
+ shift = num_points_sampled * torch.arange(num_boxes, dtype=torch.long, device=logits.device)
+ idx += shift[:, None]
+ point_coordinates = point_coordinates.view(-1, 2)[idx.view(-1), :].view(num_boxes, num_uncertain_points, 2)
+
+ if num_random_points > 0:
+ point_coordinates = torch.cat(
+ [point_coordinates, torch.rand(num_boxes, num_random_points, 2, device=logits.device)],
+ dim=1,
+ )
+ return point_coordinates
+
+ def forward(
+ self,
+ masks_queries_logits: torch.Tensor,
+ class_queries_logits: torch.Tensor,
+ mask_labels: list[torch.Tensor],
+ class_labels: list[torch.Tensor],
+ auxiliary_predictions: Optional[dict[str, torch.Tensor]] = None,
+ ) -> dict[str, torch.Tensor]:
+ """
+ This performs the loss computation.
+
+ Args:
+ masks_queries_logits (`torch.Tensor`):
+ A tensor of shape `(batch_size, num_queries, height, width)`.
+ class_queries_logits (`torch.Tensor`):
+ A tensor of shape `(batch_size, num_queries, num_labels)`.
+ mask_labels (`torch.Tensor`):
+ List of mask labels of shape `(labels, height, width)`.
+ class_labels (`list[torch.Tensor]`):
+ List of class labels of shape `(labels)`.
+ auxiliary_predictions (`dict[str, torch.Tensor]`, *optional*):
+ if `use_auxiliary_loss` was set to `true` in [`Mask2FormerConfig`], then it contains the logits from
+ the inner layers of the Mask2FormerMaskedAttentionDecoder.
+
+ Returns:
+ losses (`dict[str, Tensor]`): A dict of `torch.Tensor` containing three keys:
+ - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels.
+ - **loss_mask** -- The loss computed using sigmoid cross_entropy loss on the predicted and ground truth
+ masks.
+ - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth
+ masks.
+ if `use_auxiliary_loss` was set to `true` in [`Mask2FormerConfig`], the dictionary contains additional
+ losses for each auxiliary predictions.
+ """
+
+ # retrieve the matching between the outputs of the last layer and the labels
+ indices = self.matcher(masks_queries_logits, class_queries_logits, mask_labels, class_labels)
+ # compute the average number of target masks for normalization purposes
+ num_masks = self.get_num_masks(class_labels, device=class_labels[0].device)
+ # get all the losses
+ losses: dict[str, Tensor] = {
+ **self.loss_masks(masks_queries_logits, mask_labels, indices, num_masks),
+ **self.loss_labels(class_queries_logits, class_labels, indices),
+ }
+ # in case of auxiliary losses, we repeat this process with the output of each intermediate layer.
+ if auxiliary_predictions is not None:
+ for idx, aux_outputs in enumerate(auxiliary_predictions):
+ masks_queries_logits = aux_outputs["masks_queries_logits"]
+ class_queries_logits = aux_outputs["class_queries_logits"]
+ loss_dict = self.forward(masks_queries_logits, class_queries_logits, mask_labels, class_labels)
+ loss_dict = {f"{key}_{idx}": value for key, value in loss_dict.items()}
+ losses.update(loss_dict)
+
+ return losses
+
+ def get_num_masks(self, class_labels: torch.Tensor, device: torch.device) -> torch.Tensor:
+ """
+ Computes the average number of target masks across the batch, for normalization purposes.
+ """
+ num_masks = sum([len(classes) for classes in class_labels])
+ num_masks = torch.as_tensor(num_masks, dtype=torch.float, device=device)
+ world_size = 1
+ if is_accelerate_available():
+ if PartialState._shared_state != {}:
+ num_masks = reduce(num_masks)
+ world_size = PartialState().num_processes
+
+ num_masks = torch.clamp(num_masks / world_size, min=1)
+ return num_masks
+
+
+# Copied from transformers.models.oneformer.modeling_oneformer.multi_scale_deformable_attention
+def multi_scale_deformable_attention(
+ value: Tensor,
+ value_spatial_shapes: Union[Tensor, list[tuple]],
+ sampling_locations: Tensor,
+ attention_weights: Tensor,
+) -> Tensor:
+ batch_size, _, num_heads, hidden_dim = value.shape
+ _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
+ value_list = value.split([height * width for height, width in value_spatial_shapes], dim=1)
+ sampling_grids = 2 * sampling_locations - 1
+ sampling_value_list = []
+ for level_id, (height, width) in enumerate(value_spatial_shapes):
+ # batch_size, height*width, num_heads, hidden_dim
+ # -> batch_size, height*width, num_heads*hidden_dim
+ # -> batch_size, num_heads*hidden_dim, height*width
+ # -> batch_size*num_heads, hidden_dim, height, width
+ value_l_ = (
+ value_list[level_id].flatten(2).transpose(1, 2).reshape(batch_size * num_heads, hidden_dim, height, width)
+ )
+ # batch_size, num_queries, num_heads, num_points, 2
+ # -> batch_size, num_heads, num_queries, num_points, 2
+ # -> batch_size*num_heads, num_queries, num_points, 2
+ sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1)
+ # batch_size*num_heads, hidden_dim, num_queries, num_points
+ sampling_value_l_ = nn.functional.grid_sample(
+ value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
+ )
+ sampling_value_list.append(sampling_value_l_)
+ # (batch_size, num_queries, num_heads, num_levels, num_points)
+ # -> (batch_size, num_heads, num_queries, num_levels, num_points)
+ # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points)
+ attention_weights = attention_weights.transpose(1, 2).reshape(
+ batch_size * num_heads, 1, num_queries, num_levels * num_points
+ )
+ output = (
+ (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
+ .sum(-1)
+ .view(batch_size, num_heads * hidden_dim, num_queries)
+ )
+ return output.transpose(1, 2).contiguous()
+
+
+# Copied from transformers.models.maskformer.modeling_maskformer.MaskFormerSinePositionEmbedding with MaskFormer->Mask2Former
+class Mask2FormerSinePositionEmbedding(nn.Module):
+ """
+ This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
+ need paper, generalized to work on images.
+ """
+
+ def __init__(
+ self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: Optional[float] = None
+ ):
+ super().__init__()
+ if scale is not None and normalize is False:
+ raise ValueError("normalize should be True if scale is passed")
+ self.num_pos_feats = num_pos_feats
+ self.temperature = temperature
+ self.normalize = normalize
+ self.scale = 2 * math.pi if scale is None else scale
+
+ @compile_compatible_method_lru_cache(maxsize=1)
+ def forward(
+ self,
+ shape: torch.Size,
+ device: Union[torch.device, str],
+ dtype: torch.dtype,
+ mask: Optional[Tensor] = None,
+ ) -> Tensor:
+ if mask is None:
+ mask = torch.zeros((shape[0], shape[2], shape[3]), device=device, dtype=torch.bool)
+ not_mask = (~mask).to(dtype)
+ y_embed = not_mask.cumsum(1)
+ x_embed = not_mask.cumsum(2)
+ if self.normalize:
+ eps = 1e-6
+ y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+ x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+ dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=device).to(dtype)
+ dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats)
+
+ pos_x = x_embed[:, :, :, None] / dim_t
+ pos_y = y_embed[:, :, :, None] / dim_t
+ pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
+ pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
+ pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+ return pos
+
+
+# Modified from transformers.models.detr.modeling_deformable_detr.DeformableDetrMultiscaleDeformableAttention
+class Mask2FormerPixelDecoderEncoderMultiscaleDeformableAttention(nn.Module):
+ """
+ Multiscale deformable attention as proposed in Deformable DETR.
+ """
+
+ def __init__(self, embed_dim: int, num_heads: int, n_levels: int, n_points: int):
+ super().__init__()
+ if embed_dim % num_heads != 0:
+ raise ValueError(
+ f"embed_dim (d_model) must be divisible by num_heads, but got {embed_dim} and {num_heads}"
+ )
+ dim_per_head = embed_dim // num_heads
+ # check if dim_per_head is power of 2
+ if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0):
+ warnings.warn(
+ "You'd better set embed_dim (d_model) in DeformableDetrMultiscaleDeformableAttention to make the"
+ " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA"
+ " implementation."
+ )
+
+ self.im2col_step = 128
+
+ self.d_model = embed_dim
+ self.n_levels = n_levels
+ self.n_heads = num_heads
+ self.n_points = n_points
+
+ self.sampling_offsets = nn.Linear(embed_dim, num_heads * n_levels * n_points * 2)
+ self.attention_weights = nn.Linear(embed_dim, num_heads * n_levels * n_points)
+ self.value_proj = nn.Linear(embed_dim, embed_dim)
+ self.output_proj = nn.Linear(embed_dim, embed_dim)
+
+ def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
+ return tensor if position_embeddings is None else tensor + position_embeddings
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ position_embeddings: Optional[torch.Tensor] = None,
+ reference_points=None,
+ spatial_shapes_list=None,
+ level_start_index=None,
+ output_attentions: bool = False,
+ ):
+ # add position embeddings to the hidden states before projecting to queries and keys
+ if position_embeddings is not None:
+ hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
+
+ batch_size, num_queries, _ = hidden_states.shape
+ batch_size, sequence_length, _ = encoder_hidden_states.shape
+ total_elements = sum(height * width for height, width in spatial_shapes_list)
+ if total_elements != sequence_length:
+ raise ValueError(
+ "Make sure to align the spatial shapes with the sequence length of the encoder hidden states"
+ )
+
+ value = self.value_proj(encoder_hidden_states)
+ if attention_mask is not None:
+ # we invert the attention_mask
+ value = value.masked_fill(attention_mask[..., None], float(0))
+ value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads)
+ sampling_offsets = self.sampling_offsets(hidden_states).view(
+ batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2
+ )
+ attention_weights = self.attention_weights(hidden_states).view(
+ batch_size, num_queries, self.n_heads, self.n_levels * self.n_points
+ )
+ attention_weights = nn.functional.softmax(attention_weights, -1).view(
+ batch_size, num_queries, self.n_heads, self.n_levels, self.n_points
+ )
+ # batch_size, num_queries, n_heads, n_levels, n_points, 2
+ if reference_points.shape[-1] == 2:
+ offset_normalizer = torch.tensor(
+ [[shape[1], shape[0]] for shape in spatial_shapes_list],
+ dtype=torch.long,
+ device=reference_points.device,
+ )
+ sampling_locations = (
+ reference_points[:, :, None, :, None, :]
+ + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+ )
+ elif reference_points.shape[-1] == 4:
+ sampling_locations = (
+ reference_points[:, :, None, :, None, :2]
+ + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
+ )
+ else:
+ raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")
+
+ output = multi_scale_deformable_attention(value, spatial_shapes_list, sampling_locations, attention_weights)
+ output = self.output_proj(output)
+
+ return output, attention_weights
+
+
+class Mask2FormerPixelDecoderEncoderLayer(nn.Module):
+ def __init__(self, config: Mask2FormerConfig):
+ super().__init__()
+ self.embed_dim = config.feature_size
+ self.self_attn = Mask2FormerPixelDecoderEncoderMultiscaleDeformableAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.num_attention_heads,
+ n_levels=3,
+ n_points=4,
+ )
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.dropout = config.dropout
+ self.activation_fn = nn.functional.relu
+ self.activation_dropout = config.dropout
+ self.fc1 = nn.Linear(self.embed_dim, config.encoder_feedforward_dim)
+ self.fc2 = nn.Linear(config.encoder_feedforward_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ position_embeddings: Optional[torch.Tensor] = None,
+ reference_points=None,
+ spatial_shapes_list=None,
+ level_start_index=None,
+ output_attentions: bool = False,
+ ):
+ """
+ Args:
+ hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Input to the layer.
+ attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
+ Attention mask.
+ position_embeddings (`torch.FloatTensor`, *optional*):
+ Position embeddings, to be added to `hidden_states`.
+ reference_points (`torch.FloatTensor`, *optional*):
+ Reference points.
+ spatial_shapes_list (`list` of `tuple`):
+ Spatial shapes of the backbone feature maps as a list of tuples.
+ level_start_index (`torch.LongTensor`, *optional*):
+ Level start index.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+
+ # Apply Multi-scale Deformable Attention Module on the multi-scale feature maps.
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ encoder_hidden_states=hidden_states,
+ encoder_attention_mask=attention_mask,
+ position_embeddings=position_embeddings,
+ reference_points=reference_points,
+ spatial_shapes_list=spatial_shapes_list,
+ level_start_index=level_start_index,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ if self.training:
+ if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (attn_weights.transpose(1, 0),)
+
+ return outputs
+
+
+# Modified from from transformers.models.detr.modeling_deformable_detr.DeformableDetrEncoder with DeformableDetrEncoder->Mask2FormerPixelDecoderEncoderOnly
+class Mask2FormerPixelDecoderEncoderOnly(nn.Module):
+ """
+ Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
+ [`Mask2FormerPixelDecoderEncoderLayer`]. The encoder updates the flattened multi-scale feature maps through
+ multiple deformable attention layers.
+
+ Args:
+ config: Mask2FormerConfig
+ """
+
+ def __init__(self, config: Mask2FormerConfig):
+ super().__init__()
+
+ self.config = config
+ self.dropout = config.dropout
+ self.layers = nn.ModuleList(
+ [Mask2FormerPixelDecoderEncoderLayer(config) for _ in range(config.encoder_layers)]
+ )
+
+ @staticmethod
+ def get_reference_points(spatial_shapes_list, valid_ratios, device):
+ """
+ Get reference points for each feature map. Used in decoder.
+
+ Args:
+ spatial_shapes_list (`list` of `tuple`):
+ Spatial shapes of the backbone feature maps as a list of tuples.
+ valid_ratios (`torch.FloatTensor`):
+ Valid ratios of each feature map, has shape of `(batch_size, num_feature_levels, 2)`.
+ device (`torch.device`):
+ Device on which to create the tensors.
+ Returns:
+ `torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)`
+ """
+ reference_points_list = []
+ for lvl, (height, width) in enumerate(spatial_shapes_list):
+ ref_y, ref_x = torch.meshgrid(
+ torch.linspace(0.5, height - 0.5, height, dtype=valid_ratios.dtype, device=device),
+ torch.linspace(0.5, width - 0.5, width, dtype=valid_ratios.dtype, device=device),
+ indexing="ij",
+ )
+ ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * height)
+ ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * width)
+ ref = torch.stack((ref_x, ref_y), -1)
+ reference_points_list.append(ref)
+
+ reference_points = torch.cat(reference_points_list, 1)
+ reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+
+ return reference_points
+
+ def forward(
+ self,
+ inputs_embeds=None,
+ attention_mask=None,
+ position_embeddings=None,
+ spatial_shapes_list=None,
+ level_start_index=None,
+ valid_ratios=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
+ - 1 for pixel features that are real (i.e. **not masked**),
+ - 0 for pixel features that are padding (i.e. **masked**).
+ [What are attention masks?](../glossary#attention-mask)
+ position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Position embeddings that are added to the queries and keys in each self-attention layer.
+ spatial_shapes_list (`list` of `tuple`):
+ Spatial shapes of each feature map as a list of tuples.
+ level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`):
+ Starting index of each feature map.
+ valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
+ Ratio of valid area in each feature level.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ hidden_states = inputs_embeds
+ reference_points = self.get_reference_points(spatial_shapes_list, valid_ratios, device=inputs_embeds.device)
+
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ for i, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states.transpose(1, 0),)
+
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ position_embeddings=position_embeddings,
+ reference_points=reference_points,
+ spatial_shapes_list=spatial_shapes_list,
+ level_start_index=level_start_index,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states += (hidden_states.transpose(1, 0),)
+
+ return BaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
+ )
+
+
+# Modified from from transformers.models.detr.modeling_deformable_detr.DeformableDetrModel with DeformableDetrModel->Mask2FormerPixelDecoder
+class Mask2FormerPixelDecoder(nn.Module):
+ def __init__(self, config: Mask2FormerConfig, feature_channels):
+ super().__init__()
+
+ self.config = config
+
+ feature_dim = config.feature_size
+ mask_dim = config.mask_feature_size
+ num_pos_features = feature_dim // 2
+
+ self.position_embedding = Mask2FormerSinePositionEmbedding(num_pos_feats=num_pos_features, normalize=True)
+ self.num_feature_levels = 3
+ transformer_in_channels = feature_channels[-self.num_feature_levels :]
+
+ self.transformer_feature_strides = config.feature_strides[-self.num_feature_levels :]
+ self.feature_channels = feature_channels
+ self.level_embed = nn.Parameter(torch.Tensor(self.num_feature_levels, feature_dim))
+
+ # Create input projection layers
+ if self.num_feature_levels > 1:
+ input_projections_list = []
+ for in_channels in transformer_in_channels[::-1]:
+ input_projections_list.append(
+ nn.Sequential(
+ nn.Conv2d(in_channels, feature_dim, kernel_size=1),
+ nn.GroupNorm(32, feature_dim),
+ )
+ )
+ self.input_projections = nn.ModuleList(input_projections_list)
+ else:
+ self.input_projections = nn.ModuleList(
+ [
+ nn.Sequential(
+ nn.Conv2d(transformer_in_channels[-1], feature_dim, kernel_size=1),
+ nn.GroupNorm(32, feature_dim),
+ )
+ ]
+ )
+
+ self.encoder = Mask2FormerPixelDecoderEncoderOnly(config)
+ self.mask_projection = nn.Conv2d(feature_dim, mask_dim, kernel_size=1, stride=1, padding=0)
+
+ # Extra FPN levels
+ stride = min(self.transformer_feature_strides)
+ self.common_stride = config.common_stride
+ self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))
+
+ lateral_convs = []
+ output_convs = []
+
+ for idx, in_channels in enumerate(self.feature_channels[: self.num_fpn_levels]):
+ lateral_conv = nn.Sequential(
+ nn.Conv2d(in_channels, feature_dim, kernel_size=1, bias=False),
+ nn.GroupNorm(32, feature_dim),
+ )
+
+ output_conv = nn.Sequential(
+ nn.Conv2d(feature_dim, feature_dim, kernel_size=3, stride=1, padding=1, bias=False),
+ nn.GroupNorm(32, feature_dim),
+ nn.ReLU(),
+ )
+ self.add_module(f"adapter_{idx + 1}", lateral_conv)
+ self.add_module(f"layer_{idx + 1}", output_conv)
+
+ lateral_convs.append(lateral_conv)
+ output_convs.append(output_conv)
+
+ # Order convolutional layers from low to high resolution
+ self.lateral_convolutions = lateral_convs[::-1]
+ self.output_convolutions = output_convs[::-1]
+
+ def get_valid_ratio(self, mask, dtype=torch.float32):
+ """Get the valid ratio of all feature maps."""
+
+ _, height, width = mask.shape
+ valid_height = torch.sum(~mask[:, :, 0], 1)
+ valid_width = torch.sum(~mask[:, 0, :], 1)
+ valid_ratio_height = valid_height.to(dtype) / height
+ valid_ratio_width = valid_width.to(dtype) / width
+ valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1)
+ return valid_ratio
+
+ def forward(
+ self,
+ features,
+ encoder_outputs=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ # Apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
+ input_embeds = []
+ position_embeddings = []
+ for level, x in enumerate(features[::-1][: self.num_feature_levels]):
+ input_embeds.append(self.input_projections[level](x))
+ position_embeddings.append(self.position_embedding(x.shape, x.device, x.dtype))
+
+ masks = [
+ torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in input_embeds
+ ]
+
+ # Prepare encoder inputs (by flattening)
+ spatial_shapes_list = [(embed.shape[2], embed.shape[3]) for embed in input_embeds]
+ input_embeds_flat = torch.cat([embed.flatten(2).transpose(1, 2) for embed in input_embeds], 1)
+ spatial_shapes = torch.as_tensor(spatial_shapes_list, dtype=torch.long, device=input_embeds_flat.device)
+ masks_flat = torch.cat([mask.flatten(1) for mask in masks], 1)
+
+ position_embeddings = [embed.flatten(2).transpose(1, 2) for embed in position_embeddings]
+ level_pos_embed_flat = [x + self.level_embed[i].view(1, 1, -1) for i, x in enumerate(position_embeddings)]
+ level_pos_embed_flat = torch.cat(level_pos_embed_flat, 1)
+
+ level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
+ valid_ratios = torch.stack([self.get_valid_ratio(mask, dtype=input_embeds_flat.dtype) for mask in masks], 1)
+
+ # Send input_embeds_flat + masks_flat + level_pos_embed_flat (backbone + proj layer output) through encoder
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ inputs_embeds=input_embeds_flat,
+ attention_mask=masks_flat,
+ position_embeddings=level_pos_embed_flat,
+ spatial_shapes_list=spatial_shapes_list,
+ level_start_index=level_start_index,
+ valid_ratios=valid_ratios,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ last_hidden_state = encoder_outputs.last_hidden_state
+ batch_size = last_hidden_state.shape[0]
+
+ # We compute level_start_index_list separately from the tensor version level_start_index
+ # to avoid iterating over a tensor which breaks torch.compile/export.
+ level_start_index_list = [0]
+ for height, width in spatial_shapes_list[:-1]:
+ level_start_index_list.append(level_start_index_list[-1] + height * width)
+ split_sizes = [None] * self.num_feature_levels
+ for i in range(self.num_feature_levels):
+ if i < self.num_feature_levels - 1:
+ split_sizes[i] = level_start_index_list[i + 1] - level_start_index_list[i]
+ else:
+ split_sizes[i] = last_hidden_state.shape[1] - level_start_index_list[i]
+
+ encoder_output = torch.split(last_hidden_state, split_sizes, dim=1)
+
+ # Compute final features
+ outputs = [
+ x.transpose(1, 2).view(batch_size, -1, spatial_shapes_list[i][0], spatial_shapes_list[i][1])
+ for i, x in enumerate(encoder_output)
+ ]
+
+ # Append extra FPN levels to outputs, ordered from low to high resolution
+ for idx, feature in enumerate(features[: self.num_fpn_levels][::-1]):
+ lateral_conv = self.lateral_convolutions[idx]
+ output_conv = self.output_convolutions[idx]
+ current_fpn = lateral_conv(feature)
+
+ # Following FPN implementation, we use nearest upsampling here
+ out = current_fpn + nn.functional.interpolate(
+ outputs[-1], size=current_fpn.shape[-2:], mode="bilinear", align_corners=False
+ )
+ out = output_conv(out)
+ outputs.append(out)
+
+ num_cur_levels = 0
+ multi_scale_features = []
+
+ for out in outputs:
+ if num_cur_levels < self.num_feature_levels:
+ multi_scale_features.append(out)
+ num_cur_levels += 1
+
+ return Mask2FormerPixelDecoderOutput(
+ mask_features=self.mask_projection(outputs[-1]),
+ multi_scale_features=tuple(multi_scale_features),
+ attentions=encoder_outputs.attentions,
+ )
+
+
+class Mask2FormerPixelLevelModule(nn.Module):
+ def __init__(self, config: Mask2FormerConfig):
+ """
+ Pixel Level Module proposed in [Masked-attention Mask Transformer for Universal Image
+ Segmentation](https://huggingface.co/papers/2112.01527). It runs the input image through a backbone and a pixel
+ decoder, generating multi-scale feature maps and pixel embeddings.
+
+ Args:
+ config ([`Mask2FormerConfig`]):
+ The configuration used to instantiate this model.
+ """
+ super().__init__()
+
+ self.encoder = load_backbone(config)
+ self.decoder = Mask2FormerPixelDecoder(config, feature_channels=self.encoder.channels)
+
+ def forward(self, pixel_values: Tensor, output_hidden_states: bool = False) -> Mask2FormerPixelLevelModuleOutput:
+ backbone_features = self.encoder(pixel_values).feature_maps
+ decoder_output = self.decoder(backbone_features, output_hidden_states=output_hidden_states)
+
+ return Mask2FormerPixelLevelModuleOutput(
+ encoder_last_hidden_state=backbone_features[-1],
+ encoder_hidden_states=tuple(backbone_features) if output_hidden_states else None,
+ decoder_last_hidden_state=decoder_output.mask_features,
+ decoder_hidden_states=decoder_output.multi_scale_features,
+ )
+
+
+# Modified from transformers.models.detr.modeling_detr.DetrAttention with Detr->Mask2Former
+class Mask2FormerAttention(nn.Module):
+ """
+ Multi-headed attention from 'Attention Is All You Need' paper. Here, we add position embeddings to the queries and
+ keys (as explained in the DETR paper).
+ """
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: float = 0.0,
+ is_decoder: bool = False,
+ bias: bool = True,
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+ if self.head_dim * num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+ f" {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
+ return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
+ return tensor if position_embeddings is None else tensor + position_embeddings
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_embeddings: Optional[torch.Tensor] = None,
+ key_value_states: Optional[torch.Tensor] = None,
+ key_value_position_embeddings: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ hidden_states = hidden_states.permute(1, 0, 2) if hidden_states is not None else None
+ position_embeddings = position_embeddings.permute(1, 0, 2) if position_embeddings is not None else None
+ key_value_states = key_value_states.permute(1, 0, 2) if key_value_states is not None else None
+ key_value_position_embeddings = (
+ key_value_position_embeddings.permute(1, 0, 2) if key_value_position_embeddings is not None else None
+ )
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+ batch_size, target_len, embed_dim = hidden_states.size()
+
+ # add position embeddings to the hidden states before projecting to queries and keys
+ if position_embeddings is not None:
+ hidden_states_original = hidden_states
+ hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
+
+ # add key-value position embeddings to the key value states
+ if key_value_position_embeddings is not None:
+ key_value_states_original = key_value_states
+ key_value_states = self.with_pos_embed(key_value_states, key_value_position_embeddings)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states) * self.scaling
+ # get key, value proj
+ if is_cross_attention:
+ # cross_attentions
+ key_states = self._shape(self.k_proj(key_value_states), -1, batch_size)
+ value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size)
+ else:
+ # self_attention
+ key_states = self._shape(self.k_proj(hidden_states), -1, batch_size)
+ value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size)
+
+ proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
+ query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape)
+ key_states = key_states.view(*proj_shape)
+ value_states = value_states.view(*proj_shape)
+
+ source_len = key_states.size(1)
+
+ attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
+
+ if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len):
+ raise ValueError(
+ f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (batch_size * self.num_heads, target_len, source_len):
+ raise ValueError(
+ f"Attention mask should be of size {(target_len, batch_size * self.num_heads, source_len)}, but is"
+ f" {attention_mask.size()}"
+ )
+ attn_weights += attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ if output_attentions:
+ # this operation is a bit awkward, but it's required to
+ # make sure that attn_weights keeps its gradient.
+ # In order to do so, attn_weights have to reshaped
+ # twice and have to be reused in the following
+ attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
+ attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
+ else:
+ attn_weights_reshaped = None
+
+ attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+ attn_output = torch.bmm(attn_probs, value_states)
+
+ if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
+ attn_output = attn_output.transpose(1, 2)
+ attn_output = attn_output.reshape(batch_size, target_len, embed_dim)
+
+ attn_output = self.out_proj(attn_output).permute(1, 0, 2)
+
+ return attn_output, attn_weights_reshaped
+
+
+class Mask2FormerMaskedAttentionDecoderLayer(GradientCheckpointingLayer):
+ """
+ The Mask2FormerMaskedAttentionDecoderLayer is made up of self-attention, cross (masked) attention as well as FFN
+ blocks. The cross attention block used as part of `Mask2FormerMaskedAttentionDecoderLayer` is actually a `masked
+ attention` block that restricts the attention to localized features centered around predicted segments which leads
+ to faster convergence and improved performance. The order of self and cross (i.e. masked) attention blocks have
+ also been swapped in Mask2FormerMaskedAttentionDecoder compared to a standard DetrDecoder as an optimization
+ improvement.
+
+ Args:
+ config (`Mask2FormerConfig`):
+ The configuration used to initialize the Mask2FormerMaskedAttentionDecoder.
+ """
+
+ def __init__(self, config: Mask2FormerConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = self.config.hidden_dim
+ self.pre_norm = self.config.pre_norm
+ self.self_attn = Mask2FormerAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.num_attention_heads,
+ dropout=config.dropout,
+ is_decoder=True,
+ )
+
+ self.dropout = self.config.dropout
+ self.activation_fn = ACT2FN[self.config.activation_function]
+ self.activation_dropout = self.config.dropout
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.cross_attn = nn.MultiheadAttention(self.embed_dim, self.config.num_attention_heads, self.config.dropout)
+ self.cross_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.fc1 = nn.Linear(self.embed_dim, self.config.dim_feedforward)
+ self.fc2 = nn.Linear(self.config.dim_feedforward, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(
+ self,
+ hidden_states: torch.Tensor,
+ level_index: Optional[int] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_embeddings: Optional[torch.Tensor] = None,
+ query_position_embeddings: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ ):
+ # Masked(Cross)-Attention Block
+ cross_attn_weights = None
+ self_attn_weights = None
+
+ residual = hidden_states
+
+ hidden_states, cross_attn_weights = self.cross_attn(
+ query=self.with_pos_embed(hidden_states, query_position_embeddings),
+ key=self.with_pos_embed(encoder_hidden_states[level_index], position_embeddings[level_index]),
+ value=encoder_hidden_states[level_index],
+ attn_mask=encoder_attention_mask,
+ key_padding_mask=None,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.cross_attn_layer_norm(hidden_states)
+
+ # Self Attention Block
+ residual = hidden_states
+
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ position_embeddings=query_position_embeddings,
+ attention_mask=None,
+ output_attentions=True,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ return outputs
+
+ def forward_pre(
+ self,
+ hidden_states: torch.Tensor,
+ level_index: Optional[int] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_embeddings: Optional[torch.Tensor] = None,
+ query_position_embeddings: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ ):
+ # Masked(Cross)-Attention Block
+ cross_attn_weights = None
+ self_attn_weights = None
+
+ residual = hidden_states
+
+ hidden_states = self.cross_attn_layer_norm(hidden_states)
+
+ hidden_states, cross_attn_weights = self.cross_attn(
+ query=self.with_pos_embed(hidden_states, query_position_embeddings),
+ key=self.with_pos_embed(encoder_hidden_states[level_index], position_embeddings[level_index]),
+ value=encoder_hidden_states[level_index],
+ attn_mask=encoder_attention_mask,
+ key_padding_mask=None,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # Self Attention Block
+ residual = hidden_states
+
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ position_embeddings=query_position_embeddings,
+ attention_mask=None,
+ output_attentions=True,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ return outputs
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ level_index: Optional[int] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_embeddings: Optional[torch.Tensor] = None,
+ query_position_embeddings: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ ):
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`):
+ Input to the layer of shape `(seq_len, batch, embed_dim)`.
+ attention_mask (`torch.FloatTensor`):
+ Attention mask of shape `(1, seq_len, tgt_len, src_len)`.
+ position_embeddings (`torch.FloatTensor`, *optional*):
+ Position embeddings that are added to the keys in the masked-attention layer.
+ query_position_embeddings (`torch.FloatTensor`, *optional*):
+ Position embeddings that are added to the queries and keys in the self-attention layer.
+ encoder_hidden_states (`torch.FloatTensor`):
+ Cross attention input to the layer of shape `(seq_len, batch, embed_dim)`.
+ encoder_attention_mask (`torch.FloatTensor`):
+ Encoder attention mask of size`(1, seq_len, tgt_len, src_len)`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+
+ if self.pre_norm:
+ outputs = self.forward_pre(
+ hidden_states=hidden_states,
+ level_index=level_index,
+ position_embeddings=position_embeddings,
+ query_position_embeddings=query_position_embeddings,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ output_attentions=output_attentions,
+ )
+ else:
+ outputs = self.forward_post(
+ hidden_states=hidden_states,
+ level_index=level_index,
+ position_embeddings=position_embeddings,
+ query_position_embeddings=query_position_embeddings,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ output_attentions=output_attentions,
+ )
+
+ return outputs
+
+
+class Mask2FormerMaskedAttentionDecoder(nn.Module):
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a
+ [`Mask2FormerMaskedAttentionDecoderLayer`]. The decoder updates the query embeddings through multiple cross
+ (masked) and self-attention layers. The decoder uses a new **masked attention** mechanism instead of the standard
+ cross-attention, which extracts localized features by constraining cross-attention to within the foreground region
+ of the predicted mask for each query, instead of attending to the full feature map.
+
+ Args:
+ config (`Mask2FormerConfig`):
+ Configuration used to instantiate Mask2FormerMaskedAttentionDecoder.
+ """
+
+ def __init__(self, config: Mask2FormerConfig):
+ super().__init__()
+
+ self.config = config
+ self.mask_feature_size = config.mask_feature_size
+ self.dropout = config.dropout
+ self.layerdrop = config.dropout
+ self.num_feature_levels = 3 # level embedding (3 scales)
+ self.decoder_layers = config.decoder_layers - 1
+
+ self.layers = nn.ModuleList(
+ [Mask2FormerMaskedAttentionDecoderLayer(self.config) for _ in range(self.decoder_layers)]
+ )
+ self.layernorm = nn.LayerNorm(config.hidden_dim)
+
+ self.mask_predictor = Mask2FormerMaskPredictor(
+ hidden_size=config.hidden_dim,
+ num_heads=config.num_attention_heads,
+ mask_feature_size=self.mask_feature_size,
+ )
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ multi_stage_positional_embeddings: Optional[torch.Tensor] = None,
+ pixel_embeddings: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ query_position_embeddings: Optional[torch.Tensor] = None,
+ feature_size_list: Optional[list] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ):
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
+ The query embeddings that are passed into the decoder.
+ multi_stage_positional_embeddings (`torch.FloatTensor` of shape `(height*width, batch_size, num_channels)`):
+ Position embeddings that are added to the keys in each cross(masked)-attention layer.
+ pixel_embeddings (`torch.FloatTensor`):
+ Tensor of shape `(batch_size, num_channels, height, width)`, 1/4 scale features from the last Pixel
+ Decoder.
+ query_position_embeddings (`torch.FloatTensor` of shape `(num_queries, batch_size, hidden_size)`):
+ , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer.
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the
+ cross(masked)-attention of the decoder.
+ feature_size_list (`list[torch.Size]`):
+ This is a list containing shapes (height & width) of multi-scale features from the Pixel Decoder.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if inputs_embeds is not None:
+ hidden_states = inputs_embeds
+
+ # intermediate hidden states with layernorm applied - required for predicting class logits
+ intermediate = ()
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ attentions = () if output_attentions else None
+
+ # intermediate mask predictions from transformer decoder layers
+ intermediate_mask_predictions = ()
+
+ intermediate_hidden_states = self.layernorm(inputs_embeds)
+ intermediate += (intermediate_hidden_states,)
+
+ predicted_mask, attention_mask = self.mask_predictor(
+ intermediate_hidden_states, pixel_embeddings, feature_size_list[0]
+ )
+ intermediate_mask_predictions += (predicted_mask,)
+
+ for idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ dropout_probability = torch.rand([])
+
+ if self.training and (dropout_probability < self.layerdrop):
+ continue
+
+ level_index = idx % self.num_feature_levels
+
+ where = (attention_mask.sum(-1) != attention_mask.shape[-1]).to(attention_mask.dtype)
+ # Multiply the attention mask instead of indexing to avoid issue in torch.export.
+ attention_mask = attention_mask * where.unsqueeze(-1)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ level_index,
+ None, # attention_mask
+ multi_stage_positional_embeddings,
+ query_position_embeddings,
+ encoder_hidden_states, # as a positional argument for gradient checkpointing
+ encoder_attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+
+ intermediate_hidden_states = self.layernorm(layer_outputs[0])
+
+ predicted_mask, attention_mask = self.mask_predictor(
+ intermediate_hidden_states,
+ pixel_embeddings,
+ feature_size_list[(idx + 1) % self.num_feature_levels],
+ )
+
+ intermediate_mask_predictions += (predicted_mask,)
+
+ # add intermediate hidden states with layer norm applied which will be used for predicting class logits
+ intermediate += (intermediate_hidden_states,)
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ attentions += (layer_outputs[1],)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ hidden_states = hidden_states.transpose(1, 0)
+ if not return_dict:
+ outputs = [hidden_states, all_hidden_states, attentions, intermediate, intermediate_mask_predictions]
+ return tuple(v for v in outputs if v is not None)
+
+ return Mask2FormerMaskedAttentionDecoderOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=attentions,
+ intermediate_hidden_states=intermediate,
+ masks_queries_logits=intermediate_mask_predictions,
+ )
+
+
+# Copied from transformers.models.maskformer.modeling_maskformer.PredictionBlock with MaskFormer->Mask2Former
+class Mask2FormerPredictionBlock(nn.Module):
+ def __init__(self, in_dim: int, out_dim: int, activation: nn.Module) -> None:
+ super().__init__()
+ self.layers = [nn.Linear(in_dim, out_dim), activation]
+ # Maintain submodule indexing as if part of a Sequential block
+ for i, layer in enumerate(self.layers):
+ self.add_module(str(i), layer)
+
+ def forward(self, input: Tensor) -> Tensor:
+ hidden_state = input
+ for layer in self.layers:
+ hidden_state = layer(hidden_state)
+ return hidden_state
+
+
+class Mask2FormerMLPPredictionHead(nn.Module):
+ def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3):
+ """
+ A classic Multi Layer Perceptron (MLP).
+
+ Args:
+ input_dim (`int`):
+ The input dimensions.
+ hidden_dim (`int`):
+ The hidden dimensions.
+ output_dim (`int`):
+ The output dimensions.
+ num_layers (int, *optional*, defaults to 3):
+ The number of layers.
+ """
+ super().__init__()
+ in_dims = [input_dim] + [hidden_dim] * (num_layers - 1)
+ out_dims = [hidden_dim] * (num_layers - 1) + [output_dim]
+
+ self.layers = []
+ for i, (in_dim, out_dim) in enumerate(zip(in_dims, out_dims)):
+ activation = nn.ReLU() if i < num_layers - 1 else nn.Identity()
+ layer = Mask2FormerPredictionBlock(in_dim, out_dim, activation=activation)
+ self.layers.append(layer)
+ # Provide backwards compatibility from when the class inherited from nn.Sequential
+ # In nn.Sequential subclasses, the name given to the layer is its index in the sequence.
+ # In nn.Module subclasses they derived from the instance attribute they are assigned to e.g.
+ # self.my_layer_name = Layer()
+ # We can't give instance attributes integer names i.e. self.0 is not permitted and so need to register
+ # explicitly
+ self.add_module(str(i), layer)
+
+ def forward(self, input: Tensor) -> Tensor:
+ hidden_state = input
+ for layer in self.layers:
+ hidden_state = layer(hidden_state)
+ return hidden_state
+
+
+class Mask2FormerMaskPredictor(nn.Module):
+ def __init__(self, hidden_size: int, num_heads: int, mask_feature_size: torch.Tensor):
+ """
+ This class is used to get the predicted mask for a given Mask2FormerMaskedAttentionDecoder layer. It also
+ generates the binarized attention mask associated with the given predicted mask. The attention mask obtained
+ using predicted mask of the (l-1)th decoder layer is fed to the cross(masked)-attention block of the next
+ decoder layer as input.
+
+ Args:
+ hidden_size (`int`):
+ The feature dimension of the Mask2FormerMaskedAttentionDecoder
+ num_heads (`int`):
+ The number of heads used in the Mask2FormerMaskedAttentionDecoder
+ mask_feature_size (`torch.Tensor`):
+ one of the output dimensions of the predicted masks for each query
+ """
+ super().__init__()
+ self.hidden_size = hidden_size
+ self.num_heads = num_heads
+
+ self.mask_embedder = Mask2FormerMLPPredictionHead(self.hidden_size, self.hidden_size, mask_feature_size)
+
+ def forward(
+ self, outputs: torch.Tensor, pixel_embeddings: torch.Tensor, attention_mask_target_size: Optional[int] = None
+ ):
+ mask_embeddings = self.mask_embedder(outputs.transpose(0, 1))
+
+ # Sum up over the channels
+ outputs_mask = torch.einsum("bqc, bchw -> bqhw", mask_embeddings, pixel_embeddings)
+
+ attention_mask = nn.functional.interpolate(
+ outputs_mask, size=attention_mask_target_size, mode="bilinear", align_corners=False
+ )
+
+ attention_mask = attention_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1)
+ attention_mask = (attention_mask.flatten(0, 1) < 0.5).bool()
+ attention_mask = attention_mask.detach()
+
+ return outputs_mask, attention_mask
+
+
+class Mask2FormerTransformerModule(nn.Module):
+ """
+ The Mask2Former's transformer module.
+ """
+
+ def __init__(self, in_features: int, config: Mask2FormerConfig):
+ super().__init__()
+ hidden_dim = config.hidden_dim
+ self.num_feature_levels = 3
+ self.position_embedder = Mask2FormerSinePositionEmbedding(num_pos_feats=hidden_dim // 2, normalize=True)
+ self.queries_embedder = nn.Embedding(config.num_queries, hidden_dim)
+ self.queries_features = nn.Embedding(config.num_queries, hidden_dim)
+ self.input_projections = []
+
+ for _ in range(self.num_feature_levels):
+ if in_features != hidden_dim or config.enforce_input_projection:
+ self.input_projections.append(nn.Conv2d(in_features, hidden_dim, kernel_size=1))
+ else:
+ self.input_projections.append(nn.Sequential())
+
+ self.decoder = Mask2FormerMaskedAttentionDecoder(config=config)
+ self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)
+
+ def forward(
+ self,
+ multi_scale_features: list[Tensor],
+ mask_features: Tensor,
+ output_hidden_states: bool = False,
+ output_attentions: bool = False,
+ ) -> Mask2FormerMaskedAttentionDecoderOutput:
+ multi_stage_features = []
+ multi_stage_positional_embeddings = []
+ size_list = []
+
+ for i in range(self.num_feature_levels):
+ size_list.append(multi_scale_features[i].shape[-2:])
+ multi_stage_positional_embeddings.append(
+ self.position_embedder(
+ multi_scale_features[i].shape, multi_scale_features[i].device, multi_scale_features[i].dtype, None
+ ).flatten(2)
+ )
+ multi_stage_features.append(
+ self.input_projections[i](multi_scale_features[i]).flatten(2)
+ + self.level_embed.weight[i][None, :, None]
+ )
+
+ # Flatten (batch_size, num_channels, height, width) -> (height*width, batch_size, num_channels)
+ multi_stage_positional_embeddings[-1] = multi_stage_positional_embeddings[-1].permute(2, 0, 1)
+ multi_stage_features[-1] = multi_stage_features[-1].permute(2, 0, 1)
+
+ _, batch_size, _ = multi_stage_features[0].shape
+
+ # [num_queries, batch_size, num_channels]
+ query_embeddings = self.queries_embedder.weight.unsqueeze(1).repeat(1, batch_size, 1)
+ query_features = self.queries_features.weight.unsqueeze(1).repeat(1, batch_size, 1)
+
+ decoder_output = self.decoder(
+ inputs_embeds=query_features,
+ multi_stage_positional_embeddings=multi_stage_positional_embeddings,
+ pixel_embeddings=mask_features,
+ encoder_hidden_states=multi_stage_features,
+ query_position_embeddings=query_embeddings,
+ feature_size_list=size_list,
+ output_hidden_states=output_hidden_states,
+ output_attentions=output_attentions,
+ return_dict=True,
+ )
+
+ return decoder_output
+
+
+@auto_docstring
+class Mask2FormerPreTrainedModel(PreTrainedModel):
+ config: Mask2FormerConfig
+ base_model_prefix = "model"
+ main_input_name = "pixel_values"
+
+ def _init_weights(self, module: nn.Module):
+ xavier_std = self.config.init_xavier_std
+ std = self.config.init_std
+
+ if isinstance(module, Mask2FormerTransformerModule):
+ if module.input_projections is not None:
+ for input_projection in module.input_projections:
+ if not isinstance(input_projection, nn.Sequential):
+ nn.init.xavier_uniform_(input_projection.weight, gain=xavier_std)
+ nn.init.constant_(input_projection.bias, 0)
+
+ elif isinstance(module, Mask2FormerPixelDecoderEncoderMultiscaleDeformableAttention):
+ nn.init.constant_(module.sampling_offsets.weight.data, 0.0)
+ thetas = torch.arange(module.n_heads, dtype=torch.int64).float() * (2.0 * math.pi / module.n_heads)
+ grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
+ grid_init = (
+ (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
+ .view(module.n_heads, 1, 1, 2)
+ .repeat(1, module.n_levels, module.n_points, 1)
+ )
+ for i in range(module.n_points):
+ grid_init[:, :, i, :] *= i + 1
+ with torch.no_grad():
+ module.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
+
+ nn.init.constant_(module.attention_weights.weight.data, 0.0)
+ nn.init.constant_(module.attention_weights.bias.data, 0.0)
+ nn.init.xavier_uniform_(module.value_proj.weight.data)
+ nn.init.constant_(module.value_proj.bias.data, 0.0)
+ nn.init.xavier_uniform_(module.output_proj.weight.data)
+ nn.init.constant_(module.output_proj.bias.data, 0.0)
+
+ elif isinstance(module, Mask2FormerMaskedAttentionDecoderLayer):
+ for p in module.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p, gain=xavier_std)
+ module.cross_attn.in_proj_bias.data.zero_()
+
+ elif isinstance(module, Mask2FormerPixelDecoder):
+ nn.init.normal_(module.level_embed, std=0)
+
+ elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+
+ elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+ if hasattr(module, "reference_points"):
+ nn.init.xavier_uniform_(module.reference_points.weight.data, gain=1.0)
+ nn.init.constant_(module.reference_points.bias.data, 0.0)
+
+
+@auto_docstring
+class Mask2FormerModel(Mask2FormerPreTrainedModel):
+ main_input_name = "pixel_values"
+
+ def __init__(self, config: Mask2FormerConfig):
+ super().__init__(config)
+ self.pixel_level_module = Mask2FormerPixelLevelModule(config)
+ self.transformer_module = Mask2FormerTransformerModule(in_features=config.feature_size, config=config)
+
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Tensor,
+ pixel_mask: Optional[Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Mask2FormerModelOutput:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ batch_size, _, height, width = pixel_values.shape
+
+ if pixel_mask is None:
+ pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device)
+
+ pixel_level_module_output = self.pixel_level_module(
+ pixel_values=pixel_values, output_hidden_states=output_hidden_states
+ )
+
+ transformer_module_output = self.transformer_module(
+ multi_scale_features=pixel_level_module_output.decoder_hidden_states,
+ mask_features=pixel_level_module_output.decoder_last_hidden_state,
+ output_hidden_states=True,
+ output_attentions=output_attentions,
+ )
+
+ encoder_hidden_states = None
+ pixel_decoder_hidden_states = None
+ transformer_decoder_hidden_states = None
+ transformer_decoder_intermediate_states = None
+
+ if output_hidden_states:
+ encoder_hidden_states = pixel_level_module_output.encoder_hidden_states
+ pixel_decoder_hidden_states = pixel_level_module_output.decoder_hidden_states
+ transformer_decoder_hidden_states = transformer_module_output.hidden_states
+ transformer_decoder_intermediate_states = transformer_module_output.intermediate_hidden_states
+
+ output = Mask2FormerModelOutput(
+ encoder_last_hidden_state=pixel_level_module_output.encoder_last_hidden_state,
+ pixel_decoder_last_hidden_state=pixel_level_module_output.decoder_last_hidden_state,
+ transformer_decoder_last_hidden_state=transformer_module_output.last_hidden_state,
+ encoder_hidden_states=encoder_hidden_states,
+ pixel_decoder_hidden_states=pixel_decoder_hidden_states,
+ transformer_decoder_hidden_states=transformer_decoder_hidden_states,
+ transformer_decoder_intermediate_states=transformer_decoder_intermediate_states,
+ attentions=transformer_module_output.attentions,
+ masks_queries_logits=transformer_module_output.masks_queries_logits,
+ )
+
+ if not return_dict:
+ output = tuple(v for v in output.values() if v is not None)
+
+ return output
+
+
+@auto_docstring(
+ custom_intro="""
+ The Mask2Former Model with heads on top for instance/semantic/panoptic segmentation.
+ """
+)
+class Mask2FormerForUniversalSegmentation(Mask2FormerPreTrainedModel):
+ main_input_name = "pixel_values"
+
+ def __init__(self, config: Mask2FormerConfig):
+ super().__init__(config)
+ self.model = Mask2FormerModel(config)
+
+ self.weight_dict: dict[str, float] = {
+ "loss_cross_entropy": config.class_weight,
+ "loss_mask": config.mask_weight,
+ "loss_dice": config.dice_weight,
+ }
+
+ self.class_predictor = nn.Linear(config.hidden_dim, config.num_labels + 1)
+
+ self.criterion = Mask2FormerLoss(config=config, weight_dict=self.weight_dict)
+ self.post_init()
+
+ def get_loss_dict(
+ self,
+ masks_queries_logits: Tensor,
+ class_queries_logits: Tensor,
+ mask_labels: Tensor,
+ class_labels: Tensor,
+ auxiliary_predictions: dict[str, Tensor],
+ ) -> dict[str, Tensor]:
+ loss_dict: dict[str, Tensor] = self.criterion(
+ masks_queries_logits=masks_queries_logits,
+ class_queries_logits=class_queries_logits,
+ mask_labels=mask_labels,
+ class_labels=class_labels,
+ auxiliary_predictions=auxiliary_predictions,
+ )
+
+ # weight each loss by `self.weight_dict[]` including auxiliary losses
+ for key, weight in self.weight_dict.items():
+ for loss_key, loss in loss_dict.items():
+ if key in loss_key:
+ loss *= weight
+
+ return loss_dict
+
+ def get_loss(self, loss_dict: dict[str, Tensor]) -> Tensor:
+ return sum(loss_dict.values())
+
+ def get_auxiliary_logits(self, classes: torch.Tensor, output_masks: torch.Tensor):
+ auxiliary_logits: list[dict[str, Tensor]] = []
+
+ for aux_binary_masks, aux_classes in zip(output_masks[:-1], classes[:-1]):
+ auxiliary_logits.append({"masks_queries_logits": aux_binary_masks, "class_queries_logits": aux_classes})
+
+ return auxiliary_logits
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Tensor,
+ mask_labels: Optional[list[Tensor]] = None,
+ class_labels: Optional[list[Tensor]] = None,
+ pixel_mask: Optional[Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_auxiliary_logits: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Mask2FormerForUniversalSegmentationOutput:
+ r"""
+ mask_labels (`list[torch.Tensor]`, *optional*):
+ List of mask labels of shape `(num_labels, height, width)` to be fed to a model
+ class_labels (`list[torch.LongTensor]`, *optional*):
+ list of target class labels of shape `(num_labels, height, width)` to be fed to a model. They identify the
+ labels of `mask_labels`, e.g. the label of `mask_labels[i][j]` if `class_labels[i][j]`.
+ output_auxiliary_logits (`bool`, *optional*):
+ Whether or not to output auxiliary logits.
+
+ Examples:
+
+ Instance segmentation example:
+
+ ```python
+ >>> from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
+ >>> from PIL import Image
+ >>> import requests
+ >>> import torch
+
+ >>> # Load Mask2Former trained on COCO instance segmentation dataset
+ >>> image_processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-coco-instance")
+ >>> model = Mask2FormerForUniversalSegmentation.from_pretrained(
+ ... "facebook/mask2former-swin-small-coco-instance"
+ ... )
+
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> inputs = image_processor(image, return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+
+ >>> # Model predicts class_queries_logits of shape `(batch_size, num_queries)`
+ >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
+ >>> class_queries_logits = outputs.class_queries_logits
+ >>> masks_queries_logits = outputs.masks_queries_logits
+
+ >>> # Perform post-processing to get instance segmentation map
+ >>> pred_instance_map = image_processor.post_process_instance_segmentation(
+ ... outputs, target_sizes=[(image.height, image.width)]
+ ... )[0]
+ >>> print(pred_instance_map.shape)
+ torch.Size([480, 640])
+ ```
+
+ Semantic segmentation example:
+ ```python
+ >>> from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
+ >>> from PIL import Image
+ >>> import requests
+ >>> import torch
+
+ >>> # Load Mask2Former trained on ADE20k semantic segmentation dataset
+ >>> image_processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-ade-semantic")
+ >>> model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-small-ade-semantic")
+
+ >>> url = (
+ ... "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
+ ... )
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> inputs = image_processor(image, return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+
+ >>> # Model predicts class_queries_logits of shape `(batch_size, num_queries)`
+ >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
+ >>> class_queries_logits = outputs.class_queries_logits
+ >>> masks_queries_logits = outputs.masks_queries_logits
+
+ >>> # Perform post-processing to get semantic segmentation map
+ >>> pred_semantic_map = image_processor.post_process_semantic_segmentation(
+ ... outputs, target_sizes=[(image.height, image.width)]
+ ... )[0]
+ >>> print(pred_semantic_map.shape)
+ torch.Size([512, 683])
+ ```
+
+ Panoptic segmentation example:
+
+ ```python
+ >>> from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
+ >>> from PIL import Image
+ >>> import requests
+ >>> import torch
+
+ >>> # Load Mask2Former trained on CityScapes panoptic segmentation dataset
+ >>> image_processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-cityscapes-panoptic")
+ >>> model = Mask2FormerForUniversalSegmentation.from_pretrained(
+ ... "facebook/mask2former-swin-small-cityscapes-panoptic"
+ ... )
+
+ >>> url = "https://cdn-media.huggingface.co/Inference-API/Sample-results-on-the-Cityscapes-dataset-The-above-images-show-how-our-method-can-handle.png"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> inputs = image_processor(image, return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+
+ >>> # Model predicts class_queries_logits of shape `(batch_size, num_queries)`
+ >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
+ >>> class_queries_logits = outputs.class_queries_logits
+ >>> masks_queries_logits = outputs.masks_queries_logits
+
+ >>> # Perform post-processing to get panoptic segmentation map
+ >>> pred_panoptic_map = image_processor.post_process_panoptic_segmentation(
+ ... outputs, target_sizes=[(image.height, image.width)]
+ ... )[0]["segmentation"]
+ >>> print(pred_panoptic_map.shape)
+ torch.Size([338, 676])
+ ```
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.model(
+ pixel_values=pixel_values,
+ pixel_mask=pixel_mask,
+ output_hidden_states=output_hidden_states or self.config.use_auxiliary_loss,
+ output_attentions=output_attentions,
+ return_dict=True,
+ )
+
+ loss, loss_dict, auxiliary_logits = None, None, None
+ class_queries_logits = ()
+
+ for decoder_output in outputs.transformer_decoder_intermediate_states:
+ class_prediction = self.class_predictor(decoder_output.transpose(0, 1))
+ class_queries_logits += (class_prediction,)
+
+ masks_queries_logits = outputs.masks_queries_logits
+
+ auxiliary_logits = self.get_auxiliary_logits(class_queries_logits, masks_queries_logits)
+
+ if mask_labels is not None and class_labels is not None:
+ loss_dict = self.get_loss_dict(
+ masks_queries_logits=masks_queries_logits[-1],
+ class_queries_logits=class_queries_logits[-1],
+ mask_labels=mask_labels,
+ class_labels=class_labels,
+ auxiliary_predictions=auxiliary_logits,
+ )
+ loss = self.get_loss(loss_dict)
+
+ encoder_hidden_states = None
+ pixel_decoder_hidden_states = None
+ transformer_decoder_hidden_states = None
+
+ if output_hidden_states:
+ encoder_hidden_states = outputs.encoder_hidden_states
+ pixel_decoder_hidden_states = outputs.pixel_decoder_hidden_states
+ transformer_decoder_hidden_states = outputs.transformer_decoder_hidden_states
+
+ output_auxiliary_logits = (
+ self.config.output_auxiliary_logits if output_auxiliary_logits is None else output_auxiliary_logits
+ )
+ if not output_auxiliary_logits:
+ auxiliary_logits = None
+
+ output = Mask2FormerForUniversalSegmentationOutput(
+ loss=loss,
+ class_queries_logits=class_queries_logits[-1],
+ masks_queries_logits=masks_queries_logits[-1],
+ auxiliary_logits=auxiliary_logits,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ pixel_decoder_last_hidden_state=outputs.pixel_decoder_last_hidden_state,
+ transformer_decoder_last_hidden_state=outputs.transformer_decoder_last_hidden_state,
+ encoder_hidden_states=encoder_hidden_states,
+ pixel_decoder_hidden_states=pixel_decoder_hidden_states,
+ transformer_decoder_hidden_states=transformer_decoder_hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ if not return_dict:
+ output = tuple(v for v in output.values() if v is not None)
+ if loss is not None:
+ output = (loss) + output
+ return output
+
+
+__all__ = ["Mask2FormerForUniversalSegmentation", "Mask2FormerModel", "Mask2FormerPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/modular_mask2former.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/modular_mask2former.py
new file mode 100644
index 0000000000000000000000000000000000000000..eed1e7adc9ceaa0abbebb5d479057e424d7295be
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mask2former/modular_mask2former.py
@@ -0,0 +1,315 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Optional
+
+from transformers.models.maskformer.image_processing_maskformer_fast import MaskFormerImageProcessorFast
+
+from ...utils import (
+ TensorType,
+ is_torch_available,
+ logging,
+)
+from .image_processing_mask2former import (
+ compute_segments,
+ convert_segmentation_to_rle,
+ remove_low_and_no_objects,
+)
+
+
+if is_torch_available():
+ import torch
+ from torch import nn
+
+
+logger = logging.get_logger(__name__)
+
+
+class Mask2FormerImageProcessorFast(MaskFormerImageProcessorFast):
+ def post_process_semantic_segmentation(
+ self, outputs, target_sizes: Optional[list[tuple[int, int]]] = None
+ ) -> "torch.Tensor":
+ """
+ Converts the output of [`Mask2FormerForUniversalSegmentation`] into semantic segmentation maps. Only supports
+ PyTorch.
+
+ Args:
+ outputs ([`Mask2FormerForUniversalSegmentation`]):
+ Raw outputs of the model.
+ target_sizes (`List[Tuple[int, int]]`, *optional*):
+ List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ Returns:
+ `List[torch.Tensor]`:
+ A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
+ corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
+ `torch.Tensor` correspond to a semantic class id.
+ """
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ # Scale back to preprocessed image size - (384, 384) for all models
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits, size=(384, 384), mode="bilinear", align_corners=False
+ )
+
+ # Remove the null class `[..., :-1]`
+ masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
+ masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Semantic segmentation logits of shape (batch_size, num_classes, height, width)
+ segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
+ batch_size = class_queries_logits.shape[0]
+
+ # Resize logits and compute semantic segmentation maps
+ if target_sizes is not None:
+ if batch_size != len(target_sizes):
+ raise ValueError(
+ "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+ )
+
+ semantic_segmentation = []
+ for idx in range(batch_size):
+ resized_logits = torch.nn.functional.interpolate(
+ segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+ )
+ semantic_map = resized_logits[0].argmax(dim=0)
+ semantic_segmentation.append(semantic_map)
+ else:
+ semantic_segmentation = segmentation.argmax(dim=1)
+ semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+ return semantic_segmentation
+
+ def post_process_instance_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ return_coco_annotation: Optional[bool] = False,
+ return_binary_maps: Optional[bool] = False,
+ ) -> list[dict]:
+ """
+ Converts the output of [`Mask2FormerForUniversalSegmentationOutput`] into instance segmentation predictions.
+ Only supports PyTorch. If instances could overlap, set either return_coco_annotation or return_binary_maps
+ to `True` to get the correct segmentation result.
+
+ Args:
+ outputs ([`Mask2FormerForUniversalSegmentation`]):
+ Raw outputs of the model.
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ target_sizes (`List[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ return_coco_annotation (`bool`, *optional*, defaults to `False`):
+ If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE) format.
+ return_binary_maps (`bool`, *optional*, defaults to `False`):
+ If set to `True`, segmentation maps are returned as a concatenated tensor of binary segmentation maps
+ (one per detected instance).
+ Returns:
+ `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id`, or
+ `List[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
+ `True`, or a tensor of shape `(num_instances, height, width)` if return_binary_maps is set to `True`.
+ Set to `None` if no mask if found above `threshold`.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- An integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+ if return_coco_annotation and return_binary_maps:
+ raise ValueError("return_coco_annotation and return_binary_maps can not be both set to True.")
+
+ # [batch_size, num_queries, num_classes+1]
+ class_queries_logits = outputs.class_queries_logits
+ # [batch_size, num_queries, height, width]
+ masks_queries_logits = outputs.masks_queries_logits
+
+ # Scale back to preprocessed image size - (384, 384) for all models
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits, size=(384, 384), mode="bilinear", align_corners=False
+ )
+
+ device = masks_queries_logits.device
+ num_classes = class_queries_logits.shape[-1] - 1
+ num_queries = class_queries_logits.shape[-2]
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(class_queries_logits.shape[0]):
+ mask_pred = masks_queries_logits[i]
+ mask_cls = class_queries_logits[i]
+
+ scores = torch.nn.functional.softmax(mask_cls, dim=-1)[:, :-1]
+ labels = torch.arange(num_classes, device=device).unsqueeze(0).repeat(num_queries, 1).flatten(0, 1)
+
+ scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False)
+ labels_per_image = labels[topk_indices]
+
+ topk_indices = torch.div(topk_indices, num_classes, rounding_mode="floor")
+ mask_pred = mask_pred[topk_indices]
+ pred_masks = (mask_pred > 0).float()
+
+ # Calculate average mask prob
+ mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * pred_masks.flatten(1)).sum(1) / (
+ pred_masks.flatten(1).sum(1) + 1e-6
+ )
+ pred_scores = scores_per_image * mask_scores_per_image
+ pred_classes = labels_per_image
+
+ segmentation = torch.zeros((384, 384)) - 1
+ if target_sizes is not None:
+ segmentation = torch.zeros(target_sizes[i]) - 1
+ pred_masks = torch.nn.functional.interpolate(
+ pred_masks.unsqueeze(0), size=target_sizes[i], mode="nearest"
+ )[0]
+
+ instance_maps, segments = [], []
+ current_segment_id = 0
+ for j in range(num_queries):
+ score = pred_scores[j].item()
+
+ if not torch.all(pred_masks[j] == 0) and score >= threshold:
+ segmentation[pred_masks[j] == 1] = current_segment_id
+ segments.append(
+ {
+ "id": current_segment_id,
+ "label_id": pred_classes[j].item(),
+ "was_fused": False,
+ "score": round(score, 6),
+ }
+ )
+ current_segment_id += 1
+ instance_maps.append(pred_masks[j])
+
+ # Return segmentation map in run-length encoding (RLE) format
+ if return_coco_annotation:
+ segmentation = convert_segmentation_to_rle(segmentation)
+
+ # Return a concatenated tensor of binary instance maps
+ if return_binary_maps and len(instance_maps) != 0:
+ segmentation = torch.stack(instance_maps, dim=0)
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+ def post_process_panoptic_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ label_ids_to_fuse: Optional[set[int]] = None,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ ) -> list[dict]:
+ """
+ Converts the output of [`Mask2FormerForUniversalSegmentationOutput`] into image panoptic segmentation
+ predictions. Only supports PyTorch.
+
+ Args:
+ outputs ([`Mask2FormerForUniversalSegmentationOutput`]):
+ The outputs from [`Mask2FormerForUniversalSegmentation`].
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ label_ids_to_fuse (`Set[int]`, *optional*):
+ The labels in this state will have all their instances be fused together. For instance we could say
+ there can only be one sky in an image, but several persons, so the label ID for sky would be in that
+ set, but not the one for person.
+ target_sizes (`List[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction in batch. If left to None, predictions will not be
+ resized.
+
+ Returns:
+ `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set
+ to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized
+ to the corresponding `target_sizes` entry.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- an integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
+ Multiple instances of the same class / label were fused and assigned a single `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+
+ if label_ids_to_fuse is None:
+ logger.warning("`label_ids_to_fuse` unset. No instance will be fused.")
+ label_ids_to_fuse = set()
+
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ # Scale back to preprocessed image size - (384, 384) for all models
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits, size=(384, 384), mode="bilinear", align_corners=False
+ )
+
+ batch_size = class_queries_logits.shape[0]
+ num_labels = class_queries_logits.shape[-1] - 1
+
+ mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Predicted label and score of each query (batch_size, num_queries)
+ pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(batch_size):
+ mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
+ mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
+ )
+
+ # No mask found
+ if mask_probs_item.shape[0] <= 0:
+ height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
+ segmentation = torch.zeros((height, width)) - 1
+ results.append({"segmentation": segmentation, "segments_info": []})
+ continue
+
+ # Get segmentation map and segment information of batch item
+ target_size = target_sizes[i] if target_sizes is not None else None
+ segmentation, segments = compute_segments(
+ mask_probs=mask_probs_item,
+ pred_scores=pred_scores_item,
+ pred_labels=pred_labels_item,
+ mask_threshold=mask_threshold,
+ overlap_mask_area_threshold=overlap_mask_area_threshold,
+ label_ids_to_fuse=label_ids_to_fuse,
+ target_size=target_size,
+ )
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+ def post_process_segmentation():
+ raise NotImplementedError("Segmentation post-processing is not implemented for Mask2Former yet.")
+
+
+__all__ = ["Mask2FormerImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a91c136c2f32c434a52161342fb8df6f7b0011d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/__init__.py
@@ -0,0 +1,32 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_maskformer import *
+ from .configuration_maskformer_swin import *
+ from .feature_extraction_maskformer import *
+ from .image_processing_maskformer import *
+ from .image_processing_maskformer_fast import *
+ from .modeling_maskformer import *
+ from .modeling_maskformer_swin import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/configuration_maskformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/configuration_maskformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d988acb45e95fae720c178165c911dc410ad1748
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/configuration_maskformer.py
@@ -0,0 +1,235 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms, Inc.and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MaskFormer model configuration"""
+
+from typing import Optional
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
+from ..auto import CONFIG_MAPPING
+from ..detr import DetrConfig
+from ..swin import SwinConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class MaskFormerConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MaskFormerModel`]. It is used to instantiate a
+ MaskFormer model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the MaskFormer
+ [facebook/maskformer-swin-base-ade](https://huggingface.co/facebook/maskformer-swin-base-ade) architecture trained
+ on [ADE20k-150](https://huggingface.co/datasets/scene_parse_150).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Currently, MaskFormer only supports the [Swin Transformer](swin) as backbone.
+
+ Args:
+ mask_feature_size (`int`, *optional*, defaults to 256):
+ The masks' features size, this value will also be used to specify the Feature Pyramid Network features'
+ size.
+ no_object_weight (`float`, *optional*, defaults to 0.1):
+ Weight to apply to the null (no object) class.
+ use_auxiliary_loss(`bool`, *optional*, defaults to `False`):
+ If `True` [`MaskFormerForInstanceSegmentationOutput`] will contain the auxiliary losses computed using the
+ logits from each decoder's stage.
+ backbone_config (`Dict`, *optional*):
+ The configuration passed to the backbone, if unset, the configuration corresponding to
+ `swin-base-patch4-window12-384` will be used.
+ backbone (`str`, *optional*):
+ Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
+ will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
+ is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
+ use_pretrained_backbone (`bool`, *optional*, `False`):
+ Whether to use pretrained weights for the backbone.
+ use_timm_backbone (`bool`, *optional*, `False`):
+ Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
+ library.
+ backbone_kwargs (`dict`, *optional*):
+ Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
+ e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
+ decoder_config (`Dict`, *optional*):
+ The configuration passed to the transformer decoder model, if unset the base config for `detr-resnet-50`
+ will be used.
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ init_xavier_std (`float`, *optional*, defaults to 1):
+ The scaling factor used for the Xavier initialization gain in the HM Attention map module.
+ dice_weight (`float`, *optional*, defaults to 1.0):
+ The weight for the dice loss.
+ cross_entropy_weight (`float`, *optional*, defaults to 1.0):
+ The weight for the cross entropy loss.
+ mask_weight (`float`, *optional*, defaults to 20.0):
+ The weight for the mask loss.
+ output_auxiliary_logits (`bool`, *optional*):
+ Should the model output its `auxiliary_logits` or not.
+
+ Raises:
+ `ValueError`:
+ Raised if the backbone model type selected is not in `["swin"]` or the decoder model type selected is not
+ in `["detr"]`
+
+ Examples:
+
+ ```python
+ >>> from transformers import MaskFormerConfig, MaskFormerModel
+
+ >>> # Initializing a MaskFormer facebook/maskformer-swin-base-ade configuration
+ >>> configuration = MaskFormerConfig()
+
+ >>> # Initializing a model (with random weights) from the facebook/maskformer-swin-base-ade style configuration
+ >>> model = MaskFormerModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+
+ """
+
+ model_type = "maskformer"
+ attribute_map = {"hidden_size": "mask_feature_size"}
+ backbones_supported = ["resnet", "swin"]
+ decoders_supported = ["detr"]
+
+ def __init__(
+ self,
+ fpn_feature_size: int = 256,
+ mask_feature_size: int = 256,
+ no_object_weight: float = 0.1,
+ use_auxiliary_loss: bool = False,
+ backbone_config: Optional[dict] = None,
+ decoder_config: Optional[dict] = None,
+ init_std: float = 0.02,
+ init_xavier_std: float = 1.0,
+ dice_weight: float = 1.0,
+ cross_entropy_weight: float = 1.0,
+ mask_weight: float = 20.0,
+ output_auxiliary_logits: Optional[bool] = None,
+ backbone: Optional[str] = None,
+ use_pretrained_backbone: bool = False,
+ use_timm_backbone: bool = False,
+ backbone_kwargs: Optional[dict] = None,
+ **kwargs,
+ ):
+ if backbone_config is None and backbone is None:
+ # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
+ backbone_config = SwinConfig(
+ image_size=384,
+ num_channels=3,
+ patch_size=4,
+ embed_dim=128,
+ depths=[2, 2, 18, 2],
+ num_heads=[4, 8, 16, 32],
+ window_size=12,
+ drop_path_rate=0.3,
+ out_features=["stage1", "stage2", "stage3", "stage4"],
+ )
+ elif isinstance(backbone_config, dict):
+ backbone_model_type = backbone_config.pop("model_type")
+ config_class = CONFIG_MAPPING[backbone_model_type]
+ backbone_config = config_class.from_dict(backbone_config)
+
+ verify_backbone_config_arguments(
+ use_timm_backbone=use_timm_backbone,
+ use_pretrained_backbone=use_pretrained_backbone,
+ backbone=backbone,
+ backbone_config=backbone_config,
+ backbone_kwargs=backbone_kwargs,
+ )
+ # verify that the backbone is supported
+ if backbone_config is not None and backbone_config.model_type not in self.backbones_supported:
+ logger.warning_once(
+ f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
+ f"Supported model types: {','.join(self.backbones_supported)}"
+ )
+
+ if decoder_config is None:
+ # fall back to https://huggingface.co/facebook/detr-resnet-50
+ decoder_config = DetrConfig()
+ else:
+ # verify that the decoder is supported
+ decoder_type = (
+ decoder_config.pop("model_type") if isinstance(decoder_config, dict) else decoder_config.model_type
+ )
+ if decoder_type not in self.decoders_supported:
+ raise ValueError(
+ f"Transformer Decoder {decoder_type} not supported, please use one of"
+ f" {','.join(self.decoders_supported)}"
+ )
+ if isinstance(decoder_config, dict):
+ config_class = CONFIG_MAPPING[decoder_type]
+ decoder_config = config_class.from_dict(decoder_config)
+
+ self.backbone_config = backbone_config
+ self.decoder_config = decoder_config
+ # main feature dimension for the model
+ self.fpn_feature_size = fpn_feature_size
+ self.mask_feature_size = mask_feature_size
+ # initializer
+ self.init_std = init_std
+ self.init_xavier_std = init_xavier_std
+ # Hungarian matcher && loss
+ self.cross_entropy_weight = cross_entropy_weight
+ self.dice_weight = dice_weight
+ self.mask_weight = mask_weight
+ self.use_auxiliary_loss = use_auxiliary_loss
+ self.no_object_weight = no_object_weight
+ self.output_auxiliary_logits = output_auxiliary_logits
+
+ self.num_attention_heads = self.decoder_config.encoder_attention_heads
+ self.num_hidden_layers = self.decoder_config.num_hidden_layers
+ self.backbone = backbone
+ self.use_pretrained_backbone = use_pretrained_backbone
+ self.use_timm_backbone = use_timm_backbone
+ self.backbone_kwargs = backbone_kwargs
+ super().__init__(**kwargs)
+
+ @property
+ def sub_configs(self):
+ sub_configs = {}
+ if self.backbone_config is not None and self.backbone_config != {}:
+ sub_configs["backbone_config"] = type(self.backbone_config)
+ if self.decoder_config is not None and self.decoder_config != {}:
+ sub_configs["decoder_config"] = type(self.decoder_config)
+ return sub_configs
+
+ @classmethod
+ def from_backbone_and_decoder_configs(
+ cls, backbone_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs
+ ):
+ """Instantiate a [`MaskFormerConfig`] (or a derived class) from a pre-trained backbone model configuration and DETR model
+ configuration.
+
+ Args:
+ backbone_config ([`PretrainedConfig`]):
+ The backbone configuration.
+ decoder_config ([`PretrainedConfig`]):
+ The transformer decoder configuration to use.
+
+ Returns:
+ [`MaskFormerConfig`]: An instance of a configuration object
+ """
+ return cls(
+ backbone_config=backbone_config,
+ decoder_config=decoder_config,
+ **kwargs,
+ )
+
+
+__all__ = ["MaskFormerConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/configuration_maskformer_swin.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/configuration_maskformer_swin.py
new file mode 100644
index 0000000000000000000000000000000000000000..84157117bbf2a388fb7dbd1b4aa227e1fa016513
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/configuration_maskformer_swin.py
@@ -0,0 +1,153 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MaskFormer Swin Transformer model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
+
+
+logger = logging.get_logger(__name__)
+
+
+class MaskFormerSwinConfig(BackboneConfigMixin, PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MaskFormerSwinModel`]. It is used to instantiate
+ a Donut model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Swin
+ [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224)
+ architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ image_size (`int`, *optional*, defaults to 224):
+ The size (resolution) of each image.
+ patch_size (`int`, *optional*, defaults to 4):
+ The size (resolution) of each patch.
+ num_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ embed_dim (`int`, *optional*, defaults to 96):
+ Dimensionality of patch embedding.
+ depths (`list[int]`, *optional*, defaults to `[2, 2, 6, 2]`):
+ Depth of each layer in the Transformer encoder.
+ num_heads (`list[int]`, *optional*, defaults to `[3, 6, 12, 24]`):
+ Number of attention heads in each layer of the Transformer encoder.
+ window_size (`int`, *optional*, defaults to 7):
+ Size of windows.
+ mlp_ratio (`float`, *optional*, defaults to 4.0):
+ Ratio of MLP hidden dimensionality to embedding dimensionality.
+ qkv_bias (`bool`, *optional*, defaults to True):
+ Whether or not a learnable bias should be added to the queries, keys and values.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
+ The dropout probability for all fully connected layers in the embeddings and encoder.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ drop_path_rate (`float`, *optional*, defaults to 0.1):
+ Stochastic depth rate.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
+ `"selu"` and `"gelu_new"` are supported.
+ use_absolute_embeddings (`bool`, *optional*, defaults to False):
+ Whether or not to add absolute position embeddings to the patch embeddings.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+ out_features (`list[str]`, *optional*):
+ If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
+ (depending on how many stages the model has). If unset and `out_indices` is set, will default to the
+ corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
+ same order as defined in the `stage_names` attribute.
+ out_indices (`list[int]`, *optional*):
+ If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
+ many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
+ If unset and `out_features` is unset, will default to the last stage. Must be in the
+ same order as defined in the `stage_names` attribute.
+
+ Example:
+
+ ```python
+ >>> from transformers import MaskFormerSwinConfig, MaskFormerSwinModel
+
+ >>> # Initializing a microsoft/swin-tiny-patch4-window7-224 style configuration
+ >>> configuration = MaskFormerSwinConfig()
+
+ >>> # Initializing a model (with random weights) from the microsoft/swin-tiny-patch4-window7-224 style configuration
+ >>> model = MaskFormerSwinModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "maskformer-swin"
+
+ attribute_map = {
+ "num_attention_heads": "num_heads",
+ "num_hidden_layers": "num_layers",
+ }
+
+ def __init__(
+ self,
+ image_size=224,
+ patch_size=4,
+ num_channels=3,
+ embed_dim=96,
+ depths=[2, 2, 6, 2],
+ num_heads=[3, 6, 12, 24],
+ window_size=7,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ hidden_dropout_prob=0.0,
+ attention_probs_dropout_prob=0.0,
+ drop_path_rate=0.1,
+ hidden_act="gelu",
+ use_absolute_embeddings=False,
+ initializer_range=0.02,
+ layer_norm_eps=1e-5,
+ out_features=None,
+ out_indices=None,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.image_size = image_size
+ self.patch_size = patch_size
+ self.num_channels = num_channels
+ self.embed_dim = embed_dim
+ self.depths = depths
+ self.num_layers = len(depths)
+ self.num_heads = num_heads
+ self.window_size = window_size
+ self.mlp_ratio = mlp_ratio
+ self.qkv_bias = qkv_bias
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.drop_path_rate = drop_path_rate
+ self.hidden_act = hidden_act
+ self.use_absolute_embeddings = use_absolute_embeddings
+ self.layer_norm_eps = layer_norm_eps
+ self.initializer_range = initializer_range
+ # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
+ # this indicates the channel dimension after the last stage of the model
+ self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
+ self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
+ self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+ out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
+ )
+
+
+__all__ = ["MaskFormerSwinConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..fac17d0220335b5f9fe934705c502cff3f6917a0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py
@@ -0,0 +1,732 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import sys
+from argparse import ArgumentParser
+from collections.abc import Iterator
+from dataclasses import dataclass
+from pathlib import Path
+from pprint import pformat
+from typing import Any
+
+import requests
+import torch
+import torchvision.transforms as T
+from detectron2.checkpoint import DetectionCheckpointer
+from detectron2.config import get_cfg
+from detectron2.data import MetadataCatalog
+from detectron2.projects.deeplab import add_deeplab_config
+from PIL import Image
+from torch import Tensor, nn
+
+from transformers.models.maskformer.feature_extraction_maskformer import MaskFormerImageProcessor
+from transformers.models.maskformer.modeling_maskformer import (
+ MaskFormerConfig,
+ MaskFormerForInstanceSegmentation,
+ MaskFormerForInstanceSegmentationOutput,
+ MaskFormerModel,
+ MaskFormerModelOutput,
+)
+from transformers.utils import logging
+
+
+StateDict = dict[str, Tensor]
+
+logging.set_verbosity_info()
+logger = logging.get_logger()
+
+torch.manual_seed(0)
+
+
+class TrackedStateDict:
+ def __init__(self, to_track: dict):
+ """This class "tracks" a python dictionary by keeping track of which item is accessed.
+
+ Args:
+ to_track (Dict): The dictionary we wish to track
+ """
+ self.to_track = to_track
+ self._seen: set[str] = set()
+
+ def __getitem__(self, key: str) -> Any:
+ return self.to_track[key]
+
+ def __setitem__(self, key: str, item: Any):
+ self._seen.add(key)
+ self.to_track[key] = item
+
+ def diff(self) -> list[str]:
+ """This method returns a set difference between the keys in the tracked state dict and the one we have access so far.
+ This is an effective method to check if we have update all the keys
+
+ Returns:
+ list[str]: List of keys not yet updated
+ """
+ return set(self.to_track.keys()) - self._seen
+
+ def copy(self) -> dict:
+ # proxy the call to the internal dictionary
+ return self.to_track.copy()
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ img_data = requests.get(url, stream=True).raw
+ im = Image.open(img_data)
+ return im
+
+
+@dataclass
+class Args:
+ """Fake command line arguments needed by maskformer/detectron implementation"""
+
+ config_file: str
+
+
+def setup_cfg(args: Args):
+ # load config from file and command-line arguments
+ cfg = get_cfg()
+ add_deeplab_config(cfg)
+ add_mask_former_config(cfg)
+ cfg.merge_from_file(args.config_file)
+ cfg.freeze()
+ return cfg
+
+
+class OriginalMaskFormerConfigToOursConverter:
+ def __call__(self, original_config: object) -> MaskFormerConfig:
+ model = original_config.MODEL
+ mask_former = model.MASK_FORMER
+ swin = model.SWIN
+
+ dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST[0])
+ id2label = dict(enumerate(dataset_catalog.stuff_classes))
+ label2id = {label: idx for idx, label in id2label.items()}
+
+ config: MaskFormerConfig = MaskFormerConfig(
+ fpn_feature_size=model.SEM_SEG_HEAD.CONVS_DIM,
+ mask_feature_size=model.SEM_SEG_HEAD.MASK_DIM,
+ num_labels=model.SEM_SEG_HEAD.NUM_CLASSES,
+ no_object_weight=mask_former.NO_OBJECT_WEIGHT,
+ num_queries=mask_former.NUM_OBJECT_QUERIES,
+ backbone_config={
+ "pretrain_img_size": swin.PRETRAIN_IMG_SIZE,
+ "image_size": swin.PRETRAIN_IMG_SIZE,
+ "in_channels": 3,
+ "patch_size": swin.PATCH_SIZE,
+ "embed_dim": swin.EMBED_DIM,
+ "depths": swin.DEPTHS,
+ "num_heads": swin.NUM_HEADS,
+ "window_size": swin.WINDOW_SIZE,
+ "drop_path_rate": swin.DROP_PATH_RATE,
+ "model_type": "swin",
+ },
+ dice_weight=mask_former.DICE_WEIGHT,
+ ce_weight=1.0,
+ mask_weight=mask_former.MASK_WEIGHT,
+ decoder_config={
+ "model_type": "detr",
+ "max_position_embeddings": 1024,
+ "encoder_layers": 6,
+ "encoder_ffn_dim": 2048,
+ "encoder_attention_heads": 8,
+ "decoder_layers": mask_former.DEC_LAYERS,
+ "decoder_ffn_dim": mask_former.DIM_FEEDFORWARD,
+ "decoder_attention_heads": mask_former.NHEADS,
+ "encoder_layerdrop": 0.0,
+ "decoder_layerdrop": 0.0,
+ "d_model": mask_former.HIDDEN_DIM,
+ "dropout": mask_former.DROPOUT,
+ "attention_dropout": 0.0,
+ "activation_dropout": 0.0,
+ "init_std": 0.02,
+ "init_xavier_std": 1.0,
+ "scale_embedding": False,
+ "auxiliary_loss": False,
+ "dilation": False,
+ # default pretrained config values
+ },
+ id2label=id2label,
+ label2id=label2id,
+ )
+
+ return config
+
+
+class OriginalMaskFormerConfigToImageProcessorConverter:
+ def __call__(self, original_config: object) -> MaskFormerImageProcessor:
+ model = original_config.MODEL
+ model_input = original_config.INPUT
+ dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST[0])
+
+ return MaskFormerImageProcessor(
+ image_mean=(torch.tensor(model.PIXEL_MEAN) / 255).tolist(),
+ image_std=(torch.tensor(model.PIXEL_STD) / 255).tolist(),
+ size=model_input.MIN_SIZE_TEST,
+ max_size=model_input.MAX_SIZE_TEST,
+ num_labels=model.SEM_SEG_HEAD.NUM_CLASSES,
+ ignore_index=dataset_catalog.ignore_label,
+ size_divisibility=32, # 32 is required by swin
+ )
+
+
+class OriginalMaskFormerCheckpointToOursConverter:
+ def __init__(self, original_model: nn.Module, config: MaskFormerConfig):
+ self.original_model = original_model
+ self.config = config
+
+ def pop_all(self, renamed_keys: list[tuple[str, str]], dst_state_dict: StateDict, src_state_dict: StateDict):
+ for src_key, dst_key in renamed_keys:
+ dst_state_dict[dst_key] = src_state_dict.pop(src_key)
+
+ def replace_backbone(self, dst_state_dict: StateDict, src_state_dict: StateDict, config: MaskFormerConfig):
+ dst_prefix: str = "pixel_level_module.encoder"
+ src_prefix: str = "backbone"
+
+ renamed_keys = [
+ (
+ f"{src_prefix}.patch_embed.proj.weight",
+ f"{dst_prefix}.model.embeddings.patch_embeddings.projection.weight",
+ ),
+ (f"{src_prefix}.patch_embed.proj.bias", f"{dst_prefix}.model.embeddings.patch_embeddings.projection.bias"),
+ (f"{src_prefix}.patch_embed.norm.weight", f"{dst_prefix}.model.embeddings.norm.weight"),
+ (f"{src_prefix}.patch_embed.norm.bias", f"{dst_prefix}.model.embeddings.norm.bias"),
+ ]
+ num_layers = len(config.backbone_config.depths)
+ for layer_idx in range(num_layers):
+ for block_idx in range(config.backbone_config.depths[layer_idx]):
+ renamed_keys.extend(
+ [ # src, dst
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.bias",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_bias_table",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_bias_table",
+ ),
+ ]
+ )
+ # now we need to handle the attentions
+ # read in weights + bias of input projection layer of cross-attention
+
+ src_att_weight = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight"]
+ src_att_bias = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias"]
+
+ size = src_att_weight.shape[0]
+ offset = size // 3
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.weight"
+ ] = src_att_weight[:offset, :]
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.bias"
+ ] = src_att_bias[:offset]
+
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.weight"
+ ] = src_att_weight[offset : offset * 2, :]
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.bias"
+ ] = src_att_bias[offset : offset * 2]
+
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.weight"
+ ] = src_att_weight[-offset:, :]
+ dst_state_dict[
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.bias"
+ ] = src_att_bias[-offset:]
+
+ # let's pop them
+ src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight")
+ src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias")
+ # proj
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.bias",
+ ),
+ ]
+ )
+
+ # second norm
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.bias",
+ ),
+ ]
+ )
+
+ # mlp
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.bias",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.bias",
+ ),
+ ]
+ )
+
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_index",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_index",
+ )
+ ]
+ )
+
+ if layer_idx < num_layers - 1:
+ # patch merging
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.reduction.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.reduction.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.norm.weight",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.norm.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.norm.bias",
+ f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.norm.bias",
+ ),
+ ]
+ )
+
+ # hidden states norms
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.norm{layer_idx}.weight",
+ f"{dst_prefix}.hidden_states_norms.{layer_idx}.weight",
+ ),
+ (
+ f"{src_prefix}.norm{layer_idx}.bias",
+ f"{dst_prefix}.hidden_states_norms.{layer_idx}.bias",
+ ),
+ ]
+ )
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ def replace_pixel_module(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "pixel_level_module.decoder"
+ src_prefix: str = "sem_seg_head.pixel_decoder"
+
+ self.replace_backbone(dst_state_dict, src_state_dict, self.config)
+
+ def rename_keys_for_conv(detectron_conv: str, mine_conv: str):
+ return [
+ (f"{detectron_conv}.weight", f"{mine_conv}.0.weight"),
+ # 2 cuz the have act in the middle -> rename it
+ (f"{detectron_conv}.norm.weight", f"{mine_conv}.1.weight"),
+ (f"{detectron_conv}.norm.bias", f"{mine_conv}.1.bias"),
+ ]
+
+ renamed_keys = [
+ (f"{src_prefix}.mask_features.weight", f"{dst_prefix}.mask_projection.weight"),
+ (f"{src_prefix}.mask_features.bias", f"{dst_prefix}.mask_projection.bias"),
+ # the layers in the original one are in reverse order, stem is the last one!
+ ]
+
+ renamed_keys.extend(rename_keys_for_conv(f"{src_prefix}.layer_4", f"{dst_prefix}.fpn.stem"))
+
+ # add all the fpn layers (here we need some config parameters to know the size in advance)
+ for src_i, dst_i in zip(range(3, 0, -1), range(0, 3)):
+ renamed_keys.extend(
+ rename_keys_for_conv(f"{src_prefix}.adapter_{src_i}", f"{dst_prefix}.fpn.layers.{dst_i}.proj")
+ )
+ renamed_keys.extend(
+ rename_keys_for_conv(f"{src_prefix}.layer_{src_i}", f"{dst_prefix}.fpn.layers.{dst_i}.block")
+ )
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ def rename_keys_in_detr_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "transformer_module.decoder"
+ src_prefix: str = "sem_seg_head.predictor.transformer.decoder"
+ # not sure why we are not popping direcetly here!
+ # here we list all keys to be renamed (original name on the left, our name on the right)
+ rename_keys = []
+ for i in range(self.config.decoder_config.decoder_layers):
+ # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
+ rename_keys.append(
+ (
+ f"{src_prefix}.layers.{i}.self_attn.out_proj.weight",
+ f"{dst_prefix}.layers.{i}.self_attn.out_proj.weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"{src_prefix}.layers.{i}.self_attn.out_proj.bias",
+ f"{dst_prefix}.layers.{i}.self_attn.out_proj.bias",
+ )
+ )
+ rename_keys.append(
+ (
+ f"{src_prefix}.layers.{i}.multihead_attn.out_proj.weight",
+ f"{dst_prefix}.layers.{i}.encoder_attn.out_proj.weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"{src_prefix}.layers.{i}.multihead_attn.out_proj.bias",
+ f"{dst_prefix}.layers.{i}.encoder_attn.out_proj.bias",
+ )
+ )
+ rename_keys.append((f"{src_prefix}.layers.{i}.linear1.weight", f"{dst_prefix}.layers.{i}.fc1.weight"))
+ rename_keys.append((f"{src_prefix}.layers.{i}.linear1.bias", f"{dst_prefix}.layers.{i}.fc1.bias"))
+ rename_keys.append((f"{src_prefix}.layers.{i}.linear2.weight", f"{dst_prefix}.layers.{i}.fc2.weight"))
+ rename_keys.append((f"{src_prefix}.layers.{i}.linear2.bias", f"{dst_prefix}.layers.{i}.fc2.bias"))
+ rename_keys.append(
+ (f"{src_prefix}.layers.{i}.norm1.weight", f"{dst_prefix}.layers.{i}.self_attn_layer_norm.weight")
+ )
+ rename_keys.append(
+ (f"{src_prefix}.layers.{i}.norm1.bias", f"{dst_prefix}.layers.{i}.self_attn_layer_norm.bias")
+ )
+ rename_keys.append(
+ (f"{src_prefix}.layers.{i}.norm2.weight", f"{dst_prefix}.layers.{i}.encoder_attn_layer_norm.weight")
+ )
+ rename_keys.append(
+ (f"{src_prefix}.layers.{i}.norm2.bias", f"{dst_prefix}.layers.{i}.encoder_attn_layer_norm.bias")
+ )
+ rename_keys.append(
+ (f"{src_prefix}.layers.{i}.norm3.weight", f"{dst_prefix}.layers.{i}.final_layer_norm.weight")
+ )
+ rename_keys.append(
+ (f"{src_prefix}.layers.{i}.norm3.bias", f"{dst_prefix}.layers.{i}.final_layer_norm.bias")
+ )
+
+ return rename_keys
+
+ def replace_q_k_v_in_detr_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "transformer_module.decoder"
+ src_prefix: str = "sem_seg_head.predictor.transformer.decoder"
+ for i in range(self.config.decoder_config.decoder_layers):
+ # read in weights + bias of input projection layer of self-attention
+ in_proj_weight = src_state_dict.pop(f"{src_prefix}.layers.{i}.self_attn.in_proj_weight")
+ in_proj_bias = src_state_dict.pop(f"{src_prefix}.layers.{i}.self_attn.in_proj_bias")
+ # next, add query, keys and values (in that order) to the state dict
+ dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
+ dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
+ dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
+ dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
+ dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
+ dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
+ # read in weights + bias of input projection layer of cross-attention
+ in_proj_weight_cross_attn = src_state_dict.pop(f"{src_prefix}.layers.{i}.multihead_attn.in_proj_weight")
+ in_proj_bias_cross_attn = src_state_dict.pop(f"{src_prefix}.layers.{i}.multihead_attn.in_proj_bias")
+ # next, add query, keys and values (in that order) of cross-attention to the state dict
+ dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.q_proj.weight"] = in_proj_weight_cross_attn[:256, :]
+ dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.q_proj.bias"] = in_proj_bias_cross_attn[:256]
+ dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.k_proj.weight"] = in_proj_weight_cross_attn[
+ 256:512, :
+ ]
+ dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.k_proj.bias"] = in_proj_bias_cross_attn[256:512]
+ dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.v_proj.weight"] = in_proj_weight_cross_attn[-256:, :]
+ dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.v_proj.bias"] = in_proj_bias_cross_attn[-256:]
+
+ def replace_detr_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "transformer_module.decoder"
+ src_prefix: str = "sem_seg_head.predictor.transformer.decoder"
+ renamed_keys = self.rename_keys_in_detr_decoder(dst_state_dict, src_state_dict)
+ # add more
+ renamed_keys.extend(
+ [
+ (f"{src_prefix}.norm.weight", f"{dst_prefix}.layernorm.weight"),
+ (f"{src_prefix}.norm.bias", f"{dst_prefix}.layernorm.bias"),
+ ]
+ )
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ self.replace_q_k_v_in_detr_decoder(dst_state_dict, src_state_dict)
+
+ def replace_transformer_module(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "transformer_module"
+ src_prefix: str = "sem_seg_head.predictor"
+
+ self.replace_detr_decoder(dst_state_dict, src_state_dict)
+
+ renamed_keys = [
+ (f"{src_prefix}.query_embed.weight", f"{dst_prefix}.queries_embedder.weight"),
+ (f"{src_prefix}.input_proj.weight", f"{dst_prefix}.input_projection.weight"),
+ (f"{src_prefix}.input_proj.bias", f"{dst_prefix}.input_projection.bias"),
+ ]
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ def replace_instance_segmentation_module(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ # NOTE in our case we don't have a prefix, thus we removed the "." from the keys later on!
+ dst_prefix: str = ""
+ src_prefix: str = "sem_seg_head.predictor"
+
+ renamed_keys = [
+ (f"{src_prefix}.class_embed.weight", f"{dst_prefix}class_predictor.weight"),
+ (f"{src_prefix}.class_embed.bias", f"{dst_prefix}class_predictor.bias"),
+ ]
+
+ mlp_len = 3
+ for i in range(mlp_len):
+ renamed_keys.extend(
+ [
+ (f"{src_prefix}.mask_embed.layers.{i}.weight", f"{dst_prefix}mask_embedder.{i}.0.weight"),
+ (f"{src_prefix}.mask_embed.layers.{i}.bias", f"{dst_prefix}mask_embedder.{i}.0.bias"),
+ ]
+ )
+ logger.info(f"Replacing keys {pformat(renamed_keys)}")
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ def convert(self, mask_former: MaskFormerModel) -> MaskFormerModel:
+ dst_state_dict = TrackedStateDict(mask_former.state_dict())
+ src_state_dict = self.original_model.state_dict()
+
+ self.replace_pixel_module(dst_state_dict, src_state_dict)
+ self.replace_transformer_module(dst_state_dict, src_state_dict)
+
+ logger.info(f"Missed keys are {pformat(dst_state_dict.diff())}")
+ logger.info(f"Not copied keys are {pformat(src_state_dict.keys())}")
+ logger.info("🙌 Done")
+
+ mask_former.load_state_dict(dst_state_dict)
+
+ return mask_former
+
+ def convert_instance_segmentation(
+ self, mask_former: MaskFormerForInstanceSegmentation
+ ) -> MaskFormerForInstanceSegmentation:
+ dst_state_dict = TrackedStateDict(mask_former.state_dict())
+ src_state_dict = self.original_model.state_dict()
+
+ self.replace_instance_segmentation_module(dst_state_dict, src_state_dict)
+
+ mask_former.load_state_dict(dst_state_dict)
+
+ return mask_former
+
+ @staticmethod
+ def using_dirs(checkpoints_dir: Path, config_dir: Path) -> Iterator[tuple[object, Path, Path]]:
+ checkpoints: list[Path] = checkpoints_dir.glob("**/*.pkl")
+
+ for checkpoint in checkpoints:
+ logger.info(f"💪 Converting {checkpoint.stem}")
+ # find associated config file
+ config: Path = config_dir / checkpoint.parents[0].stem / "swin" / f"{checkpoint.stem}.yaml"
+
+ yield config, checkpoint
+
+
+def test(original_model, our_model: MaskFormerForInstanceSegmentation, image_processor: MaskFormerImageProcessor):
+ with torch.no_grad():
+ original_model = original_model.eval()
+ our_model = our_model.eval()
+
+ im = prepare_img()
+
+ tr = T.Compose(
+ [
+ T.Resize((384, 384)),
+ T.ToTensor(),
+ T.Normalize(
+ mean=torch.tensor([123.675, 116.280, 103.530]) / 255.0,
+ std=torch.tensor([58.395, 57.120, 57.375]) / 255.0,
+ ),
+ ],
+ )
+
+ x = tr(im).unsqueeze(0)
+
+ original_model_backbone_features = original_model.backbone(x.clone())
+
+ our_model_output: MaskFormerModelOutput = our_model.model(x.clone(), output_hidden_states=True)
+
+ for original_model_feature, our_model_feature in zip(
+ original_model_backbone_features.values(), our_model_output.encoder_hidden_states
+ ):
+ assert torch.allclose(original_model_feature, our_model_feature, atol=1e-3), (
+ "The backbone features are not the same."
+ )
+
+ original_model_pixel_out = original_model.sem_seg_head.pixel_decoder.forward_features(
+ original_model_backbone_features
+ )
+
+ assert torch.allclose(
+ original_model_pixel_out[0], our_model_output.pixel_decoder_last_hidden_state, atol=1e-4
+ ), "The pixel decoder feature are not the same"
+
+ # let's test the full model
+ original_model_out = original_model([{"image": x.squeeze(0)}])
+
+ original_segmentation = original_model_out[0]["sem_seg"]
+
+ our_model_out: MaskFormerForInstanceSegmentationOutput = our_model(x)
+
+ our_segmentation = image_processor.post_process_segmentation(our_model_out, target_size=(384, 384))
+
+ assert torch.allclose(original_segmentation, our_segmentation, atol=1e-3), (
+ "The segmentation image is not the same."
+ )
+
+ logger.info("✅ Test passed!")
+
+
+def get_name(checkpoint_file: Path):
+ model_name_raw: str = checkpoint_file.stem
+ # model_name_raw is something like maskformer_panoptic_swin_base_IN21k_384_bs64_554k
+ parent_name: str = checkpoint_file.parents[0].stem
+ backbone = "swin"
+ dataset = ""
+ if "coco" in parent_name:
+ dataset = "coco"
+ elif "ade" in parent_name:
+ dataset = "ade"
+ else:
+ raise ValueError(f"{parent_name} must be wrong since we didn't find 'coco' or 'ade' in it ")
+
+ backbone_types = ["tiny", "small", "base", "large"]
+
+ backbone_type = list(filter(lambda x: x in model_name_raw, backbone_types))[0]
+
+ model_name = f"maskformer-{backbone}-{backbone_type}-{dataset}"
+
+ return model_name
+
+
+if __name__ == "__main__":
+ parser = ArgumentParser(
+ description="Command line to convert the original maskformers (with swin backbone) to our implementations."
+ )
+
+ parser.add_argument(
+ "--checkpoints_dir",
+ type=Path,
+ help=(
+ "A directory containing the model's checkpoints. The directory has to have the following structure:"
+ " //.pkl\n"
+ "Given the files are in the pickle format, please be wary of passing it files you trust."
+ ),
+ )
+ parser.add_argument(
+ "--configs_dir",
+ type=Path,
+ help=(
+ "A directory containing the model's configs, see detectron2 doc. The directory has to have the following"
+ " structure: //.yaml"
+ ),
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path",
+ required=True,
+ type=Path,
+ help="Path to the folder to output PyTorch models.",
+ )
+ parser.add_argument(
+ "--maskformer_dir",
+ required=True,
+ type=Path,
+ help=(
+ "A path to MaskFormer's original implementation directory. You can download from here:"
+ " https://github.com/facebookresearch/MaskFormer"
+ ),
+ )
+
+ args = parser.parse_args()
+
+ checkpoints_dir: Path = args.checkpoints_dir
+ config_dir: Path = args.configs_dir
+ save_directory: Path = args.pytorch_dump_folder_path
+ maskformer_dir: Path = args.maskformer_dir
+ # append the path to the parents to maskformer dir
+ sys.path.append(str(maskformer_dir.parent))
+ # and import what's needed
+ from MaskFormer.mask_former import add_mask_former_config
+ from MaskFormer.mask_former.mask_former_model import MaskFormer as OriginalMaskFormer
+
+ if not save_directory.exists():
+ save_directory.mkdir(parents=True)
+
+ for config_file, checkpoint_file in OriginalMaskFormerCheckpointToOursConverter.using_dirs(
+ checkpoints_dir, config_dir
+ ):
+ image_processor = OriginalMaskFormerConfigToImageProcessorConverter()(setup_cfg(Args(config_file=config_file)))
+
+ original_config = setup_cfg(Args(config_file=config_file))
+ mask_former_kwargs = OriginalMaskFormer.from_config(original_config)
+
+ original_model = OriginalMaskFormer(**mask_former_kwargs).eval()
+
+ DetectionCheckpointer(original_model).load(str(checkpoint_file))
+
+ config: MaskFormerConfig = OriginalMaskFormerConfigToOursConverter()(original_config)
+
+ mask_former = MaskFormerModel(config=config).eval()
+
+ converter = OriginalMaskFormerCheckpointToOursConverter(original_model, config)
+
+ maskformer = converter.convert(mask_former)
+
+ mask_former_for_instance_segmentation = MaskFormerForInstanceSegmentation(config=config).eval()
+
+ mask_former_for_instance_segmentation.model = mask_former
+ mask_former_for_instance_segmentation = converter.convert_instance_segmentation(
+ mask_former_for_instance_segmentation
+ )
+
+ test(original_model, mask_former_for_instance_segmentation, image_processor)
+
+ model_name = get_name(checkpoint_file)
+ logger.info(f"🪄 Saving {model_name}")
+
+ image_processor.save_pretrained(save_directory / model_name)
+ mask_former_for_instance_segmentation.save_pretrained(save_directory / model_name)
+
+ image_processor.push_to_hub(
+ repo_path_or_name=save_directory / model_name,
+ commit_message="Add model",
+ use_temp_dir=True,
+ )
+ mask_former_for_instance_segmentation.push_to_hub(
+ repo_path_or_name=save_directory / model_name,
+ commit_message="Add model",
+ use_temp_dir=True,
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/convert_maskformer_resnet_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/convert_maskformer_resnet_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..43fbd234fb2a34820017361acc50a5a4b27bc02a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/convert_maskformer_resnet_to_pytorch.py
@@ -0,0 +1,390 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert MaskFormer checkpoints with ResNet backbone from the original repository. URL:
+https://github.com/facebookresearch/MaskFormer"""
+
+import argparse
+import json
+import pickle
+from pathlib import Path
+
+import requests
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, ResNetConfig
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def get_maskformer_config(model_name: str):
+ if "resnet101c" in model_name:
+ # TODO add support for ResNet-C backbone, which uses a "deeplab" stem
+ raise NotImplementedError("To do")
+ elif "resnet101" in model_name:
+ backbone_config = ResNetConfig.from_pretrained(
+ "microsoft/resnet-101", out_features=["stage1", "stage2", "stage3", "stage4"]
+ )
+ else:
+ backbone_config = ResNetConfig.from_pretrained(
+ "microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]
+ )
+ config = MaskFormerConfig(backbone_config=backbone_config)
+
+ repo_id = "huggingface/label-files"
+ if "ade20k-full" in model_name:
+ config.num_labels = 847
+ filename = "maskformer-ade20k-full-id2label.json"
+ elif "ade" in model_name:
+ config.num_labels = 150
+ filename = "ade20k-id2label.json"
+ elif "coco-stuff" in model_name:
+ config.num_labels = 171
+ filename = "maskformer-coco-stuff-id2label.json"
+ elif "coco" in model_name:
+ # TODO
+ config.num_labels = 133
+ filename = "coco-panoptic-id2label.json"
+ elif "cityscapes" in model_name:
+ config.num_labels = 19
+ filename = "cityscapes-id2label.json"
+ elif "vistas" in model_name:
+ config.num_labels = 65
+ filename = "mapillary-vistas-id2label.json"
+
+ id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
+ id2label = {int(k): v for k, v in id2label.items()}
+ config.id2label = id2label
+ config.label2id = {v: k for k, v in id2label.items()}
+
+ return config
+
+
+def create_rename_keys(config):
+ rename_keys = []
+ # stem
+ # fmt: off
+ rename_keys.append(("backbone.stem.conv1.weight", "model.pixel_level_module.encoder.embedder.embedder.convolution.weight"))
+ rename_keys.append(("backbone.stem.conv1.norm.weight", "model.pixel_level_module.encoder.embedder.embedder.normalization.weight"))
+ rename_keys.append(("backbone.stem.conv1.norm.bias", "model.pixel_level_module.encoder.embedder.embedder.normalization.bias"))
+ rename_keys.append(("backbone.stem.conv1.norm.running_mean", "model.pixel_level_module.encoder.embedder.embedder.normalization.running_mean"))
+ rename_keys.append(("backbone.stem.conv1.norm.running_var", "model.pixel_level_module.encoder.embedder.embedder.normalization.running_var"))
+ # fmt: on
+ # stages
+ for stage_idx in range(len(config.backbone_config.depths)):
+ for layer_idx in range(config.backbone_config.depths[stage_idx]):
+ # shortcut
+ if layer_idx == 0:
+ rename_keys.append(
+ (
+ f"backbone.res{stage_idx + 2}.{layer_idx}.shortcut.weight",
+ f"model.pixel_level_module.encoder.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"backbone.res{stage_idx + 2}.{layer_idx}.shortcut.norm.weight",
+ f"model.pixel_level_module.encoder.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"backbone.res{stage_idx + 2}.{layer_idx}.shortcut.norm.bias",
+ f"model.pixel_level_module.encoder.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
+ )
+ )
+ rename_keys.append(
+ (
+ f"backbone.res{stage_idx + 2}.{layer_idx}.shortcut.norm.running_mean",
+ f"model.pixel_level_module.encoder.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
+ )
+ )
+ rename_keys.append(
+ (
+ f"backbone.res{stage_idx + 2}.{layer_idx}.shortcut.norm.running_var",
+ f"model.pixel_level_module.encoder.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
+ )
+ )
+ # 3 convs
+ for i in range(3):
+ rename_keys.append(
+ (
+ f"backbone.res{stage_idx + 2}.{layer_idx}.conv{i + 1}.weight",
+ f"model.pixel_level_module.encoder.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"backbone.res{stage_idx + 2}.{layer_idx}.conv{i + 1}.norm.weight",
+ f"model.pixel_level_module.encoder.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
+ )
+ )
+ rename_keys.append(
+ (
+ f"backbone.res{stage_idx + 2}.{layer_idx}.conv{i + 1}.norm.bias",
+ f"model.pixel_level_module.encoder.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
+ )
+ )
+ rename_keys.append(
+ (
+ f"backbone.res{stage_idx + 2}.{layer_idx}.conv{i + 1}.norm.running_mean",
+ f"model.pixel_level_module.encoder.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
+ )
+ )
+ rename_keys.append(
+ (
+ f"backbone.res{stage_idx + 2}.{layer_idx}.conv{i + 1}.norm.running_var",
+ f"model.pixel_level_module.encoder.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
+ )
+ )
+
+ # FPN
+ # fmt: off
+ rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight"))
+ rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight"))
+ rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias"))
+ for source_index, target_index in zip(range(3, 0, -1), range(0, 3)):
+ rename_keys.append((f"sem_seg_head.adapter_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight"))
+ rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight"))
+ rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias"))
+ rename_keys.append((f"sem_seg_head.layer_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight"))
+ rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight"))
+ rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias"))
+ rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight"))
+ rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias"))
+ # fmt: on
+
+ # Transformer decoder
+ # fmt: off
+ for idx in range(config.decoder_config.decoder_layers):
+ # self-attention out projection
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias"))
+ # cross-attention out projection
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias"))
+ # MLP 1
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", f"model.transformer_module.decoder.layers.{idx}.fc1.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", f"model.transformer_module.decoder.layers.{idx}.fc1.bias"))
+ # MLP 2
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", f"model.transformer_module.decoder.layers.{idx}.fc2.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", f"model.transformer_module.decoder.layers.{idx}.fc2.bias"))
+ # layernorm 1 (self-attention layernorm)
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias"))
+ # layernorm 2 (cross-attention layernorm)
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias"))
+ # layernorm 3 (final layernorm)
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias"))
+
+ rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight"))
+ rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias"))
+ # fmt: on
+
+ # heads on top
+ # fmt: off
+ rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight"))
+
+ rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight"))
+ rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias"))
+
+ rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight"))
+ rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias"))
+
+ for i in range(3):
+ rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.weight", f"mask_embedder.{i}.0.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.bias", f"mask_embedder.{i}.0.bias"))
+ # fmt: on
+
+ return rename_keys
+
+
+def rename_key(dct, old, new):
+ val = dct.pop(old)
+ dct[new] = val
+
+
+# we split up the matrix of each encoder layer into queries, keys and values
+def read_in_decoder_q_k_v(state_dict, config):
+ # fmt: off
+ hidden_size = config.decoder_config.hidden_size
+ for idx in range(config.decoder_config.decoder_layers):
+ # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
+ in_proj_weight = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight")
+ in_proj_bias = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias")
+ # next, add query, keys and values (in that order) to the state dict
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.q_proj.weight"] = in_proj_weight[: hidden_size, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.q_proj.bias"] = in_proj_bias[:config.hidden_size]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.k_proj.weight"] = in_proj_weight[hidden_size : hidden_size * 2, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.k_proj.bias"] = in_proj_bias[hidden_size : hidden_size * 2]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.v_proj.weight"] = in_proj_weight[-hidden_size :, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.v_proj.bias"] = in_proj_bias[-hidden_size :]
+ # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
+ in_proj_weight = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight")
+ in_proj_bias = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias")
+ # next, add query, keys and values (in that order) to the state dict
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.q_proj.weight"] = in_proj_weight[: hidden_size, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.q_proj.bias"] = in_proj_bias[:config.hidden_size]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.k_proj.weight"] = in_proj_weight[hidden_size : hidden_size * 2, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.k_proj.bias"] = in_proj_bias[hidden_size : hidden_size * 2]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.v_proj.weight"] = in_proj_weight[-hidden_size :, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.v_proj.bias"] = in_proj_bias[-hidden_size :]
+ # fmt: on
+
+
+# We will verify our results on an image of cute cats
+def prepare_img() -> torch.Tensor:
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ im = Image.open(requests.get(url, stream=True).raw)
+ return im
+
+
+@torch.no_grad()
+def convert_maskformer_checkpoint(
+ model_name: str, checkpoint_path: str, pytorch_dump_folder_path: str, push_to_hub: bool = False
+):
+ """
+ Copy/paste/tweak model's weights to our MaskFormer structure.
+ """
+ config = get_maskformer_config(model_name)
+
+ # load original state_dict
+ with open(checkpoint_path, "rb") as f:
+ data = pickle.load(f)
+ state_dict = data["model"]
+
+ # rename keys
+ rename_keys = create_rename_keys(config)
+ for src, dest in rename_keys:
+ rename_key(state_dict, src, dest)
+ read_in_decoder_q_k_v(state_dict, config)
+
+ # update to torch tensors
+ for key, value in state_dict.items():
+ state_dict[key] = torch.from_numpy(value)
+
+ # load 🤗 model
+ model = MaskFormerForInstanceSegmentation(config)
+ model.eval()
+
+ model.load_state_dict(state_dict)
+
+ # verify results
+ image = prepare_img()
+ if "vistas" in model_name:
+ ignore_index = 65
+ elif "cityscapes" in model_name:
+ ignore_index = 65535
+ else:
+ ignore_index = 255
+ do_reduce_labels = "ade" in model_name
+ image_processor = MaskFormerImageProcessor(ignore_index=ignore_index, do_reduce_labels=do_reduce_labels)
+
+ inputs = image_processor(image, return_tensors="pt")
+
+ outputs = model(**inputs)
+
+ if model_name == "maskformer-resnet50-ade":
+ expected_logits = torch.tensor(
+ [[6.7710, -0.1452, -3.5687], [1.9165, -1.0010, -1.8614], [3.6209, -0.2950, -1.3813]]
+ )
+ elif model_name == "maskformer-resnet101-ade":
+ expected_logits = torch.tensor(
+ [[4.0381, -1.1483, -1.9688], [2.7083, -1.9147, -2.2555], [3.4367, -1.3711, -2.1609]]
+ )
+ elif model_name == "maskformer-resnet50-coco-stuff":
+ expected_logits = torch.tensor(
+ [[3.2309, -3.0481, -2.8695], [5.4986, -5.4242, -2.4211], [6.2100, -5.2279, -2.7786]]
+ )
+ elif model_name == "maskformer-resnet101-coco-stuff":
+ expected_logits = torch.tensor(
+ [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]
+ )
+ elif model_name == "maskformer-resnet101-cityscapes":
+ expected_logits = torch.tensor(
+ [[-1.8861, -1.5465, 0.6749], [-2.3677, -1.6707, -0.0867], [-2.2314, -1.9530, -0.9132]]
+ )
+ elif model_name == "maskformer-resnet50-vistas":
+ expected_logits = torch.tensor(
+ [[-6.3917, -1.5216, -1.1392], [-5.5335, -4.5318, -1.8339], [-4.3576, -4.0301, 0.2162]]
+ )
+ elif model_name == "maskformer-resnet50-ade20k-full":
+ expected_logits = torch.tensor(
+ [[3.6146, -1.9367, -3.2534], [4.0099, 0.2027, -2.7576], [3.3913, -2.3644, -3.9519]]
+ )
+ elif model_name == "maskformer-resnet101-ade20k-full":
+ expected_logits = torch.tensor(
+ [[3.2211, -1.6550, -2.7605], [2.8559, -2.4512, -2.9574], [2.6331, -2.6775, -2.1844]]
+ )
+
+ assert torch.allclose(outputs.class_queries_logits[0, :3, :3], expected_logits, atol=1e-4)
+ print("Looks ok!")
+
+ if pytorch_dump_folder_path is not None:
+ print(f"Saving model and image processor of {model_name} to {pytorch_dump_folder_path}")
+ Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+ model.save_pretrained(pytorch_dump_folder_path)
+ image_processor.save_pretrained(pytorch_dump_folder_path)
+
+ if push_to_hub:
+ print(f"Pushing model and image processor of {model_name} to the hub...")
+ model.push_to_hub(f"facebook/{model_name}")
+ image_processor.push_to_hub(f"facebook/{model_name}")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--model_name",
+ default="maskformer-resnet50-ade",
+ type=str,
+ required=True,
+ choices=[
+ "maskformer-resnet50-ade",
+ "maskformer-resnet101-ade",
+ "maskformer-resnet50-coco-stuff",
+ "maskformer-resnet101-coco-stuff",
+ "maskformer-resnet101-cityscapes",
+ "maskformer-resnet50-vistas",
+ "maskformer-resnet50-ade20k-full",
+ "maskformer-resnet101-ade20k-full",
+ ],
+ help=("Name of the MaskFormer model you'd like to convert",),
+ )
+ parser.add_argument(
+ "--checkpoint_path",
+ type=str,
+ required=True,
+ help="Path to the original pickle file (.pkl) of the original checkpoint.\n"
+ "Given the files are in the pickle format, please be wary of passing it files you trust.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
+ )
+ parser.add_argument(
+ "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
+ )
+
+ args = parser.parse_args()
+ convert_maskformer_checkpoint(
+ args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/convert_maskformer_swin_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/convert_maskformer_swin_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b6e32e5cc138aad3953de5a2755afcff3d3a57a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/convert_maskformer_swin_to_pytorch.py
@@ -0,0 +1,333 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert MaskFormer checkpoints with Swin backbone from the original repository. URL:
+https://github.com/facebookresearch/MaskFormer"""
+
+import argparse
+import json
+import pickle
+from pathlib import Path
+
+import requests
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def get_maskformer_config(model_name: str):
+ backbone_config = SwinConfig.from_pretrained(
+ "microsoft/swin-tiny-patch4-window7-224", out_features=["stage1", "stage2", "stage3", "stage4"]
+ )
+ config = MaskFormerConfig(backbone_config=backbone_config)
+
+ repo_id = "huggingface/label-files"
+ if "ade20k-full" in model_name:
+ # this should be ok
+ config.num_labels = 847
+ filename = "maskformer-ade20k-full-id2label.json"
+ elif "ade" in model_name:
+ # this should be ok
+ config.num_labels = 150
+ filename = "ade20k-id2label.json"
+ elif "coco-stuff" in model_name:
+ # this should be ok
+ config.num_labels = 171
+ filename = "maskformer-coco-stuff-id2label.json"
+ elif "coco" in model_name:
+ # TODO
+ config.num_labels = 133
+ filename = "coco-panoptic-id2label.json"
+ elif "cityscapes" in model_name:
+ # this should be ok
+ config.num_labels = 19
+ filename = "cityscapes-id2label.json"
+ elif "vistas" in model_name:
+ # this should be ok
+ config.num_labels = 65
+ filename = "mapillary-vistas-id2label.json"
+
+ id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
+ id2label = {int(k): v for k, v in id2label.items()}
+
+ return config
+
+
+def create_rename_keys(config):
+ rename_keys = []
+ # stem
+ # fmt: off
+ rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight"))
+ rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias"))
+ rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight"))
+ rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias"))
+ # stages
+ for i in range(len(config.backbone_config.depths)):
+ for j in range(config.backbone_config.depths[i]):
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight"))
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias"))
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table"))
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index"))
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight"))
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias"))
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight"))
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias"))
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight"))
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias"))
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight"))
+ rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias"))
+
+ if i < 3:
+ rename_keys.append((f"backbone.layers.{i}.downsample.reduction.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight"))
+ rename_keys.append((f"backbone.layers.{i}.downsample.norm.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight"))
+ rename_keys.append((f"backbone.layers.{i}.downsample.norm.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias"))
+ rename_keys.append((f"backbone.norm{i}.weight", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight"))
+ rename_keys.append((f"backbone.norm{i}.bias", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias"))
+
+ # FPN
+ rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight"))
+ rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight"))
+ rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias"))
+ for source_index, target_index in zip(range(3, 0, -1), range(0, 3)):
+ rename_keys.append((f"sem_seg_head.adapter_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight"))
+ rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight"))
+ rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias"))
+ rename_keys.append((f"sem_seg_head.layer_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight"))
+ rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight"))
+ rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias"))
+ rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight"))
+ rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias"))
+
+ # Transformer decoder
+ for idx in range(config.decoder_config.decoder_layers):
+ # self-attention out projection
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias"))
+ # cross-attention out projection
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias"))
+ # MLP 1
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", f"model.transformer_module.decoder.layers.{idx}.fc1.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", f"model.transformer_module.decoder.layers.{idx}.fc1.bias"))
+ # MLP 2
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", f"model.transformer_module.decoder.layers.{idx}.fc2.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", f"model.transformer_module.decoder.layers.{idx}.fc2.bias"))
+ # layernorm 1 (self-attention layernorm)
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias"))
+ # layernorm 2 (cross-attention layernorm)
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias"))
+ # layernorm 3 (final layernorm)
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias"))
+
+ rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight"))
+ rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias"))
+
+ # heads on top
+ rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight"))
+
+ rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight"))
+ rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias"))
+
+ rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight"))
+ rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias"))
+
+ for i in range(3):
+ rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.weight", f"mask_embedder.{i}.0.weight"))
+ rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.bias", f"mask_embedder.{i}.0.bias"))
+ # fmt: on
+
+ return rename_keys
+
+
+def rename_key(dct, old, new):
+ val = dct.pop(old)
+ dct[new] = val
+
+
+# we split up the matrix of each encoder layer into queries, keys and values
+def read_in_swin_q_k_v(state_dict, backbone_config):
+ num_features = [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
+ for i in range(len(backbone_config.depths)):
+ dim = num_features[i]
+ for j in range(backbone_config.depths[i]):
+ # fmt: off
+ # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
+ in_proj_weight = state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.weight")
+ in_proj_bias = state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.bias")
+ # next, add query, keys and values (in that order) to the state dict
+ state_dict[f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.query.weight"] = in_proj_weight[:dim, :]
+ state_dict[f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.query.bias"] = in_proj_bias[: dim]
+ state_dict[f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.key.weight"] = in_proj_weight[
+ dim : dim * 2, :
+ ]
+ state_dict[f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.key.bias"] = in_proj_bias[
+ dim : dim * 2
+ ]
+ state_dict[f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.value.weight"] = in_proj_weight[
+ -dim :, :
+ ]
+ state_dict[f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.value.bias"] = in_proj_bias[-dim :]
+ # fmt: on
+
+
+# we split up the matrix of each encoder layer into queries, keys and values
+def read_in_decoder_q_k_v(state_dict, config):
+ # fmt: off
+ hidden_size = config.decoder_config.hidden_size
+ for idx in range(config.decoder_config.decoder_layers):
+ # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
+ in_proj_weight = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight")
+ in_proj_bias = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias")
+ # next, add query, keys and values (in that order) to the state dict
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.q_proj.weight"] = in_proj_weight[: hidden_size, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.q_proj.bias"] = in_proj_bias[:config.hidden_size]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.k_proj.weight"] = in_proj_weight[hidden_size : hidden_size * 2, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.k_proj.bias"] = in_proj_bias[hidden_size : hidden_size * 2]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.v_proj.weight"] = in_proj_weight[-hidden_size :, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.self_attn.v_proj.bias"] = in_proj_bias[-hidden_size :]
+ # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
+ in_proj_weight = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight")
+ in_proj_bias = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias")
+ # next, add query, keys and values (in that order) to the state dict
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.q_proj.weight"] = in_proj_weight[: hidden_size, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.q_proj.bias"] = in_proj_bias[:config.hidden_size]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.k_proj.weight"] = in_proj_weight[hidden_size : hidden_size * 2, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.k_proj.bias"] = in_proj_bias[hidden_size : hidden_size * 2]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.v_proj.weight"] = in_proj_weight[-hidden_size :, :]
+ state_dict[f"model.transformer_module.decoder.layers.{idx}.encoder_attn.v_proj.bias"] = in_proj_bias[-hidden_size :]
+ # fmt: on
+
+
+# We will verify our results on an image of cute cats
+def prepare_img() -> torch.Tensor:
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ im = Image.open(requests.get(url, stream=True).raw)
+ return im
+
+
+@torch.no_grad()
+def convert_maskformer_checkpoint(
+ model_name: str, checkpoint_path: str, pytorch_dump_folder_path: str, push_to_hub: bool = False
+):
+ """
+ Copy/paste/tweak model's weights to our MaskFormer structure.
+ """
+ config = get_maskformer_config(model_name)
+
+ # load original state_dict
+ with open(checkpoint_path, "rb") as f:
+ data = pickle.load(f)
+ state_dict = data["model"]
+
+ # for name, param in state_dict.items():
+ # print(name, param.shape)
+
+ # rename keys
+ rename_keys = create_rename_keys(config)
+ for src, dest in rename_keys:
+ rename_key(state_dict, src, dest)
+ read_in_swin_q_k_v(state_dict, config.backbone_config)
+ read_in_decoder_q_k_v(state_dict, config)
+
+ # update to torch tensors
+ for key, value in state_dict.items():
+ state_dict[key] = torch.from_numpy(value)
+
+ # load 🤗 model
+ model = MaskFormerForInstanceSegmentation(config)
+ model.eval()
+
+ for name, param in model.named_parameters():
+ print(name, param.shape)
+
+ missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
+ assert missing_keys == [
+ "model.pixel_level_module.encoder.model.layernorm.weight",
+ "model.pixel_level_module.encoder.model.layernorm.bias",
+ ]
+ assert len(unexpected_keys) == 0, f"Unexpected keys: {unexpected_keys}"
+
+ # verify results
+ image = prepare_img()
+ if "vistas" in model_name:
+ ignore_index = 65
+ elif "cityscapes" in model_name:
+ ignore_index = 65535
+ else:
+ ignore_index = 255
+ do_reduce_labels = "ade" in model_name
+ image_processor = MaskFormerImageProcessor(ignore_index=ignore_index, do_reduce_labels=do_reduce_labels)
+
+ inputs = image_processor(image, return_tensors="pt")
+
+ outputs = model(**inputs)
+
+ print("Logits:", outputs.class_queries_logits[0, :3, :3])
+
+ if model_name == "maskformer-swin-tiny-ade":
+ expected_logits = torch.tensor(
+ [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]
+ )
+ assert torch.allclose(outputs.class_queries_logits[0, :3, :3], expected_logits, atol=1e-4)
+ print("Looks ok!")
+
+ if pytorch_dump_folder_path is not None:
+ print(f"Saving model and image processor to {pytorch_dump_folder_path}")
+ Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+ model.save_pretrained(pytorch_dump_folder_path)
+ image_processor.save_pretrained(pytorch_dump_folder_path)
+
+ if push_to_hub:
+ print("Pushing model and image processor to the hub...")
+ model.push_to_hub(f"nielsr/{model_name}")
+ image_processor.push_to_hub(f"nielsr/{model_name}")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--model_name",
+ default="maskformer-swin-tiny-ade",
+ type=str,
+ help=("Name of the MaskFormer model you'd like to convert",),
+ )
+ parser.add_argument(
+ "--checkpoint_path",
+ default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
+ type=str,
+ help="Path to the original state dict (.pth file).\n"
+ "Given the files are in the pickle format, please be wary of passing it files you trust.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
+ )
+ parser.add_argument(
+ "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
+ )
+
+ args = parser.parse_args()
+ convert_maskformer_checkpoint(
+ args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/feature_extraction_maskformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/feature_extraction_maskformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..98f7075fab83882bb84ab955bd763c2f1c1f067b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/feature_extraction_maskformer.py
@@ -0,0 +1,38 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Feature extractor class for MaskFormer."""
+
+import warnings
+
+from ...utils import logging
+from ...utils.import_utils import requires
+from .image_processing_maskformer import MaskFormerImageProcessor
+
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("vision",))
+class MaskFormerFeatureExtractor(MaskFormerImageProcessor):
+ def __init__(self, *args, **kwargs) -> None:
+ warnings.warn(
+ "The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
+ " Please use MaskFormerImageProcessor instead.",
+ FutureWarning,
+ )
+ super().__init__(*args, **kwargs)
+
+
+__all__ = ["MaskFormerFeatureExtractor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/image_processing_maskformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/image_processing_maskformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8896dad734425fda01f7736e7b028fe30ff1b5e2
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/image_processing_maskformer.py
@@ -0,0 +1,1323 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for MaskFormer."""
+
+import math
+import warnings
+from collections.abc import Iterable
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import INIT_SERVICE_KWARGS, BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import (
+ PaddingMode,
+ get_resize_output_image_size,
+ pad,
+ rescale,
+ resize,
+ to_channel_dimension_format,
+)
+from ...image_utils import (
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ get_image_size,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import (
+ IMAGENET_DEFAULT_MEAN,
+ IMAGENET_DEFAULT_STD,
+ TensorType,
+ filter_out_non_signature_kwargs,
+ is_torch_available,
+ is_torch_tensor,
+ logging,
+)
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+
+if TYPE_CHECKING:
+ from transformers import MaskFormerForInstanceSegmentationOutput
+
+
+if is_torch_available():
+ import torch
+ from torch import nn
+
+
+# Copied from transformers.models.detr.image_processing_detr.get_size_with_aspect_ratio
+def get_size_with_aspect_ratio(image_size, size, max_size=None) -> tuple[int, int]:
+ """
+ Computes the output image size given the input image size and the desired output size.
+
+ Args:
+ image_size (`tuple[int, int]`):
+ The input image size.
+ size (`int`):
+ The desired output size.
+ max_size (`int`, *optional*):
+ The maximum allowed output size.
+ """
+ height, width = image_size
+ raw_size = None
+ if max_size is not None:
+ min_original_size = float(min((height, width)))
+ max_original_size = float(max((height, width)))
+ if max_original_size / min_original_size * size > max_size:
+ raw_size = max_size * min_original_size / max_original_size
+ size = int(round(raw_size))
+
+ if (height <= width and height == size) or (width <= height and width == size):
+ oh, ow = height, width
+ elif width < height:
+ ow = size
+ if max_size is not None and raw_size is not None:
+ oh = int(raw_size * height / width)
+ else:
+ oh = int(size * height / width)
+ else:
+ oh = size
+ if max_size is not None and raw_size is not None:
+ ow = int(raw_size * width / height)
+ else:
+ ow = int(size * width / height)
+
+ return (oh, ow)
+
+
+# Copied from transformers.models.detr.image_processing_detr.max_across_indices
+def max_across_indices(values: Iterable[Any]) -> list[Any]:
+ """
+ Return the maximum value across all indices of an iterable of values.
+ """
+ return [max(values_i) for values_i in zip(*values)]
+
+
+# Copied from transformers.models.detr.image_processing_detr.get_max_height_width
+def get_max_height_width(
+ images: list[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None
+) -> list[int]:
+ """
+ Get the maximum height and width across all images in a batch.
+ """
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ if input_data_format == ChannelDimension.FIRST:
+ _, max_height, max_width = max_across_indices([img.shape for img in images])
+ elif input_data_format == ChannelDimension.LAST:
+ max_height, max_width, _ = max_across_indices([img.shape for img in images])
+ else:
+ raise ValueError(f"Invalid channel dimension format: {input_data_format}")
+ return (max_height, max_width)
+
+
+# Copied from transformers.models.detr.image_processing_detr.make_pixel_mask
+def make_pixel_mask(
+ image: np.ndarray, output_size: tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
+) -> np.ndarray:
+ """
+ Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
+
+ Args:
+ image (`np.ndarray`):
+ Image to make the pixel mask for.
+ output_size (`tuple[int, int]`):
+ Output size of the mask.
+ """
+ input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+ mask = np.zeros(output_size, dtype=np.int64)
+ mask[:input_height, :input_width] = 1
+ return mask
+
+
+# Copied from transformers.models.detr.image_processing_detr.binary_mask_to_rle
+def binary_mask_to_rle(mask):
+ """
+ Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format.
+
+ Args:
+ mask (`torch.Tensor` or `numpy.array`):
+ A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
+ segment_id or class_id.
+ Returns:
+ `List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
+ format.
+ """
+ if is_torch_tensor(mask):
+ mask = mask.numpy()
+
+ pixels = mask.flatten()
+ pixels = np.concatenate([[0], pixels, [0]])
+ runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
+ runs[1::2] -= runs[::2]
+ return list(runs)
+
+
+# Copied from transformers.models.detr.image_processing_detr.convert_segmentation_to_rle
+def convert_segmentation_to_rle(segmentation):
+ """
+ Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format.
+
+ Args:
+ segmentation (`torch.Tensor` or `numpy.array`):
+ A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
+ Returns:
+ `list[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
+ """
+ segment_ids = torch.unique(segmentation)
+
+ run_length_encodings = []
+ for idx in segment_ids:
+ mask = torch.where(segmentation == idx, 1, 0)
+ rle = binary_mask_to_rle(mask)
+ run_length_encodings.append(rle)
+
+ return run_length_encodings
+
+
+# Copied from transformers.models.detr.image_processing_detr.remove_low_and_no_objects
+def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
+ """
+ Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
+ `labels`.
+
+ Args:
+ masks (`torch.Tensor`):
+ A tensor of shape `(num_queries, height, width)`.
+ scores (`torch.Tensor`):
+ A tensor of shape `(num_queries)`.
+ labels (`torch.Tensor`):
+ A tensor of shape `(num_queries)`.
+ object_mask_threshold (`float`):
+ A number between 0 and 1 used to binarize the masks.
+ Raises:
+ `ValueError`: Raised when the first dimension doesn't match in all input tensors.
+ Returns:
+ `tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
+ < `object_mask_threshold`.
+ """
+ if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
+ raise ValueError("mask, scores and labels must have the same shape!")
+
+ to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
+
+ return masks[to_keep], scores[to_keep], labels[to_keep]
+
+
+# Copied from transformers.models.detr.image_processing_detr.check_segment_validity
+def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
+ # Get the mask associated with the k class
+ mask_k = mask_labels == k
+ mask_k_area = mask_k.sum()
+
+ # Compute the area of all the stuff in query k
+ original_area = (mask_probs[k] >= mask_threshold).sum()
+ mask_exists = mask_k_area > 0 and original_area > 0
+
+ # Eliminate disconnected tiny segments
+ if mask_exists:
+ area_ratio = mask_k_area / original_area
+ if not area_ratio.item() > overlap_mask_area_threshold:
+ mask_exists = False
+
+ return mask_exists, mask_k
+
+
+# Copied from transformers.models.detr.image_processing_detr.compute_segments
+def compute_segments(
+ mask_probs,
+ pred_scores,
+ pred_labels,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ label_ids_to_fuse: Optional[set[int]] = None,
+ target_size: Optional[tuple[int, int]] = None,
+):
+ height = mask_probs.shape[1] if target_size is None else target_size[0]
+ width = mask_probs.shape[2] if target_size is None else target_size[1]
+
+ segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
+ segments: list[dict] = []
+
+ if target_size is not None:
+ mask_probs = nn.functional.interpolate(
+ mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
+ )[0]
+
+ current_segment_id = 0
+
+ # Weigh each mask by its prediction score
+ mask_probs *= pred_scores.view(-1, 1, 1)
+ mask_labels = mask_probs.argmax(0) # [height, width]
+
+ # Keep track of instances of each class
+ stuff_memory_list: dict[str, int] = {}
+ for k in range(pred_labels.shape[0]):
+ pred_class = pred_labels[k].item()
+ should_fuse = pred_class in label_ids_to_fuse
+
+ # Check if mask exists and large enough to be a segment
+ mask_exists, mask_k = check_segment_validity(
+ mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
+ )
+
+ if mask_exists:
+ if pred_class in stuff_memory_list:
+ current_segment_id = stuff_memory_list[pred_class]
+ else:
+ current_segment_id += 1
+
+ # Add current object segment to final segmentation map
+ segmentation[mask_k] = current_segment_id
+ segment_score = round(pred_scores[k].item(), 6)
+ segments.append(
+ {
+ "id": current_segment_id,
+ "label_id": pred_class,
+ "was_fused": should_fuse,
+ "score": segment_score,
+ }
+ )
+ if should_fuse:
+ stuff_memory_list[pred_class] = current_segment_id
+
+ return segmentation, segments
+
+
+# TODO: (Amy) Move to image_transforms
+def convert_segmentation_map_to_binary_masks(
+ segmentation_map: "np.ndarray",
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+):
+ if do_reduce_labels and ignore_index is None:
+ raise ValueError("If `do_reduce_labels` is True, `ignore_index` must be provided.")
+
+ if do_reduce_labels:
+ segmentation_map = np.where(segmentation_map == 0, ignore_index, segmentation_map - 1)
+
+ # Get unique ids (class or instance ids based on input)
+ all_labels = np.unique(segmentation_map)
+
+ # Drop background label if applicable
+ if ignore_index is not None:
+ all_labels = all_labels[all_labels != ignore_index]
+
+ # Generate a binary mask for each object instance
+ binary_masks = [(segmentation_map == i) for i in all_labels]
+
+ # Stack the binary masks
+ if binary_masks:
+ binary_masks = np.stack(binary_masks, axis=0)
+ else:
+ binary_masks = np.zeros((0, *segmentation_map.shape))
+
+ # Convert instance ids to class ids
+ if instance_id_to_semantic_id is not None:
+ labels = np.zeros(all_labels.shape[0])
+
+ for label in all_labels:
+ class_id = instance_id_to_semantic_id[label + 1 if do_reduce_labels else label]
+ labels[all_labels == label] = class_id - 1 if do_reduce_labels else class_id
+ else:
+ labels = all_labels
+
+ return binary_masks.astype(np.float32), labels.astype(np.int64)
+
+
+def get_maskformer_resize_output_image_size(
+ image: np.ndarray,
+ size: Union[int, tuple[int, int], list[int], tuple[int]],
+ max_size: Optional[int] = None,
+ size_divisor: int = 0,
+ default_to_square: bool = True,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+) -> tuple[int, int]:
+ """
+ Computes the output size given the desired size.
+
+ Args:
+ image (`np.ndarray`):
+ The input image.
+ size (`int` or `tuple[int, int]` or `list[int]` or `tuple[int]`):
+ The size of the output image.
+ max_size (`int`, *optional*):
+ The maximum size of the output image.
+ size_divisor (`int`, *optional*, defaults to 0):
+ If `size_divisor` is given, the output image size will be divisible by the number.
+ default_to_square (`bool`, *optional*, defaults to `True`):
+ Whether to default to square if no size is provided.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If unset, will use the inferred format from the input.
+
+ Returns:
+ `tuple[int, int]`: The output size.
+ """
+ output_size = get_resize_output_image_size(
+ input_image=image,
+ size=size,
+ default_to_square=default_to_square,
+ max_size=max_size,
+ input_data_format=input_data_format,
+ )
+
+ if size_divisor > 0:
+ height, width = output_size
+ height = int(math.ceil(height / size_divisor) * size_divisor)
+ width = int(math.ceil(width / size_divisor) * size_divisor)
+ output_size = (height, width)
+
+ return output_size
+
+
+@requires(backends=("vision",))
+class MaskFormerImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a MaskFormer image processor. The image processor can be used to prepare image(s) and optional targets
+ for the model.
+
+ This image processor inherits from [`BaseImageProcessor`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the input to a certain `size`.
+ size (`int`, *optional*, defaults to 800):
+ Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
+ sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
+ the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
+ height / width, size)`.
+ size_divisor (`int`, *optional*, defaults to 32):
+ Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
+ Swin Transformer.
+ resample (`int`, *optional*, defaults to `Resampling.BILINEAR`):
+ An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
+ `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
+ `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
+ to `True`.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the input to a certain `scale`.
+ rescale_factor (`float`, *optional*, defaults to `1/ 255`):
+ Rescale the input by the given factor. Only has an effect if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether or not to normalize the input with mean and standard deviation.
+ image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
+ The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
+ image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
+ The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
+ ImageNet std.
+ ignore_index (`int`, *optional*):
+ Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels
+ denoted with 0 (background) will be replaced with `ignore_index`.
+ do_reduce_labels (`bool`, *optional*, defaults to `False`):
+ Whether or not to decrement all label values of segmentation maps by 1. Usually used for datasets where 0
+ is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k).
+ The background label will be replaced by `ignore_index`.
+ num_labels (`int`, *optional*):
+ The number of labels in the segmentation map.
+ pad_size (`Dict[str, int]`, *optional*):
+ The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
+ provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
+ height and width in the batch.
+
+ """
+
+ model_input_names = ["pixel_values", "pixel_mask"]
+
+ @filter_out_non_signature_kwargs(extra=["max_size", *INIT_SERVICE_KWARGS])
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ size_divisor: int = 32,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ do_rescale: bool = True,
+ rescale_factor: float = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+ num_labels: Optional[int] = None,
+ pad_size: Optional[dict[str, int]] = None,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ # We make max_size a private attribute so we can pass it as a default value in the preprocess method whilst
+ # `size` can still be pass in as an int
+ self._max_size = kwargs.pop("max_size", 1333)
+
+ size = size if size is not None else {"shortest_edge": 800, "longest_edge": self._max_size}
+ size = get_size_dict(size, max_size=self._max_size, default_to_square=False)
+
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.size_divisor = size_divisor
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
+ self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
+ self.ignore_index = ignore_index
+ self.do_reduce_labels = do_reduce_labels
+ self.num_labels = num_labels
+ self.pad_size = pad_size
+
+ def to_dict(self) -> dict[str, Any]:
+ """
+ Serializes this instance to a Python dictionary. This method calls the superclass method and then removes the
+ `_max_size` attribute from the dictionary.
+ """
+ image_processor_dict = super().to_dict()
+ image_processor_dict.pop("_max_size", None)
+ return image_processor_dict
+
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ size_divisor: int = 0,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ data_format=None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize the image to the given size. Size can be min_size (scalar) or `(height, width)` tuple. If size is an
+ int, smaller edge of the image will be matched to this number.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ The size of the output image.
+ size_divisor (`int`, *optional*, defaults to 0):
+ If `size_divisor` is given, the output image size will be divisible by the number.
+ resample (`PILImageResampling` resampling filter, *optional*, defaults to `PILImageResampling.BILINEAR`):
+ Resampling filter to use when resizing the image.
+ data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the output image. If unset, the channel dimension format of the input
+ image is used.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+
+ # Deprecated, backward compatibility
+ max_size = kwargs.pop("max_size", None)
+
+ size = get_size_dict(size, max_size=max_size, default_to_square=False)
+ if "shortest_edge" in size and "longest_edge" in size:
+ size, max_size = size["shortest_edge"], size["longest_edge"]
+ elif "height" in size and "width" in size:
+ size = (size["height"], size["width"])
+ max_size = None
+ else:
+ raise ValueError(
+ "Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
+ f" {size.keys()}."
+ )
+ size = get_maskformer_resize_output_image_size(
+ image=image,
+ size=size,
+ max_size=max_size,
+ size_divisor=size_divisor,
+ default_to_square=False,
+ input_data_format=input_data_format,
+ )
+ image = resize(
+ image, size=size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs
+ )
+ return image
+
+ # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.rescale
+ def rescale(
+ self,
+ image: np.ndarray,
+ rescale_factor: float,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Rescale the image by the given factor. image = image * rescale_factor.
+
+ Args:
+ image (`np.ndarray`):
+ Image to rescale.
+ rescale_factor (`float`):
+ The value to use for rescaling.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the output image. If unset, the channel dimension format of the input
+ image is used. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the input image. If unset, is inferred from the input image. Can be
+ one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ """
+ return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format)
+
+ def convert_segmentation_map_to_binary_masks(
+ self,
+ segmentation_map: "np.ndarray",
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+ ):
+ do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
+ ignore_index = ignore_index if ignore_index is not None else self.ignore_index
+ return convert_segmentation_map_to_binary_masks(
+ segmentation_map=segmentation_map,
+ instance_id_to_semantic_id=instance_id_to_semantic_id,
+ ignore_index=ignore_index,
+ do_reduce_labels=do_reduce_labels,
+ )
+
+ def __call__(self, images, segmentation_maps=None, **kwargs) -> BatchFeature:
+ return self.preprocess(images, segmentation_maps=segmentation_maps, **kwargs)
+
+ def _preprocess(
+ self,
+ image: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ size_divisor: Optional[int] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ if do_resize:
+ image = self.resize(
+ image, size=size, size_divisor=size_divisor, resample=resample, input_data_format=input_data_format
+ )
+ if do_rescale:
+ image = self.rescale(image, rescale_factor=rescale_factor, input_data_format=input_data_format)
+ if do_normalize:
+ image = self.normalize(image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+ return image
+
+ def _preprocess_image(
+ self,
+ image: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ size_divisor: Optional[int] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """Preprocesses a single image."""
+ # All transformations expect numpy arrays.
+ image = to_numpy_array(image)
+ if do_rescale and is_scaled_image(image):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(image)
+ image = self._preprocess(
+ image=image,
+ do_resize=do_resize,
+ size=size,
+ size_divisor=size_divisor,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ input_data_format=input_data_format,
+ )
+ if data_format is not None:
+ image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+ return image
+
+ def _preprocess_mask(
+ self,
+ segmentation_map: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ size_divisor: int = 0,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """Preprocesses a single mask."""
+ segmentation_map = to_numpy_array(segmentation_map)
+ # Add channel dimension if missing - needed for certain transformations
+ if segmentation_map.ndim == 2:
+ added_channel_dim = True
+ segmentation_map = segmentation_map[None, ...]
+ input_data_format = ChannelDimension.FIRST
+ else:
+ added_channel_dim = False
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(segmentation_map, num_channels=1)
+ # TODO: (Amy)
+ # Remork segmentation map processing to include reducing labels and resizing which doesn't
+ # drop segment IDs > 255.
+ segmentation_map = self._preprocess(
+ image=segmentation_map,
+ do_resize=do_resize,
+ resample=PILImageResampling.NEAREST,
+ size=size,
+ size_divisor=size_divisor,
+ do_rescale=False,
+ do_normalize=False,
+ input_data_format=input_data_format,
+ )
+ # Remove extra channel dimension if added for processing
+ if added_channel_dim:
+ segmentation_map = segmentation_map.squeeze(0)
+ return segmentation_map
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ segmentation_maps: Optional[ImageInput] = None,
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ size_divisor: Optional[int] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ pad_size: Optional[dict[str, int]] = None,
+ ) -> BatchFeature:
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ size = get_size_dict(size, default_to_square=False, max_size=self._max_size)
+ size_divisor = size_divisor if size_divisor is not None else self.size_divisor
+ resample = resample if resample is not None else self.resample
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ ignore_index = ignore_index if ignore_index is not None else self.ignore_index
+ do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
+ pad_size = self.pad_size if pad_size is None else pad_size
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ if segmentation_maps is not None and not valid_images(segmentation_maps):
+ raise ValueError(
+ "Invalid segmentation map type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ images = make_list_of_images(images)
+ if segmentation_maps is not None:
+ segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)
+
+ if segmentation_maps is not None and len(images) != len(segmentation_maps):
+ raise ValueError("Images and segmentation maps must have the same length.")
+
+ images = [
+ self._preprocess_image(
+ image,
+ do_resize=do_resize,
+ size=size,
+ size_divisor=size_divisor,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ for image in images
+ ]
+
+ if segmentation_maps is not None:
+ segmentation_maps = [
+ self._preprocess_mask(
+ segmentation_map, do_resize, size, size_divisor, input_data_format=input_data_format
+ )
+ for segmentation_map in segmentation_maps
+ ]
+ encoded_inputs = self.encode_inputs(
+ images,
+ segmentation_maps,
+ instance_id_to_semantic_id,
+ ignore_index,
+ do_reduce_labels,
+ return_tensors,
+ input_data_format=data_format,
+ pad_size=pad_size,
+ )
+ return encoded_inputs
+
+ # Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor._pad_image
+ def _pad_image(
+ self,
+ image: np.ndarray,
+ output_size: tuple[int, int],
+ constant_values: Union[float, Iterable[float]] = 0,
+ data_format: Optional[ChannelDimension] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Pad an image with zeros to the given size.
+ """
+ input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+ output_height, output_width = output_size
+
+ pad_bottom = output_height - input_height
+ pad_right = output_width - input_width
+ padding = ((0, pad_bottom), (0, pad_right))
+ padded_image = pad(
+ image,
+ padding,
+ mode=PaddingMode.CONSTANT,
+ constant_values=constant_values,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ return padded_image
+
+ def pad(
+ self,
+ images: list[np.ndarray],
+ constant_values: Union[float, Iterable[float]] = 0,
+ return_pixel_mask: bool = True,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Optional[ChannelDimension] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ pad_size: Optional[dict[str, int]] = None,
+ ) -> BatchFeature:
+ """
+ Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
+ in the batch and optionally returns their corresponding pixel mask.
+
+ Args:
+ image (`np.ndarray`):
+ Image to pad.
+ constant_values (`float` or `Iterable[float]`, *optional*):
+ The value to use for the padding if `mode` is `"constant"`.
+ return_pixel_mask (`bool`, *optional*, defaults to `True`):
+ Whether to return a pixel mask.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ pad_size (`Dict[str, int]`, *optional*):
+ The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
+ provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
+ height and width in the batch.
+ """
+ pad_size = pad_size if pad_size is not None else self.pad_size
+ if pad_size is not None:
+ padded_size = (pad_size["height"], pad_size["width"])
+ else:
+ padded_size = get_max_height_width(images, input_data_format=input_data_format)
+
+ padded_images = [
+ self._pad_image(
+ image,
+ padded_size,
+ constant_values=constant_values,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ for image in images
+ ]
+ data = {"pixel_values": padded_images}
+
+ if return_pixel_mask:
+ masks = [
+ make_pixel_mask(image=image, output_size=padded_size, input_data_format=input_data_format)
+ for image in images
+ ]
+ data["pixel_mask"] = masks
+
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+ def encode_inputs(
+ self,
+ pixel_values_list: list[ImageInput],
+ segmentation_maps: ImageInput = None,
+ instance_id_to_semantic_id: Optional[Union[list[dict[int, int]], dict[int, int]]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ pad_size: Optional[dict[str, int]] = None,
+ ):
+ """
+ Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
+
+ MaskFormer addresses semantic segmentation with a mask classification paradigm, thus input segmentation maps
+ will be converted to lists of binary masks and their respective labels. Let's see an example, assuming
+ `segmentation_maps = [[2,6,7,9]]`, the output will contain `mask_labels =
+ [[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]` (four binary masks) and `class_labels = [2,6,7,9]`, the labels for
+ each mask.
+
+ Args:
+ pixel_values_list (`list[ImageInput]`):
+ List of images (pixel values) to be padded. Each image should be a tensor of shape `(channels, height,
+ width)`.
+
+ segmentation_maps (`ImageInput`, *optional*):
+ The corresponding semantic segmentation maps with the pixel-wise annotations.
+
+ (`bool`, *optional*, defaults to `True`):
+ Whether or not to pad images up to the largest image in a batch and create a pixel mask.
+
+ If left to the default, will return a pixel mask that is:
+
+ - 1 for pixels that are real (i.e. **not masked**),
+ - 0 for pixels that are padding (i.e. **masked**).
+
+ instance_id_to_semantic_id (`list[dict[int, int]]` or `dict[int, int]`, *optional*):
+ A mapping between object instance ids and class ids. If passed, `segmentation_maps` is treated as an
+ instance segmentation map where each pixel represents an instance id. Can be provided as a single
+ dictionary with a global/dataset-level mapping or as a list of dictionaries (one per image), to map
+ instance ids in each image separately.
+
+ return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+ If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
+ objects.
+
+ pad_size (`Dict[str, int]`, *optional*):
+ The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
+ provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
+ height and width in the batch.
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **pixel_values** -- Pixel values to be fed to a model.
+ - **pixel_mask** -- Pixel mask to be fed to a model (when `=True` or if `pixel_mask` is in
+ `self.model_input_names`).
+ - **mask_labels** -- Optional list of mask labels of shape `(labels, height, width)` to be fed to a model
+ (when `annotations` are provided).
+ - **class_labels** -- Optional list of class labels of shape `(labels)` to be fed to a model (when
+ `annotations` are provided). They identify the labels of `mask_labels`, e.g. the label of
+ `mask_labels[i][j]` if `class_labels[i][j]`.
+ """
+ ignore_index = self.ignore_index if ignore_index is None else ignore_index
+ do_reduce_labels = self.do_reduce_labels if do_reduce_labels is None else do_reduce_labels
+
+ pixel_values_list = [to_numpy_array(pixel_values) for pixel_values in pixel_values_list]
+
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(pixel_values_list[0])
+
+ encoded_inputs = self.pad(
+ pixel_values_list, return_tensors=return_tensors, input_data_format=input_data_format, pad_size=pad_size
+ )
+
+ if segmentation_maps is not None:
+ mask_labels = []
+ class_labels = []
+ pad_size = get_max_height_width(pixel_values_list, input_data_format=input_data_format)
+ # Convert to list of binary masks and labels
+ for idx, segmentation_map in enumerate(segmentation_maps):
+ segmentation_map = to_numpy_array(segmentation_map)
+ if isinstance(instance_id_to_semantic_id, list):
+ instance_id = instance_id_to_semantic_id[idx]
+ else:
+ instance_id = instance_id_to_semantic_id
+ # Use instance2class_id mapping per image
+ masks, classes = self.convert_segmentation_map_to_binary_masks(
+ segmentation_map, instance_id, ignore_index=ignore_index, do_reduce_labels=do_reduce_labels
+ )
+ # We add an axis to make them compatible with the transformations library
+ # this will be removed in the future
+ if masks.shape[0] > 0:
+ masks = [mask[None, ...] for mask in masks]
+ masks = [
+ self._pad_image(
+ image=mask,
+ output_size=pad_size,
+ constant_values=ignore_index,
+ input_data_format=ChannelDimension.FIRST,
+ )
+ for mask in masks
+ ]
+ masks = np.concatenate(masks, axis=0)
+ else:
+ masks = np.zeros((0, *pad_size), dtype=np.float32)
+ mask_labels.append(torch.from_numpy(masks))
+ class_labels.append(torch.from_numpy(classes))
+
+ # we cannot batch them since they don't share a common class size
+ encoded_inputs["mask_labels"] = mask_labels
+ encoded_inputs["class_labels"] = class_labels
+
+ return encoded_inputs
+
+ def post_process_segmentation(
+ self, outputs: "MaskFormerForInstanceSegmentationOutput", target_size: Optional[tuple[int, int]] = None
+ ) -> "torch.Tensor":
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image segmentation predictions. Only
+ supports PyTorch.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentationOutput`]):
+ The outputs from [`MaskFormerForInstanceSegmentation`].
+
+ target_size (`tuple[int, int]`, *optional*):
+ If set, the `masks_queries_logits` will be resized to `target_size`.
+
+ Returns:
+ `torch.Tensor`:
+ A tensor of shape (`batch_size, num_class_labels, height, width`).
+ """
+ warnings.warn(
+ "`post_process_segmentation` is deprecated and will be removed in v5 of Transformers, please use"
+ " `post_process_instance_segmentation`",
+ FutureWarning,
+ )
+
+ # class_queries_logits has shape [BATCH, QUERIES, CLASSES + 1]
+ class_queries_logits = outputs.class_queries_logits
+ # masks_queries_logits has shape [BATCH, QUERIES, HEIGHT, WIDTH]
+ masks_queries_logits = outputs.masks_queries_logits
+ if target_size is not None:
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits,
+ size=target_size,
+ mode="bilinear",
+ align_corners=False,
+ )
+ # remove the null class `[..., :-1]`
+ masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
+ # mask probs has shape [BATCH, QUERIES, HEIGHT, WIDTH]
+ masks_probs = masks_queries_logits.sigmoid()
+ # now we want to sum over the queries,
+ # $ out_{c,h,w} = \sum_q p_{q,c} * m_{q,h,w} $
+ # where $ softmax(p) \in R^{q, c} $ is the mask classes
+ # and $ sigmoid(m) \in R^{q, h, w}$ is the mask probabilities
+ # b(atch)q(uery)c(lasses), b(atch)q(uery)h(eight)w(idth)
+ segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
+
+ return segmentation
+
+ def post_process_semantic_segmentation(
+ self, outputs, target_sizes: Optional[list[tuple[int, int]]] = None
+ ) -> "torch.Tensor":
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentation`] into semantic segmentation maps. Only supports
+ PyTorch.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentation`]):
+ Raw outputs of the model.
+ target_sizes (`list[tuple[int, int]]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ Returns:
+ `list[torch.Tensor]`:
+ A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
+ corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
+ `torch.Tensor` correspond to a semantic class id.
+ """
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ # Remove the null class `[..., :-1]`
+ masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
+ masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Semantic segmentation logits of shape (batch_size, num_classes, height, width)
+ segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
+ batch_size = class_queries_logits.shape[0]
+
+ # Resize logits and compute semantic segmentation maps
+ if target_sizes is not None:
+ if batch_size != len(target_sizes):
+ raise ValueError(
+ "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+ )
+
+ semantic_segmentation = []
+ for idx in range(batch_size):
+ resized_logits = torch.nn.functional.interpolate(
+ segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+ )
+ semantic_map = resized_logits[0].argmax(dim=0)
+ semantic_segmentation.append(semantic_map)
+ else:
+ semantic_segmentation = segmentation.argmax(dim=1)
+ semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+ return semantic_segmentation
+
+ def post_process_instance_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ return_coco_annotation: Optional[bool] = False,
+ return_binary_maps: Optional[bool] = False,
+ ) -> list[dict]:
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into instance segmentation predictions. Only
+ supports PyTorch. If instances could overlap, set either return_coco_annotation or return_binary_maps
+ to `True` to get the correct segmentation result.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentation`]):
+ Raw outputs of the model.
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ target_sizes (`list[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ return_coco_annotation (`bool`, *optional*, defaults to `False`):
+ If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE) format.
+ return_binary_maps (`bool`, *optional*, defaults to `False`):
+ If set to `True`, segmentation maps are returned as a concatenated tensor of binary segmentation maps
+ (one per detected instance).
+ Returns:
+ `list[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id`, or
+ `list[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
+ `True`, or a tensor of shape `(num_instances, height, width)` if return_binary_maps is set to `True`.
+ Set to `None` if no mask if found above `threshold`.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- An integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+ if return_coco_annotation and return_binary_maps:
+ raise ValueError("return_coco_annotation and return_binary_maps can not be both set to True.")
+
+ # [batch_size, num_queries, num_classes+1]
+ class_queries_logits = outputs.class_queries_logits
+ # [batch_size, num_queries, height, width]
+ masks_queries_logits = outputs.masks_queries_logits
+
+ device = masks_queries_logits.device
+ num_classes = class_queries_logits.shape[-1] - 1
+ num_queries = class_queries_logits.shape[-2]
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(class_queries_logits.shape[0]):
+ mask_pred = masks_queries_logits[i]
+ mask_cls = class_queries_logits[i]
+
+ scores = torch.nn.functional.softmax(mask_cls, dim=-1)[:, :-1]
+ labels = torch.arange(num_classes, device=device).unsqueeze(0).repeat(num_queries, 1).flatten(0, 1)
+
+ scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False)
+ labels_per_image = labels[topk_indices]
+
+ topk_indices = torch.div(topk_indices, num_classes, rounding_mode="floor")
+ mask_pred = mask_pred[topk_indices]
+ pred_masks = (mask_pred > 0).float()
+
+ # Calculate average mask prob
+ mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * pred_masks.flatten(1)).sum(1) / (
+ pred_masks.flatten(1).sum(1) + 1e-6
+ )
+ pred_scores = scores_per_image * mask_scores_per_image
+ pred_classes = labels_per_image
+
+ segmentation = torch.zeros(masks_queries_logits.shape[2:]) - 1
+ if target_sizes is not None:
+ segmentation = torch.zeros(target_sizes[i]) - 1
+ pred_masks = torch.nn.functional.interpolate(
+ pred_masks.unsqueeze(0), size=target_sizes[i], mode="nearest"
+ )[0]
+
+ instance_maps, segments = [], []
+ current_segment_id = 0
+ for j in range(num_queries):
+ score = pred_scores[j].item()
+
+ if not torch.all(pred_masks[j] == 0) and score >= threshold:
+ segmentation[pred_masks[j] == 1] = current_segment_id
+ segments.append(
+ {
+ "id": current_segment_id,
+ "label_id": pred_classes[j].item(),
+ "was_fused": False,
+ "score": round(score, 6),
+ }
+ )
+ current_segment_id += 1
+ instance_maps.append(pred_masks[j])
+
+ # Return segmentation map in run-length encoding (RLE) format
+ if return_coco_annotation:
+ segmentation = convert_segmentation_to_rle(segmentation)
+
+ # Return a concatenated tensor of binary instance maps
+ if return_binary_maps and len(instance_maps) != 0:
+ segmentation = torch.stack(instance_maps, dim=0)
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+ def post_process_panoptic_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ label_ids_to_fuse: Optional[set[int]] = None,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ ) -> list[dict]:
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image panoptic segmentation
+ predictions. Only supports PyTorch.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentationOutput`]):
+ The outputs from [`MaskFormerForInstanceSegmentation`].
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ label_ids_to_fuse (`Set[int]`, *optional*):
+ The labels in this state will have all their instances be fused together. For instance we could say
+ there can only be one sky in an image, but several persons, so the label ID for sky would be in that
+ set, but not the one for person.
+ target_sizes (`list[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction in batch. If left to None, predictions will not be
+ resized.
+
+ Returns:
+ `list[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set
+ to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized
+ to the corresponding `target_sizes` entry.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- an integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
+ Multiple instances of the same class / label were fused and assigned a single `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+
+ if label_ids_to_fuse is None:
+ logger.warning("`label_ids_to_fuse` unset. No instance will be fused.")
+ label_ids_to_fuse = set()
+
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ batch_size = class_queries_logits.shape[0]
+ num_labels = class_queries_logits.shape[-1] - 1
+
+ mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Predicted label and score of each query (batch_size, num_queries)
+ pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(batch_size):
+ mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
+ mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
+ )
+
+ # No mask found
+ if mask_probs_item.shape[0] <= 0:
+ height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
+ segmentation = torch.zeros((height, width)) - 1
+ results.append({"segmentation": segmentation, "segments_info": []})
+ continue
+
+ # Get segmentation map and segment information of batch item
+ target_size = target_sizes[i] if target_sizes is not None else None
+ segmentation, segments = compute_segments(
+ mask_probs=mask_probs_item,
+ pred_scores=pred_scores_item,
+ pred_labels=pred_labels_item,
+ mask_threshold=mask_threshold,
+ overlap_mask_area_threshold=overlap_mask_area_threshold,
+ label_ids_to_fuse=label_ids_to_fuse,
+ target_size=target_size,
+ )
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+
+__all__ = ["MaskFormerImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/image_processing_maskformer_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/image_processing_maskformer_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2e0424265eb4a7541674b2bcb9fa7758df9ae42
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/image_processing_maskformer_fast.py
@@ -0,0 +1,759 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for MaskFormer."""
+
+import math
+import warnings
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+from ...image_processing_utils import BatchFeature, get_size_dict
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ SizeDict,
+ get_image_size_for_max_height_width,
+ get_max_height_width,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_utils import (
+ IMAGENET_DEFAULT_MEAN,
+ IMAGENET_DEFAULT_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+ logging,
+)
+from .image_processing_maskformer import (
+ compute_segments,
+ convert_segmentation_to_rle,
+ get_size_with_aspect_ratio,
+ remove_low_and_no_objects,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+if TYPE_CHECKING:
+ from transformers import MaskFormerForInstanceSegmentationOutput
+
+
+if is_torch_available():
+ import torch
+ from torch import nn
+
+
+if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+
+elif is_torchvision_available():
+ from torchvision.transforms import functional as F
+
+
+def convert_segmentation_map_to_binary_masks_fast(
+ segmentation_map: "torch.Tensor",
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+):
+ if do_reduce_labels and ignore_index is None:
+ raise ValueError("If `do_reduce_labels` is True, `ignore_index` must be provided.")
+
+ if do_reduce_labels:
+ segmentation_map = torch.where(segmentation_map == 0, ignore_index, segmentation_map - 1)
+
+ all_labels = torch.unique(segmentation_map)
+
+ if ignore_index is not None:
+ all_labels = all_labels[all_labels != ignore_index] # drop background label if applicable
+
+ binary_masks = [(segmentation_map == i) for i in all_labels]
+ if binary_masks:
+ binary_masks = torch.stack(binary_masks, dim=0)
+ else:
+ binary_masks = torch.zeros((0, *segmentation_map.shape), device=segmentation_map.device)
+
+ # Convert instance ids to class ids
+ if instance_id_to_semantic_id is not None:
+ labels = torch.zeros(all_labels.shape[0], device=segmentation_map.device)
+
+ for i, label in enumerate(all_labels):
+ class_id = instance_id_to_semantic_id[(label.item() + 1 if do_reduce_labels else label.item())]
+ labels[i] = class_id - 1 if do_reduce_labels else class_id
+ else:
+ labels = all_labels
+ return binary_masks.float(), labels.long()
+
+
+class MaskFormerFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ r"""
+ size_divisor (`int`, *optional*, defaults to 32):
+ Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
+ Swin Transformer.
+ ignore_index (`int`, *optional*):
+ Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels
+ denoted with 0 (background) will be replaced with `ignore_index`.
+ do_reduce_labels (`bool`, *optional*, defaults to `False`):
+ Whether or not to decrement all label values of segmentation maps by 1. Usually used for datasets where 0
+ is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k).
+ The background label will be replaced by `ignore_index`.
+ num_labels (`int`, *optional*):
+ The number of labels in the segmentation map.
+ do_pad (`bool`, *optional*, defaults to `True`):
+ Controls whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess`
+ method. If `True`, padding will be applied to the bottom and right of the image with zeros.
+ If `pad_size` is provided, the image will be padded to the specified dimensions.
+ Otherwise, the image will be padded to the maximum height and width of the batch.
+ pad_size (`Dict[str, int]`, *optional*):
+ The size `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
+ provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
+ height and width in the batch.
+ """
+
+ size_divisor: Optional[int]
+ ignore_index: Optional[int]
+ do_reduce_labels: Optional[bool]
+ num_labels: Optional[int]
+ do_pad: Optional[bool]
+ pad_size: Optional[dict[str, int]]
+
+
+@auto_docstring
+class MaskFormerImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BILINEAR
+ image_mean = IMAGENET_DEFAULT_MEAN
+ image_std = IMAGENET_DEFAULT_STD
+ size = {"shortest_edge": 800, "longest_edge": 1333}
+ default_to_square = False
+ do_resize = True
+ do_rescale = True
+ rescale_factor = 1 / 255
+ do_normalize = True
+ do_pad = True
+ model_input_names = ["pixel_values", "pixel_mask"]
+ size_divisor = 32
+ do_reduce_labels = False
+ valid_kwargs = MaskFormerFastImageProcessorKwargs
+
+ def __init__(self, **kwargs: Unpack[MaskFormerFastImageProcessorKwargs]) -> None:
+ if "pad_and_return_pixel_mask" in kwargs:
+ kwargs["do_pad"] = kwargs.pop("pad_and_return_pixel_mask")
+
+ size = kwargs.pop("size", None)
+ max_size = kwargs.pop("max_size", None)
+
+ if size is None and max_size is not None:
+ size = self.size
+ size["longest_edge"] = max_size
+ elif size is None:
+ size = self.size
+
+ self.size = get_size_dict(size, max_size=max_size, default_to_square=False)
+
+ super().__init__(**kwargs)
+
+ def to_dict(self) -> dict[str, Any]:
+ """
+ Serializes this instance to a Python dictionary. This method calls the superclass method and then removes the
+ `_max_size` attribute from the dictionary.
+ """
+ image_processor_dict = super().to_dict()
+ image_processor_dict.pop("_max_size", None)
+ return image_processor_dict
+
+ def reduce_label(self, labels: list["torch.Tensor"]):
+ for idx in range(len(labels)):
+ label = labels[idx]
+ label = torch.where(label == 0, torch.tensor(255, dtype=label.dtype), label)
+ label = label - 1
+ label = torch.where(label == 254, torch.tensor(255, dtype=label.dtype), label)
+ labels[idx] = label
+
+ def resize(
+ self,
+ image: torch.Tensor,
+ size: SizeDict,
+ size_divisor: int = 0,
+ interpolation: "F.InterpolationMode" = None,
+ **kwargs,
+ ) -> torch.Tensor:
+ """
+ Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an
+ int, smaller edge of the image will be matched to this number.
+
+ Args:
+ image (`torch.Tensor`):
+ Image to resize.
+ size (`SizeDict`):
+ Size of the image's `(height, width)` dimensions after resizing. Available options are:
+ - `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`.
+ Do NOT keep the aspect ratio.
+ - `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting
+ the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge
+ less or equal to `longest_edge`.
+ - `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the
+ aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to
+ `max_width`.
+ size_divisor (`int`, *optional*, defaults to 0):
+ If `size_divisor` is given, the output image size will be divisible by the number.
+ interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
+ Resampling filter to use if resizing the image.
+ """
+ interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
+ if size.shortest_edge and size.longest_edge:
+ # Resize the image so that the shortest edge or the longest edge is of the given size
+ # while maintaining the aspect ratio of the original image.
+ new_size = get_size_with_aspect_ratio(
+ image.size()[-2:],
+ size["shortest_edge"],
+ size["longest_edge"],
+ )
+ elif size.max_height and size.max_width:
+ new_size = get_image_size_for_max_height_width(image.size()[-2:], size["max_height"], size["max_width"])
+ elif size.height and size.width:
+ new_size = (size["height"], size["width"])
+ else:
+ raise ValueError(
+ "Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
+ f" {size.keys()}."
+ )
+ if size_divisor > 0:
+ height, width = new_size
+ height = int(math.ceil(height / size_divisor) * size_divisor)
+ width = int(math.ceil(width / size_divisor) * size_divisor)
+ new_size = (height, width)
+
+ image = F.resize(
+ image,
+ size=new_size,
+ interpolation=interpolation,
+ **kwargs,
+ )
+ return image
+
+ def pad(
+ self,
+ images: torch.Tensor,
+ padded_size: tuple[int, int],
+ segmentation_maps: Optional[torch.Tensor] = None,
+ fill: int = 0,
+ ignore_index: int = 255,
+ ) -> BatchFeature:
+ original_size = images.size()[-2:]
+ padding_bottom = padded_size[0] - original_size[0]
+ padding_right = padded_size[1] - original_size[1]
+ if padding_bottom < 0 or padding_right < 0:
+ raise ValueError(
+ f"Padding dimensions are negative. Please make sure that the padded size is larger than the "
+ f"original size. Got padded size: {padded_size}, original size: {original_size}."
+ )
+ if original_size != padded_size:
+ padding = [0, 0, padding_right, padding_bottom]
+ images = F.pad(images, padding, fill=fill)
+ if segmentation_maps is not None:
+ segmentation_maps = F.pad(segmentation_maps, padding, fill=ignore_index)
+
+ # Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
+ pixel_mask = torch.zeros((images.shape[0], *padded_size), dtype=torch.int64, device=images.device)
+ pixel_mask[:, : original_size[0], : original_size[1]] = 1
+
+ return images, pixel_mask, segmentation_maps
+
+ @auto_docstring
+ def preprocess(
+ self,
+ images: ImageInput,
+ segmentation_maps: Optional[ImageInput] = None,
+ instance_id_to_semantic_id: Optional[Union[list[dict[int, int]], dict[int, int]]] = None,
+ **kwargs: Unpack[MaskFormerFastImageProcessorKwargs],
+ ) -> BatchFeature:
+ r"""
+ segmentation_maps (`ImageInput`, *optional*):
+ The segmentation maps.
+ instance_id_to_semantic_id (`Union[list[dict[int, int]], dict[int, int]]`, *optional*):
+ A mapping from instance IDs to semantic IDs.
+ """
+ return super().preprocess(
+ images,
+ segmentation_maps,
+ instance_id_to_semantic_id,
+ **kwargs,
+ )
+
+ def _preprocess_image_like_inputs(
+ self,
+ images: ImageInput,
+ segmentation_maps: ImageInput,
+ instance_id_to_semantic_id: Optional[Union[list[dict[int, int]], dict[int, int]]],
+ do_convert_rgb: bool,
+ input_data_format: ChannelDimension,
+ device: Optional[Union[str, "torch.device"]] = None,
+ **kwargs: Unpack[MaskFormerFastImageProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Preprocess image-like inputs.
+ To be overridden by subclasses when image-like inputs other than images should be processed.
+ It can be used for segmentation maps, depth maps, etc.
+ """
+ # Prepare input images
+ images = self._prepare_image_like_inputs(
+ images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
+ )
+ if segmentation_maps is not None:
+ segmentation_maps = self._prepare_image_like_inputs(
+ images=segmentation_maps,
+ expected_ndims=2,
+ do_convert_rgb=False,
+ input_data_format=ChannelDimension.FIRST,
+ )
+ return self._preprocess(images, segmentation_maps, instance_id_to_semantic_id, **kwargs)
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ segmentation_maps: Optional["torch.Tensor"],
+ instance_id_to_semantic_id: Optional[dict[int, int]],
+ do_resize: Optional[bool],
+ size: Optional[dict[str, int]],
+ pad_size: Optional[dict[str, int]],
+ size_divisor: Optional[int],
+ interpolation: Optional[Union["PILImageResampling", "F.InterpolationMode"]],
+ do_rescale: Optional[bool],
+ rescale_factor: Optional[float],
+ do_normalize: Optional[bool],
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ ignore_index: Optional[int],
+ do_reduce_labels: Optional[bool],
+ disable_grouping: Optional[bool],
+ return_tensors: Optional[Union[str, TensorType]],
+ **kwargs,
+ ) -> BatchFeature:
+ if segmentation_maps is not None and len(images) != len(segmentation_maps):
+ raise ValueError("Images and segmentation maps must have the same length.")
+
+ # Group images by size for batched resizing
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ resized_images_grouped = {}
+ if segmentation_maps is not None:
+ grouped_segmentation_maps, grouped_segmentation_maps_index = group_images_by_shape(
+ segmentation_maps, disable_grouping=disable_grouping
+ )
+ resized_segmentation_maps_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_resize:
+ stacked_images = self.resize(
+ image=stacked_images, size=size, size_divisor=size_divisor, interpolation=interpolation
+ )
+ if segmentation_maps is not None:
+ stacked_segmentation_maps = self.resize(
+ image=grouped_segmentation_maps[shape],
+ size=size,
+ size_divisor=size_divisor,
+ interpolation=F.InterpolationMode.NEAREST_EXACT
+ if is_torchvision_v2_available()
+ else F.InterpolationMode.NEAREST,
+ )
+ resized_images_grouped[shape] = stacked_images
+ if segmentation_maps is not None:
+ resized_segmentation_maps_grouped[shape] = stacked_segmentation_maps
+ resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+ if segmentation_maps is not None:
+ resized_segmentation_maps = reorder_images(
+ resized_segmentation_maps_grouped, grouped_segmentation_maps_index
+ )
+ if pad_size is not None:
+ padded_size = (pad_size["height"], pad_size["width"])
+ else:
+ padded_size = get_max_height_width(resized_images)
+
+ if segmentation_maps is not None:
+ mask_labels = []
+ class_labels = []
+ # Convert to list of binary masks and labels
+ for idx, segmentation_map in enumerate(resized_segmentation_maps):
+ if isinstance(instance_id_to_semantic_id, list):
+ instance_id = instance_id_to_semantic_id[idx]
+ else:
+ instance_id = instance_id_to_semantic_id
+ # Use instance2class_id mapping per image
+ masks, classes = convert_segmentation_map_to_binary_masks_fast(
+ segmentation_map.squeeze(0),
+ instance_id,
+ ignore_index=ignore_index,
+ do_reduce_labels=do_reduce_labels,
+ )
+ mask_labels.append(masks)
+ class_labels.append(classes)
+
+ grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+ processed_images_grouped = {}
+ processed_pixel_masks_grouped = {}
+ if segmentation_maps is not None:
+ grouped_segmentation_maps, grouped_segmentation_maps_index = group_images_by_shape(
+ mask_labels, disable_grouping=disable_grouping
+ )
+ processed_segmentation_maps_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ # Fused rescale and normalize
+ stacked_images = self.rescale_and_normalize(
+ stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ padded_images, pixel_masks, padded_segmentation_maps = self.pad(
+ images=stacked_images,
+ segmentation_maps=grouped_segmentation_maps[shape] if segmentation_maps is not None else None,
+ padded_size=padded_size,
+ ignore_index=ignore_index,
+ )
+ processed_images_grouped[shape] = padded_images
+ processed_pixel_masks_grouped[shape] = pixel_masks
+ if segmentation_maps is not None:
+ processed_segmentation_maps_grouped[shape] = padded_segmentation_maps.squeeze(1)
+ processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+ processed_pixel_masks = reorder_images(processed_pixel_masks_grouped, grouped_images_index)
+ encoded_inputs = BatchFeature(
+ data={
+ "pixel_values": torch.stack(processed_images, dim=0) if return_tensors else processed_images,
+ "pixel_mask": torch.stack(processed_pixel_masks, dim=0) if return_tensors else processed_pixel_masks,
+ },
+ tensor_type=return_tensors,
+ )
+ if segmentation_maps is not None:
+ mask_labels = reorder_images(processed_segmentation_maps_grouped, grouped_segmentation_maps_index)
+ # we cannot batch them since they don't share a common class size
+ encoded_inputs["mask_labels"] = mask_labels
+ encoded_inputs["class_labels"] = class_labels
+
+ return encoded_inputs
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.post_process_segmentation
+ def post_process_segmentation(
+ self, outputs: "MaskFormerForInstanceSegmentationOutput", target_size: Optional[tuple[int, int]] = None
+ ) -> "torch.Tensor":
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image segmentation predictions. Only
+ supports PyTorch.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentationOutput`]):
+ The outputs from [`MaskFormerForInstanceSegmentation`].
+
+ target_size (`tuple[int, int]`, *optional*):
+ If set, the `masks_queries_logits` will be resized to `target_size`.
+
+ Returns:
+ `torch.Tensor`:
+ A tensor of shape (`batch_size, num_class_labels, height, width`).
+ """
+ warnings.warn(
+ "`post_process_segmentation` is deprecated and will be removed in v5 of Transformers, please use"
+ " `post_process_instance_segmentation`",
+ FutureWarning,
+ )
+
+ # class_queries_logits has shape [BATCH, QUERIES, CLASSES + 1]
+ class_queries_logits = outputs.class_queries_logits
+ # masks_queries_logits has shape [BATCH, QUERIES, HEIGHT, WIDTH]
+ masks_queries_logits = outputs.masks_queries_logits
+ if target_size is not None:
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits,
+ size=target_size,
+ mode="bilinear",
+ align_corners=False,
+ )
+ # remove the null class `[..., :-1]`
+ masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
+ # mask probs has shape [BATCH, QUERIES, HEIGHT, WIDTH]
+ masks_probs = masks_queries_logits.sigmoid()
+ # now we want to sum over the queries,
+ # $ out_{c,h,w} = \sum_q p_{q,c} * m_{q,h,w} $
+ # where $ softmax(p) \in R^{q, c} $ is the mask classes
+ # and $ sigmoid(m) \in R^{q, h, w}$ is the mask probabilities
+ # b(atch)q(uery)c(lasses), b(atch)q(uery)h(eight)w(idth)
+ segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
+
+ return segmentation
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.post_process_semantic_segmentation
+ def post_process_semantic_segmentation(
+ self, outputs, target_sizes: Optional[list[tuple[int, int]]] = None
+ ) -> "torch.Tensor":
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentation`] into semantic segmentation maps. Only supports
+ PyTorch.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentation`]):
+ Raw outputs of the model.
+ target_sizes (`list[tuple[int, int]]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ Returns:
+ `list[torch.Tensor]`:
+ A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
+ corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
+ `torch.Tensor` correspond to a semantic class id.
+ """
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ # Remove the null class `[..., :-1]`
+ masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
+ masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Semantic segmentation logits of shape (batch_size, num_classes, height, width)
+ segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
+ batch_size = class_queries_logits.shape[0]
+
+ # Resize logits and compute semantic segmentation maps
+ if target_sizes is not None:
+ if batch_size != len(target_sizes):
+ raise ValueError(
+ "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+ )
+
+ semantic_segmentation = []
+ for idx in range(batch_size):
+ resized_logits = torch.nn.functional.interpolate(
+ segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+ )
+ semantic_map = resized_logits[0].argmax(dim=0)
+ semantic_segmentation.append(semantic_map)
+ else:
+ semantic_segmentation = segmentation.argmax(dim=1)
+ semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+ return semantic_segmentation
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.post_process_instance_segmentation
+ def post_process_instance_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ return_coco_annotation: Optional[bool] = False,
+ return_binary_maps: Optional[bool] = False,
+ ) -> list[dict]:
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into instance segmentation predictions. Only
+ supports PyTorch. If instances could overlap, set either return_coco_annotation or return_binary_maps
+ to `True` to get the correct segmentation result.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentation`]):
+ Raw outputs of the model.
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ target_sizes (`list[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ return_coco_annotation (`bool`, *optional*, defaults to `False`):
+ If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE) format.
+ return_binary_maps (`bool`, *optional*, defaults to `False`):
+ If set to `True`, segmentation maps are returned as a concatenated tensor of binary segmentation maps
+ (one per detected instance).
+ Returns:
+ `list[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id`, or
+ `list[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to
+ `True`, or a tensor of shape `(num_instances, height, width)` if return_binary_maps is set to `True`.
+ Set to `None` if no mask if found above `threshold`.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- An integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+ if return_coco_annotation and return_binary_maps:
+ raise ValueError("return_coco_annotation and return_binary_maps can not be both set to True.")
+
+ # [batch_size, num_queries, num_classes+1]
+ class_queries_logits = outputs.class_queries_logits
+ # [batch_size, num_queries, height, width]
+ masks_queries_logits = outputs.masks_queries_logits
+
+ device = masks_queries_logits.device
+ num_classes = class_queries_logits.shape[-1] - 1
+ num_queries = class_queries_logits.shape[-2]
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(class_queries_logits.shape[0]):
+ mask_pred = masks_queries_logits[i]
+ mask_cls = class_queries_logits[i]
+
+ scores = torch.nn.functional.softmax(mask_cls, dim=-1)[:, :-1]
+ labels = torch.arange(num_classes, device=device).unsqueeze(0).repeat(num_queries, 1).flatten(0, 1)
+
+ scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False)
+ labels_per_image = labels[topk_indices]
+
+ topk_indices = torch.div(topk_indices, num_classes, rounding_mode="floor")
+ mask_pred = mask_pred[topk_indices]
+ pred_masks = (mask_pred > 0).float()
+
+ # Calculate average mask prob
+ mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * pred_masks.flatten(1)).sum(1) / (
+ pred_masks.flatten(1).sum(1) + 1e-6
+ )
+ pred_scores = scores_per_image * mask_scores_per_image
+ pred_classes = labels_per_image
+
+ segmentation = torch.zeros(masks_queries_logits.shape[2:]) - 1
+ if target_sizes is not None:
+ segmentation = torch.zeros(target_sizes[i]) - 1
+ pred_masks = torch.nn.functional.interpolate(
+ pred_masks.unsqueeze(0), size=target_sizes[i], mode="nearest"
+ )[0]
+
+ instance_maps, segments = [], []
+ current_segment_id = 0
+ for j in range(num_queries):
+ score = pred_scores[j].item()
+
+ if not torch.all(pred_masks[j] == 0) and score >= threshold:
+ segmentation[pred_masks[j] == 1] = current_segment_id
+ segments.append(
+ {
+ "id": current_segment_id,
+ "label_id": pred_classes[j].item(),
+ "was_fused": False,
+ "score": round(score, 6),
+ }
+ )
+ current_segment_id += 1
+ instance_maps.append(pred_masks[j])
+
+ # Return segmentation map in run-length encoding (RLE) format
+ if return_coco_annotation:
+ segmentation = convert_segmentation_to_rle(segmentation)
+
+ # Return a concatenated tensor of binary instance maps
+ if return_binary_maps and len(instance_maps) != 0:
+ segmentation = torch.stack(instance_maps, dim=0)
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.post_process_panoptic_segmentation
+ def post_process_panoptic_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ label_ids_to_fuse: Optional[set[int]] = None,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ ) -> list[dict]:
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image panoptic segmentation
+ predictions. Only supports PyTorch.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentationOutput`]):
+ The outputs from [`MaskFormerForInstanceSegmentation`].
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ label_ids_to_fuse (`Set[int]`, *optional*):
+ The labels in this state will have all their instances be fused together. For instance we could say
+ there can only be one sky in an image, but several persons, so the label ID for sky would be in that
+ set, but not the one for person.
+ target_sizes (`list[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction in batch. If left to None, predictions will not be
+ resized.
+
+ Returns:
+ `list[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set
+ to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized
+ to the corresponding `target_sizes` entry.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- an integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
+ Multiple instances of the same class / label were fused and assigned a single `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+
+ if label_ids_to_fuse is None:
+ logger.warning("`label_ids_to_fuse` unset. No instance will be fused.")
+ label_ids_to_fuse = set()
+
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ batch_size = class_queries_logits.shape[0]
+ num_labels = class_queries_logits.shape[-1] - 1
+
+ mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Predicted label and score of each query (batch_size, num_queries)
+ pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(batch_size):
+ mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
+ mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
+ )
+
+ # No mask found
+ if mask_probs_item.shape[0] <= 0:
+ height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
+ segmentation = torch.zeros((height, width)) - 1
+ results.append({"segmentation": segmentation, "segments_info": []})
+ continue
+
+ # Get segmentation map and segment information of batch item
+ target_size = target_sizes[i] if target_sizes is not None else None
+ segmentation, segments = compute_segments(
+ mask_probs=mask_probs_item,
+ pred_scores=pred_scores_item,
+ pred_labels=pred_labels_item,
+ mask_threshold=mask_threshold,
+ overlap_mask_area_threshold=overlap_mask_area_threshold,
+ label_ids_to_fuse=label_ids_to_fuse,
+ target_size=target_size,
+ )
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+
+__all__ = ["MaskFormerImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/modeling_maskformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/modeling_maskformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e1c0072425b516f2c11dea7965483199ad3dd0c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/modeling_maskformer.py
@@ -0,0 +1,1803 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms, Inc.s and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MaskFormer model."""
+
+import math
+from dataclasses import dataclass
+from numbers import Number
+from typing import Optional, Union
+
+import numpy as np
+import torch
+from torch import Tensor, nn
+
+from ...activations import ACT2FN
+from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithCrossAttentions
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import compile_compatible_method_lru_cache
+from ...utils import (
+ ModelOutput,
+ auto_docstring,
+ is_accelerate_available,
+ is_scipy_available,
+ logging,
+ requires_backends,
+)
+from ...utils.backbone_utils import load_backbone
+from ..detr import DetrConfig
+from .configuration_maskformer import MaskFormerConfig
+from .configuration_maskformer_swin import MaskFormerSwinConfig
+
+
+if is_accelerate_available():
+ from accelerate import PartialState
+ from accelerate.utils import reduce
+
+if is_scipy_available():
+ from scipy.optimize import linear_sum_assignment
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of the DETR decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions,
+ namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them
+ gone through a layernorm. This is useful when training the model with auxiliary decoding losses.
+ """
+)
+# Copied from transformers.models.detr.modeling_detr.DetrDecoderOutput
+class DetrDecoderOutput(BaseModelOutputWithCrossAttentions):
+ r"""
+ cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax,
+ used to compute the weighted average in the cross-attention heads.
+ intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`):
+ Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a
+ layernorm.
+ """
+
+ intermediate_hidden_states: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ MaskFormer's pixel level module output. It returns both the last and (optionally) the hidden states from the
+ `encoder` and `decoder`. By default, the `encoder` is a MaskFormerSwin Transformer and the `decoder` is a Feature
+ Pyramid Network (FPN).
+
+ The `encoder_last_hidden_state` are referred on the paper as **images features**, while `decoder_last_hidden_state`
+ as **pixel embeddings**
+ """
+)
+class MaskFormerPixelLevelModuleOutput(ModelOutput):
+ r"""
+ encoder_last_hidden_state (`torch.FloatTensor` of shape`(batch_size, num_channels, height, width)`):
+ Last hidden states (final feature map) of the last stage of the encoder.
+ decoder_last_hidden_state (`torch.FloatTensor` of shape`(batch_size, num_channels, height, width)`):
+ Last hidden states (final feature map) of the last stage of the decoder.
+ encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at
+ the output of each stage.
+ decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at
+ the output of each stage.
+ """
+
+ encoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ decoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ MaskFormer's pixel decoder module output, practically a Feature Pyramid Network. It returns the last hidden state
+ and (optionally) the hidden states.
+ """
+)
+class MaskFormerPixelDecoderOutput(ModelOutput):
+ r"""
+ last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+ Last hidden states (final feature map) of the last stage of the model.
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Class for outputs of [`MaskFormerModel`]. This class returns all the needed hidden states to compute the logits.
+ """
+)
+class MaskFormerModelOutput(ModelOutput):
+ r"""
+ encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+ Last hidden states (final feature map) of the last stage of the encoder model (backbone).
+ pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+ Last hidden states (final feature map) of the last stage of the pixel decoder model (FPN).
+ transformer_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Last hidden states (final feature map) of the last stage of the transformer decoder model.
+ encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
+ model at the output of each stage.
+ pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
+ decoder model at the output of each stage.
+ transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the
+ transformer decoder at the output of each stage.
+ hidden_states `tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` containing `encoder_hidden_states`, `pixel_decoder_hidden_states` and
+ `decoder_hidden_states`
+ """
+
+ encoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ pixel_decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ transformer_decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Class for outputs of [`MaskFormerForInstanceSegmentation`].
+
+ This output can be directly passed to [`~MaskFormerImageProcessor.post_process_semantic_segmentation`] or or
+ [`~MaskFormerImageProcessor.post_process_instance_segmentation`] or
+ [`~MaskFormerImageProcessor.post_process_panoptic_segmentation`] depending on the task. Please, see
+ [`~MaskFormerImageProcessor] for details regarding usage.
+ """
+)
+class MaskFormerForInstanceSegmentationOutput(ModelOutput):
+ r"""
+ loss (`torch.Tensor`, *optional*):
+ The computed loss, returned when labels are present.
+ class_queries_logits (`torch.FloatTensor`):
+ A tensor of shape `(batch_size, num_queries, num_labels + 1)` representing the proposed classes for each
+ query. Note the `+ 1` is needed because we incorporate the null class.
+ masks_queries_logits (`torch.FloatTensor`):
+ A tensor of shape `(batch_size, num_queries, height, width)` representing the proposed masks for each
+ query.
+ auxiliary_logits (`Dict[str, torch.FloatTensor]`, *optional*, returned when `output_auxiliary_logits=True`):
+ Dictionary containing auxiliary predictions for each decoder layer when auxiliary losses are enabled.
+ encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+ Last hidden states (final feature map) of the last stage of the encoder model (backbone).
+ pixel_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+ Last hidden states (final feature map) of the last stage of the pixel decoder model (FPN).
+ transformer_decoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Last hidden states (final feature map) of the last stage of the transformer decoder model.
+ encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
+ model at the output of each stage.
+ pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
+ decoder model at the output of each stage.
+ transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the transformer decoder at the output
+ of each stage.
+ hidden_states `tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` containing `encoder_hidden_states`, `pixel_decoder_hidden_states` and
+ `decoder_hidden_states`.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ class_queries_logits: Optional[torch.FloatTensor] = None
+ masks_queries_logits: Optional[torch.FloatTensor] = None
+ auxiliary_logits: Optional[torch.FloatTensor] = None
+ encoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ pixel_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ transformer_decoder_last_hidden_state: Optional[torch.FloatTensor] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ pixel_decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ transformer_decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+def upsample_like(pixel_values: Tensor, like: Tensor, mode: str = "bilinear") -> Tensor:
+ """
+ An utility function that upsamples `pixel_values` to match the dimension of `like`.
+
+ Args:
+ pixel_values (`torch.Tensor`):
+ The tensor we wish to upsample.
+ like (`torch.Tensor`):
+ The tensor we wish to use as size target.
+ mode (str, *optional*, defaults to `"bilinear"`):
+ The interpolation mode.
+
+ Returns:
+ `torch.Tensor`: The upsampled tensor
+ """
+ _, _, height, width = like.shape
+ upsampled = nn.functional.interpolate(pixel_values, size=(height, width), mode=mode, align_corners=False)
+ return upsampled
+
+
+# refactored from original implementation
+def dice_loss(inputs: Tensor, labels: Tensor, num_masks: int) -> Tensor:
+ r"""
+ Compute the DICE loss, similar to generalized IOU for masks as follows:
+
+ $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x \cap y }{x \cup y + 1}} $$
+
+ In practice, since `labels` is a binary mask, (only 0s and 1s), dice can be computed as follow
+
+ $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x * y }{x + y + 1}} $$
+
+ Args:
+ inputs (`torch.Tensor`):
+ A tensor representing a mask.
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ num_masks (`int`):
+ The number of masks present in the current batch, used for normalization.
+
+ Returns:
+ `torch.Tensor`: The computed loss.
+ """
+ probs = inputs.sigmoid().flatten(1)
+ numerator = 2 * (probs * labels).sum(-1)
+ denominator = probs.sum(-1) + labels.sum(-1)
+ loss = 1 - (numerator + 1) / (denominator + 1)
+ loss = loss.sum() / num_masks
+ return loss
+
+
+# refactored from original implementation
+def sigmoid_focal_loss(
+ inputs: Tensor, labels: Tensor, num_masks: int, alpha: float = 0.25, gamma: float = 2
+) -> Tensor:
+ r"""
+ Focal loss proposed in [Focal Loss for Dense Object Detection](https://huggingface.co/papers/1708.02002) originally used in
+ RetinaNet. The loss is computed as follows:
+
+ $$ \mathcal{L}_{\text{focal loss} = -(1 - p_t)^{\gamma}\log{(p_t)} $$
+
+ where \\(CE(p_t) = -\log{(p_t)}}\\), CE is the standard Cross Entropy Loss
+
+ Please refer to equation (1,2,3) of the paper for a better understanding.
+
+ Args:
+ inputs (`torch.Tensor`):
+ A float tensor of arbitrary shape.
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ num_masks (`int`):
+ The number of masks present in the current batch, used for normalization.
+ alpha (float, *optional*, defaults to 0.25):
+ Weighting factor in range (0,1) to balance positive vs negative examples.
+ gamma (float, *optional*, defaults to 2.0):
+ Exponent of the modulating factor \\(1 - p_t\\) to balance easy vs hard examples.
+
+ Returns:
+ `torch.Tensor`: The computed loss.
+ """
+ criterion = nn.BCEWithLogitsLoss(reduction="none")
+ probs = inputs.sigmoid()
+ cross_entropy_loss = criterion(inputs, labels)
+ p_t = probs * labels + (1 - probs) * (1 - labels)
+ loss = cross_entropy_loss * ((1 - p_t) ** gamma)
+
+ if alpha >= 0:
+ alpha_t = alpha * labels + (1 - alpha) * (1 - labels)
+ loss = alpha_t * loss
+
+ loss = loss.mean(1).sum() / num_masks
+ return loss
+
+
+# refactored from original implementation
+def pair_wise_dice_loss(inputs: Tensor, labels: Tensor) -> Tensor:
+ """
+ A pair wise version of the dice loss, see `dice_loss` for usage.
+
+ Args:
+ inputs (`torch.Tensor`):
+ A tensor representing a mask
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+
+ Returns:
+ `torch.Tensor`: The computed loss between each pairs.
+ """
+ inputs = inputs.sigmoid().flatten(1)
+ numerator = 2 * torch.matmul(inputs, labels.T)
+ # using broadcasting to get a [num_queries, NUM_CLASSES] matrix
+ denominator = inputs.sum(-1)[:, None] + labels.sum(-1)[None, :]
+ loss = 1 - (numerator + 1) / (denominator + 1)
+ return loss
+
+
+# refactored from original implementation
+def pair_wise_sigmoid_focal_loss(inputs: Tensor, labels: Tensor, alpha: float = 0.25, gamma: float = 2.0) -> Tensor:
+ r"""
+ A pair wise version of the focal loss, see `sigmoid_focal_loss` for usage.
+
+ Args:
+ inputs (`torch.Tensor`):
+ A tensor representing a mask.
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ alpha (float, *optional*, defaults to 0.25):
+ Weighting factor in range (0,1) to balance positive vs negative examples.
+ gamma (float, *optional*, defaults to 2.0):
+ Exponent of the modulating factor \\(1 - p_t\\) to balance easy vs hard examples.
+
+ Returns:
+ `torch.Tensor`: The computed loss between each pairs.
+ """
+ if alpha < 0:
+ raise ValueError("alpha must be positive")
+
+ height_and_width = inputs.shape[1]
+
+ criterion = nn.BCEWithLogitsLoss(reduction="none")
+ prob = inputs.sigmoid()
+ cross_entropy_loss_pos = criterion(inputs, torch.ones_like(inputs))
+ focal_pos = ((1 - prob) ** gamma) * cross_entropy_loss_pos
+ focal_pos *= alpha
+
+ cross_entropy_loss_neg = criterion(inputs, torch.zeros_like(inputs))
+
+ focal_neg = (prob**gamma) * cross_entropy_loss_neg
+ focal_neg *= 1 - alpha
+
+ loss = torch.matmul(focal_pos, labels.T) + torch.matmul(focal_neg, (1 - labels).T)
+
+ return loss / height_and_width
+
+
+# Copied from transformers.models.detr.modeling_detr.DetrAttention
+class DetrAttention(nn.Module):
+ """
+ Multi-headed attention from 'Attention Is All You Need' paper.
+
+ Here, we add position embeddings to the queries and keys (as explained in the DETR paper).
+ """
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: float = 0.0,
+ bias: bool = True,
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+ if self.head_dim * num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+ f" {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
+ return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor]):
+ return tensor if object_queries is None else tensor + object_queries
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ object_queries: Optional[torch.Tensor] = None,
+ key_value_states: Optional[torch.Tensor] = None,
+ spatial_position_embeddings: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+ batch_size, target_len, embed_dim = hidden_states.size()
+
+ # add position embeddings to the hidden states before projecting to queries and keys
+ if object_queries is not None:
+ hidden_states_original = hidden_states
+ hidden_states = self.with_pos_embed(hidden_states, object_queries)
+
+ # add key-value position embeddings to the key value states
+ if spatial_position_embeddings is not None:
+ key_value_states_original = key_value_states
+ key_value_states = self.with_pos_embed(key_value_states, spatial_position_embeddings)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states) * self.scaling
+ # get key, value proj
+ if is_cross_attention:
+ # cross_attentions
+ key_states = self._shape(self.k_proj(key_value_states), -1, batch_size)
+ value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size)
+ else:
+ # self_attention
+ key_states = self._shape(self.k_proj(hidden_states), -1, batch_size)
+ value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size)
+
+ proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
+ query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape)
+ key_states = key_states.view(*proj_shape)
+ value_states = value_states.view(*proj_shape)
+
+ source_len = key_states.size(1)
+
+ attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
+
+ if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len):
+ raise ValueError(
+ f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (batch_size, 1, target_len, source_len):
+ raise ValueError(
+ f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is"
+ f" {attention_mask.size()}"
+ )
+ if attention_mask.dtype == torch.bool:
+ attention_mask = torch.zeros_like(attention_mask, dtype=attn_weights.dtype).masked_fill_(
+ attention_mask, -torch.inf
+ )
+ attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask
+ attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len)
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ if output_attentions:
+ # this operation is a bit awkward, but it's required to
+ # make sure that attn_weights keeps its gradient.
+ # In order to do so, attn_weights have to reshaped
+ # twice and have to be reused in the following
+ attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
+ attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
+ else:
+ attn_weights_reshaped = None
+
+ attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+ attn_output = torch.bmm(attn_probs, value_states)
+
+ if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
+ attn_output = attn_output.transpose(1, 2)
+ attn_output = attn_output.reshape(batch_size, target_len, embed_dim)
+
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights_reshaped
+
+
+# Copied from transformers.models.detr.modeling_detr.DetrDecoderLayer
+class DetrDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: DetrConfig):
+ super().__init__()
+ self.embed_dim = config.d_model
+
+ self.self_attn = DetrAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ )
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.encoder_attn = DetrAttention(
+ self.embed_dim,
+ config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ )
+ self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
+ self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ object_queries: Optional[torch.Tensor] = None,
+ query_position_embeddings: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ ):
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
+ values.
+ object_queries (`torch.FloatTensor`, *optional*):
+ object_queries that are added to the hidden states
+ in the cross-attention layer.
+ query_position_embeddings (`torch.FloatTensor`, *optional*):
+ position embeddings that are added to the queries and keys
+ in the self-attention layer.
+ encoder_hidden_states (`torch.FloatTensor`):
+ cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
+ encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
+ `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
+ values.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ object_queries=query_position_embeddings,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Cross-Attention Block
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+
+ hidden_states, cross_attn_weights = self.encoder_attn(
+ hidden_states=hidden_states,
+ object_queries=query_position_embeddings,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ spatial_position_embeddings=object_queries,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ return outputs
+
+
+class DetrDecoder(nn.Module):
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetrDecoderLayer`].
+
+ The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
+
+ Some small tweaks for DETR:
+
+ - object_queries and query_position_embeddings are added to the forward pass.
+ - if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers.
+
+ Args:
+ config: DetrConfig
+ """
+
+ def __init__(self, config: DetrConfig):
+ super().__init__()
+ self.config = config
+ self.dropout = config.dropout
+ self.layerdrop = config.decoder_layerdrop
+
+ self.layers = nn.ModuleList([DetrDecoderLayer(config) for _ in range(config.decoder_layers)])
+ # in DETR, the decoder uses layernorm after the last decoder layer output
+ self.layernorm = nn.LayerNorm(config.d_model)
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ inputs_embeds=None,
+ attention_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ object_queries=None,
+ query_position_embeddings=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ The query embeddings that are passed into the decoder.
+
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`:
+
+ - 1 for queries that are **not masked**,
+ - 0 for queries that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected
+ in `[0, 1]`:
+
+ - 1 for pixels that are real (i.e. **not masked**),
+ - 0 for pixels that are padding (i.e. **masked**).
+
+ object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Position embeddings that are added to the queries and keys in each cross-attention layer.
+ query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
+ , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if inputs_embeds is not None:
+ hidden_states = inputs_embeds
+ input_shape = inputs_embeds.size()[:-1]
+
+ # expand encoder attention mask
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask(
+ encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+ )
+
+ # optional intermediate hidden states
+ intermediate = () if self.config.auxiliary_loss else None
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
+
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ if self.training:
+ dropout_probability = torch.rand([])
+ if dropout_probability < self.layerdrop:
+ continue
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ None, # attention_mask
+ object_queries,
+ query_position_embeddings,
+ encoder_hidden_states, # as a positional argument for gradient checkpointing
+ encoder_attention_mask=encoder_attention_mask,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if self.config.auxiliary_loss:
+ hidden_states = self.layernorm(hidden_states)
+ intermediate += (hidden_states,)
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if encoder_hidden_states is not None:
+ all_cross_attentions += (layer_outputs[2],)
+
+ # finally, apply layernorm
+ hidden_states = self.layernorm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ # stack intermediate decoder activations
+ if self.config.auxiliary_loss:
+ intermediate = torch.stack(intermediate)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions, intermediate]
+ if v is not None
+ )
+ return DetrDecoderOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ intermediate_hidden_states=intermediate,
+ )
+
+
+# refactored from original implementation
+class MaskFormerHungarianMatcher(nn.Module):
+ """This class computes an assignment between the labels and the predictions of the network.
+
+ For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more
+ predictions than labels. In this case, we do a 1-to-1 matching of the best predictions, while the others are
+ un-matched (and thus treated as non-objects).
+ """
+
+ def __init__(self, cost_class: float = 1.0, cost_mask: float = 1.0, cost_dice: float = 1.0):
+ """Creates the matcher
+
+ Params:
+ cost_class (float, *optional*, defaults to 1.0):
+ This is the relative weight of the classification error in the matching cost.
+ cost_mask (float, *optional*, defaults to 1.0):
+ This is the relative weight of the focal loss of the binary mask in the matching cost.
+ cost_dice (float, *optional*, defaults to 1.0):
+ This is the relative weight of the dice loss of the binary mask in the matching cost
+ """
+ super().__init__()
+ if cost_class == 0 and cost_mask == 0 and cost_dice == 0:
+ raise ValueError("All costs can't be 0")
+ self.cost_class = cost_class
+ self.cost_mask = cost_mask
+ self.cost_dice = cost_dice
+
+ @torch.no_grad()
+ def forward(self, masks_queries_logits, class_queries_logits, mask_labels, class_labels) -> list[tuple[Tensor]]:
+ """Performs the matching
+
+ Params:
+ masks_queries_logits (`torch.Tensor`):
+ A tensor` of dim `batch_size, num_queries, num_labels` with the
+ classification logits.
+ class_queries_logits (`torch.Tensor`):
+ A tensor` of dim `batch_size, num_queries, height, width` with the
+ predicted masks.
+
+ class_labels (`torch.Tensor`):
+ A tensor` of dim `num_target_boxes` (where num_target_boxes is the number
+ of ground-truth objects in the target) containing the class labels.
+ mask_labels (`torch.Tensor`):
+ A tensor` of dim `num_target_boxes, height, width` containing the target
+ masks.
+
+ Returns:
+ `list[tuple[Tensor]]`: A list of size batch_size, containing tuples of (index_i, index_j) where:
+ - index_i is the indices of the selected predictions (in order)
+ - index_j is the indices of the corresponding selected labels (in order)
+ For each batch element, it holds:
+ len(index_i) = len(index_j) = min(num_queries, num_target_boxes).
+ """
+ indices: list[tuple[np.array]] = []
+
+ preds_masks = masks_queries_logits
+ preds_probs = class_queries_logits
+ # iterate through batch size
+ for pred_probs, pred_mask, target_mask, labels in zip(preds_probs, preds_masks, mask_labels, class_labels):
+ # downsample the target mask, save memory
+ target_mask = nn.functional.interpolate(target_mask[:, None], size=pred_mask.shape[-2:], mode="nearest")
+ pred_probs = pred_probs.softmax(-1)
+ # Compute the classification cost. Contrary to the loss, we don't use the NLL,
+ # but approximate it in 1 - proba[target class].
+ # The 1 is a constant that doesn't change the matching, it can be omitted.
+ cost_class = -pred_probs[:, labels]
+ # flatten spatial dimension "q h w -> q (h w)"
+ pred_mask_flat = pred_mask.flatten(1) # [num_queries, height*width]
+ # same for target_mask "c h w -> c (h w)"
+ target_mask_flat = target_mask[:, 0].flatten(1) # [num_total_labels, height*width]
+ # compute the focal loss between each mask pairs -> shape (num_queries, num_labels)
+ cost_mask = pair_wise_sigmoid_focal_loss(pred_mask_flat, target_mask_flat)
+ # Compute the dice loss between each mask pairs -> shape (num_queries, num_labels)
+ cost_dice = pair_wise_dice_loss(pred_mask_flat, target_mask_flat)
+ # final cost matrix
+ cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice
+ # do the assignment using the hungarian algorithm in scipy
+ assigned_indices: tuple[np.array] = linear_sum_assignment(cost_matrix.cpu())
+ indices.append(assigned_indices)
+
+ # It could be stacked in one tensor
+ matched_indices = [
+ (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices
+ ]
+ return matched_indices
+
+ def __repr__(self):
+ head = "Matcher " + self.__class__.__name__
+ body = [
+ f"cost_class: {self.cost_class}",
+ f"cost_mask: {self.cost_mask}",
+ f"cost_dice: {self.cost_dice}",
+ ]
+ _repr_indent = 4
+ lines = [head] + [" " * _repr_indent + line for line in body]
+ return "\n".join(lines)
+
+
+# copied and adapted from original implementation
+class MaskFormerLoss(nn.Module):
+ def __init__(
+ self,
+ num_labels: int,
+ matcher: MaskFormerHungarianMatcher,
+ weight_dict: dict[str, float],
+ eos_coef: float,
+ ):
+ """
+ The MaskFormer Loss. The loss is computed very similar to DETR. The process happens in two steps: 1) we compute
+ hungarian assignment between ground truth masks and the outputs of the model 2) we supervise each pair of
+ matched ground-truth / prediction (supervise class and mask)
+
+ Args:
+ num_labels (`int`):
+ The number of classes.
+ matcher (`MaskFormerHungarianMatcher`):
+ A torch module that computes the assignments between the predictions and labels.
+ weight_dict (`dict[str, float]`):
+ A dictionary of weights to be applied to the different losses.
+ eos_coef (`float`):
+ Weight to apply to the null class.
+ """
+
+ super().__init__()
+ requires_backends(self, ["scipy"])
+ self.num_labels = num_labels
+ self.matcher = matcher
+ self.weight_dict = weight_dict
+ self.eos_coef = eos_coef
+ empty_weight = torch.ones(self.num_labels + 1)
+ empty_weight[-1] = self.eos_coef
+ self.register_buffer("empty_weight", empty_weight)
+
+ def _max_by_axis(self, the_list: list[list[int]]) -> list[int]:
+ maxes = the_list[0]
+ for sublist in the_list[1:]:
+ for index, item in enumerate(sublist):
+ maxes[index] = max(maxes[index], item)
+ return maxes
+
+ def _pad_images_to_max_in_batch(self, tensors: list[Tensor]) -> tuple[Tensor, Tensor]:
+ # get the maximum size in the batch
+ max_size = self._max_by_axis([list(tensor.shape) for tensor in tensors])
+ batch_size = len(tensors)
+ # compute finel size
+ batch_shape = [batch_size] + max_size
+ b, _, h, w = batch_shape
+ # get metadata
+ dtype = tensors[0].dtype
+ device = tensors[0].device
+ padded_tensors = torch.zeros(batch_shape, dtype=dtype, device=device)
+ padding_masks = torch.ones((b, h, w), dtype=torch.bool, device=device)
+ # pad the tensors to the size of the biggest one
+ for tensor, padded_tensor, padding_mask in zip(tensors, padded_tensors, padding_masks):
+ padded_tensor[: tensor.shape[0], : tensor.shape[1], : tensor.shape[2]].copy_(tensor)
+ padding_mask[: tensor.shape[1], : tensor.shape[2]] = False
+
+ return padded_tensors, padding_masks
+
+ def loss_labels(
+ self, class_queries_logits: Tensor, class_labels: list[Tensor], indices: tuple[np.array]
+ ) -> dict[str, Tensor]:
+ """Compute the losses related to the labels using cross entropy.
+
+ Args:
+ class_queries_logits (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, num_labels`
+ class_labels (`list[torch.Tensor]`):
+ List of class labels of shape `(labels)`.
+ indices (`tuple[np.array])`:
+ The indices computed by the Hungarian matcher.
+
+ Returns:
+ `dict[str, Tensor]`: A dict of `torch.Tensor` containing the following key:
+ - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels.
+ """
+
+ pred_logits = class_queries_logits
+ batch_size, num_queries, _ = pred_logits.shape
+ criterion = nn.CrossEntropyLoss(weight=self.empty_weight)
+ idx = self._get_predictions_permutation_indices(indices)
+ # shape = (batch_size, num_queries)
+ target_classes_o = torch.cat([target[j] for target, (_, j) in zip(class_labels, indices)])
+ # shape = (batch_size, num_queries)
+ target_classes = torch.full(
+ (batch_size, num_queries), fill_value=self.num_labels, dtype=torch.int64, device=pred_logits.device
+ )
+ target_classes[idx] = target_classes_o
+ # target_classes is a (batch_size, num_labels, num_queries), we need to permute pred_logits "b q c -> b c q"
+ pred_logits_transposed = pred_logits.transpose(1, 2)
+ loss_ce = criterion(pred_logits_transposed, target_classes)
+ losses = {"loss_cross_entropy": loss_ce}
+ return losses
+
+ def loss_masks(
+ self, masks_queries_logits: Tensor, mask_labels: list[Tensor], indices: tuple[np.array], num_masks: int
+ ) -> dict[str, Tensor]:
+ """Compute the losses related to the masks using focal and dice loss.
+
+ Args:
+ masks_queries_logits (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, height, width`
+ mask_labels (`torch.Tensor`):
+ List of mask labels of shape `(labels, height, width)`.
+ indices (`tuple[np.array])`:
+ The indices computed by the Hungarian matcher.
+ num_masks (`int)`:
+ The number of masks, used for normalization.
+
+ Returns:
+ `dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys:
+ - **loss_mask** -- The loss computed using sigmoid focal loss on the predicted and ground truth masks.
+ - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth
+ masks.
+ """
+ src_idx = self._get_predictions_permutation_indices(indices)
+ tgt_idx = self._get_targets_permutation_indices(indices)
+ # shape (batch_size * num_queries, height, width)
+ pred_masks = masks_queries_logits[src_idx]
+ # shape (batch_size, num_queries, height, width)
+ # pad all and stack the targets to the num_labels dimension
+ target_masks, _ = self._pad_images_to_max_in_batch(mask_labels)
+ target_masks = target_masks[tgt_idx]
+ # upsample predictions to the target size, we have to add one dim to use interpolate
+ pred_masks = nn.functional.interpolate(
+ pred_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False
+ )
+ pred_masks = pred_masks[:, 0].flatten(1)
+
+ target_masks = target_masks.flatten(1)
+ losses = {
+ "loss_mask": sigmoid_focal_loss(pred_masks, target_masks, num_masks),
+ "loss_dice": dice_loss(pred_masks, target_masks, num_masks),
+ }
+ return losses
+
+ def _get_predictions_permutation_indices(self, indices):
+ # permute predictions following indices
+ batch_indices = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
+ predictions_indices = torch.cat([src for (src, _) in indices])
+ return batch_indices, predictions_indices
+
+ def _get_targets_permutation_indices(self, indices):
+ # permute labels following indices
+ batch_indices = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
+ target_indices = torch.cat([tgt for (_, tgt) in indices])
+ return batch_indices, target_indices
+
+ def forward(
+ self,
+ masks_queries_logits: Tensor,
+ class_queries_logits: Tensor,
+ mask_labels: list[Tensor],
+ class_labels: list[Tensor],
+ auxiliary_predictions: Optional[dict[str, Tensor]] = None,
+ ) -> dict[str, Tensor]:
+ """
+ This performs the loss computation.
+
+ Args:
+ masks_queries_logits (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, height, width`
+ class_queries_logits (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, num_labels`
+ mask_labels (`torch.Tensor`):
+ List of mask labels of shape `(labels, height, width)`.
+ class_labels (`list[torch.Tensor]`):
+ List of class labels of shape `(labels)`.
+ auxiliary_predictions (`dict[str, torch.Tensor]`, *optional*):
+ if `use_auxiliary_loss` was set to `true` in [`MaskFormerConfig`], then it contains the logits from the
+ inner layers of the Detr's Decoder.
+
+ Returns:
+ `dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys:
+ - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels.
+ - **loss_mask** -- The loss computed using sigmoid focal loss on the predicted and ground truth masks.
+ - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth
+ masks.
+ if `use_auxiliary_loss` was set to `true` in [`MaskFormerConfig`], the dictionary contains additional losses
+ for each auxiliary predictions.
+ """
+
+ # retrieve the matching between the outputs of the last layer and the labels
+ indices = self.matcher(masks_queries_logits, class_queries_logits, mask_labels, class_labels)
+ # compute the average number of target masks for normalization purposes
+ num_masks: Number = self.get_num_masks(class_labels, device=class_labels[0].device)
+ # get all the losses
+ losses: dict[str, Tensor] = {
+ **self.loss_masks(masks_queries_logits, mask_labels, indices, num_masks),
+ **self.loss_labels(class_queries_logits, class_labels, indices),
+ }
+ # in case of auxiliary losses, we repeat this process with the output of each intermediate layer.
+ if auxiliary_predictions is not None:
+ for idx, aux_outputs in enumerate(auxiliary_predictions):
+ masks_queries_logits = aux_outputs["masks_queries_logits"]
+ class_queries_logits = aux_outputs["class_queries_logits"]
+ loss_dict = self.forward(masks_queries_logits, class_queries_logits, mask_labels, class_labels)
+ loss_dict = {f"{key}_{idx}": value for key, value in loss_dict.items()}
+ losses.update(loss_dict)
+
+ return losses
+
+ def get_num_masks(self, class_labels: torch.Tensor, device: torch.device) -> torch.Tensor:
+ """
+ Computes the average number of target masks across the batch, for normalization purposes.
+ """
+ num_masks = sum([len(classes) for classes in class_labels])
+ num_masks = torch.as_tensor(num_masks, dtype=torch.float, device=device)
+ world_size = 1
+ if is_accelerate_available():
+ if PartialState._shared_state != {}:
+ num_masks = reduce(num_masks)
+ world_size = PartialState().num_processes
+
+ num_masks = torch.clamp(num_masks / world_size, min=1)
+ return num_masks
+
+
+class MaskFormerFPNConvLayer(nn.Module):
+ def __init__(self, in_features: int, out_features: int, kernel_size: int = 3, padding: int = 1):
+ """
+ A basic module that executes conv - norm - in sequence used in MaskFormer.
+
+ Args:
+ in_features (`int`):
+ The number of input features (channels).
+ out_features (`int`):
+ The number of outputs features (channels).
+ """
+ super().__init__()
+ self.layers = [
+ nn.Conv2d(in_features, out_features, kernel_size=kernel_size, padding=padding, bias=False),
+ nn.GroupNorm(32, out_features),
+ nn.ReLU(inplace=True),
+ ]
+ for i, layer in enumerate(self.layers):
+ # Provide backwards compatibility from when the class inherited from nn.Sequential
+ # In nn.Sequential subclasses, the name given to the layer is its index in the sequence.
+ # In nn.Module subclasses they derived from the instance attribute they are assigned to e.g.
+ # self.my_layer_name = Layer()
+ # We can't give instance attributes integer names i.e. self.0 is not permitted and so need to register
+ # explicitly
+ self.add_module(str(i), layer)
+
+ def forward(self, input: Tensor) -> Tensor:
+ hidden_state = input
+ for layer in self.layers:
+ hidden_state = layer(hidden_state)
+ return hidden_state
+
+
+class MaskFormerFPNLayer(nn.Module):
+ def __init__(self, in_features: int, lateral_features: int):
+ """
+ A Feature Pyramid Network Layer (FPN) layer. It creates a feature map by aggregating features from the previous
+ and backbone layer. Due to the spatial mismatch, the tensor coming from the previous layer is upsampled.
+
+ Args:
+ in_features (`int`):
+ The number of input features (channels).
+ lateral_features (`int`):
+ The number of lateral features (channels).
+ """
+ super().__init__()
+ self.proj = nn.Sequential(
+ nn.Conv2d(lateral_features, in_features, kernel_size=1, padding=0, bias=False),
+ nn.GroupNorm(32, in_features),
+ )
+
+ self.block = MaskFormerFPNConvLayer(in_features, in_features)
+
+ def forward(self, down: Tensor, left: Tensor) -> Tensor:
+ left = self.proj(left)
+ down = nn.functional.interpolate(down, size=left.shape[-2:], mode="nearest")
+ down += left
+ down = self.block(down)
+ return down
+
+
+class MaskFormerFPNModel(nn.Module):
+ def __init__(self, in_features: int, lateral_widths: list[int], feature_size: int = 256):
+ """
+ Feature Pyramid Network, given an input tensor and a set of feature map of different feature/spatial size, it
+ creates a list of feature maps with the same feature size.
+
+ Args:
+ in_features (`int`):
+ The number of input features (channels).
+ lateral_widths (`list[int]`):
+ A list with the features (channels) size of each lateral connection.
+ feature_size (int, *optional*, defaults to 256):
+ The features (channels) of the resulting feature maps.
+ """
+ super().__init__()
+ self.stem = MaskFormerFPNConvLayer(in_features, feature_size)
+ self.layers = nn.Sequential(
+ *[MaskFormerFPNLayer(feature_size, lateral_width) for lateral_width in lateral_widths[::-1]]
+ )
+
+ def forward(self, features: list[Tensor]) -> list[Tensor]:
+ fpn_features = []
+ last_feature = features[-1]
+ other_features = features[:-1]
+ output = self.stem(last_feature)
+ for layer, left in zip(self.layers, other_features[::-1]):
+ output = layer(output, left)
+ fpn_features.append(output)
+ return fpn_features
+
+
+class MaskFormerPixelDecoder(nn.Module):
+ def __init__(self, *args, feature_size: int = 256, mask_feature_size: int = 256, **kwargs):
+ r"""
+ Pixel Decoder Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
+ Segmentation](https://huggingface.co/papers/2107.06278). It first runs the backbone's features into a Feature Pyramid
+ Network creating a list of feature maps. Then, it projects the last one to the correct `mask_size`.
+
+ Args:
+ feature_size (`int`, *optional*, defaults to 256):
+ The feature size (channel dimension) of the FPN feature maps.
+ mask_feature_size (`int`, *optional*, defaults to 256):
+ The features (channels) of the target masks size \\(C_{\epsilon}\\) in the paper.
+ """
+ super().__init__()
+
+ self.fpn = MaskFormerFPNModel(*args, feature_size=feature_size, **kwargs)
+ self.mask_projection = nn.Conv2d(feature_size, mask_feature_size, kernel_size=3, padding=1)
+
+ def forward(
+ self, features: list[Tensor], output_hidden_states: bool = False, return_dict: bool = True
+ ) -> MaskFormerPixelDecoderOutput:
+ fpn_features = self.fpn(features)
+ # we use the last feature map
+ last_feature_projected = self.mask_projection(fpn_features[-1])
+
+ if not return_dict:
+ return (last_feature_projected, tuple(fpn_features)) if output_hidden_states else (last_feature_projected,)
+
+ return MaskFormerPixelDecoderOutput(
+ last_hidden_state=last_feature_projected, hidden_states=tuple(fpn_features) if output_hidden_states else ()
+ )
+
+
+# copied and adapted from original implementation, also practically equal to DetrSinePositionEmbedding
+class MaskFormerSinePositionEmbedding(nn.Module):
+ """
+ This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
+ need paper, generalized to work on images.
+ """
+
+ def __init__(
+ self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: Optional[float] = None
+ ):
+ super().__init__()
+ if scale is not None and normalize is False:
+ raise ValueError("normalize should be True if scale is passed")
+ self.num_pos_feats = num_pos_feats
+ self.temperature = temperature
+ self.normalize = normalize
+ self.scale = 2 * math.pi if scale is None else scale
+
+ @compile_compatible_method_lru_cache(maxsize=1)
+ def forward(
+ self,
+ shape: torch.Size,
+ device: Union[torch.device, str],
+ dtype: torch.dtype,
+ mask: Optional[Tensor] = None,
+ ) -> Tensor:
+ if mask is None:
+ mask = torch.zeros((shape[0], shape[2], shape[3]), device=device, dtype=torch.bool)
+ not_mask = (~mask).to(dtype)
+ y_embed = not_mask.cumsum(1)
+ x_embed = not_mask.cumsum(2)
+ if self.normalize:
+ eps = 1e-6
+ y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+ x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+ dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=device).to(dtype)
+ dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats)
+
+ pos_x = x_embed[:, :, :, None] / dim_t
+ pos_y = y_embed[:, :, :, None] / dim_t
+ pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
+ pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
+ pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+ return pos
+
+
+class PredictionBlock(nn.Module):
+ def __init__(self, in_dim: int, out_dim: int, activation: nn.Module) -> None:
+ super().__init__()
+ self.layers = [nn.Linear(in_dim, out_dim), activation]
+ # Maintain submodule indexing as if part of a Sequential block
+ for i, layer in enumerate(self.layers):
+ self.add_module(str(i), layer)
+
+ def forward(self, input: Tensor) -> Tensor:
+ hidden_state = input
+ for layer in self.layers:
+ hidden_state = layer(hidden_state)
+ return hidden_state
+
+
+class MaskformerMLPPredictionHead(nn.Module):
+ def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3):
+ """
+ A classic Multi Layer Perceptron (MLP).
+
+ Args:
+ input_dim (`int`):
+ The input dimensions.
+ hidden_dim (`int`):
+ The hidden dimensions.
+ output_dim (`int`):
+ The output dimensions.
+ num_layers (int, *optional*, defaults to 3):
+ The number of layers.
+ """
+ super().__init__()
+ in_dims = [input_dim] + [hidden_dim] * (num_layers - 1)
+ out_dims = [hidden_dim] * (num_layers - 1) + [output_dim]
+
+ self.layers = []
+ for i, (in_dim, out_dim) in enumerate(zip(in_dims, out_dims)):
+ activation = nn.ReLU() if i < num_layers - 1 else nn.Identity()
+ layer = PredictionBlock(in_dim, out_dim, activation=activation)
+ self.layers.append(layer)
+ # Provide backwards compatibility from when the class inherited from nn.Sequential
+ # In nn.Sequential subclasses, the name given to the layer is its index in the sequence.
+ # In nn.Module subclasses they derived from the instance attribute they are assigned to e.g.
+ # self.my_layer_name = Layer()
+ # We can't give instance attributes integer names i.e. self.0 is not permitted and so need to register
+ # explicitly
+ self.add_module(str(i), layer)
+
+ def forward(self, input: Tensor) -> Tensor:
+ hidden_state = input
+ for layer in self.layers:
+ hidden_state = layer(hidden_state)
+ return hidden_state
+
+
+class MaskFormerPixelLevelModule(nn.Module):
+ def __init__(self, config: MaskFormerConfig):
+ """
+ Pixel Level Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
+ Segmentation](https://huggingface.co/papers/2107.06278). It runs the input image through a backbone and a pixel
+ decoder, generating an image feature map and pixel embeddings.
+
+ Args:
+ config ([`MaskFormerConfig`]):
+ The configuration used to instantiate this model.
+ """
+ super().__init__()
+ if getattr(config, "backbone_config") is not None and config.backbone_config.model_type == "swin":
+ # for backwards compatibility
+ backbone_config = config.backbone_config
+ backbone_config = MaskFormerSwinConfig.from_dict(backbone_config.to_dict())
+ backbone_config.out_features = ["stage1", "stage2", "stage3", "stage4"]
+ config.backbone_config = backbone_config
+ self.encoder = load_backbone(config)
+
+ feature_channels = self.encoder.channels
+ self.decoder = MaskFormerPixelDecoder(
+ in_features=feature_channels[-1],
+ feature_size=config.fpn_feature_size,
+ mask_feature_size=config.mask_feature_size,
+ lateral_widths=feature_channels[:-1],
+ )
+
+ def forward(
+ self, pixel_values: Tensor, output_hidden_states: bool = False, return_dict: bool = True
+ ) -> MaskFormerPixelLevelModuleOutput:
+ features = self.encoder(pixel_values).feature_maps
+ decoder_output = self.decoder(features, output_hidden_states, return_dict=return_dict)
+
+ if not return_dict:
+ last_hidden_state = decoder_output[0]
+ outputs = (features[-1], last_hidden_state)
+ if output_hidden_states:
+ hidden_states = decoder_output[1]
+ outputs = outputs + (tuple(features),) + (hidden_states,)
+ return outputs
+
+ return MaskFormerPixelLevelModuleOutput(
+ # the last feature is actually the output from the last layer
+ encoder_last_hidden_state=features[-1],
+ decoder_last_hidden_state=decoder_output.last_hidden_state,
+ encoder_hidden_states=tuple(features) if output_hidden_states else (),
+ decoder_hidden_states=decoder_output.hidden_states if output_hidden_states else (),
+ )
+
+
+class MaskFormerTransformerModule(nn.Module):
+ """
+ The MaskFormer's transformer module.
+ """
+
+ def __init__(self, in_features: int, config: MaskFormerConfig):
+ super().__init__()
+ hidden_size = config.decoder_config.hidden_size
+ should_project = in_features != hidden_size
+ self.position_embedder = MaskFormerSinePositionEmbedding(num_pos_feats=hidden_size // 2, normalize=True)
+ self.queries_embedder = nn.Embedding(config.decoder_config.num_queries, hidden_size)
+ self.input_projection = nn.Conv2d(in_features, hidden_size, kernel_size=1) if should_project else None
+ self.decoder = DetrDecoder(config=config.decoder_config)
+
+ def forward(
+ self,
+ image_features: Tensor,
+ output_hidden_states: bool = False,
+ output_attentions: bool = False,
+ return_dict: Optional[bool] = None,
+ ) -> DetrDecoderOutput:
+ if self.input_projection is not None:
+ image_features = self.input_projection(image_features)
+ object_queries = self.position_embedder(image_features.shape, image_features.device, image_features.dtype)
+ # repeat the queries "q c -> b q c"
+ batch_size = image_features.shape[0]
+ queries_embeddings = self.queries_embedder.weight.unsqueeze(0).repeat(batch_size, 1, 1)
+ inputs_embeds = torch.zeros_like(queries_embeddings, requires_grad=self.training)
+
+ # torch.export.export does no support requires_grad
+ if self.training:
+ inputs_embeds.requires_grad_(True)
+
+ batch_size, num_channels, height, width = image_features.shape
+ # rearrange both image_features and object_queries "b c h w -> b (h w) c"
+ image_features = image_features.view(batch_size, num_channels, height * width).permute(0, 2, 1)
+ object_queries = object_queries.view(batch_size, num_channels, height * width).permute(0, 2, 1)
+
+ decoder_output: DetrDecoderOutput = self.decoder(
+ inputs_embeds=inputs_embeds,
+ attention_mask=None,
+ encoder_hidden_states=image_features,
+ encoder_attention_mask=None,
+ object_queries=object_queries,
+ query_position_embeddings=queries_embeddings,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ return decoder_output
+
+
+@auto_docstring
+class MaskFormerPreTrainedModel(PreTrainedModel):
+ config: MaskFormerConfig
+ base_model_prefix = "model"
+ main_input_name = "pixel_values"
+
+ def _init_weights(self, module: nn.Module):
+ xavier_std = self.config.init_xavier_std
+ std = self.config.init_std
+ if isinstance(module, MaskFormerTransformerModule):
+ if module.input_projection is not None:
+ nn.init.xavier_uniform_(module.input_projection.weight, gain=xavier_std)
+ nn.init.constant_(module.input_projection.bias, 0)
+ # FPN
+ elif isinstance(module, MaskFormerFPNModel):
+ nn.init.xavier_uniform_(module.stem.get_submodule("0").weight, gain=xavier_std)
+
+ elif isinstance(module, MaskFormerFPNLayer):
+ nn.init.xavier_uniform_(module.proj[0].weight, gain=xavier_std)
+
+ elif isinstance(module, MaskFormerFPNConvLayer):
+ nn.init.xavier_uniform_(module.get_submodule("0").weight, gain=xavier_std)
+ # The MLP head
+ elif isinstance(module, MaskformerMLPPredictionHead):
+ # I was not able to find the correct initializer in the original implementation
+ # we'll use xavier
+ for submodule in module.modules():
+ if isinstance(submodule, nn.Linear):
+ nn.init.xavier_uniform_(submodule.weight, gain=xavier_std)
+ nn.init.constant_(submodule.bias, 0)
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ # copied from DETR
+ if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+
+@auto_docstring
+class MaskFormerModel(MaskFormerPreTrainedModel):
+ def __init__(self, config: MaskFormerConfig):
+ super().__init__(config)
+ self.pixel_level_module = MaskFormerPixelLevelModule(config)
+ self.transformer_module = MaskFormerTransformerModule(
+ in_features=self.pixel_level_module.encoder.channels[-1], config=config
+ )
+
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Tensor,
+ pixel_mask: Optional[Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> MaskFormerModelOutput:
+ r"""
+ Examples:
+
+ ```python
+ >>> from transformers import AutoImageProcessor, MaskFormerModel
+ >>> from PIL import Image
+ >>> import requests
+
+ >>> # load MaskFormer fine-tuned on ADE20k semantic segmentation
+ >>> image_processor = AutoImageProcessor.from_pretrained("facebook/maskformer-swin-base-ade")
+ >>> model = MaskFormerModel.from_pretrained("facebook/maskformer-swin-base-ade")
+
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> inputs = image_processor(image, return_tensors="pt")
+
+ >>> # forward pass
+ >>> outputs = model(**inputs)
+
+ >>> # the decoder of MaskFormer outputs hidden states of shape (batch_size, num_queries, hidden_size)
+ >>> transformer_decoder_last_hidden_state = outputs.transformer_decoder_last_hidden_state
+ >>> list(transformer_decoder_last_hidden_state.shape)
+ [1, 100, 256]
+ ```"""
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ batch_size, _, height, width = pixel_values.shape
+
+ if pixel_mask is None:
+ pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device)
+
+ pixel_level_module_output = self.pixel_level_module(
+ pixel_values, output_hidden_states, return_dict=return_dict
+ )
+ image_features = pixel_level_module_output[0]
+ pixel_embeddings = pixel_level_module_output[1]
+
+ transformer_module_output = self.transformer_module(image_features, output_hidden_states, output_attentions)
+ queries = transformer_module_output.last_hidden_state
+
+ encoder_hidden_states = None
+ pixel_decoder_hidden_states = None
+ transformer_decoder_hidden_states = None
+ hidden_states = None
+
+ if output_hidden_states:
+ encoder_hidden_states = pixel_level_module_output[2]
+ pixel_decoder_hidden_states = pixel_level_module_output[3]
+ transformer_decoder_hidden_states = transformer_module_output[1]
+ hidden_states = encoder_hidden_states + pixel_decoder_hidden_states + transformer_decoder_hidden_states
+
+ output = MaskFormerModelOutput(
+ encoder_last_hidden_state=image_features,
+ pixel_decoder_last_hidden_state=pixel_embeddings,
+ transformer_decoder_last_hidden_state=queries,
+ encoder_hidden_states=encoder_hidden_states,
+ pixel_decoder_hidden_states=pixel_decoder_hidden_states,
+ transformer_decoder_hidden_states=transformer_decoder_hidden_states,
+ hidden_states=hidden_states,
+ attentions=transformer_module_output.attentions,
+ )
+
+ if not return_dict:
+ output = tuple(v for v in output.values())
+
+ return output
+
+
+class MaskFormerForInstanceSegmentation(MaskFormerPreTrainedModel):
+ def __init__(self, config: MaskFormerConfig):
+ super().__init__(config)
+ self.model = MaskFormerModel(config)
+ hidden_size = config.decoder_config.hidden_size
+ # + 1 because we add the "null" class
+ self.class_predictor = nn.Linear(hidden_size, config.num_labels + 1)
+ self.mask_embedder = MaskformerMLPPredictionHead(hidden_size, hidden_size, config.mask_feature_size)
+
+ self.matcher = MaskFormerHungarianMatcher(
+ cost_class=1.0, cost_dice=config.dice_weight, cost_mask=config.mask_weight
+ )
+
+ self.weight_dict: dict[str, float] = {
+ "loss_cross_entropy": config.cross_entropy_weight,
+ "loss_mask": config.mask_weight,
+ "loss_dice": config.dice_weight,
+ }
+
+ self.criterion = MaskFormerLoss(
+ config.num_labels,
+ matcher=self.matcher,
+ weight_dict=self.weight_dict,
+ eos_coef=config.no_object_weight,
+ )
+
+ self.post_init()
+
+ def get_loss_dict(
+ self,
+ masks_queries_logits: Tensor,
+ class_queries_logits: Tensor,
+ mask_labels: Tensor,
+ class_labels: Tensor,
+ auxiliary_logits: dict[str, Tensor],
+ ) -> dict[str, Tensor]:
+ loss_dict: dict[str, Tensor] = self.criterion(
+ masks_queries_logits, class_queries_logits, mask_labels, class_labels, auxiliary_logits
+ )
+ # weight each loss by `self.weight_dict[]` including auxiliary losses
+ for key, weight in self.weight_dict.items():
+ for loss_key, loss in loss_dict.items():
+ if key in loss_key:
+ loss *= weight
+
+ return loss_dict
+
+ def get_loss(self, loss_dict: dict[str, Tensor]) -> Tensor:
+ return sum(loss_dict.values())
+
+ def get_logits(self, outputs: MaskFormerModelOutput) -> tuple[Tensor, Tensor, dict[str, Tensor]]:
+ pixel_embeddings = outputs.pixel_decoder_last_hidden_state
+ # get the auxiliary predictions (one for each decoder's layer)
+ auxiliary_logits: list[str, Tensor] = []
+
+ # This code is a little bit cumbersome, an improvement can be to return a list of predictions. If we have auxiliary loss then we are going to return more than one element in the list
+ if self.config.use_auxiliary_loss:
+ stacked_transformer_decoder_outputs = torch.stack(outputs.transformer_decoder_hidden_states)
+ classes = self.class_predictor(stacked_transformer_decoder_outputs)
+ class_queries_logits = classes[-1]
+ # get the masks
+ mask_embeddings = self.mask_embedder(stacked_transformer_decoder_outputs)
+ binaries_masks = torch.einsum("lbqc, bchw -> lbqhw", mask_embeddings, pixel_embeddings)
+
+ masks_queries_logits = binaries_masks[-1]
+ # go til [:-1] because the last one is always used
+ for aux_binary_masks, aux_classes in zip(binaries_masks[:-1], classes[:-1]):
+ auxiliary_logits.append(
+ {"masks_queries_logits": aux_binary_masks, "class_queries_logits": aux_classes}
+ )
+
+ else:
+ transformer_decoder_hidden_states = outputs.transformer_decoder_last_hidden_state
+ classes = self.class_predictor(transformer_decoder_hidden_states)
+ class_queries_logits = classes
+ # get the masks
+ mask_embeddings = self.mask_embedder(transformer_decoder_hidden_states)
+ # sum up over the channels
+ masks_queries_logits = torch.einsum("bqc, bchw -> bqhw", mask_embeddings, pixel_embeddings)
+
+ return class_queries_logits, masks_queries_logits, auxiliary_logits
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Tensor,
+ mask_labels: Optional[list[Tensor]] = None,
+ class_labels: Optional[list[Tensor]] = None,
+ pixel_mask: Optional[Tensor] = None,
+ output_auxiliary_logits: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> MaskFormerForInstanceSegmentationOutput:
+ r"""
+ mask_labels (`list[torch.Tensor]`, *optional*):
+ List of mask labels of shape `(num_labels, height, width)` to be fed to a model
+ class_labels (`list[torch.LongTensor]`, *optional*):
+ list of target class labels of shape `(num_labels, height, width)` to be fed to a model. They identify the
+ labels of `mask_labels`, e.g. the label of `mask_labels[i][j]` if `class_labels[i][j]`.
+ output_auxiliary_logits (`bool`, *optional*):
+ Whether or not to output auxiliary logits.
+
+ Examples:
+
+ Semantic segmentation example:
+
+ ```python
+ >>> from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation
+ >>> from PIL import Image
+ >>> import requests
+
+ >>> # load MaskFormer fine-tuned on ADE20k semantic segmentation
+ >>> image_processor = AutoImageProcessor.from_pretrained("facebook/maskformer-swin-base-ade")
+ >>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade")
+
+ >>> url = (
+ ... "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
+ ... )
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> inputs = image_processor(images=image, return_tensors="pt")
+
+ >>> outputs = model(**inputs)
+ >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)`
+ >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
+ >>> class_queries_logits = outputs.class_queries_logits
+ >>> masks_queries_logits = outputs.masks_queries_logits
+
+ >>> # you can pass them to image_processor for postprocessing
+ >>> predicted_semantic_map = image_processor.post_process_semantic_segmentation(
+ ... outputs, target_sizes=[(image.height, image.width)]
+ ... )[0]
+
+ >>> # we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
+ >>> list(predicted_semantic_map.shape)
+ [512, 683]
+ ```
+
+ Panoptic segmentation example:
+
+ ```python
+ >>> from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation
+ >>> from PIL import Image
+ >>> import requests
+
+ >>> # load MaskFormer fine-tuned on COCO panoptic segmentation
+ >>> image_processor = AutoImageProcessor.from_pretrained("facebook/maskformer-swin-base-coco")
+ >>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-coco")
+
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> inputs = image_processor(images=image, return_tensors="pt")
+
+ >>> outputs = model(**inputs)
+ >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)`
+ >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
+ >>> class_queries_logits = outputs.class_queries_logits
+ >>> masks_queries_logits = outputs.masks_queries_logits
+
+ >>> # you can pass them to image_processor for postprocessing
+ >>> result = image_processor.post_process_panoptic_segmentation(outputs, target_sizes=[(image.height, image.width)])[0]
+
+ >>> # we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
+ >>> predicted_panoptic_map = result["segmentation"]
+ >>> list(predicted_panoptic_map.shape)
+ [480, 640]
+ ```
+ """
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ raw_outputs = self.model(
+ pixel_values,
+ pixel_mask,
+ output_hidden_states=output_hidden_states or self.config.use_auxiliary_loss,
+ return_dict=return_dict,
+ output_attentions=output_attentions,
+ )
+ # We need to have raw_outputs optionally be returned as a dict to use torch.compile. For backwards
+ # compatibility we convert to a dataclass for the rest of the model logic
+ outputs = MaskFormerModelOutput(
+ encoder_last_hidden_state=raw_outputs[0],
+ pixel_decoder_last_hidden_state=raw_outputs[1],
+ transformer_decoder_last_hidden_state=raw_outputs[2],
+ encoder_hidden_states=raw_outputs[3] if output_hidden_states else None,
+ pixel_decoder_hidden_states=raw_outputs[4] if output_hidden_states else None,
+ transformer_decoder_hidden_states=raw_outputs[5] if output_hidden_states else None,
+ hidden_states=raw_outputs[6] if output_hidden_states else None,
+ attentions=raw_outputs[-1] if output_attentions else None,
+ )
+
+ loss, loss_dict, auxiliary_logits = None, None, None
+
+ class_queries_logits, masks_queries_logits, auxiliary_logits = self.get_logits(outputs)
+
+ if mask_labels is not None and class_labels is not None:
+ loss_dict: dict[str, Tensor] = self.get_loss_dict(
+ masks_queries_logits, class_queries_logits, mask_labels, class_labels, auxiliary_logits
+ )
+ loss = self.get_loss(loss_dict)
+
+ output_auxiliary_logits = (
+ self.config.output_auxiliary_logits if output_auxiliary_logits is None else output_auxiliary_logits
+ )
+ if not output_auxiliary_logits:
+ auxiliary_logits = None
+
+ if not return_dict:
+ output = tuple(
+ v
+ for v in (loss, class_queries_logits, masks_queries_logits, auxiliary_logits, *outputs.values())
+ if v is not None
+ )
+ return output
+
+ return MaskFormerForInstanceSegmentationOutput(
+ loss=loss,
+ **outputs,
+ class_queries_logits=class_queries_logits,
+ masks_queries_logits=masks_queries_logits,
+ auxiliary_logits=auxiliary_logits,
+ )
+
+
+__all__ = ["MaskFormerForInstanceSegmentation", "MaskFormerModel", "MaskFormerPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/modeling_maskformer_swin.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/modeling_maskformer_swin.py
new file mode 100644
index 0000000000000000000000000000000000000000..417345437145868fa664d620828fa141e6cba373
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/maskformer/modeling_maskformer_swin.py
@@ -0,0 +1,927 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""MaskFormer Swin Transformer. The reason Swin Transformer is implemented here is because MaskFormer uses the hidden
+states before downsampling, which is different from the default Swin Transformer."""
+
+import collections.abc
+import math
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+from torch import Tensor, nn
+
+from ...activations import ACT2FN
+from ...file_utils import ModelOutput
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BackboneOutput
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import find_pruneable_heads_and_indices, meshgrid, prune_linear_layer
+from ...utils import auto_docstring, torch_int
+from ...utils.backbone_utils import BackboneMixin
+from .configuration_maskformer_swin import MaskFormerSwinConfig
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Class for MaskFormerSwinModel's outputs that also contains the spatial dimensions of the hidden states.
+ """
+)
+class MaskFormerSwinModelOutputWithPooling(ModelOutput):
+ r"""
+ pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
+ Last layer hidden-state after a mean pooling operation.
+ hidden_states_spatial_dimensions (`tuple(tuple(int, int))`, *optional*):
+ A tuple containing the spatial dimension of each `hidden_state` needed to reshape the `hidden_states` to
+ `batch, channels, height, width`. Due to padding, their spatial size cannot be inferred before the
+ `forward` method.
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ pooler_output: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ hidden_states_spatial_dimensions: tuple[tuple[int, int]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Class for SwinEncoder's outputs.
+ """
+)
+class MaskFormerSwinBaseModelOutput(ModelOutput):
+ r"""
+ hidden_states_spatial_dimensions (`tuple(tuple(int, int))`, *optional*):
+ A tuple containing the spatial dimension of each `hidden_state` needed to reshape the `hidden_states` to
+ `batch, channels, height, width`. Due to padding, their spatial size cannot inferred before the `forward`
+ method.
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ hidden_states_spatial_dimensions: tuple[tuple[int, int]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+# Copied from transformers.models.swin.modeling_swin.window_partition
+def window_partition(input_feature, window_size):
+ """
+ Partitions the given input into windows.
+ """
+ batch_size, height, width, num_channels = input_feature.shape
+ input_feature = input_feature.view(
+ batch_size, height // window_size, window_size, width // window_size, window_size, num_channels
+ )
+ windows = input_feature.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, num_channels)
+ return windows
+
+
+# Copied from transformers.models.swin.modeling_swin.window_reverse
+def window_reverse(windows, window_size, height, width):
+ """
+ Merges windows to produce higher resolution features.
+ """
+ num_channels = windows.shape[-1]
+ windows = windows.view(-1, height // window_size, width // window_size, window_size, window_size, num_channels)
+ windows = windows.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, height, width, num_channels)
+ return windows
+
+
+# Copied from transformers.models.swin.modeling_swin.drop_path
+def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
+ """
+ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+ Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
+ however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+ See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
+ layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
+ argument.
+ """
+ if drop_prob == 0.0 or not training:
+ return input
+ keep_prob = 1 - drop_prob
+ shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
+ random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
+ random_tensor.floor_() # binarize
+ output = input.div(keep_prob) * random_tensor
+ return output
+
+
+class MaskFormerSwinEmbeddings(nn.Module):
+ """
+ Construct the patch and position embeddings.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+
+ self.patch_embeddings = MaskFormerSwinPatchEmbeddings(config)
+ num_patches = self.patch_embeddings.num_patches
+ self.patch_grid = self.patch_embeddings.grid_size
+
+ if config.use_absolute_embeddings:
+ self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.embed_dim))
+ else:
+ self.position_embeddings = None
+
+ self.norm = nn.LayerNorm(config.embed_dim)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.patch_size = config.patch_size
+
+ # Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
+ def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
+ """
+ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
+ images. This method is also adapted to support torch.jit tracing.
+
+ Adapted from:
+ - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
+ - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
+ """
+
+ num_patches = embeddings.shape[1] - 1
+ num_positions = self.position_embeddings.shape[1] - 1
+
+ # always interpolate when tracing to ensure the exported model works for dynamic input shapes
+ if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
+ return self.position_embeddings
+
+ class_pos_embed = self.position_embeddings[:, :1]
+ patch_pos_embed = self.position_embeddings[:, 1:]
+
+ dim = embeddings.shape[-1]
+
+ new_height = height // self.patch_size
+ new_width = width // self.patch_size
+
+ sqrt_num_positions = torch_int(num_positions**0.5)
+ patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
+ patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+ patch_pos_embed = nn.functional.interpolate(
+ patch_pos_embed,
+ size=(new_height, new_width),
+ mode="bicubic",
+ align_corners=False,
+ )
+
+ patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+
+ return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
+
+ def forward(self, pixel_values, interpolate_pos_encoding):
+ _, num_channels, height, width = pixel_values.shape
+ embeddings, output_dimensions = self.patch_embeddings(pixel_values)
+ embeddings = self.norm(embeddings)
+
+ if self.position_embeddings is not None:
+ if interpolate_pos_encoding:
+ embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+ else:
+ embeddings = embeddings + self.position_embeddings
+
+ embeddings = self.dropout(embeddings)
+
+ return embeddings, output_dimensions
+
+
+# Copied from transformers.models.swin.modeling_swin.SwinPatchEmbeddings with Swin->MaskFormerSwin
+class MaskFormerSwinPatchEmbeddings(nn.Module):
+ """
+ This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
+ `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
+ Transformer.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ image_size, patch_size = config.image_size, config.patch_size
+ num_channels, hidden_size = config.num_channels, config.embed_dim
+ image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
+ patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
+ num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+ self.image_size = image_size
+ self.patch_size = patch_size
+ self.num_channels = num_channels
+ self.num_patches = num_patches
+ self.grid_size = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
+
+ self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
+
+ def maybe_pad(self, pixel_values, height, width):
+ if width % self.patch_size[1] != 0:
+ pad_values = (0, self.patch_size[1] - width % self.patch_size[1])
+ pixel_values = nn.functional.pad(pixel_values, pad_values)
+ if height % self.patch_size[0] != 0:
+ pad_values = (0, 0, 0, self.patch_size[0] - height % self.patch_size[0])
+ pixel_values = nn.functional.pad(pixel_values, pad_values)
+ return pixel_values
+
+ def forward(self, pixel_values: Optional[torch.FloatTensor]) -> tuple[torch.Tensor, tuple[int]]:
+ _, num_channels, height, width = pixel_values.shape
+ # pad the input to be divisible by self.patch_size, if needed
+ pixel_values = self.maybe_pad(pixel_values, height, width)
+ embeddings = self.projection(pixel_values)
+ _, _, height, width = embeddings.shape
+ output_dimensions = (height, width)
+ embeddings = embeddings.flatten(2).transpose(1, 2)
+
+ return embeddings, output_dimensions
+
+
+# Copied from transformers.models.swin.modeling_swin.SwinPatchMerging
+class MaskFormerSwinPatchMerging(nn.Module):
+ """
+ Patch Merging Layer.
+
+ Args:
+ input_resolution (`tuple[int]`):
+ Resolution of input feature.
+ dim (`int`):
+ Number of input channels.
+ norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
+ Normalization layer class.
+ """
+
+ def __init__(self, input_resolution: tuple[int], dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None:
+ super().__init__()
+ self.input_resolution = input_resolution
+ self.dim = dim
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+ self.norm = norm_layer(4 * dim)
+
+ def maybe_pad(self, input_feature, height, width):
+ should_pad = (height % 2 == 1) or (width % 2 == 1)
+ if should_pad:
+ pad_values = (0, 0, 0, width % 2, 0, height % 2)
+ input_feature = nn.functional.pad(input_feature, pad_values)
+
+ return input_feature
+
+ def forward(self, input_feature: torch.Tensor, input_dimensions: tuple[int, int]) -> torch.Tensor:
+ height, width = input_dimensions
+ # `dim` is height * width
+ batch_size, dim, num_channels = input_feature.shape
+
+ input_feature = input_feature.view(batch_size, height, width, num_channels)
+ # pad input to be divisible by width and height, if needed
+ input_feature = self.maybe_pad(input_feature, height, width)
+ # [batch_size, height/2, width/2, num_channels]
+ input_feature_0 = input_feature[:, 0::2, 0::2, :]
+ # [batch_size, height/2, width/2, num_channels]
+ input_feature_1 = input_feature[:, 1::2, 0::2, :]
+ # [batch_size, height/2, width/2, num_channels]
+ input_feature_2 = input_feature[:, 0::2, 1::2, :]
+ # [batch_size, height/2, width/2, num_channels]
+ input_feature_3 = input_feature[:, 1::2, 1::2, :]
+ # batch_size height/2 width/2 4*num_channels
+ input_feature = torch.cat([input_feature_0, input_feature_1, input_feature_2, input_feature_3], -1)
+ input_feature = input_feature.view(batch_size, -1, 4 * num_channels) # batch_size height/2*width/2 4*C
+
+ input_feature = self.norm(input_feature)
+ input_feature = self.reduction(input_feature)
+
+ return input_feature
+
+
+# Copied from transformers.models.swin.modeling_swin.SwinDropPath with Swin->MaskFormerSwin
+class MaskFormerSwinDropPath(nn.Module):
+ """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+ def __init__(self, drop_prob: Optional[float] = None) -> None:
+ super().__init__()
+ self.drop_prob = drop_prob
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ return drop_path(hidden_states, self.drop_prob, self.training)
+
+ def extra_repr(self) -> str:
+ return f"p={self.drop_prob}"
+
+
+# Copied from transformers.models.swin.modeling_swin.SwinSelfAttention with Swin->MaskFormerSwin
+class MaskFormerSwinSelfAttention(nn.Module):
+ def __init__(self, config, dim, num_heads, window_size):
+ super().__init__()
+ if dim % num_heads != 0:
+ raise ValueError(
+ f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
+ )
+
+ self.num_attention_heads = num_heads
+ self.attention_head_size = int(dim / num_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+ self.window_size = (
+ window_size if isinstance(window_size, collections.abc.Iterable) else (window_size, window_size)
+ )
+
+ self.relative_position_bias_table = nn.Parameter(
+ torch.zeros((2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1), num_heads)
+ )
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(self.window_size[0])
+ coords_w = torch.arange(self.window_size[1])
+ coords = torch.stack(meshgrid([coords_h, coords_w], indexing="ij"))
+ coords_flatten = torch.flatten(coords, 1)
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous()
+ relative_coords[:, :, 0] += self.window_size[0] - 1
+ relative_coords[:, :, 1] += self.window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+ relative_position_index = relative_coords.sum(-1)
+ self.register_buffer("relative_position_index", relative_position_index)
+
+ self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
+ self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
+ self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor]:
+ batch_size, dim, num_channels = hidden_states.shape
+ hidden_shape = (batch_size, dim, -1, self.attention_head_size)
+
+ query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
+ key_layer = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
+ value_layer = self.value(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+
+ relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)]
+ relative_position_bias = relative_position_bias.view(
+ self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
+ )
+
+ relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
+ attention_scores = attention_scores + relative_position_bias.unsqueeze(0)
+
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in MaskFormerSwinModel forward() function)
+ mask_shape = attention_mask.shape[0]
+ attention_scores = attention_scores.view(
+ batch_size // mask_shape, mask_shape, self.num_attention_heads, dim, dim
+ )
+ attention_scores = attention_scores + attention_mask.unsqueeze(1).unsqueeze(0)
+ attention_scores = attention_scores.view(-1, self.num_attention_heads, dim, dim)
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(new_context_layer_shape)
+
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+ return outputs
+
+
+# Copied from transformers.models.swin.modeling_swin.SwinSelfOutput with Swin->MaskFormerSwin
+class MaskFormerSwinSelfOutput(nn.Module):
+ def __init__(self, config, dim):
+ super().__init__()
+ self.dense = nn.Linear(dim, dim)
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+
+ return hidden_states
+
+
+# Copied from transformers.models.swin.modeling_swin.SwinAttention with Swin->MaskFormerSwin
+class MaskFormerSwinAttention(nn.Module):
+ def __init__(self, config, dim, num_heads, window_size):
+ super().__init__()
+ self.self = MaskFormerSwinSelfAttention(config, dim, num_heads, window_size)
+ self.output = MaskFormerSwinSelfOutput(config, dim)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+ )
+
+ # Prune linear layers
+ self.self.query = prune_linear_layer(self.self.query, index)
+ self.self.key = prune_linear_layer(self.self.key, index)
+ self.self.value = prune_linear_layer(self.self.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+ self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor]:
+ self_outputs = self.self(hidden_states, attention_mask, head_mask, output_attentions)
+ attention_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.swin.modeling_swin.SwinIntermediate with Swin->MaskFormerSwin
+class MaskFormerSwinIntermediate(nn.Module):
+ def __init__(self, config, dim):
+ super().__init__()
+ self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.swin.modeling_swin.SwinOutput with Swin->MaskFormerSwin
+class MaskFormerSwinOutput(nn.Module):
+ def __init__(self, config, dim):
+ super().__init__()
+ self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ return hidden_states
+
+
+class MaskFormerSwinLayer(nn.Module):
+ def __init__(self, config, dim, input_resolution, num_heads, drop_path_rate=0.0, shift_size=0):
+ super().__init__()
+ self.shift_size = shift_size
+ self.window_size = config.window_size
+ self.input_resolution = input_resolution
+ self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
+ self.attention = MaskFormerSwinAttention(config, dim, num_heads, self.window_size)
+ self.drop_path = MaskFormerSwinDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
+ self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
+ self.intermediate = MaskFormerSwinIntermediate(config, dim)
+ self.output = MaskFormerSwinOutput(config, dim)
+
+ def get_attn_mask(self, input_resolution):
+ if self.shift_size > 0:
+ # calculate attention mask for SW-MSA
+ height, width = input_resolution
+ img_mask = torch.zeros((1, height, width, 1))
+ height_slices = (
+ slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None),
+ )
+ width_slices = (
+ slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None),
+ )
+ count = 0
+ for height_slice in height_slices:
+ for width_slice in width_slices:
+ img_mask[:, height_slice, width_slice, :] = count
+ count += 1
+
+ mask_windows = window_partition(img_mask, self.window_size)
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, -100.0).masked_fill(attn_mask == 0, 0.0)
+ else:
+ attn_mask = None
+ return attn_mask
+
+ def maybe_pad(self, hidden_states, height, width):
+ pad_left = pad_top = 0
+ pad_rigth = (self.window_size - width % self.window_size) % self.window_size
+ pad_bottom = (self.window_size - height % self.window_size) % self.window_size
+ pad_values = (0, 0, pad_left, pad_rigth, pad_top, pad_bottom)
+ hidden_states = nn.functional.pad(hidden_states, pad_values)
+ return hidden_states, pad_values
+
+ def forward(self, hidden_states, input_dimensions, head_mask=None, output_attentions=False):
+ height, width = input_dimensions
+ batch_size, dim, channels = hidden_states.size()
+ shortcut = hidden_states
+
+ hidden_states = self.layernorm_before(hidden_states)
+ hidden_states = hidden_states.view(batch_size, height, width, channels)
+ # pad hidden_states to multiples of window size
+ hidden_states, pad_values = self.maybe_pad(hidden_states, height, width)
+
+ _, height_pad, width_pad, _ = hidden_states.shape
+ # cyclic shift
+ if self.shift_size > 0:
+ shifted_hidden_states = torch.roll(hidden_states, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+ else:
+ shifted_hidden_states = hidden_states
+
+ # partition windows
+ hidden_states_windows = window_partition(shifted_hidden_states, self.window_size)
+ hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels)
+ attn_mask = self.get_attn_mask((height_pad, width_pad))
+ if attn_mask is not None:
+ attn_mask = attn_mask.to(hidden_states_windows.device)
+
+ self_attention_outputs = self.attention(
+ hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions
+ )
+
+ attention_output = self_attention_outputs[0]
+
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ attention_windows = attention_output.view(-1, self.window_size, self.window_size, channels)
+ shifted_windows = window_reverse(
+ attention_windows, self.window_size, height_pad, width_pad
+ ) # B height' width' C
+
+ # reverse cyclic shift
+ if self.shift_size > 0:
+ attention_windows = torch.roll(shifted_windows, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+ else:
+ attention_windows = shifted_windows
+
+ was_padded = pad_values[3] > 0 or pad_values[5] > 0
+ if was_padded:
+ attention_windows = attention_windows[:, :height, :width, :].contiguous()
+
+ attention_windows = attention_windows.view(batch_size, height * width, channels)
+
+ hidden_states = shortcut + self.drop_path(attention_windows)
+
+ layer_output = self.layernorm_after(hidden_states)
+ layer_output = self.intermediate(layer_output)
+ layer_output = hidden_states + self.output(layer_output)
+
+ outputs = (layer_output,) + outputs
+
+ return outputs
+
+
+class MaskFormerSwinStage(GradientCheckpointingLayer):
+ # Copied from transformers.models.swin.modeling_swin.SwinStage.__init__ with Swin->MaskFormerSwin
+ def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, downsample):
+ super().__init__()
+ self.config = config
+ self.dim = dim
+ self.blocks = nn.ModuleList(
+ [
+ MaskFormerSwinLayer(
+ config=config,
+ dim=dim,
+ input_resolution=input_resolution,
+ num_heads=num_heads,
+ drop_path_rate=drop_path[i],
+ shift_size=0 if (i % 2 == 0) else config.window_size // 2,
+ )
+ for i in range(depth)
+ ]
+ )
+
+ # patch merging layer
+ if downsample is not None:
+ self.downsample = downsample(input_resolution, dim=dim, norm_layer=nn.LayerNorm)
+ else:
+ self.downsample = None
+
+ self.pointing = False
+
+ def forward(
+ self, hidden_states, input_dimensions, head_mask=None, output_attentions=False, output_hidden_states=False
+ ):
+ all_hidden_states = () if output_hidden_states else None
+
+ height, width = input_dimensions
+ for i, block_module in enumerate(self.blocks):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+
+ block_hidden_states = block_module(hidden_states, input_dimensions, layer_head_mask, output_attentions)
+
+ hidden_states = block_hidden_states[0]
+
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if self.downsample is not None:
+ height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
+ output_dimensions = (height, width, height_downsampled, width_downsampled)
+ hidden_states = self.downsample(hidden_states, input_dimensions)
+ else:
+ output_dimensions = (height, width, height, width)
+
+ return hidden_states, output_dimensions, all_hidden_states
+
+
+class MaskFormerSwinEncoder(nn.Module):
+ # Copied from transformers.models.swin.modeling_swin.SwinEncoder.__init__ with Swin->MaskFormerSwin
+ def __init__(self, config, grid_size):
+ super().__init__()
+ self.num_layers = len(config.depths)
+ self.config = config
+ dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths), device="cpu")]
+ self.layers = nn.ModuleList(
+ [
+ MaskFormerSwinStage(
+ config=config,
+ dim=int(config.embed_dim * 2**i_layer),
+ input_resolution=(grid_size[0] // (2**i_layer), grid_size[1] // (2**i_layer)),
+ depth=config.depths[i_layer],
+ num_heads=config.num_heads[i_layer],
+ drop_path=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
+ downsample=MaskFormerSwinPatchMerging if (i_layer < self.num_layers - 1) else None,
+ )
+ for i_layer in range(self.num_layers)
+ ]
+ )
+
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states,
+ input_dimensions,
+ head_mask=None,
+ output_attentions=False,
+ output_hidden_states=False,
+ return_dict=True,
+ ):
+ all_hidden_states = () if output_hidden_states else None
+ all_input_dimensions = ()
+ all_self_attentions = () if output_attentions else None
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ for i, layer_module in enumerate(self.layers):
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+
+ layer_hidden_states, output_dimensions, layer_all_hidden_states = layer_module(
+ hidden_states,
+ input_dimensions,
+ layer_head_mask,
+ output_attentions,
+ output_hidden_states,
+ )
+
+ input_dimensions = (output_dimensions[-2], output_dimensions[-1])
+ all_input_dimensions += (input_dimensions,)
+ if output_hidden_states:
+ all_hidden_states += (layer_all_hidden_states,)
+
+ hidden_states = layer_hidden_states
+
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_all_hidden_states[1],)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+
+ return MaskFormerSwinBaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ hidden_states_spatial_dimensions=all_input_dimensions,
+ attentions=all_self_attentions,
+ )
+
+
+@auto_docstring
+class MaskFormerSwinPreTrainedModel(PreTrainedModel):
+ config: MaskFormerSwinConfig
+ base_model_prefix = "model"
+ main_input_name = "pixel_values"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MaskFormerSwinStage"]
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, MaskFormerSwinEmbeddings):
+ if module.position_embeddings is not None:
+ module.position_embeddings.data.zero_()
+ elif isinstance(module, MaskFormerSwinSelfAttention):
+ module.relative_position_bias_table.data.zero_()
+
+
+class MaskFormerSwinModel(MaskFormerSwinPreTrainedModel):
+ def __init__(self, config, add_pooling_layer=True):
+ super().__init__(config)
+ self.config = config
+ self.num_layers = len(config.depths)
+ self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1))
+
+ self.embeddings = MaskFormerSwinEmbeddings(config)
+ self.encoder = MaskFormerSwinEncoder(config, self.embeddings.patch_grid)
+
+ self.layernorm = nn.LayerNorm(self.num_features, eps=config.layer_norm_eps)
+ self.pooler = nn.AdaptiveAvgPool1d(1) if add_pooling_layer else None
+
+ def get_input_embeddings(self):
+ return self.embeddings.patch_embeddings
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ def forward(
+ self,
+ pixel_values=None,
+ head_mask=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ interpolate_pos_encoding=False,
+ return_dict=None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, len(self.config.depths))
+
+ embedding_output, input_dimensions = self.embeddings(
+ pixel_values, interpolate_pos_encoding=interpolate_pos_encoding
+ )
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ input_dimensions,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = encoder_outputs.last_hidden_state if return_dict else encoder_outputs[0]
+ sequence_output = self.layernorm(sequence_output)
+
+ pooled_output = None
+ if self.pooler is not None:
+ pooled_output = self.pooler(sequence_output.transpose(1, 2))
+ pooled_output = torch.flatten(pooled_output, 1)
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ hidden_states_spatial_dimensions = (input_dimensions,) + encoder_outputs.hidden_states_spatial_dimensions
+
+ return MaskFormerSwinModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ hidden_states_spatial_dimensions=hidden_states_spatial_dimensions,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+class MaskFormerSwinBackbone(MaskFormerSwinPreTrainedModel, BackboneMixin):
+ """
+ MaskFormerSwin backbone, designed especially for the MaskFormer framework.
+
+ This classes reshapes `hidden_states` from (`batch_size, sequence_length, hidden_size)` to (`batch_size,
+ num_channels, height, width)`). It also adds additional layernorms after each stage.
+
+ Args:
+ config (`MaskFormerSwinConfig`):
+ The configuration used by [`MaskFormerSwinModel`].
+ """
+
+ def __init__(self, config: MaskFormerSwinConfig):
+ super().__init__(config)
+ super()._init_backbone(config)
+
+ self.model = MaskFormerSwinModel(config)
+ if "stem" in self.out_features:
+ raise ValueError("This backbone does not support 'stem' in the `out_features`.")
+ self.num_features = [config.embed_dim] + [int(config.embed_dim * 2**i) for i in range(len(config.depths))]
+ self.hidden_states_norms = nn.ModuleList(
+ [nn.LayerNorm(num_channels) for num_channels in self.num_features[1:]]
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ pixel_values: Tensor,
+ output_hidden_states: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> BackboneOutput:
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+ outputs = self.model(
+ pixel_values, output_hidden_states=True, output_attentions=output_attentions, return_dict=True
+ )
+
+ # we skip the stem
+ hidden_states = outputs.hidden_states[1:]
+
+ # we need to reshape the hidden states to their original spatial dimensions
+ # spatial dimensions contains all the heights and widths of each stage, including after the embeddings
+ spatial_dimensions: tuple[tuple[int, int]] = outputs.hidden_states_spatial_dimensions
+ feature_maps = ()
+ for i, (hidden_state, stage, (height, width)) in enumerate(
+ zip(hidden_states, self.stage_names[1:], spatial_dimensions)
+ ):
+ norm = self.hidden_states_norms[i]
+ # the last element correspond to the layer's last block output but before patch merging
+ hidden_state_unpolled = hidden_state[-1]
+ hidden_state_norm = norm(hidden_state_unpolled)
+ # the pixel decoder (FPN) expects 3D tensors (features)
+ batch_size, _, hidden_size = hidden_state_norm.shape
+ # reshape "b (h w) d -> b d h w"
+ hidden_state_permuted = (
+ hidden_state_norm.permute(0, 2, 1).view((batch_size, hidden_size, height, width)).contiguous()
+ )
+ if stage in self.out_features:
+ feature_maps += (hidden_state_permuted,)
+
+ if not return_dict:
+ output = (feature_maps,)
+ if output_hidden_states:
+ output += (outputs.hidden_states,)
+ if output_attentions:
+ output += (outputs.attentions,)
+ return output
+
+ return BackboneOutput(
+ feature_maps=feature_maps,
+ hidden_states=outputs.hidden_states if output_hidden_states else None,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = ["MaskFormerSwinBackbone", "MaskFormerSwinModel", "MaskFormerSwinPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3c5ef5767a450f2f0ce02eed8d7ae86a13b3753
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/__init__.py
@@ -0,0 +1,31 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mbart import *
+ from .modeling_flax_mbart import *
+ from .modeling_mbart import *
+ from .modeling_tf_mbart import *
+ from .tokenization_mbart import *
+ from .tokenization_mbart_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/configuration_mbart.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/configuration_mbart.py
new file mode 100644
index 0000000000000000000000000000000000000000..104e7e00d9e5afeef198fdd40836a79ee38afb64
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/configuration_mbart.py
@@ -0,0 +1,391 @@
+# coding=utf-8
+# Copyright 2021, The Facebook AI Research Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MBART model configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+from typing import Any, Optional
+
+from ... import PreTrainedTokenizer
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
+from ...onnx.utils import compute_effective_axis_dimension
+from ...utils import TensorType, is_torch_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MBartConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MBartModel`]. It is used to instantiate an MBART
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the MBART
+ [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50265):
+ Vocabulary size of the MBART model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MBartModel`] or [`TFMBartModel`].
+ d_model (`int`, *optional*, defaults to 1024):
+ Dimensionality of the layers and the pooler layer.
+ encoder_layers (`int`, *optional*, defaults to 12):
+ Number of encoder layers.
+ decoder_layers (`int`, *optional*, defaults to 12):
+ Number of decoder layers.
+ encoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ decoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ decoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ encoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ classifier_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for classifier.
+ max_position_embeddings (`int`, *optional*, defaults to 1024):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ encoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ decoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ scale_embedding (`bool`, *optional*, defaults to `False`):
+ Scale embeddings by diving by sqrt(d_model).
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models)
+ forced_eos_token_id (`int`, *optional*, defaults to 2):
+ The id of the token to force as the last generated token when `max_length` is reached. Usually set to
+ `eos_token_id`.
+
+ Example:
+
+ ```python
+ >>> from transformers import MBartConfig, MBartModel
+
+ >>> # Initializing a MBART facebook/mbart-large-cc25 style configuration
+ >>> configuration = MBartConfig()
+
+ >>> # Initializing a model (with random weights) from the facebook/mbart-large-cc25 style configuration
+ >>> model = MBartModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mbart"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
+
+ def __init__(
+ self,
+ vocab_size=50265,
+ max_position_embeddings=1024,
+ encoder_layers=12,
+ encoder_ffn_dim=4096,
+ encoder_attention_heads=16,
+ decoder_layers=12,
+ decoder_ffn_dim=4096,
+ decoder_attention_heads=16,
+ encoder_layerdrop=0.0,
+ decoder_layerdrop=0.0,
+ use_cache=True,
+ is_encoder_decoder=True,
+ activation_function="gelu",
+ d_model=1024,
+ dropout=0.1,
+ attention_dropout=0.0,
+ activation_dropout=0.0,
+ init_std=0.02,
+ classifier_dropout=0.0,
+ scale_embedding=False,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ forced_eos_token_id=2,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.d_model = d_model
+ self.encoder_ffn_dim = encoder_ffn_dim
+ self.encoder_layers = encoder_layers
+ self.encoder_attention_heads = encoder_attention_heads
+ self.decoder_ffn_dim = decoder_ffn_dim
+ self.decoder_layers = decoder_layers
+ self.decoder_attention_heads = decoder_attention_heads
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.activation_function = activation_function
+ self.init_std = init_std
+ self.encoder_layerdrop = encoder_layerdrop
+ self.decoder_layerdrop = decoder_layerdrop
+ self.classifier_dropout = classifier_dropout
+ self.use_cache = use_cache
+ self.num_hidden_layers = encoder_layers
+ self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ is_encoder_decoder=is_encoder_decoder,
+ forced_eos_token_id=forced_eos_token_id,
+ **kwargs,
+ )
+
+
+# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig with Bart->MBart
+class MBartOnnxConfig(OnnxSeq2SeqConfigWithPast):
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ if self.task in ["default", "seq2seq-lm"]:
+ common_inputs = OrderedDict(
+ [
+ ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+ ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+ ]
+ )
+
+ if self.use_past:
+ common_inputs["decoder_input_ids"] = {0: "batch"}
+ common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
+ else:
+ common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
+ common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
+
+ if self.use_past:
+ self.fill_with_past_key_values_(common_inputs, direction="inputs")
+ elif self.task == "causal-lm":
+ # TODO: figure this case out.
+ common_inputs = OrderedDict(
+ [
+ ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+ ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+ ]
+ )
+ if self.use_past:
+ num_encoder_layers, _ = self.num_layers
+ for i in range(num_encoder_layers):
+ common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
+ common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
+ else:
+ common_inputs = OrderedDict(
+ [
+ ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+ ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+ ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
+ ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
+ ]
+ )
+
+ return common_inputs
+
+ @property
+ def outputs(self) -> Mapping[str, Mapping[int, str]]:
+ if self.task in ["default", "seq2seq-lm"]:
+ common_outputs = super().outputs
+ else:
+ common_outputs = super(OnnxConfigWithPast, self).outputs
+ if self.use_past:
+ num_encoder_layers, _ = self.num_layers
+ for i in range(num_encoder_layers):
+ common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
+ common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
+ return common_outputs
+
+ def _generate_dummy_inputs_for_default_and_seq2seq_lm(
+ self,
+ tokenizer: PreTrainedTokenizer,
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+ tokenizer, batch_size, seq_length, is_pair, framework
+ )
+
+ # Generate decoder inputs
+ decoder_seq_length = seq_length if not self.use_past else 1
+ decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+ tokenizer, batch_size, decoder_seq_length, is_pair, framework
+ )
+ decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
+ common_inputs = dict(**encoder_inputs, **decoder_inputs)
+
+ if self.use_past:
+ if not is_torch_available():
+ raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+ else:
+ import torch
+ batch, encoder_seq_length = common_inputs["input_ids"].shape
+ decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
+ num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
+ encoder_shape = (
+ batch,
+ num_encoder_attention_heads,
+ encoder_seq_length,
+ self._config.hidden_size // num_encoder_attention_heads,
+ )
+ decoder_past_length = decoder_seq_length + 3
+ decoder_shape = (
+ batch,
+ num_decoder_attention_heads,
+ decoder_past_length,
+ self._config.hidden_size // num_decoder_attention_heads,
+ )
+
+ common_inputs["decoder_attention_mask"] = torch.cat(
+ [common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
+ )
+
+ common_inputs["past_key_values"] = []
+ # If the number of encoder and decoder layers are present in the model configuration, both are considered
+ num_encoder_layers, num_decoder_layers = self.num_layers
+ min_num_layers = min(num_encoder_layers, num_decoder_layers)
+ max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
+ remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
+
+ for _ in range(min_num_layers):
+ common_inputs["past_key_values"].append(
+ (
+ torch.zeros(decoder_shape),
+ torch.zeros(decoder_shape),
+ torch.zeros(encoder_shape),
+ torch.zeros(encoder_shape),
+ )
+ )
+ # TODO: test this.
+ shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
+ for _ in range(min_num_layers, max_num_layers):
+ common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))
+ return common_inputs
+
+ def _generate_dummy_inputs_for_causal_lm(
+ self,
+ tokenizer: PreTrainedTokenizer,
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+ tokenizer, batch_size, seq_length, is_pair, framework
+ )
+
+ if self.use_past:
+ if not is_torch_available():
+ raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+ else:
+ import torch
+ batch, seqlen = common_inputs["input_ids"].shape
+ # Not using the same length for past_key_values
+ past_key_values_length = seqlen + 2
+ num_encoder_layers, _ = self.num_layers
+ num_encoder_attention_heads, _ = self.num_attention_heads
+ past_shape = (
+ batch,
+ num_encoder_attention_heads,
+ past_key_values_length,
+ self._config.hidden_size // num_encoder_attention_heads,
+ )
+
+ mask_dtype = common_inputs["attention_mask"].dtype
+ common_inputs["attention_mask"] = torch.cat(
+ [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
+ )
+ common_inputs["past_key_values"] = [
+ (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers)
+ ]
+ return common_inputs
+
+ def _generate_dummy_inputs_for_sequence_classification_and_question_answering(
+ self,
+ tokenizer: PreTrainedTokenizer,
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ # Copied from OnnxConfig.generate_dummy_inputs
+ # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
+ # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
+ batch_size = compute_effective_axis_dimension(
+ batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
+ )
+
+ # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
+ token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
+ seq_length = compute_effective_axis_dimension(
+ seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
+ )
+
+ # Generate dummy inputs according to compute batch and sequence
+ dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
+ common_inputs = dict(tokenizer(dummy_input, return_tensors=framework))
+ return common_inputs
+
+ def generate_dummy_inputs(
+ self,
+ tokenizer: PreTrainedTokenizer,
+ batch_size: int = -1,
+ seq_length: int = -1,
+ is_pair: bool = False,
+ framework: Optional[TensorType] = None,
+ ) -> Mapping[str, Any]:
+ if self.task in ["default", "seq2seq-lm"]:
+ common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm(
+ tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+ )
+
+ elif self.task == "causal-lm":
+ common_inputs = self._generate_dummy_inputs_for_causal_lm(
+ tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+ )
+ else:
+ common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+ tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+ )
+
+ return common_inputs
+
+ def _flatten_past_key_values_(self, flattened_output, name, idx, t):
+ if self.task in ["default", "seq2seq-lm"]:
+ flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
+ else:
+ flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_(
+ flattened_output, name, idx, t
+ )
+
+
+__all__ = ["MBartConfig", "MBartOnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/convert_mbart_original_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/convert_mbart_original_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..909b7b41284c93a60694f8c64c77ded62dfb9346
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/convert_mbart_original_checkpoint_to_pytorch.py
@@ -0,0 +1,83 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+
+import torch
+from torch import nn
+
+from transformers import MBartConfig, MBartForConditionalGeneration
+
+
+def remove_ignore_keys_(state_dict):
+ ignore_keys = [
+ "encoder.version",
+ "decoder.version",
+ "model.encoder.version",
+ "model.decoder.version",
+ "_float_tensor",
+ "decoder.output_projection.weight",
+ ]
+ for k in ignore_keys:
+ state_dict.pop(k, None)
+
+
+def make_linear_from_emb(emb):
+ vocab_size, emb_size = emb.weight.shape
+ lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
+ lin_layer.weight.data = emb.weight.data
+ return lin_layer
+
+
+def convert_fairseq_mbart_checkpoint_from_disk(
+ checkpoint_path, hf_config_path="facebook/mbart-large-en-ro", finetuned=False, mbart_50=False
+):
+ state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=True)["model"]
+ remove_ignore_keys_(state_dict)
+ vocab_size = state_dict["encoder.embed_tokens.weight"].shape[0]
+
+ mbart_config = MBartConfig.from_pretrained(hf_config_path, vocab_size=vocab_size)
+ if mbart_50 and finetuned:
+ mbart_config.activation_function = "relu"
+
+ state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
+ model = MBartForConditionalGeneration(mbart_config)
+ model.model.load_state_dict(state_dict)
+
+ if finetuned:
+ model.lm_head = make_linear_from_emb(model.model.shared)
+
+ return model
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
+ )
+ parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
+ parser.add_argument(
+ "--hf_config",
+ default="facebook/mbart-large-cc25",
+ type=str,
+ help="Which huggingface architecture to use: mbart-large",
+ )
+ parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
+ parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
+ args = parser.parse_args()
+ model = convert_fairseq_mbart_checkpoint_from_disk(
+ args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_50=args.mbart_50
+ )
+ model.save_pretrained(args.pytorch_dump_folder_path)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/modeling_flax_mbart.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/modeling_flax_mbart.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad269860a9595f4eb9c9679c0ad8e6c9dc5f5381
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/modeling_flax_mbart.py
@@ -0,0 +1,1780 @@
+# coding=utf-8
+# Copyright 2021, The Facebook AI Research Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Flax MBart model."""
+
+import math
+import random
+from functools import partial
+from typing import Callable, Optional
+
+import flax.linen as nn
+import jax
+import jax.numpy as jnp
+from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
+from flax.linen import combine_masks, make_causal_mask
+from flax.linen.attention import dot_product_attention_weights
+from flax.traverse_util import flatten_dict, unflatten_dict
+from jax import lax
+from jax.random import PRNGKey
+
+from ...modeling_flax_outputs import (
+ FlaxBaseModelOutput,
+ FlaxBaseModelOutputWithPastAndCrossAttentions,
+ FlaxCausalLMOutputWithCrossAttentions,
+ FlaxSeq2SeqLMOutput,
+ FlaxSeq2SeqModelOutput,
+ FlaxSeq2SeqQuestionAnsweringModelOutput,
+ FlaxSeq2SeqSequenceClassifierOutput,
+)
+from ...modeling_flax_utils import (
+ ACT2FN,
+ FlaxPreTrainedModel,
+ append_call_sample_docstring,
+ append_replace_return_docstrings,
+ overwrite_call_docstring,
+)
+from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from .configuration_mbart import MBartConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "facebook/mbart-large-cc25"
+_CONFIG_FOR_DOC = "MBartConfig"
+
+
+MBART_START_DOCSTRING = r"""
+ This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a Flax Linen
+ [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
+ regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
+
+ Finally, this model supports inherent JAX features such as:
+
+ - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
+ - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
+ - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
+ - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
+
+ Parameters:
+ config ([`MBartConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
+ dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
+ The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
+ `jax.numpy.bfloat16` (on TPUs).
+
+ This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
+ specified all the computation will be performed with the given `dtype`.
+
+ **Note that this only specifies the dtype of the computation and does not influence the dtype of model
+ parameters.**
+
+ If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
+ [`~FlaxPreTrainedModel.to_bf16`].
+"""
+
+MBART_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
+ paper](https://huggingface.co/papers/1910.13461) for more information on the default strategy.
+ position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
+ range `[0, config.max_position_embeddings - 1]`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+MBART_ENCODE_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+MBART_DECODE_INPUTS_DOCSTRING = r"""
+ Args:
+ decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ encoder_outputs (`tuple(tuple(jnp.ndarray)`):
+ Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
+ `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
+ hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
+ encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
+ paper](https://huggingface.co/papers/1910.13461) for more information on the default strategy.
+ decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
+ range `[0, config.max_position_embeddings - 1]`.
+ past_key_values (`dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
+ Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
+ auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int) -> jnp.ndarray:
+ """
+ Shift input ids one token to the right, and wrap the last non pad token (the token) Note that MBart does not
+ have a single `decoder_start_token_id` in contrast to other Bart-like models.
+ """
+ prev_output_tokens = jnp.array(input_ids).copy()
+
+ if pad_token_id is None:
+ raise ValueError("self.model.config.pad_token_id has to be defined.")
+
+ # replace possible -100 values in labels by `pad_token_id`
+ prev_output_tokens = jnp.where(prev_output_tokens == -100, pad_token_id, input_ids)
+ index_of_eos = (jnp.where(prev_output_tokens != pad_token_id, 1, 0).sum(axis=-1) - 1).reshape(-1, 1)
+ decoder_start_tokens = jnp.array(
+ [prev_output_tokens[i, eos_idx] for i, eos_idx in enumerate(index_of_eos)], dtype=jnp.int32
+ ).squeeze()
+
+ prev_output_tokens = prev_output_tokens.at[:, 1:].set(prev_output_tokens[:, :-1])
+ prev_output_tokens = prev_output_tokens.at[:, 0].set(decoder_start_tokens)
+
+ return prev_output_tokens
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->MBart
+class FlaxMBartAttention(nn.Module):
+ config: MBartConfig
+ embed_dim: int
+ num_heads: int
+ dropout: float = 0.0
+ causal: bool = False
+ bias: bool = True
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self) -> None:
+ self.head_dim = self.embed_dim // self.num_heads
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ dense = partial(
+ nn.Dense,
+ self.embed_dim,
+ use_bias=self.bias,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+
+ self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
+ self.out_proj = dense()
+
+ self.dropout_layer = nn.Dropout(rate=self.dropout)
+
+ if self.causal:
+ self.causal_mask = make_causal_mask(
+ jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
+ )
+
+ def _split_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
+
+ def _merge_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
+
+ @nn.compact
+ def _concatenate_to_cache(self, key, value, query, attention_mask):
+ """
+ This function takes projected key, value states from a single input token and concatenates the states to cached
+ states from previous steps. This function is slightly adapted from the official Flax repository:
+ https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
+ """
+ # detect if we're initializing by absence of existing cache data.
+ is_initialized = self.has_variable("cache", "cached_key")
+ cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
+ cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
+ cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
+
+ if is_initialized:
+ *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
+ # update key, value caches with our new 1d spatial slices
+ cur_index = cache_index.value
+ indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
+ key = lax.dynamic_update_slice(cached_key.value, key, indices)
+ value = lax.dynamic_update_slice(cached_value.value, value, indices)
+ cached_key.value = key
+ cached_value.value = value
+ num_updated_cache_vectors = query.shape[1]
+ cache_index.value = cache_index.value + num_updated_cache_vectors
+ # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
+ pad_mask = jnp.broadcast_to(
+ jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
+ tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
+ )
+ attention_mask = combine_masks(pad_mask, attention_mask)
+ return key, value, attention_mask
+
+ def __call__(
+ self,
+ hidden_states: jnp.ndarray,
+ key_value_states: Optional[jnp.ndarray] = None,
+ attention_mask: Optional[jnp.ndarray] = None,
+ init_cache: bool = False,
+ deterministic: bool = True,
+ ) -> tuple[jnp.ndarray]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+ batch_size = hidden_states.shape[0]
+
+ # get query proj
+ query_states = self.q_proj(hidden_states)
+ # get key, value proj
+ if is_cross_attention:
+ # cross_attentions
+ key_states = self.k_proj(key_value_states)
+ value_states = self.v_proj(key_value_states)
+ else:
+ # self_attention
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = self._split_heads(query_states)
+ key_states = self._split_heads(key_states)
+ value_states = self._split_heads(value_states)
+
+ # handle cache prepare causal attention mask
+ if self.causal:
+ query_length, key_length = query_states.shape[1], key_states.shape[1]
+ if self.has_variable("cache", "cached_key"):
+ mask_shift = self.variables["cache"]["cache_index"]
+ max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
+ causal_mask = lax.dynamic_slice(
+ self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
+ )
+ else:
+ causal_mask = self.causal_mask[:, :, :query_length, :key_length]
+ causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
+
+ # combine masks if needed
+ if attention_mask is not None and self.causal:
+ attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
+ attention_mask = combine_masks(attention_mask, causal_mask)
+ elif self.causal:
+ attention_mask = causal_mask
+ elif attention_mask is not None:
+ attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
+
+ # During fast autoregressive decoding, we feed one position at a time,
+ # and cache the keys and values step by step.
+ if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
+ key_states, value_states, attention_mask = self._concatenate_to_cache(
+ key_states, value_states, query_states, attention_mask
+ )
+
+ # Convert the boolean attention mask to an attention bias.
+ if attention_mask is not None:
+ # attention mask in the form of attention bias
+ attention_bias = lax.select(
+ attention_mask > 0,
+ jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
+ jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
+ )
+ else:
+ attention_bias = None
+
+ dropout_rng = None
+ if not deterministic and self.dropout > 0.0:
+ dropout_rng = self.make_rng("dropout")
+
+ attn_weights = dot_product_attention_weights(
+ query_states,
+ key_states,
+ bias=attention_bias,
+ dropout_rng=dropout_rng,
+ dropout_rate=self.dropout,
+ broadcast_dropout=True,
+ deterministic=deterministic,
+ dtype=self.dtype,
+ precision=None,
+ )
+
+ attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
+ attn_output = self._merge_heads(attn_output)
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+class FlaxMBartEncoderLayer(nn.Module):
+ config: MBartConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self) -> None:
+ self.embed_dim = self.config.d_model
+ self.self_attn = FlaxMBartAttention(
+ config=self.config,
+ embed_dim=self.embed_dim,
+ num_heads=self.config.encoder_attention_heads,
+ dropout=self.config.attention_dropout,
+ dtype=self.dtype,
+ )
+ self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+ self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+ self.activation_fn = ACT2FN[self.config.activation_function]
+ self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
+ self.fc1 = nn.Dense(
+ self.config.encoder_ffn_dim,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+ self.fc2 = nn.Dense(
+ self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
+ )
+ self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+
+ def __call__(
+ self,
+ hidden_states: jnp.ndarray,
+ attention_mask: jnp.ndarray,
+ output_attentions: bool = True,
+ deterministic: bool = True,
+ ) -> tuple[jnp.ndarray]:
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+ hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask)
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayerCollection with Bart->MBart
+class FlaxMBartEncoderLayerCollection(nn.Module):
+ config: MBartConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.layers = [
+ FlaxMBartEncoderLayer(self.config, name=str(i), dtype=self.dtype)
+ for i in range(self.config.encoder_layers)
+ ]
+ self.layerdrop = self.config.encoder_layerdrop
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask,
+ deterministic: bool = True,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ):
+ all_attentions = () if output_attentions else None
+ all_hidden_states = () if output_hidden_states else None
+
+ for encoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = random.uniform(0, 1)
+ if not deterministic and (dropout_probability < self.layerdrop): # skip the layer
+ layer_outputs = (None, None)
+ else:
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ output_attentions,
+ deterministic,
+ )
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ outputs = (hidden_states, all_hidden_states, all_attentions)
+
+ if not return_dict:
+ return tuple(v for v in outputs if v is not None)
+
+ return FlaxBaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
+ )
+
+
+class FlaxMBartDecoderLayer(nn.Module):
+ config: MBartConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self) -> None:
+ self.embed_dim = self.config.d_model
+ self.self_attn = FlaxMBartAttention(
+ config=self.config,
+ embed_dim=self.embed_dim,
+ num_heads=self.config.decoder_attention_heads,
+ dropout=self.config.attention_dropout,
+ causal=True,
+ dtype=self.dtype,
+ )
+ self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+ self.activation_fn = ACT2FN[self.config.activation_function]
+ self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
+
+ self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+ self.encoder_attn = FlaxMBartAttention(
+ config=self.config,
+ embed_dim=self.embed_dim,
+ num_heads=self.config.decoder_attention_heads,
+ dropout=self.config.attention_dropout,
+ dtype=self.dtype,
+ )
+ self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+ self.fc1 = nn.Dense(
+ self.config.decoder_ffn_dim,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+ self.fc2 = nn.Dense(
+ self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
+ )
+ self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+
+ def __call__(
+ self,
+ hidden_states: jnp.ndarray,
+ attention_mask: jnp.ndarray,
+ encoder_hidden_states: Optional[jnp.ndarray] = None,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ init_cache: bool = False,
+ output_attentions: bool = True,
+ deterministic: bool = True,
+ ) -> tuple[jnp.ndarray]:
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache
+ )
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = residual + hidden_states
+
+ # Cross-Attention Block
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+ hidden_states, cross_attn_weights = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ )
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ return outputs
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayerCollection with Bart->MBart
+class FlaxMBartDecoderLayerCollection(nn.Module):
+ config: MBartConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.layers = [
+ FlaxMBartDecoderLayer(self.config, name=str(i), dtype=self.dtype)
+ for i in range(self.config.decoder_layers)
+ ]
+ self.layerdrop = self.config.decoder_layerdrop
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask,
+ encoder_hidden_states: Optional[jnp.ndarray] = None,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ):
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = random.uniform(0, 1)
+ if not deterministic and (dropout_probability < self.layerdrop):
+ layer_outputs = (None, None, None)
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ deterministic=deterministic,
+ )
+
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if encoder_hidden_states is not None:
+ all_cross_attentions += (layer_outputs[2],)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions]
+
+ if not return_dict:
+ return tuple(v for v in outputs if v is not None)
+
+ return FlaxBaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartClassificationHead with Bart->MBart
+class FlaxMBartClassificationHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ config: MBartConfig
+ inner_dim: int
+ num_classes: int
+ pooler_dropout: float
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.dense = nn.Dense(
+ self.inner_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
+ )
+ self.dropout = nn.Dropout(rate=self.pooler_dropout)
+ self.out_proj = nn.Dense(
+ self.num_classes,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+
+ def __call__(self, hidden_states: jnp.ndarray, deterministic: bool):
+ hidden_states = self.dropout(hidden_states, deterministic=deterministic)
+ hidden_states = self.dense(hidden_states)
+ hidden_states = jnp.tanh(hidden_states)
+ hidden_states = self.dropout(hidden_states, deterministic=deterministic)
+ hidden_states = self.out_proj(hidden_states)
+ return hidden_states
+
+
+class FlaxMBartEncoder(nn.Module):
+ config: MBartConfig
+ embed_tokens: nn.Embed
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+
+ embed_dim = self.config.d_model
+ self.padding_idx = self.config.pad_token_id
+ self.max_source_positions = self.config.max_position_embeddings
+ self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0
+
+ # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
+ # and adjust num_embeddings appropriately. Other models don't have this hack
+ self.offset = 2
+ self.embed_positions = nn.Embed(
+ self.config.max_position_embeddings + self.offset,
+ embed_dim,
+ embedding_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+ self.layers = FlaxMBartEncoderLayerCollection(self.config, self.dtype)
+ self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+ self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ position_ids,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ input_shape = input_ids.shape
+ input_ids = input_ids.reshape(-1, input_shape[-1])
+
+ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+ embed_pos = self.embed_positions(position_ids + self.offset)
+
+ hidden_states = inputs_embeds + embed_pos
+ hidden_states = self.layernorm_embedding(hidden_states)
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+
+ outputs = self.layers(
+ hidden_states,
+ attention_mask,
+ deterministic=deterministic,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ last_hidden_states = outputs[0]
+ last_hidden_states = self.layer_norm(last_hidden_states)
+
+ # update the last element in `hidden_states` after applying `layernorm` above
+ hidden_states = None
+ if output_hidden_states:
+ hidden_states = outputs[1]
+ hidden_states = hidden_states[:-1] + (last_hidden_states,)
+
+ if not return_dict:
+ outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
+ return tuple(v for v in outputs if v is not None)
+
+ return FlaxBaseModelOutput(
+ last_hidden_state=last_hidden_states,
+ hidden_states=hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+class FlaxMBartDecoder(nn.Module):
+ config: MBartConfig
+ embed_tokens: nn.Embed
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+
+ embed_dim = self.config.d_model
+ self.padding_idx = self.config.pad_token_id
+ self.max_target_positions = self.config.max_position_embeddings
+ self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0
+
+ # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
+ # and adjust num_embeddings appropriately. Other models don't have this hack
+ self.offset = 2
+ self.embed_positions = nn.Embed(
+ self.config.max_position_embeddings + self.offset,
+ embed_dim,
+ embedding_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+
+ self.layers = FlaxMBartDecoderLayerCollection(self.config, self.dtype)
+ self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+ self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ position_ids,
+ encoder_hidden_states: Optional[jnp.ndarray] = None,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ input_shape = input_ids.shape
+ input_ids = input_ids.reshape(-1, input_shape[-1])
+
+ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+ # embed positions
+ positions = self.embed_positions(position_ids + self.offset)
+
+ hidden_states = inputs_embeds + positions
+ hidden_states = self.layernorm_embedding(hidden_states)
+
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+
+ outputs = self.layers(
+ hidden_states,
+ attention_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ last_hidden_states = outputs[0]
+ last_hidden_states = self.layer_norm(last_hidden_states)
+
+ # update the last element in `hidden_states` after applying `layernorm` above
+ hidden_states = None
+ if output_hidden_states:
+ hidden_states = outputs[1]
+ hidden_states = hidden_states[:-1] + (last_hidden_states,)
+
+ if not return_dict:
+ outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
+ return tuple(v for v in outputs if v is not None)
+
+ return FlaxBaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=last_hidden_states,
+ hidden_states=hidden_states,
+ attentions=outputs.attentions,
+ cross_attentions=outputs.cross_attentions,
+ )
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModule with Bart->MBart
+class FlaxMBartModule(nn.Module):
+ config: MBartConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.shared = nn.Embed(
+ self.config.vocab_size,
+ self.config.d_model,
+ embedding_init=jax.nn.initializers.normal(self.config.init_std),
+ dtype=self.dtype,
+ )
+
+ self.encoder = FlaxMBartEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
+ self.decoder = FlaxMBartDecoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
+
+ def _get_encoder_module(self):
+ return self.encoder
+
+ def _get_decoder_module(self):
+ return self.decoder
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ decoder_input_ids,
+ decoder_attention_mask,
+ position_ids,
+ decoder_position_ids,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ position_ids=decoder_position_ids,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return FlaxSeq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+class FlaxMBartPreTrainedModel(FlaxPreTrainedModel):
+ config_class = MBartConfig
+ base_model_prefix: str = "model"
+ module_class: nn.Module = None
+
+ def __init__(
+ self,
+ config: MBartConfig,
+ input_shape: tuple[int] = (1, 1),
+ seed: int = 0,
+ dtype: jnp.dtype = jnp.float32,
+ _do_init: bool = True,
+ **kwargs,
+ ):
+ module = self.module_class(config=config, dtype=dtype, **kwargs)
+ super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
+
+ def init_weights(self, rng: jax.random.PRNGKey, input_shape: tuple, params: FrozenDict = None) -> FrozenDict:
+ # init input tensors
+ input_ids = jnp.zeros(input_shape, dtype="i4")
+ # make sure initialization pass will work for FlaxMBartForSequenceClassificationModule
+ input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
+ attention_mask = jnp.ones_like(input_ids)
+ decoder_input_ids = input_ids
+ decoder_attention_mask = jnp.ones_like(input_ids)
+
+ batch_size, sequence_length = input_ids.shape
+ position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+ decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+
+ params_rng, dropout_rng = jax.random.split(rng)
+ rngs = {"params": params_rng, "dropout": dropout_rng}
+
+ random_params = self.module.init(
+ rngs,
+ input_ids,
+ attention_mask,
+ decoder_input_ids,
+ decoder_attention_mask,
+ position_ids,
+ decoder_position_ids,
+ )["params"]
+
+ if params is not None:
+ random_params = flatten_dict(unfreeze(random_params))
+ params = flatten_dict(unfreeze(params))
+ for missing_key in self._missing_keys:
+ params[missing_key] = random_params[missing_key]
+ self._missing_keys = set()
+ return freeze(unflatten_dict(params))
+ else:
+ return random_params
+
+ # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartPreTrainedModel.init_cache with Bart->MBart
+ def init_cache(self, batch_size, max_length, encoder_outputs):
+ r"""
+ Args:
+ batch_size (`int`):
+ batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
+ max_length (`int`):
+ maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
+ cache.
+ encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
+ `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
+ `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*)
+ is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
+ cross-attention of the decoder.
+ """
+ # init input variables to retrieve cache
+ decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
+ decoder_attention_mask = jnp.ones_like(decoder_input_ids)
+ decoder_position_ids = jnp.broadcast_to(
+ jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
+ )
+
+ def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
+ decoder_module = module._get_decoder_module()
+ return decoder_module(
+ decoder_input_ids,
+ decoder_attention_mask,
+ decoder_position_ids,
+ **kwargs,
+ )
+
+ init_variables = self.module.init(
+ jax.random.PRNGKey(0),
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ decoder_position_ids=decoder_position_ids,
+ encoder_hidden_states=encoder_outputs[0],
+ init_cache=True,
+ method=_decoder_forward, # we only need to call the decoder to init the cache
+ )
+ return unfreeze(init_variables["cache"])
+
+ @add_start_docstrings(MBART_ENCODE_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=MBartConfig)
+ def encode(
+ self,
+ input_ids: jnp.ndarray,
+ attention_mask: Optional[jnp.ndarray] = None,
+ position_ids: Optional[jnp.ndarray] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ r"""
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxMBartForConditionalGeneration
+
+ >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
+
+ >>> text = "My friends are cool but they eat too many carbs."
+ >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
+ >>> encoder_outputs = model.encode(**inputs)
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ if attention_mask is None:
+ attention_mask = jnp.ones_like(input_ids)
+ if position_ids is None:
+ batch_size, sequence_length = input_ids.shape
+ position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs):
+ encode_module = module._get_encoder_module()
+ return encode_module(input_ids, attention_mask, position_ids, **kwargs)
+
+ return self.module.apply(
+ {"params": params or self.params},
+ input_ids=jnp.array(input_ids, dtype="i4"),
+ attention_mask=jnp.array(attention_mask, dtype="i4"),
+ position_ids=jnp.array(position_ids, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ method=_encoder_forward,
+ )
+
+ @add_start_docstrings(MBART_DECODE_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=MBartConfig)
+ def decode(
+ self,
+ decoder_input_ids,
+ encoder_outputs,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ decoder_attention_mask: Optional[jnp.ndarray] = None,
+ decoder_position_ids: Optional[jnp.ndarray] = None,
+ past_key_values: Optional[dict] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ r"""
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxMBartForConditionalGeneration
+
+ >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
+
+ >>> text = "My friends are cool but they eat too many carbs."
+ >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
+ >>> encoder_outputs = model.encode(**inputs)
+
+ >>> decoder_start_token_id = model.config.decoder_start_token_id
+ >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
+
+ >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
+ >>> last_decoder_hidden_states = outputs.last_hidden_state
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ encoder_hidden_states = encoder_outputs[0]
+ if encoder_attention_mask is None:
+ batch_size, sequence_length = encoder_hidden_states.shape[:2]
+ encoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ batch_size, sequence_length = decoder_input_ids.shape
+ if decoder_attention_mask is None:
+ decoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ if decoder_position_ids is None:
+ if past_key_values is not None:
+ raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
+
+ decoder_position_ids = jnp.broadcast_to(
+ jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
+ )
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ inputs = {"params": params or self.params}
+
+ # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
+ # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
+ # it can be changed by FlaxMBartAttention module
+ if past_key_values:
+ inputs["cache"] = past_key_values
+ mutable = ["cache"]
+ else:
+ mutable = False
+
+ def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
+ decoder_module = module._get_decoder_module()
+ return decoder_module(
+ decoder_input_ids,
+ decoder_attention_mask,
+ decoder_position_ids,
+ **kwargs,
+ )
+
+ outputs = self.module.apply(
+ inputs,
+ decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+ decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+ decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ mutable=mutable,
+ method=_decoder_forward,
+ )
+
+ # add updated cache to model output
+ if past_key_values is not None and return_dict:
+ outputs, past = outputs
+ outputs["past_key_values"] = unfreeze(past["cache"])
+ return outputs
+ elif past_key_values is not None and not return_dict:
+ outputs, past = outputs
+ outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
+
+ return outputs
+
+ @add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
+ def __call__(
+ self,
+ input_ids: jnp.ndarray,
+ attention_mask: Optional[jnp.ndarray] = None,
+ decoder_input_ids: Optional[jnp.ndarray] = None,
+ decoder_attention_mask: Optional[jnp.ndarray] = None,
+ position_ids: Optional[jnp.ndarray] = None,
+ decoder_position_ids: Optional[jnp.ndarray] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ # prepare encoder inputs
+ if attention_mask is None:
+ attention_mask = jnp.ones_like(input_ids)
+ if position_ids is None:
+ batch_size, sequence_length = input_ids.shape
+ position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+
+ # prepare decoder inputs
+ if decoder_input_ids is None:
+ decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id)
+ if decoder_attention_mask is None:
+ decoder_attention_mask = jnp.ones_like(decoder_input_ids)
+ if decoder_position_ids is None:
+ batch_size, sequence_length = decoder_input_ids.shape
+ decoder_position_ids = jnp.broadcast_to(
+ jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
+ )
+
+ # Handle any PRNG if needed
+ rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
+
+ return self.module.apply(
+ {"params": params or self.params},
+ input_ids=jnp.array(input_ids, dtype="i4"),
+ attention_mask=jnp.array(attention_mask, dtype="i4"),
+ position_ids=jnp.array(position_ids, dtype="i4"),
+ decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+ decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+ decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ )
+
+
+@add_start_docstrings(
+ "The bare MBart Model transformer outputting raw hidden-states without any specific head on top.",
+ MBART_START_DOCSTRING,
+)
+class FlaxMBartModel(FlaxMBartPreTrainedModel):
+ config: MBartConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+ module_class = FlaxMBartModule
+
+
+append_call_sample_docstring(FlaxMBartModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForConditionalGenerationModule with Bart->MBart
+class FlaxMBartForConditionalGenerationModule(nn.Module):
+ config: MBartConfig
+ dtype: jnp.dtype = jnp.float32
+ bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
+
+ def setup(self):
+ self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
+ self.lm_head = nn.Dense(
+ self.model.shared.num_embeddings,
+ use_bias=False,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+ self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings))
+
+ def _get_encoder_module(self):
+ return self.model.encoder
+
+ def _get_decoder_module(self):
+ return self.model.decoder
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ decoder_input_ids,
+ decoder_attention_mask,
+ position_ids,
+ decoder_position_ids,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ position_ids=position_ids,
+ decoder_position_ids=decoder_position_ids,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ hidden_states = outputs[0]
+
+ if self.config.tie_word_embeddings:
+ shared_embedding = self.model.variables["params"]["shared"]["embedding"]
+ lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
+ else:
+ lm_logits = self.lm_head(hidden_states)
+
+ lm_logits += jax.lax.stop_gradient(self.final_logits_bias.astype(self.dtype))
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return output
+
+ return FlaxSeq2SeqLMOutput(
+ logits=lm_logits,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+@add_start_docstrings(
+ "The MMBart Model with a language modeling head. Can be used for summarization.", MBART_START_DOCSTRING
+)
+class FlaxMBartForConditionalGeneration(FlaxMBartPreTrainedModel):
+ module_class = FlaxMBartForConditionalGenerationModule
+ dtype: jnp.dtype = jnp.float32
+
+ @add_start_docstrings(MBART_DECODE_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=MBartConfig)
+ def decode(
+ self,
+ decoder_input_ids,
+ encoder_outputs,
+ encoder_attention_mask: Optional[jnp.ndarray] = None,
+ decoder_attention_mask: Optional[jnp.ndarray] = None,
+ decoder_position_ids: Optional[jnp.ndarray] = None,
+ past_key_values: Optional[dict] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ train: bool = False,
+ params: Optional[dict] = None,
+ dropout_rng: PRNGKey = None,
+ ):
+ r"""
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxMBartForConditionalGeneration
+
+ >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
+
+ >>> text = "My friends are cool but they eat too many carbs."
+ >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax")
+ >>> encoder_outputs = model.encode(**inputs)
+
+ >>> decoder_start_token_id = model.config.decoder_start_token_id
+ >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
+
+ >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
+ >>> logits = outputs.logits
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ encoder_hidden_states = encoder_outputs[0]
+ if encoder_attention_mask is None:
+ batch_size, sequence_length = encoder_hidden_states.shape[:2]
+ encoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ batch_size, sequence_length = decoder_input_ids.shape
+ if decoder_attention_mask is None:
+ decoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+ if decoder_position_ids is None:
+ if past_key_values is not None:
+ raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
+
+ decoder_position_ids = jnp.broadcast_to(
+ jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
+ )
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ inputs = {"params": params or self.params}
+
+ # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
+ # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
+ # it can be changed by FlaxMBartAttention module
+ if past_key_values:
+ inputs["cache"] = past_key_values
+ mutable = ["cache"]
+ else:
+ mutable = False
+
+ def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
+ decoder_module = module._get_decoder_module()
+ outputs = decoder_module(
+ decoder_input_ids,
+ decoder_attention_mask,
+ decoder_position_ids,
+ **kwargs,
+ )
+ hidden_states = outputs[0]
+
+ if self.config.tie_word_embeddings:
+ shared_embedding = module.model.variables["params"]["shared"]["embedding"]
+ lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
+ else:
+ lm_logits = module.lm_head(hidden_states)
+
+ lm_logits += module.final_logits_bias.astype(self.dtype)
+ return lm_logits, outputs
+
+ outputs = self.module.apply(
+ inputs,
+ decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+ decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+ decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=not train,
+ rngs=rngs,
+ mutable=mutable,
+ method=_decoder_forward,
+ )
+
+ if past_key_values is None:
+ lm_logits, decoder_outputs = outputs
+ else:
+ (lm_logits, decoder_outputs), past = outputs
+
+ if return_dict:
+ outputs = FlaxCausalLMOutputWithCrossAttentions(
+ logits=lm_logits,
+ hidden_states=decoder_outputs.hidden_states,
+ attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ )
+ else:
+ outputs = (lm_logits,) + decoder_outputs[1:]
+
+ # add updated cache to model output
+ if past_key_values is not None and return_dict:
+ outputs["past_key_values"] = unfreeze(past["cache"])
+ return outputs
+ elif past_key_values is not None and not return_dict:
+ outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
+
+ return outputs
+
+ def prepare_inputs_for_generation(
+ self,
+ decoder_input_ids,
+ max_length,
+ attention_mask: Optional[jax.Array] = None,
+ decoder_attention_mask: Optional[jax.Array] = None,
+ encoder_outputs=None,
+ **kwargs,
+ ):
+ # initializing the cache
+ batch_size, seq_length = decoder_input_ids.shape
+
+ past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
+ # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
+ # But since the decoder uses a causal mask, those positions are masked anyways.
+ # Thus we can create a single static attention_mask here, which is more efficient for compilation
+ extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
+ if decoder_attention_mask is not None:
+ position_ids = decoder_attention_mask.cumsum(axis=-1) - 1
+ extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
+ else:
+ position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
+
+ return {
+ "past_key_values": past_key_values,
+ "encoder_outputs": encoder_outputs,
+ "encoder_attention_mask": attention_mask,
+ "decoder_attention_mask": extended_attention_mask,
+ "decoder_position_ids": position_ids,
+ }
+
+ def update_inputs_for_generation(self, model_outputs, model_kwargs):
+ model_kwargs["past_key_values"] = model_outputs.past_key_values
+ model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
+ return model_kwargs
+
+
+FLAX_MBART_CONDITIONAL_GENERATION_DOCSTRING = r"""
+ Returns:
+
+ Summarization example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxMBartForConditionalGeneration, MBartConfig
+
+ >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
+
+ >>> ARTICLE_TO_SUMMARIZE = "Meine Freunde sind cool, aber sie essen zu viel Kuchen."
+ >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="np")
+
+ >>> # Generate Summary
+ >>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=5).sequences
+ >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
+ ```
+
+ Mask filling example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, FlaxMBartForConditionalGeneration
+
+ >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
+
+ >>> # de_DE is the language symbol id for German
+ >>> TXT = " Meine Freunde sind nett aber sie essen zu viel Kuchen. de_DE"
+ >>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors="np")["input_ids"]
+
+ >>> logits = model(input_ids).logits
+ >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero()[0].item()
+ >>> probs = logits[0, masked_index].softmax(dim=0)
+ >>> values, predictions = probs.topk(5)
+
+ >>> tokenizer.decode(predictions).split()
+ ```
+"""
+
+overwrite_call_docstring(
+ FlaxMBartForConditionalGeneration, MBART_INPUTS_DOCSTRING + FLAX_MBART_CONDITIONAL_GENERATION_DOCSTRING
+)
+append_replace_return_docstrings(
+ FlaxMBartForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
+)
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForSequenceClassificationModule with Bart->MBart
+class FlaxMBartForSequenceClassificationModule(nn.Module):
+ config: MBartConfig
+ dtype: jnp.dtype = jnp.float32
+ num_labels: Optional[int] = None
+
+ def setup(self):
+ self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
+ self.classification_head = FlaxMBartClassificationHead(
+ config=self.config,
+ inner_dim=self.config.d_model,
+ num_classes=self.num_labels if self.num_labels is not None else self.config.num_labels,
+ pooler_dropout=self.config.classifier_dropout,
+ )
+
+ def _get_encoder_module(self):
+ return self.model.encoder
+
+ def _get_decoder_module(self):
+ return self.model.decoder
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ decoder_input_ids,
+ decoder_attention_mask,
+ position_ids,
+ decoder_position_ids,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ position_ids=position_ids,
+ decoder_position_ids=decoder_position_ids,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ hidden_states = outputs[0] # last hidden state
+
+ eos_mask = jnp.where(input_ids == self.config.eos_token_id, 1, 0)
+
+ # The first condition is necessary to overcome jax._src.errors.ConcretizationTypeError during JIT compilation
+ if not isinstance(eos_mask, jax.interpreters.partial_eval.DynamicJaxprTracer):
+ if len(jnp.unique(eos_mask.sum(1))) > 1:
+ raise ValueError("All examples must have the same number of tokens.")
+
+ if any(eos_mask.sum(1) == 0):
+ raise ValueError("There are missing tokens in input_ids")
+
+ # Ensure to keep 1 only for the last token for each example
+ eos_mask_noised = eos_mask + jnp.arange(eos_mask.shape[1]) * 1e-6
+ eos_mask = jnp.where(eos_mask_noised == eos_mask_noised.max(1).reshape(-1, 1), 1, 0)
+
+ sentence_representation = jnp.einsum("ijk, ij -> ijk", hidden_states, eos_mask).sum(1)
+ logits = self.classification_head(sentence_representation, deterministic=deterministic)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return output
+
+ return FlaxSeq2SeqSequenceClassifierOutput(
+ logits=logits,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+@add_start_docstrings(
+ """
+ MBart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
+ tasks.
+ """,
+ MBART_START_DOCSTRING,
+)
+class FlaxMBartForSequenceClassification(FlaxMBartPreTrainedModel):
+ module_class = FlaxMBartForSequenceClassificationModule
+ dtype = jnp.float32
+
+
+append_call_sample_docstring(
+ FlaxMBartForSequenceClassification,
+ _CHECKPOINT_FOR_DOC,
+ FlaxSeq2SeqSequenceClassifierOutput,
+ _CONFIG_FOR_DOC,
+)
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForQuestionAnsweringModule with Bart->MBart
+class FlaxMBartForQuestionAnsweringModule(nn.Module):
+ config: MBartConfig
+ dtype: jnp.dtype = jnp.float32
+ num_labels = 2
+
+ def setup(self):
+ self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
+ self.qa_outputs = nn.Dense(
+ self.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
+ )
+
+ def _get_encoder_module(self):
+ return self.model.encoder
+
+ def _get_decoder_module(self):
+ return self.model.decoder
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ decoder_input_ids,
+ decoder_attention_mask,
+ position_ids,
+ decoder_position_ids,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ position_ids=position_ids,
+ decoder_position_ids=decoder_position_ids,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = jnp.split(logits, logits.shape[-1], axis=-1)
+ start_logits = start_logits.squeeze(-1)
+ end_logits = end_logits.squeeze(-1)
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[1:]
+ return output
+
+ return FlaxSeq2SeqQuestionAnsweringModelOutput(
+ start_logits=start_logits,
+ end_logits=end_logits,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+@add_start_docstrings(
+ """
+ MBart Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
+ layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
+ """,
+ MBART_START_DOCSTRING,
+)
+class FlaxMBartForQuestionAnswering(FlaxMBartPreTrainedModel):
+ module_class = FlaxMBartForQuestionAnsweringModule
+ dtype = jnp.float32
+
+
+append_call_sample_docstring(
+ FlaxMBartForQuestionAnswering,
+ _CHECKPOINT_FOR_DOC,
+ FlaxSeq2SeqQuestionAnsweringModelOutput,
+ _CONFIG_FOR_DOC,
+)
+
+
+__all__ = [
+ "FlaxMBartForConditionalGeneration",
+ "FlaxMBartForQuestionAnswering",
+ "FlaxMBartForSequenceClassification",
+ "FlaxMBartModel",
+ "FlaxMBartPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/modeling_mbart.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/modeling_mbart.py
new file mode 100644
index 0000000000000000000000000000000000000000..499a626f4440a8749b1f93548500e6bb22d6e989
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/modeling_mbart.py
@@ -0,0 +1,1913 @@
+# coding=utf-8
+# Copyright 2021, The Facebook AI Research Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MBART model."""
+
+import math
+from typing import Callable, Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import (
+ AttentionMaskConverter,
+ _prepare_4d_attention_mask,
+ _prepare_4d_attention_mask_for_sdpa,
+)
+from ...modeling_flash_attention_utils import (
+ FlashAttentionKwargs,
+)
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPastAndCrossAttentions,
+ CausalLMOutputWithCrossAttentions,
+ Seq2SeqLMOutput,
+ Seq2SeqModelOutput,
+ Seq2SeqQuestionAnsweringModelOutput,
+ Seq2SeqSequenceClassifierOutput,
+)
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import (
+ auto_docstring,
+ is_torch_flex_attn_available,
+ is_torchdynamo_compiling,
+ logging,
+)
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_mbart import MBartConfig
+
+
+if is_torch_flex_attn_available():
+ from ...integrations.flex_attention import BlockMask, make_flex_block_causal_mask
+
+
+logger = logging.get_logger(__name__)
+
+
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int):
+ """
+ Shift input ids one token to the right, and wrap the last non pad token (the token) Note that MBart does not
+ have a single `decoder_start_token_id` in contrast to other Bart-like models.
+ """
+ prev_output_tokens = input_ids.clone()
+
+ if pad_token_id is None:
+ raise ValueError("self.model.config.pad_token_id has to be defined.")
+ # replace possible -100 values in labels by `pad_token_id`
+ prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id)
+
+ index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
+ decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
+ prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
+ prev_output_tokens[:, 0] = decoder_start_tokens
+
+ return prev_output_tokens
+
+
+# Copied from transformers.models.bart.modeling_bart.BartLearnedPositionalEmbedding with Bart->MBart
+class MBartLearnedPositionalEmbedding(nn.Embedding):
+ """
+ This module learns positional embeddings up to a fixed maximum size.
+ """
+
+ def __init__(self, num_embeddings: int, embedding_dim: int):
+ # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
+ # and adjust num_embeddings appropriately. Other models don't have this hack
+ self.offset = 2
+ super().__init__(num_embeddings + self.offset, embedding_dim)
+
+ def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0, position_ids: torch.Tensor = None):
+ """`input_ids' shape is expected to be [bsz x seqlen]."""
+
+ if position_ids is None:
+ bsz, seq_len = input_ids.shape[:2]
+ position_ids = torch.arange(
+ past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
+ ).expand(bsz, -1)
+ else:
+ position_ids = position_ids.unsqueeze(0)
+
+ return super().forward(position_ids + self.offset)
+
+
+# Copied from transformers.models.bart.modeling_bart.BartScaledWordEmbedding with Bart->MBart
+class MBartScaledWordEmbedding(nn.Embedding):
+ """
+ This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
+ """
+
+ def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
+ super().__init__(num_embeddings, embedding_dim, padding_idx)
+ self.embed_scale = embed_scale
+
+ def forward(self, input_ids: torch.Tensor):
+ return super().forward(input_ids) * self.embed_scale
+
+
+# Copied from transformers.models.bart.modeling_bart.eager_attention_forward
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: Optional[float] = None,
+ dropout: float = 0.0,
+ head_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+):
+ if scaling is None:
+ scaling = query.size(-1) ** -0.5
+
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ if head_mask is not None:
+ attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
+
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->MBart
+class MBartAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: float = 0.0,
+ is_decoder: bool = False,
+ bias: bool = True,
+ is_causal: bool = False,
+ config: Optional[MBartConfig] = None,
+ layer_idx: Optional[int] = None,
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+ self.config = config
+
+ if (self.head_dim * num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+ self.is_causal = is_causal
+ self.layer_idx = layer_idx
+ if layer_idx is None and self.is_decoder:
+ logger.warning_once(
+ f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
+ "will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ key_value_states: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ cache_position: Optional[torch.Tensor] = None,
+ # TODO: we need a refactor so that the different attention modules can get their specific kwargs
+ # ATM, we have mixed things encoder, decoder, and encoder-decoder attn
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+
+ # determine input shapes
+ bsz, tgt_len = hidden_states.shape[:-1]
+ src_len = key_value_states.shape[1] if is_cross_attention else tgt_len
+
+ q_input_shape = (bsz, tgt_len, -1, self.head_dim)
+ kv_input_shape = (bsz, src_len, -1, self.head_dim)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states).view(*q_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ if isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_states from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = key_value_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_states = curr_past_key_value.layers[self.layer_idx].keys
+ value_states = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_states = self.k_proj(current_states)
+ value_states = self.v_proj(current_states)
+ key_states = key_states.view(*kv_input_shape).transpose(1, 2)
+ value_states = value_states.view(*kv_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_states, value_states = curr_past_key_value.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=self.scaling,
+ output_attentions=output_attentions,
+ head_mask=layer_head_mask,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+class MBartEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MBartConfig):
+ super().__init__()
+ self.embed_dim = config.d_model
+
+ self.self_attn = MBartAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.encoder_attention_heads,
+ dropout=config.attention_dropout,
+ config=config,
+ )
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+ self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
+ self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ layer_head_mask: torch.Tensor,
+ output_attentions: bool = False,
+ ) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ if hidden_states.dtype == torch.float16:
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ return hidden_states, attn_weights
+
+
+class MBartDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MBartConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.embed_dim = config.d_model
+
+ self.self_attn = MBartAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ is_causal=True,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.encoder_attn = MBartAttention(
+ self.embed_dim,
+ config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
+ self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = True,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ encoder_hidden_states (`torch.FloatTensor`):
+ cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
+ encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`.
+ cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
+ size `(decoder_attention_heads,)`.
+ past_key_values (`Tuple(torch.FloatTensor)`): cached past key and value projection states
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
+ cache in the correct position and to infer the complete sequence length.
+ """
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # Cross-Attention Block
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ hidden_states, cross_attn_weights = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ layer_head_mask=cross_attn_layer_head_mask,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ return outputs
+
+
+# Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->MBart
+class MBartClassificationHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(
+ self,
+ input_dim: int,
+ inner_dim: int,
+ num_classes: int,
+ pooler_dropout: float,
+ ):
+ super().__init__()
+ self.dense = nn.Linear(input_dim, inner_dim)
+ self.dropout = nn.Dropout(p=pooler_dropout)
+ self.out_proj = nn.Linear(inner_dim, num_classes)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.dense(hidden_states)
+ hidden_states = torch.tanh(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.out_proj(hidden_states)
+ return hidden_states
+
+
+@auto_docstring
+class MBartPreTrainedModel(PreTrainedModel):
+ config: MBartConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MBartDecoderLayer", "MBartEncoderLayer", "MBartAttention"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ _can_compile_fullgraph = True
+
+ def _init_weights(self, module):
+ std = self.config.init_std
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+
+ @property
+ def dummy_inputs(self):
+ pad_token = self.config.pad_token_id
+ input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
+ dummy_inputs = {
+ "attention_mask": input_ids.ne(pad_token),
+ "input_ids": input_ids,
+ }
+ return dummy_inputs
+
+ # Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_full_mask
+ def _update_full_mask(
+ self,
+ attention_mask: Union[torch.Tensor, None],
+ inputs_embeds: torch.Tensor,
+ ):
+ if attention_mask is not None:
+ if self.config._attn_implementation == "flash_attention_2":
+ attention_mask = attention_mask if 0 in attention_mask else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & head_mask can not be supported when using SDPA, fall back to
+ # the manual implementation that requires a 4D causal mask in all cases.
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, inputs_embeds.dtype)
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask, is_causal=False)
+ else:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
+
+ return attention_mask
+
+ # Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_causal_mask
+ def _update_causal_mask(
+ self,
+ attention_mask: Optional[Union[torch.Tensor, "BlockMask"]],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ ):
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ # Other attention flavors support in-built causal (when `mask is None`)
+ # while we need to create our specific block mask regardless
+ elif attention_mask is None:
+ attention_mask = make_flex_block_causal_mask(
+ torch.ones(
+ size=(input_tensor.shape[0], input_tensor.shape[1]),
+ device=attention_mask.device,
+ )
+ )
+ return attention_mask
+
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and (attention_mask == 0.0).any():
+ return attention_mask
+ return None
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if self.config._attn_implementation == "sdpa" and not using_compilable_cache:
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ sequence_length = input_tensor.shape[1]
+ if using_compilable_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+ # Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_cross_attn_mask
+ def _update_cross_attn_mask(
+ self,
+ encoder_hidden_states: Union[torch.Tensor, None],
+ encoder_attention_mask: Union[torch.Tensor, None],
+ input_shape: torch.Size,
+ inputs_embeds: torch.Tensor,
+ ):
+ # expand encoder attention mask
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ if self.config._attn_implementation == "flash_attention_2":
+ encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
+ encoder_attention_mask,
+ inputs_embeds.dtype,
+ tgt_len=input_shape[-1],
+ )
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(encoder_attention_mask, torch.Tensor):
+ encoder_attention_mask = make_flex_block_causal_mask(
+ encoder_attention_mask,
+ query_length=input_shape[-1],
+ is_causal=False,
+ )
+ else:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask(
+ encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+ )
+
+ return encoder_attention_mask
+
+
+class MBartEncoder(MBartPreTrainedModel):
+ """
+ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
+ [`MBartEncoderLayer`].
+
+ Args:
+ config: MBartConfig
+ embed_tokens (nn.Embedding): output embedding
+ """
+
+ def __init__(self, config: MBartConfig, embed_tokens: Optional[nn.Embedding] = None):
+ super().__init__(config)
+
+ self.dropout = config.dropout
+ self.layerdrop = config.encoder_layerdrop
+
+ embed_dim = config.d_model
+ self.padding_idx = config.pad_token_id
+ self.max_source_positions = config.max_position_embeddings
+ embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
+
+ self.embed_tokens = MBartScaledWordEmbedding(
+ config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale
+ )
+
+ if embed_tokens is not None:
+ self.embed_tokens.weight = embed_tokens.weight
+
+ self.embed_positions = MBartLearnedPositionalEmbedding(
+ config.max_position_embeddings,
+ embed_dim,
+ )
+ self.layers = nn.ModuleList([MBartEncoderLayer(config) for _ in range(config.encoder_layers)])
+ self.config = config
+ self.layernorm_embedding = nn.LayerNorm(embed_dim)
+ self.layer_norm = nn.LayerNorm(config.d_model)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def _backward_compatibility_gradient_checkpointing(self):
+ # Override to not delete the attribute from the config
+ if self.supports_gradient_checkpointing and getattr(self.config, "gradient_checkpointing", False):
+ self.gradient_checkpointing_enable()
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutput]:
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input = input_ids
+ input_shape = input.shape
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input = inputs_embeds[:, :, -1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ embed_pos = self.embed_positions(input)
+
+ hidden_states = inputs_embeds + embed_pos.to(inputs_embeds.device)
+ hidden_states = self.layernorm_embedding(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ attention_mask = self._update_full_mask(
+ attention_mask,
+ inputs_embeds,
+ )
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ if head_mask.size()[0] != len(self.layers):
+ raise ValueError(
+ f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ to_drop = False
+ if self.training:
+ dropout_probability = torch.rand([])
+ if dropout_probability < self.layerdrop: # skip the layer
+ to_drop = True
+
+ if to_drop:
+ layer_outputs = (None, None)
+ else:
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ hidden_states = self.layer_norm(hidden_states)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+ )
+
+
+class MBartDecoder(MBartPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MBartDecoderLayer`]
+
+ Args:
+ config: MBartConfig
+ embed_tokens (nn.Embedding): output embedding
+ """
+
+ def __init__(self, config: MBartConfig, embed_tokens: Optional[nn.Embedding] = None):
+ super().__init__(config)
+ self.dropout = config.dropout
+ self.layerdrop = config.decoder_layerdrop
+ self.padding_idx = config.pad_token_id
+ self.max_target_positions = config.max_position_embeddings
+ embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+
+ self.embed_tokens = MBartScaledWordEmbedding(
+ config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale
+ )
+
+ if embed_tokens is not None:
+ self.embed_tokens.weight = embed_tokens.weight
+
+ self.embed_positions = MBartLearnedPositionalEmbedding(
+ config.max_position_embeddings,
+ config.d_model,
+ )
+ self.layers = nn.ModuleList([MBartDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)])
+ self.config = config
+
+ self.layernorm_embedding = nn.LayerNorm(config.d_model)
+ self.layer_norm = nn.LayerNorm(config.d_model)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPastAndCrossAttentions]:
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
+ cross-attention on hidden heads. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+ shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+ cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+ that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+ all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
+ cache in the correct position and to infer the complete sequence length.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ input = input_ids
+ input_shape = input.shape
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ input = inputs_embeds[:, :, -1]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input)
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing`. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ # initialize `past_key_values`
+ if use_cache and past_key_values is None:
+ past_key_values = (
+ EncoderDecoderCache(DynamicCache(), DynamicCache())
+ if encoder_hidden_states is not None
+ else DynamicCache()
+ )
+ if use_cache and isinstance(past_key_values, tuple):
+ logger.warning_once(
+ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
+ "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
+ "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
+ )
+ past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
+
+ batch_size, seq_length = inputs_embeds.size()[:-1]
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+ if cache_position is None:
+ cache_position = torch.arange(
+ past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
+ )
+
+ if attention_mask is None and not is_torchdynamo_compiling():
+ # required mask seq length can be calculated via length of past cache
+ mask_seq_length = past_key_values_length + seq_length
+ attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
+
+ self_attn_cache = (
+ past_key_values.self_attention_cache
+ if isinstance(past_key_values, EncoderDecoderCache)
+ else past_key_values
+ )
+
+ causal_mask = self._update_causal_mask(
+ attention_mask,
+ inputs_embeds,
+ cache_position,
+ self_attn_cache,
+ )
+ encoder_attention_mask = self._update_cross_attn_mask(
+ encoder_hidden_states,
+ encoder_attention_mask,
+ input_shape,
+ inputs_embeds,
+ )
+
+ # embed positions
+ position_ids = self.embed_positions(input, past_key_values_length, position_ids=cache_position)
+
+ hidden_states = inputs_embeds + position_ids.to(inputs_embeds.device)
+ hidden_states = self.layernorm_embedding(hidden_states)
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
+
+ # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
+ for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
+ if attn_mask is not None:
+ if attn_mask.size()[0] != len(self.layers):
+ raise ValueError(
+ f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+ f" {attn_mask.size()[0]}."
+ )
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ if self.training:
+ dropout_probability = torch.rand([])
+ if dropout_probability < self.layerdrop:
+ continue
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ causal_mask,
+ encoder_hidden_states, # as a positional argument for gradient checkpointing
+ encoder_attention_mask=encoder_attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if encoder_hidden_states is not None:
+ all_cross_attentions += (layer_outputs[2],)
+
+ hidden_states = self.layer_norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions]
+ if v is not None
+ )
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+@auto_docstring
+class MBartModel(MBartPreTrainedModel):
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+ def __init__(self, config: MBartConfig):
+ super().__init__(config)
+
+ padding_idx, vocab_size = config.pad_token_id, config.vocab_size
+ embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+ self.shared = MBartScaledWordEmbedding(vocab_size, config.d_model, padding_idx, embed_scale=embed_scale)
+
+ self.encoder = MBartEncoder(config, self.shared)
+ self.decoder = MBartDecoder(config, self.shared)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, value):
+ self.shared = value
+ self.encoder.embed_tokens = self.shared
+ self.decoder.embed_tokens = self.shared
+
+ def get_encoder(self):
+ return self.encoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ def _tie_weights(self):
+ if self.config.tie_word_embeddings:
+ self._tie_or_clone_weights(self.encoder.embed_tokens, self.get_input_embeddings())
+ self._tie_or_clone_weights(self.decoder.embed_tokens, self.get_input_embeddings())
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[Seq2SeqModelOutput, tuple[torch.FloatTensor]]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ MBart uses a specific language id token as the starting token for `decoder_input_ids` generation that
+ varies according to source and target language, *e.g.* 25004 for *en_XX*, and 25003 for *de_DE*. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # different to other models, MBart automatically creates decoder_input_ids from
+ # input_ids if no decoder_input_ids are provided
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id)
+
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
+ elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+ encoder_outputs = BaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+
+ # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return Seq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The MBART Model with a language modeling head. Can be used for summarization, after fine-tuning the pretrained models.
+ """
+)
+class MBartForConditionalGeneration(MBartPreTrainedModel, GenerationMixin):
+ base_model_prefix = "model"
+ _keys_to_ignore_on_load_missing = ["final_logits_bias"]
+ _tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight", "lm_head.weight"]
+
+ def __init__(self, config: MBartConfig):
+ super().__init__(config)
+ self.model = MBartModel(config)
+ self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
+ self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_encoder(self):
+ return self.model.get_encoder()
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def resize_token_embeddings(
+ self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+ ) -> nn.Embedding:
+ new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
+ self._resize_final_logits_bias(new_embeddings.weight.shape[0])
+ return new_embeddings
+
+ def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
+ old_num_tokens = self.final_logits_bias.shape[-1]
+ if new_num_tokens <= old_num_tokens:
+ new_bias = self.final_logits_bias[:, :new_num_tokens]
+ else:
+ extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
+ new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
+ self.register_buffer("final_logits_bias", new_bias)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[Seq2SeqLMOutput, tuple[torch.FloatTensor]]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ MBart uses a specific language id token as the starting token for `decoder_input_ids` generation that
+ varies according to source and target language, *e.g.* 25004 for *en_XX*, and 25003 for *de_DE*. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example Translation:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MBartForConditionalGeneration
+
+ >>> model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-en-ro")
+
+ >>> example_english_phrase = "42 is the answer"
+ >>> inputs = tokenizer(example_english_phrase, return_tensors="pt")
+
+ >>> # Translate
+ >>> generated_ids = model.generate(**inputs, num_beams=4, max_length=5)
+ >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ '42 este răspuns'
+ ```
+
+ Mask filling example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MBartForConditionalGeneration
+
+ >>> model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
+
+ >>> # de_DE is the language symbol id for German
+ >>> TXT = " Meine Freunde sind nett aber sie essen zu viel Kuchen. de_DE"
+
+ >>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors="pt")["input_ids"]
+ >>> logits = model(input_ids).logits
+
+ >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
+ >>> probs = logits[0, masked_index].softmax(dim=0)
+ >>> values, predictions = probs.topk(5)
+
+ >>> tokenizer.decode(predictions).split()
+ ['nett', 'sehr', 'ganz', 'nicht', 'so']
+ ```
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if labels is not None:
+ if use_cache:
+ logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+ use_cache = False
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id)
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ encoder_outputs=encoder_outputs,
+ decoder_attention_mask=decoder_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+ lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return Seq2SeqLMOutput(
+ loss=masked_lm_loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+ def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+ return shift_tokens_right(labels, self.config.pad_token_id)
+
+
+@auto_docstring(
+ custom_intro="""
+ MBart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
+ tasks.
+ """
+)
+class MBartForSequenceClassification(MBartPreTrainedModel):
+ _tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight"]
+
+ def __init__(self, config: MBartConfig, **kwargs):
+ super().__init__(config, **kwargs)
+ self.model = MBartModel(config)
+ self.classification_head = MBartClassificationHead(
+ config.d_model,
+ config.d_model,
+ config.num_labels,
+ config.classifier_dropout,
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ # Copied from transformers.models.bart.modeling_bart.BartForSequenceClassification.forward
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, Seq2SeqSequenceClassifierOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ Bart uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should read [`modeling_bart._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more
+ information on the default strategy.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ if labels is not None:
+ use_cache = False
+
+ if input_ids is None and inputs_embeds is not None:
+ raise NotImplementedError(
+ f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
+ )
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ encoder_outputs=encoder_outputs,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+ hidden_states = outputs[0] # last hidden state
+
+ eos_mask = input_ids.eq(self.config.eos_token_id).to(hidden_states.device)
+
+ if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
+ raise ValueError("All examples must have the same number of tokens.")
+ sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
+ :, -1, :
+ ]
+ logits = self.classification_head(sentence_representation)
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.config.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.config.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return Seq2SeqSequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+@auto_docstring
+class MBartForQuestionAnswering(MBartPreTrainedModel):
+ _tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ config.num_labels = 2
+ self.num_labels = config.num_labels
+
+ self.model = MBartModel(config)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ # Copied from transformers.models.bart.modeling_bart.BartForQuestionAnswering.forward
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[list[torch.FloatTensor]] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, Seq2SeqQuestionAnsweringModelOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ Bart uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should read [`modeling_bart._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more
+ information on the default strategy.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ if start_positions is not None and end_positions is not None:
+ use_cache = False
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ encoder_outputs=encoder_outputs,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (
+ start_logits,
+ end_logits,
+ ) + outputs[1:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return Seq2SeqQuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->MBart
+class MBartDecoderWrapper(MBartPreTrainedModel):
+ """
+ This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
+ used in combination with the [`EncoderDecoderModel`] framework.
+ """
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.decoder = MBartDecoder(config)
+
+ def forward(self, *args, **kwargs):
+ return self.decoder(*args, **kwargs)
+
+
+# Copied from transformers.models.bart.modeling_bart.BartForCausalLM with Bart->MBart, facebook/bart-base->facebook/mbart-large-cc25
+class MBartForCausalLM(MBartPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ config.is_decoder = True
+ config.is_encoder_decoder = False
+ super().__init__(config)
+ self.model = MBartDecoderWrapper(config)
+
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.decoder.embed_tokens = value
+
+ def set_decoder(self, decoder):
+ self.model.decoder = decoder
+
+ def get_decoder(self):
+ return self.model.decoder
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
+ r"""
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MBartForCausalLM
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
+ >>> model = MBartForCausalLM.from_pretrained("facebook/mbart-large-cc25", add_cross_attention=False)
+ >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
+ >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+ >>> outputs = model(**inputs)
+
+ >>> logits = outputs.logits
+ >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
+ >>> list(logits.shape) == expected_shape
+ True
+ ```"""
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model.decoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ head_mask=head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ logits = self.lm_head(outputs[0])
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithCrossAttentions(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ cross_attentions=outputs.cross_attentions,
+ )
+
+
+__all__ = [
+ "MBartForCausalLM",
+ "MBartForConditionalGeneration",
+ "MBartForQuestionAnswering",
+ "MBartForSequenceClassification",
+ "MBartModel",
+ "MBartPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/modeling_tf_mbart.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/modeling_tf_mbart.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac29bfeac76ff333483ea7396f70f9f2fded5c39
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/modeling_tf_mbart.py
@@ -0,0 +1,1572 @@
+# coding=utf-8
+# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TF 2.0 MBart model."""
+
+from __future__ import annotations
+
+import random
+
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_outputs import (
+ TFBaseModelOutput,
+ TFBaseModelOutputWithPastAndCrossAttentions,
+ TFSeq2SeqLMOutput,
+ TFSeq2SeqModelOutput,
+)
+
+# Public API
+from ...modeling_tf_utils import (
+ TFCausalLanguageModelingLoss,
+ TFModelInputType,
+ TFPreTrainedModel,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
+from ...utils import (
+ add_code_sample_docstrings,
+ add_end_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+ replace_return_docstrings,
+)
+from .configuration_mbart import MBartConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "facebook/mbart-large-cc25"
+_CONFIG_FOR_DOC = "MBartConfig"
+
+
+LARGE_NEGATIVE = -1e8
+
+
+def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int):
+ """
+ Shift input ids one token to the right, and wrap the last non pad token (the token) Note that MBart does not
+ have a single `decoder_start_token_id` in contrast to other Bart-like models.
+ """
+ if pad_token_id is None:
+ raise ValueError("self.model.config.pad_token_id has to be defined.")
+ # replace possible -100 values in labels by `pad_token_id`
+ input_ids = tf.where(
+ input_ids == -100, tf.fill(shape_list(input_ids), tf.cast(pad_token_id, input_ids.dtype)), input_ids
+ )
+ language_id_index = (
+ tf.reduce_sum(tf.cast(tf.math.not_equal(input_ids, pad_token_id), dtype=input_ids.dtype), axis=-1) - 1
+ )
+ language_id_index = tf.stack(
+ [tf.range(shape_list(input_ids)[0], dtype=input_ids.dtype), language_id_index], axis=-1
+ )
+ languages_ids = tf.gather_nd(input_ids, language_id_index)
+
+ shifted_input_ids = tf.concat([tf.expand_dims(languages_ids, axis=-1), input_ids[:, :-1]], axis=-1)
+
+ return shifted_input_ids
+
+
+# Copied from transformers.models.bart.modeling_tf_bart._make_causal_mask
+def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
+ """
+ Make causal mask used for bi-directional self-attention.
+ """
+ bsz = input_ids_shape[0]
+ tgt_len = input_ids_shape[1]
+ mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE
+ mask_cond = tf.range(shape_list(mask)[-1])
+
+ mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask)
+
+ if past_key_values_length > 0:
+ mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
+
+ return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
+
+
+# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
+def _expand_mask(mask: tf.Tensor, tgt_len: int | None = None):
+ """
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+ """
+ src_len = shape_list(mask)[1]
+ tgt_len = tgt_len if tgt_len is not None else src_len
+ one_cst = tf.constant(1.0)
+ mask = tf.cast(mask, dtype=one_cst.dtype)
+ expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
+
+ return (one_cst - expanded_mask) * LARGE_NEGATIVE
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.TFBartLearnedPositionalEmbedding with Bart->MBart
+class TFMBartLearnedPositionalEmbedding(keras.layers.Embedding):
+ """
+ This module learns positional embeddings up to a fixed maximum size.
+ """
+
+ def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
+ # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
+ # and adjust num_embeddings appropriately. Other models don't have this hack
+ self.offset = 2
+ super().__init__(num_embeddings + self.offset, embedding_dim, **kwargs)
+
+ def call(
+ self,
+ input_shape: tf.TensorShape | None = None,
+ past_key_values_length: int = 0,
+ position_ids: tf.Tensor | None = None,
+ ):
+ """Input is expected to be of size [bsz x seqlen]."""
+ if position_ids is None:
+ seq_len = input_shape[1]
+ position_ids = tf.range(seq_len, delta=1, name="range")
+ position_ids += past_key_values_length
+
+ offset_dtype = position_ids.dtype if isinstance(position_ids, tf.Tensor) else tf.int32
+ return super().call(position_ids + tf.constant(self.offset, dtype=offset_dtype))
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with Bart->MBart
+class TFMBartAttention(keras.layers.Layer):
+ """Multi-headed attention from "Attention Is All You Need"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: float = 0.0,
+ is_decoder: bool = False,
+ bias: bool = True,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.embed_dim = embed_dim
+
+ self.num_heads = num_heads
+ self.dropout = keras.layers.Dropout(dropout)
+ self.head_dim = embed_dim // num_heads
+ if (self.head_dim * num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+
+ self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
+ self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
+ self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
+ self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
+
+ def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
+ return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ key_value_states: tf.Tensor | None = None,
+ past_key_value: tuple[tuple[tf.Tensor]] | None = None,
+ attention_mask: tf.Tensor | None = None,
+ layer_head_mask: tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple[tf.Tensor, tf.Tensor | None]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+ bsz, tgt_len, embed_dim = shape_list(hidden_states)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states) * self.scaling
+ # get key, value proj
+ if is_cross_attention and past_key_value is not None:
+ # reuse k,v, cross_attentions
+ key_states = past_key_value[0]
+ value_states = past_key_value[1]
+ elif is_cross_attention:
+ # cross_attentions
+ key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
+ value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
+ elif past_key_value is not None:
+ # reuse k, v, self_attention
+ key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+ value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+ key_states = tf.concat([past_key_value[0], key_states], axis=2)
+ value_states = tf.concat([past_key_value[1], value_states], axis=2)
+ else:
+ # self_attention
+ key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+ value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+
+ if self.is_decoder:
+ # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
+ # Further calls to cross_attention layer can then reuse all cross-attention
+ # key/value_states (first "if" case)
+ # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
+ # all previous decoder key/value_states. Further calls to uni-directional self-attention
+ # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+ # if encoder bi-directional self-attention `past_key_value` is always `None`
+ past_key_value = (key_states, value_states)
+
+ proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+ query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
+ key_states = tf.reshape(key_states, proj_shape)
+ value_states = tf.reshape(value_states, proj_shape)
+
+ src_len = shape_list(key_states)[1]
+ attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
+
+ tf.debugging.assert_equal(
+ shape_list(attn_weights),
+ [bsz * self.num_heads, tgt_len, src_len],
+ message=(
+ f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
+ f" {shape_list(attn_weights)}"
+ ),
+ )
+
+ if attention_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(attention_mask),
+ [bsz, 1, tgt_len, src_len],
+ message=(
+ f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
+ f" {shape_list(attention_mask)}"
+ ),
+ )
+
+ attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
+ attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
+ attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
+
+ attn_weights = stable_softmax(attn_weights, axis=-1)
+
+ if layer_head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(layer_head_mask),
+ [self.num_heads],
+ message=(
+ f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
+ f" {shape_list(layer_head_mask)}"
+ ),
+ )
+
+ attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
+ attn_weights, (bsz, self.num_heads, tgt_len, src_len)
+ )
+ attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
+
+ attn_probs = self.dropout(attn_weights, training=training)
+ attn_output = tf.matmul(attn_probs, value_states)
+
+ tf.debugging.assert_equal(
+ shape_list(attn_output),
+ [bsz * self.num_heads, tgt_len, self.head_dim],
+ message=(
+ f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
+ f" {shape_list(attn_output)}"
+ ),
+ )
+
+ attn_output = tf.transpose(
+ tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
+ )
+ attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
+
+ attn_output = self.out_proj(attn_output)
+ attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
+
+ return attn_output, attn_weights, past_key_value
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "k_proj", None) is not None:
+ with tf.name_scope(self.k_proj.name):
+ self.k_proj.build([None, None, self.embed_dim])
+ if getattr(self, "q_proj", None) is not None:
+ with tf.name_scope(self.q_proj.name):
+ self.q_proj.build([None, None, self.embed_dim])
+ if getattr(self, "v_proj", None) is not None:
+ with tf.name_scope(self.v_proj.name):
+ self.v_proj.build([None, None, self.embed_dim])
+ if getattr(self, "out_proj", None) is not None:
+ with tf.name_scope(self.out_proj.name):
+ self.out_proj.build([None, None, self.embed_dim])
+
+
+class TFMBartEncoderLayer(keras.layers.Layer):
+ def __init__(self, config: MBartConfig, **kwargs):
+ super().__init__(**kwargs)
+ self.embed_dim = config.d_model
+ self.self_attn = TFMBartAttention(
+ self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn"
+ )
+ self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
+ self.dropout = keras.layers.Dropout(config.dropout)
+ self.activation_fn = get_tf_activation(config.activation_function)
+ self.activation_dropout = keras.layers.Dropout(config.activation_dropout)
+ self.fc1 = keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
+ self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2")
+ self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+ self.config = config
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: tf.Tensor,
+ layer_head_mask: tf.Tensor,
+ training: bool | None = False,
+ ):
+ """
+ Args:
+ hidden_states (`tf.Tensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
+ attention_mask (`tf.Tensor`): attention mask of size
+ *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
+ layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
+ *(encoder_attention_heads,)*
+ """
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+ hidden_states, self_attn_weights, _ = self.self_attn(
+ hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask
+ )
+
+ tf.debugging.assert_equal(
+ shape_list(hidden_states),
+ shape_list(residual),
+ message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
+ )
+
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = self.activation_dropout(hidden_states, training=training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+
+ return hidden_states, self_attn_weights
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self_attn", None) is not None:
+ with tf.name_scope(self.self_attn.name):
+ self.self_attn.build(None)
+ if getattr(self, "self_attn_layer_norm", None) is not None:
+ with tf.name_scope(self.self_attn_layer_norm.name):
+ self.self_attn_layer_norm.build([None, None, self.embed_dim])
+ if getattr(self, "fc1", None) is not None:
+ with tf.name_scope(self.fc1.name):
+ self.fc1.build([None, None, self.embed_dim])
+ if getattr(self, "fc2", None) is not None:
+ with tf.name_scope(self.fc2.name):
+ self.fc2.build([None, None, self.config.encoder_ffn_dim])
+ if getattr(self, "final_layer_norm", None) is not None:
+ with tf.name_scope(self.final_layer_norm.name):
+ self.final_layer_norm.build([None, None, self.embed_dim])
+
+
+class TFMBartDecoderLayer(keras.layers.Layer):
+ def __init__(self, config: MBartConfig, **kwargs):
+ super().__init__(**kwargs)
+ self.embed_dim = config.d_model
+ self.self_attn = TFMBartAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ name="self_attn",
+ is_decoder=True,
+ )
+ self.dropout = keras.layers.Dropout(config.dropout)
+ self.activation_fn = get_tf_activation(config.activation_function)
+ self.activation_dropout = keras.layers.Dropout(config.activation_dropout)
+
+ self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
+ self.encoder_attn = TFMBartAttention(
+ self.embed_dim,
+ config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ name="encoder_attn",
+ is_decoder=True,
+ )
+ self.encoder_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
+ self.fc1 = keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
+ self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2")
+ self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+ self.config = config
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: tf.Tensor | None = None,
+ encoder_hidden_states: tf.Tensor | None = None,
+ encoder_attention_mask: tf.Tensor | None = None,
+ layer_head_mask: tf.Tensor | None = None,
+ cross_attn_layer_head_mask: tf.Tensor | None = None,
+ past_key_value: tuple[tf.Tensor] | None = None,
+ training: bool | None = False,
+ ) -> tuple[tf.Tensor, tf.Tensor, tuple[tuple[tf.Tensor]]]:
+ """
+ Args:
+ hidden_states (`tf.Tensor`): input to the layer of shape *(batch, seq_len, embed_dim)*
+ attention_mask (`tf.Tensor`): attention mask of size
+ *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
+ encoder_hidden_states (`tf.Tensor`):
+ cross attention input to the layer of shape *(batch, seq_len, embed_dim)*
+ encoder_attention_mask (`tf.Tensor`): encoder attention mask of size
+ *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
+ layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
+ *(decoder_attention_heads,)*
+ cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-attention module.
+ *(decoder_attention_heads,)*
+ past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states
+ """
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Self Attention
+ # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+ self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+ # add present self-attn cache to positions 1,2 of present_key_value tuple
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_value=self_attn_past_key_value,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ )
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+
+ # Cross-Attention Block
+ cross_attn_present_key_value = None
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
+ cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
+ hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ layer_head_mask=cross_attn_layer_head_mask,
+ past_key_value=cross_attn_past_key_value,
+ )
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+
+ # add cross-attn to positions 3,4 of present_key_value tuple
+ present_key_value = present_key_value + cross_attn_present_key_value
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = self.activation_dropout(hidden_states, training=training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+
+ return (
+ hidden_states,
+ self_attn_weights,
+ cross_attn_weights,
+ present_key_value,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self_attn", None) is not None:
+ with tf.name_scope(self.self_attn.name):
+ self.self_attn.build(None)
+ if getattr(self, "self_attn_layer_norm", None) is not None:
+ with tf.name_scope(self.self_attn_layer_norm.name):
+ self.self_attn_layer_norm.build([None, None, self.embed_dim])
+ if getattr(self, "encoder_attn", None) is not None:
+ with tf.name_scope(self.encoder_attn.name):
+ self.encoder_attn.build(None)
+ if getattr(self, "encoder_attn_layer_norm", None) is not None:
+ with tf.name_scope(self.encoder_attn_layer_norm.name):
+ self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+ if getattr(self, "fc1", None) is not None:
+ with tf.name_scope(self.fc1.name):
+ self.fc1.build([None, None, self.embed_dim])
+ if getattr(self, "fc2", None) is not None:
+ with tf.name_scope(self.fc2.name):
+ self.fc2.build([None, None, self.config.decoder_ffn_dim])
+ if getattr(self, "final_layer_norm", None) is not None:
+ with tf.name_scope(self.final_layer_norm.name):
+ self.final_layer_norm.build([None, None, self.embed_dim])
+
+
+class TFMBartPreTrainedModel(TFPreTrainedModel):
+ config_class = MBartConfig
+ base_model_prefix = "model"
+
+
+MBART_START_DOCSTRING = r"""
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Args:
+ config ([`MBartConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+MBART_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`tf.Tensor` of shape `({0})`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `({0})`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ MBart uses a specific language id token as the starting token for `decoder_input_ids` generation that
+ varies according to source and target language, *e.g.* 25004 for *en_XX*, and 25003 for *de_DE*. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
+ decoder_position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
+ range `[0, config.max_position_embeddings - 1]`.
+ head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ encoder_outputs (`tf.FloatTensor`, *optional*):
+ hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
+ of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
+ past_key_values (`tuple[tuple[tf.Tensor]]` of length `config.n_layers`)
+ contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`). Set to `False` during training, `True` during generation
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+ config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+ used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+ eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+"""
+
+MBART_GENERATION_EXAMPLE = r"""
+ Translation example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, TFMBartForConditionalGeneration
+
+ >>> model = TFMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-en-ro")
+
+ >>> example_english_phrase = "42 is the answer"
+ >>> inputs = tokenizer(example_english_phrase, return_tensors="tf")
+
+ >>> # Translate
+ >>> generated_ids = model.generate(**inputs, num_beams=4, max_length=5)
+ >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ '42 este răspuns'
+ ```
+
+ Mask filling example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, TFMBartForConditionalGeneration
+ >>> import tensorflow as tf
+
+ >>> model = TFMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
+
+ >>> # de_DE is the language symbol id for German
+ >>> TXT = " Meine Freunde sind nett aber sie essen zu viel Kuchen. de_DE"
+
+ >>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors="tf")["input_ids"]
+ >>> logits = model(input_ids).logits
+
+ >>> masked_index = tf.where(input_ids[0] == tokenizer.mask_token_id)[0, 0]
+ >>> probs = tf.nn.softmax(logits[0, masked_index], axis=0)
+ >>> values, predictions = tf.math.top_k(probs, 5)
+
+ >>> tokenizer.decode(predictions).split()
+ ['nett', 'sehr', 'ganz', 'nicht', 'so']
+ ```
+"""
+
+
+@keras_serializable
+class TFMBartEncoder(keras.layers.Layer):
+ config_class = MBartConfig
+ """
+ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
+ [`TFMBartEncoderLayer`].
+
+ Args:
+ config: MBartConfig
+ """
+
+ def __init__(self, config: MBartConfig, embed_tokens: keras.layers.Embedding | None = None, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.dropout = keras.layers.Dropout(config.dropout)
+ self.layerdrop = config.encoder_layerdrop
+ self.padding_idx = config.pad_token_id
+ self.max_source_positions = config.max_position_embeddings
+ self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
+
+ self.embed_tokens = embed_tokens
+ self.embed_positions = TFMBartLearnedPositionalEmbedding(
+ config.max_position_embeddings,
+ config.d_model,
+ name="embed_positions",
+ )
+ self.layers = [TFMBartEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
+ self.layernorm_embedding = keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
+ self.layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
+ self.embed_dim = config.d_model
+
+ def get_embed_tokens(self):
+ return self.embed_tokens
+
+ def set_embed_tokens(self, embed_tokens):
+ self.embed_tokens = embed_tokens
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ ) -> TFBaseModelOutput | tuple[tf.Tensor]:
+ """
+ Args:
+ input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
+ in the config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail. This argument can be used only in eager mode, in graph mode the value in the config
+ will be used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
+ in eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+ """
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
+ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+ embed_pos = self.embed_positions(input_shape)
+ hidden_states = inputs_embeds + embed_pos
+ hidden_states = self.layernorm_embedding(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+
+ # check attention mask and invert
+ if attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _expand_mask(attention_mask)
+ else:
+ attention_mask = None
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(head_mask)[0],
+ len(self.layers),
+ message=(
+ f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
+ f" {shape_list(head_mask)[0]}."
+ ),
+ )
+
+ # encoder layers
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = random.uniform(0, 1)
+ if training and (dropout_probability < self.layerdrop): # skip the layer
+ continue
+
+ hidden_states, attn = encoder_layer(
+ hidden_states,
+ attention_mask,
+ head_mask[idx] if head_mask is not None else None,
+ )
+
+ if output_attentions:
+ all_attentions += (attn,)
+
+ hidden_states = self.layer_norm(hidden_states)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+ return TFBaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embed_positions", None) is not None:
+ with tf.name_scope(self.embed_positions.name):
+ self.embed_positions.build(None)
+ if getattr(self, "layernorm_embedding", None) is not None:
+ with tf.name_scope(self.layernorm_embedding.name):
+ self.layernorm_embedding.build([None, None, self.embed_dim])
+ if getattr(self, "layer_norm", None) is not None:
+ with tf.name_scope(self.layer_norm.name):
+ self.layer_norm.build([None, None, self.config.d_model])
+ if getattr(self, "layers", None) is not None:
+ for layer in self.layers:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+@keras_serializable
+class TFMBartDecoder(keras.layers.Layer):
+ config_class = MBartConfig
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFMBartDecoderLayer`]
+
+ Args:
+ config: MBartConfig
+ embed_tokens: output embedding
+ """
+
+ def __init__(self, config: MBartConfig, embed_tokens: keras.layers.Embedding | None = None, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.padding_idx = config.pad_token_id
+ self.embed_tokens = embed_tokens
+ self.layerdrop = config.decoder_layerdrop
+ self.embed_positions = TFMBartLearnedPositionalEmbedding(
+ config.max_position_embeddings,
+ config.d_model,
+ name="embed_positions",
+ )
+ self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0
+ self.layers = [TFMBartDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
+ self.layernorm_embedding = keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
+ self.layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
+
+ self.dropout = keras.layers.Dropout(config.dropout)
+
+ def get_embed_tokens(self):
+ return self.embed_tokens
+
+ def set_embed_tokens(self, embed_tokens):
+ self.embed_tokens = embed_tokens
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: TFModelInputType = None,
+ inputs_embeds: tf.Tensor | None = None,
+ attention_mask: tf.Tensor | None = None,
+ position_ids: tf.Tensor | None = None,
+ encoder_hidden_states: tf.Tensor | None = None,
+ encoder_attention_mask: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ cross_attn_head_mask: tf.Tensor | None = None,
+ past_key_values: tuple[tuple[tf.Tensor]] | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ ) -> TFBaseModelOutputWithPastAndCrossAttentions | tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]:
+ r"""
+ Args:
+ input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
+ range `[0, config.max_position_embeddings - 1]`.
+ encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`tf.Tensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`tuple[tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+ Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
+ decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+ that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+ all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
+ in the config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail. This argument can be used only in eager mode, in graph mode the value in the config
+ will be used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
+ in eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+ """
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
+
+ # embed positions
+ if position_ids is None:
+ positions = self.embed_positions(input_shape, past_key_values_length)
+ else:
+ positions = self.embed_positions(input_shape, position_ids=position_ids)
+
+ if inputs_embeds is None:
+ check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim)
+ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+ hidden_states = inputs_embeds
+
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ if input_shape[-1] > 1:
+ combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
+ else:
+ combined_attention_mask = _expand_mask(
+ tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
+ )
+
+ if attention_mask is not None:
+ combined_attention_mask = combined_attention_mask + _expand_mask(attention_mask, tgt_len=input_shape[-1])
+
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1])
+
+ hidden_states = self.layernorm_embedding(hidden_states + positions)
+ hidden_states = self.dropout(hidden_states, training=training)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None
+ present_key_values = () if use_cache else None
+
+ # check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired
+ for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]:
+ if attn_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(attn_mask)[0],
+ len(self.layers),
+ message=(
+ f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for"
+ f" {shape_list(attn_mask)[0]}."
+ ),
+ )
+
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ dropout_probability = random.uniform(0, 1)
+
+ if training and (dropout_probability < self.layerdrop):
+ continue
+
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+ hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
+ hidden_states,
+ attention_mask=combined_attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ layer_head_mask=head_mask[idx] if head_mask is not None else None,
+ cross_attn_layer_head_mask=cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None,
+ past_key_value=past_key_value,
+ )
+
+ if use_cache:
+ present_key_values += (present_key_value,)
+
+ if output_attentions:
+ all_self_attns += (layer_self_attn,)
+
+ if encoder_hidden_states is not None:
+ all_cross_attns += (layer_cross_attn,)
+
+ hidden_states = self.layer_norm(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns
+ else:
+ return TFBaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=present_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attns,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embed_positions", None) is not None:
+ with tf.name_scope(self.embed_positions.name):
+ self.embed_positions.build(None)
+ if getattr(self, "layernorm_embedding", None) is not None:
+ with tf.name_scope(self.layernorm_embedding.name):
+ self.layernorm_embedding.build([None, None, self.config.d_model])
+ if getattr(self, "layer_norm", None) is not None:
+ with tf.name_scope(self.layer_norm.name):
+ self.layer_norm.build([None, None, self.config.d_model])
+ if getattr(self, "layers", None) is not None:
+ for layer in self.layers:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+@keras_serializable
+class TFMBartMainLayer(keras.layers.Layer):
+ config_class = MBartConfig
+
+ def __init__(self, config: MBartConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.shared = keras.layers.Embedding(
+ input_dim=config.vocab_size,
+ output_dim=config.d_model,
+ embeddings_initializer=keras.initializers.TruncatedNormal(stddev=self.config.init_std),
+ name="model.shared",
+ )
+ # Additional attribute to specify the expected name scope of the layer (for loading/storing weights)
+ self.shared.load_weight_prefix = "model.shared"
+
+ self.encoder = TFMBartEncoder(config, self.shared, name="encoder")
+ self.decoder = TFMBartDecoder(config, self.shared, name="decoder")
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, new_embeddings):
+ self.shared = new_embeddings
+ self.encoder.embed_tokens = self.shared
+ self.decoder.embed_tokens = self.shared
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: TFModelInputType = None,
+ attention_mask: tf.Tensor | None = None,
+ decoder_input_ids: tf.Tensor | None = None,
+ decoder_attention_mask: tf.Tensor | None = None,
+ decoder_position_ids: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ decoder_head_mask: tf.Tensor | None = None,
+ cross_attn_head_mask: tf.Tensor | None = None,
+ encoder_outputs: tuple | TFBaseModelOutput | None = None,
+ past_key_values: tuple[tuple[tf.Tensor]] | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ decoder_inputs_embeds: tf.Tensor | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ **kwargs,
+ ) -> TFSeq2SeqModelOutput | tf.Tensor:
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ use_cache = False
+
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ if decoder_input_ids is None and input_ids is not None:
+ decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id)
+
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ # If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
+ elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput):
+ encoder_outputs = TFBaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+ # If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
+ elif not return_dict and not isinstance(encoder_outputs, tuple):
+ encoder_outputs = encoder_outputs.to_tuple()
+
+ decoder_outputs = self.decoder(
+ decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ position_ids=decoder_position_ids,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return TFSeq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ # The shared/tied weights expect to be in the model base namespace
+ # Adding "/" to the end (not the start!) of a tf.name_scope puts it in the root namespace rather than
+ # the current one.
+ with tf.name_scope(self.shared.load_weight_prefix + "/" + self.shared.name + "/"):
+ self.shared.build(None)
+ if getattr(self, "encoder", None) is not None:
+ with tf.name_scope(self.encoder.name):
+ self.encoder.build(None)
+ if getattr(self, "decoder", None) is not None:
+ with tf.name_scope(self.decoder.name):
+ self.decoder.build(None)
+
+
+@add_start_docstrings(
+ "The bare MBART Model outputting raw hidden-states without any specific head on top.",
+ MBART_START_DOCSTRING,
+)
+class TFMBartModel(TFMBartPreTrainedModel):
+ def __init__(self, config: MBartConfig, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.model = TFMBartMainLayer(config, name="model")
+
+ def get_encoder(self):
+ return self.model.encoder
+
+ def get_decoder(self):
+ return self.model.decoder
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFSeq2SeqModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType = None,
+ attention_mask: tf.Tensor | None = None,
+ decoder_input_ids: tf.Tensor | None = None,
+ decoder_attention_mask: tf.Tensor | None = None,
+ decoder_position_ids: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ decoder_head_mask: tf.Tensor | None = None,
+ cross_attn_head_mask: tf.Tensor | None = None,
+ encoder_outputs: tuple | TFBaseModelOutput | None = None,
+ past_key_values: tuple[tuple[tf.Tensor]] | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ decoder_inputs_embeds: tf.Tensor | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ **kwargs,
+ ) -> TFSeq2SeqModelOutput | tuple[tf.Tensor]:
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ decoder_position_ids=decoder_position_ids,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ encoder_outputs=encoder_outputs,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ return outputs
+
+ # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
+ def serving_output(self, output):
+ pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
+ dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
+ dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
+ cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
+ enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
+ enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
+
+ return TFSeq2SeqModelOutput(
+ last_hidden_state=output.last_hidden_state,
+ past_key_values=pkv,
+ decoder_hidden_states=dec_hs,
+ decoder_attentions=dec_attns,
+ cross_attentions=cross_attns,
+ encoder_last_hidden_state=output.encoder_last_hidden_state,
+ encoder_hidden_states=enc_hs,
+ encoder_attentions=enc_attns,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "model", None) is not None:
+ with tf.name_scope(self.model.name):
+ self.model.build(None)
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
+class BiasLayer(keras.layers.Layer):
+ """
+ Bias as a layer. It is used for serialization purposes: `keras.Model.save_weights` stores on a per-layer basis,
+ so all weights have to be registered in a layer.
+ """
+
+ def __init__(self, shape, initializer, trainable, name, **kwargs):
+ super().__init__(name=name, **kwargs)
+ # Note: the name of this variable will NOT be scoped when serialized, i.e. it will not be in the format of
+ # "outer_layer/inner_layer/.../name:0". Instead, it will be "name:0". For further details, see:
+ # https://github.com/huggingface/transformers/pull/18833#issuecomment-1233090214
+ self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable)
+
+ def call(self, x):
+ return x + self.bias
+
+
+@add_start_docstrings(
+ "The MBART Model with a language modeling head. Can be used for summarization, after fine-tuning the pretrained models.",
+ MBART_START_DOCSTRING,
+)
+class TFMBartForConditionalGeneration(TFMBartPreTrainedModel, TFCausalLanguageModelingLoss):
+ _keys_to_ignore_on_load_unexpected = [
+ r"model.encoder.embed_tokens.weight",
+ r"model.decoder.embed_tokens.weight",
+ ]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.model = TFMBartMainLayer(config, name="model")
+ self.use_cache = config.use_cache
+ # final_bias_logits is registered as a buffer in pytorch, so not trainable for the sake of consistency.
+ self.bias_layer = BiasLayer(
+ name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
+ )
+
+ def get_decoder(self):
+ return self.model.decoder
+
+ def get_encoder(self):
+ return self.model.encoder
+
+ def get_output_embeddings(self):
+ return self.get_input_embeddings()
+
+ def set_output_embeddings(self, value):
+ self.set_input_embeddings(value)
+
+ def get_bias(self):
+ return {"final_logits_bias": self.bias_layer.bias}
+
+ def set_bias(self, value):
+ # Replaces the existing layers containing bias for correct (de)serialization.
+ vocab_size = value["final_logits_bias"].shape[-1]
+ self.bias_layer = BiasLayer(
+ name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False
+ )
+ self.bias_layer.bias.assign(value["final_logits_bias"])
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
+ @add_end_docstrings(MBART_GENERATION_EXAMPLE)
+ def call(
+ self,
+ input_ids: TFModelInputType = None,
+ attention_mask: tf.Tensor | None = None,
+ decoder_input_ids: tf.Tensor | None = None,
+ decoder_attention_mask: tf.Tensor | None = None,
+ decoder_position_ids: tf.Tensor | None = None,
+ head_mask: tf.Tensor | None = None,
+ decoder_head_mask: tf.Tensor | None = None,
+ cross_attn_head_mask: tf.Tensor | None = None,
+ encoder_outputs: TFBaseModelOutput | None = None,
+ past_key_values: tuple[tuple[tf.Tensor]] | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ decoder_inputs_embeds: tf.Tensor | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> TFSeq2SeqLMOutput | tuple[tf.Tensor]:
+ """
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Returns:
+
+ """
+
+ if labels is not None:
+ labels = tf.where(
+ labels == self.config.pad_token_id,
+ tf.cast(tf.fill(shape_list(labels), -100), labels.dtype),
+ labels,
+ )
+ use_cache = False
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id)
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ encoder_outputs=encoder_outputs,
+ decoder_attention_mask=decoder_attention_mask,
+ decoder_position_ids=decoder_position_ids,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ lm_logits = tf.matmul(outputs[0], self.model.shared.weights, transpose_b=True)
+ lm_logits = self.bias_layer(lm_logits)
+ masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits)
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+ return TFSeq2SeqLMOutput(
+ loss=masked_lm_loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values, # index 1 of d outputs
+ decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs
+ decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs
+ cross_attentions=outputs.cross_attentions, # index 4 of d outputs
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs
+ encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out
+ encoder_attentions=outputs.encoder_attentions, # 2 of e out
+ )
+
+ # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
+ def serving_output(self, output):
+ pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
+ dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
+ dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
+ cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
+ enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
+ enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
+
+ return TFSeq2SeqLMOutput(
+ logits=output.logits,
+ past_key_values=pkv,
+ decoder_hidden_states=dec_hs,
+ decoder_attentions=dec_attns,
+ cross_attentions=cross_attns,
+ encoder_last_hidden_state=output.encoder_last_hidden_state,
+ encoder_hidden_states=enc_hs,
+ encoder_attentions=enc_attns,
+ )
+
+ # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.prepare_inputs_for_generation
+ def prepare_inputs_for_generation(
+ self,
+ decoder_input_ids,
+ past_key_values=None,
+ attention_mask=None,
+ decoder_attention_mask=None,
+ head_mask=None,
+ decoder_head_mask=None,
+ cross_attn_head_mask=None,
+ use_cache=None,
+ encoder_outputs=None,
+ **kwargs,
+ ):
+ # cut decoder_input_ids if past_key_values is used
+ if past_key_values is not None:
+ decoder_input_ids = decoder_input_ids[:, -1:]
+
+ if decoder_attention_mask is not None: # xla
+ decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
+ elif past_key_values is not None: # no xla + past_key_values
+ decoder_position_ids = past_key_values[0][0].shape[2]
+ else: # no xla + no past_key_values
+ decoder_position_ids = tf.range(decoder_input_ids.shape[1])
+
+ return {
+ "input_ids": None, # encoder_outputs is defined. input_ids not needed
+ "encoder_outputs": encoder_outputs,
+ "past_key_values": past_key_values,
+ "decoder_input_ids": decoder_input_ids,
+ "attention_mask": attention_mask,
+ "decoder_attention_mask": decoder_attention_mask,
+ "decoder_position_ids": decoder_position_ids,
+ "head_mask": head_mask,
+ "decoder_head_mask": decoder_head_mask,
+ "cross_attn_head_mask": cross_attn_head_mask,
+ "use_cache": use_cache, # change this to avoid caching (presumably for debugging)
+ }
+
+ def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
+ return shift_tokens_right(labels, self.config.pad_token_id)
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "model", None) is not None:
+ with tf.name_scope(self.model.name):
+ self.model.build(None)
+ if getattr(self, "bias_layer", None) is not None:
+ with tf.name_scope(self.bias_layer.name):
+ self.bias_layer.build(None)
+
+
+__all__ = ["TFMBartForConditionalGeneration", "TFMBartModel", "TFMBartPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/tokenization_mbart.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/tokenization_mbart.py
new file mode 100644
index 0000000000000000000000000000000000000000..852357c9a2669d3739b156f53d3da0cde60051e1
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/tokenization_mbart.py
@@ -0,0 +1,342 @@
+# coding=utf-8
+# Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from shutil import copyfile
+from typing import Any, Optional
+
+import sentencepiece as spm
+
+from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
+from ...utils import logging
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+SPIECE_UNDERLINE = "▁"
+
+VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
+
+
+FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"] # fmt: skip
+
+
+@requires(backends=("sentencepiece",))
+class MBartTokenizer(PreTrainedTokenizer):
+ """
+ Construct an MBART tokenizer.
+
+ Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
+ [SentencePiece](https://github.com/google/sentencepiece).
+
+ The tokenization method is ` ` for source language documents, and `
+ ` for target language documents.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MBartTokenizer
+
+ >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX", tgt_lang="ro_RO")
+ >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
+ >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
+ >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors="pt")
+ ```"""
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ prefix_tokens: list[int] = []
+ suffix_tokens: list[int] = []
+
+ def __init__(
+ self,
+ vocab_file,
+ bos_token="",
+ eos_token=" ",
+ sep_token="",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ tokenizer_file=None,
+ src_lang=None,
+ tgt_lang=None,
+ sp_model_kwargs: Optional[dict[str, Any]] = None,
+ additional_special_tokens=None,
+ **kwargs,
+ ):
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = (
+ AddedToken(mask_token, lstrip=True, normalized=False) if isinstance(mask_token, str) else mask_token
+ )
+
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.Load(str(vocab_file))
+ self.vocab_file = vocab_file
+
+ # Original fairseq vocab and spm vocab must be "aligned":
+ # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
+ # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
+ # fairseq | '' | '' | ' ' | '' | ',' | '.' | '▁' | 's' | '▁de' | '-'
+ # spm | '' | '' | ' ' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
+
+ # Mimic fairseq token-to-id alignment for the first 4 token
+ self.fairseq_tokens_to_ids = {"": 0, "": 1, " ": 2, "": 3}
+
+ # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
+ self.fairseq_offset = 1
+
+ self.sp_model_size = len(self.sp_model)
+ self.lang_code_to_id = {
+ code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(FAIRSEQ_LANGUAGE_CODES)
+ }
+ self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()}
+ self.fairseq_tokens_to_ids[""] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset
+
+ self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
+ self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
+ _additional_special_tokens = list(self.lang_code_to_id.keys())
+
+ if additional_special_tokens is not None:
+ # Only add those special tokens if they are not already there.
+ _additional_special_tokens.extend(
+ [t for t in additional_special_tokens if t not in _additional_special_tokens]
+ )
+
+ super().__init__(
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ tokenizer_file=None,
+ src_lang=src_lang,
+ tgt_lang=tgt_lang,
+ additional_special_tokens=_additional_special_tokens,
+ sp_model_kwargs=self.sp_model_kwargs,
+ **kwargs,
+ )
+
+ self._src_lang = src_lang if src_lang is not None else "en_XX"
+ self.cur_lang_code_id = self.lang_code_to_id[self._src_lang]
+ self.tgt_lang = tgt_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+ state["sp_model"] = None
+ state["sp_model_proto"] = self.sp_model.serialized_model_proto()
+ return state
+
+ def __setstate__(self, d):
+ self.__dict__ = d
+
+ # for backward compatibility
+ if not hasattr(self, "sp_model_kwargs"):
+ self.sp_model_kwargs = {}
+
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+ @property
+ def vocab_size(self):
+ return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token
+
+ @property
+ def src_lang(self) -> str:
+ return self._src_lang
+
+ @src_lang.setter
+ def src_lang(self, new_src_lang: str) -> None:
+ self._src_lang = new_src_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ prefix_ones = [1] * len(self.prefix_tokens)
+ suffix_ones = [1] * len(self.suffix_tokens)
+ if token_ids_1 is None:
+ return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
+ return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. An MBART sequence has the following format, where `X` represents the sequence:
+
+ - `input_ids` (for encoder) `X [eos, src_lang_code]`
+ - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
+
+ BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
+ separator.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return self.prefix_tokens + token_ids_0 + self.suffix_tokens
+ # We don't expect to process pairs, but leave the pair logic for API consistency
+ return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. mBART does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+
+ """
+
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ def _build_translation_inputs(
+ self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
+ ):
+ """Used by translation pipeline, to prepare inputs for the generate function"""
+ if src_lang is None or tgt_lang is None:
+ raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+ self.src_lang = src_lang
+ inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
+ tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
+ inputs["forced_bos_token_id"] = tgt_lang_id
+ return inputs
+
+ def get_vocab(self):
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ def _tokenize(self, text: str) -> list[str]:
+ return self.sp_model.encode(text, out_type=str)
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ if token in self.fairseq_tokens_to_ids:
+ return self.fairseq_tokens_to_ids[token]
+ spm_id = self.sp_model.PieceToId(token)
+
+ # Need to return unknown token if the SP model returned 0
+ return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ if index in self.fairseq_ids_to_tokens:
+ return self.fairseq_ids_to_tokens[index]
+ return self.sp_model.IdToPiece(index - self.fairseq_offset)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (strings for sub-words) in a single string."""
+ out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
+ return out_string
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+ elif not os.path.isfile(self.vocab_file):
+ with open(out_vocab_file, "wb") as fi:
+ content_spiece_model = self.sp_model.serialized_model_proto()
+ fi.write(content_spiece_model)
+
+ return (out_vocab_file,)
+
+ def prepare_seq2seq_batch(
+ self,
+ src_texts: list[str],
+ src_lang: str = "en_XX",
+ tgt_texts: Optional[list[str]] = None,
+ tgt_lang: str = "ro_RO",
+ **kwargs,
+ ) -> BatchEncoding:
+ self.src_lang = src_lang
+ self.tgt_lang = tgt_lang
+ return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
+
+ def _switch_to_input_mode(self):
+ return self.set_src_lang_special_tokens(self.src_lang)
+
+ def _switch_to_target_mode(self):
+ return self.set_tgt_lang_special_tokens(self.tgt_lang)
+
+ def set_src_lang_special_tokens(self, src_lang) -> None:
+ """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
+ self.cur_lang_code = self.lang_code_to_id[src_lang]
+ self.prefix_tokens = []
+ self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
+
+ def set_tgt_lang_special_tokens(self, lang: str) -> None:
+ """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
+ self.cur_lang_code = self.lang_code_to_id[lang]
+ self.prefix_tokens = []
+ self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
+
+
+__all__ = ["MBartTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/tokenization_mbart_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/tokenization_mbart_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cf4d468c7df37524456a48960a7f0254cd8dd66
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart/tokenization_mbart_fast.py
@@ -0,0 +1,269 @@
+# coding=utf-8
+# Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from shutil import copyfile
+from typing import Optional
+
+from tokenizers import processors
+
+from ...tokenization_utils import AddedToken, BatchEncoding
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import is_sentencepiece_available, logging
+
+
+if is_sentencepiece_available():
+ from .tokenization_mbart import MBartTokenizer
+else:
+ MBartTokenizer = None
+
+
+logger = logging.get_logger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
+
+
+FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"] # fmt: skip
+
+
+class MBartTokenizerFast(PreTrainedTokenizerFast):
+ """
+ Construct a "fast" MBART tokenizer (backed by HuggingFace's *tokenizers* library). Based on
+ [BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ The tokenization method is ` ` for source language documents, and `
+ ` for target language documents.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MBartTokenizerFast
+
+ >>> tokenizer = MBartTokenizerFast.from_pretrained(
+ ... "facebook/mbart-large-en-ro", src_lang="en_XX", tgt_lang="ro_RO"
+ ... )
+ >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
+ >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
+ >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors="pt")
+ ```"""
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+ slow_tokenizer_class = MBartTokenizer
+
+ prefix_tokens: list[int] = []
+ suffix_tokens: list[int] = []
+
+ def __init__(
+ self,
+ vocab_file=None,
+ tokenizer_file=None,
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ src_lang=None,
+ tgt_lang=None,
+ additional_special_tokens=None,
+ **kwargs,
+ ):
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+ _additional_special_tokens = FAIRSEQ_LANGUAGE_CODES.copy()
+
+ if additional_special_tokens is not None:
+ # Only add those special tokens if they are not already there.
+ _additional_special_tokens.extend(
+ [t for t in additional_special_tokens if t not in _additional_special_tokens]
+ )
+
+ super().__init__(
+ vocab_file=vocab_file,
+ tokenizer_file=tokenizer_file,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ src_lang=src_lang,
+ tgt_lang=tgt_lang,
+ additional_special_tokens=_additional_special_tokens,
+ **kwargs,
+ )
+
+ self.vocab_file = vocab_file
+ self.lang_code_to_id = {
+ lang_code: self.convert_tokens_to_ids(lang_code) for lang_code in FAIRSEQ_LANGUAGE_CODES
+ }
+
+ self._src_lang = src_lang if src_lang is not None else "en_XX"
+ self.cur_lang_code = self.convert_tokens_to_ids(self._src_lang)
+ self.tgt_lang = tgt_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ @property
+ def src_lang(self) -> str:
+ return self._src_lang
+
+ @src_lang.setter
+ def src_lang(self, new_src_lang: str) -> None:
+ self._src_lang = new_src_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. The special tokens depend on calling set_lang.
+
+ An MBART sequence has the following format, where `X` represents the sequence:
+
+ - `input_ids` (for encoder) `X [eos, src_lang_code]`
+ - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
+
+ BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
+ separator.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return self.prefix_tokens + token_ids_0 + self.suffix_tokens
+ # We don't expect to process pairs, but leave the pair logic for API consistency
+ return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. mBART does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+
+ """
+
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ def _build_translation_inputs(
+ self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
+ ):
+ """Used by translation pipeline, to prepare inputs for the generate function"""
+ if src_lang is None or tgt_lang is None:
+ raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+ self.src_lang = src_lang
+ inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
+ tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
+ inputs["forced_bos_token_id"] = tgt_lang_id
+ return inputs
+
+ def prepare_seq2seq_batch(
+ self,
+ src_texts: list[str],
+ src_lang: str = "en_XX",
+ tgt_texts: Optional[list[str]] = None,
+ tgt_lang: str = "ro_RO",
+ **kwargs,
+ ) -> BatchEncoding:
+ self.src_lang = src_lang
+ self.tgt_lang = tgt_lang
+ return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
+
+ def _switch_to_input_mode(self):
+ return self.set_src_lang_special_tokens(self.src_lang)
+
+ def _switch_to_target_mode(self):
+ return self.set_tgt_lang_special_tokens(self.tgt_lang)
+
+ def set_src_lang_special_tokens(self, src_lang) -> None:
+ """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
+ self.cur_lang_code = self.convert_tokens_to_ids(src_lang)
+ self.prefix_tokens = []
+ self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
+
+ prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
+ suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
+
+ self._tokenizer.post_processor = processors.TemplateProcessing(
+ single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
+ pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
+ special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
+ )
+
+ def set_tgt_lang_special_tokens(self, lang: str) -> None:
+ """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
+ self.cur_lang_code = self.convert_tokens_to_ids(lang)
+ self.prefix_tokens = []
+ self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
+
+ prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
+ suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
+
+ self._tokenizer.post_processor = processors.TemplateProcessing(
+ single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
+ pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
+ special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
+ )
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not self.can_save_slow_tokenizer:
+ raise ValueError(
+ "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
+ "tokenizer."
+ )
+
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory.")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+
+ return (out_vocab_file,)
+
+
+__all__ = ["MBartTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart50/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart50/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7cd8c28da631fdd44cc09458380527b6323d044
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart50/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .tokenization_mbart50 import *
+ from .tokenization_mbart50_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart50/tokenization_mbart50.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart50/tokenization_mbart50.py
new file mode 100644
index 0000000000000000000000000000000000000000..413beaa03a83e4eddb9eb5a050d85c347ecbe2b5
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart50/tokenization_mbart50.py
@@ -0,0 +1,359 @@
+# coding=utf-8
+# Copyright 2021 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from shutil import copyfile
+from typing import Any, Optional
+
+import sentencepiece as spm
+
+from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
+from ...utils import logging
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+SPIECE_UNDERLINE = "▁"
+
+VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
+
+
+FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"] # fmt: skip
+
+
+@requires(backends=("sentencepiece",))
+class MBart50Tokenizer(PreTrainedTokenizer):
+ """
+ Construct a MBart50 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ src_lang (`str`, *optional*):
+ A string representing the source language.
+ tgt_lang (`str`, *optional*):
+ A string representing the target language.
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+ sep_token (`str`, *optional*, defaults to `""`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ sp_model_kwargs (`dict`, *optional*):
+ Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+ SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+ to set:
+
+ - `enable_sampling`: Enable subword regularization.
+ - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+ - `nbest_size = {0,1}`: No sampling is performed.
+ - `nbest_size > 1`: samples from the nbest_size results.
+ - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+ using forward-filtering-and-backward-sampling algorithm.
+
+ - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+ BPE-dropout.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MBart50Tokenizer
+
+ >>> tokenizer = MBart50Tokenizer.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO")
+ >>> src_text = " UN Chief Says There Is No Military Solution in Syria"
+ >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
+ >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
+ >>> # model(**model_inputs) should work
+ ```"""
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ prefix_tokens: list[int] = []
+ suffix_tokens: list[int] = []
+
+ def __init__(
+ self,
+ vocab_file,
+ src_lang=None,
+ tgt_lang=None,
+ eos_token=" ",
+ sep_token="",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ sp_model_kwargs: Optional[dict[str, Any]] = None,
+ **kwargs,
+ ) -> None:
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+ kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) or []
+ kwargs["additional_special_tokens"] += [
+ code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
+ ]
+
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.Load(str(vocab_file))
+ self.vocab_file = vocab_file
+
+ # Original fairseq vocab and spm vocab must be "aligned":
+ # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
+ # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
+ # fairseq | '' | '' | ' ' | '' | ',' | '.' | '▁' | 's' | '▁de' | '-'
+ # spm | '' | '' | ' ' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
+
+ # Mimic fairseq token-to-id alignment for the first 4 token
+ self.fairseq_tokens_to_ids = {"": 0, "": 1, " ": 2, "": 3}
+
+ # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
+ self.fairseq_offset = 1
+
+ self.sp_model_size = len(self.sp_model)
+ self.lang_code_to_id = {
+ code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(FAIRSEQ_LANGUAGE_CODES)
+ }
+ self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()}
+ self.fairseq_tokens_to_ids[""] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset
+
+ self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
+ self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
+
+ super().__init__(
+ src_lang=src_lang,
+ tgt_lang=tgt_lang,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ sp_model_kwargs=self.sp_model_kwargs,
+ **kwargs,
+ )
+
+ self._src_lang = src_lang if src_lang is not None else "en_XX"
+ self.cur_lang_code_id = self.lang_code_to_id[self._src_lang]
+ self.tgt_lang = tgt_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ @property
+ def vocab_size(self) -> int:
+ return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token
+
+ @property
+ def src_lang(self) -> str:
+ return self._src_lang
+
+ @src_lang.setter
+ def src_lang(self, new_src_lang: str) -> None:
+ self._src_lang = new_src_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ def __getstate__(self) -> dict:
+ state = self.__dict__.copy()
+ state["sp_model"] = None
+ return state
+
+ def __setstate__(self, d: dict) -> None:
+ self.__dict__ = d
+
+ # for backward compatibility
+ if not hasattr(self, "sp_model_kwargs"):
+ self.sp_model_kwargs = {}
+
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.Load(self.vocab_file)
+
+ def get_vocab(self) -> dict:
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ def _tokenize(self, text: str) -> list[str]:
+ return self.sp_model.encode(text, out_type=str)
+
+ def _convert_token_to_id(self, token: str) -> int:
+ """Converts a token (str) in an id using the vocab."""
+ if token in self.fairseq_tokens_to_ids:
+ return self.fairseq_tokens_to_ids[token]
+ spm_id = self.sp_model.PieceToId(token)
+
+ # Need to return unknown token if the SP model returned 0
+ return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
+
+ def _convert_id_to_token(self, index: int) -> str:
+ """Converts an index (integer) in a token (str) using the vocab."""
+ if index in self.fairseq_ids_to_tokens:
+ return self.fairseq_ids_to_tokens[index]
+ return self.sp_model.IdToPiece(index - self.fairseq_offset)
+
+ # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.convert_tokens_to_string
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ current_sub_tokens = []
+ out_string = ""
+ prev_is_special = False
+ for token in tokens:
+ # make sure that special tokens are not decoded using sentencepiece model
+ if token in self.all_special_tokens:
+ if not prev_is_special:
+ out_string += " "
+ out_string += self.sp_model.decode(current_sub_tokens) + token
+ prev_is_special = True
+ current_sub_tokens = []
+ else:
+ current_sub_tokens.append(token)
+ prev_is_special = False
+ out_string += self.sp_model.decode(current_sub_tokens)
+ return out_string.strip()
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+ elif not os.path.isfile(self.vocab_file):
+ with open(out_vocab_file, "wb") as fi:
+ content_spiece_model = self.sp_model.serialized_model_proto()
+ fi.write(content_spiece_model)
+
+ return (out_vocab_file,)
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ prefix_ones = [1] * len(self.prefix_tokens)
+ suffix_ones = [1] * len(self.suffix_tokens)
+ if token_ids_1 is None:
+ return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
+ return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. An MBART-50 sequence has the following format, where `X` represents the sequence:
+
+ - `input_ids` (for encoder) `[src_lang_code] X [eos]`
+ - `labels`: (for decoder) `[tgt_lang_code] X [eos]`
+
+ BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
+ separator.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return self.prefix_tokens + token_ids_0 + self.suffix_tokens
+ # We don't expect to process pairs, but leave the pair logic for API consistency
+ return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
+
+ def _build_translation_inputs(
+ self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
+ ):
+ """Used by translation pipeline, to prepare inputs for the generate function"""
+ if src_lang is None or tgt_lang is None:
+ raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+ self.src_lang = src_lang
+ inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
+ tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
+ inputs["forced_bos_token_id"] = tgt_lang_id
+ return inputs
+
+ def prepare_seq2seq_batch(
+ self,
+ src_texts: list[str],
+ src_lang: str = "en_XX",
+ tgt_texts: Optional[list[str]] = None,
+ tgt_lang: str = "ro_RO",
+ **kwargs,
+ ) -> BatchEncoding:
+ self.src_lang = src_lang
+ self.tgt_lang = tgt_lang
+ return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
+
+ def _switch_to_input_mode(self):
+ return self.set_src_lang_special_tokens(self.src_lang)
+
+ def _switch_to_target_mode(self):
+ return self.set_tgt_lang_special_tokens(self.tgt_lang)
+
+ def set_src_lang_special_tokens(self, src_lang: str) -> None:
+ """Reset the special tokens to the source lang setting. prefix=[src_lang_code] and suffix=[eos]."""
+ self.cur_lang_code_id = self.lang_code_to_id[src_lang]
+ self.prefix_tokens = [self.cur_lang_code_id]
+ self.suffix_tokens = [self.eos_token_id]
+
+ def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
+ """Reset the special tokens to the target language setting. prefix=[tgt_lang_code] and suffix=[eos]."""
+ self.cur_lang_code_id = self.lang_code_to_id[tgt_lang]
+ self.prefix_tokens = [self.cur_lang_code_id]
+ self.suffix_tokens = [self.eos_token_id]
+
+
+__all__ = ["MBart50Tokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart50/tokenization_mbart50_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart50/tokenization_mbart50_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..985b0929f87c5516a2edd8e02c6aaaa8926d496e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mbart50/tokenization_mbart50_fast.py
@@ -0,0 +1,258 @@
+# coding=utf-8
+# Copyright 2021 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from shutil import copyfile
+from typing import Optional
+
+from tokenizers import processors
+
+from ...tokenization_utils import AddedToken, BatchEncoding
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import is_sentencepiece_available, logging
+
+
+if is_sentencepiece_available():
+ from .tokenization_mbart50 import MBart50Tokenizer
+else:
+ MBart50Tokenizer = None
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
+
+
+FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"] # fmt: skip
+
+
+class MBart50TokenizerFast(PreTrainedTokenizerFast):
+ """
+ Construct a "fast" MBART tokenizer for mBART-50 (backed by HuggingFace's *tokenizers* library). Based on
+ [BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ src_lang (`str`, *optional*):
+ A string representing the source language.
+ tgt_lang (`str`, *optional*):
+ A string representing the target language.
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+ sep_token (`str`, *optional*, defaults to `""`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MBart50TokenizerFast
+
+ >>> tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO")
+ >>> src_text = " UN Chief Says There Is No Military Solution in Syria"
+ >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
+ >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
+ >>> # model(**model_inputs) should work
+ ```"""
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+ slow_tokenizer_class = MBart50Tokenizer
+
+ prefix_tokens: list[int] = []
+ suffix_tokens: list[int] = []
+
+ def __init__(
+ self,
+ vocab_file=None,
+ src_lang=None,
+ tgt_lang=None,
+ tokenizer_file=None,
+ eos_token=" ",
+ sep_token="",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ **kwargs,
+ ):
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+ kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) or []
+ kwargs["additional_special_tokens"] += [
+ code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
+ ]
+
+ super().__init__(
+ vocab_file,
+ src_lang=src_lang,
+ tgt_lang=tgt_lang,
+ tokenizer_file=tokenizer_file,
+ eos_token=eos_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ **kwargs,
+ )
+
+ self.vocab_file = vocab_file
+
+ self.lang_code_to_id = {
+ lang_code: self.convert_tokens_to_ids(lang_code) for lang_code in FAIRSEQ_LANGUAGE_CODES
+ }
+
+ self._src_lang = src_lang if src_lang is not None else "en_XX"
+ self.tgt_lang = tgt_lang
+ self.cur_lang_code_id = self.lang_code_to_id[self._src_lang]
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ @property
+ def src_lang(self) -> str:
+ return self._src_lang
+
+ @src_lang.setter
+ def src_lang(self, new_src_lang: str) -> None:
+ self._src_lang = new_src_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. The special tokens depend on calling set_lang.
+
+ An MBART-50 sequence has the following format, where `X` represents the sequence:
+
+ - `input_ids` (for encoder) `[src_lang_code] X [eos]`
+ - `labels`: (for decoder) `[tgt_lang_code] X [eos]`
+
+ BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
+ separator.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return self.prefix_tokens + token_ids_0 + self.suffix_tokens
+ # We don't expect to process pairs, but leave the pair logic for API consistency
+ return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
+
+ def prepare_seq2seq_batch(
+ self,
+ src_texts: list[str],
+ src_lang: str = "en_XX",
+ tgt_texts: Optional[list[str]] = None,
+ tgt_lang: str = "ro_RO",
+ **kwargs,
+ ) -> BatchEncoding:
+ self.src_lang = src_lang
+ self.tgt_lang = tgt_lang
+ return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
+
+ def _switch_to_input_mode(self):
+ return self.set_src_lang_special_tokens(self.src_lang)
+
+ def _switch_to_target_mode(self):
+ return self.set_tgt_lang_special_tokens(self.tgt_lang)
+
+ def set_src_lang_special_tokens(self, src_lang: str) -> None:
+ """Reset the special tokens to the source lang setting. prefix=[src_lang_code] and suffix=[eos]."""
+ self.cur_lang_code_id = self.convert_tokens_to_ids(src_lang)
+ self.prefix_tokens = [self.cur_lang_code_id]
+ self.suffix_tokens = [self.eos_token_id]
+
+ prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
+ suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
+
+ self._tokenizer.post_processor = processors.TemplateProcessing(
+ single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
+ pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
+ special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
+ )
+
+ def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
+ """Reset the special tokens to the target language setting. prefix=[src_lang_code] and suffix=[eos]."""
+ self.cur_lang_code_id = self.convert_tokens_to_ids(tgt_lang)
+ self.prefix_tokens = [self.cur_lang_code_id]
+ self.suffix_tokens = [self.eos_token_id]
+
+ prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
+ suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
+
+ self._tokenizer.post_processor = processors.TemplateProcessing(
+ single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
+ pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
+ special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
+ )
+
+ def _build_translation_inputs(
+ self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
+ ):
+ """Used by translation pipeline, to prepare inputs for the generate function"""
+ if src_lang is None or tgt_lang is None:
+ raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+ self.src_lang = src_lang
+ inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
+ tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
+ inputs["forced_bos_token_id"] = tgt_lang_id
+ return inputs
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not self.can_save_slow_tokenizer:
+ raise ValueError(
+ "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
+ "tokenizer."
+ )
+
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+
+ return (out_vocab_file,)
+
+
+__all__ = ["MBart50TokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..09d805dc1bf67d12cd7855d961a51d9461f03431
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_megatron_bert import *
+ from .modeling_megatron_bert import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/configuration_megatron_bert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/configuration_megatron_bert.py
new file mode 100644
index 0000000000000000000000000000000000000000..1505388e2925788fddcf4a40613da1ca9d13a82d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/configuration_megatron_bert.py
@@ -0,0 +1,129 @@
+# coding=utf-8
+# Copyright 2021- NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MEGATRON_BERT model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MegatronBertConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MegatronBertModel`]. It is used to instantiate a
+ MEGATRON_BERT model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the MEGATRON_BERT
+ [nvidia/megatron-bert-uncased-345m](https://huggingface.co/nvidia/megatron-bert-uncased-345m) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 29056):
+ Vocabulary size of the MEGATRON_BERT model. Defines the number of different tokens that can be represented
+ by the `inputs_ids` passed when calling [`MegatronBertModel`].
+ hidden_size (`int`, *optional*, defaults to 1024):
+ Dimensionality of the encoder layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 24):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 2):
+ The vocabulary size of the `token_type_ids` passed when calling [`MegatronBertModel`].
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+ position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
+ Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
+ positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
+ [Self-Attention with Relative Position Representations (Shaw et al.)](https://huggingface.co/papers/1803.02155).
+ For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
+ with Better Relative Position Embeddings (Huang et al.)](https://huggingface.co/papers/2009.13658).
+ is_decoder (`bool`, *optional*, defaults to `False`):
+ Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MegatronBertConfig, MegatronBertModel
+
+ >>> # Initializing a MEGATRON_BERT google-bert/bert-base-uncased style configuration
+ >>> configuration = MegatronBertConfig()
+
+ >>> # Initializing a model (with random weights) from the google-bert/bert-base-uncased style configuration
+ >>> model = MegatronBertModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "megatron-bert"
+
+ def __init__(
+ self,
+ vocab_size=29056,
+ hidden_size=1024,
+ num_hidden_layers=24,
+ num_attention_heads=16,
+ intermediate_size=4096,
+ hidden_act="gelu",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ type_vocab_size=2,
+ initializer_range=0.02,
+ layer_norm_eps=1e-12,
+ pad_token_id=0,
+ position_embedding_type="absolute",
+ use_cache=True,
+ **kwargs,
+ ):
+ super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.hidden_act = hidden_act
+ self.intermediate_size = intermediate_size
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.max_position_embeddings = max_position_embeddings
+ self.type_vocab_size = type_vocab_size
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.position_embedding_type = position_embedding_type
+ self.use_cache = use_cache
+
+
+__all__ = ["MegatronBertConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..69ebed5aa8e0f1a5d908654938fc85623e27cace
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py
@@ -0,0 +1,334 @@
+####################################################################################################
+
+# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+####################################################################################################
+
+#
+# Note: If when running this conversion script you're getting an exception:
+# ModuleNotFoundError: No module named 'megatron.model.enums'
+# you need to tell python where to find the clone of Megatron-LM, e.g.:
+#
+# cd /tmp
+# git clone https://github.com/NVIDIA/Megatron-LM
+# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
+#
+# if you already have it cloned elsewhere, simply adjust the path to the existing path
+#
+# If the training was done using a Megatron-LM fork, e.g.,
+# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
+# in your path, i.e., /path/to/Megatron-DeepSpeed/
+#
+
+import argparse
+import os
+import re
+import zipfile
+
+import torch
+
+from transformers import MegatronBertConfig
+
+
+####################################################################################################
+
+
+def recursive_print(name, val, spaces=0):
+ # Format the message.
+ if name is None:
+ msg = None
+ else:
+ fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
+ msg = fmt.format(name)
+
+ # Print and recurse (if needed).
+ if isinstance(val, dict):
+ if msg is not None:
+ print(msg)
+ for k in val:
+ recursive_print(k, val[k], spaces + 2)
+ elif isinstance(val, torch.Tensor):
+ print(msg, ":", val.size())
+ else:
+ print(msg, ":", val)
+
+
+def fix_query_key_value_ordering(param, checkpoint_version, num_splits, num_heads, hidden_size):
+ # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
+ # for compatibility with later versions of NVIDIA Megatron-LM.
+ # The inverse operation is performed inside Megatron-LM to read checkpoints:
+ # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
+ # If param is the weight tensor of the self-attention block, the returned tensor
+ # will have to be transposed one more time to be read by HuggingFace BERT.
+ input_shape = param.size()
+ if checkpoint_version == 1.0:
+ # version 1.0 stores [num_heads * hidden_size * num_splits, :]
+ saved_shape = (num_heads, hidden_size, num_splits) + input_shape[1:]
+ param = param.view(*saved_shape)
+ param = param.transpose(0, 2)
+ param = param.transpose(1, 2).contiguous()
+ elif checkpoint_version >= 2.0:
+ # other versions store [num_heads * num_splits * hidden_size, :]
+ saved_shape = (num_heads, num_splits, hidden_size) + input_shape[1:]
+ param = param.view(*saved_shape)
+ param = param.transpose(0, 1).contiguous()
+ param = param.view(*input_shape)
+ return param
+
+
+####################################################################################################
+
+
+def convert_megatron_checkpoint(args, input_state_dict, config):
+ # The converted output model.
+ output_state_dict = {}
+
+ # old versions did not store training args
+ ds_args = input_state_dict.get("args", None)
+ if ds_args is not None:
+ # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
+ # from pprint import pprint
+ # pprint(vars(ds_args))
+
+ config.tokenizer_type = ds_args.tokenizer_type
+ config.vocab_size = ds_args.padded_vocab_size
+ config.max_position_embeddings = ds_args.max_position_embeddings
+ config.hidden_size = ds_args.hidden_size
+ config.num_hidden_layers = ds_args.num_layers
+ config.num_attention_heads = ds_args.num_attention_heads
+ config.intermediate_size = ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
+ # pprint(config)
+
+ # The number of heads.
+ heads = config.num_attention_heads
+ # The hidden_size per head.
+ hidden_size_per_head = config.hidden_size // heads
+ # Megatron-LM checkpoint version
+ if "checkpoint_version" in input_state_dict:
+ checkpoint_version = input_state_dict["checkpoint_version"]
+ else:
+ checkpoint_version = 0.0
+
+ # The model.
+ model = input_state_dict["model"]
+ # The language model.
+ lm = model["language_model"]
+ # The embeddings.
+ embeddings = lm["embedding"]
+
+ # The word embeddings.
+ word_embeddings = embeddings["word_embeddings"]["weight"]
+ # Truncate the embedding table to vocab_size rows.
+ word_embeddings = word_embeddings[: config.vocab_size, :]
+ # Store the word embeddings.
+ output_state_dict["bert.embeddings.word_embeddings.weight"] = word_embeddings
+
+ # The position embeddings.
+ pos_embeddings = embeddings["position_embeddings"]["weight"]
+ assert pos_embeddings.size(0) == config.max_position_embeddings and pos_embeddings.size(1) == config.hidden_size
+ # Store the position embeddings.
+ output_state_dict["bert.embeddings.position_embeddings.weight"] = pos_embeddings
+
+ # The token-type embeddings.
+ tokentype_embeddings = embeddings["tokentype_embeddings"]["weight"]
+ # Store the position embeddings.
+ output_state_dict["bert.embeddings.token_type_embeddings.weight"] = tokentype_embeddings
+
+ # The transformer.
+ transformer = lm["transformer"] if "transformer" in lm else lm["encoder"]
+
+ # The regex to extract layer names.
+ layer_re = re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")
+
+ # The simple map of names for "automated" rules.
+ megatron_to_transformers = {
+ "attention.dense": ".attention.output.dense.",
+ "self_attention.dense": ".attention.output.dense.",
+ "mlp.dense_h_to_4h": ".intermediate.dense.",
+ "mlp.dense_4h_to_h": ".output.dense.",
+ }
+
+ # Keep track of the attention/query/value tensor.
+ attention_qkv_weight = None
+
+ # Extract the layers.
+ for key, val in transformer.items():
+ # Match the name.
+ m = layer_re.match(key)
+
+ # Stop if that's not a layer
+ if m is None:
+ break
+
+ # The index of the layer.
+ layer_idx = int(m.group(1))
+ # The name of the operation.
+ op_name = m.group(2)
+ # Is it a weight or a bias?
+ weight_or_bias = m.group(3)
+
+ # The name of the layer.
+ layer_name = f"bert.encoder.layer.{layer_idx}"
+
+ # For layernorm(s), simply store the layer norm.
+ if op_name.endswith("layernorm"):
+ ln_name = "attention.ln" if op_name.startswith("input") else "ln"
+ output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = val
+
+ # Transpose the QKV matrix.
+ elif (
+ op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
+ ) and weight_or_bias == "weight":
+ # Make sure the QKV pointer is nil.
+ assert attention_qkv_weight is None, ""
+
+ out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
+ # Store the tensor as we need the bias as well to interleave QKV and biases.
+ attention_qkv_weight = out_val
+
+ # Transpose the bias.
+ elif (
+ op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
+ ) and weight_or_bias == "bias":
+ # Make sure we read the weight tensor.
+ assert attention_qkv_weight is not None, ""
+
+ # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
+ q = attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
+ k = attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
+ v = attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]
+
+ out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
+ # Split the bias.
+ q_bias = out_val[0 * config.hidden_size : 1 * config.hidden_size]
+ k_bias = out_val[1 * config.hidden_size : 2 * config.hidden_size]
+ v_bias = out_val[2 * config.hidden_size : 3 * config.hidden_size]
+
+ # Store.
+ output_state_dict[f"{layer_name}.attention.self.query.weight"] = q
+ output_state_dict[f"{layer_name}.attention.self.query.bias"] = q_bias
+ output_state_dict[f"{layer_name}.attention.self.key.weight"] = k
+ output_state_dict[f"{layer_name}.attention.self.key.bias"] = k_bias
+ output_state_dict[f"{layer_name}.attention.self.value.weight"] = v
+ output_state_dict[f"{layer_name}.attention.self.value.bias"] = v_bias
+
+ # Clear the stored tensor.
+ attention_qkv_weight = None
+
+ # Copy weights and biases as is.
+ elif weight_or_bias in ["weight", "bias"]:
+ out_name = megatron_to_transformers[op_name]
+ output_state_dict[layer_name + out_name + weight_or_bias] = val
+
+ # The final layernorm.
+ output_state_dict["bert.encoder.ln.weight"] = transformer["final_layernorm.weight"]
+ output_state_dict["bert.encoder.ln.bias"] = transformer["final_layernorm.bias"]
+
+ # The pooler.
+ pooler = lm["pooler"]
+
+ # Store the matrix and the bias.
+ output_state_dict["bert.pooler.dense.weight"] = pooler["dense.weight"]
+ output_state_dict["bert.pooler.dense.bias"] = pooler["dense.bias"]
+
+ # The LM head from Megatron (for RACE).
+ lm_head = model["lm_head"]
+
+ # The transform matrix.
+ output_state_dict["cls.predictions.transform.dense.weight"] = lm_head["dense.weight"]
+ output_state_dict["cls.predictions.transform.dense.bias"] = lm_head["dense.bias"]
+
+ # The transform LN.
+ output_state_dict["cls.predictions.transform.LayerNorm.weight"] = lm_head["layernorm.weight"]
+ output_state_dict["cls.predictions.transform.LayerNorm.bias"] = lm_head["layernorm.bias"]
+
+ # For the decoder, we replicate the weights.
+ output_state_dict["cls.predictions.decoder.weight"] = word_embeddings
+ output_state_dict["cls.predictions.bias"] = lm_head["bias"]
+
+ # The classifier from Megatron (for MLNI).
+ binary_head = model["binary_head"]
+
+ # Store the classifier.
+ output_state_dict["cls.seq_relationship.weight"] = binary_head["weight"]
+ output_state_dict["cls.seq_relationship.bias"] = binary_head["bias"]
+
+ # It should be done!
+ return output_state_dict
+
+
+####################################################################################################
+
+
+def main():
+ # Create the argument parser.
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--print-checkpoint-structure", action="store_true")
+ parser.add_argument("path_to_checkpoint", type=str, help="Path to the ZIP file containing the checkpoint")
+ parser.add_argument(
+ "--config_file",
+ default="",
+ type=str,
+ help="An optional config json file describing the pre-trained model.",
+ )
+ args = parser.parse_args()
+
+ # Extract the basename.
+ basename = os.path.dirname(args.path_to_checkpoint)
+
+ # Load the model.
+ # the .zip is very optional, let's keep it for backward compatibility
+ print(f'Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"')
+ if args.path_to_checkpoint.endswith(".zip"):
+ with zipfile.ZipFile(args.path_to_checkpoint, "r") as checkpoint:
+ with checkpoint.open("release/mp_rank_00/model_optim_rng.pt") as pytorch_dict:
+ input_state_dict = torch.load(pytorch_dict, map_location="cpu", weights_only=True)
+ else:
+ input_state_dict = torch.load(args.path_to_checkpoint, map_location="cpu", weights_only=True)
+
+ if args.config_file == "":
+ # Default config of megatron-bert 345m
+ config = MegatronBertConfig()
+
+ # different megatron-bert-*-345m models have different vocab sizes, so override the default
+ # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
+ config.vocab_size = input_state_dict["model"]["lm_head"]["bias"].numel()
+ else:
+ config = MegatronBertConfig.from_json_file(args.config_file)
+
+ # Convert.
+ print("Converting")
+ output_state_dict = convert_megatron_checkpoint(args, input_state_dict, config)
+
+ # Print the structure of converted state dict.
+ if args.print_checkpoint_structure:
+ recursive_print(None, output_state_dict)
+
+ # Store the config to file.
+ print("Saving config")
+ config.save_pretrained(basename)
+
+ # Store the state_dict to file.
+ output_checkpoint_file = os.path.join(basename, "pytorch_model.bin")
+ print(f'Saving checkpoint to "{output_checkpoint_file}"')
+ torch.save(output_state_dict, output_checkpoint_file)
+
+
+####################################################################################################
+
+if __name__ == "__main__":
+ main()
+
+####################################################################################################
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/modeling_megatron_bert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/modeling_megatron_bert.py
new file mode 100644
index 0000000000000000000000000000000000000000..69239a0b468f89c2e50e054b69d5dc3bdcd287f8
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_bert/modeling_megatron_bert.py
@@ -0,0 +1,1649 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018-2021, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MegatronBERT model."""
+
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutputWithPastAndCrossAttentions,
+ BaseModelOutputWithPoolingAndCrossAttentions,
+ CausalLMOutputWithCrossAttentions,
+ MaskedLMOutput,
+ MultipleChoiceModelOutput,
+ NextSentencePredictorOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import ModelOutput, auto_docstring, logging
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_megatron_bert import MegatronBertConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def load_tf_weights_in_megatron_bert(model, config, tf_checkpoint_path):
+ """Load tf checkpoints in a pytorch model."""
+ try:
+ import re
+
+ import numpy as np
+ import tensorflow as tf
+ except ImportError:
+ logger.error(
+ "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
+ "https://www.tensorflow.org/install/ for installation instructions."
+ )
+ raise
+ tf_path = os.path.abspath(tf_checkpoint_path)
+ logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
+ # Load weights from TF model
+ init_vars = tf.train.list_variables(tf_path)
+ names = []
+ arrays = []
+ for name, shape in init_vars:
+ logger.info(f"Loading TF weight {name} with shape {shape}")
+ array = tf.train.load_variable(tf_path, name)
+ names.append(name)
+ arrays.append(array)
+
+ for name, array in zip(names, arrays):
+ name = name.split("/")
+ # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+ # which are not required for using pretrained model
+ if any(
+ n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
+ for n in name
+ ):
+ logger.info(f"Skipping {'/'.join(name)}")
+ continue
+ pointer = model
+ for m_name in name:
+ if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
+ scope_names = re.split(r"_(\d+)", m_name)
+ else:
+ scope_names = [m_name]
+ if scope_names[0] == "kernel" or scope_names[0] == "gamma":
+ pointer = getattr(pointer, "weight")
+ elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
+ pointer = getattr(pointer, "bias")
+ elif scope_names[0] == "output_weights":
+ pointer = getattr(pointer, "weight")
+ elif scope_names[0] == "squad":
+ pointer = getattr(pointer, "classifier")
+ else:
+ try:
+ pointer = getattr(pointer, scope_names[0])
+ except AttributeError:
+ logger.info(f"Skipping {'/'.join(name)}")
+ continue
+ if len(scope_names) >= 2:
+ num = int(scope_names[1])
+ pointer = pointer[num]
+ if m_name[-11:] == "_embeddings":
+ pointer = getattr(pointer, "weight")
+ elif m_name == "kernel":
+ array = np.transpose(array)
+ if pointer.shape != array.shape:
+ raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
+ logger.info(f"Initialize PyTorch weight {name}")
+ pointer.data = torch.from_numpy(array)
+ return model
+
+
+class MegatronBertEmbeddings(nn.Module):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+
+ # In Megatron, layer-norm is applied after the 1st dropout.
+ # self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+ self.register_buffer(
+ "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+ )
+ self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.LongTensor] = None,
+ past_key_values_length: int = 0,
+ ) -> torch.Tensor:
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+
+ if position_ids is None:
+ position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = inputs_embeds + token_type_embeddings
+ if self.position_embedding_type == "absolute":
+ position_embeddings = self.position_embeddings(position_ids)
+ embeddings += position_embeddings
+
+ # Megatron BERT moves that layer norm after the drop-out (and to each layer).
+ # embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->MegatronBert
+class MegatronBertSelfAttention(nn.Module):
+ def __init__(self, config, position_embedding_type=None, layer_idx=None):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+ self.position_embedding_type = position_embedding_type or getattr(
+ config, "position_embedding_type", "absolute"
+ )
+ if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+ self.max_position_embeddings = config.max_position_embeddings
+ self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
+
+ self.is_decoder = config.is_decoder
+ self.layer_idx = layer_idx
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> tuple[torch.Tensor]:
+ batch_size, seq_length, _ = hidden_states.shape
+ query_layer = self.query(hidden_states)
+ query_layer = query_layer.view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(
+ 1, 2
+ )
+
+ is_cross_attention = encoder_hidden_states is not None
+ if past_key_values is not None:
+ if isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_layer from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = encoder_hidden_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_layer = curr_past_key_value.layers[self.layer_idx].keys
+ value_layer = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_layer = self.key(current_states)
+ key_layer = key_layer.view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(
+ 1, 2
+ )
+ value_layer = self.value(current_states)
+ value_layer = value_layer.view(
+ batch_size, -1, self.num_attention_heads, self.attention_head_size
+ ).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_layer to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_layer, value_layer = curr_past_key_value.update(
+ key_layer, value_layer, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+ if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+ query_length, key_length = query_layer.shape[2], key_layer.shape[2]
+ if past_key_values is not None:
+ position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
+ -1, 1
+ )
+ else:
+ position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
+ position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
+ distance = position_ids_l - position_ids_r
+
+ positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
+ positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
+
+ if self.position_embedding_type == "relative_key":
+ relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+ attention_scores = attention_scores + relative_position_scores
+ elif self.position_embedding_type == "relative_key_query":
+ relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+ relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
+ attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
+
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in MegatronBertModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(new_context_layer_shape)
+
+ return context_layer, attention_probs
+
+
+# Based transformers.models.bert.modeling_bert.BertSelfOutput. Moved LayerNorm to MegatronBertAttention below.
+class MegatronBertSelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ return residual + hidden_states
+
+
+# Based transformers.models.bert.modeling_bert.BertAttention. Added LayerNorm.
+class MegatronBertAttention(nn.Module):
+ def __init__(self, config, layer_idx=None):
+ super().__init__()
+ self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.self = MegatronBertSelfAttention(config, layer_idx=layer_idx)
+ self.output = MegatronBertSelfOutput(config)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+ )
+
+ # Prune linear layers
+ self.self.query = prune_linear_layer(self.self.query, index)
+ self.self.key = prune_linear_layer(self.self.key, index)
+ self.self.value = prune_linear_layer(self.self.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+ self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> tuple[torch.Tensor]:
+ ln_outputs = self.ln(hidden_states)
+ self_outputs = self.self(
+ ln_outputs,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ attention_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->MegatronBert
+class MegatronBertIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Based on transformers.models.bert.modeling_bert.BertOutput. Moved LayerNorm to MegatronBertLayer below.
+class MegatronBertOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ return input_tensor + hidden_states
+
+
+# Based on transformers.models.bert.modeling_bert.BertLayer. Added LayerNorm.
+class MegatronBertLayer(GradientCheckpointingLayer):
+ def __init__(self, config, layer_idx=None):
+ super().__init__()
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = MegatronBertAttention(config, layer_idx=layer_idx)
+ self.is_decoder = config.is_decoder
+ self.add_cross_attention = config.add_cross_attention
+ if self.add_cross_attention:
+ if not self.is_decoder:
+ raise TypeError(f"{self} should be used as a decoder model if cross attention is added")
+ self.crossattention = MegatronBertAttention(config, layer_idx=layer_idx)
+ self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.intermediate = MegatronBertIntermediate(config)
+ self.output = MegatronBertOutput(config)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> tuple[torch.Tensor]:
+ # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+ self_attention_outputs = self.attention(
+ hidden_states,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ )
+ attention_output = self_attention_outputs[0]
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ if self.is_decoder and encoder_hidden_states is not None:
+ if not hasattr(self, "crossattention"):
+ raise AttributeError(
+ f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
+ " by setting `config.add_cross_attention=True`"
+ )
+
+ cross_attention_outputs = self.crossattention(
+ attention_output,
+ attention_mask=encoder_attention_mask,
+ head_mask=head_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ attention_output = cross_attention_outputs[0]
+ outputs = outputs + cross_attention_outputs[1:] # add cross attentions if we output attention weights
+
+ layer_output = apply_chunking_to_forward(
+ self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+ )
+ return (layer_output,) + outputs
+
+ def feed_forward_chunk(self, attention_output):
+ ln_output = self.ln(attention_output)
+ intermediate_output = self.intermediate(ln_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+
+class MegatronBertEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList([MegatronBertLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])
+
+ # The final layer norm. We removed the 1st LN, moved LN to each hidden layer and this one
+ # is simply the final LN (Transformer's BERT has it attached to each hidden layer).
+ self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = False,
+ output_hidden_states: Optional[bool] = False,
+ return_dict: Optional[bool] = True,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPastAndCrossAttentions]:
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+ if use_cache and past_key_values is None:
+ past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
+ if use_cache and isinstance(past_key_values, tuple):
+ logger.warning_once(
+ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
+ "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
+ "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
+ )
+ past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
+
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+ all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_head_mask = head_mask[i] if head_mask is not None else None
+
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ past_key_values,
+ output_attentions,
+ cache_position,
+ )
+
+ # Because we moved the layer-norm at the end of the hidden layer, we have non-normali-
+ # zed data here. If that's really needed, we must apply LN to match Transformer's BERT.
+
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+ if self.config.add_cross_attention:
+ all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
+
+ # Finalize the hidden states.
+ hidden_states = self.ln(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ hidden_states,
+ past_key_values,
+ all_hidden_states,
+ all_self_attentions,
+ all_cross_attentions,
+ ]
+ if v is not None
+ )
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->MegatronBert
+class MegatronBertPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->MegatronBert
+class MegatronBertPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ if isinstance(config.hidden_act, str):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->MegatronBert
+class MegatronBertLMPredictionHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.transform = MegatronBertPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+ # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+ self.decoder.bias = self.bias
+
+ def _tie_weights(self):
+ self.decoder.bias = self.bias
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->MegatronBert
+class MegatronBertOnlyMLMHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.predictions = MegatronBertLMPredictionHead(config)
+
+ def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
+ prediction_scores = self.predictions(sequence_output)
+ return prediction_scores
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->MegatronBert
+class MegatronBertOnlyNSPHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+ def forward(self, pooled_output):
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return seq_relationship_score
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->MegatronBert
+class MegatronBertPreTrainingHeads(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.predictions = MegatronBertLMPredictionHead(config)
+ self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+ def forward(self, sequence_output, pooled_output):
+ prediction_scores = self.predictions(sequence_output)
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return prediction_scores, seq_relationship_score
+
+
+@auto_docstring
+class MegatronBertPreTrainedModel(PreTrainedModel):
+ config: MegatronBertConfig
+ load_tf_weights = load_tf_weights_in_megatron_bert
+ base_model_prefix = "bert"
+ supports_gradient_checkpointing = True
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, (nn.Linear, nn.Embedding)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if hasattr(module, "bias") and module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, MegatronBertLMPredictionHead):
+ module.bias.data.zero_()
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Output type of [`MegatronBertForPreTraining`].
+ """
+)
+# Copied from transformers.models.bert.modeling_bert.BertForPreTrainingOutput with Bert->MegatronBert
+class MegatronBertForPreTrainingOutput(ModelOutput):
+ r"""
+ loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
+ Total loss as the sum of the masked language modeling loss and the next sequence prediction
+ (classification) loss.
+ prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
+ Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
+ before SoftMax).
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ prediction_logits: Optional[torch.FloatTensor] = None
+ seq_relationship_logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@auto_docstring
+class MegatronBertModel(MegatronBertPreTrainedModel):
+ """
+
+ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+ cross-attention is added between the self-attention layers, following the architecture described in [Attention is
+ all you need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+ Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+
+ To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
+ to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
+ `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
+ """
+
+ def __init__(self, config, add_pooling_layer=True):
+ r"""
+ add_pooling_layer (bool, *optional*, defaults to `True`):
+ Whether to add a pooling layer
+ """
+ super().__init__(config)
+ self.config = config
+
+ self.embeddings = MegatronBertEmbeddings(config)
+ self.encoder = MegatronBertEncoder(config)
+
+ self.pooler = MegatronBertPooler(config) if add_pooling_layer else None
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.Tensor]]] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if self.config.is_decoder:
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ else:
+ use_cache = False
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ batch_size, seq_length = input_shape
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ past_key_values_length = 0
+ if past_key_values is not None:
+ past_key_values_length = (
+ past_key_values[0][0].shape[-2]
+ if not isinstance(past_key_values, Cache)
+ else past_key_values.get_seq_length()
+ )
+
+ if attention_mask is None:
+ attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+ # If a 2D or 3D attention mask is provided for the cross-attention
+ # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+ if self.config.is_decoder and encoder_hidden_states is not None:
+ encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+ encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+ if encoder_attention_mask is None:
+ encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+ encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+ else:
+ encoder_extended_attention_mask = None
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ past_key_values_length=past_key_values_length,
+ )
+ encoder_outputs = self.encoder(
+ embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_extended_attention_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPoolingAndCrossAttentions(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ past_key_values=encoder_outputs.past_key_values,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ cross_attentions=encoder_outputs.cross_attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MegatronBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
+ `next sentence prediction (classification)` head.
+ """
+)
+class MegatronBertForPreTraining(MegatronBertPreTrainedModel):
+ _tied_weights_keys = ["cls.predictions.decoder"]
+
+ def __init__(self, config, add_binary_head=True):
+ r"""
+ add_binary_head (`bool`, *optional*, defaults to `True`):
+ Whether or not to add a binary head.
+ """
+ super().__init__(config)
+
+ self.bert = MegatronBertModel(config)
+ self.cls = MegatronBertPreTrainingHeads(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.cls.predictions.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.cls.predictions.decoder = new_embeddings
+ self.cls.predictions.bias = new_embeddings.bias
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ next_sentence_label: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, MegatronBertForPreTrainingOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
+ (see `input_ids` docstring) Indices should be in `[0, 1]`:
+
+ - 0 indicates sequence B is a continuation of sequence A,
+ - 1 indicates sequence B is a random sequence.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MegatronBertForPreTraining
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("nvidia/megatron-bert-cased-345m")
+ >>> model = MegatronBertForPreTraining.from_pretrained("nvidia/megatron-bert-cased-345m")
+
+ >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+ >>> outputs = model(**inputs)
+
+ >>> prediction_logits = outputs.prediction_logits
+ >>> seq_relationship_logits = outputs.seq_relationship_logits
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.bert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output, pooled_output = outputs[:2]
+ prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
+
+ total_loss = None
+ if labels is not None and next_sentence_label is not None:
+ loss_fct = CrossEntropyLoss()
+ masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+ next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
+ total_loss = masked_lm_loss + next_sentence_loss
+
+ if not return_dict:
+ output = (prediction_scores, seq_relationship_score) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return MegatronBertForPreTrainingOutput(
+ loss=total_loss,
+ prediction_logits=prediction_scores,
+ seq_relationship_logits=seq_relationship_score,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MegatronBert Model with a `language modeling` head on top for CLM fine-tuning.
+ """
+)
+class MegatronBertForCausalLM(MegatronBertPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["cls.predictions.decoder"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ if not config.is_decoder:
+ logger.warning("If you want to use `MegatronBertForCausalLM` as a standalone, add `is_decoder=True.`")
+
+ self.bert = MegatronBertModel(config, add_pooling_layer=False)
+ self.cls = MegatronBertOnlyMLMHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.cls.predictions.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.cls.predictions.decoder = new_embeddings
+ self.cls.predictions.bias = new_embeddings.bias
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.Tensor]]] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
+ `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
+ ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MegatronBertForCausalLM, MegatronBertConfig
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("nvidia/megatron-bert-cased-345m")
+ >>> model = MegatronBertForCausalLM.from_pretrained("nvidia/megatron-bert-cased-345m", is_decoder=True)
+
+ >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+ >>> outputs = model(**inputs)
+
+ >>> prediction_logits = outputs.logits
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ if labels is not None:
+ use_cache = False
+
+ outputs = self.bert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ sequence_output = outputs[0]
+ prediction_scores = self.cls(sequence_output)
+
+ lm_loss = None
+ if labels is not None:
+ lm_loss = self.loss_function(
+ prediction_scores,
+ labels,
+ vocab_size=self.config.vocab_size,
+ **kwargs,
+ )
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return ((lm_loss,) + output) if lm_loss is not None else output
+
+ return CausalLMOutputWithCrossAttentions(
+ loss=lm_loss,
+ logits=prediction_scores,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ cross_attentions=outputs.cross_attentions,
+ )
+
+
+@auto_docstring
+class MegatronBertForMaskedLM(MegatronBertPreTrainedModel):
+ _tied_weights_keys = ["cls.predictions.decoder"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ if config.is_decoder:
+ logger.warning(
+ "If you want to use `MegatronBertForMaskedLM` make sure `config.is_decoder=False` for "
+ "bi-directional self-attention."
+ )
+
+ self.bert = MegatronBertModel(config, add_pooling_layer=False)
+ self.cls = MegatronBertOnlyMLMHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.cls.predictions.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.cls.predictions.decoder = new_embeddings
+ self.cls.predictions.bias = new_embeddings.bias
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, MaskedLMOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.bert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+ prediction_scores = self.cls(sequence_output)
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss() # -100 index = padding token
+ masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return MaskedLMOutput(
+ loss=masked_lm_loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
+ input_shape = input_ids.shape
+ effective_batch_size = input_shape[0]
+
+ # add a dummy token
+ if self.config.pad_token_id is None:
+ raise ValueError("The PAD token should be defined for generation")
+ attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
+ dummy_token = torch.full(
+ (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
+ )
+ input_ids = torch.cat([input_ids, dummy_token], dim=1)
+
+ return {"input_ids": input_ids, "attention_mask": attention_mask}
+
+
+@auto_docstring(
+ custom_intro="""
+ MegatronBert Model with a `next sentence prediction (classification)` head on top.
+ """
+)
+class MegatronBertForNextSentencePrediction(MegatronBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.bert = MegatronBertModel(config)
+ self.cls = MegatronBertOnlyNSPHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple, NextSentencePredictorOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
+ (see `input_ids` docstring). Indices should be in `[0, 1]`:
+
+ - 0 indicates sequence B is a continuation of sequence A,
+ - 1 indicates sequence B is a random sequence.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MegatronBertForNextSentencePrediction
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("nvidia/megatron-bert-cased-345m")
+ >>> model = MegatronBertForNextSentencePrediction.from_pretrained("nvidia/megatron-bert-cased-345m")
+
+ >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+ >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
+ >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
+
+ >>> outputs = model(**encoding, labels=torch.LongTensor([1]))
+ >>> logits = outputs.logits
+ >>> assert logits[0, 0] < logits[0, 1] # next sentence was random
+ ```"""
+
+ if "next_sentence_label" in kwargs:
+ warnings.warn(
+ "The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
+ " `labels` instead.",
+ FutureWarning,
+ )
+ labels = kwargs.pop("next_sentence_label")
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.bert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ pooled_output = outputs[1]
+
+ seq_relationship_scores = self.cls(pooled_output)
+
+ next_sentence_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
+
+ if not return_dict:
+ output = (seq_relationship_scores,) + outputs[2:]
+ return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
+
+ return NextSentencePredictorOutput(
+ loss=next_sentence_loss,
+ logits=seq_relationship_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MegatronBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+ pooled output) e.g. for GLUE tasks.
+ """
+)
+class MegatronBertForSequenceClassification(MegatronBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.bert = MegatronBertModel(config)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, SequenceClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.bert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ pooled_output = outputs[1]
+
+ pooled_output = self.dropout(pooled_output)
+ logits = self.classifier(pooled_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class MegatronBertForMultipleChoice(MegatronBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.bert = MegatronBertModel(config)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, 1)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, MultipleChoiceModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+ num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+ `input_ids` above)
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+ input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+ attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+ token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+ position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+ inputs_embeds = (
+ inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+ if inputs_embeds is not None
+ else None
+ )
+
+ outputs = self.bert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ pooled_output = outputs[1]
+
+ pooled_output = self.dropout(pooled_output)
+ logits = self.classifier(pooled_output)
+ reshaped_logits = logits.view(-1, num_choices)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(reshaped_logits, labels)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return MultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class MegatronBertForTokenClassification(MegatronBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.bert = MegatronBertModel(config, add_pooling_layer=False)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, TokenClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.bert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class MegatronBertForQuestionAnswering(MegatronBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.bert = MegatronBertModel(config, add_pooling_layer=False)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, QuestionAnsweringModelOutput]:
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.bert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "MegatronBertForCausalLM",
+ "MegatronBertForMaskedLM",
+ "MegatronBertForMultipleChoice",
+ "MegatronBertForNextSentencePrediction",
+ "MegatronBertForPreTraining",
+ "MegatronBertForQuestionAnswering",
+ "MegatronBertForSequenceClassification",
+ "MegatronBertForTokenClassification",
+ "MegatronBertModel",
+ "MegatronBertPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_gpt2/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_gpt2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab866834a2c974469f6eed9693732f0526b8e84d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py
@@ -0,0 +1,925 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import importlib.util
+import json
+import os
+import re
+import sys
+import types
+
+import torch
+from huggingface_hub import split_torch_state_dict_into_shards
+from packaging import version
+
+from transformers import AutoTokenizer, GPT2Config
+from transformers.modeling_utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
+from transformers.utils import check_torch_load_is_safe
+
+
+def add_checkpointing_args(parser):
+ parser.add_argument("--megatron-path", type=str, default=None, help="Base directory of Megatron repository")
+ parser.add_argument(
+ "--convert_checkpoint_from_megatron_to_transformers",
+ action="store_true",
+ help=(
+ "If True, convert a Megatron checkpoint to a Transformers checkpoint. "
+ "If False, convert a Transformers checkpoint to a Megatron checkpoint."
+ ),
+ )
+ parser.add_argument(
+ "--load_path",
+ type=str,
+ required=True,
+ help="Path to the checkpoint to convert.",
+ )
+ parser.add_argument(
+ "--save_path",
+ type=str,
+ required=True,
+ help="Path to the converted checkpoint.",
+ )
+ parser.add_argument("--print-checkpoint-structure", action="store_true")
+ return parser
+
+
+def add_megatron_checkpoint_args(parser):
+ parser.add_argument(
+ "--target_tensor_model_parallel_size",
+ type=int,
+ default=1,
+ help=(
+ "The tensor model parallel size of the converted checkpoint. "
+ "Only used when converting a Transformers checkpoint to a Megatron checkpoint."
+ ),
+ )
+ parser.add_argument(
+ "--target_pipeline_model_parallel_size",
+ type=int,
+ default=1,
+ help=(
+ "The pipeline model parallel size of the converted checkpoint. "
+ "Only used when converting a Transformers checkpoint to a Megatron checkpoint."
+ ),
+ )
+ parser.add_argument(
+ "--target_data_parallel_size",
+ type=int,
+ default=1,
+ help=(
+ "The data parallel size of the converted checkpoint. "
+ "Only used when converting a Transformers checkpoint to a Megatron checkpoint."
+ ),
+ )
+ parser.add_argument(
+ "--target_params_dtype",
+ type=str,
+ default="fp32",
+ help=(
+ "The dtype of the converted checkpoint. "
+ "Only used when converting a Transformers checkpoint to a Megatron checkpoint."
+ ),
+ )
+ parser.add_argument(
+ "--make_vocab_size_divisible_by",
+ type=int,
+ default=128,
+ help=(
+ "Pad the vocab size to be divisible by this value. "
+ "This is added for computational efficiency reasons. "
+ "Only used when converting a Transformers checkpoint to a Megatron checkpoint."
+ ),
+ )
+ parser.add_argument(
+ "--use_distributed_optimizer",
+ action="store_true",
+ help=(
+ "If True, use the distributed optimizer. "
+ "Only used when converting a Transformers checkpoint to a Megatron checkpoint."
+ ),
+ )
+ return parser
+
+
+def add_transformers_checkpoint_args(parser):
+ parser.add_argument(
+ "--tokenizer_name",
+ type=str,
+ default=None,
+ help=(
+ "The name of the pre-trained tokenizer to save. "
+ "If not None, the tokenizer will be saved. "
+ "Only used when converting a Megatron checkpoint to a Transformers checkpoint."
+ ),
+ )
+ parser.add_argument(
+ "--max_shard_size",
+ type=str,
+ default="10GB",
+ help=(
+ "The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size "
+ "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`). "
+ "Only used when converting a Megatron checkpoint to a Transformers checkpoint."
+ ),
+ )
+
+ return parser
+
+
+# The simple map of names for "automated" rules.
+megatron_to_transformers = {
+ "attention.dense": ".attn.c_proj.",
+ "self_attention.dense": ".attn.c_proj.",
+ "mlp.dense_h_to_4h": ".mlp.c_fc.",
+ "mlp.dense_4h_to_h": ".mlp.c_proj.",
+}
+transformers_to_megatron = {v[1:-1]: k for k, v in megatron_to_transformers.items()}
+
+tensor_parallel_params = [
+ # megatron-lm layers to merge across tp ranks
+ "self_attention.query_key_value.weight",
+ "self_attention.query_key_value.bias",
+ "self_attention.dense.weight",
+ "mlp.dense_h_to_4h.weight",
+ "mlp.dense_h_to_4h.bias",
+ "mlp.dense_4h_to_h.weight",
+ # deprecated
+ "attention.query_key_value.weight",
+ "attention.query_key_value.bias",
+ "attention.dense.weight",
+ # transformers layers to split across tp ranks
+ "attn.c_attn.weight",
+ "attn.c_attn.bias",
+ "attn.c_proj.weight",
+ "mlp.c_fc.weight",
+ "mlp.c_fc.bias",
+ "mlp.c_proj.weight",
+]
+
+
+def recursive_print(name, val, spaces=0):
+ """
+ Recursively print the structure of a checkpoint. This function is taken from `convert_megatron_gpt2_checkpoint.py`
+
+ Args:
+ name (str): the name of the current tensor parameter
+ val (Tuple(int)): the shape of the current tensor parameter
+ spaces (int): the number of spaces to print before the output for a nested structure
+ """
+ # Format the message.
+ if name is None:
+ msg = None
+ else:
+ fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
+ msg = fmt.format(name)
+
+ # Print and recurse (if needed).
+ if isinstance(val, dict):
+ if msg is not None:
+ print(msg)
+ for k in val:
+ recursive_print(k, val[k], spaces + 2)
+ elif isinstance(val, torch.Tensor):
+ print(msg, ":", val.size())
+ else:
+ print(msg, ":", val)
+
+
+def megatron_to_transformers_fix_query_key_value_ordering(
+ param, checkpoint_version, num_splits, num_heads, hidden_size
+):
+ """
+ Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] for compatibility with later versions
+ of NVIDIA Megatron-LM. The inverse operation is performed inside Megatron-LM to read checkpoints:
+ https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 If param is the weight tensor of the
+ self-attention block, the returned tensor will have to be transposed one more time to be read by HuggingFace GPT2.
+ This function is taken from `convert_megatron_gpt2_checkpoint.py`
+
+ Args:
+ param (torch.Tensor): the tensor to permute
+ checkpoint_version (int): the version of the checkpoint.
+ num_splits (int): the number of projections, usually 3 for (Query, Key, Value)
+ num_heads (int): the number of attention heads
+ hidden_size (int): the hidden size per head
+ """
+
+ input_shape = param.size()
+ if checkpoint_version == 1.0:
+ # version 1.0 stores [num_heads * hidden_size * num_splits, :]
+ saved_shape = (num_heads, hidden_size, num_splits) + input_shape[1:]
+ param = param.view(*saved_shape)
+ param = param.transpose(0, 2)
+ param = param.transpose(1, 2).contiguous()
+ elif checkpoint_version >= 2.0:
+ # other versions store [num_heads * num_splits * hidden_size, :]
+ saved_shape = (num_heads, num_splits, hidden_size) + input_shape[1:]
+ param = param.view(*saved_shape)
+ param = param.transpose(0, 1).contiguous()
+ param = param.view(*input_shape)
+ return param
+
+
+def transformers_to_megatron_fix_query_key_value_ordering(
+ param, checkpoint_version, num_splits, num_heads, hidden_size
+):
+ """
+ Permutes layout of param tensor to the one compatible with respective NVIDIA Megatron-LM checkpoint versions. Input
+ is [num_splits * num_heads * hidden_size, :] and output is [num_heads * hidden_size * num_splits, :] for version
+ 1.0 and [num_heads * num_splits * hidden_size, :] for version 2.0 and later. If param is the weight tensor of the
+ self-attention block, the param needs to be already transposed before calling this function.
+
+ Args:
+ param (torch.Tensor): the tensor to permute
+ checkpoint_version (int): the version of the checkpoint.
+ num_splits (int): the number of projections, usually 3 for (Query, Key, Value)
+ num_heads (int): the number of attention heads
+ hidden_size (int): the hidden size per head
+ """
+
+ # Input is [num_splits * num_heads * hidden_size, :]
+ input_shape = param.size()
+ if checkpoint_version == 1.0:
+ # version 1.0 stores [num_heads * hidden_size * num_splits, :]
+ current_shape = (num_splits, num_heads, hidden_size) + input_shape[1:]
+ param = param.view(*current_shape)
+ param = param.transpose(0, 2)
+ param = param.transpose(1, 2).contiguous()
+ elif checkpoint_version >= 2.0:
+ # other versions store [num_heads * num_splits * hidden_size, :]
+ current_shape = (num_splits, num_heads, hidden_size) + input_shape[1:]
+ param = param.view(*current_shape)
+ param = param.transpose(0, 1).contiguous()
+ param = param.view(*input_shape)
+ return param
+
+
+def merge_transformers_sharded_states(path, num_checkpoints):
+ """
+ Merge sharded checkpoints from transformers into a single checkpoint.
+
+ Args:
+ path (str): the path to the sharded checkpoints
+ num_checkpoints (int): the number of checkpoints to merge
+ """
+ state_dict = {}
+ for i in range(1, num_checkpoints + 1):
+ checkpoint_path = os.path.join(path, f"pytorch_model-{i:05d}-of-{num_checkpoints:05d}.bin")
+ check_torch_load_is_safe()
+ current_chunk = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
+ state_dict.update(current_chunk)
+ return state_dict
+
+
+def get_megatron_sharded_states(args, tp_size, pp_size, pp_rank):
+ """
+ Get sharded checkpoints from NVIDIA Megatron-LM checkpoint based on the provided tensor parallel size, pipeline
+ parallel size and pipeline parallel rank.
+
+ Args:
+ args (argparse.Namespace): the arguments to the script
+ tp_size (int): the tensor parallel size
+ pp_size (int): the pipeline parallel size
+ pp_rank (int): the pipeline parallel rank
+ """
+ tp_state_dicts = []
+ for i in range(tp_size):
+ sub_dir_name = f"mp_rank_{i:02d}" if pp_size == 1 else f"mp_rank_{i:02d}_{pp_rank:03d}"
+ for checkpoint_name in ["model_optim_rng.pt", "model_rng.pt"]:
+ checkpoint_path = os.path.join(args.load_path, sub_dir_name, checkpoint_name)
+ if os.path.isfile(checkpoint_path):
+ break
+ check_torch_load_is_safe()
+ state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
+ tp_state_dicts.append(state_dict)
+ return tp_state_dicts
+
+
+def get_element_from_dict_by_path(d, path):
+ """
+ Get element from dictionary by path. If element is not present, recursively add empty dictionaries.
+
+ Args:
+ d (dict): the dictionary to get the element from
+ path (list): the path to the element which is delimited by "."
+ """
+ path = path.split(".")
+ for k in path:
+ if k not in d:
+ d[k] = {}
+ d = d[k]
+ return d
+
+
+def convert_checkpoint_from_megatron_to_transformers(args):
+ """
+ Convert NVIDIA Megatron-LM checkpoint to HuggingFace Transformers checkpoint. This handles Megatron checkpoints
+ with different tensor parallelism and pipeline parallelism sizes. It saves the converted checkpoint into shards
+ using HuggingFace Transformers checkpoint sharding functionality. This greatly extends the functionality of
+ `convert_megatron_gpt2_checkpoint.py`
+
+ Args:
+ args (argparse.Namespace): the arguments to the script
+ """
+ # Load Megatron-LM checkpoint arguments from the state dict
+ sub_dirs = os.listdir(args.load_path)
+ possible_sub_dirs = ["mp_rank_00", "mp_rank_00_000"]
+ for sub_dir in possible_sub_dirs:
+ if sub_dir in sub_dirs:
+ rank0_checkpoint_name = os.listdir(os.path.join(args.load_path, sub_dir))[0]
+ rank0_checkpoint_path = os.path.join(args.load_path, sub_dir, rank0_checkpoint_name)
+ break
+ print(f"Loading Megatron-LM checkpoint arguments from: {rank0_checkpoint_path}")
+ check_torch_load_is_safe()
+ state_dict = torch.load(rank0_checkpoint_path, map_location="cpu", weights_only=True)
+ megatron_args = state_dict.get("args", None)
+ if megatron_args is None:
+ raise ValueError(
+ "Megatron-LM checkpoint does not contain arguments. This utility only supports Megatron-LM checkpoints"
+ " containing all the megatron arguments. This is because it loads all config related to model"
+ " architecture, the tensor and pipeline model parallel size from the checkpoint instead of user having to"
+ " manually specify all the details. Please save Megatron-LM checkpoint along with all the megatron"
+ " arguments to use this utility."
+ )
+
+ # Create Transformers GPT2 config from Megatron-LM arguments
+ if megatron_args is not None:
+ if megatron_args.bias_gelu_fusion:
+ activation_function = "gelu_fast"
+ elif megatron_args.openai_gelu:
+ activation_function = "gelu_new"
+ else:
+ activation_function = "gelu"
+ else:
+ # in the very early days this used to be "gelu_new"
+ activation_function = "gelu_new"
+ vocab_size = (
+ megatron_args.padded_vocab_size
+ if getattr(megatron_args, "orig_vocab_size", None) is None
+ else megatron_args.orig_vocab_size
+ )
+ print(vocab_size)
+
+ config = GPT2Config(
+ vocab_size=vocab_size,
+ n_positions=megatron_args.max_position_embeddings,
+ n_embd=megatron_args.hidden_size,
+ n_layer=megatron_args.num_layers,
+ n_head=megatron_args.num_attention_heads,
+ n_inner=megatron_args.ffn_hidden_size,
+ activation_function=activation_function,
+ resid_pdrop=0.1,
+ embd_pdrop=0.1,
+ attn_pdrop=0.1,
+ layer_norm_epsilon=1e-5,
+ initializer_range=0.02,
+ summary_type="cls_index",
+ summary_use_proj=True,
+ summary_activation=None,
+ summary_proj_to_labels=True,
+ summary_first_dropout=0.1,
+ scale_attn_weights=True,
+ use_cache=True,
+ bos_token_id=vocab_size - 1,
+ eos_token_id=vocab_size - 1,
+ architectures=["GPT2LMHeadModel"],
+ )
+
+ output_state_dict = {}
+
+ checkpoint_version = state_dict.get("checkpoint_version", 0.0)
+ tp_size = megatron_args.tensor_model_parallel_size
+ pp_size = megatron_args.pipeline_model_parallel_size
+ dtype = torch.float32
+ # The regex to extract layer names.
+ layer_re = re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")
+
+ # Convert.
+ print("Converting")
+
+ # Embeddings
+ print("Converting embeddings")
+ tp_state_dicts = get_megatron_sharded_states(args, tp_size, pp_size, 0)
+
+ # Convert and store the position embeddings.
+ position_embeddings = get_element_from_dict_by_path(
+ tp_state_dicts[0], "model.language_model.embedding.position_embeddings.weight"
+ )
+ output_state_dict["transformer.wpe.weight"] = position_embeddings.to(dtype)
+
+ # Convert and store the word embeddings.
+ word_embeddings = torch.cat(
+ [
+ get_element_from_dict_by_path(
+ tp_state_dicts[tp_rank], "model.language_model.embedding.word_embeddings.weight"
+ )
+ for tp_rank in range(tp_size)
+ ],
+ dim=0,
+ )
+ word_embeddings = word_embeddings[:vocab_size].to(dtype)
+ output_state_dict["transformer.wte.weight"] = word_embeddings
+
+ # Transformer Layers
+ print("Converting transformer layers")
+ # The number of heads.
+ heads = config.n_head
+ # The hidden_size per head.
+ hidden_size_per_head = config.n_embd // config.n_head
+ n_positions = config.n_positions
+ num_layers = config.num_hidden_layers // pp_size
+
+ for pp_rank in range(pp_size):
+ if pp_size > 0:
+ print(f"Converting pipeline parallel rank {pp_rank}")
+ tp_state_dicts = get_megatron_sharded_states(args, tp_size, pp_size, pp_rank)
+
+ # The transformer.
+ path = (
+ "model.language_model.transformer"
+ if "transformer" in get_element_from_dict_by_path(tp_state_dicts[0], "model.language_model")
+ else "model.language_model.encoder"
+ )
+ # Extract the layers.
+ for key, val in get_element_from_dict_by_path(tp_state_dicts[0], path).items():
+ # Match the name.
+ m = layer_re.match(key)
+ # Stop if that's not a layer
+ if m is None:
+ break
+
+ # The index of the layer.
+ layer_idx = int(m.group(1)) + pp_rank * num_layers
+ # The name of the operation.
+ op_name = m.group(2)
+ # Is it a weight or a bias?
+ weight_or_bias = m.group(3)
+
+ # The name of the layer.
+ layer_name = f"transformer.h.{layer_idx}"
+
+ if op_name + "." + weight_or_bias not in tensor_parallel_params:
+ params = val.to(dtype)
+ else:
+ dim = 1 if op_name in ["self_attention.dense", "mlp.dense_4h_to_h", "attention.dense"] else 0
+ params = torch.cat(
+ [val]
+ + [
+ get_element_from_dict_by_path(tp_state_dicts[tp_rank], f"{path}")[key]
+ for tp_rank in range(1, tp_size)
+ ],
+ dim=dim,
+ ).to(dtype)
+
+ # For layernorm(s), simply store the layer norm.
+ if op_name.endswith("layernorm"):
+ ln_name = "ln_1" if op_name.startswith("input") else "ln_2"
+ output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = params
+
+ # Transpose the QKV matrix.
+ elif (
+ op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
+ ) and weight_or_bias == "weight":
+ # Insert a tensor of 1x1xDxD bias.
+ causal_mask = torch.tril(torch.ones((n_positions, n_positions), dtype=dtype)).view(
+ 1, 1, n_positions, n_positions
+ )
+ output_state_dict[layer_name + ".attn.bias"] = causal_mask
+
+ # Insert a "dummy" tensor for masked_bias.
+ masked_bias = torch.tensor(-1e4, dtype=dtype)
+ output_state_dict[layer_name + ".attn.masked_bias"] = masked_bias
+
+ out_val = megatron_to_transformers_fix_query_key_value_ordering(
+ params,
+ checkpoint_version,
+ 3,
+ heads,
+ hidden_size_per_head,
+ )
+ # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
+ out_val = out_val.transpose(0, 1).contiguous()
+ # Store.
+ output_state_dict[layer_name + ".attn.c_attn.weight"] = out_val
+
+ # Transpose the bias.
+ elif (
+ op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
+ ) and weight_or_bias == "bias":
+ out_val = megatron_to_transformers_fix_query_key_value_ordering(
+ params, checkpoint_version, 3, heads, hidden_size_per_head
+ )
+ # Store. No change of shape.
+ output_state_dict[layer_name + ".attn.c_attn.bias"] = out_val
+
+ # Transpose the weights.
+ elif weight_or_bias == "weight":
+ out_name = megatron_to_transformers[op_name]
+ output_state_dict[layer_name + out_name + "weight"] = params.transpose(0, 1)
+
+ # Copy the bias.
+ elif weight_or_bias == "bias":
+ out_name = megatron_to_transformers[op_name]
+ output_state_dict[layer_name + out_name + "bias"] = params
+
+ if config.n_layer != (layer_idx + 1):
+ raise ValueError(f"Expected {config.n_layer} layers but found {layer_idx + 1}")
+
+ # The final layernorm.
+ print("Converting final layernorm")
+ params = get_element_from_dict_by_path(tp_state_dicts[0], str(path))
+ output_state_dict["transformer.ln_f.weight"] = params["final_layernorm.weight"].to(dtype)
+ output_state_dict["transformer.ln_f.bias"] = params["final_layernorm.bias"].to(dtype)
+
+ # For LM head, transformers' wants the matrix to weight embeddings.
+ print("Converting LM head")
+ output_state_dict["lm_head.weight"] = word_embeddings.to(dtype)
+
+ # It should be done!
+ print("Conversion from Megatron-LM to Transformers is done!")
+
+ # Print the structure of converted state dict.
+ if args.print_checkpoint_structure:
+ recursive_print(None, output_state_dict)
+
+ # Add tokenizer class info to config
+ # see https://github.com/huggingface/transformers/issues/13906)
+
+ if args.tokenizer_name is None:
+ tokenizer_name = "openai-community/gpt2"
+ else:
+ tokenizer_name = args.tokenizer_name
+
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+ tokenizer_class = type(tokenizer).__name__
+ config.tokenizer_class = tokenizer_class
+
+ # Store the config to file.
+ print("Saving config")
+ config.save_pretrained(args.save_path)
+
+ # Save tokenizer based on args
+ if args.tokenizer_name is not None:
+ print(f"Adding {tokenizer_class} tokenizer files")
+ tokenizer.save_pretrained(args.save_path)
+
+ # Store the state_dict to file.
+ max_shard_size = int(args.max_shard_size) if args.max_shard_size.isdigit() else args.max_shard_size
+ state_dict_split = split_torch_state_dict_into_shards(output_state_dict, max_shard_size=max_shard_size)
+ shards = index = None
+ for tensors in state_dict_split.filename_to_tensors.values():
+ shards = {tensor: state_dict[tensor] for tensor in tensors}
+ if state_dict_split.is_sharded:
+ index = {
+ "metadata": state_dict_split.metadata,
+ "weight_map": state_dict_split.tensor_to_filename,
+ }
+
+ # Save the model
+ for shard_file, shard in shards.items():
+ torch.save(shard, os.path.join(args.save_path, shard_file))
+
+ if index is None:
+ print(f"Model weights saved in {os.path.join(args.save_path, WEIGHTS_NAME)}")
+ else:
+ save_index_file = os.path.join(args.save_path, WEIGHTS_INDEX_NAME)
+ # Save the index as well
+ with open(save_index_file, "w", encoding="utf-8") as f:
+ content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+ f.write(content)
+ print(
+ f"The model is bigger than the maximum size per checkpoint ({args.max_shard_size}) and is going to be "
+ f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the "
+ f"index located at {save_index_file}."
+ )
+
+
+def convert_checkpoint_from_transformers_to_megatron(args):
+ """
+ Convert a checkpoint from HuggingFace Transformers to Megatron-LM. This allows converted checkpoints with variable
+ tensor parallelism and pipeline parallelism sizes. It takes as input a checkpoint from HuggingFace Transformers
+ which can have multiple shards.
+
+ Args:
+ args (argparse.Namespace): the arguments to the script
+
+ """
+ os.makedirs(args.save_path, exist_ok=True)
+ # Search in directory above this
+ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
+ if args.megatron_path is not None:
+ sys.path.insert(0, args.megatron_path)
+
+ megatron_exists = importlib.util.find_spec("megatron") is not None
+ if megatron_exists:
+ from megatron.core import package_info
+
+ if version.parse(package_info.__version__) >= version.parse("0.6.0"):
+ from megatron.training.tokenizer.tokenizer import _vocab_size_with_padding
+ else:
+ from megatron.tokenizer.tokenizer import _vocab_size_with_padding
+
+ else:
+ print("Unable to import Megatron, please specify the path to Megatron using --megatron-path. Exiting.")
+ exit(1)
+
+ # load the transformers model state dict and config
+ sub_dirs = [x for x in os.listdir(args.load_path) if x.startswith("pytorch_model")]
+ if len(sub_dirs) == 1:
+ checkpoint_name = "pytorch_model.bin"
+ check_torch_load_is_safe()
+ state_dict = torch.load(os.path.join(args.load_path, checkpoint_name), map_location="cpu", weights_only=True)
+ else:
+ num_checkpoints = len(sub_dirs) - 1
+ state_dict = merge_transformers_sharded_states(args.load_path, num_checkpoints)
+
+ config = GPT2Config.from_pretrained(args.load_path)
+
+ # Saving the tracker file
+ tracker_filepath = os.path.join(args.save_path, "latest_checkpointed_iteration.txt")
+ with open(tracker_filepath, "w") as f:
+ f.write("release")
+
+ # create `release` dir in args.load_path
+ release_dir = os.path.join(args.save_path, "release")
+ os.makedirs(release_dir, exist_ok=True)
+
+ # megatron args
+ megatron_args = {
+ "orig_vocab_size": config.vocab_size,
+ "max_position_embeddings": config.n_positions,
+ "hidden_size": config.n_embd,
+ "num_layers": config.n_layer,
+ "num_attention_heads": config.n_head,
+ "ffn_hidden_size": config.n_inner,
+ "tensor_model_parallel_size": args.target_tensor_model_parallel_size,
+ "pipeline_model_parallel_size": args.target_pipeline_model_parallel_size,
+ "data_parallel_size": args.target_data_parallel_size,
+ "make_vocab_size_divisible_by": args.make_vocab_size_divisible_by,
+ "rank": 0,
+ "tokenizer_type": "GPT2BPETokenizer",
+ }
+
+ if config.activation_function == "gelu":
+ megatron_args["bias_gelu_fusion"] = False
+ megatron_args["openai_gelu"] = False
+ elif config.activation_function == "gelu_fast":
+ megatron_args["bias_gelu_fusion"] = True
+ megatron_args["openai_gelu"] = False
+ elif config.activation_function == "gelu_new":
+ megatron_args["bias_gelu_fusion"] = False
+ megatron_args["openai_gelu"] = True
+
+ margs = types.SimpleNamespace()
+ for k, v in megatron_args.items():
+ setattr(margs, k, v)
+
+ # params dtype
+ if args.target_params_dtype == "fp16":
+ dtype = torch.float16
+ elif args.target_params_dtype == "bf16":
+ dtype = torch.bfloat16
+ else:
+ dtype = torch.float32
+ setattr(margs, "params_dtype", dtype)
+
+ # save dummy optim state dict
+ dummy_optim_state_dict = {}
+ dummy_optim_state_dict["optimizer"] = {
+ "step": 0,
+ "param_groups": [
+ {
+ "lr": 0.0,
+ "beta1": 0.0,
+ "beta2": 0.0,
+ "eps": 0.0,
+ "weight_decay": 0.0,
+ "correct_bias": False,
+ "params": [],
+ }
+ ],
+ }
+ if args.use_distributed_optimizer:
+ for i in range(args.target_pipeline_model_parallel_size):
+ for j in range(args.target_tensor_model_parallel_size):
+ for k in range(args.target_data_parallel_size):
+ if args.target_pipeline_model_parallel_size == 1:
+ checkpoint_dir = f"mp_rank_{j:02d}_{k:03d}"
+ else:
+ checkpoint_dir = f"mp_rank_{j:02d}_{i:03d}_{k:03d}"
+ checkpoint_dir = os.path.join(release_dir, checkpoint_dir)
+ os.makedirs(checkpoint_dir, exist_ok=True)
+ torch.save(
+ dummy_optim_state_dict,
+ os.path.join(checkpoint_dir, "optim.pt"),
+ )
+
+ # Convert.
+ print("Converting")
+ output_state_dict = []
+ for i in range(args.target_tensor_model_parallel_size):
+ output_state_dict.append({})
+
+ # Embedding layer
+ print("converting embedding layer")
+ pos_embedding = state_dict["transformer.wpe.weight"].to(dtype)
+ word_embedding = state_dict["transformer.wte.weight"].to(dtype)
+ orig_vocab_size = config.vocab_size
+ padded_vocab_size = _vocab_size_with_padding(orig_vocab_size, margs)
+ setattr(margs, "padded_vocab_size", padded_vocab_size)
+ # Cut out extra padding we don't need
+ if orig_vocab_size > padded_vocab_size:
+ full_word_embed = word_embedding[0:padded_vocab_size, :]
+ # Expanding embedding to larger size by replicating final entry
+ elif orig_vocab_size < padded_vocab_size:
+ padding_size = padded_vocab_size - orig_vocab_size
+ full_word_embed = torch.cat((word_embedding, word_embedding[-1].unsqueeze(0).expand(padding_size, -1)))
+ # Same size!
+ else:
+ full_word_embed = word_embedding
+
+ # Split into new tensor model parallel sizes
+ out_word_embed = torch.chunk(full_word_embed, args.target_tensor_model_parallel_size, dim=0)
+ for i in range(args.target_tensor_model_parallel_size):
+ pos_emb_dict = get_element_from_dict_by_path(
+ output_state_dict[i], "model.language_model.embedding.position_embeddings"
+ )
+ pos_emb_dict["weight"] = pos_embedding
+
+ word_emb_dict = get_element_from_dict_by_path(
+ output_state_dict[i], "model.language_model.embedding.word_embeddings"
+ )
+ word_emb_dict["weight"] = out_word_embed[i].clone()
+
+ # Transformer layers
+ print("converting transformer layers")
+ if config.num_attention_heads % args.target_tensor_model_parallel_size != 0:
+ raise ValueError(
+ f"Number of attention heads ({config.num_attention_heads}) must be divisible by number of tensor parallelism"
+ f" ({args.target_tensor_model_parallel_size})"
+ )
+
+ if config.num_hidden_layers % args.target_pipeline_model_parallel_size != 0:
+ raise ValueError(
+ f"Number of layers ({config.num_hidden_layers}) must be divisible by number of pipeline parallelism"
+ f" ({args.target_pipeline_model_parallel_size})"
+ )
+
+ num_layers = config.num_hidden_layers // args.target_pipeline_model_parallel_size
+
+ layer_re = re.compile(r"transformer.h\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")
+ # The number of heads.
+ heads = config.n_head
+ # The hidden_size per head.
+ hidden_size_per_head = config.n_embd // config.n_head
+ for pp_rank in range(args.target_pipeline_model_parallel_size):
+ layer_offset = pp_rank * num_layers
+ if pp_rank > 0:
+ output_state_dict = []
+ for i in range(args.target_tensor_model_parallel_size):
+ output_state_dict.append({})
+
+ for layer in range(num_layers):
+ pp_layer_id = layer + layer_offset
+ layers_to_copy = [
+ layer_name for layer_name in state_dict if layer_name.startswith(f"transformer.h.{pp_layer_id}.")
+ ]
+
+ for layer_name in layers_to_copy:
+ m = layer_re.match(layer_name)
+ # Stop if that's not a layer
+ if m is None:
+ break
+
+ # The index of the layer.
+ _ = int(m.group(1))
+ # The name of the operation.
+ op_name = m.group(2)
+ # Is it a weight or a bias?
+ weight_or_bias = m.group(3)
+
+ params = state_dict[layer_name].to(dtype)
+ # handle layernorm
+ if op_name.startswith("ln"):
+ out_name = "input_layernorm" if op_name.endswith("1") else "post_attention_layernorm"
+ layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}"
+
+ # handle attention K, V, Q weights
+ elif op_name.startswith("attn.c_attn") and weight_or_bias == "weight":
+ # transformers stores D X (3*D) but Megatron-LM expects (3*D) X D.
+ params = params.transpose(0, 1).contiguous()
+
+ params = transformers_to_megatron_fix_query_key_value_ordering(
+ params,
+ 3.0,
+ 3,
+ heads,
+ hidden_size_per_head,
+ )
+ layer_name = f"layers.{layer}.self_attention.query_key_value.{weight_or_bias}"
+
+ # handle attention K, V, Q bias
+ elif op_name.startswith("attn.c_attn") and weight_or_bias == "bias":
+ params = transformers_to_megatron_fix_query_key_value_ordering(
+ params,
+ 3.0,
+ 3,
+ heads,
+ hidden_size_per_head,
+ )
+ layer_name = f"layers.{layer}.self_attention.query_key_value.{weight_or_bias}"
+
+ # handle attention and mlp weights
+ elif weight_or_bias == "weight":
+ out_name = transformers_to_megatron.get(op_name)
+ if out_name is None:
+ continue
+ params = params.transpose(0, 1)
+ layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}"
+
+ # handle attention and mlp bias
+ elif weight_or_bias == "bias":
+ out_name = transformers_to_megatron.get(op_name)
+ if out_name is None:
+ continue
+ layer_name = f"layers.{layer}.{out_name}.{weight_or_bias}"
+
+ # skip
+ else:
+ continue
+
+ if op_name + "." + weight_or_bias in tensor_parallel_params:
+ dim = 1 if op_name in ["attn.c_proj", "mlp.c_proj"] else 0
+ params = torch.chunk(params, args.target_tensor_model_parallel_size, dim=dim)
+
+ for i in range(args.target_tensor_model_parallel_size):
+ params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.language_model.encoder")
+ params_dict[layer_name] = (
+ params[i].clone() if (op_name + "." + weight_or_bias in tensor_parallel_params) else params
+ )
+
+ if pp_rank == args.target_pipeline_model_parallel_size - 1:
+ # handle final layernorm
+ for weight_or_bias in ["weight", "bias"]:
+ params = state_dict[f"transformer.ln_f.{weight_or_bias}"].to(dtype)
+ layer_name = f"final_layernorm.{weight_or_bias}"
+ for i in range(args.target_tensor_model_parallel_size):
+ params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.language_model.encoder")
+ params_dict[layer_name] = params
+
+ # add the LM head
+ for i in range(args.target_tensor_model_parallel_size):
+ params_dict = get_element_from_dict_by_path(output_state_dict[i], "model.word_embeddings_for_head")
+ params_dict["weight"] = out_word_embed[i].clone()
+
+ # saving the state dict as per the tp_rank and pp_rank
+ for tp_rank in range(args.target_tensor_model_parallel_size):
+ output_state_dict[tp_rank]["checkpoint_version"] = 3.0
+ output_state_dict[tp_rank]["args"] = margs
+ checkpoint_dir = (
+ f"mp_rank_{tp_rank:02d}"
+ if args.target_pipeline_model_parallel_size == 1
+ else f"mp_rank_{tp_rank:02d}_{pp_rank:03d}"
+ )
+ if args.use_distributed_optimizer:
+ checkpoint_name = "model_rng.pt"
+ else:
+ checkpoint_name = "model_optim_rng.pt"
+ output_state_dict[tp_rank]["optimizer"] = dummy_optim_state_dict["optimizer"]
+ checkpoint_dir = os.path.join(release_dir, checkpoint_dir)
+ os.makedirs(checkpoint_dir, exist_ok=True)
+ checkpoint_path = os.path.join(checkpoint_dir, checkpoint_name)
+ if args.print_checkpoint_structure:
+ print(
+ f"Checkpoint structure of model state dict shard belonging to TP rank {tp_rank} and PP rank"
+ f" {pp_rank}:"
+ )
+ recursive_print(None, output_state_dict[tp_rank])
+ torch.save(output_state_dict[tp_rank], checkpoint_path)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser = add_checkpointing_args(parser)
+ parser = add_megatron_checkpoint_args(parser)
+ parser = add_transformers_checkpoint_args(parser)
+ args = parser.parse_args()
+ if args.convert_checkpoint_from_megatron_to_transformers:
+ convert_checkpoint_from_megatron_to_transformers(args)
+ else:
+ convert_checkpoint_from_transformers_to_megatron(args)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1953f50baed91d8f3f09edcea9356865fa6091f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py
@@ -0,0 +1,430 @@
+####################################################################################################
+
+# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+####################################################################################################
+
+#
+# Note: If when running this conversion script you're getting an exception:
+# ModuleNotFoundError: No module named 'megatron.model.enums'
+# you need to tell python where to find the clone of Megatron-LM, e.g.:
+#
+# cd /tmp
+# git clone https://github.com/NVIDIA/Megatron-LM
+# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
+#
+# if you already have it cloned elsewhere, simply adjust the path to the existing path
+#
+# If the training was done using a Megatron-LM fork, e.g.,
+# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
+# in your path, i.e., /path/to/Megatron-DeepSpeed/
+#
+
+import argparse
+import os
+import re
+import zipfile
+
+import torch
+
+from transformers import AutoTokenizer, GPT2Config
+
+
+####################################################################################################
+
+
+def recursive_print(name, val, spaces=0):
+ # Format the message.
+ if name is None:
+ msg = None
+ else:
+ fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
+ msg = fmt.format(name)
+
+ # Print and recurse (if needed).
+ if isinstance(val, dict):
+ if msg is not None:
+ print(msg)
+ for k in val:
+ recursive_print(k, val[k], spaces + 2)
+ elif isinstance(val, torch.Tensor):
+ print(msg, ":", val.size())
+ else:
+ print(msg, ":", val)
+
+
+def fix_query_key_value_ordering(param, checkpoint_version, num_splits, num_heads, hidden_size):
+ # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
+ # for compatibility with later versions of NVIDIA Megatron-LM.
+ # The inverse operation is performed inside Megatron-LM to read checkpoints:
+ # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
+ # If param is the weight tensor of the self-attention block, the returned tensor
+ # will have to be transposed one more time to be read by HuggingFace GPT2.
+ input_shape = param.size()
+ if checkpoint_version == 1.0:
+ # version 1.0 stores [num_heads * hidden_size * num_splits, :]
+ saved_shape = (num_heads, hidden_size, num_splits) + input_shape[1:]
+ param = param.view(*saved_shape)
+ param = param.transpose(0, 2)
+ param = param.transpose(1, 2).contiguous()
+ elif checkpoint_version >= 2.0:
+ # other versions store [num_heads * num_splits * hidden_size, :]
+ saved_shape = (num_heads, num_splits, hidden_size) + input_shape[1:]
+ param = param.view(*saved_shape)
+ param = param.transpose(0, 1).contiguous()
+ param = param.view(*input_shape)
+ return param
+
+
+####################################################################################################
+
+
+def convert_megatron_checkpoint(args, input_state_dict, config):
+ # The converted output model.
+ output_state_dict = {}
+
+ # old versions did not store training args
+ ds_args = input_state_dict.get("args", None)
+ if ds_args is not None:
+ # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
+ # from pprint import pprint
+ # pprint(vars(ds_args))
+
+ config.vocab_size = ds_args.padded_vocab_size
+ config.n_positions = ds_args.max_position_embeddings
+ config.n_embd = ds_args.hidden_size
+ config.n_layer = ds_args.num_layers
+ config.n_head = ds_args.num_attention_heads
+ config.n_inner = ds_args.ffn_hidden_size
+ # pprint(config)
+
+ # The number of heads.
+ heads = config.n_head
+ # The hidden_size per head.
+ hidden_size_per_head = config.n_embd // config.n_head
+ # Megatron-LM checkpoint version
+ if "checkpoint_version" in input_state_dict:
+ checkpoint_version = input_state_dict["checkpoint_version"]
+ else:
+ checkpoint_version = 0.0
+
+ # The model.
+ model = input_state_dict["model"]
+ # The language model.
+ lm = model["language_model"]
+ # The embeddings.
+ embeddings = lm["embedding"]
+
+ # The word embeddings.
+ word_embeddings = embeddings["word_embeddings"]["weight"]
+ # Truncate the embedding table to vocab_size rows.
+ word_embeddings = word_embeddings[: config.vocab_size, :]
+ output_state_dict["transformer.wte.weight"] = word_embeddings
+
+ # The position embeddings.
+ pos_embeddings = embeddings["position_embeddings"]["weight"]
+ # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
+ n_positions = pos_embeddings.size(0)
+ if n_positions != config.n_positions:
+ raise ValueError(
+ f"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"
+ )
+ # Store the position embeddings.
+ output_state_dict["transformer.wpe.weight"] = pos_embeddings
+
+ # The transformer.
+ transformer = lm["transformer"] if "transformer" in lm else lm["encoder"]
+
+ # The regex to extract layer names.
+ layer_re = re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z0-9_]+)")
+
+ # The simple map of names for "automated" rules.
+ megatron_to_transformers = {
+ "attention.dense": ".attn.c_proj.",
+ "self_attention.dense": ".attn.c_proj.",
+ "self_attention.proj": ".attn.c_proj.", # New format
+ "mlp.dense_h_to_4h": ".mlp.c_fc.",
+ "mlp.dense_4h_to_h": ".mlp.c_proj.",
+ "layernorm_mlp.fc1": ".mlp.c_fc.", # New format
+ "layernorm_mlp.fc2": ".mlp.c_proj.", # New format
+ }
+
+ # Extract the layers.
+ for key, val in transformer.items():
+ # Match the name.
+ m = layer_re.match(key)
+
+ # Stop if that's not a layer
+ if m is None:
+ continue
+
+ # The index of the layer.
+ layer_idx = int(m.group(1))
+ # The name of the operation.
+ op_name = m.group(2)
+ # Is it a weight or a bias?
+ weight_or_bias = m.group(3)
+ # The name of the layer.
+ layer_name = f"transformer.h.{layer_idx}"
+
+ # Handle _extra_state keys (skip them)
+ if weight_or_bias == "_extra_state":
+ continue
+
+ # For layernorm(s), simply store the layer norm.
+ if op_name.endswith("layernorm") or weight_or_bias.startswith("layer_norm"):
+ if weight_or_bias.startswith("layer_norm"):
+ # New format: layers.X.self_attention.layernorm_qkv.layer_norm_weight
+ if op_name == "self_attention.layernorm_qkv":
+ ln_name = "ln_1" # Pre-attention layer norm
+ elif op_name == "layernorm_mlp":
+ ln_name = "ln_2" # Pre-MLP layer norm
+ else:
+ ln_name = "ln_1" if op_name.startswith("input") else "ln_2"
+
+ param_name = "weight" if weight_or_bias == "layer_norm_weight" else "bias"
+ output_state_dict[layer_name + "." + ln_name + "." + param_name] = val
+ else:
+ # Old format
+ ln_name = "ln_1" if op_name.startswith("input") else "ln_2"
+ output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = val
+
+ # Handle QKV projections - new format: self_attention.layernorm_qkv.weight/bias
+ elif op_name == "self_attention.layernorm_qkv" and weight_or_bias in ["weight", "bias"]:
+ if weight_or_bias == "weight":
+ # Insert a tensor of 1x1xDxD bias.
+ causal_mask = torch.tril(torch.ones((n_positions, n_positions), dtype=torch.float16)).view(
+ 1, 1, n_positions, n_positions
+ )
+ output_state_dict[layer_name + ".attn.bias"] = causal_mask
+
+ # Insert a "dummy" tensor for masked_bias.
+ masked_bias = torch.tensor(-1e4, dtype=torch.float16)
+ output_state_dict[layer_name + ".attn.masked_bias"] = masked_bias
+
+ out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
+ # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
+ out_val = out_val.transpose(0, 1).contiguous()
+ # Store.
+ output_state_dict[layer_name + ".attn.c_attn.weight"] = out_val
+ else: # bias
+ out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
+ # Store. No change of shape.
+ output_state_dict[layer_name + ".attn.c_attn.bias"] = out_val
+
+ # Transpose the QKV matrix - old format.
+ elif (
+ op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
+ ) and weight_or_bias == "weight":
+ # Insert a tensor of 1x1xDxD bias.
+ causal_mask = torch.tril(torch.ones((n_positions, n_positions), dtype=torch.float16)).view(
+ 1, 1, n_positions, n_positions
+ )
+ output_state_dict[layer_name + ".attn.bias"] = causal_mask
+
+ # Insert a "dummy" tensor for masked_bias.
+ masked_bias = torch.tensor(-1e4, dtype=torch.float16)
+ output_state_dict[layer_name + ".attn.masked_bias"] = masked_bias
+
+ out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
+ # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
+ out_val = out_val.transpose(0, 1).contiguous()
+ # Store.
+ output_state_dict[layer_name + ".attn.c_attn.weight"] = out_val
+
+ # Transpose the bias - old format.
+ elif (
+ op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
+ ) and weight_or_bias == "bias":
+ out_val = fix_query_key_value_ordering(val, checkpoint_version, 3, heads, hidden_size_per_head)
+ # Store. No change of shape.
+ output_state_dict[layer_name + ".attn.c_attn.bias"] = out_val
+
+ # Transpose the weights.
+ elif weight_or_bias == "weight":
+ # DEBUG: Check if op_name exists in the mapping
+ if op_name not in megatron_to_transformers:
+ continue
+ out_name = megatron_to_transformers[op_name]
+ output_state_dict[layer_name + out_name + "weight"] = val.transpose(0, 1)
+
+ # Copy the bias.
+ elif weight_or_bias == "bias":
+ # DEBUG: Check if op_name exists in the mapping
+ if op_name not in megatron_to_transformers:
+ continue
+ out_name = megatron_to_transformers[op_name]
+ output_state_dict[layer_name + out_name + "bias"] = val
+
+ # Handle new format MLP weights/biases
+ elif weight_or_bias in ["fc1_weight", "fc2_weight", "fc1_bias", "fc2_bias"]:
+ if weight_or_bias == "fc1_weight":
+ output_state_dict[layer_name + ".mlp.c_fc.weight"] = val.transpose(0, 1)
+ elif weight_or_bias == "fc1_bias":
+ output_state_dict[layer_name + ".mlp.c_fc.bias"] = val
+ elif weight_or_bias == "fc2_weight":
+ output_state_dict[layer_name + ".mlp.c_proj.weight"] = val.transpose(0, 1)
+ elif weight_or_bias == "fc2_bias":
+ output_state_dict[layer_name + ".mlp.c_proj.bias"] = val
+
+ else:
+ print(
+ f"DEBUG: Unhandled key: {key} (layer {layer_idx}, op_name: '{op_name}', weight_or_bias: '{weight_or_bias}')"
+ )
+
+ # DEBUG.
+ assert config.n_layer == layer_idx + 1
+
+ # The final layernorm - handle both old and new formats.
+ if "final_layernorm.weight" in transformer:
+ # Old format
+ output_state_dict["transformer.ln_f.weight"] = transformer["final_layernorm.weight"]
+ output_state_dict["transformer.ln_f.bias"] = transformer["final_layernorm.bias"]
+ elif "final_norm.weight" in transformer:
+ # New format
+ output_state_dict["transformer.ln_f.weight"] = transformer["final_norm.weight"]
+ output_state_dict["transformer.ln_f.bias"] = transformer["final_norm.bias"]
+ else:
+ print("WARNING: Could not find final layer norm weights!")
+
+ # For LM head, transformers' wants the matrix to weight embeddings.
+ output_state_dict["lm_head.weight"] = word_embeddings
+
+ # It should be done!
+ return output_state_dict
+
+
+####################################################################################################
+
+
+def main():
+ # Create the argument parser.
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--print-checkpoint-structure", action="store_true")
+ parser.add_argument(
+ "path_to_checkpoint",
+ type=str,
+ help="Path to the checkpoint file (.zip archive or direct .pt file)",
+ )
+ parser.add_argument(
+ "--config_file",
+ default="",
+ type=str,
+ help="An optional config json file describing the pre-trained model.",
+ )
+ args = parser.parse_args()
+
+ # Extract the basename.
+ basename = os.path.dirname(args.path_to_checkpoint)
+
+ # Load the model.
+ # the .zip is very optional, let's keep it for backward compatibility
+ print(f"Extracting PyTorch state dictionary from {args.path_to_checkpoint}")
+ if args.path_to_checkpoint.endswith(".zip"):
+ with zipfile.ZipFile(args.path_to_checkpoint, "r") as checkpoint:
+ with checkpoint.open("release/mp_rank_00/model_optim_rng.pt") as pytorch_dict:
+ input_state_dict = torch.load(pytorch_dict, map_location="cpu", weights_only=True)
+ else:
+ input_state_dict = torch.load(args.path_to_checkpoint, map_location="cpu", weights_only=False)
+
+ ds_args = input_state_dict.get("args", None)
+
+ # Read the config, or default to the model released by NVIDIA.
+ if args.config_file == "":
+ if ds_args is not None:
+ if ds_args.bias_gelu_fusion:
+ activation_function = "gelu_fast"
+ elif ds_args.openai_gelu:
+ activation_function = "gelu_new"
+ else:
+ activation_function = "gelu"
+ else:
+ # in the very early days this used to be "gelu_new"
+ activation_function = "gelu_new"
+
+ # Spell out all parameters in case the defaults change.
+ config = GPT2Config(
+ vocab_size=50257,
+ n_positions=1024,
+ n_embd=1024,
+ n_layer=24,
+ n_head=16,
+ n_inner=4096,
+ activation_function=activation_function,
+ resid_pdrop=0.1,
+ embd_pdrop=0.1,
+ attn_pdrop=0.1,
+ layer_norm_epsilon=1e-5,
+ initializer_range=0.02,
+ summary_type="cls_index",
+ summary_use_proj=True,
+ summary_activation=None,
+ summary_proj_to_labels=True,
+ summary_first_dropout=0.1,
+ scale_attn_weights=True,
+ use_cache=True,
+ bos_token_id=50256,
+ eos_token_id=50256,
+ )
+ else:
+ config = GPT2Config.from_json_file(args.config_file)
+
+ config.architectures = ["GPT2LMHeadModel"]
+
+ # Convert.
+ print("Converting")
+ output_state_dict = convert_megatron_checkpoint(args, input_state_dict, config)
+
+ # Print the structure of converted state dict.
+ if args.print_checkpoint_structure:
+ recursive_print(None, output_state_dict)
+
+ # Add tokenizer class info to config
+ # see https://github.com/huggingface/transformers/issues/13906)
+ if ds_args is not None:
+ tokenizer_type = ds_args.tokenizer_type
+ if tokenizer_type == "GPT2BPETokenizer":
+ tokenizer_model_name = "openai-community/gpt2"
+ elif tokenizer_type == "PretrainedFromHF":
+ tokenizer_model_name = ds_args.tokenizer_name_or_path
+ else:
+ raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}")
+ else:
+ tokenizer_model_name = "openai-community/gpt2"
+
+ tokenizer = AutoTokenizer.from_pretrained(tokenizer_model_name)
+ tokenizer_class = type(tokenizer).__name__
+ config.tokenizer_class = tokenizer_class
+
+ # Store the config to file.
+ print("Saving config")
+ config.save_pretrained(basename)
+
+ # Save tokenizer based on args
+ print(f"Adding {tokenizer_class} tokenizer files")
+ tokenizer.save_pretrained(basename)
+
+ # Store the state_dict to file.
+ output_checkpoint_file = os.path.join(basename, "pytorch_model.bin")
+ print(f'Saving checkpoint to "{output_checkpoint_file}"')
+ torch.save(output_state_dict, output_checkpoint_file)
+
+
+####################################################################################################
+
+if __name__ == "__main__":
+ main()
+
+####################################################################################################
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a20c4cd4efc3ee8da6b22d0d4edf3dab25dcfc06
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mgp_str import *
+ from .modeling_mgp_str import *
+ from .processing_mgp_str import *
+ from .tokenization_mgp_str import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/configuration_mgp_str.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/configuration_mgp_str.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f6b355c7718054f2522a6892f5a3212c4d85ced
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/configuration_mgp_str.py
@@ -0,0 +1,137 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MGP-STR model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MgpstrConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of an [`MgpstrModel`]. It is used to instantiate an
+ MGP-STR model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the MGP-STR
+ [alibaba-damo/mgp-str-base](https://huggingface.co/alibaba-damo/mgp-str-base) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ image_size (`list[int]`, *optional*, defaults to `[32, 128]`):
+ The size (resolution) of each image.
+ patch_size (`int`, *optional*, defaults to 4):
+ The size (resolution) of each patch.
+ num_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ max_token_length (`int`, *optional*, defaults to 27):
+ The max number of output tokens.
+ num_character_labels (`int`, *optional*, defaults to 38):
+ The number of classes for character head .
+ num_bpe_labels (`int`, *optional*, defaults to 50257):
+ The number of classes for bpe head .
+ num_wordpiece_labels (`int`, *optional*, defaults to 30522):
+ The number of classes for wordpiece head .
+ hidden_size (`int`, *optional*, defaults to 768):
+ The embedding dimension.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ mlp_ratio (`float`, *optional*, defaults to 4.0):
+ The ratio of mlp hidden dim to embedding dim.
+ qkv_bias (`bool`, *optional*, defaults to `True`):
+ Whether to add a bias to the queries, keys and values.
+ distilled (`bool`, *optional*, defaults to `False`):
+ Model includes a distillation token and head as in DeiT models.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the layer normalization layers.
+ drop_rate (`float`, *optional*, defaults to 0.0):
+ The dropout probability for all fully connected layers in the embeddings, encoder.
+ attn_drop_rate (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ drop_path_rate (`float`, *optional*, defaults to 0.0):
+ The stochastic depth rate.
+ output_a3_attentions (`bool`, *optional*, defaults to `False`):
+ Whether or not the model should returns A^3 module attentions.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+
+ Example:
+
+ ```python
+ >>> from transformers import MgpstrConfig, MgpstrForSceneTextRecognition
+
+ >>> # Initializing a Mgpstr mgp-str-base style configuration
+ >>> configuration = MgpstrConfig()
+
+ >>> # Initializing a model (with random weights) from the mgp-str-base style configuration
+ >>> model = MgpstrForSceneTextRecognition(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mgp-str"
+
+ def __init__(
+ self,
+ image_size=[32, 128],
+ patch_size=4,
+ num_channels=3,
+ max_token_length=27,
+ num_character_labels=38,
+ num_bpe_labels=50257,
+ num_wordpiece_labels=30522,
+ hidden_size=768,
+ num_hidden_layers=12,
+ num_attention_heads=12,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ distilled=False,
+ layer_norm_eps=1e-5,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.0,
+ output_a3_attentions=False,
+ initializer_range=0.02,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.image_size = image_size
+ self.patch_size = patch_size
+ self.num_channels = num_channels
+ self.max_token_length = max_token_length
+ self.num_character_labels = num_character_labels
+ self.num_bpe_labels = num_bpe_labels
+ self.num_wordpiece_labels = num_wordpiece_labels
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.mlp_ratio = mlp_ratio
+ self.distilled = distilled
+ self.layer_norm_eps = layer_norm_eps
+ self.drop_rate = drop_rate
+ self.qkv_bias = qkv_bias
+ self.attn_drop_rate = attn_drop_rate
+ self.drop_path_rate = drop_path_rate
+ self.output_a3_attentions = output_a3_attentions
+ self.initializer_range = initializer_range
+
+
+__all__ = ["MgpstrConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/modeling_mgp_str.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/modeling_mgp_str.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e6ab26a4b98434758ff96049fba76631edb418b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/modeling_mgp_str.py
@@ -0,0 +1,460 @@
+# coding=utf-8
+# Copyright 2023 Alibaba Research and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MGP-STR model."""
+
+import collections.abc
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+
+from ...modeling_outputs import BaseModelOutput
+from ...modeling_utils import PreTrainedModel
+from ...utils import ModelOutput, auto_docstring, logging
+from .configuration_mgp_str import MgpstrConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.beit.modeling_beit.drop_path
+def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
+ """
+ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+ Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
+ however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+ See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
+ layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
+ argument.
+ """
+ if drop_prob == 0.0 or not training:
+ return input
+ keep_prob = 1 - drop_prob
+ shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
+ random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
+ random_tensor.floor_() # binarize
+ output = input.div(keep_prob) * random_tensor
+ return output
+
+
+# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Mgpstr
+class MgpstrDropPath(nn.Module):
+ """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+ def __init__(self, drop_prob: Optional[float] = None) -> None:
+ super().__init__()
+ self.drop_prob = drop_prob
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ return drop_path(hidden_states, self.drop_prob, self.training)
+
+ def extra_repr(self) -> str:
+ return f"p={self.drop_prob}"
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
+ """
+)
+class MgpstrModelOutput(ModelOutput):
+ r"""
+ logits (`tuple(torch.FloatTensor)` of shape `(batch_size, config.num_character_labels)`):
+ Tuple of `torch.FloatTensor` (one for the output of character of shape `(batch_size,
+ config.max_token_length, config.num_character_labels)`, + one for the output of bpe of shape `(batch_size,
+ config.max_token_length, config.num_bpe_labels)`, + one for the output of wordpiece of shape `(batch_size,
+ config.max_token_length, config.num_wordpiece_labels)`) .
+
+ Classification scores (before SoftMax) of character, bpe and wordpiece.
+ a3_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_a3_attentions=True` is passed or when `config.output_a3_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for the attention of character, + one for the attention of bpe`, + one
+ for the attention of wordpiece) of shape `(batch_size, config.max_token_length, sequence_length)`.
+
+ Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+ heads.
+ """
+
+ logits: tuple[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ a3_attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+class MgpstrEmbeddings(nn.Module):
+ """2D Image to Patch Embedding"""
+
+ def __init__(self, config: MgpstrConfig):
+ super().__init__()
+ image_size = (
+ config.image_size
+ if isinstance(config.image_size, collections.abc.Iterable)
+ else (config.image_size, config.image_size)
+ )
+ patch_size = (
+ config.patch_size
+ if isinstance(config.patch_size, collections.abc.Iterable)
+ else (config.patch_size, config.patch_size)
+ )
+ self.image_size = image_size
+ self.patch_size = patch_size
+ self.grid_size = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
+ self.num_patches = self.grid_size[0] * self.grid_size[1]
+ self.num_tokens = 2 if config.distilled else 1
+
+ self.proj = nn.Conv2d(config.num_channels, config.hidden_size, kernel_size=patch_size, stride=patch_size)
+
+ self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+
+ self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches + self.num_tokens, config.hidden_size))
+ self.pos_drop = nn.Dropout(p=config.drop_rate)
+
+ def forward(self, pixel_values):
+ batch_size, channel, height, width = pixel_values.shape
+ if height != self.image_size[0] or width != self.image_size[1]:
+ raise ValueError(
+ f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
+ )
+
+ patch_embeddings = self.proj(pixel_values)
+ patch_embeddings = patch_embeddings.flatten(2).transpose(1, 2) # BCHW -> BNC
+
+ cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+ embedding_output = torch.cat((cls_tokens, patch_embeddings), dim=1)
+ embedding_output = embedding_output + self.pos_embed
+ embedding_output = self.pos_drop(embedding_output)
+
+ return embedding_output
+
+
+class MgpstrMlp(nn.Module):
+ """MLP as used in Vision Transformer, MLP-Mixer and related networks"""
+
+ def __init__(self, config: MgpstrConfig, hidden_features):
+ super().__init__()
+ hidden_features = hidden_features or config.hidden_size
+ self.fc1 = nn.Linear(config.hidden_size, hidden_features)
+ self.act = nn.GELU()
+ self.fc2 = nn.Linear(hidden_features, config.hidden_size)
+ self.drop = nn.Dropout(config.drop_rate)
+
+ def forward(self, hidden_states):
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.drop(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.drop(hidden_states)
+ return hidden_states
+
+
+class MgpstrAttention(nn.Module):
+ def __init__(self, config: MgpstrConfig):
+ super().__init__()
+ self.num_heads = config.num_attention_heads
+ head_dim = config.hidden_size // config.num_attention_heads
+ self.scale = head_dim**-0.5
+
+ self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias)
+ self.attn_drop = nn.Dropout(config.attn_drop_rate)
+ self.proj = nn.Linear(config.hidden_size, config.hidden_size)
+ self.proj_drop = nn.Dropout(config.drop_rate)
+
+ def forward(self, hidden_states):
+ batch_size, num, channel = hidden_states.shape
+ qkv = (
+ self.qkv(hidden_states)
+ .reshape(batch_size, num, 3, self.num_heads, channel // self.num_heads)
+ .permute(2, 0, 3, 1, 4)
+ )
+ query, key, value = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
+
+ attention_probs = (query @ key.transpose(-2, -1)) * self.scale
+ attention_probs = attention_probs.softmax(dim=-1)
+ attention_probs = self.attn_drop(attention_probs)
+
+ context_layer = (attention_probs @ value).transpose(1, 2).reshape(batch_size, num, channel)
+ context_layer = self.proj(context_layer)
+ context_layer = self.proj_drop(context_layer)
+ return (context_layer, attention_probs)
+
+
+class MgpstrLayer(nn.Module):
+ def __init__(self, config: MgpstrConfig, drop_path=None):
+ super().__init__()
+ self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.attn = MgpstrAttention(config)
+ # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+ self.drop_path = MgpstrDropPath(drop_path) if drop_path is not None else nn.Identity()
+ self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ mlp_hidden_dim = int(config.hidden_size * config.mlp_ratio)
+ self.mlp = MgpstrMlp(config, mlp_hidden_dim)
+
+ def forward(self, hidden_states):
+ self_attention_outputs = self.attn(self.norm1(hidden_states))
+ attention_output = self_attention_outputs[0]
+ outputs = self_attention_outputs[1]
+
+ # first residual connection
+ hidden_states = self.drop_path(attention_output) + hidden_states
+
+ # second residual connection is done here
+ layer_output = hidden_states + self.drop_path(self.mlp(self.norm2(hidden_states)))
+
+ outputs = (layer_output, outputs)
+ return outputs
+
+
+class MgpstrEncoder(nn.Module):
+ def __init__(self, config: MgpstrConfig):
+ super().__init__()
+ # stochastic depth decay rule
+ dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers, device="cpu")]
+
+ self.blocks = nn.Sequential(
+ *[MgpstrLayer(config=config, drop_path=dpr[i]) for i in range(config.num_hidden_layers)]
+ )
+
+ def forward(self, hidden_states, output_attentions=False, output_hidden_states=False, return_dict=True):
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+
+ for _, blk in enumerate(self.blocks):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = blk(hidden_states)
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+
+
+class MgpstrA3Module(nn.Module):
+ def __init__(self, config: MgpstrConfig):
+ super().__init__()
+ self.token_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.tokenLearner = nn.Sequential(
+ nn.Conv2d(config.hidden_size, config.hidden_size, kernel_size=(1, 1), stride=1, groups=8, bias=False),
+ nn.Conv2d(config.hidden_size, config.max_token_length, kernel_size=(1, 1), stride=1, bias=False),
+ )
+ self.feat = nn.Conv2d(
+ config.hidden_size, config.hidden_size, kernel_size=(1, 1), stride=1, groups=8, bias=False
+ )
+ self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states):
+ hidden_states = self.token_norm(hidden_states)
+ hidden_states = hidden_states.transpose(1, 2).unsqueeze(-1)
+ selected = self.tokenLearner(hidden_states)
+ selected = selected.flatten(2)
+ attentions = F.softmax(selected, dim=-1)
+
+ feat = self.feat(hidden_states)
+ feat = feat.flatten(2).transpose(1, 2)
+ feat = torch.einsum("...si,...id->...sd", attentions, feat)
+ a3_out = self.norm(feat)
+
+ return (a3_out, attentions)
+
+
+@auto_docstring
+class MgpstrPreTrainedModel(PreTrainedModel):
+ config: MgpstrConfig
+ base_model_prefix = "mgp_str"
+ _no_split_modules = []
+
+ def _init_weights(self, module: nn.Module) -> None:
+ """Initialize the weights"""
+ std = self.config.initializer_range
+ if isinstance(module, MgpstrEmbeddings):
+ nn.init.trunc_normal_(module.pos_embed, mean=0.0, std=std)
+ nn.init.trunc_normal_(module.cls_token, mean=0.0, std=std)
+ elif isinstance(module, (nn.Linear, nn.Conv2d)):
+ nn.init.trunc_normal_(module.weight.data, mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class MgpstrModel(MgpstrPreTrainedModel):
+ def __init__(self, config: MgpstrConfig):
+ super().__init__(config)
+ self.config = config
+ self.embeddings = MgpstrEmbeddings(config)
+ self.encoder = MgpstrEncoder(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self) -> nn.Module:
+ return self.embeddings.proj
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: torch.FloatTensor,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.FloatTensor], BaseModelOutput]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ embedding_output = self.embeddings(pixel_values)
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ if not return_dict:
+ return encoder_outputs
+ return BaseModelOutput(
+ last_hidden_state=encoder_outputs.last_hidden_state,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MGP-STR Model transformer with three classification heads on top (three A^3 modules and three linear layer on top
+ of the transformer encoder output) for scene text recognition (STR) .
+ """
+)
+class MgpstrForSceneTextRecognition(MgpstrPreTrainedModel):
+ config: MgpstrConfig
+ main_input_name = "pixel_values"
+
+ def __init__(self, config: MgpstrConfig) -> None:
+ super().__init__(config)
+
+ self.num_labels = config.num_labels
+ self.mgp_str = MgpstrModel(config)
+
+ self.char_a3_module = MgpstrA3Module(config)
+ self.bpe_a3_module = MgpstrA3Module(config)
+ self.wp_a3_module = MgpstrA3Module(config)
+
+ self.char_head = nn.Linear(config.hidden_size, config.num_character_labels)
+ self.bpe_head = nn.Linear(config.hidden_size, config.num_bpe_labels)
+ self.wp_head = nn.Linear(config.hidden_size, config.num_wordpiece_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: torch.FloatTensor,
+ output_attentions: Optional[bool] = None,
+ output_a3_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.FloatTensor], MgpstrModelOutput]:
+ r"""
+ output_a3_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of a3 modules. See `a3_attentions` under returned tensors
+ for more detail.
+
+ Example:
+
+ ```python
+ >>> from transformers import (
+ ... MgpstrProcessor,
+ ... MgpstrForSceneTextRecognition,
+ ... )
+ >>> import requests
+ >>> from PIL import Image
+
+ >>> # load image from the IIIT-5k dataset
+ >>> url = "https://i.postimg.cc/ZKwLg2Gw/367-14.png"
+ >>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+
+ >>> processor = MgpstrProcessor.from_pretrained("alibaba-damo/mgp-str-base")
+ >>> pixel_values = processor(images=image, return_tensors="pt").pixel_values
+
+ >>> model = MgpstrForSceneTextRecognition.from_pretrained("alibaba-damo/mgp-str-base")
+
+ >>> # inference
+ >>> outputs = model(pixel_values)
+ >>> out_strs = processor.batch_decode(outputs.logits)
+ >>> out_strs["generated_text"]
+ '["ticket"]'
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ mgp_outputs = self.mgp_str(
+ pixel_values,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = mgp_outputs[0]
+
+ char_a3_out, char_attention = self.char_a3_module(sequence_output)
+ bpe_a3_out, bpe_attention = self.bpe_a3_module(sequence_output)
+ wp_a3_out, wp_attention = self.wp_a3_module(sequence_output)
+
+ char_logits = self.char_head(char_a3_out)
+ bpe_logits = self.bpe_head(bpe_a3_out)
+ wp_logits = self.wp_head(wp_a3_out)
+
+ all_a3_attentions = (char_attention, bpe_attention, wp_attention) if output_a3_attentions else None
+ all_logits = (char_logits, bpe_logits, wp_logits)
+
+ if not return_dict:
+ outputs = (all_logits, all_a3_attentions) + mgp_outputs[1:]
+ return tuple(output for output in outputs if output is not None)
+ return MgpstrModelOutput(
+ logits=all_logits,
+ hidden_states=mgp_outputs.hidden_states,
+ attentions=mgp_outputs.attentions,
+ a3_attentions=all_a3_attentions,
+ )
+
+
+__all__ = ["MgpstrModel", "MgpstrPreTrainedModel", "MgpstrForSceneTextRecognition"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/processing_mgp_str.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/processing_mgp_str.py
new file mode 100644
index 0000000000000000000000000000000000000000..c224e0fecc4a87144f20f41d4ba24d26fae2edfa
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/processing_mgp_str.py
@@ -0,0 +1,235 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Processor class for MGP-STR."""
+
+import warnings
+
+from transformers import AutoTokenizer
+from transformers.utils import is_torch_available
+from transformers.utils.generic import ExplicitEnum
+
+from ...processing_utils import ProcessorMixin
+from ...utils.import_utils import requires
+
+
+if is_torch_available():
+ import torch
+
+
+class DecodeType(ExplicitEnum):
+ CHARACTER = "char"
+ BPE = "bpe"
+ WORDPIECE = "wp"
+
+
+SUPPORTED_ANNOTATION_FORMATS = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
+
+
+@requires(backends=("sentencepiece",))
+class MgpstrProcessor(ProcessorMixin):
+ r"""
+ Constructs a MGP-STR processor which wraps an image processor and MGP-STR tokenizers into a single
+
+ [`MgpstrProcessor`] offers all the functionalities of `ViTImageProcessor`] and [`MgpstrTokenizer`]. See the
+ [`~MgpstrProcessor.__call__`] and [`~MgpstrProcessor.batch_decode`] for more information.
+
+ Args:
+ image_processor (`ViTImageProcessor`, *optional*):
+ An instance of `ViTImageProcessor`. The image processor is a required input.
+ tokenizer ([`MgpstrTokenizer`], *optional*):
+ The tokenizer is a required input.
+ """
+
+ attributes = ["image_processor", "char_tokenizer"]
+ image_processor_class = ("ViTImageProcessor", "ViTImageProcessorFast")
+ char_tokenizer_class = "MgpstrTokenizer"
+
+ def __init__(self, image_processor=None, tokenizer=None, **kwargs):
+ feature_extractor = None
+ if "feature_extractor" in kwargs:
+ warnings.warn(
+ "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
+ " instead.",
+ FutureWarning,
+ )
+ feature_extractor = kwargs.pop("feature_extractor")
+
+ image_processor = image_processor if image_processor is not None else feature_extractor
+ if image_processor is None:
+ raise ValueError("You need to specify an `image_processor`.")
+ if tokenizer is None:
+ raise ValueError("You need to specify a `tokenizer`.")
+
+ self.char_tokenizer = tokenizer
+ self.bpe_tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
+ self.wp_tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+
+ super().__init__(image_processor, tokenizer)
+
+ def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
+ """
+ When used in normal mode, this method forwards all its arguments to ViTImageProcessor's
+ [`~ViTImageProcessor.__call__`] and returns its output. This method also forwards the `text` and `kwargs`
+ arguments to MgpstrTokenizer's [`~MgpstrTokenizer.__call__`] if `text` is not `None` to encode the text. Please
+ refer to the docstring of the above methods for more information.
+ """
+ if images is None and text is None:
+ raise ValueError("You need to specify either an `images` or `text` input to process.")
+
+ if images is not None:
+ inputs = self.image_processor(images, return_tensors=return_tensors, **kwargs)
+ if text is not None:
+ encodings = self.char_tokenizer(text, return_tensors=return_tensors, **kwargs)
+
+ if text is None:
+ return inputs
+ elif images is None:
+ return encodings
+ else:
+ inputs["labels"] = encodings["input_ids"]
+ return inputs
+
+ def batch_decode(self, sequences):
+ """
+ Convert a list of lists of token ids into a list of strings by calling decode.
+
+ Args:
+ sequences (`torch.Tensor`):
+ List of tokenized input ids.
+
+ Returns:
+ `dict[str, any]`: Dictionary of all the outputs of the decoded results.
+ generated_text (`list[str]`): The final results after fusion of char, bpe, and wp. scores
+ (`list[float]`): The final scores after fusion of char, bpe, and wp. char_preds (`list[str]`): The list
+ of character decoded sentences. bpe_preds (`list[str]`): The list of bpe decoded sentences. wp_preds
+ (`list[str]`): The list of wp decoded sentences.
+
+ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ char_preds, bpe_preds, wp_preds = sequences
+ batch_size = char_preds.size(0)
+
+ char_strs, char_scores = self._decode_helper(char_preds, "char")
+ bpe_strs, bpe_scores = self._decode_helper(bpe_preds, "bpe")
+ wp_strs, wp_scores = self._decode_helper(wp_preds, "wp")
+
+ final_strs = []
+ final_scores = []
+ for i in range(batch_size):
+ scores = [char_scores[i], bpe_scores[i], wp_scores[i]]
+ strs = [char_strs[i], bpe_strs[i], wp_strs[i]]
+ max_score_index = scores.index(max(scores))
+ final_strs.append(strs[max_score_index])
+ final_scores.append(scores[max_score_index])
+
+ out = {}
+ out["generated_text"] = final_strs
+ out["scores"] = final_scores
+ out["char_preds"] = char_strs
+ out["bpe_preds"] = bpe_strs
+ out["wp_preds"] = wp_strs
+ return out
+
+ def _decode_helper(self, pred_logits, format):
+ """
+ Convert a list of lists of bpe token ids into a list of strings by calling bpe tokenizer.
+
+ Args:
+ pred_logits (`torch.Tensor`):
+ List of model prediction logits.
+ format (`Union[DecoderType, str]`):
+ Type of model prediction. Must be one of ['char', 'bpe', 'wp'].
+ Returns:
+ `tuple`:
+ dec_strs(`str`): The decode strings of model prediction. conf_scores(`list[float]`): The confidence
+ score of model prediction.
+ """
+ if format == DecodeType.CHARACTER:
+ decoder = self.char_decode
+ eos_token = 1
+ eos_str = "[s]"
+ elif format == DecodeType.BPE:
+ decoder = self.bpe_decode
+ eos_token = 2
+ eos_str = "#"
+ elif format == DecodeType.WORDPIECE:
+ decoder = self.wp_decode
+ eos_token = 102
+ eos_str = "[SEP]"
+ else:
+ raise ValueError(f"Format {format} is not supported.")
+
+ dec_strs, conf_scores = [], []
+ batch_size = pred_logits.size(0)
+ batch_max_length = pred_logits.size(1)
+ _, preds_index = pred_logits.topk(1, dim=-1, largest=True, sorted=True)
+ preds_index = preds_index.view(-1, batch_max_length)[:, 1:]
+ preds_str = decoder(preds_index)
+ preds_max_prob, _ = torch.nn.functional.softmax(pred_logits, dim=2).max(dim=2)
+ preds_max_prob = preds_max_prob[:, 1:]
+
+ for index in range(batch_size):
+ pred_eos = preds_str[index].find(eos_str)
+ pred = preds_str[index][:pred_eos]
+ pred_index = preds_index[index].tolist()
+ pred_eos_index = pred_index.index(eos_token) if eos_token in pred_index else -1
+ pred_max_prob = preds_max_prob[index][: pred_eos_index + 1]
+ confidence_score = pred_max_prob.cumprod(dim=0)[-1] if pred_max_prob.nelement() != 0 else 0.0
+ dec_strs.append(pred)
+ conf_scores.append(confidence_score)
+
+ return dec_strs, conf_scores
+
+ def char_decode(self, sequences):
+ """
+ Convert a list of lists of char token ids into a list of strings by calling char tokenizer.
+
+ Args:
+ sequences (`torch.Tensor`):
+ List of tokenized input ids.
+ Returns:
+ `list[str]`: The list of char decoded sentences.
+ """
+ decode_strs = [seq.replace(" ", "") for seq in self.char_tokenizer.batch_decode(sequences)]
+ return decode_strs
+
+ def bpe_decode(self, sequences):
+ """
+ Convert a list of lists of bpe token ids into a list of strings by calling bpe tokenizer.
+
+ Args:
+ sequences (`torch.Tensor`):
+ List of tokenized input ids.
+ Returns:
+ `list[str]`: The list of bpe decoded sentences.
+ """
+ return self.bpe_tokenizer.batch_decode(sequences)
+
+ def wp_decode(self, sequences):
+ """
+ Convert a list of lists of word piece token ids into a list of strings by calling word piece tokenizer.
+
+ Args:
+ sequences (`torch.Tensor`):
+ List of tokenized input ids.
+ Returns:
+ `list[str]`: The list of wp decoded sentences.
+ """
+ decode_strs = [seq.replace(" ", "") for seq in self.wp_tokenizer.batch_decode(sequences)]
+ return decode_strs
+
+
+__all__ = ["MgpstrProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/tokenization_mgp_str.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/tokenization_mgp_str.py
new file mode 100644
index 0000000000000000000000000000000000000000..f29b8a8348ee3771210df17de79be6c30a65c34a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mgp_str/tokenization_mgp_str.py
@@ -0,0 +1,104 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for MGT-STR CHAR."""
+
+import json
+import os
+from typing import Optional
+
+from ...tokenization_utils import PreTrainedTokenizer
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json"}
+
+
+class MgpstrTokenizer(PreTrainedTokenizer):
+ """
+ Construct a MGP-STR char tokenizer.
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ unk_token (`str`, *optional*, defaults to `"[GO]"`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ bos_token (`str`, *optional*, defaults to `"[GO]"`):
+ The beginning of sequence token.
+ eos_token (`str`, *optional*, defaults to `"[s]"`):
+ The end of sequence token.
+ pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"[GO]"`):
+ A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
+ attention mechanisms or loss computation.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+
+ def __init__(self, vocab_file, unk_token="[GO]", bos_token="[GO]", eos_token="[s]", pad_token="[GO]", **kwargs):
+ with open(vocab_file, encoding="utf-8") as vocab_handle:
+ self.vocab = json.load(vocab_handle)
+ self.decoder = {v: k for k, v in self.vocab.items()}
+ super().__init__(
+ unk_token=unk_token,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ pad_token=pad_token,
+ **kwargs,
+ )
+
+ @property
+ def vocab_size(self):
+ return len(self.vocab)
+
+ def get_vocab(self):
+ vocab = dict(self.vocab).copy()
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ def _tokenize(self, text):
+ """Tokenize a string."""
+ char_tokens = []
+ for s in text:
+ char_tokens.extend(s)
+ return char_tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.decoder.get(index)
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ with open(vocab_file, "w", encoding="utf-8") as f:
+ f.write(json.dumps(self.vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+ return (vocab_file,)
+
+
+__all__ = ["MgpstrTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ed44a4324ddbf586f5aa9e1971422c98755eb3a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mimi import *
+ from .modeling_mimi import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/configuration_mimi.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/configuration_mimi.py
new file mode 100644
index 0000000000000000000000000000000000000000..b213359886dc9805b8fae61158488ed39d8164fc
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/configuration_mimi.py
@@ -0,0 +1,279 @@
+# coding=utf-8
+# Copyright 2024 Meta Platforms, Inc. and affiliates, and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Mimi model configuration"""
+
+import math
+
+import numpy as np
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MimiConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of an [`MimiModel`]. It is used to instantiate a
+ Mimi model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the
+ [kyutai/mimi](https://huggingface.co/kyutai/mimi) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ sampling_rate (`int`, *optional*, defaults to 24000):
+ The sampling rate at which the audio waveform should be digitalized expressed in hertz (Hz).
+ frame_rate (`float`, *optional*):
+ Should be computed from the other parameters, yet kept for backward compatibility.
+ audio_channels (`int`, *optional*, defaults to 1):
+ Number of channels in the audio data. Either 1 for mono or 2 for stereo.
+ hidden_size (`int`, *optional*, defaults to 512):
+ Intermediate representation dimension.
+ num_filters (`int`, *optional*, defaults to 64):
+ Number of convolution kernels of first `MimiConv1d` down sampling layer.
+ num_residual_layers (`int`, *optional*, defaults to 1):
+ Number of residual layers.
+ upsampling_ratios (`Sequence[int]`, *optional*):
+ Kernel size and stride ratios. The encoder uses downsampling ratios instead of upsampling ratios, hence it
+ will use the ratios in the reverse order to the ones specified here that must match the decoder order.
+ If not specified, will defaults to `[8, 6, 5, 4]`
+ kernel_size (`int`, *optional*, defaults to 7):
+ Kernel size for the initial convolution.
+ last_kernel_size (`int`, *optional*, defaults to 3):
+ Kernel size for the last convolution layer.
+ residual_kernel_size (`int`, *optional*, defaults to 3):
+ Kernel size for the residual layers.
+ dilation_growth_rate (`int`, *optional*, defaults to 2):
+ How much to increase the dilation with each layer.
+ use_causal_conv (`bool`, *optional*, defaults to `True`):
+ Whether to use fully causal convolution.
+ pad_mode (`str`, *optional*, defaults to `"constant"`):
+ Padding mode for the convolutions.
+ compress (`int`, *optional*, defaults to 2):
+ Reduced dimensionality in residual branches.
+ trim_right_ratio (`float`, *optional*, defaults to 1.0):
+ Ratio for trimming at the right of the transposed convolution under the `use_causal_conv = True` setup. If
+ equal to 1.0, it means that all the trimming is done at the right.
+ codebook_size (`int`, *optional*, defaults to 2048):
+ Number of discret codes in each codebooks.
+ codebook_dim (`int`, *optional*, defaults to 256):
+ Dimension of the unquantized codebook vectors. If not defined, uses `hidden_size`.
+ num_quantizers (`int`, *optional*, defaults to 32):
+ Number of quantizer channels, or codebooks, in the quantizer.
+ use_conv_shortcut (`bool`, *optional*, defaults to `False`):
+ Whether to use a convolutional layer as the 'skip' connection in the `MimiResnetBlock` block. If False,
+ an identity function will be used, giving a generic residual connection.
+ vector_quantization_hidden_dimension (`int`, *optional*, defaults to 256):
+ Intermediate representation dimension in the residual vector quantization space.
+ num_semantic_quantizers (`int`, *optional*, defaults to 1):
+ Number of semantic quantizer channels, or codebooks, in the semantic quantizer. Must be lower than `num_quantizers`.
+ upsample_groups (`int`, *optional*, defaults to 512):
+ If `frame_rate!=encodec_frame_rate`, indicates the number of groups used in the upsampling operation to go from one rate to another.
+ num_hidden_layers (`int`, *optional*, defaults to 8):
+ Number of hidden layers in the Transformer models.
+ intermediate_size (`int`, *optional*, defaults to 2048):
+ Dimension of the MLP representations.
+ num_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_key_value_heads (`int`, *optional*, defaults to 8):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
+ head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
+ The attention head dimension.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 8000):
+ The maximum sequence length that this model might ever be used with. Mimi's sliding window attention
+ allows sequence of up to 8000 tokens.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the LayerNorm normalization layers.
+ use_cache (`bool`, *optional*, defaults to `False`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ use_streaming (`bool`, *optional*, defaults to `False`):
+ Whether to use streaming mode. If `True`, the model encode method will return the padding cache that can be used in a subsequent call to the encode method.
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ sliding_window (`int`, *optional*, defaults to 250):
+ Sliding window attention window size. If not specified, will default to `250`.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ layer_scale_initial_scale (`float`, *optional*, defaults to 0.01):
+ Initiale scale of the residual rescaling operation done in the Transformer models.
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ Example:
+
+ ```python
+ >>> from transformers import MimiModel, MimiConfig
+
+ >>> # Initializing a "kyutai/mimi" style configuration
+ >>> configuration = MimiConfig()
+
+ >>> # Initializing a model (with random weights) from the "kyutai/mimi" style configuration
+ >>> model = MimiModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mimi"
+
+ def __init__(
+ self,
+ sampling_rate=24_000,
+ frame_rate=None,
+ audio_channels=1,
+ hidden_size=512,
+ num_filters=64,
+ num_residual_layers=1,
+ upsampling_ratios=None,
+ kernel_size=7,
+ last_kernel_size=3,
+ residual_kernel_size=3,
+ dilation_growth_rate=2,
+ use_causal_conv=True,
+ pad_mode="constant",
+ compress=2,
+ trim_right_ratio=1.0,
+ codebook_size=2048,
+ codebook_dim=256,
+ num_quantizers=32,
+ use_conv_shortcut=False,
+ vector_quantization_hidden_dimension=256,
+ num_semantic_quantizers=1,
+ upsample_groups=512,
+ num_hidden_layers=8,
+ intermediate_size=2048,
+ num_attention_heads=8,
+ num_key_value_heads=8,
+ head_dim=None,
+ hidden_act="gelu",
+ max_position_embeddings=8000,
+ initializer_range=0.02,
+ norm_eps=1e-5,
+ use_cache=False,
+ use_streaming=False,
+ rope_theta=10000.0,
+ sliding_window=250,
+ attention_dropout=0.0,
+ layer_scale_initial_scale=0.01,
+ attention_bias=False,
+ **kwargs,
+ ):
+ self.sampling_rate = sampling_rate
+ self.audio_channels = audio_channels
+ self.hidden_size = hidden_size
+ self.num_filters = num_filters
+ self.num_residual_layers = num_residual_layers
+ self.upsampling_ratios = upsampling_ratios if upsampling_ratios else [8, 6, 5, 4]
+ self.kernel_size = kernel_size
+ self.last_kernel_size = last_kernel_size
+ self.residual_kernel_size = residual_kernel_size
+ self.dilation_growth_rate = dilation_growth_rate
+ self.use_causal_conv = use_causal_conv
+ self.pad_mode = pad_mode
+ self.compress = compress
+ self.trim_right_ratio = trim_right_ratio
+ self.codebook_size = codebook_size
+ self.codebook_dim = codebook_dim if codebook_dim is not None else hidden_size
+ self.num_quantizers = num_quantizers
+ self.use_conv_shortcut = use_conv_shortcut
+ self.vector_quantization_hidden_dimension = vector_quantization_hidden_dimension
+ self.upsample_groups = upsample_groups
+ self.num_hidden_layers = num_hidden_layers
+ self.intermediate_size = intermediate_size
+ self.num_attention_heads = num_attention_heads
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.max_position_embeddings = max_position_embeddings
+ self.initializer_range = initializer_range
+ self.norm_eps = norm_eps
+ self.use_cache = use_cache
+ self.use_streaming = use_streaming
+ self.rope_theta = rope_theta
+ self.sliding_window = sliding_window
+ self.attention_dropout = attention_dropout
+ self.head_dim = head_dim or hidden_size // num_attention_heads
+ self.layer_scale_initial_scale = layer_scale_initial_scale
+ self.attention_bias = attention_bias
+
+ # Handle backward compatibility for frame_rate:
+ # If frame_rate is explicitly provided, use it (backward compatibility)
+ # Otherwise, compute it from other parameters (correctly)
+ if frame_rate is not None:
+ self._frame_rate = frame_rate
+ else:
+ self._frame_rate = None
+
+ if num_semantic_quantizers >= self.num_quantizers:
+ raise ValueError(
+ f"The number of semantic quantizers should be lower than the total number of quantizers {self.num_quantizers}, but is currently {num_semantic_quantizers}."
+ )
+ self.num_semantic_quantizers = num_semantic_quantizers
+ super().__init__(**kwargs)
+
+ @property
+ def encodec_frame_rate(self) -> int:
+ hop_length = np.prod(self.upsampling_ratios)
+ return math.ceil(self.sampling_rate / hop_length)
+
+ @property
+ def num_codebooks(self) -> int:
+ # alias to num_quantizers
+ return self.num_quantizers
+
+ @property
+ def frame_size(self) -> int:
+ # 1. we need each encoder conv stride
+ # first conv
+ strides = [1]
+
+ # layer convs
+ for ratio in reversed(self.upsampling_ratios):
+ for j in range(self.num_residual_layers):
+ len_kernel_sizes = len(self.residual_kernel_size) if isinstance(self.residual_kernel_size, list) else 1
+ strides.extend([1] * (len_kernel_sizes + 1))
+ if self.use_conv_shortcut: # skip connection
+ strides.append(1)
+
+ strides.append(ratio)
+
+ # last conv
+ strides.append(1)
+
+ # downsampling layer
+ strides.append(2)
+
+ return math.prod(strides)
+
+ @property
+ def frame_rate(self) -> float:
+ # handle backward compatibility
+ if self._frame_rate is not None:
+ return self._frame_rate
+ return self.sampling_rate / self.frame_size
+
+
+__all__ = ["MimiConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/convert_mimi_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/convert_mimi_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..75702aadd314a8f5cfd6dc51389abe322d6c9328
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/convert_mimi_checkpoint_to_pytorch.py
@@ -0,0 +1,198 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert Mimi checkpoints."""
+
+import argparse
+
+import safetensors
+import torch
+
+from transformers import (
+ EncodecFeatureExtractor,
+ MimiConfig,
+ MimiModel,
+ logging,
+)
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger("transformers.models.mimi")
+
+
+def assert_param_count(model_1, model_2):
+ count_1 = sum(p[1].numel() for p in model_1.named_parameters() if "final_proj" not in p[0])
+ count_2 = sum(p[1].numel() for p in model_2.named_parameters() if "final_proj" not in p[0])
+ assert count_1 == count_2, f"{model_1.__class__}: {count_1} != {model_2.__class__}: {count_2}"
+
+
+def param_count(model):
+ return sum(p[1].numel() for p in model.named_parameters() if "final_proj" not in p[0])
+
+
+def _grab_best_device(use_gpu=True):
+ if torch.cuda.device_count() > 0 and use_gpu:
+ device = "cuda"
+ else:
+ device = "cpu"
+ return torch.device(device)
+
+
+convert_list = [
+ # GENERAL
+ ("conv.conv.conv", "conv"),
+ ("convtr.convtr.convtr", "conv"),
+ ("conv.conv", "conv"),
+ ("convtr.convtr", "conv"),
+ # QUANTIZER
+ ("quantizer.rvq_first.vq", "quantizer.semantic_residual_vector_quantizer"),
+ ("quantizer.rvq_first", "quantizer.semantic_residual_vector_quantizer"),
+ ("quantizer.rvq_rest.vq", "quantizer.acoustic_residual_vector_quantizer"),
+ ("quantizer.rvq_rest", "quantizer.acoustic_residual_vector_quantizer"),
+ ("_codebook", "codebook"),
+ ("_initialized", "initialized"),
+ ("embedding_sum", "embed_sum"),
+ # ENCODER PART
+ ("encoder.model", "encoder.layers"),
+ ("decoder.model", "decoder.layers"),
+ # TRANSFORMERS PART
+ ("encoder_transformer.transformer", "encoder_transformer"),
+ ("decoder_transformer.transformer", "decoder_transformer"),
+ ("linear1", "mlp.fc1"),
+ ("linear2", "mlp.fc2"),
+ ("self_attn.out_proj", "self_attn.o_proj"),
+ ("norm1", "input_layernorm"),
+ ("norm2", "post_attention_layernorm"),
+ ("layer_scale_1", "self_attn_layer_scale"),
+ ("layer_scale_2", "mlp_layer_scale"),
+]
+
+
+def _convert_model(
+ state_dict,
+ hf_model,
+ convert_list,
+ device,
+ config,
+ unwanted_prefix=None,
+):
+ hidden_size = config.hidden_size
+ head_dim = config.head_dim
+ num_heads = int(config.hidden_size // config.head_dim)
+ num_key_value_heads = config.num_key_value_heads
+ key_value_head_dim = config.num_key_value_heads * head_dim
+
+ # permute for sliced rotary
+ def permute(w, n_heads, dim1=hidden_size, dim2=hidden_size):
+ return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
+
+ for k, v in list(state_dict.items()):
+ new_k = k if unwanted_prefix is None else k[len(unwanted_prefix) :]
+ for old_layer_name, new_layer_name in convert_list:
+ if old_layer_name in new_k:
+ new_k = new_k.replace(old_layer_name, new_layer_name)
+
+ if "in_proj_weight" in new_k:
+ # split qkv into query key and value
+ mixed_qkv = state_dict.pop(k)
+ qkv_dim = mixed_qkv.size(0) // 3
+
+ query_layer = mixed_qkv[:qkv_dim]
+ key_layer = mixed_qkv[qkv_dim : qkv_dim * 2]
+ value_layer = mixed_qkv[qkv_dim * 2 :]
+
+ state_dict[new_k.replace("in_proj_weight", "q_proj.weight")] = permute(query_layer, num_heads)
+ state_dict[new_k.replace("in_proj_weight", "k_proj.weight")] = permute(
+ key_layer, num_key_value_heads, dim1=key_value_head_dim
+ )
+ state_dict[new_k.replace("in_proj_weight", "v_proj.weight")] = value_layer
+ else:
+ state_dict[new_k] = state_dict.pop(k)
+
+ extra_keys = set(state_dict.keys()) - set(hf_model.state_dict().keys())
+ missing_keys = set(hf_model.state_dict().keys()) - set(state_dict.keys())
+ if len(extra_keys) != 0:
+ raise ValueError(f"extra keys found: {extra_keys}")
+ if len(missing_keys) != 0:
+ raise ValueError(f"missing keys: {missing_keys}")
+ hf_model.load_state_dict(state_dict, strict=True)
+ n_params = param_count(hf_model)
+
+ logger.info(f"model loaded: {round(n_params / 1e6, 1)}M params")
+
+ hf_model.eval()
+ hf_model.to(device)
+ del state_dict
+
+ return hf_model
+
+
+@torch.no_grad()
+def convert_checkpoint(
+ checkpoint_path,
+ pytorch_dump_folder_path,
+ config_path=None,
+ repo_id=None,
+):
+ """
+ Copy/paste/tweak model's weights to transformers design.
+ """
+ device = _grab_best_device()
+
+ if config_path is not None:
+ config = MimiConfig.from_pretrained(config_path)
+ else:
+ config = MimiConfig()
+
+ model = MimiModel(config)
+
+ feature_extractor = EncodecFeatureExtractor(
+ feature_size=config.audio_channels,
+ sampling_rate=config.sampling_rate,
+ )
+ feature_extractor.save_pretrained(pytorch_dump_folder_path)
+
+ original_checkpoint = safetensors.torch.load_file(checkpoint_path)
+ if "best_state" in original_checkpoint:
+ # we might have a training state saved, in which case discard the yaml results and just retain the weights
+ original_checkpoint = original_checkpoint["best_state"]
+
+ model = _convert_model(original_checkpoint, model, convert_list, device, config)
+
+ model.save_pretrained(pytorch_dump_folder_path)
+
+ if repo_id:
+ print("Pushing to the hub...")
+ feature_extractor.push_to_hub(repo_id)
+ model.push_to_hub(repo_id)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
+ parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
+ parser.add_argument(
+ "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
+ )
+ parser.add_argument(
+ "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
+ )
+
+ args = parser.parse_args()
+ convert_checkpoint(
+ args.checkpoint_path,
+ args.pytorch_dump_folder_path,
+ args.config_path,
+ args.push_to_hub,
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/modeling_mimi.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/modeling_mimi.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cabf4849c1d0546d00501cff9ac7dcfcb4a7f6e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mimi/modeling_mimi.py
@@ -0,0 +1,1772 @@
+# coding=utf-8
+# Copyright 2024 Kyutai, and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Mimi model."""
+
+import math
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...masking_utils import create_causal_mask
+from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import PreTrainedModel
+from ...utils import ModelOutput, auto_docstring, logging
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_mimi import MimiConfig
+
+
+if is_flash_attn_available():
+ from ...modeling_flash_attention_utils import _flash_attention_forward
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring
+class MimiOutput(ModelOutput):
+ r"""
+ audio_codes (`torch.LongTensor` of shape `(batch_size, num_quantizers, codes_length)`, *optional*):
+ Discret code embeddings computed using `model.encode`.
+ audio_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Decoded audio values, obtained using the decoder part of Mimi.
+ encoder_past_key_values (`Cache`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks) that can be used to speed up sequential decoding of the encoder transformer.
+ This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ The model will output the same cache format that is fed as input.
+
+ If `past_key_values` are used, the user can optionally input only the last `audio_values` or `audio_codes (those that don't
+ have their past key value states given to this model).
+ decoder_past_key_values (`Cache`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks) that can be used to speed up sequential decoding of the decoder transformer.
+ This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ The model will output the same cache format that is fed as input.
+
+ If `past_key_values` are used, the user can optionally input only the last `audio_values` or `audio_codes (those that don't
+ have their past key value states given to this model).
+ """
+
+ audio_codes: Optional[torch.LongTensor] = None
+ audio_values: Optional[torch.FloatTensor] = None
+ encoder_past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None
+ decoder_past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None
+
+
+class MimiConv1dPaddingCache:
+ """
+ Padding cache for MimiConv1d causal convolutions in order to support streaming via cache padding.
+ See: https://arxiv.org/pdf/2005.06720 & https://arxiv.org/pdf/2204.07064
+
+ A padding cache is a list of cached partial hidden states for each convolution layer.
+ Hidden states are cached from the previous call to the MimiConv1d forward pass, given the padding size.
+ """
+
+ def __init__(
+ self,
+ num_layers: int,
+ per_layer_padding: list[int],
+ per_layer_padding_mode: list[str],
+ per_layer_in_channels: list[int],
+ ):
+ # ensure correct number of layers for each arg
+ from_args_num_layers = {len(per_layer_padding), len(per_layer_padding_mode), len(per_layer_in_channels)}
+
+ if len(from_args_num_layers) != 1 or from_args_num_layers.pop() != num_layers:
+ raise ValueError(
+ f"Expected `num_layers` ({num_layers}) values in `per_layer_padding`, `per_layer_padding_mode` and `per_layer_in_channels`"
+ )
+ elif not all(mode in ["constant", "replicate"] for mode in per_layer_padding_mode):
+ raise NotImplementedError(
+ "`padding_cache` is not supported for convolutions using other than `constant` or `replicate` padding mode"
+ )
+
+ self.per_layer_padding = per_layer_padding
+ self.per_layer_padding_mode = per_layer_padding_mode
+ self.per_layer_in_channels = per_layer_in_channels
+ self.per_layer_is_init = [True] * num_layers
+
+ self.padding_cache = [None] * num_layers
+
+ def update(self, hidden_states: torch.Tensor, layer_idx: int):
+ """
+ Updates the padding cache with the new padding states for the layer `layer_idx` and returns the current cache.
+
+ Parameters:
+ hidden_states (`torch.Tensor`):
+ The hidden states to be partially cached.
+ layer_idx (`int`):
+ The index of the layer to cache the states for.
+ Returns:
+ `torch.Tensor` or `None`, the current padding cache.
+ """
+ batch_size, dtype, device = hidden_states.shape[0], hidden_states.dtype, hidden_states.device
+ padding = self.per_layer_padding[layer_idx]
+ padding_mode = self.per_layer_padding_mode[layer_idx]
+ in_channels = self.per_layer_in_channels[layer_idx]
+
+ if self.padding_cache[layer_idx] is None:
+ if padding_mode == "constant":
+ current_cache = torch.zeros(
+ batch_size,
+ in_channels,
+ padding,
+ device=device,
+ dtype=dtype,
+ )
+ elif padding_mode == "replicate":
+ current_cache = (
+ torch.ones(
+ batch_size,
+ in_channels,
+ padding,
+ device=device,
+ dtype=dtype,
+ )
+ * hidden_states[..., :1]
+ )
+ else:
+ current_cache = self.padding_cache[layer_idx]
+
+ # update the cache
+ if padding > 0:
+ padding_states = hidden_states[:, :, -padding:]
+ else:
+ padding_states = torch.empty(batch_size, in_channels, padding, dtype=dtype, device=device)
+ self.padding_cache[layer_idx] = padding_states
+
+ return current_cache
+
+
+@dataclass
+@auto_docstring
+class MimiEncoderOutput(ModelOutput):
+ r"""
+ audio_codes (`torch.LongTensor` of shape `(batch_size, num_quantizers, codes_length)`, *optional*):
+ Discret code embeddings computed using `model.encode`.
+ encoder_past_key_values (`Cache`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks) that can be used to speed up sequential decoding of the encoder transformer.
+ This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ The model will output the same cache format that is fed as input.
+
+ If `past_key_values` are used, the user can optionally input only the last `audio_values` or `audio_codes (those that don't
+ have their past key value states given to this model).
+ padding_cache (`MimiConv1dPaddingCache`, *optional*):
+ Padding cache for MimiConv1d causal convolutions in order to support streaming via cache padding.
+ """
+
+ audio_codes: Optional[torch.LongTensor] = None
+ encoder_past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None
+ padding_cache: Optional[MimiConv1dPaddingCache] = None
+
+
+@dataclass
+@auto_docstring
+class MimiDecoderOutput(ModelOutput):
+ r"""
+ audio_values (`torch.FloatTensor` of shape `(batch_size, segment_length)`, *optional*):
+ Decoded audio values, obtained using the decoder part of Mimi.
+ decoder_past_key_values (`Cache`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks) that can be used to speed up sequential decoding of the decoder transformer.
+ This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ The model will output the same cache format that is fed as input.
+
+ If `past_key_values` are used, the user can optionally input only the last `audio_values` or `audio_codes (those that don't
+ have their past key value states given to this model).
+ """
+
+ audio_values: Optional[torch.FloatTensor] = None
+ decoder_past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None
+
+
+class MimiConv1d(nn.Module):
+ """Conv1d with asymmetric or causal padding and normalization."""
+
+ def __init__(
+ self,
+ config,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ stride: int = 1,
+ dilation: int = 1,
+ groups: int = 1,
+ pad_mode: Optional[str] = None,
+ bias: bool = True,
+ layer_idx: Optional[int] = None,
+ ):
+ super().__init__()
+ self.causal = config.use_causal_conv
+ self.pad_mode = config.pad_mode if pad_mode is None else pad_mode
+ self.layer_idx = layer_idx
+ self.in_channels = in_channels
+
+ # warn user on unusual setup between dilation and stride
+ if stride > 1 and dilation > 1:
+ logger.warning(
+ "MimiConv1d has been initialized with stride > 1 and dilation > 1"
+ f" (kernel_size={kernel_size} stride={stride}, dilation={dilation})."
+ )
+
+ self.conv = nn.Conv1d(
+ in_channels, out_channels, kernel_size, stride, dilation=dilation, groups=groups, bias=bias
+ )
+
+ kernel_size = self.conv.kernel_size[0]
+ stride = torch.tensor(self.conv.stride[0], dtype=torch.int64)
+ dilation = self.conv.dilation[0]
+
+ # Effective kernel size with dilations.
+ kernel_size = torch.tensor((kernel_size - 1) * dilation + 1, dtype=torch.int64)
+
+ self.register_buffer("stride", stride, persistent=False)
+ self.register_buffer("kernel_size", kernel_size, persistent=False)
+ self.register_buffer("padding_total", kernel_size - stride, persistent=False)
+
+ # Asymmetric padding required for odd strides
+ self.padding_right = self.padding_total // 2
+ self.padding_left = self.padding_total - self.padding_right
+
+ def apply_weight_norm(self):
+ weight_norm = nn.utils.weight_norm
+ if hasattr(nn.utils.parametrizations, "weight_norm"):
+ weight_norm = nn.utils.parametrizations.weight_norm
+
+ weight_norm(self.conv)
+
+ def remove_weight_norm(self):
+ nn.utils.remove_weight_norm(self.conv)
+
+ # Copied from transformers.models.encodec.modeling_encodec.EncodecConv1d._get_extra_padding_for_conv1d
+ def _get_extra_padding_for_conv1d(
+ self,
+ hidden_states: torch.Tensor,
+ ) -> torch.Tensor:
+ """See `pad_for_conv1d`."""
+ length = hidden_states.shape[-1]
+ n_frames = (length - self.kernel_size + self.padding_total) / self.stride + 1
+ n_frames = torch.ceil(n_frames).to(torch.int64) - 1
+ ideal_length = n_frames * self.stride + self.kernel_size - self.padding_total
+
+ return ideal_length - length
+
+ @staticmethod
+ # Copied from transformers.models.encodec.modeling_encodec.EncodecConv1d._pad1d
+ def _pad1d(hidden_states: torch.Tensor, paddings: tuple[int, int], mode: str = "zero", value: float = 0.0):
+ """Tiny wrapper around torch.nn.functional.pad, just to allow for reflect padding on small input.
+ If this is the case, we insert extra 0 padding to the right before the reflection happens.
+ """
+ length = hidden_states.shape[-1]
+ padding_left, padding_right = paddings
+ if mode != "reflect":
+ return nn.functional.pad(hidden_states, paddings, mode, value)
+
+ max_pad = max(padding_left, padding_right)
+ extra_pad = 0
+ if length <= max_pad:
+ extra_pad = max_pad - length + 1
+ hidden_states = nn.functional.pad(hidden_states, (0, extra_pad))
+ padded = nn.functional.pad(hidden_states, paddings, mode, value)
+ end = padded.shape[-1] - extra_pad
+ return padded[..., :end]
+
+ def _get_output_length(self, input_length: torch.LongTensor) -> torch.LongTensor:
+ """
+ Return the length of the output of the MimiConv1d.
+ """
+ # padding size
+ n_frames = (input_length - self.kernel_size + self.padding_total) / self.stride + 1
+ n_frames = torch.ceil(n_frames).to(torch.int64) - 1
+ ideal_length = n_frames * self.stride + self.kernel_size - self.padding_total
+ extra_padding = ideal_length - input_length
+
+ if self.causal:
+ padding_left = self.padding_total
+ padding_right = extra_padding
+ else:
+ padding_left = self.padding_left
+ padding_right = self.padding_right + extra_padding
+
+ # padding
+ input_length = input_length + padding_left + padding_right
+
+ # conv
+ output_lenght = (
+ input_length + 2 * self.conv.padding[0] - self.conv.dilation[0] * (self.conv.kernel_size[0] - 1) - 1
+ ) // self.conv.stride[0] + 1
+ return output_lenght
+
+ def forward(self, hidden_states, padding_cache=None):
+ extra_padding = self._get_extra_padding_for_conv1d(hidden_states)
+
+ if not self.causal and padding_cache is not None:
+ raise ValueError("`padding_cache` is not supported for non-causal convolutions.")
+
+ if self.causal and padding_cache is not None:
+ layer_padding_cache = padding_cache.update(hidden_states, self.layer_idx)
+ hidden_states = torch.cat([layer_padding_cache, hidden_states], dim=2)
+
+ elif self.causal:
+ # Left padding for causal
+ hidden_states = self._pad1d(hidden_states, (self.padding_total, extra_padding), mode=self.pad_mode)
+
+ else:
+ hidden_states = self._pad1d(
+ hidden_states, (self.padding_left, self.padding_right + extra_padding), mode=self.pad_mode
+ )
+
+ hidden_states = self.conv(hidden_states)
+ return hidden_states
+
+
+class MimiConvTranspose1d(nn.Module):
+ """ConvTranspose1d with asymmetric or causal padding and normalization."""
+
+ def __init__(
+ self,
+ config,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ stride: int = 1,
+ groups: int = 1,
+ bias=True,
+ ):
+ super().__init__()
+ self.causal = config.use_causal_conv
+ self.trim_right_ratio = config.trim_right_ratio
+ self.conv = nn.ConvTranspose1d(in_channels, out_channels, kernel_size, stride, groups=groups, bias=bias)
+
+ if not (self.causal or self.trim_right_ratio == 1.0):
+ raise ValueError("`trim_right_ratio` != 1.0 only makes sense for causal convolutions")
+
+ kernel_size = self.conv.kernel_size[0]
+ stride = self.conv.stride[0]
+ padding_total = kernel_size - stride
+
+ # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
+ # removed at the very end, when keeping only the right length for the output,
+ # as removing it here would require also passing the length at the matching layer
+ # in the encoder.
+ if self.causal:
+ # Trim the padding on the right according to the specified ratio
+ # if trim_right_ratio = 1.0, trim everything from right
+ self.padding_right = math.ceil(padding_total * self.trim_right_ratio)
+ else:
+ # Asymmetric padding required for odd strides
+ self.padding_right = padding_total // 2
+
+ self.padding_left = padding_total - self.padding_right
+
+ def apply_weight_norm(self):
+ weight_norm = nn.utils.weight_norm
+ if hasattr(nn.utils.parametrizations, "weight_norm"):
+ weight_norm = nn.utils.parametrizations.weight_norm
+
+ weight_norm(self.conv)
+
+ def remove_weight_norm(self):
+ nn.utils.remove_weight_norm(self.conv)
+
+ def forward(self, hidden_states):
+ hidden_states = self.conv(hidden_states)
+
+ # unpad
+ end = hidden_states.shape[-1] - self.padding_right
+ hidden_states = hidden_states[..., self.padding_left : end]
+ return hidden_states
+
+
+class MimiResnetBlock(nn.Module):
+ """
+ Residual block from SEANet model as used by Mimi.
+ """
+
+ def __init__(self, config: MimiConfig, dim: int, dilations: list[int]):
+ super().__init__()
+ kernel_sizes = (config.residual_kernel_size, 1)
+ if len(kernel_sizes) != len(dilations):
+ raise ValueError("Number of kernel sizes should match number of dilations")
+
+ hidden = dim // config.compress
+ block = []
+ for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)):
+ in_chs = dim if i == 0 else hidden
+ out_chs = dim if i == len(kernel_sizes) - 1 else hidden
+ block += [nn.ELU()]
+ block += [MimiConv1d(config, in_chs, out_chs, kernel_size, dilation=dilation)]
+ self.block = nn.ModuleList(block)
+
+ if config.use_conv_shortcut:
+ self.shortcut = MimiConv1d(config, dim, dim, kernel_size=1)
+ else:
+ self.shortcut = nn.Identity()
+
+ def forward(self, hidden_states, padding_cache=None):
+ residual = hidden_states
+
+ for layer in self.block:
+ if isinstance(layer, MimiConv1d):
+ hidden_states = layer(hidden_states, padding_cache=padding_cache)
+ else:
+ hidden_states = layer(hidden_states)
+
+ if isinstance(self.shortcut, MimiConv1d):
+ residual = self.shortcut(residual, padding_cache=padding_cache)
+ else:
+ residual = self.shortcut(residual)
+
+ return residual + hidden_states
+
+
+class MimiEncoder(nn.Module):
+ """SEANet encoder as used by Mimi."""
+
+ def __init__(self, config: MimiConfig):
+ super().__init__()
+ model = [MimiConv1d(config, config.audio_channels, config.num_filters, config.kernel_size)]
+ scaling = 1
+
+ # keep track of MimiConv1d submodule layer names for easy encoded length computation
+ mimiconv1d_layer_names = ["layers.0"]
+
+ # Downsample to raw audio scale
+ for ratio in reversed(config.upsampling_ratios):
+ current_scale = scaling * config.num_filters
+ # Add residual layers
+ for j in range(config.num_residual_layers):
+ mimiconv1d_layer_names.extend([f"layers.{len(model)}.block.1", f"layers.{len(model)}.block.3"])
+ model += [MimiResnetBlock(config, current_scale, [config.dilation_growth_rate**j, 1])]
+ # Add downsampling layers
+ model += [nn.ELU()]
+ mimiconv1d_layer_names.append(f"layers.{len(model)}")
+ model += [MimiConv1d(config, current_scale, current_scale * 2, kernel_size=ratio * 2, stride=ratio)]
+ scaling *= 2
+
+ model += [nn.ELU()]
+ mimiconv1d_layer_names.append(f"layers.{len(model)}")
+ model += [MimiConv1d(config, scaling * config.num_filters, config.hidden_size, config.last_kernel_size)]
+
+ self.layers = nn.ModuleList(model)
+ self._mimiconv1d_layer_names = mimiconv1d_layer_names
+
+ # initialize layer_idx for MimiConv1d submodules, necessary for padding_cache
+ for layer_idx, layername in enumerate(self._mimiconv1d_layer_names):
+ conv_layer = self.get_submodule(layername)
+ setattr(conv_layer, "layer_idx", layer_idx)
+
+ def forward(self, hidden_states, padding_cache=None):
+ for layer in self.layers:
+ if isinstance(layer, (MimiConv1d, MimiResnetBlock)):
+ hidden_states = layer(hidden_states, padding_cache=padding_cache)
+ else:
+ hidden_states = layer(hidden_states)
+ return hidden_states
+
+
+class MimiLayerScale(nn.Module):
+ """Layer scale from [Touvron et al 2021] (https://huggingface.co/papers/2103.17239).
+ This rescales diagonally the residual outputs close to 0, with a learnt scale.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ channels = config.hidden_size
+ initial_scale = config.layer_scale_initial_scale
+ self.scale = nn.Parameter(torch.full((channels,), initial_scale, requires_grad=True))
+
+ def forward(self, x: torch.Tensor):
+ return self.scale * x
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Mimi
+class MimiRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: MimiConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class MimiMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.activation_fn = ACT2FN[config.hidden_act]
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+
+ # Copied from transformers.models.clip.modeling_clip.CLIPMLP.forward
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# copied from transformers.models.gemma.modeling_gemma.GemmaAttention with Gemma->Mimi
+# no longer copied after attention refactors
+class MimiAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: MimiConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = config.head_dim
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.rope_theta = config.rope_theta
+ self.is_causal = True
+ self.scaling = 1 / math.sqrt(config.head_dim)
+
+ if self.hidden_size % self.num_heads != 0:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+ self.rotary_emb = MimiRotaryEmbedding(config)
+ self.sliding_window = config.sliding_window # Ignore copy
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ cos, sin = self.rotary_emb(value_states, position_ids)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
+
+ if attention_mask is not None: # no matter the length, we just slice it
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.view(bsz, q_len, -1)
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights
+
+
+# NO LONGER EXIST Copied from transformers.models.gemma.modeling_gemma.GemmaFlashAttention2 with Gemma->Mimi
+# TODO cyril: modular
+class MimiFlashAttention2(MimiAttention):
+ """
+ Mimi flash attention module. This module inherits from `MimiAttention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+ self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ if isinstance(past_key_values, StaticCache):
+ raise ValueError(
+ "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
+ "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
+ )
+
+ output_attentions = False
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ # Flash attention requires the input to have the shape
+ # batch_size x seq_length x head_dim x hidden_dim
+ # therefore we just need to keep the original shape
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ cos, sin = self.rotary_emb(value_states, position_ids)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+ # to be able to avoid many of these transpose/reshape/view.
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ dropout_rate = self.attention_dropout if self.training else 0.0
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in the correct dtype just to be sure everything works as expected.
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+ # in fp32. (MimiRMSNorm handles it correctly)
+
+ input_dtype = query_states.dtype
+ device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
+ if input_dtype == torch.float32:
+ if torch.is_autocast_enabled():
+ target_dtype = (
+ torch.get_autocast_dtype(device_type)
+ if hasattr(torch, "get_autocast_dtype")
+ else torch.get_autocast_gpu_dtype()
+ )
+ # Handle the case where the model is quantized
+ elif hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ else:
+ target_dtype = self.q_proj.weight.dtype
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ attn_output = _flash_attention_forward(
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ q_len,
+ position_ids=position_ids,
+ dropout=dropout_rate,
+ sliding_window=getattr(self, "sliding_window", None),
+ is_causal=self.is_causal,
+ use_top_left_mask=self._flash_attn_uses_top_left_mask,
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights
+
+
+# NO LONGER EXIST Copied from transformers.models.gemma.modeling_gemma.GemmaSdpaAttention with Gemma->Mimi
+# TODO cyril: modular
+class MimiSdpaAttention(MimiAttention):
+ """
+ Mimi attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `MimiAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+ SDPA API.
+ """
+
+ # Adapted from MimiAttention.forward
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ if output_attentions:
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+ logger.warning_once(
+ "MimiModel is using MimiSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ return super().forward(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ cos, sin = self.rotary_emb(value_states, position_ids)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ causal_mask = attention_mask
+ if attention_mask is not None:
+ causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ if query_states.device.type == "cuda" and causal_mask is not None:
+ query_states = query_states.contiguous()
+ key_states = key_states.contiguous()
+ value_states = value_states.contiguous()
+
+ # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+ # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+ is_causal = causal_mask is None and q_len > 1
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=causal_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ is_causal=is_causal,
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.view(bsz, q_len, -1)
+
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, None
+
+
+MIMI_ATTENTION_CLASSES = {
+ "eager": MimiAttention,
+ "flash_attention_2": MimiFlashAttention2,
+ "sdpa": MimiSdpaAttention,
+}
+
+
+class MimiTransformerLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MimiConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = MIMI_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+ self.mlp = MimiMLP(config)
+ self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+ self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
+ self.self_attn_layer_scale = MimiLayerScale(config)
+ self.mlp_layer_scale = MimiLayerScale(config)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence
+ kwargs (`dict`, *optional*):
+ Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+ into the model
+ """
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = residual + self.self_attn_layer_scale(hidden_states)
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + self.mlp_layer_scale(hidden_states)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ return outputs
+
+
+class MimiTransformerModel(nn.Module):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MimiTransformerLayer`]
+
+ Args:
+ config: MimiConfig
+ """
+
+ def __init__(self, config: MimiConfig):
+ super().__init__()
+
+ self.layers = nn.ModuleList(
+ [MimiTransformerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self._attn_implementation = config._attn_implementation
+
+ self.gradient_checkpointing = False
+ self.config = config
+
+ def forward(
+ self,
+ hidden_states: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Embedded representation that will be contextualized by the model
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ Two formats are allowed:
+ - a [`~cache_utils.Cache`] instance;
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+ cache format.
+
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+ legacy cache format will be returned.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+ of shape `(batch_size, sequence_length)`.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+ )
+ use_cache = False
+
+ # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
+ if not isinstance(past_key_values, (type(None), Cache)):
+ raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = create_causal_mask(
+ config=self.config,
+ input_embeds=hidden_states,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None
+ )
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class MimiDecoder(nn.Module):
+ """SEANet decoder as used by Mimi."""
+
+ def __init__(self, config: MimiConfig):
+ super().__init__()
+ scaling = int(2 ** len(config.upsampling_ratios))
+ model = [MimiConv1d(config, config.hidden_size, scaling * config.num_filters, config.kernel_size)]
+
+ # Upsample to raw audio scale
+ for ratio in config.upsampling_ratios:
+ current_scale = scaling * config.num_filters
+ # Add upsampling layers
+ model += [nn.ELU()]
+ model += [
+ MimiConvTranspose1d(config, current_scale, current_scale // 2, kernel_size=ratio * 2, stride=ratio)
+ ]
+ # Add residual layers
+ for j in range(config.num_residual_layers):
+ model += [MimiResnetBlock(config, current_scale // 2, (config.dilation_growth_rate**j, 1))]
+ scaling //= 2
+
+ # Add final layers
+ model += [nn.ELU()]
+ model += [MimiConv1d(config, config.num_filters, config.audio_channels, config.last_kernel_size)]
+ self.layers = nn.ModuleList(model)
+
+ # Copied from transformers.models.encodec.modeling_encodec.EncodecDecoder.forward
+ def forward(self, hidden_states):
+ for layer in self.layers:
+ hidden_states = layer(hidden_states)
+ return hidden_states
+
+
+class MimiEuclideanCodebook(nn.Module):
+ """Codebook with Euclidean distance."""
+
+ def __init__(self, config: MimiConfig, epsilon: float = 1e-5):
+ super().__init__()
+ embed = torch.zeros(config.codebook_size, config.codebook_dim)
+
+ self.codebook_size = config.codebook_size
+
+ self.register_buffer("initialized", torch.tensor([True], dtype=torch.float32))
+ self.register_buffer("cluster_usage", torch.ones(config.codebook_size))
+ self.register_buffer("embed_sum", embed)
+ self._embed = None
+ self.epsilon = epsilon
+
+ @property
+ def embed(self) -> torch.Tensor:
+ if self._embed is None:
+ self._embed = self.embed_sum / self.cluster_usage.clamp(min=self.epsilon)[:, None]
+ return self._embed
+
+ def quantize(self, hidden_states):
+ # Projects each vector in `hidden_states` over the nearest centroid and return its index.
+ # `hidden_states` should be `[N, D]` with `N` the number of input vectors and `D` the dimension.
+ dists = torch.cdist(hidden_states[None].float(), self.embed[None].float(), p=2)[0]
+ embed_ind = dists.argmin(dim=-1)
+ return embed_ind
+
+ # Copied from transformers.models.encodec.modeling_encodec.EncodecEuclideanCodebook.encode
+ def encode(self, hidden_states):
+ shape = hidden_states.shape
+ # pre-process
+ hidden_states = hidden_states.reshape((-1, shape[-1]))
+ # quantize
+ embed_ind = self.quantize(hidden_states)
+ # post-process
+ embed_ind = embed_ind.view(*shape[:-1])
+ return embed_ind
+
+ # Copied from transformers.models.encodec.modeling_encodec.EncodecEuclideanCodebook.decode
+ def decode(self, embed_ind):
+ quantize = nn.functional.embedding(embed_ind, self.embed)
+ return quantize
+
+
+# Copied from transformers.models.encodec.modeling_encodec.EncodecVectorQuantization with Encodec->Mimi
+class MimiVectorQuantization(nn.Module):
+ """
+ Vector quantization implementation. Currently supports only euclidean distance.
+ """
+
+ def __init__(self, config: MimiConfig):
+ super().__init__()
+ self.codebook = MimiEuclideanCodebook(config)
+
+ def encode(self, hidden_states):
+ hidden_states = hidden_states.permute(0, 2, 1)
+ embed_in = self.codebook.encode(hidden_states)
+ return embed_in
+
+ def decode(self, embed_ind):
+ quantize = self.codebook.decode(embed_ind)
+ quantize = quantize.permute(0, 2, 1)
+ return quantize
+
+
+class MimiResidualVectorQuantizer(nn.Module):
+ """Residual Vector Quantizer."""
+
+ def __init__(self, config: MimiConfig, num_quantizers: Optional[int] = None):
+ super().__init__()
+ self.codebook_size = config.codebook_size
+ self.frame_rate = config.frame_rate
+ self.num_quantizers = num_quantizers if num_quantizers is not None else config.num_quantizers
+ self.layers = nn.ModuleList([MimiVectorQuantization(config) for _ in range(self.num_quantizers)])
+
+ self.input_proj = None
+ self.output_proj = None
+ if config.vector_quantization_hidden_dimension != config.hidden_size:
+ self.input_proj = torch.nn.Conv1d(
+ config.hidden_size, config.vector_quantization_hidden_dimension, 1, bias=False
+ )
+ self.output_proj = torch.nn.Conv1d(
+ config.vector_quantization_hidden_dimension, config.hidden_size, 1, bias=False
+ )
+
+ def encode(self, embeddings: torch.Tensor, num_quantizers: Optional[int] = None) -> torch.Tensor:
+ """
+ Encode a given input tensor with the specified frame rate at the given number of quantizers / codebooks. The RVQ encode method sets
+ the appropriate number of quantizers to use and returns indices for each quantizer.
+ """
+ if self.input_proj is not None:
+ embeddings = self.input_proj(embeddings)
+
+ num_quantizers = num_quantizers if num_quantizers is not None else self.num_quantizers
+
+ residual = embeddings
+ all_indices = []
+ for layer in self.layers[:num_quantizers]:
+ indices = layer.encode(residual)
+ quantized = layer.decode(indices)
+ residual = residual - quantized
+ all_indices.append(indices)
+ out_indices = torch.stack(all_indices)
+ return out_indices
+
+ def decode(self, codes: torch.Tensor) -> torch.Tensor:
+ """Decode the given codes of shape [B, K, T] to the quantized representation."""
+ quantized_out = torch.tensor(0.0, device=codes.device)
+ codes = codes.transpose(0, 1)
+ for i, indices in enumerate(codes):
+ layer = self.layers[i]
+ quantized = layer.decode(indices)
+ quantized_out = quantized_out + quantized
+
+ if self.output_proj is not None:
+ quantized_out = self.output_proj(quantized_out)
+ return quantized_out
+
+
+class MimiSplitResidualVectorQuantizer(nn.Module):
+ """Split Residual Vector Quantizer."""
+
+ def __init__(self, config: MimiConfig):
+ super().__init__()
+ self.codebook_size = config.codebook_size
+ self.frame_rate = config.frame_rate
+ self.max_num_quantizers = config.num_quantizers
+
+ self.num_semantic_quantizers = config.num_semantic_quantizers
+ self.num_acoustic_quantizers = config.num_quantizers - config.num_semantic_quantizers
+
+ self.semantic_residual_vector_quantizer = MimiResidualVectorQuantizer(config, self.num_semantic_quantizers)
+ self.acoustic_residual_vector_quantizer = MimiResidualVectorQuantizer(config, self.num_acoustic_quantizers)
+
+ def encode(self, embeddings: torch.Tensor, num_quantizers: Optional[float] = None) -> torch.Tensor:
+ """
+ Encode a given input tensor with the specified frame rate at the given number of quantizers / codebooks. The RVQ encode method sets
+ the appropriate number of quantizers to use and returns indices for each quantizer.
+ """
+
+ num_quantizers = self.max_num_quantizers if num_quantizers is None else num_quantizers
+
+ if num_quantizers > self.max_num_quantizers:
+ raise ValueError(
+ f"The number of quantizers (i.e codebooks) asked should be lower than the total number of quantizers {self.max_num_quantizers}, but is currently {num_quantizers}."
+ )
+
+ if num_quantizers < self.num_semantic_quantizers:
+ raise ValueError(
+ f"The number of quantizers (i.e codebooks) asked should be higher than the number of semantic quantizers {self.num_semantic_quantizers}, but is currently {num_quantizers}."
+ )
+
+ # codes is [K, B, T], with T frames, K nb of codebooks.
+ codes = self.semantic_residual_vector_quantizer.encode(embeddings)
+
+ if num_quantizers > self.num_semantic_quantizers:
+ acoustic_codes = self.acoustic_residual_vector_quantizer.encode(
+ embeddings, num_quantizers=num_quantizers - self.num_semantic_quantizers
+ )
+ codes = torch.cat([codes, acoustic_codes], dim=0)
+
+ return codes
+
+ def decode(self, codes: torch.Tensor) -> torch.Tensor:
+ """Decode the given codes to the quantized representation."""
+
+ # The first num_semantic_quantizers codebooks are decoded using the semantic RVQ
+ quantized_out = self.semantic_residual_vector_quantizer.decode(codes[:, : self.num_semantic_quantizers])
+
+ # The rest of the codebooks are decoded using the acoustic RVQ
+ if codes.shape[1] > self.num_semantic_quantizers:
+ quantized_out += self.acoustic_residual_vector_quantizer.decode(codes[:, self.num_semantic_quantizers :])
+ return quantized_out
+
+
+@auto_docstring
+class MimiPreTrainedModel(PreTrainedModel):
+ config: MimiConfig
+ base_model_prefix = "mimi"
+ main_input_name = "input_values"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MimiDecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, (nn.Conv1d, nn.ConvTranspose1d)):
+ nn.init.kaiming_normal_(module.weight)
+ if module.bias is not None:
+ k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0]))
+ nn.init.uniform_(module.bias, a=-k, b=k)
+ elif isinstance(module, MimiLayerScale):
+ module.scale.data.fill_(self.config.layer_scale_initial_scale)
+
+
+@auto_docstring(
+ custom_intro="""
+ The Mimi neural audio codec model.
+ """
+)
+class MimiModel(MimiPreTrainedModel):
+ def __init__(self, config: MimiConfig):
+ super().__init__(config)
+ self.config = config
+
+ self.encoder = MimiEncoder(config)
+ self.encoder_transformer = MimiTransformerModel(config)
+
+ self.downsample = None
+ self.upsample = None
+ if config.frame_rate != config.encodec_frame_rate:
+ self.downsample = MimiConv1d(
+ config,
+ config.hidden_size,
+ config.hidden_size,
+ kernel_size=2 * int(config.encodec_frame_rate / config.frame_rate),
+ stride=2,
+ bias=False,
+ pad_mode="replicate",
+ layer_idx=len(self.encoder._mimiconv1d_layer_names),
+ )
+
+ self.upsample = MimiConvTranspose1d(
+ config,
+ config.hidden_size,
+ config.hidden_size,
+ kernel_size=2 * int(config.encodec_frame_rate / config.frame_rate),
+ stride=2,
+ bias=False,
+ groups=config.upsample_groups,
+ )
+
+ self.decoder_transformer = MimiTransformerModel(config)
+ self.decoder = MimiDecoder(config)
+
+ self.quantizer = MimiSplitResidualVectorQuantizer(config)
+
+ self.bits_per_codebook = int(math.log2(self.config.codebook_size))
+ if 2**self.bits_per_codebook != self.config.codebook_size:
+ raise ValueError("The codebook_size must be a power of 2.")
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_encoder(self):
+ return self.encoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ def _encode_frame(
+ self,
+ input_values: torch.Tensor,
+ num_quantizers: int,
+ padding_mask: int,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ padding_cache: Optional[MimiConv1dPaddingCache] = None,
+ return_dict: Optional[bool] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ """
+ Encodes the given input using the underlying VQVAE. The padding mask is required to compute the correct scale.
+ """
+
+ # TODO: @eustlb, let's make the encoder support padding_mask so that batched inputs are supported.
+ embeddings = self.encoder(input_values, padding_cache=padding_cache)
+
+ # TODO: @eustlb, convert the padding mask to attention mask.
+ encoder_outputs = self.encoder_transformer(
+ embeddings.transpose(1, 2), past_key_values=past_key_values, return_dict=return_dict
+ )
+ if return_dict:
+ past_key_values = encoder_outputs.get("past_key_values")
+ elif len(encoder_outputs) > 1:
+ past_key_values = encoder_outputs[1]
+ embeddings = encoder_outputs[0].transpose(1, 2)
+ embeddings = self.downsample(embeddings, padding_cache=padding_cache)
+
+ codes = self.quantizer.encode(embeddings, num_quantizers)
+ codes = codes.transpose(0, 1)
+ return codes, past_key_values, padding_cache
+
+ def get_encoded_length(self, input_length: torch.LongTensor) -> torch.LongTensor:
+ """
+ Return the number of frames of the encoded audio waveform.
+ """
+ output_length = input_length
+
+ # encoder
+ for layer_name in self.encoder._mimiconv1d_layer_names:
+ output_length = self.encoder.get_submodule(layer_name)._get_output_length(output_length)
+
+ # downsample
+ output_length = self.downsample._get_output_length(output_length)
+
+ return output_length
+
+ def get_audio_codes_mask(self, padding_mask: torch.Tensor, padding_side: str = "right"):
+ """
+ Get the mask for the audio codes from the original padding mask.
+ """
+ encoded_lengths = self.get_encoded_length(padding_mask.sum(dim=-1))
+
+ audio_codes_mask = torch.arange(encoded_lengths.max(), device=encoded_lengths.device).expand(
+ len(encoded_lengths), -1
+ )
+ audio_codes_mask = audio_codes_mask < encoded_lengths.unsqueeze(1)
+ audio_codes_mask = audio_codes_mask.to(padding_mask.device)
+
+ if padding_side == "right":
+ return audio_codes_mask
+ else:
+ return audio_codes_mask.flip(dims=[-1])
+
+ def encode(
+ self,
+ input_values: torch.Tensor,
+ padding_mask: Optional[torch.Tensor] = None,
+ num_quantizers: Optional[float] = None,
+ encoder_past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ padding_cache: Optional[MimiConv1dPaddingCache] = None,
+ use_streaming: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor, Optional[torch.Tensor]], MimiEncoderOutput]:
+ """
+ Encodes the input audio waveform into discrete codes.
+
+ Args:
+ input_values (`torch.Tensor` of shape `(batch_size, channels, sequence_length)`):
+ Float values of the input audio waveform.
+ padding_mask (`torch.Tensor` of shape `(batch_size, channels, sequence_length)`):
+ Indicates which inputs are to be ignored due to padding, where elements are either 1 for *not masked* or 0
+ for *masked*.
+ num_quantizers (`int`, *optional*):
+ Number of quantizers (i.e codebooks) to use. By default, all quantizers are used.
+ encoder_past_key_values (`Cache`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks) that can be used to speed up sequential decoding of the encoder transformer.
+ This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ The model will output the same cache format that is fed as input.
+
+ If `past_key_values` are used, the user can optionally input only the last `audio_values` or `audio_codes (those that don't
+ have their past key value states given to this model).
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+
+ Returns:
+ `codebook` of shape `[batch_size, num_codebooks, frames]`, the discrete encoded codes for the input audio waveform.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+ use_streaming = use_streaming if use_streaming is not None else self.config.use_streaming
+
+ num_quantizers = self.config.num_quantizers if num_quantizers is None else num_quantizers
+
+ if num_quantizers > self.config.num_quantizers:
+ raise ValueError(
+ f"The number of quantizers (i.e codebooks) asked should be lower than the total number of quantizers {self.config.num_quantizers}, but is currently {num_quantizers}."
+ )
+
+ _, channels, input_length = input_values.shape
+
+ if channels < 1 or channels > 2:
+ raise ValueError(f"Number of audio channels must be 1 or 2, but got {channels}")
+
+ if padding_mask is None:
+ padding_mask = torch.ones_like(input_values).bool()
+
+ if use_streaming and padding_cache is None:
+ per_layer_padding, per_layer_padding_mode, per_layer_in_channels = [], [], []
+ for layer_name in self.encoder._mimiconv1d_layer_names:
+ per_layer_padding.append(self.encoder.get_submodule(layer_name).padding_total)
+ per_layer_padding_mode.append(self.encoder.get_submodule(layer_name).pad_mode)
+ per_layer_in_channels.append(self.encoder.get_submodule(layer_name).in_channels)
+
+ # downsample layer
+ per_layer_padding.append(self.downsample.padding_total)
+ per_layer_padding_mode.append(self.downsample.pad_mode)
+ per_layer_in_channels.append(self.downsample.in_channels)
+
+ padding_cache = MimiConv1dPaddingCache(
+ num_layers=len(self.encoder._mimiconv1d_layer_names) + 1,
+ per_layer_padding=per_layer_padding,
+ per_layer_padding_mode=per_layer_padding_mode,
+ per_layer_in_channels=per_layer_in_channels,
+ )
+
+ encoded_frames, encoder_past_key_values, padding_cache = self._encode_frame(
+ input_values,
+ num_quantizers,
+ padding_mask.bool(),
+ past_key_values=encoder_past_key_values,
+ padding_cache=padding_cache,
+ return_dict=return_dict,
+ )
+
+ if not return_dict:
+ return (
+ encoded_frames,
+ encoder_past_key_values,
+ padding_cache,
+ )
+
+ return MimiEncoderOutput(encoded_frames, encoder_past_key_values, padding_cache)
+
+ def _decode_frame(
+ self,
+ codes: torch.Tensor,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ return_dict: Optional[bool] = None,
+ ) -> torch.Tensor:
+ embeddings = self.quantizer.decode(codes)
+
+ embeddings = self.upsample(embeddings)
+ decoder_outputs = self.decoder_transformer(
+ embeddings.transpose(1, 2), past_key_values=past_key_values, return_dict=return_dict
+ )
+ if return_dict:
+ past_key_values = decoder_outputs.get("past_key_values")
+ elif len(decoder_outputs) > 1:
+ past_key_values = decoder_outputs[1]
+ embeddings = decoder_outputs[0].transpose(1, 2)
+ outputs = self.decoder(embeddings)
+ return outputs, past_key_values
+
+ def decode(
+ self,
+ audio_codes: torch.Tensor,
+ padding_mask: Optional[torch.Tensor] = None,
+ decoder_past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor, torch.Tensor], MimiDecoderOutput]:
+ """
+ Decodes the given frames into an output audio waveform.
+
+ Note that the output might be a bit bigger than the input. In that case, any extra steps at the end can be
+ trimmed.
+
+ Args:
+ audio_codes (`torch.LongTensor` of shape `(batch_size, num_quantizers, codes_length)`, *optional*):
+ Discret code embeddings computed using `model.encode`.
+ padding_mask (`torch.Tensor` of shape `(batch_size, channels, sequence_length)`):
+ Indicates which inputs are to be ignored due to padding, where elements are either 1 for *not masked* or 0
+ for *masked*.
+ decoder_past_key_values (`Cache`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks) that can be used to speed up sequential decoding of the decoder transformer.
+ This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ The model will output the same cache format that is fed as input.
+
+ If `past_key_values` are used, the user can optionally input only the last `audio_values` or `audio_codes (those that don't
+ have their past key value states given to this model).
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+
+ """
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ audio_values, decoder_past_key_values = self._decode_frame(
+ audio_codes, past_key_values=decoder_past_key_values, return_dict=return_dict
+ )
+
+ # truncate based on padding mask
+ if padding_mask is not None and padding_mask.shape[-1] < audio_values.shape[-1]:
+ audio_values = audio_values[..., : padding_mask.shape[-1]]
+
+ if not return_dict:
+ return (
+ audio_values,
+ decoder_past_key_values,
+ )
+ return MimiDecoderOutput(audio_values, decoder_past_key_values)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_values: torch.Tensor,
+ padding_mask: Optional[torch.Tensor] = None,
+ num_quantizers: Optional[int] = None,
+ audio_codes: Optional[torch.Tensor] = None,
+ encoder_past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ decoder_past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor, torch.Tensor], MimiOutput]:
+ r"""
+ input_values (`torch.FloatTensor` of shape `(batch_size, channels, sequence_length)`, *optional*):
+ Raw audio input converted to Float.
+ padding_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indicates which inputs are to be ignored due to padding, where elements are either 1 for *not masked* or 0
+ for *masked*.
+ num_quantizers (`int`, *optional*):
+ Number of quantizers (i.e codebooks) to use. By default, all quantizers are used.
+ audio_codes (`torch.LongTensor` of shape `(batch_size, num_quantizers, codes_length)`, *optional*):
+ Discret code embeddings computed using `model.encode`.
+ encoder_past_key_values (`Cache`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks) that can be used to speed up sequential decoding of the encoder transformer.
+ This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ The model will output the same cache format that is fed as input.
+
+ If `past_key_values` are used, the user can optionally input only the last `audio_values` or `audio_codes (those that don't
+ have their past key value states given to this model).
+ decoder_past_key_values (`Cache`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks) that can be used to speed up sequential decoding of the decoder transformer.
+ This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ The model will output the same cache format that is fed as input.
+
+ If `past_key_values` are used, the user can optionally input only the last `audio_values` or `audio_codes (those that don't
+ have their past key value states given to this model).
+
+ Examples:
+
+ ```python
+ >>> from datasets import load_dataset
+ >>> from transformers import AutoFeatureExtractor, MimiModel
+
+ >>> dataset = load_dataset("hf-internal-testing/ashraq-esc50-1-dog-example")
+ >>> audio_sample = dataset["train"]["audio"][0]["array"]
+
+ >>> model_id = "kyutai/mimi"
+ >>> model = MimiModel.from_pretrained(model_id)
+ >>> feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+
+ >>> inputs = feature_extractor(raw_audio=audio_sample, return_tensors="pt")
+
+ >>> outputs = model(**inputs)
+ >>> audio_codes = outputs.audio_codes
+ >>> audio_values = outputs.audio_values
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ if padding_mask is None:
+ padding_mask = torch.ones_like(input_values).bool()
+
+ if audio_codes is None:
+ encoder_outputs = self.encode(
+ input_values, padding_mask, num_quantizers, encoder_past_key_values, return_dict=return_dict
+ )
+ audio_codes = encoder_outputs[0]
+ if return_dict:
+ encoder_past_key_values = encoder_outputs.get("past_key_values")
+ elif len(encoder_outputs) > 1:
+ encoder_past_key_values = encoder_outputs[1]
+
+ decoder_outputs = self.decode(audio_codes, padding_mask, decoder_past_key_values, return_dict=return_dict)
+ audio_values = decoder_outputs[0]
+ if return_dict:
+ decoder_past_key_values = decoder_outputs.get("past_key_values")
+ elif len(decoder_outputs) > 1:
+ decoder_past_key_values = decoder_outputs[1]
+
+ if not return_dict:
+ return (audio_codes, audio_values, encoder_past_key_values, decoder_past_key_values)
+
+ return MimiOutput(
+ audio_codes=audio_codes,
+ audio_values=audio_values,
+ encoder_past_key_values=encoder_past_key_values,
+ decoder_past_key_values=decoder_past_key_values,
+ )
+
+
+__all__ = ["MimiModel", "MimiPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..91834eb6a2246ff1a5790116c94f3b0b4a559725
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/__init__.py
@@ -0,0 +1,29 @@
+# coding=utf-8
+# Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_minimax import *
+ from .modeling_minimax import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/configuration_minimax.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/configuration_minimax.py
new file mode 100644
index 0000000000000000000000000000000000000000..86369db078b0fc8bb7f127fc8ece01604804cff3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/configuration_minimax.py
@@ -0,0 +1,230 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/minimax/modular_minimax.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_minimax.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from ...configuration_utils import PretrainedConfig, layer_type_validation
+
+
+class MiniMaxConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MiniMaxModel`]. It is used to instantiate an
+ MiniMax model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the MiniMax.
+
+ [MiniMaxAI/MiniMax-Text-01-hf](https://huggingface.co/MiniMaxAI/MiniMax-Text-01-hf)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the MiniMax model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MiniMaxModel`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 14336):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_key_value_heads (`int`, *optional*, defaults to 8):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
+ head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
+ The attention head dimension.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+ The maximum sequence length that this model might ever be used with. MiniMax's sliding window attention
+ allows sequence of up to 4096*32 tokens.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*):
+ The id of the padding token.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ The id of the "beginning-of-sequence" token.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ The id of the "end-of-sequence" token.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether the model's input and output word embeddings should be tied.
+ rope_theta (`float`, *optional*, defaults to 1000000.0):
+ The base period of the RoPE embeddings.
+ sliding_window (`int`, *optional*):
+ Sliding window attention window size. If not specified, will default to `4096`.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ num_experts_per_tok (`int`, *optional*, defaults to 2):
+ The number of experts to route per-token, can be also interpreted as the `top-k` routing
+ parameter
+ num_local_experts (`int`, *optional*, defaults to 8):
+ Number of experts per Sparse MLP layer.
+ output_router_logits (`bool`, *optional*, defaults to `False`):
+ Whether or not the router logits should be returned by the model. Enabeling this will also
+ allow the model to output the auxiliary loss. See [here]() for more details
+ router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+ The aux loss factor for the total loss.
+ router_jitter_noise (`float`, *optional*, defaults to 0.0):
+ Amount of noise to add to the router.
+ layer_types (`list`, *optional*):
+ Attention pattern for each layer.
+ block_size (`int`, *optional*, defaults to 256):
+ The length of each attention block, determining how queries, keys, and values
+ are grouped and processed for intra- and inter-block attention.
+ full_attn_alpha_factor (`float`, *optional*, defaults to 1):
+ Weight for residual value in residual connection after normal attention.
+ full_attn_beta_factor (`float`, *optional*, defaults to 1):
+ Weight for hidden state value in residual connection after normal attention.
+ linear_attn_alpha_factor (`float`, *optional*, defaults to 1):
+ Weight for residual value in residual connection after lightning attention.
+ linear_attn_beta_factor (`float`, *optional*, defaults to 1):
+ Weight for hidden state value in residual connection after lightning attention.
+ mlp_alpha_factor (`float`, *optional*, defaults to 1):
+ Weight for residual value in residual connection after MLP.
+ mlp_beta_factor (`float`, *optional*, defaults to 1):
+ Weight for hidden state value in residual connection after MLP.
+
+ ```python
+ >>> from transformers import MiniMaxModel, MiniMaxConfig
+
+ >>> # Initializing a MiniMax style configuration
+ >>> configuration = MiniMaxConfig()
+
+ >>> # Initializing a model from the MiniMax style configuration
+ >>> model = MiniMaxModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "minimax"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ base_model_tp_plan = {
+ "layers.*.self_attn.q_proj": "colwise",
+ "layers.*.self_attn.k_proj": "colwise",
+ "layers.*.self_attn.v_proj": "colwise",
+ "layers.*.self_attn.o_proj": "rowwise",
+ "layers.*.block_sparse_moe.gate": "colwise_rep", # we need to replicate here to correctly route experts
+ "layers.*.block_sparse_moe.experts.*.w1": "colwise",
+ "layers.*.block_sparse_moe.experts.*.w2": "rowwise",
+ "layers.*.block_sparse_moe.experts.*.w3": "colwise",
+ }
+ base_model_pp_plan = {
+ "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+ "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+ "norm": (["hidden_states"], ["hidden_states"]),
+ }
+
+ def __init__(
+ self,
+ vocab_size=32000,
+ hidden_size=4096,
+ intermediate_size=14336,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=8,
+ head_dim=None,
+ hidden_act="silu",
+ max_position_embeddings=4096 * 32,
+ initializer_range=0.02,
+ rms_norm_eps=1e-5,
+ use_cache=True,
+ pad_token_id=None,
+ bos_token_id=1,
+ eos_token_id=2,
+ tie_word_embeddings=False,
+ rope_theta=1e6,
+ sliding_window=None,
+ attention_dropout=0.0,
+ num_experts_per_tok=2,
+ num_local_experts=8,
+ output_router_logits=False,
+ router_aux_loss_coef=0.001,
+ router_jitter_noise=0.0,
+ layer_types=None,
+ block_size=256,
+ full_attn_alpha_factor=1,
+ full_attn_beta_factor=1,
+ linear_attn_alpha_factor=1,
+ linear_attn_beta_factor=1,
+ mlp_alpha_factor=1,
+ mlp_beta_factor=1,
+ **kwargs,
+ ):
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.sliding_window = sliding_window
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.attention_dropout = attention_dropout
+ self.head_dim = head_dim
+
+ self.num_experts_per_tok = num_experts_per_tok
+ self.num_local_experts = num_local_experts
+ self.output_router_logits = output_router_logits
+ self.router_aux_loss_coef = router_aux_loss_coef
+ self.router_jitter_noise = router_jitter_noise
+ self.layer_types = layer_types
+ self.block_size = block_size
+ self.full_attn_alpha_factor = full_attn_alpha_factor
+ self.full_attn_beta_factor = full_attn_beta_factor
+ self.linear_attn_alpha_factor = linear_attn_alpha_factor
+ self.linear_attn_beta_factor = linear_attn_beta_factor
+ self.mlp_alpha_factor = mlp_alpha_factor
+ self.mlp_beta_factor = mlp_beta_factor
+
+ if self.layer_types is None:
+ self.layer_types = [
+ "full_attention" if bool((i + 1) % 2) else "linear_attention" for i in range(self.num_hidden_layers)
+ ]
+ layer_type_validation(self.layer_types)
+
+
+__all__ = ["MiniMaxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/modeling_minimax.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/modeling_minimax.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfd1af9cecee39440ecf35323dce48b29ca4f779
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/modeling_minimax.py
@@ -0,0 +1,939 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/minimax/modular_minimax.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_minimax.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers.utils.generic import check_model_inputs
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import (
+ GenericForQuestionAnswering,
+ GenericForSequenceClassification,
+ GenericForTokenClassification,
+ GradientCheckpointingLayer,
+)
+from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import OutputRecorder
+from .configuration_minimax import MiniMaxConfig
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class MiniMaxRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ MiniMaxRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class MiniMaxCache(DynamicCache):
+ def __init__(self):
+ super().__init__()
+ self.linear_cache: list[torch.Tensor] = []
+
+ def set_linear_cache(self, layer_idx, linear_cache):
+ # There may be skipped layers, fill them with empty lists
+ for _ in range(len(self.linear_cache), layer_idx + 1):
+ self.linear_cache.append([])
+ self.linear_cache[layer_idx] = linear_cache
+
+ def get_linear_cache(self, layer_idx: int):
+ if layer_idx < len(self):
+ return self.linear_cache[layer_idx]
+ return None
+
+ def __len__(self):
+ return max(super().__len__(), len(self.linear_cache))
+
+ def __getitem__(self, layer_idx: int):
+ if layer_idx < len(self.linear_cache) and self.linear_cache[layer_idx] != []:
+ return (self.linear_cache[layer_idx],)
+ return super().__getitem__(layer_idx)
+
+ def __iter__(self):
+ for layer_idx in range(len(self)):
+ yield self[layer_idx]
+
+ def batch_repeat_interleave(self, repeats: int):
+ for layer_idx in range(len(self)):
+ if self.linear_cache[layer_idx] != []:
+ self.linear_cache[layer_idx] = self.linear_cache[layer_idx].repeat_interleave(repeats, dim=0)
+ else:
+ self.layers[layer_idx].batch_repeat_interleave(repeats)
+
+ def batch_select_indices(self, indices: torch.Tensor):
+ for layer_idx in range(len(self)):
+ if self.linear_cache[layer_idx] != []:
+ self.linear_cache[layer_idx] = self.linear_cache[layer_idx][indices, ...]
+ else:
+ self.layers[layer_idx].batch_select_indices(indices)
+
+ def crop(self, max_length: int):
+ raise RuntimeError("MiniMaxCache doesnot support `crop` method")
+
+
+class MiniMaxLightningAttention(nn.Module):
+ def __init__(self, config: MiniMaxConfig, layer_idx: int):
+ super().__init__()
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+ self.num_attention_heads = config.num_attention_heads
+ self.num_hidden_layers = config.num_hidden_layers
+ self.block_size = config.block_size
+
+ self.act_fn = ACT2FN[config.hidden_act]
+ self.norm = MiniMaxRMSNorm(self.head_dim * self.num_attention_heads)
+ self.qkv_proj = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim * 3, bias=False)
+ self.out_proj = nn.Linear(self.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+ self.output_gate = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim, bias=False)
+
+ slope_rate = self.get_slope_rate()
+ query_decay, key_decay, diagonal_decay = self.decay_factors(slope_rate)
+
+ self.register_buffer("slope_rate", slope_rate)
+ self.register_buffer("query_decay", query_decay)
+ self.register_buffer("key_decay", key_decay)
+ self.register_buffer("diagonal_decay", diagonal_decay)
+
+ def get_slope_rate(self):
+ base = 1 / (2 ** (8 / self.num_attention_heads))
+ exponent = torch.arange(self.num_attention_heads) + 1
+ factor = 1 - self.layer_idx / (self.num_hidden_layers - 1 + 1e-5) + 1e-5
+
+ rate = base**exponent
+ rate = rate * factor
+ rate = rate[:, None, None]
+
+ return rate
+
+ def decay_factors(self, slope_rate):
+ block_size_range = torch.arange(self.block_size) + 1
+
+ query_decay = torch.exp(-slope_rate * block_size_range[:, None])
+ key_decay = torch.exp(-slope_rate * (self.block_size - block_size_range[:, None]))
+
+ diagonal_decay = block_size_range[:, None] - block_size_range[None, :]
+ diagonal_decay = diagonal_decay[None, None, :, :]
+ diagonal_decay = slope_rate * diagonal_decay
+ diagonal_decay = torch.where(diagonal_decay >= 0, -diagonal_decay, float("-inf"))
+ diagonal_decay = torch.exp(diagonal_decay)
+
+ return query_decay, key_decay, diagonal_decay
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ batch_size, seq_len, hidden_size = hidden_states.shape
+ num_blocks = (seq_len + self.block_size - 1) // self.block_size
+
+ qkv_states = self.act_fn(self.qkv_proj(hidden_states))
+ qkv_states = qkv_states.reshape(batch_size, seq_len, self.num_attention_heads, 3 * self.head_dim)
+
+ query_states, key_states, value_states = torch.split(qkv_states, self.head_dim, dim=3)
+
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ # calculated (K.T @ V) and saved as cache
+ attn_weights_inter = None
+ if past_key_values is not None:
+ attn_weights_inter = past_key_values.get_linear_cache(self.layer_idx)
+
+ if attn_weights_inter is None:
+ attn_weights_inter = torch.zeros(batch_size, self.num_attention_heads, self.head_dim, self.head_dim).to(
+ value_states
+ )
+
+ # apply attention_mask
+ if attention_mask is not None:
+ attention_mask = attention_mask.to(dtype=torch.bool) # Ensure it's a boolean tensor
+ value_states = value_states.masked_fill(~attention_mask.unsqueeze(1).unsqueeze(-1), 0)
+
+ attn_output = []
+ for i in range(num_blocks):
+ start_idx = i * self.block_size
+ end_idx = min(start_idx + self.block_size, seq_len)
+ current_block_size = end_idx - start_idx
+
+ current_query_states = query_states[:, :, start_idx:end_idx]
+ current_key_states = key_states[:, :, start_idx:end_idx]
+ current_value_states = value_states[:, :, start_idx:end_idx]
+
+ current_query_decay = self.query_decay[:, :current_block_size]
+ current_key_decay = self.key_decay[:, -current_block_size:]
+ current_diagonal_decay = self.diagonal_decay[:, :, :current_block_size, :current_block_size]
+ block_decay = torch.exp(-self.slope_rate * current_block_size)
+
+ # intra: ( Q @ K.T ) @ V -> QK * V
+ attn_weights_intra = torch.matmul(current_query_states, current_key_states.transpose(-1, -2))
+ attn_output_intra = torch.matmul(attn_weights_intra * current_diagonal_decay, current_value_states)
+
+ # inter: Q @ ( K.T @ V ) -> Q * KV
+ attn_output_inter = torch.matmul(current_query_states * current_query_decay, attn_weights_inter)
+
+ # final attention output
+ current_attn_output = attn_output_inter + attn_output_intra
+ attn_output.append(current_attn_output)
+
+ # cacluate attn_weights_inter for next block or cache
+ next_attn_weights_inter = torch.matmul(
+ (current_key_states * current_key_decay).transpose(-1, -2), current_value_states
+ )
+ attn_weights_inter = attn_weights_inter * block_decay + next_attn_weights_inter
+
+ else:
+ ratio = torch.exp(-self.slope_rate)
+ attn_output = []
+ for i in range(seq_len):
+ current_query_states = query_states[:, :, i : i + 1]
+ current_key_states = key_states[:, :, i : i + 1]
+ current_value_states = value_states[:, :, i : i + 1]
+
+ current_attn_weights_inter = torch.matmul(current_key_states.transpose(-1, -2), current_value_states)
+ attn_weights_inter = ratio * attn_weights_inter + current_attn_weights_inter
+ current_attn_output = torch.matmul(current_query_states, attn_weights_inter)
+
+ attn_output.append(current_attn_output)
+
+ # concatenate attention outputs over all blocks
+ attn_output = torch.cat(attn_output, dim=-2)
+
+ # final output projection
+ attn_output = attn_output.transpose(1, 2)
+ attn_output = attn_output.reshape(batch_size, seq_len, self.num_attention_heads * self.head_dim)
+ attn_output = self.norm(attn_output)
+ attn_output = F.sigmoid(self.output_gate(hidden_states)) * attn_output
+ attn_output = self.out_proj(attn_output)
+
+ # update cache
+ if past_key_values is not None:
+ past_key_values.set_linear_cache(self.layer_idx, attn_weights_inter)
+
+ return attn_output, attn_weights_inter
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class MiniMaxAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: MiniMaxConfig, layer_idx: int):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = config.attention_dropout
+ self.is_causal = True
+ self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
+ self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ sliding_window=getattr(self.config, "sliding_window", None), # main diff with Llama
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class MiniMaxBlockSparseTop2MLP(nn.Module):
+ def __init__(self, config: MiniMaxConfig):
+ super().__init__()
+ self.ffn_dim = config.intermediate_size
+ self.hidden_dim = config.hidden_size
+
+ self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+ self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+ self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_states):
+ current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+ current_hidden_states = self.w2(current_hidden_states)
+ return current_hidden_states
+
+
+class MiniMaxSparseMoeBlock(nn.Module):
+ """
+ This implementation is
+ strictly equivalent to standard MoE with full capacity (no
+ dropped tokens). It's faster since it formulates MoE operations
+ in terms of block-sparse operations to accommodate imbalanced
+ assignments of tokens to experts, whereas standard MoE either
+ (1) drop tokens at the cost of reduced performance or (2) set
+ capacity factor to number of experts and thus waste computation
+ and memory on padding.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.hidden_dim = config.hidden_size
+ self.ffn_dim = config.intermediate_size
+ self.num_experts = config.num_local_experts
+ self.top_k = config.num_experts_per_tok
+
+ # gating
+ self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+
+ self.experts = nn.ModuleList([MiniMaxBlockSparseTop2MLP(config) for _ in range(self.num_experts)])
+
+ # Jitter parameters
+ self.jitter_noise = config.router_jitter_noise
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ """ """
+ batch_size, sequence_length, hidden_dim = hidden_states.shape
+ if self.training and self.jitter_noise > 0:
+ hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
+ hidden_states = hidden_states.view(-1, hidden_dim)
+ # router_logits: (batch * sequence_length, n_experts)
+ router_logits = self.gate(hidden_states)
+
+ routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+ routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+ routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+ # we cast back to the input dtype
+ routing_weights = routing_weights.to(hidden_states.dtype)
+
+ final_hidden_states = torch.zeros(
+ (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+ )
+
+ # One hot encode the selected experts to create an expert mask
+ # this will be used to easily index which expert is going to be sollicitated
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+ expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+ for expert_idx in expert_hit:
+ expert_layer = self.experts[expert_idx]
+ idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
+ # Index the correct hidden states and compute the expert hidden state for
+ # the current expert. We need to make sure to multiply the output hidden
+ # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+ current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+ current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
+
+ # However `index_add_` only support torch tensors for indexing so we'll use
+ # the `top_x` tensor here.
+ final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+ final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+ return final_hidden_states, router_logits
+
+
+class MiniMaxDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MiniMaxConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = MiniMaxAttention(config, layer_idx)
+
+ self.block_sparse_moe = MiniMaxSparseMoeBlock(config)
+ self.input_layernorm = MiniMaxRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = MiniMaxRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.layer_idx = layer_idx
+ self.layer_type = config.layer_types[layer_idx]
+ self.mlp_alpha_factor = config.mlp_alpha_factor
+ self.mlp_beta_factor = config.mlp_beta_factor
+
+ if self.layer_type == "linear_attention":
+ self.self_attn = MiniMaxLightningAttention(config, layer_idx)
+ self.attn_alpha_factor = config.linear_attn_alpha_factor
+ self.attn_beta_factor = config.linear_attn_beta_factor
+ else:
+ self.self_attn = MiniMaxAttention(config, layer_idx)
+ self.attn_alpha_factor = config.full_attn_alpha_factor
+ self.attn_beta_factor = config.full_attn_beta_factor
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ output_router_logits: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`):
+ Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+ with `head_dim` being the embedding dimension of each attention head.
+ attention_mask (`torch.Tensor`, *optional*): attention mask of size
+ `(batch, sequence_length)` where padding elements are indicated by 0.
+ past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_router_logits (`bool`, *optional*):
+ Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+ should not be returned during inference.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ kwargs (`dict`, *optional*):
+ Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+ into the model
+ """
+
+ hidden_states = self.input_layernorm(hidden_states)
+ residual = hidden_states
+
+ # Self Attention
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = residual * self.attn_alpha_factor + hidden_states * self.attn_beta_factor
+
+ # Fully Connected
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ residual = hidden_states
+ hidden_states, _ = self.block_sparse_moe(hidden_states)
+ hidden_states = residual * self.mlp_alpha_factor + hidden_states * self.mlp_beta_factor
+
+ return hidden_states
+
+
+@auto_docstring
+class MiniMaxPreTrainedModel(PreTrainedModel):
+ config: MiniMaxConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MiniMaxDecoderLayer"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+ _can_compile_fullgraph = False
+ _supports_attention_backend = True
+ _can_record_outputs = {
+ "router_logits": OutputRecorder(MiniMaxSparseMoeBlock, index=1),
+ "hidden_states": MiniMaxDecoderLayer,
+ "attentions": [MiniMaxAttention, MiniMaxLightningAttention],
+ }
+
+
+class MiniMaxRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: MiniMaxConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+@auto_docstring
+class MiniMaxModel(MiniMaxPreTrainedModel):
+ def __init__(self, config: MiniMaxConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [MiniMaxDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.norm = MiniMaxRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.rotary_emb = MiniMaxRotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[MiniMaxCache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> MoeModelOutputWithPast:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if use_cache and past_key_values is None:
+ past_key_values = MiniMaxCache()
+ elif use_cache and not isinstance(past_key_values, MiniMaxCache):
+ raise ValueError(
+ f"MiniMax uses cache of its own and is not compatible with `past_key_values` of type {type(past_key_values)}."
+ )
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
+ causal_mask = mask_function(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+
+ # create position embeddings to be shared across the decoder layers
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ for decoder_layer in self.layers:
+ if decoder_layer.layer_type == "full_attention":
+ input_attention_mask = causal_mask
+ else:
+ # lightning attention uses original attention_mask, and uses it only for the first step
+ input_attention_mask = attention_mask
+
+ hidden_states = decoder_layer(
+ hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=input_attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = self.norm(hidden_states)
+
+ return MoeModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+def load_balancing_loss_func(
+ gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
+ num_experts: Optional[int] = None,
+ top_k=2,
+ attention_mask: Optional[torch.Tensor] = None,
+) -> Union[torch.Tensor, int]:
+ r"""
+ Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+ See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
+ function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+ experts is too unbalanced.
+
+ Args:
+ gate_logits:
+ Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+ shape [batch_size X sequence_length, num_experts].
+ num_experts:
+ Number of experts
+ top_k:
+ The number of experts to route per-token, can be also interpreted as the `top-k` routing
+ parameter.
+ attention_mask (`torch.Tensor`, *optional*):
+ The attention_mask used in forward function
+ shape [batch_size X sequence_length] if not None.
+
+ Returns:
+ The auxiliary loss.
+ """
+ if gate_logits is None or not isinstance(gate_logits, tuple):
+ return 0
+
+ if isinstance(gate_logits, tuple):
+ compute_device = gate_logits[0].device
+ concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+ routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+ _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+ if attention_mask is None:
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.mean(routing_weights, dim=0)
+ else:
+ batch_size, sequence_length = attention_mask.shape
+ num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+ expert_attention_mask = (
+ attention_mask[None, :, :, None, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+ .reshape(-1, top_k, num_experts)
+ .to(compute_device)
+ )
+
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+ expert_attention_mask, dim=0
+ )
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+ router_per_expert_attention_mask = (
+ attention_mask[None, :, :, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
+ .reshape(-1, num_experts)
+ .to(compute_device)
+ )
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+ router_per_expert_attention_mask, dim=0
+ )
+
+ overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
+ return overall_loss * num_experts
+
+
+@auto_docstring
+class MiniMaxForCausalLM(MiniMaxPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+ _tp_plan = {"lm_head": "colwise_rep"}
+ _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = MiniMaxModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ self.router_aux_loss_coef = config.router_aux_loss_coef
+ self.num_experts = config.num_local_experts
+ self.num_experts_per_tok = config.num_experts_per_tok
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> MoeCausalLMOutputWithPast:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MiniMaxForCausalLM
+
+ >>> model = MiniMaxForCausalLM.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
+ >>> tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+
+ output_router_logits = (
+ output_router_logits if output_router_logits is not None else self.config.output_router_logits
+ )
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs: MoeModelOutputWithPast = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_router_logits=output_router_logits,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+ aux_loss = None
+ if output_router_logits:
+ aux_loss = load_balancing_loss_func(
+ outputs.router_logits,
+ self.num_experts,
+ self.num_experts_per_tok,
+ attention_mask,
+ )
+ if labels is not None:
+ loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
+
+ return MoeCausalLMOutputWithPast(
+ loss=loss,
+ aux_loss=aux_loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ router_logits=outputs.router_logits,
+ )
+
+
+class MiniMaxForSequenceClassification(GenericForSequenceClassification, MiniMaxPreTrainedModel):
+ pass
+
+
+class MiniMaxForTokenClassification(GenericForTokenClassification, MiniMaxPreTrainedModel):
+ pass
+
+
+class MiniMaxForQuestionAnswering(GenericForQuestionAnswering, MiniMaxPreTrainedModel):
+ pass
+
+
+__all__ = [
+ "MiniMaxPreTrainedModel",
+ "MiniMaxModel",
+ "MiniMaxForCausalLM",
+ "MiniMaxForSequenceClassification",
+ "MiniMaxForTokenClassification",
+ "MiniMaxForQuestionAnswering",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/modular_minimax.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/modular_minimax.py
new file mode 100644
index 0000000000000000000000000000000000000000..9582e5f392ac8ba0a50a3e21c171cae6e6606357
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/minimax/modular_minimax.py
@@ -0,0 +1,604 @@
+# coding=utf-8
+# Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MiniMax model."""
+
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...configuration_utils import layer_type_validation
+from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import MoeModelOutputWithPast
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, logging
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import OutputRecorder
+from ..mixtral.configuration_mixtral import MixtralConfig
+from ..mixtral.modeling_mixtral import (
+ MixtralAttention,
+ MixtralDecoderLayer,
+ MixtralForCausalLM,
+ MixtralForQuestionAnswering,
+ MixtralForSequenceClassification,
+ MixtralForTokenClassification,
+ MixtralModel,
+ MixtralPreTrainedModel,
+ MixtralRMSNorm,
+ MixtralSparseMoeBlock,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+class MiniMaxConfig(MixtralConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MiniMaxModel`]. It is used to instantiate an
+ MiniMax model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the MiniMax.
+
+ [MiniMaxAI/MiniMax-Text-01-hf](https://huggingface.co/MiniMaxAI/MiniMax-Text-01-hf)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the MiniMax model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MiniMaxModel`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 14336):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_key_value_heads (`int`, *optional*, defaults to 8):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
+ head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
+ The attention head dimension.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+ The maximum sequence length that this model might ever be used with. MiniMax's sliding window attention
+ allows sequence of up to 4096*32 tokens.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*):
+ The id of the padding token.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ The id of the "beginning-of-sequence" token.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ The id of the "end-of-sequence" token.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether the model's input and output word embeddings should be tied.
+ rope_theta (`float`, *optional*, defaults to 1000000.0):
+ The base period of the RoPE embeddings.
+ sliding_window (`int`, *optional*):
+ Sliding window attention window size. If not specified, will default to `4096`.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ num_experts_per_tok (`int`, *optional*, defaults to 2):
+ The number of experts to route per-token, can be also interpreted as the `top-k` routing
+ parameter
+ num_local_experts (`int`, *optional*, defaults to 8):
+ Number of experts per Sparse MLP layer.
+ output_router_logits (`bool`, *optional*, defaults to `False`):
+ Whether or not the router logits should be returned by the model. Enabeling this will also
+ allow the model to output the auxiliary loss. See [here]() for more details
+ router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+ The aux loss factor for the total loss.
+ router_jitter_noise (`float`, *optional*, defaults to 0.0):
+ Amount of noise to add to the router.
+ layer_types (`list`, *optional*):
+ Attention pattern for each layer.
+ block_size (`int`, *optional*, defaults to 256):
+ The length of each attention block, determining how queries, keys, and values
+ are grouped and processed for intra- and inter-block attention.
+ full_attn_alpha_factor (`float`, *optional*, defaults to 1):
+ Weight for residual value in residual connection after normal attention.
+ full_attn_beta_factor (`float`, *optional*, defaults to 1):
+ Weight for hidden state value in residual connection after normal attention.
+ linear_attn_alpha_factor (`float`, *optional*, defaults to 1):
+ Weight for residual value in residual connection after lightning attention.
+ linear_attn_beta_factor (`float`, *optional*, defaults to 1):
+ Weight for hidden state value in residual connection after lightning attention.
+ mlp_alpha_factor (`float`, *optional*, defaults to 1):
+ Weight for residual value in residual connection after MLP.
+ mlp_beta_factor (`float`, *optional*, defaults to 1):
+ Weight for hidden state value in residual connection after MLP.
+
+ ```python
+ >>> from transformers import MiniMaxModel, MiniMaxConfig
+
+ >>> # Initializing a MiniMax style configuration
+ >>> configuration = MiniMaxConfig()
+
+ >>> # Initializing a model from the MiniMax style configuration
+ >>> model = MiniMaxModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ def __init__(
+ self,
+ layer_types=None,
+ block_size=256,
+ full_attn_alpha_factor=1,
+ full_attn_beta_factor=1,
+ linear_attn_alpha_factor=1,
+ linear_attn_beta_factor=1,
+ mlp_alpha_factor=1,
+ mlp_beta_factor=1,
+ **super_kwargs,
+ ):
+ super().__init__(**super_kwargs)
+ self.layer_types = layer_types
+ self.block_size = block_size
+ self.full_attn_alpha_factor = full_attn_alpha_factor
+ self.full_attn_beta_factor = full_attn_beta_factor
+ self.linear_attn_alpha_factor = linear_attn_alpha_factor
+ self.linear_attn_beta_factor = linear_attn_beta_factor
+ self.mlp_alpha_factor = mlp_alpha_factor
+ self.mlp_beta_factor = mlp_beta_factor
+
+ if self.layer_types is None:
+ self.layer_types = [
+ "full_attention" if bool((i + 1) % 2) else "linear_attention" for i in range(self.num_hidden_layers)
+ ]
+ layer_type_validation(self.layer_types)
+
+
+class MiniMaxRMSNorm(MixtralRMSNorm):
+ pass
+
+
+class MiniMaxCache(DynamicCache):
+ def __init__(self):
+ super().__init__()
+ self.linear_cache: list[torch.Tensor] = []
+
+ def set_linear_cache(self, layer_idx, linear_cache):
+ # There may be skipped layers, fill them with empty lists
+ for _ in range(len(self.linear_cache), layer_idx + 1):
+ self.linear_cache.append([])
+ self.linear_cache[layer_idx] = linear_cache
+
+ def get_linear_cache(self, layer_idx: int):
+ if layer_idx < len(self):
+ return self.linear_cache[layer_idx]
+ return None
+
+ def __len__(self):
+ return max(super().__len__(), len(self.linear_cache))
+
+ def __getitem__(self, layer_idx: int):
+ if layer_idx < len(self.linear_cache) and self.linear_cache[layer_idx] != []:
+ return (self.linear_cache[layer_idx],)
+ return super().__getitem__(layer_idx)
+
+ def __iter__(self):
+ for layer_idx in range(len(self)):
+ yield self[layer_idx]
+
+ def batch_repeat_interleave(self, repeats: int):
+ for layer_idx in range(len(self)):
+ if self.linear_cache[layer_idx] != []:
+ self.linear_cache[layer_idx] = self.linear_cache[layer_idx].repeat_interleave(repeats, dim=0)
+ else:
+ self.layers[layer_idx].batch_repeat_interleave(repeats)
+
+ def batch_select_indices(self, indices: torch.Tensor):
+ for layer_idx in range(len(self)):
+ if self.linear_cache[layer_idx] != []:
+ self.linear_cache[layer_idx] = self.linear_cache[layer_idx][indices, ...]
+ else:
+ self.layers[layer_idx].batch_select_indices(indices)
+
+ def crop(self, max_length: int):
+ raise RuntimeError("MiniMaxCache doesnot support `crop` method")
+
+
+class MiniMaxLightningAttention(nn.Module):
+ def __init__(self, config: MiniMaxConfig, layer_idx: int):
+ super().__init__()
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+ self.num_attention_heads = config.num_attention_heads
+ self.num_hidden_layers = config.num_hidden_layers
+ self.block_size = config.block_size
+
+ self.act_fn = ACT2FN[config.hidden_act]
+ self.norm = MiniMaxRMSNorm(self.head_dim * self.num_attention_heads)
+ self.qkv_proj = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim * 3, bias=False)
+ self.out_proj = nn.Linear(self.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+ self.output_gate = nn.Linear(config.hidden_size, self.num_attention_heads * self.head_dim, bias=False)
+
+ slope_rate = self.get_slope_rate()
+ query_decay, key_decay, diagonal_decay = self.decay_factors(slope_rate)
+
+ self.register_buffer("slope_rate", slope_rate)
+ self.register_buffer("query_decay", query_decay)
+ self.register_buffer("key_decay", key_decay)
+ self.register_buffer("diagonal_decay", diagonal_decay)
+
+ def get_slope_rate(self):
+ base = 1 / (2 ** (8 / self.num_attention_heads))
+ exponent = torch.arange(self.num_attention_heads) + 1
+ factor = 1 - self.layer_idx / (self.num_hidden_layers - 1 + 1e-5) + 1e-5
+
+ rate = base**exponent
+ rate = rate * factor
+ rate = rate[:, None, None]
+
+ return rate
+
+ def decay_factors(self, slope_rate):
+ block_size_range = torch.arange(self.block_size) + 1
+
+ query_decay = torch.exp(-slope_rate * block_size_range[:, None])
+ key_decay = torch.exp(-slope_rate * (self.block_size - block_size_range[:, None]))
+
+ diagonal_decay = block_size_range[:, None] - block_size_range[None, :]
+ diagonal_decay = diagonal_decay[None, None, :, :]
+ diagonal_decay = slope_rate * diagonal_decay
+ diagonal_decay = torch.where(diagonal_decay >= 0, -diagonal_decay, float("-inf"))
+ diagonal_decay = torch.exp(diagonal_decay)
+
+ return query_decay, key_decay, diagonal_decay
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ batch_size, seq_len, hidden_size = hidden_states.shape
+ num_blocks = (seq_len + self.block_size - 1) // self.block_size
+
+ qkv_states = self.act_fn(self.qkv_proj(hidden_states))
+ qkv_states = qkv_states.reshape(batch_size, seq_len, self.num_attention_heads, 3 * self.head_dim)
+
+ query_states, key_states, value_states = torch.split(qkv_states, self.head_dim, dim=3)
+
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ # calculated (K.T @ V) and saved as cache
+ attn_weights_inter = None
+ if past_key_values is not None:
+ attn_weights_inter = past_key_values.get_linear_cache(self.layer_idx)
+
+ if attn_weights_inter is None:
+ attn_weights_inter = torch.zeros(batch_size, self.num_attention_heads, self.head_dim, self.head_dim).to(
+ value_states
+ )
+
+ # apply attention_mask
+ if attention_mask is not None:
+ attention_mask = attention_mask.to(dtype=torch.bool) # Ensure it's a boolean tensor
+ value_states = value_states.masked_fill(~attention_mask.unsqueeze(1).unsqueeze(-1), 0)
+
+ attn_output = []
+ for i in range(num_blocks):
+ start_idx = i * self.block_size
+ end_idx = min(start_idx + self.block_size, seq_len)
+ current_block_size = end_idx - start_idx
+
+ current_query_states = query_states[:, :, start_idx:end_idx]
+ current_key_states = key_states[:, :, start_idx:end_idx]
+ current_value_states = value_states[:, :, start_idx:end_idx]
+
+ current_query_decay = self.query_decay[:, :current_block_size]
+ current_key_decay = self.key_decay[:, -current_block_size:]
+ current_diagonal_decay = self.diagonal_decay[:, :, :current_block_size, :current_block_size]
+ block_decay = torch.exp(-self.slope_rate * current_block_size)
+
+ # intra: ( Q @ K.T ) @ V -> QK * V
+ attn_weights_intra = torch.matmul(current_query_states, current_key_states.transpose(-1, -2))
+ attn_output_intra = torch.matmul(attn_weights_intra * current_diagonal_decay, current_value_states)
+
+ # inter: Q @ ( K.T @ V ) -> Q * KV
+ attn_output_inter = torch.matmul(current_query_states * current_query_decay, attn_weights_inter)
+
+ # final attention output
+ current_attn_output = attn_output_inter + attn_output_intra
+ attn_output.append(current_attn_output)
+
+ # cacluate attn_weights_inter for next block or cache
+ next_attn_weights_inter = torch.matmul(
+ (current_key_states * current_key_decay).transpose(-1, -2), current_value_states
+ )
+ attn_weights_inter = attn_weights_inter * block_decay + next_attn_weights_inter
+
+ else:
+ ratio = torch.exp(-self.slope_rate)
+ attn_output = []
+ for i in range(seq_len):
+ current_query_states = query_states[:, :, i : i + 1]
+ current_key_states = key_states[:, :, i : i + 1]
+ current_value_states = value_states[:, :, i : i + 1]
+
+ current_attn_weights_inter = torch.matmul(current_key_states.transpose(-1, -2), current_value_states)
+ attn_weights_inter = ratio * attn_weights_inter + current_attn_weights_inter
+ current_attn_output = torch.matmul(current_query_states, attn_weights_inter)
+
+ attn_output.append(current_attn_output)
+
+ # concatenate attention outputs over all blocks
+ attn_output = torch.cat(attn_output, dim=-2)
+
+ # final output projection
+ attn_output = attn_output.transpose(1, 2)
+ attn_output = attn_output.reshape(batch_size, seq_len, self.num_attention_heads * self.head_dim)
+ attn_output = self.norm(attn_output)
+ attn_output = F.sigmoid(self.output_gate(hidden_states)) * attn_output
+ attn_output = self.out_proj(attn_output)
+
+ # update cache
+ if past_key_values is not None:
+ past_key_values.set_linear_cache(self.layer_idx, attn_weights_inter)
+
+ return attn_output, attn_weights_inter
+
+
+class MiniMaxAttention(MixtralAttention):
+ pass
+
+
+class MiniMaxSparseMoeBlock(MixtralSparseMoeBlock):
+ pass
+
+
+class MiniMaxDecoderLayer(MixtralDecoderLayer, GradientCheckpointingLayer):
+ def __init__(self, config: MiniMaxConfig, layer_idx: int):
+ super().__init__(config, layer_idx)
+
+ self.layer_idx = layer_idx
+ self.layer_type = config.layer_types[layer_idx]
+ self.mlp_alpha_factor = config.mlp_alpha_factor
+ self.mlp_beta_factor = config.mlp_beta_factor
+
+ if self.layer_type == "linear_attention":
+ self.self_attn = MiniMaxLightningAttention(config, layer_idx)
+ self.attn_alpha_factor = config.linear_attn_alpha_factor
+ self.attn_beta_factor = config.linear_attn_beta_factor
+ else:
+ self.self_attn = MiniMaxAttention(config, layer_idx)
+ self.attn_alpha_factor = config.full_attn_alpha_factor
+ self.attn_beta_factor = config.full_attn_beta_factor
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ output_router_logits: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`):
+ Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+ with `head_dim` being the embedding dimension of each attention head.
+ attention_mask (`torch.Tensor`, *optional*): attention mask of size
+ `(batch, sequence_length)` where padding elements are indicated by 0.
+ past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_router_logits (`bool`, *optional*):
+ Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+ should not be returned during inference.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ kwargs (`dict`, *optional*):
+ Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+ into the model
+ """
+
+ hidden_states = self.input_layernorm(hidden_states)
+ residual = hidden_states
+
+ # Self Attention
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = residual * self.attn_alpha_factor + hidden_states * self.attn_beta_factor
+
+ # Fully Connected
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ residual = hidden_states
+ hidden_states, _ = self.block_sparse_moe(hidden_states)
+ hidden_states = residual * self.mlp_alpha_factor + hidden_states * self.mlp_beta_factor
+
+ return hidden_states
+
+
+class MiniMaxPreTrainedModel(MixtralPreTrainedModel):
+ _can_compile_fullgraph = False
+ _can_record_outputs = {
+ "router_logits": OutputRecorder(MiniMaxSparseMoeBlock, index=1),
+ "hidden_states": MiniMaxDecoderLayer,
+ "attentions": [MiniMaxAttention, MiniMaxLightningAttention],
+ }
+
+
+class MiniMaxModel(MixtralModel):
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[MiniMaxCache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> MoeModelOutputWithPast:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if use_cache and past_key_values is None:
+ past_key_values = MiniMaxCache()
+ elif use_cache and not isinstance(past_key_values, MiniMaxCache):
+ raise ValueError(
+ f"MiniMax uses cache of its own and is not compatible with `past_key_values` of type {type(past_key_values)}."
+ )
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
+ causal_mask = mask_function(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+
+ # create position embeddings to be shared across the decoder layers
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ for decoder_layer in self.layers:
+ if decoder_layer.layer_type == "full_attention":
+ input_attention_mask = causal_mask
+ else:
+ # lightning attention uses original attention_mask, and uses it only for the first step
+ input_attention_mask = attention_mask
+
+ hidden_states = decoder_layer(
+ hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=input_attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = self.norm(hidden_states)
+
+ return MoeModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+class MiniMaxForCausalLM(MixtralForCausalLM):
+ def forward(self, **super_kwargs):
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MiniMaxForCausalLM
+
+ >>> model = MiniMaxForCausalLM.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
+ >>> tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-Text-01-hf")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ return super().forward(**super_kwargs)
+
+
+class MiniMaxForSequenceClassification(MixtralForSequenceClassification):
+ pass
+
+
+class MiniMaxForTokenClassification(MixtralForTokenClassification):
+ pass
+
+
+class MiniMaxForQuestionAnswering(MixtralForQuestionAnswering):
+ pass
+
+
+__all__ = [
+ "MiniMaxConfig",
+ "MiniMaxPreTrainedModel",
+ "MiniMaxModel",
+ "MiniMaxForCausalLM",
+ "MiniMaxForSequenceClassification",
+ "MiniMaxForTokenClassification",
+ "MiniMaxForQuestionAnswering",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..18a5657cd2ec6dbf39c6e794fae09bf65753da8f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2023 Mistral AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mistral import *
+ from .modeling_flax_mistral import *
+ from .modeling_mistral import *
+ from .modeling_tf_mistral import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/configuration_mistral.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/configuration_mistral.py
new file mode 100644
index 0000000000000000000000000000000000000000..893853a07c6d958d3ad770bdd486498fd37ba628
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/configuration_mistral.py
@@ -0,0 +1,169 @@
+# coding=utf-8
+# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Mistral model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MistralConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
+ Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
+
+ [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+ [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MistralModel`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 14336):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_key_value_heads (`int`, *optional*, defaults to 8):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
+ head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
+ The attention head dimension.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+ The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
+ allows sequence of up to 4096*32 tokens.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*):
+ The id of the padding token.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ The id of the "beginning-of-sequence" token.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ The id of the "end-of-sequence" token.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether the model's input and output word embeddings should be tied.
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ sliding_window (`int`, *optional*, defaults to 4096):
+ Sliding window attention window size. If not specified, will default to `4096`.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+
+ ```python
+ >>> from transformers import MistralModel, MistralConfig
+
+ >>> # Initializing a Mistral 7B style configuration
+ >>> configuration = MistralConfig()
+
+ >>> # Initializing a model from the Mistral 7B style configuration
+ >>> model = MistralModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mistral"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ # Default tensor parallel plan for base model `MistralModel`
+ base_model_tp_plan = {
+ "layers.*.self_attn.q_proj": "colwise",
+ "layers.*.self_attn.k_proj": "colwise",
+ "layers.*.self_attn.v_proj": "colwise",
+ "layers.*.self_attn.o_proj": "rowwise",
+ "layers.*.mlp.gate_proj": "colwise",
+ "layers.*.mlp.up_proj": "colwise",
+ "layers.*.mlp.down_proj": "rowwise",
+ }
+ base_model_pp_plan = {
+ "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+ "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+ "norm": (["hidden_states"], ["hidden_states"]),
+ }
+
+ def __init__(
+ self,
+ vocab_size=32000,
+ hidden_size=4096,
+ intermediate_size=14336,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=8,
+ head_dim=None,
+ hidden_act="silu",
+ max_position_embeddings=4096 * 32,
+ initializer_range=0.02,
+ rms_norm_eps=1e-6,
+ use_cache=True,
+ pad_token_id=None,
+ bos_token_id=1,
+ eos_token_id=2,
+ tie_word_embeddings=False,
+ rope_theta=10000.0,
+ sliding_window=4096,
+ attention_dropout=0.0,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.sliding_window = sliding_window
+ self.head_dim = head_dim
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.attention_dropout = attention_dropout
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+
+__all__ = ["MistralConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/convert_mistral_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/convert_mistral_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..a790fed81d1bdf33948a55305f7bfa0e8f5deae6
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/convert_mistral_weights_to_hf.py
@@ -0,0 +1,284 @@
+# Copyright 2023 Mistral AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import json
+import os
+import re
+
+import torch
+from safetensors.torch import load_file
+
+from transformers import AutoTokenizer, LlamaTokenizerFast, MistralConfig, MistralForCausalLM
+from transformers.integrations.mistral import convert_tekken_tokenizer
+
+
+# fmt: off
+STATE_DICT_MAPPING = {
+ # CausalLM keys
+ r"^output.weight": r"lm_head.weight",
+
+ # Model keys
+ r"^norm.weight": r"model.norm.weight",
+ r"^tok_embeddings.weight": r"model.embed_tokens.weight",
+
+ # Layers keys
+ r"^layers.(\d+).attention_norm.weight": r"model.layers.\1.input_layernorm.weight",
+ r"^layers.(\d+).ffn_norm.weight": r"model.layers.\1.post_attention_layernorm.weight",
+
+ # Attention keys
+ r"^layers.(\d+).attention.w(q|k|v|o).weight": r"model.layers.\1.self_attn.\2_proj.weight",
+
+
+ # MLP keys
+ r"^layers.(\d+).feed_forward.w1.weight": r"model.layers.\1.mlp.gate_proj.weight",
+ r"^layers.(\d+).feed_forward.w2.weight": r"model.layers.\1.mlp.down_proj.weight",
+ r"^layers.(\d+).feed_forward.w3.weight": r"model.layers.\1.mlp.up_proj.weight",
+}
+# fmt: on
+
+
+def map_old_key_to_new(old_key):
+ """Map of a key of the original state dict to the equivalent key in HF format"""
+ for pattern, replacement in STATE_DICT_MAPPING.items():
+ new_key, n_replace = re.subn(pattern, replacement, old_key)
+ # Early exit of the loop
+ if n_replace > 0:
+ return new_key
+
+ raise ValueError(f"Key: {old_key} could not be mapped (check the mapping).")
+
+
+def read_json(path):
+ with open(path, "r") as f:
+ return json.load(f)
+
+
+def permute_for_rope(tensor, n_heads, dim1, dim2):
+ """Permute the weights for the ROPE formulation."""
+ tensor = tensor.view(n_heads, dim1 // n_heads // 2, 2, dim2)
+ tensor = tensor.transpose(1, 2)
+ tensor = tensor.reshape(dim1, dim2)
+ return tensor
+
+
+def convert_state_dict(original_state_dict: dict, config: MistralConfig):
+ """Convert a state dict file, when a single `nn.Module` is never sharded in different files (usual case)."""
+ new_dict = {}
+
+ num_attention_heads = config.num_attention_heads
+ hidden_size = config.hidden_size
+ head_dim = config.head_dim
+ num_key_value_heads = config.num_key_value_heads
+ key_value_dim = head_dim * num_key_value_heads
+ query_dim = head_dim * num_attention_heads
+
+ for old_key, tensor in original_state_dict.items():
+ new_key = map_old_key_to_new(old_key)
+
+ if "q_proj" in new_key:
+ tensor = tensor.view(num_attention_heads, head_dim, hidden_size).reshape(query_dim, hidden_size)
+ tensor = permute_for_rope(tensor, num_attention_heads, query_dim, hidden_size)
+ elif "k_proj" in new_key:
+ tensor = tensor.view(num_key_value_heads, head_dim, hidden_size).reshape(key_value_dim, hidden_size)
+ tensor = permute_for_rope(tensor, num_key_value_heads, key_value_dim, hidden_size)
+ elif "v_proj" in new_key:
+ tensor = tensor.view(num_key_value_heads, head_dim, hidden_size).reshape(key_value_dim, hidden_size)
+
+ new_dict[new_key] = tensor
+ return new_dict
+
+
+def get_concat_dim(key):
+ """Return the dimension to concatenate the weights on."""
+ concat_dim_1 = [
+ r"model.embed_tokens.weight",
+ r"model.layers.(\d+).self_attn.o_proj.weight",
+ r"model.layers.(\d+).mlp.down_proj.weight",
+ ]
+ if any(re.search(pattern, key) for pattern in concat_dim_1):
+ return 1
+ return 0
+
+
+def convert_state_dict_sharded(loaded_shards: list[dict], config: MistralConfig):
+ """Convert the state dict, when a single `nn.Module` is sharded across different files."""
+ new_dict = {}
+
+ num_shards = len(loaded_shards)
+
+ n_heads = config.num_attention_heads
+ dim = config.hidden_size
+ dims_per_head = dim // n_heads
+ num_key_value_heads = config.num_key_value_heads
+ n_heads_per_shard = n_heads // num_shards
+ num_local_key_value_heads = num_key_value_heads // num_shards
+ key_value_dim = dim if n_heads == num_key_value_heads else dims_per_head * num_local_key_value_heads
+
+ original_keys = loaded_shards[0].keys()
+ for old_key in original_keys:
+ new_key = map_old_key_to_new(old_key)
+ cat_dim = get_concat_dim(new_key)
+
+ if "q_proj" in new_key:
+ tensor = torch.cat(
+ [shard.pop(old_key).view(n_heads_per_shard, dims_per_head, dim) for shard in loaded_shards],
+ dim=cat_dim,
+ ).reshape(dim, dim)
+ tensor = permute_for_rope(tensor, n_heads, dim, dim)
+ elif "k_proj" in new_key:
+ tensor = torch.cat(
+ [shard.pop(old_key).view(num_local_key_value_heads, dims_per_head, dim) for shard in loaded_shards],
+ dim=cat_dim,
+ ).reshape(key_value_dim, dim)
+ tensor = permute_for_rope(tensor, num_key_value_heads, key_value_dim, dim)
+ elif "v_proj" in new_key:
+ tensor = torch.cat(
+ [shard.pop(old_key).view(num_local_key_value_heads, dims_per_head, dim) for shard in loaded_shards],
+ dim=cat_dim,
+ ).reshape(key_value_dim, dim)
+ elif "input_layernorm" in new_key or "post_attention_layernorm" in new_key:
+ tensor = loaded_shards[0][old_key].clone()
+ elif "model.norm.weight" in new_key:
+ tensor = loaded_shards[0][old_key]
+ else:
+ tensor = torch.cat([shard.pop(old_key) for shard in loaded_shards], dim=cat_dim)
+
+ new_dict[new_key] = tensor
+
+ return new_dict
+
+
+def convert_config(original_config: dict, max_position_embeddings: int = 32768):
+ key_mapping = {
+ "hidden_size": "dim",
+ "num_hidden_layers": "n_layers",
+ "intermediate_size": "hidden_dim",
+ "num_attention_heads": "n_heads",
+ "rms_norm_eps": "norm_eps",
+ }
+ similar_keys_to_keep = [
+ "head_dim",
+ "vocab_size",
+ ]
+
+ new_config_kwargs = {k: original_config[v] for k, v in key_mapping.items()}
+ new_config_kwargs.update({k: v for k, v in original_config.items() if k in similar_keys_to_keep})
+
+ # These are not always defined depending on `params.json`
+ new_config_kwargs["sliding_window"] = original_config.get("sliding_window")
+ new_config_kwargs["num_key_value_heads"] = original_config.get(
+ "n_kv_heads", new_config_kwargs["num_attention_heads"]
+ )
+ new_config_kwargs["rope_theta"] = original_config.get("rope_theta", 10000.0)
+ new_config_kwargs["max_position_embeddings"] = original_config.get("max_seq_len", max_position_embeddings)
+
+ # This may sometimes be a string in `params.json`
+ if new_config_kwargs["sliding_window"] is not None:
+ new_config_kwargs["sliding_window"] = int(new_config_kwargs["sliding_window"])
+
+ new_config = MistralConfig(**new_config_kwargs)
+ return new_config
+
+
+def convert_and_write_model(input_dir: str, output_dir: str, max_position_embeddings: int, modules_are_split: bool):
+ """Convert the model and save it (this implicitly save the config as well)."""
+ params = read_json(os.path.join(input_dir, "params.json"))
+ config = convert_config(params, max_position_embeddings)
+
+ full_state_dict = {}
+ # The model may be split between different files, but a single nn.Module is always fully present in a single file
+ if not modules_are_split:
+ shards = [file for file in os.listdir(input_dir) if file.endswith(".safetensors")]
+ for shard_file in shards:
+ original_state_dict = load_file(os.path.join(input_dir, shard_file))
+ new_dict = convert_state_dict(original_state_dict, config)
+ full_state_dict.update(new_dict)
+ # A single nn.Module is split between different checkpoint files
+ else:
+ shards = [file for file in os.listdir(input_dir) if re.match(r"consolidated.\d+.pth", file)]
+ shards = sorted(shards, key=lambda x: int(x.split(".")[1]))
+ loaded_shards = [
+ torch.load(os.path.join(input_dir, file), map_location="cpu", weights_only=True) for file in shards
+ ]
+ full_state_dict = convert_state_dict_sharded(loaded_shards, config)
+
+ # Load weights into model and resave them
+ with torch.device("meta"):
+ model = MistralForCausalLM(config)
+ model.load_state_dict(full_state_dict, strict=True, assign=True)
+ model.save_pretrained(output_dir)
+
+
+def convert_and_write_tokenizer(input_dir: str, output_dir: str, tokenizer_template_name: str = ""):
+ """Convert the tokenizer and save it."""
+ # Tekken format
+ if "tekken.json" in os.listdir(input_dir):
+ tokenizer_file = os.path.join(input_dir, "tekken.json")
+ tokenizer = convert_tekken_tokenizer(tokenizer_file)
+ else:
+ # May have .v3 or .v7 at the end
+ tokenizer_file = [file for file in os.listdir(input_dir) if "tokenizer.model" in file][0]
+ tokenizer = LlamaTokenizerFast(os.path.join(input_dir, tokenizer_file))
+
+ # Load a chat template from another model
+ if tokenizer_template_name != "":
+ template_tok = AutoTokenizer.from_pretrained(tokenizer_template_name)
+ tokenizer.chat_template = template_tok.chat_template
+
+ # Finally save it
+ tokenizer.save_pretrained(output_dir)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "input_dir",
+ help="Location of Mistral weights, which contains tokenizer.model and model folders",
+ )
+ parser.add_argument(
+ "output_dir",
+ help="Location to write HF model and tokenizer",
+ )
+ parser.add_argument(
+ "--template_name",
+ type=str,
+ default="",
+ help="Another model name from which to copy the chat template.",
+ )
+ parser.add_argument(
+ "--max_position_embeddings",
+ type=int,
+ default=32768,
+ help="`max_position_embeddings` field in the config. This needs to be manually passed (not present anywhere otherwise).",
+ )
+ parser.add_argument(
+ "--modules_are_split",
+ action="store_true",
+ help="If passed, then the weights of a single `nn.Module` are assumed to be split between different files.",
+ )
+ parser.add_argument(
+ "--tokenizer_only",
+ action="store_true",
+ help="If passed, will only convert the tokenizer.",
+ )
+
+ args = parser.parse_args()
+
+ if not args.tokenizer_only:
+ convert_and_write_model(args.input_dir, args.output_dir, args.max_position_embeddings, args.modules_are_split)
+ convert_and_write_tokenizer(args.input_dir, args.output_dir, args.template_name)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modeling_flax_mistral.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modeling_flax_mistral.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b34a5144168627d644f560491d8f5ac1a832bde
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modeling_flax_mistral.py
@@ -0,0 +1,744 @@
+# coding=utf-8
+# Copyright 2024 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Flax Mistral model."""
+
+from typing import Optional
+
+import flax.linen as nn
+import jax
+import jax.numpy as jnp
+import numpy as np
+from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
+from flax.linen import combine_masks, make_causal_mask
+from flax.linen.attention import dot_product_attention_weights
+from flax.traverse_util import flatten_dict, unflatten_dict
+from jax import lax
+
+from ...modeling_flax_outputs import (
+ FlaxBaseModelOutput,
+ FlaxBaseModelOutputWithPast,
+ FlaxCausalLMOutput,
+ FlaxCausalLMOutputWithCrossAttentions,
+)
+from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, logging
+from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward
+from .configuration_mistral import MistralConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "MistralConfig"
+_REAL_CHECKPOINT_FOR_DOC = "mistralai/Mistral-7B-v0.1"
+_CHECKPOINT_FOR_DOC = "ksmcg/Mistral-tiny"
+
+MISTRAL_START_DOCSTRING = r"""
+
+ This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a Flax Linen
+ [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
+ regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
+
+ Finally, this model supports inherent JAX features such as:
+
+ - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
+ - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
+ - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
+ - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
+
+ Parameters:
+ config ([`MistralConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
+ dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
+ The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16`, or
+ `jax.numpy.bfloat16`.
+
+ This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
+ specified all the computation will be performed with the given `dtype`.
+
+ **Note that this only specifies the dtype of the computation and does not influence the dtype of model
+ parameters.**
+
+ If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
+ [`~FlaxPreTrainedModel.to_bf16`].
+"""
+
+MISTRAL_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`numpy.ndarray` of shape `(batch_size, input_ids_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`numpy.ndarray` of shape `(batch_size, input_ids_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
+ Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
+ auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+# Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaRMSNorm with Llama->Mistral
+class FlaxMistralRMSNorm(nn.Module):
+ config: MistralConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.epsilon = self.config.rms_norm_eps
+ self.weight = self.param("weight", lambda _, shape: jnp.ones(shape), self.config.hidden_size)
+
+ def __call__(self, hidden_states):
+ variance = jnp.asarray(hidden_states, dtype=jnp.float32)
+ variance = jnp.power(variance, 2)
+ variance = variance.mean(-1, keepdims=True)
+ # use `jax.numpy.sqrt` as `jax.lax.rsqrt` does not match `torch.rsqrt`
+ hidden_states = hidden_states / jnp.sqrt(variance + self.epsilon)
+
+ return self.weight * jnp.asarray(hidden_states, dtype=self.dtype)
+
+
+# Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaRotaryEmbedding with Llama->Mistral
+class FlaxMistralRotaryEmbedding(nn.Module):
+ config: MistralConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ head_dim = self.config.hidden_size // self.config.num_attention_heads
+ self.sincos = create_sinusoidal_positions(self.config.max_position_embeddings, head_dim)
+
+ def __call__(self, key, query, position_ids):
+ sincos = self.sincos[position_ids]
+ sin_pos, cos_pos = jnp.split(sincos, 2, axis=-1)
+
+ key = apply_rotary_pos_emb(key, sin_pos, cos_pos)
+ query = apply_rotary_pos_emb(query, sin_pos, cos_pos)
+
+ key = jnp.asarray(key, dtype=self.dtype)
+ query = jnp.asarray(query, dtype=self.dtype)
+
+ return key, query
+
+
+# Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaMLP with Llama->Mistral
+class FlaxMistralMLP(nn.Module):
+ config: MistralConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ embed_dim = self.config.hidden_size
+ inner_dim = self.config.intermediate_size if self.config.intermediate_size is not None else 4 * embed_dim
+
+ kernel_init = jax.nn.initializers.normal(self.config.initializer_range)
+ self.act = ACT2FN[self.config.hidden_act]
+
+ self.gate_proj = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init)
+ self.down_proj = nn.Dense(embed_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init)
+ self.up_proj = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init)
+
+ def __call__(self, hidden_states):
+ up_proj_states = self.up_proj(hidden_states)
+ gate_states = self.act(self.gate_proj(hidden_states))
+
+ hidden_states = self.down_proj(up_proj_states * gate_states)
+ return hidden_states
+
+
+# Copied from transformers.models.llama.modeling_flax_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(tensor, sin_pos, cos_pos):
+ return (tensor * cos_pos) + (rotate_half(tensor) * sin_pos)
+
+
+# Copied from transformers.models.llama.modeling_flax_llama.create_sinusoidal_positions
+def create_sinusoidal_positions(num_pos, dim):
+ inv_freq = 1.0 / (10000 ** (np.arange(0, dim, 2) / dim))
+ freqs = np.einsum("i , j -> i j", np.arange(num_pos), inv_freq).astype("float32")
+
+ emb = np.concatenate((freqs, freqs), axis=-1)
+ out = np.concatenate((np.sin(emb)[:, None, :], np.cos(emb)[:, None, :]), axis=-1)
+ return jnp.array(out[:, :, :num_pos])
+
+
+# Copied from transformers.models.llama.modeling_flax_llama.rotate_half
+def rotate_half(tensor):
+ """Rotates half the hidden dims of the input."""
+ rotate_half_tensor = jnp.concatenate(
+ (-tensor[..., tensor.shape[-1] // 2 :], tensor[..., : tensor.shape[-1] // 2]), axis=-1
+ )
+ return rotate_half_tensor
+
+
+class FlaxMistralAttention(nn.Module):
+ config: MistralConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ config = self.config
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.attention_softmax_in_fp32 = self.dtype is not jnp.float32
+ self.rope_theta = config.rope_theta
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+ self.q_proj = nn.Dense(self.num_heads * self.head_dim, use_bias=False, dtype=self.dtype)
+ self.k_proj = nn.Dense(self.num_key_value_heads * self.head_dim, use_bias=False, dtype=self.dtype)
+ self.v_proj = nn.Dense(self.num_key_value_heads * self.head_dim, use_bias=False, dtype=self.dtype)
+ self.o_proj = nn.Dense(self.hidden_size, use_bias=False, dtype=self.dtype)
+ casual_mask = make_causal_mask(jnp.ones((1, config.max_position_embeddings), dtype="bool"), dtype="bool")
+ self.causal_mask = jnp.triu(casual_mask, k=-(config.sliding_window or 0))
+ self.rotary_emb = FlaxMistralRotaryEmbedding(self.config, dtype=self.dtype)
+
+ def _split_heads(self, hidden_states, num_heads):
+ return hidden_states.reshape(hidden_states.shape[:2] + (num_heads, self.head_dim))
+
+ def _merge_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.hidden_size,))
+
+ @nn.compact
+ # Copied from transformers.models.gpt_neo.modeling_flax_gpt_neo.FlaxGPTNeoSelfAttention._concatenate_to_cache
+ def _concatenate_to_cache(self, key, value, query, attention_mask):
+ """
+ This function takes projected key, value states from a single input token and concatenates the states to cached
+ states from previous steps. This function is slightly adapted from the official Flax repository:
+ https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
+ """
+ # detect if we're initializing by absence of existing cache data.
+ is_initialized = self.has_variable("cache", "cached_key")
+ cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
+ cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
+ cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
+
+ if is_initialized:
+ *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
+ # update key, value caches with our new 1d spatial slices
+ cur_index = cache_index.value
+ indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
+ key = lax.dynamic_update_slice(cached_key.value, key, indices)
+ value = lax.dynamic_update_slice(cached_value.value, value, indices)
+ cached_key.value = key
+ cached_value.value = value
+ num_updated_cache_vectors = query.shape[1]
+ cache_index.value = cache_index.value + num_updated_cache_vectors
+ # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
+ pad_mask = jnp.broadcast_to(
+ jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
+ tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
+ )
+ attention_mask = combine_masks(pad_mask, attention_mask)
+ return key, value, attention_mask
+
+ def __call__(
+ self,
+ hidden_states: jnp.ndarray,
+ attention_mask: Optional[jnp.ndarray] = None,
+ position_ids: Optional[jnp.ndarray] = None,
+ deterministic: bool = True,
+ output_attentions: bool = False,
+ init_cache: bool = False,
+ ) -> tuple[jnp.ndarray, jnp.ndarray]:
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = self._split_heads(query_states, self.num_heads)
+ key_states = self._split_heads(key_states, self.num_key_value_heads)
+ value_states = self._split_heads(value_states, self.num_key_value_heads)
+
+ key_states, query_states = self.rotary_emb(key_states, query_states, position_ids)
+ query_length, key_length = query_states.shape[1], key_states.shape[1]
+ if self.has_variable("cache", "cached_key"):
+ mask_shift = self.variables["cache"]["cache_index"]
+ max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
+ causal_mask = lax.dynamic_slice(
+ self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
+ )
+ else:
+ causal_mask = self.causal_mask[:, :, :query_length, :key_length]
+
+ batch_size = hidden_states.shape[0]
+ causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
+ attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
+ attention_mask = combine_masks(attention_mask, causal_mask)
+
+ if self.has_variable("cache", "cached_key") or init_cache:
+ key_states, value_states, attention_mask = self._concatenate_to_cache(
+ key_states, value_states, query_states, attention_mask
+ )
+ key_states = jnp.repeat(key_states, self.num_key_value_groups, axis=2)
+ value_states = jnp.repeat(value_states, self.num_key_value_groups, axis=2)
+
+ attention_bias = lax.select(
+ attention_mask > 0,
+ jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
+ jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
+ )
+
+ # usual dot product attention
+ attention_dtype = jnp.float32 if self.attention_softmax_in_fp32 else self.dtype
+ attn_weights = dot_product_attention_weights(
+ query_states,
+ key_states,
+ bias=attention_bias,
+ deterministic=deterministic,
+ dropout_rate=self.config.attention_dropout,
+ dtype=attention_dtype,
+ )
+
+ if self.attention_softmax_in_fp32:
+ attn_weights = attn_weights.astype(self.dtype)
+
+ attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
+ attn_output = self._merge_heads(attn_output)
+ attn_output = self.o_proj(attn_output)
+
+ outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
+ return outputs
+
+
+# Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaDecoderLayer with Llama->Mistral
+class FlaxMistralDecoderLayer(nn.Module):
+ config: MistralConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.input_layernorm = FlaxMistralRMSNorm(self.config, dtype=self.dtype)
+ self.self_attn = FlaxMistralAttention(self.config, dtype=self.dtype)
+ self.post_attention_layernorm = FlaxMistralRMSNorm(self.config, dtype=self.dtype)
+ self.mlp = FlaxMistralMLP(self.config, dtype=self.dtype)
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_ids=None,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ ):
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+ outputs = self.self_attn(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ )
+ # residual connection
+ attn_output = outputs[0]
+ hidden_states = residual + attn_output
+
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ # residual connection
+ hidden_states = residual + hidden_states
+
+ return (hidden_states,) + outputs[1:]
+
+
+# Copied from transformers.models.gpt_neo.modeling_flax_gpt_neo.FlaxGPTNeoPreTrainedModel with GPTNeo->Mistral, GPT_NEO->MISTRAL, transformer->model
+class FlaxMistralPreTrainedModel(FlaxPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = MistralConfig
+ base_model_prefix = "model"
+ module_class: nn.Module = None
+
+ def __init__(
+ self,
+ config: MistralConfig,
+ input_shape: tuple = (1, 1),
+ seed: int = 0,
+ dtype: jnp.dtype = jnp.float32,
+ _do_init: bool = True,
+ **kwargs,
+ ):
+ module = self.module_class(config=config, dtype=dtype, **kwargs)
+ super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
+
+ def init_weights(self, rng: jax.random.PRNGKey, input_shape: tuple, params: FrozenDict = None) -> FrozenDict:
+ # init input tensors
+ input_ids = jnp.zeros(input_shape, dtype="i4")
+ attention_mask = jnp.ones_like(input_ids)
+ position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
+ params_rng, dropout_rng = jax.random.split(rng)
+ rngs = {"params": params_rng, "dropout": dropout_rng}
+
+ random_params = self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False)["params"]
+
+ if params is not None:
+ random_params = flatten_dict(unfreeze(random_params))
+ params = flatten_dict(unfreeze(params))
+ for missing_key in self._missing_keys:
+ params[missing_key] = random_params[missing_key]
+ self._missing_keys = set()
+ return freeze(unflatten_dict(params))
+ else:
+ return random_params
+
+ def init_cache(self, batch_size, max_length):
+ r"""
+ Args:
+ batch_size (`int`):
+ batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
+ max_length (`int`):
+ maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
+ cache.
+ """
+ # init input variables to retrieve cache
+ input_ids = jnp.ones((batch_size, max_length))
+ attention_mask = jnp.ones_like(input_ids)
+ position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
+
+ init_variables = self.module.init(
+ jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
+ )
+ return unfreeze(init_variables["cache"])
+
+ @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+ def __call__(
+ self,
+ input_ids,
+ attention_mask=None,
+ position_ids=None,
+ params: Optional[dict] = None,
+ past_key_values: Optional[dict] = None,
+ dropout_rng: jax.random.PRNGKey = None,
+ train: bool = False,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ batch_size, sequence_length = input_ids.shape
+
+ if position_ids is None:
+ if past_key_values is not None:
+ raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
+
+ position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+
+ if attention_mask is None:
+ attention_mask = jnp.ones((batch_size, sequence_length))
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ inputs = {"params": params or self.params}
+
+ # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be changed by FlaxMistralAttention module
+ if past_key_values:
+ inputs["cache"] = past_key_values
+ mutable = ["cache"]
+ else:
+ mutable = False
+
+ outputs = self.module.apply(
+ inputs,
+ jnp.array(input_ids, dtype="i4"),
+ jnp.array(attention_mask, dtype="i4"),
+ jnp.array(position_ids, dtype="i4"),
+ not train,
+ False,
+ output_attentions,
+ output_hidden_states,
+ return_dict,
+ rngs=rngs,
+ mutable=mutable,
+ )
+
+ # add updated cache to model output
+ if past_key_values is not None and return_dict:
+ outputs, past_key_values = outputs
+ outputs["past_key_values"] = unfreeze(past_key_values["cache"])
+ return outputs
+ elif past_key_values is not None and not return_dict:
+ outputs, past_key_values = outputs
+ outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
+
+ return outputs
+
+
+# Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaLayerCollection with Llama->Mistral
+class FlaxMistralLayerCollection(nn.Module):
+ config: MistralConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.blocks = [
+ FlaxMistralDecoderLayer(self.config, dtype=self.dtype, name=str(i))
+ for i in range(self.config.num_hidden_layers)
+ ]
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_ids=None,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = False,
+ ):
+ all_attentions = () if output_attentions else None
+ all_hidden_states = () if output_hidden_states else None
+
+ for block in self.blocks:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ layer_outputs = block(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ )
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions += (layer_outputs[1],)
+
+ # this contains possible `None` values - `FlaxMistralModule` will filter them out
+ outputs = (hidden_states, all_hidden_states, all_attentions)
+
+ return outputs
+
+
+# Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaModule with Llama->Mistral
+class FlaxMistralModule(nn.Module):
+ config: MistralConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.hidden_size = self.config.hidden_size
+ embedding_init = jax.nn.initializers.normal(stddev=self.config.initializer_range)
+ self.embed_tokens = nn.Embed(
+ self.config.vocab_size,
+ self.hidden_size,
+ embedding_init=embedding_init,
+ dtype=self.dtype,
+ )
+ self.layers = FlaxMistralLayerCollection(self.config, dtype=self.dtype)
+ self.norm = FlaxMistralRMSNorm(self.config, dtype=self.dtype)
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask=None,
+ position_ids=None,
+ deterministic=True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ):
+ input_embeds = self.embed_tokens(input_ids.astype("i4"))
+
+ outputs = self.layers(
+ input_embeds,
+ position_ids=position_ids,
+ attention_mask=attention_mask,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ hidden_states = self.norm(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = outputs[1] + (hidden_states,)
+ outputs = (hidden_states, all_hidden_states) + outputs[2:]
+ else:
+ outputs = (hidden_states,) + outputs[1:]
+
+ if not return_dict:
+ return tuple(v for v in outputs if v is not None)
+
+ return FlaxBaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=outputs[1],
+ attentions=outputs[-1],
+ )
+
+
+@add_start_docstrings(
+ "The bare Mistral Model transformer outputting raw hidden-states without any specific head on top.",
+ MISTRAL_START_DOCSTRING,
+)
+class FlaxMistralModel(FlaxMistralPreTrainedModel):
+ module_class = FlaxMistralModule
+
+
+append_call_sample_docstring(
+ FlaxMistralModel,
+ _CHECKPOINT_FOR_DOC,
+ FlaxBaseModelOutputWithPast,
+ _CONFIG_FOR_DOC,
+ real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
+)
+
+
+# Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaForCausalLMModule with Llama->Mistral
+class FlaxMistralForCausalLMModule(nn.Module):
+ config: MistralConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.model = FlaxMistralModule(self.config, dtype=self.dtype)
+ self.lm_head = nn.Dense(
+ self.config.vocab_size,
+ use_bias=False,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
+ )
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask=None,
+ position_ids=None,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ):
+ outputs = self.model(
+ input_ids,
+ position_ids=position_ids,
+ attention_mask=attention_mask,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ lm_logits = self.lm_head(hidden_states)
+
+ if not return_dict:
+ return (lm_logits,) + outputs[1:]
+
+ return FlaxCausalLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
+
+
+@add_start_docstrings(
+ """
+ The Mistral Model transformer with a language modeling head (linear layer) on top.
+ """,
+ MISTRAL_START_DOCSTRING,
+)
+
+# Copied from transformers.models.gptj.modeling_flax_gptj.FlaxGPTJForCausalLM with GPTJ->Mistral
+class FlaxMistralForCausalLM(FlaxMistralPreTrainedModel):
+ module_class = FlaxMistralForCausalLMModule
+
+ def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
+ # initializing the cache
+ batch_size, seq_length = input_ids.shape
+
+ past_key_values = self.init_cache(batch_size, max_length)
+ # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
+ # But since Mistral uses a causal mask, those positions are masked anyways.
+ # Thus we can create a single static attention_mask here, which is more efficient for compilation
+ extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
+ if attention_mask is not None:
+ position_ids = attention_mask.cumsum(axis=-1) - 1
+ extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
+ else:
+ position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
+
+ return {
+ "past_key_values": past_key_values,
+ "attention_mask": extended_attention_mask,
+ "position_ids": position_ids,
+ }
+
+ def update_inputs_for_generation(self, model_outputs, model_kwargs):
+ model_kwargs["past_key_values"] = model_outputs.past_key_values
+ model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
+ return model_kwargs
+
+
+append_call_sample_docstring(
+ FlaxMistralForCausalLM,
+ _CHECKPOINT_FOR_DOC,
+ FlaxCausalLMOutputWithCrossAttentions,
+ _CONFIG_FOR_DOC,
+ real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
+)
+
+__all__ = ["FlaxMistralForCausalLM", "FlaxMistralModel", "FlaxMistralPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modeling_mistral.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modeling_mistral.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7e6ee8c628f8f69f0de60ebe4c50c6060dc4b56
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modeling_mistral.py
@@ -0,0 +1,486 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/mistral/modular_mistral.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_mistral.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+from typing import Callable, Optional, Union
+
+import torch
+from torch import nn
+
+from transformers.utils.generic import check_model_inputs
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import (
+ GenericForQuestionAnswering,
+ GenericForSequenceClassification,
+ GenericForTokenClassification,
+ GradientCheckpointingLayer,
+)
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_mistral import MistralConfig
+
+
+class MistralMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+ return down_proj
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class MistralAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: MistralConfig, layer_idx: int):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = config.attention_dropout
+ self.is_causal = True
+ self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
+ self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ sliding_window=getattr(self.config, "sliding_window", None), # main diff with Llama
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class MistralRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ MistralRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class MistralDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MistralConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.self_attn = MistralAttention(config=config, layer_idx=layer_idx)
+ self.mlp = MistralMLP(config)
+ self.input_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> torch.Tensor:
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+ # Self Attention
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+ return hidden_states
+
+
+@auto_docstring
+class MistralPreTrainedModel(PreTrainedModel):
+ config: MistralConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MistralDecoderLayer"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ _can_compile_fullgraph = True
+ _supports_attention_backend = True
+ _can_record_outputs = {
+ "hidden_states": MistralDecoderLayer,
+ "attentions": MistralAttention,
+ }
+
+
+class MistralRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: MistralConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+@auto_docstring
+class MistralModel(MistralPreTrainedModel):
+ def __init__(self, config: MistralConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [MistralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.rotary_emb = MistralRotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> BaseModelOutputWithPast:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache(config=self.config)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
+ causal_mask = mask_function(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ hidden_states = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = self.norm(hidden_states)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values if use_cache else None,
+ )
+
+
+@auto_docstring
+class MistralForCausalLM(MistralPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+ _tp_plan = {"lm_head": "colwise_rep"}
+ _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = MistralModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> CausalLMOutputWithPast:
+ r"""
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MistralForCausalLM
+
+ >>> model = MistralForCausalLM.from_pretrained("meta-mistral/Mistral-2-7b-hf")
+ >>> tokenizer = AutoTokenizer.from_pretrained("meta-mistral/Mistral-2-7b-hf")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ outputs: BaseModelOutputWithPast = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+class MistralForTokenClassification(GenericForTokenClassification, MistralPreTrainedModel):
+ pass
+
+
+class MistralForSequenceClassification(GenericForSequenceClassification, MistralPreTrainedModel):
+ pass
+
+
+class MistralForQuestionAnswering(GenericForQuestionAnswering, MistralPreTrainedModel): ...
+
+
+__all__ = [
+ "MistralForCausalLM",
+ "MistralForQuestionAnswering",
+ "MistralModel",
+ "MistralPreTrainedModel",
+ "MistralForSequenceClassification",
+ "MistralForTokenClassification",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modeling_tf_mistral.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modeling_tf_mistral.py
new file mode 100644
index 0000000000000000000000000000000000000000..30ca8d5769be56fdb733270f24b8e40f79921e83
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modeling_tf_mistral.py
@@ -0,0 +1,1022 @@
+# coding=utf-8
+# Copyright 2024 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TF 2.0 Mistral model."""
+
+import math
+import warnings
+from typing import Optional, Union
+
+import tensorflow as tf
+
+from ...modeling_tf_outputs import (
+ TFBaseModelOutputWithPast,
+ TFCausalLMOutputWithPast,
+ TFSequenceClassifierOutputWithPast,
+)
+from ...modeling_tf_utils import (
+ TFCausalLanguageModelingLoss,
+ TFPreTrainedModel,
+ TFSequenceClassificationLoss,
+ get_initializer,
+ get_tf_activation,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
+from ...utils import (
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+)
+from .configuration_mistral import MistralConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "MistralConfig"
+
+
+def _make_causal_mask(input_ids_shape, dtype, past_key_values_length=0):
+ """
+ Make causal mask used for bi-directional self-attention, supporting both static and dynamic shapes.
+ """
+ bsz, tgt_len = input_ids_shape
+
+ # Create a matrix where only the lower triangle and diagonal are filled with zeros (causal mask)
+ mask = tf.fill((tgt_len, tgt_len), tf.dtypes.as_dtype(dtype).min)
+ mask_cond = tf.range(tgt_len)
+ mask = tf.where(mask_cond[:, None] >= mask_cond[None, :], 0.0, mask)
+
+ if past_key_values_length > 0:
+ mask = tf.concat([tf.zeros((tgt_len, past_key_values_length), dtype=dtype), mask], axis=-1)
+
+ if bsz is None:
+ # When batch size is dynamic, expand and tile
+ # so we can compile a functional model
+ mask = tf.expand_dims(mask, 0)
+ mask = tf.expand_dims(mask, 0) # shape: (1, 1, tgt_len, tgt_len + past_key_values_length)
+ mask = tf.tile(mask, [bsz, 1, 1, 1])
+ else:
+ # When batch size is static, directly use broadcast_to
+ mask = tf.broadcast_to(mask[None, None, :, :], (bsz, 1, tgt_len, tgt_len + past_key_values_length))
+
+ return mask
+
+
+def _expand_mask(mask, dtype, tgt_len=None):
+ """
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+ """
+ bsz, src_len = shape_list(mask)
+ tgt_len = tgt_len if tgt_len is not None else src_len
+
+ expanded_mask = tf.expand_dims(tf.expand_dims(mask, 1), 1)
+ expanded_mask = tf.broadcast_to(expanded_mask, [bsz, 1, tgt_len, src_len])
+
+ inverted_mask = 1.0 - tf.cast(expanded_mask, dtype)
+
+ return tf.where(
+ tf.cast(inverted_mask, bool), tf.fill(dims=shape_list(inverted_mask), value=tf.float32.min), inverted_mask
+ )
+
+
+class TFMistralRMSNorm(keras.layers.Layer):
+ def __init__(self, hidden_size, eps=1e-6, **kwargs):
+ """
+ TFMistralRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__(**kwargs)
+ self.hidden_size = hidden_size
+ self.variance_epsilon = eps
+
+ def build(self, input_shape=None):
+ self.weight = self.add_weight(
+ name="weight",
+ shape=self.hidden_size,
+ initializer="ones",
+ )
+ if self.built:
+ return
+ self.built = True
+
+ def call(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = tf.cast(hidden_states, tf.float32)
+ variance = tf.reduce_mean(tf.square(hidden_states), axis=-1, keepdims=True)
+ hidden_states = tf.divide(hidden_states, tf.sqrt(variance + self.variance_epsilon))
+ return self.weight * tf.cast(hidden_states, input_dtype)
+
+
+# Verification: https://colab.research.google.com/gist/ariG23498/f8d8131b795a131b93d99e70ee93c192/scratchpad.ipynb
+class TFMistralRotaryEmbedding(keras.layers.Layer):
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, **kwargs):
+ super().__init__(**kwargs)
+ self.dim = dim
+ self.max_position_embeddings = max_position_embeddings
+ self.base = base
+ self.inv_freq = 1.0 / (self.base ** (tf.range(start=0, limit=self.dim, delta=2, dtype=tf.float32) / self.dim))
+
+ def call(self, x, seq_len=None):
+ # x: [bs, num_attention_heads, seq_len, head_size]
+ t = tf.cast(tf.range(seq_len, dtype=tf.int64), self.inv_freq.dtype)
+ freqs = tf.einsum("i,j->ij", t, self.inv_freq)
+ emb = tf.concat([freqs, freqs], axis=-1)
+ cos_values = tf.cast(tf.cos(emb), x.dtype)
+ sin_values = tf.cast(tf.sin(emb), x.dtype)
+
+ cos_values = cos_values[:seq_len]
+ cos_values = tf.cast(cos_values, dtype=x.dtype)
+ sin_values = sin_values[:seq_len]
+ sin_values = tf.cast(sin_values, dtype=x.dtype)
+ return (cos_values, sin_values)
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ mid_length = shape_list(x)[-1] // 2
+ x1 = x[..., :mid_length]
+ x2 = x[..., mid_length:]
+ return tf.concat([-x2, x1], axis=-1)
+
+
+# Verification: https://colab.research.google.com/gist/ariG23498/bb8474baeb33f4ae6ed7d77da5f7e7a4/scratchpad.ipynb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`tf.Tensor`): The query tensor.
+ k (`tf.Tensor`): The key tensor.
+ cos (`tf.Tensor`): The cosine part of the rotary embedding.
+ sin (`tf.Tensor`): The sine part of the rotary embedding.
+ position_ids (`tf.Tensor`):
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+ used to pass offsetted position ids when working with a KV-cache.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(tf.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = tf.expand_dims(tf.gather(cos, position_ids), unsqueeze_dim)
+ sin = tf.expand_dims(tf.gather(sin, position_ids), unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class TFMistralMLP(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = keras.layers.Dense(self.intermediate_size, use_bias=False, name="gate_proj")
+ self.up_proj = keras.layers.Dense(self.intermediate_size, use_bias=False, name="up_proj")
+ self.down_proj = keras.layers.Dense(self.hidden_size, use_bias=False, name="down_proj")
+ self.act_fn = get_tf_activation(config.hidden_act)
+
+ def call(self, x):
+ return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "gate_proj", None) is not None:
+ with tf.name_scope(self.gate_proj.name):
+ self.gate_proj.build((self.hidden_size,))
+ if getattr(self, "up_proj", None) is not None:
+ with tf.name_scope(self.up_proj.name):
+ self.up_proj.build((self.hidden_size,))
+ if getattr(self, "down_proj", None) is not None:
+ with tf.name_scope(self.down_proj.name):
+ self.down_proj.build((self.intermediate_size,))
+
+
+# Verification: https://colab.research.google.com/gist/ariG23498/556d443d491966763ce2e7eee336efed/scratchpad.ipynb
+def repeat_kv(hidden_states: tf.Tensor, n_rep: int) -> tf.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = shape_list(hidden_states)
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = tf.expand_dims(hidden_states, 2)
+ hidden_states = tf.repeat(hidden_states, repeats=n_rep, axis=2)
+ return tf.reshape(hidden_states, (batch, num_key_value_heads * n_rep, slen, head_dim))
+
+
+class TFMistralAttention(keras.layers.Layer):
+ """
+ Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+ and "Generating Long Sequences with Sparse Transformers".
+ """
+
+ def __init__(self, config: MistralConfig, layer_idx: Optional[int] = None, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.rope_theta = config.rope_theta
+ self.is_causal = True
+ self.attention_dropout = config.attention_dropout
+
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+ self.q_proj = keras.layers.Dense(self.num_heads * self.head_dim, use_bias=False, name="q_proj")
+ self.k_proj = keras.layers.Dense(self.num_key_value_heads * self.head_dim, use_bias=False, name="k_proj")
+ self.v_proj = keras.layers.Dense(self.num_key_value_heads * self.head_dim, use_bias=False, name="v_proj")
+ self.o_proj = keras.layers.Dense(self.hidden_size, use_bias=False, name="o_proj")
+
+ self.rotary_emb = TFMistralRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ base=self.rope_theta,
+ name="rotary_emb",
+ )
+ self.dropout = keras.layers.Dropout(rate=self.attention_dropout)
+
+ def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
+ tensor = tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim))
+ tensor = tf.transpose(tensor, perm=(0, 2, 1, 3))
+ return tensor
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: Optional[tf.Tensor] = None,
+ position_ids: Optional[tf.Tensor] = None,
+ past_key_value: Optional[tuple[tf.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ training=None,
+ **kwargs,
+ ) -> tuple[tf.Tensor, Optional[tf.Tensor], Optional[tuple[tf.Tensor]]]:
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+ bsz, q_len, _ = shape_list(hidden_states)
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = tf.transpose(
+ tf.reshape(query_states, (bsz, q_len, self.num_heads, self.head_dim)), perm=(0, 2, 1, 3)
+ )
+ key_states = tf.transpose(
+ tf.reshape(key_states, (bsz, q_len, self.num_key_value_heads, self.head_dim)), perm=(0, 2, 1, 3)
+ )
+ value_states = tf.transpose(
+ tf.reshape(value_states, (bsz, q_len, self.num_key_value_heads, self.head_dim)), perm=(0, 2, 1, 3)
+ )
+
+ kv_seq_len = shape_list(key_states)[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value[0].shape[-2]
+ cos, sin = self.rotary_emb(
+ x=value_states,
+ seq_len=kv_seq_len,
+ )
+ query_states, key_states = apply_rotary_pos_emb(
+ q=query_states,
+ k=key_states,
+ cos=cos,
+ sin=sin,
+ position_ids=position_ids,
+ )
+
+ if past_key_value is not None:
+ # reuse k, v, self_attention
+ key_states = tf.concat([past_key_value[0], key_states], axis=2)
+ value_states = tf.concat([past_key_value[1], value_states], axis=2)
+
+ past_key_value = (key_states, value_states) if use_cache else None
+
+ # repeat k/v heads if n_kv_heads < n_heads
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = tf.matmul(query_states, key_states, transpose_b=True) / math.sqrt(self.head_dim)
+
+ if attention_mask is not None:
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = stable_softmax(attn_weights, axis=-1)
+ attn_weights = tf.cast(attn_weights, query_states.dtype)
+ attn_weights = self.dropout(
+ attn_weights,
+ training=training,
+ )
+ attn_output = tf.matmul(attn_weights, value_states)
+
+ attn_output = tf.transpose(attn_output, perm=(0, 2, 1, 3))
+ attn_output = tf.reshape(attn_output, (bsz, q_len, self.hidden_size))
+
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "q_proj", None) is not None:
+ with tf.name_scope(self.q_proj.name):
+ self.q_proj.build((self.hidden_size,))
+ if getattr(self, "k_proj", None) is not None:
+ with tf.name_scope(self.k_proj.name):
+ self.k_proj.build((self.hidden_size,))
+ if getattr(self, "v_proj", None) is not None:
+ with tf.name_scope(self.v_proj.name):
+ self.v_proj.build((self.hidden_size,))
+ if getattr(self, "o_proj", None) is not None:
+ with tf.name_scope(self.o_proj.name):
+ self.o_proj.build((self.num_heads * self.head_dim,))
+
+
+class TFMistralDecoderLayer(keras.layers.Layer):
+ def __init__(self, config: MistralConfig, layer_idx: int, **kwargs):
+ super().__init__(**kwargs)
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = TFMistralAttention(config, layer_idx, name="self_attn")
+
+ self.mlp = TFMistralMLP(config, name="mlp")
+ self.input_layernorm = TFMistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps, name="input_layernorm")
+ self.post_attention_layernorm = TFMistralRMSNorm(
+ config.hidden_size, eps=config.rms_norm_eps, name="post_attention_layernorm"
+ )
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: Optional[tf.Tensor] = None,
+ position_ids: Optional[tf.Tensor] = None,
+ past_key_value: Optional[tuple[tf.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ **kwargs,
+ ) -> tuple[tf.Tensor, Optional[tuple[tf.Tensor, tf.Tensor]]]:
+ """
+ Args:
+ hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`tf.Tensor`, *optional*): attention mask of size
+ `(batch, sequence_length)` where padding elements are indicated by 0.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_value (`Tuple(tf.Tensor)`, *optional*): cached past key and value projection states
+ """
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self_attn", None) is not None:
+ with tf.name_scope(self.self_attn.name):
+ self.self_attn.build(None)
+ if getattr(self, "mlp", None) is not None:
+ with tf.name_scope(self.mlp.name):
+ self.mlp.build(None)
+ if getattr(self, "input_layernorm", None) is not None:
+ with tf.name_scope(self.input_layernorm.name):
+ self.input_layernorm.build(None)
+ if getattr(self, "post_attention_layernorm", None) is not None:
+ with tf.name_scope(self.post_attention_layernorm.name):
+ self.post_attention_layernorm.build(None)
+
+
+@keras_serializable
+class TFMistralMainLayer(keras.layers.Layer):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]
+
+ Args:
+ config: MistralConfig
+ """
+
+ config_class = MistralConfig
+
+ def __init__(self, config: MistralConfig, **kwargs):
+ super().__init__(**kwargs)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+ self.hidden_size = config.hidden_size
+
+ # TF and PT Embedding check: https://colab.research.google.com/gist/ariG23498/2b9826818875c9c4968c79cb19f55f2c/scratchpad.ipynb
+ self.embed_tokens = keras.layers.Embedding(
+ input_dim=config.vocab_size,
+ output_dim=config.hidden_size,
+ name="embed_tokens",
+ )
+ self.layers = [
+ TFMistralDecoderLayer(config, layer_idx, name=f"layers.{layer_idx}")
+ for layer_idx in range(config.num_hidden_layers)
+ ]
+ self._attn_implementation = config._attn_implementation
+ self.norm = TFMistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps, name="norm")
+ self.config = config
+
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+ # create causal mask
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ combined_attention_mask = None
+ # if input_shape[-1] > 1:
+ combined_attention_mask = _make_causal_mask(
+ input_shape,
+ inputs_embeds.dtype,
+ past_key_values_length=past_key_values_length,
+ )
+
+ if attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
+ combined_attention_mask = (
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+ )
+
+ return combined_attention_mask
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: Optional[tf.Tensor] = None,
+ attention_mask: Optional[tf.Tensor] = None,
+ position_ids: Optional[tf.Tensor] = None,
+ past_key_values: Optional[list[tf.Tensor]] = None,
+ inputs_embeds: Optional[tf.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, TFBaseModelOutputWithPast]:
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ batch_size, seq_length = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ batch_size, seq_length, _ = shape_list(inputs_embeds)
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ seq_length_with_past = seq_length
+ past_key_values_length = 0
+
+ if past_key_values is not None:
+ past_key_values_length = shape_list(past_key_values[0][0])[2]
+ seq_length_with_past = seq_length_with_past + past_key_values_length
+
+ if position_ids is None:
+ position_ids = tf.range(
+ start=past_key_values_length, limit=seq_length + past_key_values_length, dtype=tf.int64
+ )
+ position_ids = tf.reshape(tf.expand_dims(position_ids, 0), (-1, seq_length))
+
+ else:
+ position_ids = tf.cast(tf.reshape(position_ids, (-1, seq_length)), tf.int64)
+
+ if inputs_embeds is None:
+ check_embeddings_within_bounds(input_ids, self.config.vocab_size)
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if attention_mask is None:
+ attention_mask = tf.ones((batch_size, seq_length_with_past), dtype=tf.bool)
+ attention_mask = self._prepare_decoder_attention_mask(
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+ )
+
+ hidden_states = inputs_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ next_decoder_cache = () if use_cache else None
+
+ for idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ next_cache = next_decoder_cache if use_cache else None
+ if not return_dict:
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+ return TFBaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embed_tokens", None) is not None:
+ with tf.name_scope(self.embed_tokens.name):
+ self.embed_tokens.build(None)
+ if getattr(self, "norm", None) is not None:
+ with tf.name_scope(self.norm.name):
+ self.norm.build(None)
+ if getattr(self, "layers", None) is not None:
+ for layer in self.layers:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+MISTRAL_START_DOCSTRING = r"""
+
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `model` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Parameters:
+ config ([`MistralConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
+ MISTRAL_START_DOCSTRING,
+)
+class TFMistralPreTrainedModel(TFPreTrainedModel):
+ config_class = MistralConfig
+ base_model_prefix = "model"
+
+
+MISTRAL_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`Cache` or `tuple(tuple(tf.Tensor))`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ One formats is allowed:
+ - Tuple of `tuple(tf.Tensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+ cache format.
+
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+ legacy cache format will be returned.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+ of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
+ MISTRAL_START_DOCSTRING,
+)
+class TFMistralModel(TFMistralPreTrainedModel):
+ def __init__(self, config: MistralConfig, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.model = TFMistralMainLayer(config, name="model")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+ def call(
+ self,
+ input_ids: Optional[tf.Tensor] = None,
+ attention_mask: Optional[tf.Tensor] = None,
+ position_ids: Optional[tf.Tensor] = None,
+ past_key_values: Optional[list[tf.Tensor]] = None,
+ inputs_embeds: Optional[tf.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, TFBaseModelOutputWithPast]:
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "model", None) is not None:
+ with tf.name_scope(self.model.name):
+ self.model.build(None)
+
+
+class TFMistralForCausalLM(TFMistralPreTrainedModel, TFCausalLanguageModelingLoss):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.model = TFMistralMainLayer(config, name="model")
+ self.vocab_size = config.vocab_size
+ self.lm_head = keras.layers.Dense(
+ config.vocab_size,
+ use_bias=False,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="lm_head",
+ )
+ self.config = config
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ def call(
+ self,
+ input_ids: Optional[tf.Tensor] = None,
+ attention_mask: Optional[tf.Tensor] = None,
+ position_ids: Optional[tf.Tensor] = None,
+ past_key_values: Optional[list[tf.Tensor]] = None,
+ inputs_embeds: Optional[tf.Tensor] = None,
+ labels: Optional[tf.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, TFCausalLMOutputWithPast]:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]`
+ or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ """
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ logits = self.lm_head(hidden_states)
+ logits = tf.cast(logits, tf.float32)
+
+ loss = None
+ if labels is not None:
+ # shift labels to the left and cut last logit token
+ shifted_logits = logits[:, :-1]
+ labels = labels[:, 1:]
+ loss = self.hf_compute_loss(labels, shifted_logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+ ):
+ # Omit tokens covered by past_key_values
+ if past_key_values:
+ input_ids = tf.expand_dims(input_ids[:, -1], -1)
+
+ position_ids = kwargs.get("position_ids")
+ if attention_mask is not None and position_ids is None:
+ position_ids = tf.math.cumsum(attention_mask, axis=-1, exclusive=True)
+ if past_key_values:
+ position_ids = tf.expand_dims(position_ids[:, -1], -1)
+
+ return {
+ "input_ids": input_ids,
+ "attention_mask": attention_mask,
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ }
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "model", None) is not None:
+ with tf.name_scope(self.model.name):
+ self.model.build(None)
+ if getattr(self, "lm_head", None) is not None:
+ with tf.name_scope(self.lm_head.name):
+ self.lm_head.build((self.config.hidden_size,))
+
+
+@add_start_docstrings(
+ """
+ The Mistral Model transformer with a sequence classification head on top (linear layer).
+
+ [`MistralForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """,
+ MISTRAL_START_DOCSTRING,
+)
+class TFMistralForSequenceClassification(TFMistralPreTrainedModel, TFSequenceClassificationLoss):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.num_labels = config.num_labels
+ self.model = TFMistralMainLayer(config, name="model")
+ self.score = keras.layers.Dense(
+ self.num_labels,
+ use_bias=False,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="score",
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ def call(
+ self,
+ input_ids: Optional[tf.Tensor] = None,
+ attention_mask: Optional[tf.Tensor] = None,
+ position_ids: Optional[tf.Tensor] = None,
+ past_key_values: Optional[list[tf.Tensor]] = None,
+ inputs_embeds: Optional[tf.Tensor] = None,
+ labels: Optional[tf.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, TFSequenceClassifierOutputWithPast]:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ """
+
+ transformer_outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+ logits_shape = shape_list(logits)
+ batch_size = logits_shape[0]
+
+ if self.config.pad_token_id is None:
+ last_non_pad_token = tf.fill((batch_size,), value=logits_shape[1] - 1)
+ else:
+ if input_ids is not None:
+ token_indices = tf.range(shape_list(input_ids)[-1])
+ non_pad_mask = tf.cast(input_ids != self.config.pad_token_id, token_indices.dtype)
+ last_non_pad_token = tf.reduce_max(token_indices * non_pad_mask, axis=-1)
+ else:
+ last_non_pad_token = tf.fill((batch_size,), value=logits_shape[1] - 1)
+ logger.warning_once(
+ f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+ "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+ )
+ loss = None
+
+ pooled_logits = tf.gather(logits, last_non_pad_token, batch_dims=1, axis=1)
+
+ if labels is not None:
+ if self.config.pad_token_id is None and logits_shape[0] != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+
+ loss = self.hf_compute_loss(tf.reshape(labels, [-1]), tf.reshape(pooled_logits, [-1, self.num_labels]))
+
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFSequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "model", None) is not None:
+ with tf.name_scope(self.model.name):
+ self.model.build(None)
+ if getattr(self, "score", None) is not None:
+ with tf.name_scope(self.score.name):
+ self.score.build((self.config.hidden_size,))
+
+
+__all__ = ["TFMistralModel", "TFMistralForCausalLM", "TFMistralForSequenceClassification", "TFMistralPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modular_mistral.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modular_mistral.py
new file mode 100644
index 0000000000000000000000000000000000000000..470c552ce647b409fdbe89a574c20b52b7a38192
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral/modular_mistral.py
@@ -0,0 +1,199 @@
+from typing import Callable, Optional
+
+import torch
+from torch import nn
+
+from transformers.utils.generic import check_model_inputs
+
+from ...cache_utils import Cache, DynamicCache
+from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import (
+ GenericForQuestionAnswering,
+)
+from ...modeling_outputs import BaseModelOutputWithPast
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, logging
+from ...utils.deprecation import deprecate_kwarg
+from ..llama.modeling_llama import (
+ LlamaAttention,
+ LlamaDecoderLayer,
+ LlamaForCausalLM,
+ LlamaForSequenceClassification,
+ LlamaForTokenClassification,
+ LlamaMLP,
+ LlamaModel,
+ LlamaPreTrainedModel,
+ apply_rotary_pos_emb,
+ eager_attention_forward,
+)
+from .configuration_mistral import MistralConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class MistralMLP(LlamaMLP):
+ def __init__(self, config):
+ super().__init__(config)
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+
+
+class MistralAttention(LlamaAttention):
+ def __init__(self, config: MistralConfig, layer_idx: int):
+ super().__init__()
+ self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+ self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
+ self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ sliding_window=getattr(self.config, "sliding_window", None), # main diff with Llama
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class MistralDecoderLayer(LlamaDecoderLayer):
+ def __init__(self, config: MistralConfig, layer_idx: int):
+ super().__init__(config, layer_idx)
+ self.self_attn = MistralAttention(config=config, layer_idx=layer_idx)
+ self.mlp = MistralMLP(config)
+
+
+class MistralPreTrainedModel(LlamaPreTrainedModel):
+ _can_record_outputs = {
+ "hidden_states": MistralDecoderLayer,
+ "attentions": MistralAttention,
+ }
+
+
+class MistralModel(LlamaModel):
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> BaseModelOutputWithPast:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache(config=self.config)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
+ causal_mask = mask_function(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ hidden_states = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = self.norm(hidden_states)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values if use_cache else None,
+ )
+
+
+class MistralForCausalLM(LlamaForCausalLM):
+ pass
+
+
+class MistralForTokenClassification(LlamaForTokenClassification):
+ pass
+
+
+class MistralForSequenceClassification(LlamaForSequenceClassification):
+ pass
+
+
+class MistralForQuestionAnswering(GenericForQuestionAnswering, MistralPreTrainedModel): ...
+
+
+__all__ = [
+ "MistralForCausalLM",
+ "MistralForQuestionAnswering",
+ "MistralModel",
+ "MistralPreTrainedModel",
+ "MistralForSequenceClassification",
+ "MistralForTokenClassification",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..11a9fcbdc4ed3935dc549e3cd1992b7550d9bbad
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mistral3 import *
+ from .modeling_mistral3 import *
+ from .processing_mistral3 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/configuration_mistral3.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/configuration_mistral3.py
new file mode 100644
index 0000000000000000000000000000000000000000..90ae36abcec371cc0871452005981628e5219413
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/configuration_mistral3.py
@@ -0,0 +1,141 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ...configuration_utils import PretrainedConfig
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+class Mistral3Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Mistral3ForConditionalGeneration`]. It is used to instantiate an
+ Mistral3 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of
+ [mistralai/Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `PixtralVisionConfig`):
+ The config object or dictionary of the vision backbone.
+ text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MistralConfig`):
+ The config object or dictionary of the text backbone.
+ image_token_index (`int`, *optional*, defaults to 10):
+ The image token index to encode the image prompt.
+ projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
+ The activation function used by the multimodal projector.
+ vision_feature_layer (`Union[int, list[int]]`, *optional*, defaults to -1):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ multimodal_projector_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the multimodal projector.
+ spatial_merge_size (`int`, *optional*, defaults to 2):
+ The downsampling factor for the spatial merge operation.
+
+ Example:
+
+ ```python
+ >>> from transformers import Mistral3ForConditionalGeneration, Mistral3Config, PixtralVisionConfig, MistralConfig
+
+ >>> # Initializing a Pixtral-vision config
+ >>> vision_config = PixtralVisionConfig()
+
+ >>> # Initializing a Mistral config
+ >>> text_config = MistralConfig()
+
+ >>> # Initializing a Mistral3 configuration
+ >>> configuration = Mistral3Config(vision_config, text_config)
+
+ >>> # Initializing a model from the mistral3.1 configuration
+ >>> model = Mistral3ForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mistral3"
+ attribute_map = {
+ "image_token_id": "image_token_index",
+ }
+ sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
+ is_composition = True
+
+ def __init__(
+ self,
+ vision_config=None,
+ text_config=None,
+ image_token_index=10,
+ projector_hidden_act="gelu",
+ vision_feature_layer=-1,
+ multimodal_projector_bias=False,
+ spatial_merge_size=2,
+ **kwargs,
+ ):
+ self.image_token_index = image_token_index
+ self.projector_hidden_act = projector_hidden_act
+
+ self.vision_feature_layer = vision_feature_layer
+
+ if isinstance(vision_config, dict):
+ vision_config["model_type"] = vision_config.get("model_type", "pixtral")
+ vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
+ elif vision_config is None:
+ vision_config = CONFIG_MAPPING["pixtral"](
+ intermediate_size=4096,
+ hidden_size=1024,
+ patch_size=14,
+ image_size=1540,
+ num_hidden_layers=24,
+ num_attention_heads=16,
+ vocab_size=32000,
+ head_dim=64,
+ hidden_act="gelu",
+ )
+
+ self.vision_config = vision_config
+
+ if isinstance(text_config, dict):
+ text_config["model_type"] = text_config.get("model_type", "mistral")
+ text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+ elif text_config is None:
+ text_config = CONFIG_MAPPING["mistral"](
+ attention_dropout=0.0,
+ head_dim=128,
+ hidden_act="silu",
+ hidden_size=5120,
+ initializer_range=0.02,
+ intermediate_size=32768,
+ max_position_embeddings=131072,
+ model_type="mistral",
+ num_attention_heads=32,
+ num_hidden_layers=40,
+ num_key_value_heads=8,
+ rms_norm_eps=1e-05,
+ rope_theta=1000000000.0,
+ sliding_window=None,
+ use_cache=True,
+ vocab_size=131072,
+ )
+
+ self.text_config = text_config
+ self.multimodal_projector_bias = multimodal_projector_bias
+ self.spatial_merge_size = spatial_merge_size
+
+ super().__init__(**kwargs)
+
+
+__all__ = ["Mistral3Config"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/convert_mistral3_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/convert_mistral3_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8f9b64ab1f6022d9087e6e7a98172cb0d5558d3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/convert_mistral3_weights_to_hf.py
@@ -0,0 +1,241 @@
+# Copyright 2023 Mistral AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import json
+import os
+import re
+
+import torch
+from safetensors.torch import load_file
+
+from transformers import (
+ Mistral3Config,
+ Mistral3ForConditionalGeneration,
+ MistralConfig,
+ PixtralImageProcessorFast,
+ PixtralProcessor,
+ PixtralVisionConfig,
+)
+from transformers.integrations.mistral import convert_tekken_tokenizer
+
+
+# fmt: off
+STATE_DICT_MAPPING = {
+ # Text model keys
+ r"^output.weight": r"language_model.lm_head.weight",
+ r"^norm.weight": r"language_model.model.norm.weight",
+ r"^tok_embeddings.weight": r"language_model.model.embed_tokens.weight",
+ r"^layers.(\d+).attention_norm.weight": r"language_model.model.layers.\1.input_layernorm.weight",
+ r"^layers.(\d+).ffn_norm.weight": r"language_model.model.layers.\1.post_attention_layernorm.weight",
+ r"^layers.(\d+).attention.w(q|k|v|o).weight": r"language_model.model.layers.\1.self_attn.\2_proj.weight",
+ r"^layers.(\d+).feed_forward.w1.weight": r"language_model.model.layers.\1.mlp.gate_proj.weight",
+ r"^layers.(\d+).feed_forward.w2.weight": r"language_model.model.layers.\1.mlp.down_proj.weight",
+ r"^layers.(\d+).feed_forward.w3.weight": r"language_model.model.layers.\1.mlp.up_proj.weight",
+
+ # Vision model keys
+ r"vision_encoder.transformer.layers.(\d+).attention_norm.weight": r"vision_tower.transformer.layers.\1.attention_norm.weight",
+ r"^vision_encoder.transformer.layers.(\d+).ffn_norm.weight": r"vision_tower.transformer.layers.\1.ffn_norm.weight",
+ r"^vision_encoder.transformer.layers.(\d+).attention.w(q|k|v|o).weight": r"vision_tower.transformer.layers.\1.attention.\2_proj.weight",
+ r"^vision_encoder.transformer.layers.(\d+).feed_forward.w1.weight": r"vision_tower.transformer.layers.\1.feed_forward.gate_proj.weight",
+ r"^vision_encoder.transformer.layers.(\d+).feed_forward.w2.weight": r"vision_tower.transformer.layers.\1.feed_forward.down_proj.weight",
+ r"^vision_encoder.transformer.layers.(\d+).feed_forward.w3.weight": r"vision_tower.transformer.layers.\1.feed_forward.up_proj.weight",
+ r"^vision_language_adapter.w_in": r"multi_modal_projector.linear_1",
+ r"^vision_language_adapter.w_out": r"multi_modal_projector.linear_2",
+ r"^vision_encoder.ln_pre.weight": r"vision_tower.ln_pre.weight",
+ r"^vision_encoder.patch_conv.weight": r"vision_tower.patch_conv.weight",
+ r"^patch_merger.merging_layer.weight": r"multi_modal_projector.patch_merger.merging_layer.weight",
+ r"^pre_mm_projector_norm.weight": r"multi_modal_projector.norm.weight",
+}
+# fmt: on
+
+
+def map_old_key_to_new(old_key):
+ """Map of a key of the original state dict to the equivalent key in HF format"""
+ for pattern, replacement in STATE_DICT_MAPPING.items():
+ new_key, n_replace = re.subn(pattern, replacement, old_key)
+ # Early exit of the loop
+ if n_replace > 0:
+ return new_key
+
+ raise ValueError(f"Key: {old_key} could not be mapped (check the mapping).")
+
+
+def read_json(path):
+ with open(path, "r") as f:
+ return json.load(f)
+
+
+def permute_for_rope(tensor, n_heads, dim1, dim2):
+ """Permute the weights for the ROPE formulation."""
+ tensor = tensor.view(n_heads, dim1 // n_heads // 2, 2, dim2)
+ tensor = tensor.transpose(1, 2)
+ tensor = tensor.reshape(dim1, dim2)
+ return tensor
+
+
+def convert_state_dict(original_state_dict: dict, config: MistralConfig):
+ """Convert a state dict file, when a single `nn.Module` is never sharded in different files (usual case)."""
+ new_dict = {}
+
+ for old_key, tensor in original_state_dict.items():
+ new_key = map_old_key_to_new(old_key)
+
+ if "vision" in old_key:
+ num_attention_heads = config.vision_config.num_attention_heads
+ num_key_value_heads = num_attention_heads
+ hidden_size = config.vision_config.hidden_size
+ head_dim = config.vision_config.head_dim
+ key_value_dim = head_dim * num_attention_heads
+ query_dim = head_dim * num_attention_heads
+ else:
+ num_attention_heads = config.text_config.num_attention_heads
+ hidden_size = config.text_config.hidden_size
+ head_dim = config.text_config.head_dim
+ num_key_value_heads = config.text_config.num_key_value_heads
+ key_value_dim = head_dim * num_key_value_heads
+ query_dim = head_dim * num_attention_heads
+
+ if "q_proj" in new_key:
+ tensor = permute_for_rope(tensor, num_attention_heads, query_dim, hidden_size)
+ elif "k_proj" in new_key:
+ tensor = permute_for_rope(tensor, num_key_value_heads, key_value_dim, hidden_size)
+
+ new_dict[new_key] = tensor
+ return new_dict
+
+
+def convert_config(original_config: dict, max_position_embeddings: int = 131072):
+ original_vision_config = original_config.pop("vision_encoder")
+ original_text_config = original_config
+
+ # Text config
+ text_key_mapping = {
+ "hidden_size": "dim",
+ "num_hidden_layers": "n_layers",
+ "intermediate_size": "hidden_dim",
+ "num_attention_heads": "n_heads",
+ "num_key_value_heads": "n_kv_heads",
+ "rms_norm_eps": "norm_eps",
+ }
+ similar_text_keys_to_keep = [
+ "head_dim",
+ "vocab_size",
+ "rope_theta",
+ ]
+ new_text_config_kwargs = {k: original_text_config[v] for k, v in text_key_mapping.items()}
+ new_text_config_kwargs.update({k: v for k, v in original_text_config.items() if k in similar_text_keys_to_keep})
+ # These are not always defined depending on `params.json`
+ new_text_config_kwargs["sliding_window"] = original_text_config.get("sliding_window", None)
+ new_text_config_kwargs["max_position_embeddings"] = original_text_config.get(
+ "max_seq_len", max_position_embeddings
+ )
+ # This may sometimes be a string in `params.json`
+ if new_text_config_kwargs["sliding_window"] is not None:
+ new_text_config_kwargs["sliding_window"] = int(new_text_config_kwargs["sliding_window"])
+ new_text_config = MistralConfig(**new_text_config_kwargs)
+
+ # Vision config
+ new_vision_config = original_vision_config
+ adapter_bias = new_vision_config.pop("adapter_bias", False)
+ _ = new_vision_config.pop("mm_projector_id", None)
+ _ = new_vision_config.pop("add_pre_mm_projector_layer_norm", None)
+ spatial_merge_size = new_vision_config.pop("spatial_merge_size")
+ image_token_id = new_vision_config.pop("image_token_id", 10)
+ _ = new_vision_config.pop("image_break_token_id", 12)
+ _ = new_vision_config.pop("image_end_token_id", 13)
+ _ = new_vision_config.pop("max_image_size")
+ new_vision_config = PixtralVisionConfig(**new_vision_config)
+
+ new_config = Mistral3Config(
+ vision_config=new_vision_config,
+ text_config=new_text_config,
+ multimodal_projector_bias=adapter_bias,
+ image_token_id=image_token_id,
+ spatial_merge_size=spatial_merge_size,
+ vision_feature_layer=-1,
+ )
+ return new_config
+
+
+def convert_and_write_model(input_dir: str, output_dir: str, max_position_embeddings: int):
+ """Convert the model and save it (this implicitly save the config as well)."""
+ params = read_json(os.path.join(input_dir, "params.json"))
+ config = convert_config(params, max_position_embeddings)
+
+ full_state_dict = {}
+ # The model may be split between different files, but a single nn.Module is always fully present in a single file
+ shards = [file for file in os.listdir(input_dir) if file.endswith(".safetensors")]
+ for shard_file in shards:
+ original_state_dict = load_file(os.path.join(input_dir, shard_file))
+ new_dict = convert_state_dict(original_state_dict, config)
+ full_state_dict.update(new_dict)
+
+ # Load weights into model and resave them
+ with torch.device("meta"):
+ model = Mistral3ForConditionalGeneration(config)
+ model.load_state_dict(full_state_dict, strict=True, assign=True)
+ model.save_pretrained(output_dir)
+
+
+def convert_and_write_processor(input_dir: str, output_dir: str):
+ """Convert the tokenizer and save it."""
+ tokenizer_file = os.path.join(input_dir, "tekken.json")
+ tokenizer = convert_tekken_tokenizer(tokenizer_file)
+ tokenizer.add_special_tokens({"pad_token": ""})
+ chat_template = '{%- if messages[0]["role"] == "system" %}{%- set system_message = messages[0]["content"] %}{%- set loop_messages = messages[1:] %}\n{%- else %}{%- set loop_messages = messages %}{%- endif %}{{- bos_token }}{%- for message in loop_messages %}{%- if (message[\'role\'] == \'user\') != (loop.index0 % 2 == 0) %}{{- raise_exception(\'After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\') }}{%- endif %}{%- if message["role"] == "user" %}{%- if loop.last and system_message is defined %}{{- "[INST]" + system_message + "\n\n" }}{%- else %}{{ "[INST]" }}{%- endif %}{%- endif %}{%- if message["content"] is not string %}{%- for chunk in message["content"] %}{%- if chunk["type"] == "text" %}{%- if "content" in chunk %}{{- chunk["content"] }}{%- elif "text" in chunk %}{{- chunk["text"] }}{%- endif %}{%- elif chunk["type"] == "image" %}{{- "[IMG]" }}{%- else %}{{- raise_exception("Unrecognized content type!") }}{%- endif %}{%- endfor %}{%- else %}{{- message["content"] }}{%- endif %}{%- if message["role"] == "user" %}{{- "[/INST]" }}{%- elif message["role"] == "assistant" %}{{- eos_token}}{%- else %}{{- raise_exception("Only user and assistant roles are supported, with the exception of an initial optional system message!") }}{%- endif %}{%- endfor %}'
+
+ config = read_json(os.path.join(input_dir, "params.json"))
+ patch_size = config["vision_encoder"]["patch_size"]
+ spatial_merge_size = config["vision_encoder"]["spatial_merge_size"]
+ max_image_size = config["vision_encoder"]["max_image_size"]
+ image_processor = PixtralImageProcessorFast(patch_size=patch_size, size={"longest_edge": max_image_size})
+
+ processor = PixtralProcessor(
+ tokenizer=tokenizer,
+ image_processor=image_processor,
+ image_token="[IMG]",
+ patch_size=patch_size,
+ chat_template=chat_template,
+ spatial_merge_size=spatial_merge_size,
+ )
+
+ # Finally save it
+ processor.save_pretrained(output_dir)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "input_dir",
+ help="Location of Mistral weights, which contains tokenizer.model and model folders",
+ )
+ parser.add_argument(
+ "output_dir",
+ help="Location to write HF model and tokenizer",
+ )
+ parser.add_argument(
+ "--max_position_embeddings",
+ type=int,
+ default=131072,
+ help="`max_position_embeddings` field in the config. This needs to be manually passed (not present anywhere otherwise).",
+ )
+
+ args = parser.parse_args()
+
+ convert_and_write_model(args.input_dir, args.output_dir, args.max_position_embeddings)
+ convert_and_write_processor(args.input_dir, args.output_dir)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/modeling_mistral3.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/modeling_mistral3.py
new file mode 100644
index 0000000000000000000000000000000000000000..afa6bf44734c7b2ba2040c206f7ab2020534c3c1
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/modeling_mistral3.py
@@ -0,0 +1,539 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/mistral3/modular_mistral3.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_mistral3.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_outputs import BaseModelOutputWithPast, ModelOutput
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ..auto import AutoModel
+from .configuration_mistral3 import Mistral3Config
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class Mistral3RMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ Mistral3RMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class Mistral3PatchMerger(nn.Module):
+ """
+ Learned merging of spatial_merge_size ** 2 patches
+ """
+
+ def __init__(self, config: Mistral3Config):
+ super().__init__()
+ self.config = config
+
+ hidden_size = config.vision_config.hidden_size
+ self.spatial_merge_size = config.spatial_merge_size
+ self.patch_size = self.config.vision_config.patch_size
+ self.merging_layer = nn.Linear(hidden_size * self.spatial_merge_size**2, hidden_size, bias=False)
+
+ def forward(self, image_features: torch.Tensor, image_sizes: torch.Tensor) -> torch.Tensor:
+ image_sizes = [
+ (image_size[0] // self.patch_size, image_size[1] // self.patch_size) for image_size in image_sizes
+ ]
+
+ tokens_per_image = [h * w for h, w in image_sizes]
+ d = image_features.shape[-1]
+
+ permuted_tensor = []
+ for image_index, image_tokens in enumerate(image_features.split(tokens_per_image)):
+ # Reshape image_tokens into a 2D grid
+ h, w = image_sizes[image_index]
+ image_grid = image_tokens.view(h, w, d).permute(2, 0, 1).unsqueeze(0)
+ grid = torch.nn.functional.unfold(
+ image_grid, kernel_size=self.spatial_merge_size, stride=self.spatial_merge_size
+ )
+ grid = grid.view(d * self.spatial_merge_size**2, -1).t()
+ permuted_tensor.append(grid)
+
+ image_features = torch.cat(permuted_tensor, dim=0)
+ image_features = self.merging_layer(image_features)
+ return image_features
+
+
+class Mistral3MultiModalProjector(nn.Module):
+ def __init__(self, config: Mistral3Config):
+ super().__init__()
+ self.norm = Mistral3RMSNorm(config.vision_config.hidden_size, eps=config.text_config.rms_norm_eps)
+ self.patch_merger = Mistral3PatchMerger(config)
+ # We have hidden_size * the number of vision feature layers
+ num_feature_layers = 1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer)
+ self.linear_1 = nn.Linear(
+ config.vision_config.hidden_size * num_feature_layers,
+ config.text_config.hidden_size,
+ bias=config.multimodal_projector_bias,
+ )
+ self.act = ACT2FN[config.projector_hidden_act]
+ self.linear_2 = nn.Linear(
+ config.text_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
+ )
+
+ def forward(self, image_features: torch.Tensor, image_sizes: torch.Tensor):
+ image_features = self.norm(image_features)
+ image_features = self.patch_merger(image_features, image_sizes)
+ hidden_states = self.linear_1(image_features)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.linear_2(hidden_states)
+ return hidden_states
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Mistral3 causal language model (or autoregressive) outputs.
+ """
+)
+class Mistral3CausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Mistral3 outputs, with hidden states and attentions.
+ """
+)
+class Mistral3ModelOutputWithPast(BaseModelOutputWithPast):
+ r"""
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+
+@auto_docstring
+class Mistral3PreTrainedModel(PreTrainedModel):
+ config: Mistral3Config
+ base_model_prefix = ""
+ supports_gradient_checkpointing = True
+ _skip_keys_device_placement = "past_key_values"
+
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+ _supports_flex_attn = True
+ _supports_attention_backend = True
+
+
+@auto_docstring(
+ custom_intro="""
+ The Mistral3 model which consists of a vision backbone and a language model, without a language modeling head.
+ """
+)
+class Mistral3Model(Mistral3PreTrainedModel):
+ _checkpoint_conversion_mapping = {"language_model.model": "language_model"}
+
+ def __init__(self, config: Mistral3Config):
+ super().__init__(config)
+ self.vision_tower = AutoModel.from_config(config.vision_config)
+
+ self.multi_modal_projector = Mistral3MultiModalProjector(config)
+ self.language_model = AutoModel.from_config(config.text_config)
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def set_decoder(self, decoder):
+ self.language_model = decoder
+
+ def get_decoder(self):
+ return self.language_model
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ **kwargs,
+ ):
+ """
+ Obtains image last hidden states from the vision tower and apply multimodal projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
+ The tensors corresponding to the input images.
+ vision_feature_layer (`Union[int, list[int]]`, *optional*):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ image_sizes (`torch.Tensor`, *optional*):
+ Tensor containing the image sizes as returned by the processor.
+ Returns:
+ image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
+ """
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+
+ kwargs = {k: v for k, v in kwargs.items() if v is not None}
+ # this is not memory efficient at all (output_hidden_states=True) will save all the hidden states.
+ image_outputs = self.vision_tower(pixel_values, image_sizes=image_sizes, output_hidden_states=True, **kwargs)
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
+ else:
+ hs_pool = [image_outputs.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ selected_image_feature = torch.cat(hs_pool, dim=-1)
+
+ image_features = self.multi_modal_projector(selected_image_feature.squeeze(0), image_sizes)
+ downsample_ratio = self.vision_tower.patch_size * self.config.spatial_merge_size
+ split_sizes = [(height // downsample_ratio) * (width // downsample_ratio) for height, width in image_sizes]
+ image_features = torch.split(image_features.squeeze(0), split_sizes)
+ return image_features
+
+ def get_placeholder_mask(
+ self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
+ ):
+ """
+ Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ n_image_features = image_features.shape[0] * image_features.shape[1]
+ if inputs_embeds[special_image_mask].numel() != image_features.numel():
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+ )
+ return special_image_mask
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ image_sizes: torch.Tensor = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, Mistral3ModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ if pixel_values is not None:
+ image_features = self.get_image_features(
+ pixel_values=pixel_values,
+ vision_feature_layer=vision_feature_layer,
+ image_sizes=image_sizes,
+ )
+ image_features = torch.cat(image_features, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+ special_image_mask = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+ outputs = self.language_model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return Mistral3ModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=image_features if pixel_values is not None else None,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The MISTRAL3 model which consists of a vision backbone and a language model.
+ """
+)
+class Mistral3ForConditionalGeneration(Mistral3PreTrainedModel, GenerationMixin):
+ _checkpoint_conversion_mapping = {
+ "^language_model.model": "model.language_model",
+ "^vision_tower": "model.vision_tower",
+ "^multi_modal_projector": "model.multi_modal_projector",
+ "^language_model.lm_head": "lm_head",
+ }
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: Mistral3Config):
+ super().__init__(config)
+ self.model = Mistral3Model(config)
+ self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ def get_output_embeddings(self) -> nn.Module:
+ return self.lm_head
+
+ def set_decoder(self, decoder):
+ self.model.set_decoder(decoder)
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ **kwargs,
+ ):
+ return self.model.get_image_features(
+ pixel_values=pixel_values,
+ image_sizes=image_sizes,
+ vision_feature_layer=vision_feature_layer,
+ **kwargs,
+ )
+
+ # Make modules available through conditional class for BC
+ @property
+ def language_model(self):
+ return self.model.language_model
+
+ @property
+ def vision_tower(self):
+ return self.model.vision_tower
+
+ @property
+ def multi_modal_projector(self):
+ return self.model.multi_modal_projector
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ image_sizes: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, Mistral3CausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import AutoProcessor, Mistral3ForConditionalGeneration
+
+ >>> model = Mistral3ForConditionalGeneration.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
+ >>> processor = AutoProcessor.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
+
+ >>> prompt = "[INST][IMG]What is the image?[/INST]"
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(**inputs, max_new_tokens=15)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "What is the image?The image depicts two cats lying on a pink blanket."
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ image_sizes=image_sizes,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return Mistral3CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ inputs_embeds=None,
+ pixel_values=None,
+ attention_mask=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ if cache_position[0] == 0:
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ model_inputs["pixel_values"] = pixel_values
+
+ return model_inputs
+
+
+__all__ = ["Mistral3Model", "Mistral3PreTrainedModel", "Mistral3ForConditionalGeneration"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/modular_mistral3.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/modular_mistral3.py
new file mode 100644
index 0000000000000000000000000000000000000000..454904c16b365b42a6afaae7434f5c07fa95f7f0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mistral3/modular_mistral3.py
@@ -0,0 +1,338 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Optional, Union
+
+import torch
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...processing_utils import Unpack
+from ...utils import logging
+from ..llava.modeling_llava import (
+ LlavaCausalLMOutputWithPast,
+ LlavaForConditionalGeneration,
+ LlavaModel,
+ LlavaModelOutputWithPast,
+ LlavaPreTrainedModel,
+ TransformersKwargs,
+)
+from ..mistral.modeling_mistral import MistralRMSNorm
+from .configuration_mistral3 import Mistral3Config
+
+
+logger = logging.get_logger(__name__)
+
+
+class Mistral3RMSNorm(MistralRMSNorm):
+ pass
+
+
+class Mistral3PatchMerger(nn.Module):
+ """
+ Learned merging of spatial_merge_size ** 2 patches
+ """
+
+ def __init__(self, config: Mistral3Config):
+ super().__init__()
+ self.config = config
+
+ hidden_size = config.vision_config.hidden_size
+ self.spatial_merge_size = config.spatial_merge_size
+ self.patch_size = self.config.vision_config.patch_size
+ self.merging_layer = nn.Linear(hidden_size * self.spatial_merge_size**2, hidden_size, bias=False)
+
+ def forward(self, image_features: torch.Tensor, image_sizes: torch.Tensor) -> torch.Tensor:
+ image_sizes = [
+ (image_size[0] // self.patch_size, image_size[1] // self.patch_size) for image_size in image_sizes
+ ]
+
+ tokens_per_image = [h * w for h, w in image_sizes]
+ d = image_features.shape[-1]
+
+ permuted_tensor = []
+ for image_index, image_tokens in enumerate(image_features.split(tokens_per_image)):
+ # Reshape image_tokens into a 2D grid
+ h, w = image_sizes[image_index]
+ image_grid = image_tokens.view(h, w, d).permute(2, 0, 1).unsqueeze(0)
+ grid = torch.nn.functional.unfold(
+ image_grid, kernel_size=self.spatial_merge_size, stride=self.spatial_merge_size
+ )
+ grid = grid.view(d * self.spatial_merge_size**2, -1).t()
+ permuted_tensor.append(grid)
+
+ image_features = torch.cat(permuted_tensor, dim=0)
+ image_features = self.merging_layer(image_features)
+ return image_features
+
+
+class Mistral3MultiModalProjector(nn.Module):
+ def __init__(self, config: Mistral3Config):
+ super().__init__()
+ self.norm = Mistral3RMSNorm(config.vision_config.hidden_size, eps=config.text_config.rms_norm_eps)
+ self.patch_merger = Mistral3PatchMerger(config)
+ # We have hidden_size * the number of vision feature layers
+ num_feature_layers = 1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer)
+ self.linear_1 = nn.Linear(
+ config.vision_config.hidden_size * num_feature_layers,
+ config.text_config.hidden_size,
+ bias=config.multimodal_projector_bias,
+ )
+ self.act = ACT2FN[config.projector_hidden_act]
+ self.linear_2 = nn.Linear(
+ config.text_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
+ )
+
+ def forward(self, image_features: torch.Tensor, image_sizes: torch.Tensor):
+ image_features = self.norm(image_features)
+ image_features = self.patch_merger(image_features, image_sizes)
+ hidden_states = self.linear_1(image_features)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.linear_2(hidden_states)
+ return hidden_states
+
+
+class Mistral3CausalLMOutputWithPast(LlavaCausalLMOutputWithPast):
+ pass
+
+
+class Mistral3ModelOutputWithPast(LlavaModelOutputWithPast):
+ pass
+
+
+class Mistral3PreTrainedModel(LlavaPreTrainedModel):
+ pass
+
+
+class Mistral3Model(LlavaModel):
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ **kwargs,
+ ):
+ """
+ Obtains image last hidden states from the vision tower and apply multimodal projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
+ The tensors corresponding to the input images.
+ vision_feature_layer (`Union[int, list[int]]`, *optional*):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ image_sizes (`torch.Tensor`, *optional*):
+ Tensor containing the image sizes as returned by the processor.
+ Returns:
+ image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
+ """
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+
+ kwargs = {k: v for k, v in kwargs.items() if v is not None}
+ # this is not memory efficient at all (output_hidden_states=True) will save all the hidden states.
+ image_outputs = self.vision_tower(pixel_values, image_sizes=image_sizes, output_hidden_states=True, **kwargs)
+ # If we have one vision feature layer, return the corresponding hidden states,
+ # otherwise, select the hidden states of each feature layer and concatenate them
+ if isinstance(vision_feature_layer, int):
+ selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
+ else:
+ hs_pool = [image_outputs.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
+ selected_image_feature = torch.cat(hs_pool, dim=-1)
+
+ image_features = self.multi_modal_projector(selected_image_feature.squeeze(0), image_sizes)
+ downsample_ratio = self.vision_tower.patch_size * self.config.spatial_merge_size
+ split_sizes = [(height // downsample_ratio) * (width // downsample_ratio) for height, width in image_sizes]
+ image_features = torch.split(image_features.squeeze(0), split_sizes)
+ return image_features
+
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ image_sizes: torch.Tensor = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, Mistral3ModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ vision_feature_layer = (
+ vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+ )
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ if pixel_values is not None:
+ image_features = self.get_image_features(
+ pixel_values=pixel_values,
+ vision_feature_layer=vision_feature_layer,
+ image_sizes=image_sizes,
+ )
+ image_features = torch.cat(image_features, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+ special_image_mask = self.get_placeholder_mask(
+ input_ids, inputs_embeds=inputs_embeds, image_features=image_features
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+ outputs = self.language_model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return Mistral3ModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=image_features if pixel_values is not None else None,
+ )
+
+
+class Mistral3ForConditionalGeneration(LlavaForConditionalGeneration):
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ image_sizes: torch.Tensor,
+ vision_feature_layer: Optional[Union[int, list[int]]] = None,
+ **kwargs,
+ ):
+ return self.model.get_image_features(
+ pixel_values=pixel_values,
+ image_sizes=image_sizes,
+ vision_feature_layer=vision_feature_layer,
+ **kwargs,
+ )
+
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ image_sizes: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, Mistral3CausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import AutoProcessor, Mistral3ForConditionalGeneration
+
+ >>> model = Mistral3ForConditionalGeneration.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
+ >>> processor = AutoProcessor.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
+
+ >>> prompt = "[INST][IMG]What is the image?[/INST]"
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(**inputs, max_new_tokens=15)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "What is the image?The image depicts two cats lying on a pink blanket."
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ image_sizes=image_sizes,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return Mistral3CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ )
+
+
+__all__ = [
+ "Mistral3Model",
+ "Mistral3PreTrainedModel", # noqa
+ "Mistral3ForConditionalGeneration",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4ca36bacbee79261b8fe19e1df5cd2fb91b7344
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2023 Mixtral AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mixtral import *
+ from .modeling_mixtral import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/configuration_mixtral.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/configuration_mixtral.py
new file mode 100644
index 0000000000000000000000000000000000000000..35ae3f10dbc67a8040def0e641d73009c0505098
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/configuration_mixtral.py
@@ -0,0 +1,191 @@
+# coding=utf-8
+# Copyright 2023 Mixtral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Mixtral model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MixtralConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MixtralModel`]. It is used to instantiate an
+ Mixtral model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Mixtral-7B-v0.1 or Mixtral-7B-Instruct-v0.1.
+
+ [mixtralai/Mixtral-8x7B](https://huggingface.co/mixtralai/Mixtral-8x7B)
+ [mixtralai/Mixtral-7B-Instruct-v0.1](https://huggingface.co/mixtralai/Mixtral-7B-Instruct-v0.1)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the Mixtral model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MixtralModel`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 14336):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_key_value_heads (`int`, *optional*, defaults to 8):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
+ head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
+ The attention head dimension.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+ The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention
+ allows sequence of up to 4096*32 tokens.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*):
+ The id of the padding token.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ The id of the "beginning-of-sequence" token.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ The id of the "end-of-sequence" token.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether the model's input and output word embeddings should be tied.
+ rope_theta (`float`, *optional*, defaults to 1000000.0):
+ The base period of the RoPE embeddings.
+ sliding_window (`int`, *optional*):
+ Sliding window attention window size. If not specified, will default to `4096`.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ num_experts_per_tok (`int`, *optional*, defaults to 2):
+ The number of experts to route per-token, can be also interpreted as the `top-k` routing
+ parameter
+ num_local_experts (`int`, *optional*, defaults to 8):
+ Number of experts per Sparse MLP layer.
+ output_router_logits (`bool`, *optional*, defaults to `False`):
+ Whether or not the router logits should be returned by the model. Enabling this will also
+ allow the model to output the auxiliary loss. See [here]() for more details
+ router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+ The aux loss factor for the total loss.
+ router_jitter_noise (`float`, *optional*, defaults to 0.0):
+ Amount of noise to add to the router.
+
+ ```python
+ >>> from transformers import MixtralModel, MixtralConfig
+
+ >>> # Initializing a Mixtral 7B style configuration
+ >>> configuration = MixtralConfig()
+
+ >>> # Initializing a model from the Mixtral 7B style configuration
+ >>> model = MixtralModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mixtral"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ base_model_tp_plan = {
+ "layers.*.self_attn.q_proj": "colwise",
+ "layers.*.self_attn.k_proj": "colwise",
+ "layers.*.self_attn.v_proj": "colwise",
+ "layers.*.self_attn.o_proj": "rowwise",
+ "layers.*.block_sparse_moe.gate": "colwise_rep", # we need to replicate here to correctly route experts
+ "layers.*.block_sparse_moe.experts.*.w1": "colwise",
+ "layers.*.block_sparse_moe.experts.*.w2": "rowwise",
+ "layers.*.block_sparse_moe.experts.*.w3": "colwise",
+ }
+ base_model_pp_plan = {
+ "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+ "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+ "norm": (["hidden_states"], ["hidden_states"]),
+ }
+
+ def __init__(
+ self,
+ vocab_size=32000,
+ hidden_size=4096,
+ intermediate_size=14336,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=8,
+ head_dim=None,
+ hidden_act="silu",
+ max_position_embeddings=4096 * 32,
+ initializer_range=0.02,
+ rms_norm_eps=1e-5,
+ use_cache=True,
+ pad_token_id=None,
+ bos_token_id=1,
+ eos_token_id=2,
+ tie_word_embeddings=False,
+ rope_theta=1e6,
+ sliding_window=None,
+ attention_dropout=0.0,
+ num_experts_per_tok=2,
+ num_local_experts=8,
+ output_router_logits=False,
+ router_aux_loss_coef=0.001,
+ router_jitter_noise=0.0,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.sliding_window = sliding_window
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.attention_dropout = attention_dropout
+ self.head_dim = head_dim
+
+ self.num_experts_per_tok = num_experts_per_tok
+ self.num_local_experts = num_local_experts
+ self.output_router_logits = output_router_logits
+ self.router_aux_loss_coef = router_aux_loss_coef
+ self.router_jitter_noise = router_jitter_noise
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+
+__all__ = ["MixtralConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6df5901feff99635204d305063be5729817959d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py
@@ -0,0 +1,245 @@
+# Copyright 2023 Mistral AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import json
+import os
+
+import torch
+
+from transformers import (
+ MixtralConfig,
+ MixtralForCausalLM,
+)
+
+
+"""
+Sample usage:
+
+```
+python src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py \
+ --input_dir /path/to/downloaded/mixtral/weights --model_size 7B --output_dir /output/path
+```
+
+Thereafter, models can be loaded via:
+
+```py
+from transformers import MixtralForCausalLM
+
+model = MixtralForCausalLM.from_pretrained("/output/path")
+```
+
+Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
+come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
+"""
+
+
+def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
+ return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
+
+
+def read_json(path):
+ with open(path, "r") as f:
+ return json.load(f)
+
+
+def write_json(text, path):
+ with open(path, "w") as f:
+ json.dump(text, f)
+
+
+def write_model(model_path, input_base_path, model_size, safe_serialization=True):
+ os.makedirs(model_path, exist_ok=True)
+
+ params = read_json(os.path.join(input_base_path, "params.json"))
+ num_shards = 1
+
+ # For some reason this is a string in the params.json
+ sliding_window = int(params["sliding_window"]) if "sliding_window" in params else None
+ n_layers = params["num_hidden_layers"]
+ n_heads = params["num_attention_heads"]
+ n_heads_per_shard = n_heads // num_shards
+ dim = params["hidden_size"]
+ dims_per_head = dim // n_heads
+ base = params.get("rope_theta", 10000.0)
+ max_position_embeddings = 4096 * 8
+ num_local_experts = params["num_local_experts"]
+ ffn_dim = params["intermediate_size"]
+
+ vocab_size = params["vocab_size"]
+
+ if "num_key_value_heads" in params:
+ num_key_value_heads = params["num_key_value_heads"] # for GQA / MQA
+ num_local_key_value_heads = num_key_value_heads // num_shards
+ key_value_dim = dims_per_head * num_local_key_value_heads
+ else: # compatibility with other checkpoints
+ num_key_value_heads = n_heads
+ num_local_key_value_heads = n_heads_per_shard
+ key_value_dim = dim
+
+ # permute for sliced rotary
+ def permute(w, n_heads=n_heads, dim1=dim, dim2=dim):
+ return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
+
+ print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
+ # Load weights
+ loaded = [
+ torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pt"), map_location="cpu", weights_only=True)
+ for i in range(8)
+ ]
+
+ merged_state_dict = {}
+ for state_dict in loaded:
+ merged_state_dict.update(state_dict)
+
+ state_dict = {}
+
+ for layer_i in range(n_layers):
+ # Sharded
+ # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
+ # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
+ # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
+
+ state_dict.update(
+ {
+ f"model.layers.{layer_i}.input_layernorm.weight": merged_state_dict[
+ f"layers.{layer_i}.attention_norm.weight"
+ ].clone(),
+ f"model.layers.{layer_i}.post_attention_layernorm.weight": merged_state_dict[
+ f"layers.{layer_i}.ffn_norm.weight"
+ ].clone(),
+ }
+ )
+
+ state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
+ merged_state_dict[f"layers.{layer_i}.attention.wq.weight"]
+ .view(n_heads_per_shard, dims_per_head, dim)
+ .reshape(dim, dim)
+ )
+ state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
+ merged_state_dict[f"layers.{layer_i}.attention.wk.weight"]
+ .view(num_local_key_value_heads, dims_per_head, dim)
+ .reshape(key_value_dim, dim),
+ num_key_value_heads,
+ key_value_dim,
+ dim,
+ )
+ state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = (
+ merged_state_dict[f"layers.{layer_i}.attention.wv.weight"]
+ .view(num_local_key_value_heads, dims_per_head, dim)
+ .reshape(key_value_dim, dim)
+ )
+
+ state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = merged_state_dict[
+ f"layers.{layer_i}.attention.wo.weight"
+ ]
+
+ w1 = merged_state_dict[f"layers.{layer_i}.block_sparse_moe.w1"]
+ w2 = merged_state_dict[f"layers.{layer_i}.block_sparse_moe.w2"]
+ w3 = merged_state_dict[f"layers.{layer_i}.block_sparse_moe.w3"]
+
+ experts_w1 = [
+ w1[ffn_dim * expert_idx : ffn_dim * (expert_idx + 1), :].clone(memory_format=torch.contiguous_format)
+ for expert_idx in range(num_local_experts)
+ ]
+
+ for idx, expert_block in enumerate(experts_w1):
+ expert_key = f"model.layers.{layer_i}.block_sparse_moe.experts.{idx}.w1"
+ state_dict[expert_key + ".weight"] = expert_block.clone()
+
+ experts_w2 = [
+ w2[ffn_dim * expert_idx : ffn_dim * (expert_idx + 1), :].clone(memory_format=torch.contiguous_format)
+ for expert_idx in range(num_local_experts)
+ ]
+
+ for idx, expert_block in enumerate(experts_w2):
+ expert_key = f"model.layers.{layer_i}.block_sparse_moe.experts.{idx}.w2"
+ state_dict[expert_key + ".weight"] = expert_block.T.clone(memory_format=torch.contiguous_format)
+
+ experts_w3 = [
+ w3[ffn_dim * expert_idx : ffn_dim * (expert_idx + 1), :].clone(memory_format=torch.contiguous_format)
+ for expert_idx in range(num_local_experts)
+ ]
+
+ for idx, expert_block in enumerate(experts_w3):
+ expert_key = f"model.layers.{layer_i}.block_sparse_moe.experts.{idx}.w3"
+ state_dict[expert_key + ".weight"] = expert_block.clone()
+
+ state_dict[f"model.layers.{layer_i}.block_sparse_moe.gate.weight"] = merged_state_dict[
+ f"layers.{layer_i}.block_sparse_moe.gate.weight"
+ ]
+
+ state_dict.update(
+ {
+ "model.norm.weight": merged_state_dict["norm.weight"],
+ "model.embed_tokens.weight": merged_state_dict["tok_embeddings.weight"],
+ "lm_head.weight": merged_state_dict["output.weight"],
+ }
+ )
+
+ config = MixtralConfig(
+ hidden_size=dim,
+ intermediate_size=ffn_dim,
+ num_attention_heads=params["num_attention_heads"],
+ num_hidden_layers=params["num_hidden_layers"],
+ rms_norm_eps=params["rms_norm_eps"],
+ num_key_value_heads=num_key_value_heads,
+ vocab_size=vocab_size,
+ rope_theta=base,
+ max_position_embeddings=max_position_embeddings,
+ sliding_window=sliding_window,
+ num_local_experts=num_local_experts,
+ )
+
+ print("Loading the checkpoint in a Mixtral model.")
+ with torch.device("meta"):
+ model = MixtralForCausalLM(config)
+ # Avoid saving this as part of the config.
+ del model.config._name_or_path
+ model.config.torch_dtype = torch.float16
+ print("Saving in the Transformers format.")
+
+ model.load_state_dict(state_dict, strict=True, assign=True)
+
+ for n, p in model.named_parameters():
+ assert p.device.type != "meta", f"{n} has not been loaded!"
+
+ model.save_pretrained(model_path, safe_serialization=safe_serialization)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--input_dir",
+ help="Location of Mixtral weights, which contains tokenizer.model and model folders",
+ required=True,
+ )
+ parser.add_argument(
+ "--model_size",
+ choices=["7B"],
+ help="'f' models correspond to the finetuned versions, and are specific to the Mixtral official release. For more details on Mixtral, check out the original repo: https://huggingface.co/mistral-ai",
+ default="7B",
+ )
+ parser.add_argument("--output_dir", help="Location to write HF model", required=True)
+ parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
+ args = parser.parse_args()
+ write_model(
+ model_path=args.output_dir,
+ input_base_path=args.input_dir,
+ model_size=args.model_size,
+ safe_serialization=args.safe_serialization,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/modeling_mixtral.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/modeling_mixtral.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6b5883f4a775a0713b9b33e8a8c90c3554be703
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/modeling_mixtral.py
@@ -0,0 +1,695 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/mixtral/modular_mixtral.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_mixtral.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers.utils.generic import check_model_inputs
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import (
+ GenericForQuestionAnswering,
+ GenericForSequenceClassification,
+ GenericForTokenClassification,
+ GradientCheckpointingLayer,
+)
+from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import OutputRecorder
+from .configuration_mixtral import MixtralConfig
+
+
+class MixtralBlockSparseTop2MLP(nn.Module):
+ def __init__(self, config: MixtralConfig):
+ super().__init__()
+ self.ffn_dim = config.intermediate_size
+ self.hidden_dim = config.hidden_size
+
+ self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+ self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+ self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_states):
+ current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+ current_hidden_states = self.w2(current_hidden_states)
+ return current_hidden_states
+
+
+class MixtralSparseMoeBlock(nn.Module):
+ """
+ This implementation is
+ strictly equivalent to standard MoE with full capacity (no
+ dropped tokens). It's faster since it formulates MoE operations
+ in terms of block-sparse operations to accommodate imbalanced
+ assignments of tokens to experts, whereas standard MoE either
+ (1) drop tokens at the cost of reduced performance or (2) set
+ capacity factor to number of experts and thus waste computation
+ and memory on padding.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.hidden_dim = config.hidden_size
+ self.ffn_dim = config.intermediate_size
+ self.num_experts = config.num_local_experts
+ self.top_k = config.num_experts_per_tok
+
+ # gating
+ self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+
+ self.experts = nn.ModuleList([MixtralBlockSparseTop2MLP(config) for _ in range(self.num_experts)])
+
+ # Jitter parameters
+ self.jitter_noise = config.router_jitter_noise
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ """ """
+ batch_size, sequence_length, hidden_dim = hidden_states.shape
+ if self.training and self.jitter_noise > 0:
+ hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
+ hidden_states = hidden_states.view(-1, hidden_dim)
+ # router_logits: (batch * sequence_length, n_experts)
+ router_logits = self.gate(hidden_states)
+
+ routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+ routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+ routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+ # we cast back to the input dtype
+ routing_weights = routing_weights.to(hidden_states.dtype)
+
+ final_hidden_states = torch.zeros(
+ (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+ )
+
+ # One hot encode the selected experts to create an expert mask
+ # this will be used to easily index which expert is going to be sollicitated
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+ expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+ for expert_idx in expert_hit:
+ expert_layer = self.experts[expert_idx]
+ idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
+ # Index the correct hidden states and compute the expert hidden state for
+ # the current expert. We need to make sure to multiply the output hidden
+ # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+ current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+ current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
+
+ # However `index_add_` only support torch tensors for indexing so we'll use
+ # the `top_x` tensor here.
+ final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+ final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+ return final_hidden_states, router_logits
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class MixtralRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ MixtralRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class MixtralAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: MixtralConfig, layer_idx: int):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = config.attention_dropout
+ self.is_causal = True
+ self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
+ self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
+ self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ sliding_window=getattr(self.config, "sliding_window", None), # main diff with Llama
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class MixtralDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MixtralConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = MixtralAttention(config, layer_idx)
+
+ self.block_sparse_moe = MixtralSparseMoeBlock(config)
+ self.input_layernorm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[torch.Tensor]] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> torch.FloatTensor:
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states, _ = self.block_sparse_moe(hidden_states)
+ hidden_states = residual + hidden_states
+
+ return hidden_states
+
+
+class MixtralRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: MixtralConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+@auto_docstring
+class MixtralPreTrainedModel(PreTrainedModel):
+ config: MixtralConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MixtralDecoderLayer"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+ _can_compile_fullgraph = False # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
+ _supports_attention_backend = True
+ _can_record_outputs = {
+ "router_logits": OutputRecorder(MixtralSparseMoeBlock, index=1),
+ "hidden_states": MixtralDecoderLayer,
+ "attentions": MixtralAttention,
+ }
+
+
+@auto_docstring
+class MixtralModel(MixtralPreTrainedModel):
+ def __init__(self, config: MixtralConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [MixtralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.norm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.rotary_emb = MixtralRotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> MoeModelOutputWithPast:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache(config=self.config)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
+ causal_mask = mask_function(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+
+ # create position embeddings to be shared across the decoder layers
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ hidden_states = decoder_layer(
+ hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = self.norm(hidden_states)
+
+ return MoeModelOutputWithPast( # only diff with Mistral is the output type, we need MoE
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+def load_balancing_loss_func(
+ gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
+ num_experts: Optional[int] = None,
+ top_k=2,
+ attention_mask: Optional[torch.Tensor] = None,
+) -> Union[torch.Tensor, int]:
+ r"""
+ Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+ See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
+ function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+ experts is too unbalanced.
+
+ Args:
+ gate_logits:
+ Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+ shape [batch_size X sequence_length, num_experts].
+ num_experts:
+ Number of experts
+ top_k:
+ The number of experts to route per-token, can be also interpreted as the `top-k` routing
+ parameter.
+ attention_mask (`torch.Tensor`, *optional*):
+ The attention_mask used in forward function
+ shape [batch_size X sequence_length] if not None.
+
+ Returns:
+ The auxiliary loss.
+ """
+ if gate_logits is None or not isinstance(gate_logits, tuple):
+ return 0
+
+ if isinstance(gate_logits, tuple):
+ compute_device = gate_logits[0].device
+ concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+ routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+ _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+ if attention_mask is None:
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.mean(routing_weights, dim=0)
+ else:
+ batch_size, sequence_length = attention_mask.shape
+ num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+ expert_attention_mask = (
+ attention_mask[None, :, :, None, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+ .reshape(-1, top_k, num_experts)
+ .to(compute_device)
+ )
+
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+ expert_attention_mask, dim=0
+ )
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+ router_per_expert_attention_mask = (
+ attention_mask[None, :, :, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
+ .reshape(-1, num_experts)
+ .to(compute_device)
+ )
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+ router_per_expert_attention_mask, dim=0
+ )
+
+ overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
+ return overall_loss * num_experts
+
+
+@auto_docstring
+class MixtralForCausalLM(MixtralPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+ _tp_plan = {"lm_head": "colwise_rep"}
+ _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = MixtralModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ self.router_aux_loss_coef = config.router_aux_loss_coef
+ self.num_experts = config.num_local_experts
+ self.num_experts_per_tok = config.num_experts_per_tok
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> MoeCausalLMOutputWithPast:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MixtralForCausalLM
+
+ >>> model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
+ >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+
+ output_router_logits = (
+ output_router_logits if output_router_logits is not None else self.config.output_router_logits
+ )
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs: MoeModelOutputWithPast = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_router_logits=output_router_logits,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+ aux_loss = None
+ if output_router_logits:
+ aux_loss = load_balancing_loss_func(
+ outputs.router_logits,
+ self.num_experts,
+ self.num_experts_per_tok,
+ attention_mask,
+ )
+ if labels is not None:
+ loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
+
+ return MoeCausalLMOutputWithPast(
+ loss=loss,
+ aux_loss=aux_loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ router_logits=outputs.router_logits,
+ )
+
+
+class MixtralForSequenceClassification(GenericForSequenceClassification, MixtralPreTrainedModel):
+ pass
+
+
+class MixtralForTokenClassification(GenericForTokenClassification, MixtralPreTrainedModel):
+ pass
+
+
+class MixtralForQuestionAnswering(GenericForQuestionAnswering, MixtralPreTrainedModel):
+ pass
+
+
+__all__ = [
+ "MixtralForCausalLM",
+ "MixtralForQuestionAnswering",
+ "MixtralModel",
+ "MixtralPreTrainedModel",
+ "MixtralForSequenceClassification",
+ "MixtralForTokenClassification",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/modular_mixtral.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/modular_mixtral.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffcf8224353fd643068f76bf6152e40b21fe8ab9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mixtral/modular_mixtral.py
@@ -0,0 +1,468 @@
+# coding=utf-8
+# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Mixtral model."""
+
+from typing import Optional, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, logging
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import OutputRecorder
+from ..mistral.modeling_mistral import (
+ MistralAttention,
+ MistralForCausalLM,
+ MistralForQuestionAnswering,
+ MistralForSequenceClassification,
+ MistralForTokenClassification,
+ MistralModel,
+ MistralPreTrainedModel,
+ MistralRMSNorm,
+ MistralRotaryEmbedding,
+)
+from .configuration_mixtral import MixtralConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def load_balancing_loss_func(
+ gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
+ num_experts: Optional[int] = None,
+ top_k=2,
+ attention_mask: Optional[torch.Tensor] = None,
+) -> Union[torch.Tensor, int]:
+ r"""
+ Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+ See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
+ function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+ experts is too unbalanced.
+
+ Args:
+ gate_logits:
+ Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+ shape [batch_size X sequence_length, num_experts].
+ num_experts:
+ Number of experts
+ top_k:
+ The number of experts to route per-token, can be also interpreted as the `top-k` routing
+ parameter.
+ attention_mask (`torch.Tensor`, *optional*):
+ The attention_mask used in forward function
+ shape [batch_size X sequence_length] if not None.
+
+ Returns:
+ The auxiliary loss.
+ """
+ if gate_logits is None or not isinstance(gate_logits, tuple):
+ return 0
+
+ if isinstance(gate_logits, tuple):
+ compute_device = gate_logits[0].device
+ concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+ routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+ _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+ if attention_mask is None:
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.mean(routing_weights, dim=0)
+ else:
+ batch_size, sequence_length = attention_mask.shape
+ num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+ expert_attention_mask = (
+ attention_mask[None, :, :, None, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+ .reshape(-1, top_k, num_experts)
+ .to(compute_device)
+ )
+
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+ expert_attention_mask, dim=0
+ )
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+ router_per_expert_attention_mask = (
+ attention_mask[None, :, :, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
+ .reshape(-1, num_experts)
+ .to(compute_device)
+ )
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+ router_per_expert_attention_mask, dim=0
+ )
+
+ overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
+ return overall_loss * num_experts
+
+
+class MixtralBlockSparseTop2MLP(nn.Module):
+ def __init__(self, config: MixtralConfig):
+ super().__init__()
+ self.ffn_dim = config.intermediate_size
+ self.hidden_dim = config.hidden_size
+
+ self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+ self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+ self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, hidden_states):
+ current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+ current_hidden_states = self.w2(current_hidden_states)
+ return current_hidden_states
+
+
+class MixtralSparseMoeBlock(nn.Module):
+ """
+ This implementation is
+ strictly equivalent to standard MoE with full capacity (no
+ dropped tokens). It's faster since it formulates MoE operations
+ in terms of block-sparse operations to accommodate imbalanced
+ assignments of tokens to experts, whereas standard MoE either
+ (1) drop tokens at the cost of reduced performance or (2) set
+ capacity factor to number of experts and thus waste computation
+ and memory on padding.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.hidden_dim = config.hidden_size
+ self.ffn_dim = config.intermediate_size
+ self.num_experts = config.num_local_experts
+ self.top_k = config.num_experts_per_tok
+
+ # gating
+ self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+
+ self.experts = nn.ModuleList([MixtralBlockSparseTop2MLP(config) for _ in range(self.num_experts)])
+
+ # Jitter parameters
+ self.jitter_noise = config.router_jitter_noise
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ """ """
+ batch_size, sequence_length, hidden_dim = hidden_states.shape
+ if self.training and self.jitter_noise > 0:
+ hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
+ hidden_states = hidden_states.view(-1, hidden_dim)
+ # router_logits: (batch * sequence_length, n_experts)
+ router_logits = self.gate(hidden_states)
+
+ routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+ routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+ routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+ # we cast back to the input dtype
+ routing_weights = routing_weights.to(hidden_states.dtype)
+
+ final_hidden_states = torch.zeros(
+ (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+ )
+
+ # One hot encode the selected experts to create an expert mask
+ # this will be used to easily index which expert is going to be sollicitated
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+ expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+ for expert_idx in expert_hit:
+ expert_layer = self.experts[expert_idx]
+ idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
+ # Index the correct hidden states and compute the expert hidden state for
+ # the current expert. We need to make sure to multiply the output hidden
+ # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+ current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+ current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
+
+ # However `index_add_` only support torch tensors for indexing so we'll use
+ # the `top_x` tensor here.
+ final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+ final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+ return final_hidden_states, router_logits
+
+
+class MixtralRMSNorm(MistralRMSNorm):
+ pass
+
+
+class MixtralAttention(MistralAttention):
+ pass
+
+
+class MixtralDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MixtralConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = MixtralAttention(config, layer_idx)
+
+ self.block_sparse_moe = MixtralSparseMoeBlock(config)
+ self.input_layernorm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[torch.Tensor]] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> torch.FloatTensor:
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states, _ = self.block_sparse_moe(hidden_states)
+ hidden_states = residual + hidden_states
+
+ return hidden_states
+
+
+class MixtralRotaryEmbedding(MistralRotaryEmbedding):
+ pass
+
+
+class MixtralPreTrainedModel(MistralPreTrainedModel):
+ _can_compile_fullgraph = False # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
+ _can_record_outputs = {
+ "router_logits": OutputRecorder(MixtralSparseMoeBlock, index=1),
+ "hidden_states": MixtralDecoderLayer,
+ "attentions": MixtralAttention,
+ }
+
+
+class MixtralModel(MistralModel):
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> MoeModelOutputWithPast:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache(config=self.config)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
+ causal_mask = mask_function(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+
+ # create position embeddings to be shared across the decoder layers
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ hidden_states = decoder_layer(
+ hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = self.norm(hidden_states)
+
+ return MoeModelOutputWithPast( # only diff with Mistral is the output type, we need MoE
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+class MixtralForCausalLM(MistralForCausalLM):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = MixtralModel(config)
+ self.router_aux_loss_coef = config.router_aux_loss_coef
+ self.num_experts = config.num_local_experts
+ self.num_experts_per_tok = config.num_experts_per_tok
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> MoeCausalLMOutputWithPast:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MixtralForCausalLM
+
+ >>> model = MixtralForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
+ >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+
+ output_router_logits = (
+ output_router_logits if output_router_logits is not None else self.config.output_router_logits
+ )
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs: MoeModelOutputWithPast = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_router_logits=output_router_logits,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+ aux_loss = None
+ if output_router_logits:
+ aux_loss = load_balancing_loss_func(
+ outputs.router_logits,
+ self.num_experts,
+ self.num_experts_per_tok,
+ attention_mask,
+ )
+ if labels is not None:
+ loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
+
+ return MoeCausalLMOutputWithPast(
+ loss=loss,
+ aux_loss=aux_loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ router_logits=outputs.router_logits,
+ )
+
+
+class MixtralForSequenceClassification(MistralForSequenceClassification):
+ pass
+
+
+class MixtralForTokenClassification(MistralForTokenClassification):
+ pass
+
+
+class MixtralForQuestionAnswering(MistralForQuestionAnswering):
+ pass
+
+
+__all__ = [
+ "MixtralForCausalLM",
+ "MixtralForQuestionAnswering",
+ "MixtralModel",
+ "MixtralPreTrainedModel",
+ "MixtralForSequenceClassification",
+ "MixtralForTokenClassification",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3556177d5eb8e493aa5e051785b980b0e10d9c4a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mlcd import *
+ from .modeling_mlcd import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/configuration_mlcd.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/configuration_mlcd.py
new file mode 100644
index 0000000000000000000000000000000000000000..f28a5f1a7cab3d453c3bf07caea9fa9fd25a593b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/configuration_mlcd.py
@@ -0,0 +1,117 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/mlcd/modular_mlcd.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_mlcd.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ...configuration_utils import PretrainedConfig
+
+
+class MLCDVisionConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MLCDVisionModel`]. It is used to instantiate a MLCD
+ vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the vision encoder of the MLCD
+ [DeepGlint-AI/mlcd-vit-bigG-patch14-336](https://huggingface.co/DeepGlint-AI/mlcd-vit-bigG-patch14-336) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ hidden_size (`int`, *optional*, defaults to 1664):
+ Dimensionality of the encoder layers and the pooler layer.
+ intermediate_size (`int`, *optional*, defaults to 8192):
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ projection_dim (`int`, *optional*, defaults to 1024):
+ Dimensionality of text and vision projection layers.
+ num_hidden_layers (`int`, *optional*, defaults to 48):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ image_size (`int`, *optional*, defaults to 336):
+ The size (resolution) of each image.
+ patch_size (`int`, *optional*, defaults to 14):
+ The size (resolution) of each patch.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the layer normalization layers.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ initializer_factor (`float`, *optional*, defaults to 1.0):
+ A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+ testing).
+
+ Example:
+
+ ```python
+ >>> from transformers import MLCDVisionConfig, MLCDVisionModel
+
+ >>> # Initializing a MLCDVisionConfig with DeepGlint-AI/mlcd-vit-bigG-patch14-336 style configuration
+ >>> configuration = MLCDVisionConfig()
+
+ >>> # Initializing a MLCDVisionModel (with random weights) from the DeepGlint-AI/mlcd-vit-bigG-patch14-336 style configuration
+ >>> model = MLCDVisionModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mlcd_vision_model"
+ base_config_key = "vision_config"
+
+ def __init__(
+ self,
+ hidden_size=1664,
+ intermediate_size=8192,
+ num_hidden_layers=48,
+ num_attention_heads=16,
+ num_key_value_groups=1,
+ num_channels=3,
+ image_size=336,
+ patch_size=14,
+ hidden_act="gelu",
+ layer_norm_eps=1e-5,
+ attention_dropout=0.0,
+ initializer_range=0.02,
+ initializer_factor=1.0,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.num_key_value_groups = num_key_value_groups
+ self.num_channels = num_channels
+ self.patch_size = patch_size
+ self.image_size = image_size
+ self.initializer_range = initializer_range
+ self.initializer_factor = initializer_factor
+ self.attention_dropout = attention_dropout
+ self.layer_norm_eps = layer_norm_eps
+ self.hidden_act = hidden_act
+
+
+__all__ = ["MLCDVisionConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/convert_mlcd_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/convert_mlcd_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f74b64737a217a87d63cccb1f48c209328a6494
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/convert_mlcd_weights_to_hf.py
@@ -0,0 +1,336 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert MLCD checkpoints from the original repository.
+
+URL: https://github.com/deepglint/unicom/tree/main
+"""
+
+import argparse
+import collections
+import os
+import re
+
+import numpy as np
+import requests
+import torch
+from PIL import Image
+
+from transformers import CLIPImageProcessor
+
+from ...utils import logging
+from .configuration_mlcd import MLCDVisionConfig
+from .modeling_mlcd import MLCDVisionModel
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+COMMON_CONFIG_PARAMS = {
+ "mlcd-vit-bigG-patch14-336": {
+ "hidden_size": 1664,
+ "image_size": 336,
+ "intermediate_size": 8192,
+ "num_attention_heads": 16,
+ "num_hidden_layers": 48,
+ "patch_size": 14,
+ "projection_dim": 1024,
+ },
+ "mlcd-vit-bigG-patch14-448": {
+ "hidden_size": 1664,
+ "image_size": 448,
+ "intermediate_size": 8192,
+ "num_attention_heads": 16,
+ "num_hidden_layers": 48,
+ "patch_size": 14,
+ "projection_dim": 1024,
+ },
+}
+
+MODEL_NAME_TO_CHECKPOINT_PATH = {
+ # base checkpoints
+ "mlcd-vit-bigG-patch14-336": "MLCD_ViT_bigG_14_336px_pytorch.pt",
+ "mlcd-vit-bigG-patch14-448": "MLCD_ViT_bigG_14_448px_pytorch.pt",
+}
+
+# fmt: off
+EXPECTED_OUTPUTS = {
+ "mlcd-vit-bigG-patch14-336": torch.tensor([
+ [-0.8921, -0.1069, 0.2989, 0.6018, -0.5892],
+ [ 0.4093, -1.4592, 0.6048, -0.5147, -0.5929],
+ [ 0.7796, -0.7133, -0.5649, -0.7843, -0.5548],
+ [ 0.0041, 0.0286, 0.4310, -0.1403, -0.2399],
+ [ 0.0839, 0.2152, -0.3822, -0.1668, -0.7886]
+ ]),
+ "mlcd-vit-bigG-patch14-448": torch.tensor([
+ [-0.8978, -0.1181, 0.4769, 0.4761, -0.5779],
+ [ 0.2640, -2.6150, 0.4853, 0.5743, -1.1003],
+ [ 0.3314, -0.3328, -0.4305, -0.1874, -0.7701],
+ [-1.5174, -1.0238, -1.1854, 0.1749, -0.8786],
+ [ 0.2323, -0.8346, -0.9680, -0.2951, 0.0867],
+ ]),
+}
+# fmt: on
+
+# fmt: off
+ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
+ # Vision embeddings
+ r"conv1.weight": r"vision_model.embeddings.patch_embedding.weight",
+ r"class_embedding": r"vision_model.embeddings.class_embedding",
+ r"vision_rotary_embedding": r"vision_model.vision_rotary_embedding",
+ r"class_pos_emb": r"vision_model.class_pos_emb",
+ # Vision encoder
+ r"transformer.resblocks_(\d+).ln_1.weight": r"vision_model.encoder.layers.\1.layer_norm1.weight",
+ r"transformer.resblocks_(\d+).ln_1.bias": r"vision_model.encoder.layers.\1.layer_norm1.bias",
+ r"transformer.resblocks_(\d+).ln_2.weight": r"vision_model.encoder.layers.\1.layer_norm2.weight",
+ r"transformer.resblocks_(\d+).ln_2.bias": r"vision_model.encoder.layers.\1.layer_norm2.bias",
+ r"transformer.resblocks_(\d+).mlp.c_fc.weight": r"vision_model.encoder.layers.\1.mlp.fc1.weight",
+ r"transformer.resblocks_(\d+).mlp.c_fc.bias": r"vision_model.encoder.layers.\1.mlp.fc1.bias",
+ r"transformer.resblocks_(\d+).mlp.c_proj.weight": r"vision_model.encoder.layers.\1.mlp.fc2.weight",
+ r"transformer.resblocks_(\d+).mlp.c_proj.bias": r"vision_model.encoder.layers.\1.mlp.fc2.bias",
+ r"transformer.resblocks_(\d+).attn.(q|k|v|out)_proj.weight": r"vision_model.encoder.layers.\1.self_attn.\2_proj.weight",
+ r"transformer.resblocks_(\d+).attn.(q|k|v|out)_proj.bias": r"vision_model.encoder.layers.\1.self_attn.\2_proj.bias",
+ # Vision norm
+ r"ln_post.weight": r"vision_model.post_layernorm.weight",
+ r"ln_post.bias": r"vision_model.post_layernorm.bias",
+ r"ln_pre.weight": r"vision_model.pre_layernorm.weight",
+ r"ln_pre.bias": r"vision_model.pre_layernorm.bias",
+}
+# fmt: on
+
+
+# --------------------------------------------------------------------------------------------
+# Model objects: configuration, image processor
+# --------------------------------------------------------------------------------------------
+
+
+def get_mlcd_config(model_name: str) -> MLCDVisionConfig:
+ """
+ Create a configuration for the MLCD model based on the model name.
+ """
+ assert model_name in COMMON_CONFIG_PARAMS, f"Model {model_name} not found in the list of COMMON_CONFIG_PARAMS."
+ config_params = COMMON_CONFIG_PARAMS[model_name]
+ config = MLCDVisionConfig(
+ hidden_size=config_params["hidden_size"],
+ image_size=config_params["image_size"],
+ intermediate_size=config_params["intermediate_size"],
+ num_attention_heads=config_params["num_attention_heads"],
+ num_hidden_layers=config_params["num_hidden_layers"],
+ patch_size=config_params["patch_size"],
+ projection_dim=config_params["projection_dim"],
+ )
+ return config
+
+
+def get_mlcd_image_processor(model_name: str) -> CLIPImageProcessor:
+ """
+ Create an image processor for the MLCD model based on the model name.
+ """
+ assert model_name in COMMON_CONFIG_PARAMS, f"Model {model_name} not found in the list of COMMON_CONFIG_PARAMS."
+ config_params = COMMON_CONFIG_PARAMS[model_name]
+ image_processor = CLIPImageProcessor(
+ do_center_crop=True,
+ do_normalize=True,
+ do_resize=True,
+ feature_extractor_type="CLIPFeatureExtractor",
+ image_mean=[0.48145466, 0.4578275, 0.40821073],
+ image_std=[0.26862954, 0.26130258, 0.27577711],
+ resample=3,
+ size=config_params["image_size"],
+ crop_size=config_params["image_size"],
+ )
+ return image_processor
+
+
+# --------------------------------------------------------------------------------------------
+# Helper functions for state dict conversion
+# --------------------------------------------------------------------------------------------
+
+
+def flatten_nested_dict(params: dict, parent_key: str = "", sep: str = ".") -> dict:
+ """
+ Flatten a nested original checkpoint dictionary into a flat dictionary.
+ """
+ items = []
+ for k, v in params.items():
+ new_key = parent_key + sep + k if parent_key else k
+ if isinstance(v, collections.abc.MutableMapping):
+ items.extend(flatten_nested_dict(v, new_key, sep=sep).items())
+ else:
+ items.append((new_key, v))
+ return dict(items)
+
+
+def split_resblocks_layers(state_dict: dict) -> dict:
+ """
+ Split the resblocks weight into layers. In some cases they are concatenated in
+ the original checkpoints.
+ """
+ # Make shallow copy
+ state_dict = state_dict.copy()
+ # Split resblocks weight into layers
+ keys = list(state_dict.keys())
+ for key in keys:
+ if ".resblocks." in key:
+ weight = state_dict.pop(key)
+ for i, weight_i in enumerate(weight):
+ new_name = key.replace("resblocks", f"resblocks_{i}")
+ state_dict[new_name] = weight_i
+ return state_dict
+
+
+def chunk_qkv_for_attn(state_dict: dict) -> dict:
+ """
+ Chunk the q/k/v weights and biases for the attention layers.
+ """
+ # Make shallow copy
+ state_dict = state_dict.copy()
+ # Read and process q/k/v weights and biases
+ keys = list(state_dict.keys())
+ for key in keys:
+ if ".in_proj." in key:
+ weight = state_dict.pop(key)
+ qkv_weights = weight.chunk(3, dim=0)
+ for name, weight_i in zip(["q_proj", "k_proj", "v_proj"], qkv_weights):
+ new_name = key.replace("in_proj", name)
+ state_dict[new_name] = weight_i
+ return state_dict
+
+
+def convert_old_keys_to_new_keys(state_dict_keys: list) -> dict:
+ """
+ This function should be applied only once, on the concatenated keys to efficiently rename using
+ the key mappings.
+ """
+ output_dict = {}
+ if state_dict_keys is not None:
+ old_text = "\n".join(state_dict_keys)
+ new_text = old_text
+ for pattern, replacement in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items():
+ if replacement is None:
+ new_text = re.sub(pattern, "", new_text) # an empty line
+ continue
+ new_text = re.sub(pattern, replacement, new_text)
+ output_dict = dict(zip(old_text.split("\n"), new_text.split("\n")))
+ return output_dict
+
+
+# --------------------------------------------------------------------------------------------
+# Convert model
+# --------------------------------------------------------------------------------------------
+
+
+@torch.no_grad()
+def convert_mlcd_checkpoint(model_name, input_dir, output_dir, verify_hidden_state=True, push_to_hub=False):
+ """
+ Copy/paste/tweak model's weights to our MLCD structure.
+ """
+
+ # Define MLCD configuration
+ config = get_mlcd_config(model_name)
+
+ checkpoint = MODEL_NAME_TO_CHECKPOINT_PATH[model_name]
+ checkpoint_path = os.path.join(input_dir, checkpoint)
+ assert os.path.exists(checkpoint_path), f"Checkpoint path ({checkpoint_path}) not found."
+
+ # Load original checkpoint
+ print(f"Loading checkpoint from {checkpoint_path}...")
+ state_dict = torch.load(checkpoint_path, "cpu")
+
+ # Flatten nested dictionary
+ print("Flattening nested dictionary...")
+ state_dict = {k.replace("_orig_mod.", ""): v for k, v in state_dict.items()}
+ if "positional_embedding" in state_dict:
+ state_dict.pop("positional_embedding")
+ state_dict = flatten_nested_dict(state_dict)
+ state_dict = split_resblocks_layers(state_dict)
+ state_dict = chunk_qkv_for_attn(state_dict)
+
+ # Rename and transform weights
+ print("Renaming and transforming weights...")
+ original_keys = list(state_dict.keys())
+ hf_keys = convert_old_keys_to_new_keys(original_keys)
+ new_state_dict = {}
+ for original_key in original_keys:
+ new_key = hf_keys[original_key]
+ parameter = state_dict.pop(original_key)
+ new_state_dict[new_key] = torch.from_numpy(parameter)
+
+ # load HuggingFace model
+ print("Loading HuggingFace model...")
+ model = MLCDVisionModel(config).eval()
+ model.load_state_dict(new_state_dict)
+
+ # Create processor
+ print("Creating processor...")
+ image_processor = get_mlcd_image_processor(model_name)
+
+ # Verify hidden state
+ if verify_hidden_state:
+ print("Verifying hidden state for {model_name}...")
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ image = Image.open(requests.get(url, stream=True).raw)
+ pixel_values = image_processor(image, return_tensors="pt")["pixel_values"]
+ last_hidden_state = model(pixel_values, output_hidden_states=True).last_hidden_state[0, :5, :5]
+ expected_hidden_state = EXPECTED_OUTPUTS[model_name]
+ np.testing.assert_allclose(last_hidden_state.cpu().numpy(), expected_hidden_state.numpy(), atol=1e-4)
+
+ # Save model
+ if output_dir is not None:
+ dst_dir = os.path.join(output_dir, model_name)
+ print(f"Saving model {model_name} to {dst_dir}...")
+ model.save_pretrained(dst_dir)
+ print(f"Saving processor to {dst_dir}...")
+ image_processor.save_pretrained(dst_dir)
+
+ if push_to_hub:
+ print(f"Pushing model and processor for {model_name} to the HuggingFace Hub...")
+ model.push_to_hub(f"deepglint-hf/{model_name}", private=True)
+ image_processor.push_to_hub(f"deepglint-hf/{model_name}", private=True)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--model_name",
+ default="mlcd-vit-bigG-patch14-448",
+ type=str,
+ choices=MODEL_NAME_TO_CHECKPOINT_PATH.keys(),
+ help="Name of the model you'd like to convert.",
+ )
+ parser.add_argument(
+ "--input_dir",
+ default="mlcd/original",
+ help="Location of MLCD original weights",
+ )
+ parser.add_argument(
+ "--output_dir",
+ default="mlcd/checkpoint",
+ help="Location to write HF model and processor",
+ )
+ parser.add_argument(
+ "--verify_hidden_state",
+ action="store_true",
+ help="Whether to verify hidden_state against the original implementation.",
+ )
+ parser.add_argument(
+ "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
+ )
+
+ args = parser.parse_args()
+ convert_mlcd_checkpoint(
+ args.model_name, args.input_dir, args.output_dir, args.verify_hidden_state, args.push_to_hub
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/modeling_mlcd.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/modeling_mlcd.py
new file mode 100644
index 0000000000000000000000000000000000000000..5379c5313726dd2da0401b766580b569e354e277
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/modeling_mlcd.py
@@ -0,0 +1,611 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/mlcd/modular_mlcd.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_mlcd.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn as nn
+
+from ...activations import ACT2FN
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, torch_int
+from .configuration_mlcd import MLCDVisionConfig
+
+
+class MLCDMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.activation_fn = ACT2FN[config.hidden_act]
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+class MLCDRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, dim: int, theta: float = 10000.0) -> None:
+ super().__init__()
+ inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ def forward(self, num_patches_height: int, num_patches_width: int) -> torch.Tensor:
+ """
+ Calculate the Rotary Position Embedding (RoPE) for MLCDVisionModel based on the grid size.
+
+ Args:
+ num_patches_height (int): Number of patches in the height dimension.
+ num_patches_width (int): Number of patches in the width dimension.
+
+ Returns:
+ torch.Tensor: Rotary positional embeddings for the given grid size.
+ """
+ # Generate position IDs for height and width dimensions
+ hpos_ids = (
+ torch.arange(num_patches_height, device=self.inv_freq.device).unsqueeze(1).expand(-1, num_patches_width)
+ )
+ wpos_ids = (
+ torch.arange(num_patches_width, device=self.inv_freq.device).unsqueeze(0).expand(num_patches_height, -1)
+ )
+
+ # Flatten and stack the position IDs
+ pos_ids = torch.stack([hpos_ids.flatten(), wpos_ids.flatten()], dim=-1)
+
+ # Generate the full rotary positional embeddings for the maximum grid size
+ max_grid_size = max(num_patches_height, num_patches_width)
+ seq = torch.arange(max_grid_size, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+ rotary_pos_emb_full = torch.outer(seq, self.inv_freq)
+
+ # Select and flatten the embeddings based on the position IDs
+ rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+
+ return rotary_pos_emb
+
+
+class MLCDVisionEmbeddings(nn.Module):
+ def __init__(self, config: MLCDVisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.image_size = config.image_size
+ self.patch_size = config.patch_size
+
+ self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
+
+ self.patch_embedding = nn.Conv2d(
+ in_channels=config.num_channels,
+ out_channels=self.embed_dim,
+ kernel_size=self.patch_size,
+ stride=self.patch_size,
+ bias=False,
+ )
+
+ self.num_patches = (self.image_size // self.patch_size) ** 2
+ self.num_positions = self.num_patches + 1
+ self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
+
+ def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
+ """
+ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
+ images. This method is also adapted to support torch.jit tracing.
+
+ Adapted from:
+ - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
+ - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
+ """
+
+ num_patches = embeddings.shape[1] - 1
+ position_embedding = self.position_embedding.weight.unsqueeze(0)
+ num_positions = position_embedding.shape[1] - 1
+
+ # always interpolate when tracing to ensure the exported model works for dynamic input shapes
+ if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
+ return self.position_embedding(self.position_ids)
+
+ class_pos_embed = position_embedding[:, :1]
+ patch_pos_embed = position_embedding[:, 1:]
+
+ dim = embeddings.shape[-1]
+
+ new_height = height // self.patch_size
+ new_width = width // self.patch_size
+
+ sqrt_num_positions = torch_int(num_positions**0.5)
+ patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
+ patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+ patch_pos_embed = nn.functional.interpolate(
+ patch_pos_embed,
+ size=(new_height, new_width),
+ mode="bicubic",
+ align_corners=False,
+ )
+
+ patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+
+ return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
+
+ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+ batch_size = pixel_values.shape[0]
+ target_dtype = self.patch_embedding.weight.dtype
+ # patch_embeds -> shape = [batch, width, grid, grid]
+ patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
+ patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+
+ class_embeds = self.class_embedding.expand(batch_size, 1, -1)
+ embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+
+ return embeddings
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def apply_rotary_pos_emb_vision(
+ q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+ orig_q_dtype = q.dtype
+ orig_k_dtype = k.dtype
+ q, k = q.float(), k.float()
+ cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float()
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ q_embed = q_embed.to(orig_q_dtype)
+ k_embed = k_embed.to(orig_k_dtype)
+ return q_embed, k_embed
+
+
+class MLCDAttention(nn.Module):
+ """Multi-headed attention with RoPE. Refer to papers:
+ - Attention is all you need:
+ https://huggingface.co/papers/1706.03762
+ - RoFormer: Enhanced Transformer with Rotary Position Embedding:
+ https://huggingface.co/papers/2104.09864
+ """
+
+ def __init__(self, config: MLCDVisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.embed_dim // self.num_heads
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+ f" {self.num_heads})."
+ )
+ self.scale = self.head_dim**-0.5
+ self.dropout = config.attention_dropout
+ self.is_causal = False
+
+ self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
+ self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
+ self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
+ self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
+ self.num_key_value_groups = config.num_key_value_groups
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ """Input shape: Batch x Time x Channel"""
+ batch_size, seq_length = hidden_states.shape[:-1]
+
+ # Each of shape: [batch_size, seq_length, num_heads, head_dim]
+ query_states = self.q_proj(hidden_states).reshape((batch_size, seq_length, self.num_heads, self.head_dim))
+ key_states = self.k_proj(hidden_states).reshape((batch_size, seq_length, self.num_heads, self.head_dim))
+ value_states = self.v_proj(hidden_states).reshape((batch_size, seq_length, self.num_heads, self.head_dim))
+
+ # Apply positional embeddings
+ cos = position_embeddings[0].unsqueeze(0).float()
+ sin = position_embeddings[1].unsqueeze(0).float()
+ query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin)
+
+ # Each of shape: [batch_size, num_heads, seq_length, head_dim]
+ query_states = query_states.permute(0, 2, 1, 3).contiguous()
+ key_states = key_states.permute(0, 2, 1, 3).contiguous()
+ value_states = value_states.permute(0, 2, 1, 3).contiguous()
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=self.scale,
+ is_causal=self.is_causal,
+ **kwargs,
+ )
+
+ attn_output = attn_output.permute(1, 0, 2, 3).contiguous() # [seq_length, batch_size, num_heads, head_dim]
+ attn_output = attn_output.view(seq_length, batch_size, -1) # [seq_length, batch_size, embedding_dim]
+ attn_output = self.out_proj(attn_output)
+ attn_output = attn_output.permute(1, 0, 2).contiguous() # [batch_size, seq_length, embedding_dim]
+ return attn_output, attn_weights
+
+
+class MLCDEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MLCDVisionConfig):
+ super().__init__()
+ self.embed_dim = config.hidden_size
+ self.self_attn = MLCDAttention(config)
+ self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+ self.mlp = MLCDMLP(config)
+ self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.FloatTensor]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`):
+ Input to the layer of shape `(batch, seq_len, embed_dim)`.
+ Represents the hidden states from the previous layer or the input embeddings.
+ position_embeddings (`tuple[torch.Tensor, torch.Tensor]`):
+ A tuple of two tensors, each of shape `(batch, seq_len, embed_dim)`.
+ Represents absolute positional embeddings for the query and key in the attention mechanism.
+ attention_mask (`torch.FloatTensor`):
+ Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
+ output_attentions (`bool`, *optional*, defaults to `False`):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+
+ hidden_states = self.layer_norm1(hidden_states)
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+ hidden_states = self.layer_norm2(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs
+
+
+class MLCDEncoder(nn.Module):
+ """
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+ [`MLCDEncoderLayer`].
+
+ Args:
+ config: MLCDVisionConfig
+ """
+
+ def __init__(self, config: MLCDVisionConfig):
+ """Overwrite dummy `MLCDConfig` to `MLCDVisionConfig`."""
+ super().__init__()
+ self.config = config
+ self.layers = nn.ModuleList([MLCDEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ inputs_embeds: torch.FloatTensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutput]:
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ position_embeddings (`tuple[torch.Tensor, torch.Tensor]`):
+ A tuple of two tensors, each of shape `(batch, seq_len, embed_dim)`.
+ Represents absolute positional embeddings for the query and key in the attention mechanism.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ [What are attention masks?](../glossary#attention-mask)
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ hidden_states = inputs_embeds
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ layer_outputs = encoder_layer(
+ hidden_states=hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=encoder_states,
+ attentions=all_attentions,
+ )
+
+
+class MLCDVisionTransformer(nn.Module):
+ def __init__(self, config: MLCDVisionConfig):
+ super().__init__()
+ self.config = config
+ embed_dim = config.hidden_size
+
+ self.embeddings = MLCDVisionEmbeddings(config)
+ self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+ self.encoder = MLCDEncoder(config)
+ self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+ self.vision_rotary_embedding = MLCDRotaryEmbedding(config.hidden_size // config.num_attention_heads // 2)
+ self.class_pos_emb = nn.Parameter(torch.randn(1, config.hidden_size // config.num_attention_heads // 2))
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPooling]:
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ num_patches_height = pixel_values.shape[-2] // self.config.patch_size
+ num_patches_width = pixel_values.shape[-1] // self.config.patch_size
+ rotary_pos_emb = self.vision_rotary_embedding(num_patches_height, num_patches_width)
+ rotary_pos_emb = rotary_pos_emb.to(self.class_pos_emb.device)
+ rotary_pos_emb = torch.cat([self.class_pos_emb, rotary_pos_emb], dim=0)
+ emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+ position_embeddings = (emb.cos(), emb.sin())
+
+ hidden_states = self.embeddings(pixel_values)
+ hidden_states = self.pre_layrnorm(hidden_states)
+
+ encoder_outputs = self.encoder(
+ inputs_embeds=hidden_states,
+ position_embeddings=position_embeddings,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ last_hidden_state = encoder_outputs[0]
+ pooled_output = last_hidden_state[:, 0, :]
+ pooled_output = self.post_layernorm(pooled_output)
+
+ if not return_dict:
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring
+class MLCDPreTrainedModel(PreTrainedModel):
+ config: MLCDVisionConfig
+ base_model_prefix = "mlcd"
+ supports_gradient_checkpointing = True
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ factor = self.config.initializer_factor
+ if isinstance(module, MLCDVisionEmbeddings):
+ factor = self.config.initializer_factor
+ nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
+ nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
+ elif isinstance(module, MLCDAttention):
+ factor = self.config.initializer_factor
+ in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
+ out_proj_std = (module.embed_dim**-0.5) * factor
+ nn.init.normal_(module.q_proj.weight, std=in_proj_std)
+ nn.init.normal_(module.k_proj.weight, std=in_proj_std)
+ nn.init.normal_(module.v_proj.weight, std=in_proj_std)
+ nn.init.normal_(module.out_proj.weight, std=out_proj_std)
+ elif isinstance(module, MLCDMLP):
+ factor = self.config.initializer_factor
+ in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
+ fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
+ nn.init.normal_(module.fc1.weight, std=fc_std)
+ nn.init.normal_(module.fc2.weight, std=in_proj_std)
+ elif isinstance(module, MLCDVisionTransformer):
+ factor = self.config.initializer_factor
+ pos_emb_std = (module.config.hidden_size // module.config.num_attention_heads // 2) ** -0.5 * factor
+ nn.init.normal_(module.class_pos_emb, mean=0.0, std=pos_emb_std)
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, nn.Linear) and module.bias is not None:
+ module.bias.data.zero_()
+
+
+@auto_docstring(
+ custom_intro="""
+ The vision model from M_L_C_D without any head or projection on top.
+ """
+)
+class MLCDVisionModel(MLCDPreTrainedModel):
+ config: MLCDVisionConfig
+ main_input_name = "pixel_values"
+ _no_split_modules = ["MLCDEncoderLayer"]
+
+ def __init__(self, config: MLCDVisionConfig):
+ super().__init__(config)
+ self.vision_model = MLCDVisionTransformer(config)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self) -> nn.Module:
+ return self.vision_model.embeddings.patch_embedding
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPooling]:
+ r"""
+ Example:
+
+ ```python
+ >>> import requests
+ >>> from PIL import Image
+ >>> from transformers import AutoProcessor, MLCDVisionModel
+ >>> model = MLCDVisionModel.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448")
+ >>> processor = AutoProcessor.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448")
+
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> inputs = processor(images=image, return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs, output_attentions=True)
+
+ >>> features = outputs.last_hidden_state
+ >>> print(f"Extracted features shape: {features.shape}")
+ >>> print(f"Number of attention layers: {len(outputs.attentions)}")
+ >>> print(f"Attention shape: {outputs.attentions[0].shape}")
+ ```"""
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+ return self.vision_model(
+ pixel_values=pixel_values,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+
+__all__ = ["MLCDPreTrainedModel", "MLCDVisionModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/modular_mlcd.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/modular_mlcd.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcc18ab2b1c8f123be84c502d534c6000882a683
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mlcd/modular_mlcd.py
@@ -0,0 +1,529 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn as nn
+
+from ...configuration_utils import PretrainedConfig
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import auto_docstring, logging
+from ..clip.modeling_clip import (
+ CLIPMLP,
+ CLIPAttention,
+ CLIPEncoder,
+ CLIPEncoderLayer,
+ CLIPVisionEmbeddings,
+ CLIPVisionModel,
+ CLIPVisionTransformer,
+)
+from ..llama.modeling_llama import eager_attention_forward
+from ..qwen2_vl.modeling_qwen2_vl import VisionRotaryEmbedding, apply_rotary_pos_emb_vision
+
+
+logger = logging.get_logger(__name__)
+
+
+class MLCDVisionConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MLCDVisionModel`]. It is used to instantiate a MLCD
+ vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the vision encoder of the MLCD
+ [DeepGlint-AI/mlcd-vit-bigG-patch14-336](https://huggingface.co/DeepGlint-AI/mlcd-vit-bigG-patch14-336) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ hidden_size (`int`, *optional*, defaults to 1664):
+ Dimensionality of the encoder layers and the pooler layer.
+ intermediate_size (`int`, *optional*, defaults to 8192):
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ projection_dim (`int`, *optional*, defaults to 1024):
+ Dimensionality of text and vision projection layers.
+ num_hidden_layers (`int`, *optional*, defaults to 48):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ image_size (`int`, *optional*, defaults to 336):
+ The size (resolution) of each image.
+ patch_size (`int`, *optional*, defaults to 14):
+ The size (resolution) of each patch.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the layer normalization layers.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ initializer_factor (`float`, *optional*, defaults to 1.0):
+ A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+ testing).
+
+ Example:
+
+ ```python
+ >>> from transformers import MLCDVisionConfig, MLCDVisionModel
+
+ >>> # Initializing a MLCDVisionConfig with DeepGlint-AI/mlcd-vit-bigG-patch14-336 style configuration
+ >>> configuration = MLCDVisionConfig()
+
+ >>> # Initializing a MLCDVisionModel (with random weights) from the DeepGlint-AI/mlcd-vit-bigG-patch14-336 style configuration
+ >>> model = MLCDVisionModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mlcd_vision_model"
+ base_config_key = "vision_config"
+
+ def __init__(
+ self,
+ hidden_size=1664,
+ intermediate_size=8192,
+ num_hidden_layers=48,
+ num_attention_heads=16,
+ num_key_value_groups=1,
+ num_channels=3,
+ image_size=336,
+ patch_size=14,
+ hidden_act="gelu",
+ layer_norm_eps=1e-5,
+ attention_dropout=0.0,
+ initializer_range=0.02,
+ initializer_factor=1.0,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.num_key_value_groups = num_key_value_groups
+ self.num_channels = num_channels
+ self.patch_size = patch_size
+ self.image_size = image_size
+ self.initializer_range = initializer_range
+ self.initializer_factor = initializer_factor
+ self.attention_dropout = attention_dropout
+ self.layer_norm_eps = layer_norm_eps
+ self.hidden_act = hidden_act
+
+
+class MLCDMLP(CLIPMLP):
+ pass
+
+
+class MLCDRotaryEmbedding(VisionRotaryEmbedding):
+ def forward(self, num_patches_height: int, num_patches_width: int) -> torch.Tensor:
+ """
+ Calculate the Rotary Position Embedding (RoPE) for MLCDVisionModel based on the grid size.
+
+ Args:
+ num_patches_height (int): Number of patches in the height dimension.
+ num_patches_width (int): Number of patches in the width dimension.
+
+ Returns:
+ torch.Tensor: Rotary positional embeddings for the given grid size.
+ """
+ # Generate position IDs for height and width dimensions
+ hpos_ids = (
+ torch.arange(num_patches_height, device=self.inv_freq.device).unsqueeze(1).expand(-1, num_patches_width)
+ )
+ wpos_ids = (
+ torch.arange(num_patches_width, device=self.inv_freq.device).unsqueeze(0).expand(num_patches_height, -1)
+ )
+
+ # Flatten and stack the position IDs
+ pos_ids = torch.stack([hpos_ids.flatten(), wpos_ids.flatten()], dim=-1)
+
+ # Generate the full rotary positional embeddings for the maximum grid size
+ max_grid_size = max(num_patches_height, num_patches_width)
+ seq = torch.arange(max_grid_size, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+ rotary_pos_emb_full = torch.outer(seq, self.inv_freq)
+
+ # Select and flatten the embeddings based on the position IDs
+ rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+
+ return rotary_pos_emb
+
+
+class MLCDVisionEmbeddings(CLIPVisionEmbeddings):
+ def __init__(self, config: MLCDVisionConfig):
+ super().__init__(config)
+ del self.position_embedding
+
+ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+ batch_size = pixel_values.shape[0]
+ target_dtype = self.patch_embedding.weight.dtype
+ # patch_embeds -> shape = [batch, width, grid, grid]
+ patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
+ patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+
+ class_embeds = self.class_embedding.expand(batch_size, 1, -1)
+ embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+
+ return embeddings
+
+
+class MLCDAttention(CLIPAttention):
+ """Multi-headed attention with RoPE. Refer to papers:
+ - Attention is all you need:
+ https://huggingface.co/papers/1706.03762
+ - RoFormer: Enhanced Transformer with Rotary Position Embedding:
+ https://huggingface.co/papers/2104.09864
+ """
+
+ def __init__(self, config: MLCDVisionConfig):
+ super().__init__(config)
+ self.num_key_value_groups = config.num_key_value_groups
+ self.is_causal = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ batch_size, seq_length = hidden_states.shape[:-1]
+
+ # Each of shape: [batch_size, seq_length, num_heads, head_dim]
+ query_states = self.q_proj(hidden_states).reshape((batch_size, seq_length, self.num_heads, self.head_dim))
+ key_states = self.k_proj(hidden_states).reshape((batch_size, seq_length, self.num_heads, self.head_dim))
+ value_states = self.v_proj(hidden_states).reshape((batch_size, seq_length, self.num_heads, self.head_dim))
+
+ # Apply positional embeddings
+ cos = position_embeddings[0].unsqueeze(0).float()
+ sin = position_embeddings[1].unsqueeze(0).float()
+ query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin)
+
+ # Each of shape: [batch_size, num_heads, seq_length, head_dim]
+ query_states = query_states.permute(0, 2, 1, 3).contiguous()
+ key_states = key_states.permute(0, 2, 1, 3).contiguous()
+ value_states = value_states.permute(0, 2, 1, 3).contiguous()
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=self.scale,
+ is_causal=self.is_causal,
+ **kwargs,
+ )
+
+ attn_output = attn_output.permute(1, 0, 2, 3).contiguous() # [seq_length, batch_size, num_heads, head_dim]
+ attn_output = attn_output.view(seq_length, batch_size, -1) # [seq_length, batch_size, embedding_dim]
+ attn_output = self.out_proj(attn_output)
+ attn_output = attn_output.permute(1, 0, 2).contiguous() # [batch_size, seq_length, embedding_dim]
+ return attn_output, attn_weights
+
+
+class MLCDEncoderLayer(CLIPEncoderLayer):
+ def __init__(self, config: MLCDVisionConfig):
+ super().__init__(config)
+ self.self_attn = MLCDAttention(config)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.FloatTensor]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`):
+ Input to the layer of shape `(batch, seq_len, embed_dim)`.
+ Represents the hidden states from the previous layer or the input embeddings.
+ position_embeddings (`tuple[torch.Tensor, torch.Tensor]`):
+ A tuple of two tensors, each of shape `(batch, seq_len, embed_dim)`.
+ Represents absolute positional embeddings for the query and key in the attention mechanism.
+ attention_mask (`torch.FloatTensor`):
+ Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
+ output_attentions (`bool`, *optional*, defaults to `False`):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+
+ hidden_states = self.layer_norm1(hidden_states)
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+ hidden_states = self.layer_norm2(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs
+
+
+class MLCDEncoder(CLIPEncoder):
+ """
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+ [`MLCDEncoderLayer`].
+
+ Args:
+ config: MLCDVisionConfig
+ """
+
+ def __init__(self, config: MLCDVisionConfig):
+ """Overwrite dummy `MLCDConfig` to `MLCDVisionConfig`."""
+ super().__init__(config)
+
+ def forward(
+ self,
+ inputs_embeds: torch.FloatTensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutput]:
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ position_embeddings (`tuple[torch.Tensor, torch.Tensor]`):
+ A tuple of two tensors, each of shape `(batch, seq_len, embed_dim)`.
+ Represents absolute positional embeddings for the query and key in the attention mechanism.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ [What are attention masks?](../glossary#attention-mask)
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ hidden_states = inputs_embeds
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ layer_outputs = encoder_layer(
+ hidden_states=hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=encoder_states,
+ attentions=all_attentions,
+ )
+
+
+class MLCDVisionTransformer(CLIPVisionTransformer):
+ def __init__(self, config: MLCDVisionConfig):
+ super().__init__(config)
+ self.vision_rotary_embedding = MLCDRotaryEmbedding(config.hidden_size // config.num_attention_heads // 2)
+ self.class_pos_emb = nn.Parameter(torch.randn(1, config.hidden_size // config.num_attention_heads // 2))
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPooling]:
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ num_patches_height = pixel_values.shape[-2] // self.config.patch_size
+ num_patches_width = pixel_values.shape[-1] // self.config.patch_size
+ rotary_pos_emb = self.vision_rotary_embedding(num_patches_height, num_patches_width)
+ rotary_pos_emb = rotary_pos_emb.to(self.class_pos_emb.device)
+ rotary_pos_emb = torch.cat([self.class_pos_emb, rotary_pos_emb], dim=0)
+ emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+ position_embeddings = (emb.cos(), emb.sin())
+
+ hidden_states = self.embeddings(pixel_values)
+ hidden_states = self.pre_layrnorm(hidden_states)
+
+ encoder_outputs = self.encoder(
+ inputs_embeds=hidden_states,
+ position_embeddings=position_embeddings,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ last_hidden_state = encoder_outputs[0]
+ pooled_output = last_hidden_state[:, 0, :]
+ pooled_output = self.post_layernorm(pooled_output)
+
+ if not return_dict:
+ return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring
+class MLCDPreTrainedModel(PreTrainedModel):
+ config: MLCDVisionConfig
+ base_model_prefix = "mlcd"
+ supports_gradient_checkpointing = True
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ factor = self.config.initializer_factor
+ if isinstance(module, MLCDVisionEmbeddings):
+ factor = self.config.initializer_factor
+ nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
+ nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
+ elif isinstance(module, MLCDAttention):
+ factor = self.config.initializer_factor
+ in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
+ out_proj_std = (module.embed_dim**-0.5) * factor
+ nn.init.normal_(module.q_proj.weight, std=in_proj_std)
+ nn.init.normal_(module.k_proj.weight, std=in_proj_std)
+ nn.init.normal_(module.v_proj.weight, std=in_proj_std)
+ nn.init.normal_(module.out_proj.weight, std=out_proj_std)
+ elif isinstance(module, MLCDMLP):
+ factor = self.config.initializer_factor
+ in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
+ fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
+ nn.init.normal_(module.fc1.weight, std=fc_std)
+ nn.init.normal_(module.fc2.weight, std=in_proj_std)
+ elif isinstance(module, MLCDVisionTransformer):
+ factor = self.config.initializer_factor
+ pos_emb_std = (module.config.hidden_size // module.config.num_attention_heads // 2) ** -0.5 * factor
+ nn.init.normal_(module.class_pos_emb, mean=0.0, std=pos_emb_std)
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, nn.Linear) and module.bias is not None:
+ module.bias.data.zero_()
+
+
+class MLCDVisionModel(CLIPVisionModel):
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPooling]:
+ r"""
+ Example:
+
+ ```python
+ >>> import requests
+ >>> from PIL import Image
+ >>> from transformers import AutoProcessor, MLCDVisionModel
+ >>> model = MLCDVisionModel.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448")
+ >>> processor = AutoProcessor.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448")
+
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> inputs = processor(images=image, return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs, output_attentions=True)
+
+ >>> features = outputs.last_hidden_state
+ >>> print(f"Extracted features shape: {features.shape}")
+ >>> print(f"Number of attention layers: {len(outputs.attentions)}")
+ >>> print(f"Attention shape: {outputs.attentions[0].shape}")
+ ```"""
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+ return self.vision_model(
+ pixel_values=pixel_values,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+
+__all__ = [
+ "MLCDVisionConfig",
+ "MLCDPreTrainedModel",
+ "MLCDVisionModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..08d3994bbbe13e07fc6e89da627d7d14ce40be2c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mllama import *
+ from .image_processing_mllama import *
+ from .modeling_mllama import *
+ from .processing_mllama import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/configuration_mllama.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/configuration_mllama.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d0518ab9b8d64b7fa663f4787c1792fda4a48fa
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/configuration_mllama.py
@@ -0,0 +1,373 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc. team. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Mllama model configuration"""
+
+from typing import Optional
+
+from ...configuration_utils import PretrainedConfig
+from ...modeling_rope_utils import rope_config_validation
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MllamaVisionConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MllamaVisionModel`]. It is used to instantiate an
+ Mllama vision model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Mllama-11B.
+
+ e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ hidden_size (`int`, *optional*, defaults to 1280):
+ Dimensionality of the encoder layers and the pooler layer.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer encoder.
+ num_global_layers (`int`, *optional*, defaults to 8):
+ Number of global layers in the Transformer encoder.
+ Vision model has a second transformer encoder, called global.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_channels (`int`, *optional*, defaults to 3):
+ Number of channels in the input image.
+ intermediate_size (`int`, *optional*, defaults to 5120):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ vision_output_dim (`int`, *optional*, defaults to 7680):
+ Dimensionality of the vision model output. Includes output of transformer
+ encoder with intermediate layers and global transformer encoder.
+ image_size (`int`, *optional*, defaults to 448):
+ The size (resolution) of each image *tile*.
+ patch_size (`int`, *optional*, defaults to 14):
+ The size (resolution) of each patch.
+ norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the layer normalization layers.
+ max_num_tiles (`int`, *optional*, defaults to 4):
+ Maximum number of tiles for image splitting.
+ intermediate_layers_indices (`list[int]`, *optional*, defaults to [3, 7, 15, 23, 30]):
+ Indices of intermediate layers of transformer encoder from which to extract and output features.
+ These output features are concatenated with final hidden state of transformer encoder.
+ supported_aspect_ratios (`list[list[int]]`, *optional*):
+ List of supported aspect ratios for image splitting. If not specified, the default supported aspect ratios
+ are [[1, 1], [1, 2], [1, 3], [1, 4], [2, 1], [2, 2], [3, 1], [4, 1]] for `max_num_tiles=4`.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+
+ Example:
+
+ ```python
+ >>> from transformers import MllamaVisionConfig, MllamaVisionModel
+
+ >>> # Initializing a Llama config
+ >>> config = MllamaVisionConfig()
+
+ >>> # Initializing a vision model from the mllama-11b style configuration
+ >>> model = MllamaVisionModel(config)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mllama_vision_model"
+ base_config_key = "vision_config"
+
+ def __init__(
+ self,
+ hidden_size: int = 1280,
+ hidden_act: str = "gelu",
+ num_hidden_layers: int = 32,
+ num_global_layers: int = 8,
+ num_attention_heads: int = 16,
+ num_channels: int = 3,
+ intermediate_size: int = 5120,
+ vision_output_dim: int = 7680,
+ image_size: int = 448,
+ patch_size: int = 14,
+ norm_eps: float = 1e-5,
+ max_num_tiles: int = 4,
+ intermediate_layers_indices: Optional[list[int]] = None,
+ supported_aspect_ratios: Optional[list[list[int]]] = None,
+ initializer_range: float = 0.02,
+ **kwargs,
+ ):
+ if supported_aspect_ratios is None:
+ if max_num_tiles != 4:
+ raise ValueError("max_num_tiles must be 4 for default supported aspect ratios")
+ supported_aspect_ratios = [[1, 1], [1, 2], [1, 3], [1, 4], [2, 1], [2, 2], [3, 1], [4, 1]]
+
+ if intermediate_layers_indices is None:
+ intermediate_layers_indices = [3, 7, 15, 23, 30]
+
+ self.hidden_size = hidden_size
+ self.hidden_act = hidden_act
+ self.num_hidden_layers = num_hidden_layers
+ self.num_channels = num_channels
+ self.intermediate_size = intermediate_size
+ self.image_size = image_size
+ self.vision_output_dim = vision_output_dim
+ self.patch_size = patch_size
+ self.intermediate_layers_indices = intermediate_layers_indices
+ self.num_global_layers = num_global_layers
+ self.max_num_tiles = max_num_tiles
+ self.norm_eps = norm_eps
+ self.attention_heads = num_attention_heads
+ self.supported_aspect_ratios = supported_aspect_ratios
+ self.initializer_range = initializer_range
+ super().__init__(**kwargs)
+
+ @property
+ def max_aspect_ratio_id(self) -> int:
+ return len(self.supported_aspect_ratios)
+
+
+class MllamaTextConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MllamaTextModel`]. It is used to instantiate an
+ Mllama text model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Mllama-11B.
+
+ e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 128256):
+ Vocabulary size of the Mllama text model. Defines the maximum number of different tokens that can be represented
+ by the `inputs_ids` passed when calling [`MllamaTextModel`].
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimensionality of the embeddings and hidden states.
+ hidden_act (`str` or `Callable`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the encoder and pooler.
+ num_hidden_layers (`int`, *optional*, defaults to 40):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_key_value_heads (`int`, *optional*, defaults to 8):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If not
+ specified, will default to `num_attention_heads`.
+ intermediate_size (`int`, *optional*, defaults to 14336):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ rope_theta (`float`, *optional*, defaults to `500000.0`):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+ and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+ accordingly.
+ Expected contents:
+ `rope_type` (`str`):
+ The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+ 'llama3'], with 'default' being the original RoPE implementation.
+ `factor` (`float`, *optional*):
+ Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+ most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+ original maximum pre-trained length.
+ `original_max_position_embeddings` (`int`, *optional*):
+ Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+ pretraining.
+ `attention_factor` (`float`, *optional*):
+ Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+ computation. If unspecified, it defaults to value recommended by the implementation, using the
+ `factor` field to infer the suggested value.
+ `beta_fast` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+ ramp function. If unspecified, it defaults to 32.
+ `beta_slow` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+ ramp function. If unspecified, it defaults to 1.
+ `short_factor` (`list[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+ size divided by the number of attention heads divided by 2
+ `long_factor` (`list[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+ size divided by the number of attention heads divided by 2
+ `low_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+ `high_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+ rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+ max_position_embeddings (`int`, *optional*, defaults to 131072):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ cross_attention_layers (`list[int]`, *optional*):
+ Indices of the cross attention layers. If not specified, will default to [3, 8, 13, 18, 23, 28, 33, 38].
+ dropout (`float`, *optional*, defaults to 0):
+ The dropout probability for self- and cross-attention layers.
+ bos_token_id (`int`, *optional*, defaults to 128000):
+ The id of the beginning of sentence token.
+ eos_token_id (`int`, *optional*, defaults to 128001):
+ The id of the end of sentence token.
+ pad_token_id (`int`, *optional*, defaults to 128004):
+ The id of the padding token.
+
+ Example:
+
+ ```python
+ >>> from transformers import MllamaTextModel, MllamaTextConfig
+
+ >>> # Initializing a Mllama text config
+ >>> config = MllamaTextConfig()
+
+ >>> # Initializing a model from the Mllama text configuration
+ >>> model = MllamaTextModel(config)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mllama_text_model"
+ base_config_key = "text_config"
+
+ def __init__(
+ self,
+ vocab_size: int = 128256,
+ hidden_size: int = 4096,
+ hidden_act: str = "silu",
+ num_hidden_layers: int = 40,
+ num_attention_heads: int = 32,
+ num_key_value_heads: int = 8,
+ intermediate_size: int = 14_336,
+ rope_theta: float = 500_000,
+ rope_scaling: Optional[dict] = None,
+ rms_norm_eps: float = 1e-5,
+ max_position_embeddings: int = 131_072,
+ initializer_range: float = 0.02,
+ use_cache: bool = True,
+ tie_word_embeddings: bool = False,
+ cross_attention_layers: Optional[list[int]] = None,
+ dropout: float = 0,
+ bos_token_id: int = 128000,
+ eos_token_id: int = 128001,
+ pad_token_id: Optional[int] = 128004,
+ **kwargs,
+ ):
+ if cross_attention_layers is None:
+ cross_attention_layers = [3, 8, 13, 18, 23, 28, 33, 38]
+
+ self.vocab_size = vocab_size
+ self.num_hidden_layers = num_hidden_layers
+ self.cross_attention_layers = cross_attention_layers
+ self.hidden_size = hidden_size
+ self.num_attention_heads = num_attention_heads
+ self.num_key_value_heads = num_key_value_heads
+ self.initializer_range = initializer_range
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rms_norm_eps = rms_norm_eps
+ self.intermediate_size = intermediate_size
+ self.dropout = dropout
+ self.hidden_act = hidden_act
+ self.rope_scaling = rope_scaling
+ self.max_position_embeddings = max_position_embeddings
+ rope_config_validation(self)
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+
+class MllamaConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MllamaForConditionalGeneration`]. It is used to instantiate an
+ Mllama model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the Mllama-9B.
+
+ e.g. [meta-llama/Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaVisionConfig`):
+ The config object or dictionary of the vision backbone.
+ text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `MllamaTextConfig`):
+ The config object or dictionary of the text backbone.
+ image_token_index (`int`, *optional*, defaults to 128256):
+ The image token index to encode the image prompt.
+
+ Example:
+
+ ```python
+ >>> from transformers import MllamaForConditionalGeneration, MllamaConfig, MllamaVisionConfig, MllamaTextConfig
+
+ >>> # Initializing a CLIP-vision config
+ >>> vision_config = MllamaVisionConfig()
+
+ >>> # Initializing a Llama config
+ >>> text_config = MllamaTextConfig()
+
+ >>> # Initializing a mllama-11b style configuration
+ >>> configuration = MllamaConfig(vision_config, text_config)
+
+ >>> # Initializing a model from the mllama-11b style configuration
+ >>> model = MllamaForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mllama"
+ attribute_map = {
+ "image_token_id": "image_token_index",
+ }
+ sub_configs = {"text_config": MllamaTextConfig, "vision_config": MllamaVisionConfig}
+
+ def __init__(
+ self,
+ vision_config=None,
+ text_config=None,
+ image_token_index=128256,
+ **kwargs,
+ ):
+ if vision_config is None:
+ self.vision_config = MllamaVisionConfig()
+ logger.info("vision_config is None, using default mllama vision config")
+ elif isinstance(vision_config, dict):
+ self.vision_config = MllamaVisionConfig(**vision_config)
+ elif isinstance(vision_config, MllamaVisionConfig):
+ self.vision_config = vision_config
+
+ self.image_token_index = image_token_index
+
+ if text_config is None:
+ self.text_config = MllamaTextConfig()
+ logger.info("text_config is None, using default mllama text config")
+ elif isinstance(text_config, dict):
+ self.text_config = MllamaTextConfig(**text_config)
+ elif isinstance(text_config, MllamaTextConfig):
+ self.text_config = text_config
+
+ super().__init__(**kwargs)
+
+
+__all__ = ["MllamaConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/convert_mllama_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/convert_mllama_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a6060eba412dd8c125fa242d6f258953f815114
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/convert_mllama_weights_to_hf.py
@@ -0,0 +1,644 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import gc
+import json
+import math
+import os
+from typing import Optional
+
+import regex as re
+import torch
+import torch.nn.functional as F
+
+from transformers import (
+ GenerationConfig,
+ MllamaConfig,
+ MllamaForConditionalGeneration,
+ MllamaImageProcessor,
+ PreTrainedTokenizerFast,
+)
+from transformers.convert_slow_tokenizer import TikTokenConverter
+from transformers.models.mllama.configuration_mllama import MllamaTextConfig, MllamaVisionConfig
+from transformers.models.mllama.image_processing_mllama import get_all_supported_aspect_ratios
+
+
+# fmt: off
+# If a weight needs to be split in two or more keys, use `|` to indicate it. ex:
+# r"text_model.layers.(\d+).attention.wqkv.weight": r"language_model.model.layers.\1.self_attn.q|k|v|_proj.weight"
+ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
+ r"text_model.norm.weight": r"language_model.model.norm.weight",
+ r"text_model.output.weight": r"language_model.lm_head.weight",
+ r"text_model.tok_embeddings": r"language_model.model.embed_tokens",
+ r"text_model.learnable_embedding": r"language_model.model.learnable_embedding",
+ r"text_model.rope.freqs": None, # meaning we skip it and don't want it
+ # For every cross attention layer, the layer needs to be updated
+ r"text_model.cross_attention_layers.(\d+).gate_attn": r"language_model.model.layers.\1.cross_attn_attn_gate",
+ r"text_model.cross_attention_layers.(\d+).gate_ffwd": r"language_model.model.layers.\1.cross_attn_mlp_gate",
+ # special key, wqkv needs to be split afterwards
+ r"text_model.cross_attention_layers.(\d+).attention.w(q|k|v|o)": r"language_model.model.layers.\1.cross_attn.\2_proj",
+ r"text_model.cross_attention_layers.(\d+).attention.(q|k)_norm": r"language_model.model.layers.\1.cross_attn.\2_norm",
+ r"text_model.cross_attention_layers.(\d+).attention_norm.weight": r"language_model.model.layers.\1.input_layernorm.weight",
+ r"text_model.cross_attention_layers.(\d+).attention.wk.layer_norm_weight": r"language_model.model.layers.\1.post_attention_layernorm.weight",
+ r"text_model.cross_attention_layers.(\d+).feed_forward.w1.weight": r"language_model.model.layers.\1.mlp.gate_proj.weight",
+ r"text_model.cross_attention_layers.(\d+).feed_forward.w2.weight": r"language_model.model.layers.\1.mlp.down_proj.weight",
+ r"text_model.cross_attention_layers.(\d+).feed_forward.w3.weight": r"language_model.model.layers.\1.mlp.up_proj.weight",
+ r"text_model.cross_attention_layers.(\d+).ffn_norm.weight": r"language_model.model.layers.\1.post_attention_layernorm.weight",
+ # self attention layers
+ r"text_model.layers.(\d+).attention.w(q|k|v|o).weight": r"language_model.model.layers.\1.self_attn.\2_proj.weight",
+ r"text_model.layers.(\d+).attention_norm.weight": r"language_model.model.layers.\1.input_layernorm.weight",
+ r"text_model.layers.(\d+).feed_forward.w1.": r"language_model.model.layers.\1.mlp.gate_proj.",
+ r"text_model.layers.(\d+).feed_forward.w2.": r"language_model.model.layers.\1.mlp.down_proj.",
+ r"text_model.layers.(\d+).feed_forward.w3.": r"language_model.model.layers.\1.mlp.up_proj.",
+ r"text_model.layers.(\d+).ffn_norm.weight": r"language_model.model.layers.\1.post_attention_layernorm.weight",
+ # Vision encoder mapping
+ r"vision_model.vision_encoder.conv1._linear": r"vision_model.patch_embedding",
+ r'vision_model.vision_projection.': r"multi_modal_projector.",
+ r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).attn.wq": r"vision_model.\1.layers.\2.self_attn.q_proj",
+ r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).attn.wk": r"vision_model.\1.layers.\2.self_attn.k_proj",
+ r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).attn.wv": r"vision_model.\1.layers.\2.self_attn.v_proj",
+ r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).attn.wo": r"vision_model.\1.layers.\2.self_attn.o_proj",
+ r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).mlp.c_fc": r"vision_model.\1.layers.\2.mlp.fc1",
+ r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).mlp.c_proj": r"vision_model.\1.layers.\2.mlp.fc2",
+ r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).ln_1": r"vision_model.\1.layers.\2.input_layernorm",
+ r"vision_model.vision_encoder.(global_transformer|transformer).resblocks.(\d+).ln_2": r"vision_model.\1.layers.\2.post_attention_layernorm",
+ r"vision_model.vision_encoder.global_transformer.resblocks.(\d+).(gate_ffn|gate_attn)": r"vision_model.global_transformer.layers.\1.\2",
+ r'vision_model.vision_encoder.ln_(pre|post).(weight|bias)': r'vision_model.vision_encoder.layernorm_\1.\2',
+ r'vision_model.vision_encoder.positional_embedding\b': r'vision_model.gated_positional_embedding.embedding',
+ r'vision_model.vision_encoder.gated_positional_embedding\b': r'vision_model.gated_positional_embedding.tile_embedding.weight',
+ r'vision_model.vision_encoder.gated_positional_embedding_gate': r'vision_model.gated_positional_embedding.gate',
+ r"vision_model.vision_encoder.pre_tile_pos_embed.embedding": r"vision_model.pre_tile_positional_embedding.embedding.weight",
+ r"vision_model.vision_encoder.post_tile_pos_embed.embedding": r"vision_model.post_tile_positional_embedding.embedding.weight",
+ r"vision_model.vision_encoder.pre_tile_pos_embed.gate": r"vision_model.pre_tile_positional_embedding.gate",
+ r"vision_model.vision_encoder.post_tile_pos_embed.gate": r"vision_model.post_tile_positional_embedding.gate",
+ r"vision_model.vision_encoder.(?=\w)": r"vision_model.",
+}
+# fmt: on
+
+CONTEXT_LENGTH = 131072
+
+
+def convert_old_keys_to_new_keys(state_dict_keys: Optional[dict] = None):
+ """
+ This function should be applied only once, on the concatenated keys to efficiently rename using
+ the key mappings.
+ """
+ output_dict = {}
+ if state_dict_keys is not None:
+ old_text = "\n".join(state_dict_keys)
+ new_text = old_text
+ for pattern, replacement in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items():
+ if replacement is None:
+ new_text = re.sub(pattern, "", new_text) # an empty line
+ continue
+ new_text = re.sub(pattern, replacement, new_text)
+ output_dict = dict(zip(old_text.split("\n"), new_text.split("\n")))
+ return output_dict
+
+
+def permute_for_rope(input_tensor, n_heads, dim1, dim2):
+ """
+ When you go from the complex ROPE formulation to sin and cos one, you need
+ to permute the query and key weights (to avoid doing it on the fly)
+ """
+ input_tensor = input_tensor.reshape(dim1, dim2)
+ input_tensor = input_tensor.view(n_heads, dim1 // n_heads // 2, 2, dim2)
+ input_tensor = input_tensor.transpose(1, 2).reshape(dim1, dim2)
+ return input_tensor
+
+
+def pre_compute_positional_embedding(embedding):
+ """
+ Instead of iterating of the batch of images, and the ratios inside, we pre-compute the
+ positional embeddings depending on the aspect ratio id. This is done to support `torch.compile`
+ and efficient inference / training with different aspect ratios.
+ """
+ max_num_tiles, *shapes = embedding.shape
+ hidden_size = shapes[-1]
+ supported_aspect_ratios = get_all_supported_aspect_ratios(max_num_tiles)
+ max_aspect_ratio_id = len(supported_aspect_ratios) # we keep 0 index for padding
+ # tile embedding does not have patches
+ num_patches = 1 if len(shapes) == 2 else shapes[1]
+ precomputed_embeddings = torch.zeros(
+ max_aspect_ratio_id + 1,
+ max_num_tiles,
+ num_patches,
+ hidden_size,
+ device=embedding.device,
+ dtype=embedding.dtype,
+ )
+
+ for i, (height, width) in enumerate(supported_aspect_ratios):
+ aspect_ratio_id = i + 1 # we keep 0 index for padding
+ current_embedding = embedding[:height, :width].reshape(height * width, num_patches, hidden_size)
+ precomputed_embeddings[aspect_ratio_id, : height * width] = current_embedding
+ precomputed_embeddings = precomputed_embeddings.flatten(1)
+ return precomputed_embeddings
+
+
+def is_param_different_across_shards(key):
+ """
+ Return `True` if the parameter is different across checkpoint shards
+ and needs to be concatenated.
+ """
+ patterns = [r"vision_model.patch_embedding.weight",r"vision_model.(transformer|global_transformer).layers.(\d+).self_attn.(q|k|v|o)_proj.weight",r"vision_model.(transformer|global_transformer).layers.(\d+).mlp.fc1.(weight|bias)",r"vision_model.(transformer|global_transformer).layers.(\d+).mlp.fc2.weight", r"multi_modal_projector.(weight|bias)",r"language_model.model.embed_tokens.weight",r"language_model.lm_head.weight",r"language_model.model.layers.(\d+).self_attn.(q|k|v|o)_proj.weight",r"language_model.model.layers.(\d+).cross_attn.(q|k|v|o)_proj.weight",r"language_model.model.layers.(\d+).mlp.(up|down|gate)_proj.weight",r"language_model.model.learnable_embedding.weight"] # fmt: skip
+ return any(re.search(pattern, key) for pattern in patterns)
+
+
+def get_concat_dim(key):
+ """
+ Return the dimension to concatenate the weights on.
+ """
+ concat_dim_1 = [r"vision_model.(transformer|global_transformer).layers.(\d+).mlp.fc2.weight",r"vision_model.(transformer|global_transformer).layers.(\d+).self_attn.o_proj.weight",r"language_model.model.layers.(\d+).cross_attn.o_proj.weight",r"language_model.model.layers.(\d+).self_attn.o_proj.weight",r"language_model.model.layers.(\d+).mlp.down_proj.weight"] # fmt: off
+ if any(re.search(pattern, key) for pattern in concat_dim_1):
+ return 1
+ return 0
+
+
+def compute_intermediate_size(hidden_dim, multiple_of=1024, ffn_dim_multiplier=1.3):
+ hidden_dim = 4 * int(2 * hidden_dim / 3)
+ hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+ hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+ return hidden_dim
+
+
+def interpolate_positional_embedding(
+ embeddings: torch.Tensor, vision_tile_size: int, vision_patch_size: int
+) -> torch.Tensor:
+ """
+ This method allows to interpolate the pre-trained position embeddings, to be able to use the model on higher resolution
+ images.
+ """
+ cls_embedding, positional_embedding = embeddings[:1], embeddings[1:]
+ total_num_patches, dim = positional_embedding.shape
+
+ # compute current and target number of patches for height and width
+ num_patches = int(round(total_num_patches**0.5))
+ new_num_patches = vision_tile_size // vision_patch_size
+
+ # Check if the number of patches is already the desired size
+ if num_patches == new_num_patches:
+ return embeddings
+
+ positional_embedding = positional_embedding.transpose(0, 1)
+ positional_embedding = positional_embedding.reshape(1, dim, num_patches, num_patches)
+ positional_embedding = F.interpolate(
+ positional_embedding,
+ size=(new_num_patches, new_num_patches),
+ mode="bicubic",
+ align_corners=False,
+ )
+ positional_embedding = positional_embedding.reshape(dim, -1).transpose(0, 1)
+
+ embeddings = torch.cat([cls_embedding, positional_embedding], dim=0)
+ return embeddings
+
+
+def write_model(
+ model_path,
+ input_base_path,
+ num_shards,
+ safe_serialization=True,
+ instruct=False,
+):
+ os.makedirs(model_path, exist_ok=True)
+
+ with open(os.path.join(input_base_path, "params.json"), "r") as f:
+ params = json.load(f)
+
+ params = params.get("model", params)
+ torch_dtype = "bfloat16"
+
+ # ------------------------------------------------------------
+ # Text model params and config
+ # ------------------------------------------------------------
+
+ # params from config
+ text_vocab_size = params["vocab_size"]
+ text_num_layers = params["n_layers"]
+ text_dim = params["dim"]
+ text_num_heads = params["n_heads"]
+ text_rms_norm_eps = params["norm_eps"]
+ text_rope_theta = params["rope_theta"]
+ cross_attention_num_layers = params["vision_num_cross_attention_layers"]
+
+ # some constants from original code
+ rope_scaling = {
+ "rope_type": "llama3",
+ "factor": 8.0,
+ "low_freq_factor": 1.0,
+ "high_freq_factor": 4.0,
+ "original_max_position_embeddings": 8192,
+ }
+ max_position_embeddings = CONTEXT_LENGTH
+
+ # compute additional params for weight conversion
+ text_num_heads_per_shard = text_num_heads // num_shards
+ text_dim_per_head = text_dim // text_num_heads
+ text_intermediate_size = compute_intermediate_size(text_dim, multiple_of=params["multiple_of"])
+
+ if params.get("n_kv_heads", None) is not None:
+ text_num_key_value_heads = params["n_kv_heads"] # for GQA / MQA
+ text_num_key_value_heads_per_shard = text_num_key_value_heads // num_shards
+ text_key_value_dim = text_dim_per_head * text_num_key_value_heads
+ else: # compatibility with other checkpoints
+ text_num_key_value_heads = text_num_heads
+ text_num_key_value_heads_per_shard = text_num_heads_per_shard
+ text_key_value_dim = text_dim
+
+ # cross-attention layers: 20 for 90B, 8 for 11B
+ cross_attention_frequency = math.ceil(text_num_layers / cross_attention_num_layers)
+ text_num_total_layers = text_num_layers + cross_attention_num_layers
+ cross_attention_layers_shift = list(
+ range(cross_attention_frequency - 1, text_num_total_layers, cross_attention_frequency + 1)
+ )
+ self_attention_layers_shift = [k for k in range(text_num_total_layers) if k not in cross_attention_layers_shift]
+
+ bos_token_id = 128000
+ eos_token_id = [128001, 128008, 128009] if instruct else 128001
+ pad_token_id = 128004
+
+ text_config = MllamaTextConfig(
+ num_attention_heads=text_num_heads,
+ vocab_size=text_vocab_size,
+ hidden_size=text_dim,
+ rms_norm_eps=text_rms_norm_eps,
+ rope_theta=text_rope_theta,
+ num_hidden_layers=text_num_total_layers,
+ cross_attention_layers=cross_attention_layers_shift,
+ intermediate_size=text_intermediate_size,
+ max_position_embeddings=max_position_embeddings,
+ rope_scaling=rope_scaling,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ pad_token_id=pad_token_id,
+ tie_word_embeddings=False, # Constant set to False
+ torch_dtype=torch_dtype,
+ )
+
+ # ------------------------------------------------------------
+ # Vision model params and config
+ # ------------------------------------------------------------
+
+ # params from config
+ vision_tile_size = params["vision_chunk_size"]
+ vision_max_num_tiles = params["vision_max_num_chunks"]
+
+ # some constants from original code
+ vision_patch_size = 14
+ vision_num_channels = 3
+ vision_num_layers = 32
+ vision_num_layers_global = 8
+ vision_dim = 1280
+ vision_num_heads = 16
+ vision_intermediate_layers_indices = [3, 7, 15, 23, 30]
+
+ # compute additional params for weight conversion
+ vision_dim_per_head = vision_dim // vision_num_heads
+ vision_num_heads_per_shard = vision_num_heads // num_shards
+ vision_intermediate_size = vision_dim * 4
+ vision_supported_aspect_ratios = get_all_supported_aspect_ratios(vision_max_num_tiles)
+
+ vision_config = MllamaVisionConfig(
+ hidden_size=vision_dim,
+ patch_size=vision_patch_size,
+ num_channels=vision_num_channels,
+ intermediate_size=vision_intermediate_size,
+ num_hidden_layers=vision_num_layers,
+ num_attention_heads=vision_num_heads,
+ num_global_layers=vision_num_layers_global,
+ intermediate_layers_indices=vision_intermediate_layers_indices,
+ image_size=vision_tile_size,
+ max_num_tiles=vision_max_num_tiles,
+ supported_aspect_ratios=vision_supported_aspect_ratios,
+ torch_dtype=torch_dtype,
+ )
+
+ # save config
+ config = MllamaConfig(vision_config=vision_config, text_config=text_config, torch_dtype=torch_dtype)
+ config.architectures = ["MllamaForConditionalGeneration"]
+ config.save_pretrained(model_path)
+ print("Model config saved successfully...")
+
+ # ------------------------------------------------------------
+ # Convert weights
+ # ------------------------------------------------------------
+
+ print(f"Fetching all parameters from the checkpoint at {input_base_path}...")
+ if num_shards == 1:
+ if os.path.exists(os.path.join(input_base_path, "consolidated.00.pth")):
+ path = os.path.join(input_base_path, "consolidated.00.pth")
+ else:
+ path = os.path.join(input_base_path, "consolidated.pth")
+ loaded = [torch.load(path, map_location="cpu", mmap=True, weights_only=True)]
+ else:
+ loaded = [
+ torch.load(
+ os.path.join(input_base_path, f"consolidated.{i:02d}.pth"),
+ map_location="cpu",
+ mmap=True,
+ weights_only=True,
+ )
+ for i in range(num_shards)
+ ]
+
+ print("Converting model...")
+ all_keys = list(loaded[0].keys())
+ new_keys = convert_old_keys_to_new_keys(all_keys)
+
+ state_dict = {}
+ for key in all_keys:
+ new_key = new_keys[key]
+
+ # In the original model, self-attention layers and cross-attention layers are different lists of layers.
+ # In the converted model, they are merged into one list with corresponding index shift to preserve the order.
+ if ("cross_attention" in key or "text_model.layers" in key) and "language_model" in new_key:
+ shift = cross_attention_layers_shift if "cross_attention" in key else self_attention_layers_shift
+ new_key = re.sub(r"layers.(\d+).", lambda _match: f"layers.{shift[int(_match.groups()[0])]}.", new_key)
+
+ current_parameter = [chunk.pop(key).contiguous().clone() for chunk in loaded]
+ if not is_param_different_across_shards(new_key):
+ current_parameter = current_parameter[0]
+
+ concat_dim = get_concat_dim(new_key)
+
+ # Post-process the current_parameter.
+ if re.search("(k|v|q)_proj.weight", new_key) and "language_model" in new_key:
+ if "q_proj" in new_key:
+ param_num_heads = text_num_heads
+ param_num_head_per_shard = text_num_heads_per_shard
+ param_dim = text_dim
+ else:
+ param_num_heads = text_num_key_value_heads
+ param_num_head_per_shard = text_num_key_value_heads_per_shard
+ param_dim = text_key_value_dim
+ shards = [param.view(param_num_head_per_shard, text_dim_per_head, text_dim) for param in current_parameter]
+ current_parameter = torch.cat(shards, dim=concat_dim)
+ if "cross_attn" not in new_key and "v_proj.weight" not in new_key:
+ current_parameter = permute_for_rope(current_parameter, param_num_heads, param_dim, text_dim)
+ state_dict[new_key] = current_parameter.reshape(param_num_heads * text_dim_per_head, text_dim)
+
+ elif "vision_model" in new_key and re.search("(k|v|q)_proj", new_key):
+ shards = [
+ param.view(vision_num_heads_per_shard, vision_dim_per_head, vision_dim) for param in current_parameter
+ ]
+ param = torch.cat(shards, dim=concat_dim)
+ state_dict[new_key] = param.reshape(vision_num_heads * vision_dim_per_head, vision_dim)
+
+ elif new_key == "vision_model.patch_embedding.weight":
+ current_parameter = torch.cat(current_parameter, dim=concat_dim)
+ state_dict[new_key] = current_parameter.reshape(
+ -1, vision_num_channels, vision_patch_size, vision_patch_size
+ )
+
+ elif new_key.endswith("gate"):
+ state_dict[new_key] = current_parameter[0].view(1)
+
+ elif "vision_model.gated_positional_embedding.embedding" in new_key:
+ current_parameter = interpolate_positional_embedding(
+ current_parameter, vision_tile_size, vision_patch_size
+ )
+ state_dict[new_key] = current_parameter
+
+ elif "vision_model.gated_positional_embedding.tile_embedding.weight" in new_key:
+ current_parameter = current_parameter.permute(2, 0, 1, 3).flatten(1)
+ current_parameter = interpolate_positional_embedding(
+ current_parameter, vision_tile_size, vision_patch_size
+ )
+ current_parameter = current_parameter.reshape(
+ -1, vision_max_num_tiles, vision_max_num_tiles, vision_dim
+ ).permute(1, 2, 0, 3)
+ state_dict[new_key] = pre_compute_positional_embedding(current_parameter)
+
+ elif "tile_positional_embedding.embedding" in new_key:
+ state_dict[new_key] = pre_compute_positional_embedding(current_parameter)
+
+ elif new_key != "":
+ if isinstance(current_parameter, list):
+ current_parameter = torch.cat(current_parameter, dim=concat_dim)
+ state_dict[new_key] = current_parameter
+
+ state_dict["language_model.model.embed_tokens.weight"] = torch.cat(
+ [
+ state_dict["language_model.model.embed_tokens.weight"],
+ state_dict.pop("language_model.model.learnable_embedding.weight"),
+ ],
+ dim=0,
+ )
+ del loaded
+ gc.collect()
+
+ print("Loading the checkpoint in a Mllama model.")
+ with torch.device("meta"):
+ model = MllamaForConditionalGeneration(config)
+ model.load_state_dict(state_dict, strict=True, assign=True)
+ print("Checkpoint loaded successfully.")
+ del model.config._name_or_path
+
+ print("Saving the model.")
+ model.save_pretrained(model_path, safe_serialization=safe_serialization)
+ del state_dict, model
+
+ # Safety check: reload the converted model
+ gc.collect()
+ print("Reloading the model to check if it's saved correctly.")
+ MllamaForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map="auto")
+ print("Model reloaded successfully.")
+
+ # generation config
+ if instruct:
+ print("Saving generation config...")
+ generation_config = GenerationConfig(
+ do_sample=True,
+ temperature=0.6,
+ top_p=0.9,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ pad_token_id=pad_token_id,
+ )
+ generation_config.save_pretrained(model_path)
+
+
+class MllamaConverter(TikTokenConverter):
+ def __init__(
+ self,
+ vocab_file,
+ special_tokens: list[str],
+ pattern: str,
+ model_max_length: int,
+ chat_template: Optional[str] = None,
+ **kwargs,
+ ):
+ super().__init__(vocab_file, pattern=pattern)
+ self.additional_special_tokens = special_tokens
+ tokenizer = self.converted()
+ if chat_template is not None:
+ kwargs["chat_template"] = chat_template
+ self.tokenizer = PreTrainedTokenizerFast(
+ tokenizer_object=tokenizer,
+ model_input_names=["input_ids", "attention_mask"],
+ model_max_length=model_max_length,
+ **kwargs,
+ )
+
+
+def write_tokenizer(tokenizer_path: str, save_dir: str, instruct: bool = False):
+ model_max_length = CONTEXT_LENGTH
+ pattern = r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+" # noqa: W605
+
+ # Special tokens
+ num_reserved_special_tokens = 256
+ special_tokens = [
+ "<|begin_of_text|>",
+ "<|end_of_text|>",
+ "<|reserved_special_token_0|>",
+ "<|reserved_special_token_1|>",
+ "<|finetune_right_pad_id|>",
+ "<|step_id|>",
+ "<|start_header_id|>",
+ "<|end_header_id|>",
+ "<|eom_id|>", # end of message
+ "<|eot_id|>", # end of turn
+ "<|python_tag|>",
+ ]
+ special_tokens += [
+ f"<|reserved_special_token_{i + 2}|>" for i in range(num_reserved_special_tokens - len(special_tokens))
+ ]
+ # original tokenizer has <|image|> with 128011 token_id,
+ # however, later in the code it is replaced with 128256 token_id
+ special_tokens.append("<|image|>")
+
+ # Chat template
+ chat_template = (
+ "{% for message in messages %}"
+ "{% if loop.index0 == 0 %}"
+ "{{ bos_token }}"
+ "{% endif %}"
+ "{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' }}"
+ "{% if message['content'] is string %}"
+ "{{ message['content'] }}"
+ "{% else %}"
+ "{% for content in message['content'] %}"
+ "{% if content['type'] == 'image' %}"
+ "{{ '<|image|>' }}"
+ "{% elif content['type'] == 'text' %}"
+ "{{ content['text'] }}"
+ "{% endif %}"
+ "{% endfor %}"
+ "{% endif %}"
+ "{{ '<|eot_id|>' }}"
+ "{% endfor %}"
+ "{% if add_generation_prompt %}"
+ "{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}"
+ "{% endif %}"
+ )
+
+ converter = MllamaConverter(
+ vocab_file=tokenizer_path,
+ pattern=pattern,
+ special_tokens=special_tokens,
+ model_max_length=model_max_length,
+ chat_template=chat_template if instruct else None,
+ bos_token="<|begin_of_text|>",
+ eos_token="<|end_of_text|>" if not instruct else "<|eot_id|>",
+ pad_token="<|finetune_right_pad_id|>",
+ )
+ tokenizer = converter.tokenizer
+ tokenizer.save_pretrained(save_dir)
+
+ if instruct:
+ print("Saving chat template...")
+ chat_template_path = os.path.join(save_dir, "chat_template.json")
+ with open(chat_template_path, "w") as f:
+ json.dump({"chat_template": chat_template}, f, indent=2)
+
+
+def write_image_processor(config_path: str, save_dir: str):
+ with open(config_path, "r") as f:
+ params = json.load(f)
+
+ tile_size = params["vision_chunk_size"]
+ max_image_tiles = params["vision_max_num_chunks"]
+
+ image_processor = MllamaImageProcessor(
+ do_resize=True,
+ size={"height": tile_size, "width": tile_size},
+ do_rescale=True,
+ rescale_factor=1 / 255,
+ do_normalize=True,
+ image_mean=[0.48145466, 0.4578275, 0.40821073],
+ image_std=[0.26862954, 0.26130258, 0.27577711],
+ do_pad=True,
+ max_image_tiles=max_image_tiles,
+ )
+
+ image_processor.save_pretrained(save_dir)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--input_dir",
+ default="Llama-3.2-11B-Vision/original",
+ help="Location of LLaMA weights, which contains tokenizer.model and model folders",
+ )
+ parser.add_argument(
+ "--output_dir",
+ default="Llama-3.2-11B-Vision",
+ help="Location to write HF model and tokenizer",
+ )
+ parser.add_argument(
+ "--safe_serialization", default=True, type=bool, help="Whether or not to save using `safetensors`."
+ )
+ parser.add_argument(
+ "--special_tokens",
+ default=None,
+ type=list[str],
+ help="The list of special tokens that should be added to the model.",
+ )
+ parser.add_argument(
+ "--num_shards",
+ default=1,
+ type=int,
+ help="The number of individual shards used for the model. Does not have to be the same as the number of consolidated_xx.pth",
+ )
+ parser.add_argument(
+ "--instruct",
+ action="store_true",
+ help="Whether the model is an instruct model",
+ )
+ args = parser.parse_args()
+ write_model(
+ model_path=args.output_dir,
+ input_base_path=args.input_dir,
+ safe_serialization=args.safe_serialization,
+ num_shards=args.num_shards,
+ instruct=args.instruct,
+ )
+
+ write_tokenizer(
+ tokenizer_path=os.path.join(args.input_dir, "tokenizer.model"),
+ save_dir=args.output_dir,
+ instruct=args.instruct,
+ )
+
+ write_image_processor(
+ config_path=os.path.join(args.input_dir, "params.json"),
+ save_dir=args.output_dir,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/image_processing_mllama.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/image_processing_mllama.py
new file mode 100644
index 0000000000000000000000000000000000000000..01ae14c9f5cb21c984a0ae54ed678b4f2734390b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/image_processing_mllama.py
@@ -0,0 +1,904 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from functools import lru_cache
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature
+from ...image_transforms import (
+ PaddingMode,
+ get_image_size,
+ pad,
+ resize,
+)
+from ...image_utils import (
+ IMAGENET_STANDARD_MEAN,
+ IMAGENET_STANDARD_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ infer_channel_dimension_format,
+ is_vision_available,
+ make_nested_list_of_images,
+ to_numpy_array,
+ validate_preprocess_arguments,
+)
+from ...utils import TensorType, logging
+
+
+if is_vision_available():
+ import PIL
+ from PIL import Image
+
+
+logger = logging.get_logger(__name__)
+
+
+@lru_cache(maxsize=10)
+def get_all_supported_aspect_ratios(max_image_tiles: int) -> list[tuple[int, int]]:
+ """
+ Computes all allowed aspect ratios for a given maximum number of input tiles.
+
+ This function calculates all possible arrangements of tiles that can be formed
+ within the constraint of the maximum number of tiles. Each arrangement is
+ represented by its aspect ratio (width/height) and the corresponding tile configuration.
+
+ Args:
+ max_image_tiles (`int`):
+ The maximum number of tiles allowed.
+
+ Returns:
+ `list[tuple[int, int]]`: A list of tuples, each tuple representing a valid (width, height)
+ configuration in terms of number of tiles.
+
+ Example:
+ >>> get_all_supported_aspect_ratios(4)
+ [(1, 1), (1, 2), (1, 3), (1, 4), (2, 1), (2, 2), (3, 1), (4, 1)]
+
+ """
+ aspect_ratios = []
+ for width in range(1, max_image_tiles + 1):
+ for height in range(1, max_image_tiles + 1):
+ if width * height <= max_image_tiles:
+ aspect_ratios.append((width, height))
+ return aspect_ratios
+
+
+def get_image_size_fit_to_canvas(
+ image_height: int,
+ image_width: int,
+ canvas_height: int,
+ canvas_width: int,
+ tile_size: int,
+) -> tuple[int, int]:
+ """
+ Calculates the new size of an image to fit within a canvas while maintaining aspect ratio.
+
+ This function calculates the optimal size for an image to fit within a canvas defined by
+ canvas_height and canvas_width, while ensuring that the image dimensions are not smaller than
+ tile_size. If the image is larger than the canvas, the returned size will fit within the canvas.
+ If the image already fits within the canvas, the size remains unchanged.
+ The aspect ratio of the original image is preserved as much as possible.
+
+ Args:
+ image_height (`int`):
+ The height of the original image.
+ image_width (`int`):
+ The width of the original image.
+ canvas_height (`int`):
+ The height of the canvas.
+ canvas_width (`int`):
+ The width of the canvas.
+ tile_size (`int`):
+ The tile size.
+
+ Returns:
+ `tuple[int, int]`: A tuple containing the new height and width of the image.
+
+ """
+ # Set target image size in between `tile_size` and canvas_size
+ target_width = np.clip(image_width, tile_size, canvas_width)
+ target_height = np.clip(image_height, tile_size, canvas_height)
+
+ scale_h = target_height / image_height
+ scale_w = target_width / image_width
+
+ if scale_w < scale_h:
+ new_width = target_width
+ # minimum height is 1 to avoid invalid height of 0
+ new_height = min(math.floor(image_height * scale_w) or 1, target_height)
+ else:
+ new_height = target_height
+ # minimum width is 1 to avoid invalid width of 0
+ new_width = min(math.floor(image_width * scale_h) or 1, target_width)
+
+ return new_height, new_width
+
+
+@lru_cache(maxsize=100)
+def get_optimal_tiled_canvas(
+ image_height: int,
+ image_width: int,
+ max_image_tiles: int,
+ tile_size: int,
+) -> tuple[int, int]:
+ r"""
+ Determines the best canvas based on image and tile size and maximum number of tiles.
+
+ First, calculates possible resolutions based on the maximum number of tiles and tile size.
+ For example for max_image_tiles=2, tile_size=100, possible tile arrangements are:
+ [(1, 1), (1, 2), (2, 1)] and corresponding canvas sizes are:
+ [(100, 100), (100, 200), (200, 100)]
+
+ For each possible resolution, calculates the scaling factors for
+ width and height, and selects the smallest one, which is the limiting side.
+ E.g. to match the canvas you can upscale height by 2x, and width by 1.5x,
+ therefore, the maximum upscaling you can do is min(2, 1.5) = 1.5.
+
+ If upscaling is possible (any of the scaling factors is greater than 1),
+ then picks the smallest upscaling factor > 1.
+
+ If upscaling is not possible, then picks the largest scaling factor <= 1, i.e.
+ reduce downscaling as much as possible.
+
+ If there are multiple resolutions with the same max scale, we pick the one with the lowest area,
+ to minimize padding. E.g., the same image can be upscaled to 224x224 and 224x448, but the latter
+ has more padding.
+
+ Example of canvases made from tiles:
+
+ To visualize how the image can fit onto different tile grids, let's try fitting an ASCII cat into the tiles.
+
+ Here's an ASCII cat image you want to fit into the tiles:
+
+ /\_/\
+ ( o.o )
+ > ^ <
+
+ If `num_tiles=6`, possible tile grids would look like this:
+
+ **2x3 Canvas (2 tiles wide, 3 tiles tall)**: -> total of 6 tiles
+ +-------+-------+
+ | /\_/\ | 0 | <- Cat image split across two tiles horizontally
+ +-------+-------+
+ | > ^ < | 0 | <- Remaining part of the cat occupies the left tile
+ +-------+-------+
+ |( o.o )| 0 |
+ +-------+-------+
+
+ **3x2 Canvas (3 tiles wide, 2 tiles tall)**: -> total of 6 tiles
+ +-------+-------+-------+
+ | /\_/\ |( o.o )| 0 | <- Cat image occupies the first two tiles, 1 tile remains empty
+ +-------+-------+-------+
+ | > ^ < | 0 | 0 | <- Remaining part of the cat occupies the left tile
+ +-------+-------+-------+
+
+ **1x6 Canvas (1 tile wide, 6 tiles tall)**: -> total of 6 tiles
+ +-------+
+ | /\_/\ | <- Top part of the cat
+ +-------+
+ |( o.o )| <- Middle part of the cat
+ +-------+
+ | > ^ < | <- Bottom part of the cat
+ +-------+
+ | 0 |
+ +-------+
+ | 0 |
+ +-------+
+ | 0 |
+ +-------+
+
+ Given that the tiles you get depend on the chosen aspect ratio, you have to add
+ embedding in the modeling code to help it know if it got a 3x2 or a 1x6 or a 2x3
+ aspect ratio.
+
+ The function tests these arrangements to find the smallest canvas where the image fits.
+ If multiple canvases fit, it selects the one where the dimensions are closest to the image size.
+
+ In this case the first canvas is the closest to the original image.
+
+ You then feed all of the tiles to the model:
+
+ +-------+-------+-------+-------+-------+-------+
+ - | /\_/\ |( o.o )| > ^ < | 0 | 0 | 0 | <- Last canvas
+ +-------+-------+-------+-------+-------+-------+
+
+ +-------+-------+-------+-------+-------+-------+
+ - | /\_/\ | 0 |( o.o )| 0 | > ^ < | 0 | <- First canvas
+ +-------+-------+-------+-------+-------+-------+
+
+ +-------+-------+-------+-------+-------+-------+
+ - | /\_/\ |( o.o )| 0 | > ^ < | 0 | 0 | <- second canvas
+ +-------+-------+-------+-------+-------+-------+
+
+ For each tile, you have num_channels (usually RGB so 3), tile_width, tile_height
+
+ Args:
+ image_height (`int`):
+ The height of the image.
+ image_width (`int`):
+ The width of the image.
+ max_image_tiles (`int`):
+ The maximum number of tiles any image can be split into.
+ tile_size (`int`):
+ The tile size.
+
+ Returns:
+ `tuple[int, int]`: The best canvas resolution [height, width] for the given image.
+ """
+ possible_tile_arrangements = get_all_supported_aspect_ratios(max_image_tiles)
+ possible_canvas_sizes = np.array(possible_tile_arrangements) * tile_size
+
+ # get all possible resolutions heights/widths
+ target_heights, target_widths = np.array(possible_canvas_sizes).T
+
+ # get scaling factors to resize the image without distortion
+ scale_h = target_heights / image_height
+ scale_w = target_widths / image_width
+
+ # get the min scale between width and height (limiting side -> no distortion)
+ scales = np.where(scale_w > scale_h, scale_h, scale_w)
+
+ # filter only scales that allow upscaling
+ upscaling_options = scales[scales >= 1]
+ if len(upscaling_options) > 0:
+ selected_scale = np.min(upscaling_options)
+ else:
+ # no upscaling possible,
+ # get the minimum downscaling (max scale for scales<1)
+ downscaling_options = scales[scales < 1]
+ selected_scale = np.max(downscaling_options)
+
+ # get all resolutions that support this scaling factor,
+ # e.g. you can upscale to 224x224, 224x448, 224x672 without distortion
+ chosen_canvas = possible_canvas_sizes[scales == selected_scale]
+
+ # if there are multiple resolutions,
+ # get the one with minimum area to reduce padding
+ if len(chosen_canvas) > 1:
+ areas = chosen_canvas[:, 0] * chosen_canvas[:, 1]
+ optimal_idx = np.argmin(areas)
+ optimal_canvas = chosen_canvas[optimal_idx]
+ else:
+ optimal_canvas = chosen_canvas[0]
+
+ return optimal_canvas
+
+
+def split_to_tiles(image: np.ndarray, num_tiles_height: int, num_tiles_width: int) -> np.ndarray:
+ """
+ Split an image into a specified number of tiles along its width and height dimensions.
+
+ Args:
+ image (`np.ndarray`):
+ Input image with shape (num_channels, height, width).
+ num_tiles_height (`int`):
+ Number of tiles to split the image into along its height.
+ num_tiles_width (`int`):
+ Number of tiles to split the image into along its width.
+
+ Returns:
+ `np.ndarray`:
+ Array of image tiles with shape (num_tiles_width * num_tiles_height, num_channels, tile_height, tile_width).
+ """
+ num_channels, height, width = image.shape
+ tile_height = height // num_tiles_height
+ tile_width = width // num_tiles_width
+
+ image = image.reshape(num_channels, num_tiles_height, tile_height, num_tiles_width, tile_width)
+
+ # Permute to (num_tiles_height, num_tiles_width, num_channels, tile_height, tile_width)
+ image = image.transpose(1, 3, 0, 2, 4)
+
+ # Reshape into the desired output shape (num_tiles_width * num_tiles_height, num_channels, tile_height, tile_width)
+ image = image.reshape(num_tiles_width * num_tiles_height, num_channels, tile_height, tile_width)
+
+ return np.ascontiguousarray(image)
+
+
+def build_aspect_ratio_mask(aspect_ratios: list[list[tuple[int, int]]], max_image_tiles: int) -> np.ndarray:
+ """
+ Builds a mask for the aspect ratios of the images.
+
+ Args:
+ aspect_ratios (`list[list[tuple[int, int]]]`):
+ A list of lists containing aspect ratios for each image in the batch.
+ Each aspect ratio is represented as a tuple of (width, height) in terms of number of tiles.
+ max_image_tiles (`int`):
+ The maximum number of tiles any image can be split into.
+
+ Returns:
+ `np.ndarray`: A 3D numpy array of shape (batch_size, max_num_images, max_image_tiles).
+ The mask contains 1s for valid tiles and 0s for padding.
+ """
+ batch_size = len(aspect_ratios)
+ max_num_images = max([len(row) for row in aspect_ratios])
+
+ aspect_ratio_mask = np.zeros((batch_size, max_num_images, max_image_tiles), dtype=np.int64)
+
+ # Set the first tile to 1 for all aspect ratios
+ # because in original implementation aspect ratios are padded with (1, 1),
+ # but original code examples are not built to handle batches, so we might remove it later
+ aspect_ratio_mask[:, :, 0] = 1
+
+ # Set the aspect ratio mask for the rest of the tiles
+ for i, sample_aspect_ratios in enumerate(aspect_ratios):
+ for j, (num_tiles_w, num_tiles_h) in enumerate(sample_aspect_ratios):
+ aspect_ratio_mask[i, j, : num_tiles_w * num_tiles_h] = 1
+
+ return aspect_ratio_mask
+
+
+def pack_images(
+ batch_images: list[list[np.ndarray]],
+ max_image_tiles: int,
+) -> tuple[np.ndarray, list[list[int]]]:
+ """
+ Stack a list of lists of images with variable lengths into a numpy array, applying zero padding as needed.
+ Each list in the input represents a batch sample, and each image within a list is expected to be
+ pre-split into tiles. The resulting array will have a shape of
+ (batch_size, max_num_images, max_image_tiles, channels, tile_height, tile_width).
+
+ Args:
+ batch_images (`list[list[np.ndarray]]`):
+ A list of lists of image tiles. Each inner list represents
+ a batch sample containing multiple images, where each image is pre-split into tiles.
+ The shape of each tile array is (num_tiles, channels, tile_height, tile_width).
+ max_image_tiles (int):
+ The maximum number of tiles any image was potantially split.
+
+ Returns:
+ `tuple[np.ndarray, list[list[int]]]`: A tuple containing:
+ - stacked_images (`np.ndarray`):
+ A numpy array of stacked images with shape
+ (batch_size, max_num_images, max_image_tiles, channels, tile_height, tile_width).
+ - all_num_tiles (`list[list[int]]`):
+ A list of lists containing the number of tiles
+ for each image in each batch sample.
+ """
+
+ # Determine output shape
+ batch_size = len(batch_images)
+ max_num_images = max([len(images) for images in batch_images])
+ shapes = [image.shape for images in batch_images for image in images]
+ _, channels, tile_height, tile_width = shapes[0]
+
+ # Initialize the stacked images array with zeros
+ stacked_images = np.zeros(
+ (batch_size, max_num_images, max_image_tiles, channels, tile_height, tile_width),
+ dtype=np.float32,
+ )
+
+ # Fill the stacked images array with the tiled images from the batch
+ all_num_tiles = []
+ for i, images in enumerate(batch_images):
+ num_sample_tiles = []
+ for j, image in enumerate(images):
+ num_tiles = image.shape[0]
+ stacked_images[i, j, :num_tiles] = image
+ num_sample_tiles.append(num_tiles)
+ all_num_tiles.append(num_sample_tiles)
+
+ return stacked_images, all_num_tiles
+
+
+def pack_aspect_ratios(aspect_ratios: list[list[tuple[int, int]]], pad_value: int = 1) -> np.ndarray:
+ """
+ Stack a list of aspect ratios into a numpy array.
+
+ Args:
+ aspect_ratios (`list[list[tuple[int, int]]]`):
+ A list of aspect ratios.
+ pad_value (`int`, *optional*, defaults to 1):
+ The value to pad the aspect ratios with.
+
+ Returns:
+ `np.ndarray`:
+ The aspect ratios stacked into a numpy array with shape (batch_size, max_num_images, 2).
+ """
+ batch_size = len(aspect_ratios)
+ max_num_images = max([len(row) for row in aspect_ratios])
+
+ aspect_ratios_stacked = np.full((batch_size, max_num_images, 2), pad_value, dtype=np.int64)
+ for i, row in enumerate(aspect_ratios):
+ if len(row) > 0:
+ aspect_ratios_stacked[i, : len(row)] = np.array(row)
+ return aspect_ratios_stacked
+
+
+def convert_aspect_ratios_to_ids(aspect_ratios: list[list[tuple[int, int]]], max_image_tiles: int) -> np.ndarray:
+ """
+ Convert aspect ratio tuples to unique ids.
+
+ For batch padding we use 0, because there might be different number of images in each batch.
+ The aspect ratio ids start from 1, with 1 corresponding to the first supported aspect ratio.
+
+ Args:
+ aspect_ratios (`list[list[tuple[int, int]]]`):
+ A list of aspect ratios for each image in the batch.
+ max_image_tiles (`int`):
+ The maximum number of tiles any image can be split into.
+
+ Returns:
+ `np.ndarray`:
+ The aspect ratios ids as a numpy array with shape (batch_size, max_num_images).
+ Each id corresponds to the index of the aspect ratio in the list of supported aspect ratios,
+ offset by 1 (so 0 can be used for padding).
+ """
+
+ batch_size = len(aspect_ratios)
+ max_num_images = max([len(row) for row in aspect_ratios])
+ supported_aspect_ratios = get_all_supported_aspect_ratios(max_image_tiles)
+
+ aspect_ratios_ids = np.zeros((batch_size, max_num_images), dtype=np.int64)
+ for i, sample_aspect_ratios in enumerate(aspect_ratios):
+ for j, (num_tiles_h, num_tiles_w) in enumerate(sample_aspect_ratios):
+ aspect_ratios_ids[i, j] = supported_aspect_ratios.index((num_tiles_h, num_tiles_w)) + 1
+ return aspect_ratios_ids
+
+
+def to_channel_dimension_format(
+ image: np.ndarray,
+ channel_dim: Union[ChannelDimension, str],
+ input_channel_dim: Optional[Union[ChannelDimension, str]] = None,
+) -> np.ndarray:
+ """
+ Converts `image` to the channel dimension format specified by `channel_dim`.
+
+ Args:
+ image (`numpy.ndarray`):
+ The image to have its channel dimension set.
+ channel_dim (`ChannelDimension`):
+ The channel dimension format to use.
+ input_channel_dim (`ChannelDimension`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred from the input image.
+
+ Returns:
+ `np.ndarray`:
+ The image with the channel dimension set to `channel_dim`.
+ """
+ if not isinstance(image, np.ndarray):
+ raise TypeError(f"Input image must be of type np.ndarray, got {type(image)}")
+
+ if input_channel_dim is None:
+ input_channel_dim = infer_channel_dimension_format(image)
+
+ target_channel_dim = ChannelDimension(channel_dim)
+ if input_channel_dim == target_channel_dim:
+ return image
+
+ if target_channel_dim == ChannelDimension.FIRST:
+ image = image.transpose((2, 0, 1))
+ elif target_channel_dim == ChannelDimension.LAST:
+ image = image.transpose((1, 2, 0))
+ else:
+ raise ValueError(f"Unsupported channel dimension format: {channel_dim}")
+
+ return image
+
+
+# Copied from transformers.models.idefics2.image_processing_idefics2.convert_to_rgb
+def convert_to_rgb(image: ImageInput) -> ImageInput:
+ """
+ Converts an image to RGB format. Only converts if the image is of type PIL.Image.Image, otherwise returns the image
+ as is.
+ Args:
+ image (Image):
+ The image to convert.
+ """
+ if not isinstance(image, PIL.Image.Image):
+ return image
+
+ # `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background
+ # for transparent images. The call to `alpha_composite` handles this case
+ if image.mode == "RGB":
+ return image
+
+ image_rgba = image.convert("RGBA")
+ background = Image.new("RGBA", image_rgba.size, (255, 255, 255))
+ alpha_composite = Image.alpha_composite(background, image_rgba)
+ alpha_composite = alpha_composite.convert("RGB")
+ return alpha_composite
+
+
+def _validate_size(size: dict[str, int]) -> None:
+ if not ("height" in size and "width" in size):
+ raise ValueError(f"Argument `size` must be a dictionary with keys 'height' and 'width'. Got: {size}")
+ if size["height"] != size["width"]:
+ raise ValueError(f"Argument `size` must have the same height and width, got {size}")
+
+
+def _validate_mllama_preprocess_arguments(do_resize, size, do_pad, max_image_tiles):
+ if not do_pad:
+ raise ValueError("MllamaImageProcessor doesn't support `do_pad=False` mode.")
+ if not do_resize:
+ raise ValueError("MllamaImageProcessor doesn't support `do_resize=False` mode.")
+ if max_image_tiles is None or max_image_tiles <= 0:
+ raise ValueError(f"MllamaImageProcessor `max_image_tiles` must be a positive integer, got {max_image_tiles}.")
+ _validate_size(size)
+
+
+class MllamaImageProcessor(BaseImageProcessor):
+ """
+ Constructs a Mllama image processor.
+
+ Args:
+ do_convert_rgb (`bool`, *optional*, defaults to `True`):
+ Whether to convert the image to RGB. This is useful if the input image is of a different format e.g. RGBA.
+ Only has an effect if the input image is in the PIL format.
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image tile. Should be a dictionary containing 'height' and 'width' keys, both with integer values.
+ The height and width values should be equal.
+ resample (`int`, *optional*, defaults to `Resampling.BILINEAR`):
+ Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+ has an effect if `do_resize` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image.
+ rescale_factor (`float`, *optional*, defaults to 0.0):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+ `True`.
+ do_pad (`bool`, *optional*, defaults to `True`):
+ Whether or not to pad the images to the largest height and width in the batch.
+ max_image_tiles (`int`, *optional*, defaults to 4):
+ The maximum number of tiles to split the image into.
+ """
+
+ model_input_names = ["pixel_values", "num_tiles", "aspect_ratio_ids", "aspect_ratio_mask"]
+
+ def __init__(
+ self,
+ do_convert_rgb: bool = True,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ do_rescale: bool = True,
+ rescale_factor: float = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_pad: bool = True,
+ max_image_tiles: int = 4,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ self.do_convert_rgb = do_convert_rgb
+ self.do_resize = do_resize
+ self.size = size if size is not None else {"height": 224, "width": 224}
+ self.resample = resample
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
+ self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
+ self.do_pad = do_pad
+ self.max_image_tiles = max_image_tiles
+
+ _validate_mllama_preprocess_arguments(self.do_resize, self.size, self.do_pad, self.max_image_tiles)
+
+ def preprocess(
+ self,
+ images: ImageInput,
+ do_convert_rgb: Optional[bool] = None,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: Optional[PILImageResampling] = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_pad: Optional[bool] = None,
+ max_image_tiles: Optional[int] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ ):
+ """
+ Preprocess a batch of images.
+
+ Args:
+ images (`ImageInput`):
+ A list of images to preprocess.
+ do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+ Whether to convert the image to RGB.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image tile. Should be a dictionary containing 'height' and 'width' keys, both with integer values.
+ The height and width values should be equal.
+ resample (`int`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+ has an effect if `do_resize` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image.
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+ `True`.
+ do_pad (`bool`, *optional*, defaults to `self.do_pad`):
+ Whether or not to pad the images to the largest height and width in the batch.
+ max_image_tiles (`int`, *optional*, defaults to `self.max_image_tiles`):
+ The maximum number of tiles to split the image into.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+
+ Returns:
+ `BatchFeature` of the following structure:
+ - **pixel_values** (`TensorType`): The preprocessed pixel values.
+ - **aspect_ratio_ids** (`TensorType`): The aspect ratio ids of the images.
+ - **num_tiles** (`list[list[int]]`): The number of tiles for each image in the batch.
+ """
+ do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ resample = resample if resample is not None else self.resample
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ do_pad = do_pad if do_pad is not None else self.do_pad
+ max_image_tiles = max_image_tiles if max_image_tiles is not None else self.max_image_tiles
+
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ # extra validation
+ _validate_mllama_preprocess_arguments(do_resize, size, do_pad, max_image_tiles)
+
+ images_list = make_nested_list_of_images(images)
+
+ if self.do_convert_rgb:
+ images_list = [[convert_to_rgb(image) for image in images] for images in images_list]
+
+ batch_images = []
+ batch_aspect_ratios = []
+
+ # iterate over batch samples
+ for images in images_list:
+ sample_images = []
+ sample_aspect_ratios = []
+
+ # iterate over images in a batch sample
+ for image in images:
+ # default PIL images to channels_last
+ if input_data_format is None and isinstance(image, PIL.Image.Image):
+ input_data_format = ChannelDimension.LAST
+
+ # convert to numpy array for processing
+ image = to_numpy_array(image)
+
+ # convert images to channels first format for faster processing
+ # LAST is slower for `pad` and not supported by `split_to_tiles`
+ data_format = ChannelDimension.FIRST
+ image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+
+ # do_resize=False is not supported, validated
+ image, aspect_ratio = self.resize(
+ image=image,
+ size=size,
+ resample=resample,
+ max_image_tiles=max_image_tiles,
+ input_data_format=data_format,
+ data_format=data_format,
+ )
+
+ # do_pad=False is not supported, validated
+ image = self.pad(
+ image=image,
+ size=size,
+ aspect_ratio=aspect_ratio,
+ input_data_format=data_format,
+ data_format=data_format,
+ )
+
+ if do_rescale:
+ image = self.rescale(
+ image=image,
+ scale=rescale_factor,
+ input_data_format=data_format,
+ data_format=data_format,
+ )
+
+ if do_normalize:
+ image = self.normalize(
+ image=image,
+ mean=image_mean,
+ std=image_std,
+ input_data_format=data_format,
+ data_format=data_format,
+ )
+
+ num_tiles_height, num_tiles_width = aspect_ratio
+ image = split_to_tiles(image, num_tiles_height, num_tiles_width)
+
+ sample_images.append(image)
+ sample_aspect_ratios.append((num_tiles_height, num_tiles_width))
+
+ batch_images.append(sample_images)
+ batch_aspect_ratios.append(sample_aspect_ratios)
+
+ images, num_tiles = pack_images(batch_images, max_image_tiles)
+
+ aspect_ratio_ids = convert_aspect_ratios_to_ids(batch_aspect_ratios, max_image_tiles=max_image_tiles)
+ aspect_ratio_mask = build_aspect_ratio_mask(batch_aspect_ratios, max_image_tiles=max_image_tiles)
+
+ # images (np.ndarray) with shape (batch_size, max_num_images, max_image_tiles, channels, tile_height, tile_width)
+ # aspect_ratio_ids (np.ndarray) with shape (batch_size, max_num_images) - aspect ratio ids for each image, padded to max_num_images with 0
+ # num_tiles (list[list[int]]) with (batch_size, num_images_in_batch) - real number of tiles for each image, not padded
+ # aspect_ratio_mask (np.ndarray) with shape (batch_size, max_num_images, max_image_tiles) - number of tiles for each image, padded to max_num_images with 0
+ encoded_inputs = BatchFeature(
+ data={
+ "pixel_values": images,
+ "aspect_ratio_ids": aspect_ratio_ids,
+ "aspect_ratio_mask": aspect_ratio_mask,
+ },
+ tensor_type=return_tensors,
+ )
+ encoded_inputs["num_tiles"] = num_tiles
+
+ return encoded_inputs
+
+ def pad(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ aspect_ratio: tuple[int, int],
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Pad an image to the `size` x `aspect_ratio`. For example, if size is {height: 224, width: 224} and aspect ratio is
+ (1, 2), the image will be padded to 224x448.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Size of the output image.
+ aspect_ratio (`tuple[int, int]`):
+ The aspect ratio of the image.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+
+ Returns:
+ `np.ndarray`: The padded image.
+ """
+
+ _validate_size(size)
+
+ image_height, image_width = get_image_size(image, channel_dim=input_data_format)
+ num_tiles_height, num_tiles_width = aspect_ratio
+ padded_height = num_tiles_height * size["height"]
+ padded_width = num_tiles_width * size["width"]
+ pad_size = ((0, padded_height - image_height), (0, padded_width - image_width))
+
+ image = pad(
+ image,
+ pad_size,
+ mode=PaddingMode.CONSTANT,
+ constant_values=0,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+
+ return image
+
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ max_image_tiles: int,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> Union[np.ndarray, tuple[int, int]]:
+ """
+ Resizes an image to fit within a tiled canvas while maintaining its aspect ratio.
+ The optimal canvas size is calculated based on the maximum number of tiles and the tile size.
+
+ The function first determines the best tile arrangement for the image, then resizes the image
+ to fit within this canvas. The resized image and the number of tiles along the height and width
+ dimensions are returned.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Size of the output image.
+ max_image_tiles (`int`):
+ The maximum number of tiles to split the image into.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ Resampling filter to use when resizing the image.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+
+ Returns:
+ `Union[np.ndarray, tuple[int, int]]`: The resized image and a tuple containing the number of tiles
+ along the height and width dimensions.
+ """
+
+ _validate_size(size)
+
+ image_height, image_width = get_image_size(image, channel_dim=input_data_format)
+ tile_size = size["height"]
+
+ canvas_height, canvas_width = get_optimal_tiled_canvas(
+ image_height=image_height,
+ image_width=image_width,
+ max_image_tiles=max_image_tiles,
+ tile_size=tile_size,
+ )
+ num_tiles_height = canvas_height // tile_size
+ num_tiles_width = canvas_width // tile_size
+
+ new_height, new_width = get_image_size_fit_to_canvas(
+ image_height=image_height,
+ image_width=image_width,
+ canvas_height=canvas_height,
+ canvas_width=canvas_width,
+ tile_size=tile_size,
+ )
+
+ image = resize(
+ image,
+ (new_height, new_width),
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+
+ return image, (num_tiles_height, num_tiles_width)
+
+
+__all__ = ["MllamaImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/modeling_mllama.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/modeling_mllama.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8aa1e6cc1a8b186dd90a82cd5a47b735376d83f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/modeling_mllama.py
@@ -0,0 +1,1763 @@
+# coding=utf-8
+# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Mllama model."""
+
+import math
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import AttentionMaskConverter
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import OutputRecorder, check_model_inputs
+from .configuration_mllama import MllamaConfig, MllamaTextConfig, MllamaVisionConfig
+
+
+if is_torch_flex_attn_available():
+ from torch.nn.attention.flex_attention import BlockMask
+
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+logger = logging.get_logger(__name__)
+
+
+def _prepare_cross_attention_mask(
+ cross_attention_mask: torch.Tensor,
+ num_vision_tokens: int,
+ dtype: str,
+) -> tuple[torch.Tensor, torch.Tensor]:
+ # reshape so it can be used by attn module
+ batch_size, text_total_length, *_ = cross_attention_mask.shape
+ cross_attention_mask = cross_attention_mask.repeat_interleave(num_vision_tokens, dim=3)
+ cross_attention_mask = cross_attention_mask.view(batch_size, text_total_length, -1)
+ cross_attention_mask = cross_attention_mask.unsqueeze(1)
+
+ # invert the mask
+ inverted_cross_attn_mask = (1.0 - cross_attention_mask).to(dtype)
+ cross_attention_mask = inverted_cross_attn_mask.masked_fill(
+ inverted_cross_attn_mask.to(torch.bool), torch.finfo(dtype).min
+ )
+
+ # apply full-row bias, which return 4D tensor of shape [B, H, S1, 1] where value is 0 if the a full row in cross attn mask's
+ # last dimension contains negative infinity values, otherwise it's 1
+ negative_inf_value = torch.finfo(dtype).min
+ full_text_row_masked_out_mask = (
+ (cross_attention_mask != negative_inf_value).any(dim=-1).type_as(cross_attention_mask)[..., None]
+ )
+ cross_attention_mask *= full_text_row_masked_out_mask
+
+ return cross_attention_mask, full_text_row_masked_out_mask
+
+
+def _prepare_aspect_ratio_attention_mask(
+ aspect_ratio_mask: torch.Tensor,
+ num_patches: int,
+ target_length: int,
+ dtype: torch.dtype,
+) -> torch.Tensor:
+ # Expand aspect ratio mask to target_length
+ batch_size, max_num_tiles = aspect_ratio_mask.shape
+ attention_mask = aspect_ratio_mask.view(batch_size, max_num_tiles, 1, 1).to(dtype)
+ attention_mask = attention_mask.repeat(1, 1, target_length, 1)
+
+ # Mask padding patches
+ pad_patches = target_length - num_patches
+ attention_mask[:, :, -pad_patches:] = 0
+
+ # Invert the mask (0 -> 1, 1 -> 0)
+ attention_mask = 1 - attention_mask
+
+ # Reshape to 2D and create 4D attention mask
+ # (batch_size, 1, max_num_tiles * target_length, max_num_tiles * target_length)
+ attention_mask = attention_mask.reshape(batch_size, max_num_tiles * target_length, 1)
+ attention_mask = attention_mask @ attention_mask.transpose(-1, -2) * torch.finfo(dtype).min
+ attention_mask = attention_mask.unsqueeze(1)
+
+ return attention_mask
+
+
+class MllamaPrecomputedAspectRatioEmbedding(nn.Module):
+ def __init__(self, config: MllamaVisionConfig, is_gated: bool = True):
+ super().__init__()
+ self.max_num_tiles = config.max_num_tiles
+ self.hidden_size = config.hidden_size
+ self.max_aspect_ratio_id = config.max_aspect_ratio_id
+ self.is_gated = is_gated
+
+ self.embedding = nn.Embedding(self.max_aspect_ratio_id + 1, self.max_num_tiles * self.hidden_size)
+ if is_gated:
+ self.gate = nn.Parameter(torch.zeros(1))
+
+ def forward(self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor) -> torch.Tensor:
+ embeddings = self.embedding(aspect_ratio_ids)
+ embeddings = embeddings.reshape(-1, self.max_num_tiles, 1, self.hidden_size)
+
+ if self.is_gated:
+ embeddings = embeddings * self.gate.tanh()
+
+ hidden_state = hidden_state + embeddings
+ return hidden_state
+
+
+class MllamaPrecomputedPositionEmbedding(nn.Module):
+ def __init__(self, config: MllamaVisionConfig):
+ super().__init__()
+ self.max_num_tiles = config.max_num_tiles
+ self.max_aspect_ratio_id = config.max_aspect_ratio_id
+ self.num_patches = (config.image_size // config.patch_size) ** 2 + 1
+ self.hidden_size = config.hidden_size
+ self.scale = config.hidden_size**-0.5
+
+ self.gate = nn.Parameter(torch.zeros(1))
+
+ # position embedding
+ position_embedding = torch.randn(self.num_patches, self.hidden_size)
+ self.embedding = nn.Parameter(self.scale * position_embedding)
+
+ # tile position embedding
+ self.tile_embedding = nn.Embedding(
+ self.max_aspect_ratio_id + 1, self.max_num_tiles * self.num_patches * self.hidden_size
+ )
+
+ def forward(self, hidden_state: torch.Tensor, aspect_ratio_ids: torch.Tensor) -> torch.Tensor:
+ # position embeddings
+ gated_position_embedding = (1 - self.gate.tanh()) * self.embedding
+ hidden_state = hidden_state + gated_position_embedding.view(1, 1, self.num_patches, self.hidden_size)
+
+ # precomputed tile position embeddings
+ tile_position_embedding = self.tile_embedding(aspect_ratio_ids)
+ batch_size = hidden_state.shape[0]
+ tile_position_embedding = tile_position_embedding.reshape(
+ batch_size, self.max_num_tiles, self.num_patches, self.hidden_size
+ )
+ gated_tile_position_embedding = self.gate.tanh() * tile_position_embedding
+ hidden_state = hidden_state + gated_tile_position_embedding
+
+ return hidden_state
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->MllamaVision
+class MllamaVisionMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.activation_fn = ACT2FN[config.hidden_act]
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Copied from transformers.models.llama.modeling_llama.eager_attention_forward
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class MllamaVisionAttention(nn.Module):
+ def __init__(self, config: MllamaVisionConfig):
+ super().__init__()
+
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.attention_heads
+ self.head_dim = config.hidden_size // config.attention_heads
+ self.scaling = self.head_dim**-0.5
+ self.num_key_value_groups = 1
+
+ self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=False)
+ self.k_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=False)
+ self.v_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=False)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.embed_dim, bias=False)
+
+ def forward(
+ self,
+ hidden_state: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ query = self.q_proj(hidden_state)
+ key = self.k_proj(hidden_state)
+ value = self.v_proj(hidden_state)
+
+ batch_size, q_seq_len, _ = query.shape
+ _, kv_seq_len, _ = key.shape
+
+ query = query.view(batch_size, q_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key = key.view(batch_size, kv_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+ value = value.view(batch_size, kv_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+ attention_interface: Callable = eager_attention_forward
+
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query,
+ key,
+ value,
+ attention_mask,
+ dropout=0.0,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(batch_size, q_seq_len, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+class MllamaVisionEncoderLayer(nn.Module):
+ def __init__(self, config: MllamaVisionConfig, is_gated: bool = False):
+ super().__init__()
+
+ self.hidden_size = config.hidden_size
+ self.num_attention_heads = config.attention_heads
+ self.is_gated = is_gated
+ self.intermediate_size = config.intermediate_size
+
+ self.self_attn = MllamaVisionAttention(config)
+ self.mlp = MllamaVisionMLP(config)
+
+ self.input_layernorm = nn.LayerNorm(self.hidden_size, eps=config.norm_eps)
+ self.post_attention_layernorm = nn.LayerNorm(self.hidden_size, eps=config.norm_eps)
+
+ if is_gated:
+ self.gate_attn = nn.Parameter(torch.ones(1) * math.pi / 4)
+ self.gate_ffn = nn.Parameter(torch.ones(1) * math.pi / 4)
+
+ def forward(
+ self,
+ hidden_state: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ ):
+ # Self Attention
+ residual = hidden_state
+ hidden_state = self.input_layernorm(hidden_state)
+ hidden_state, attn_weights = self.self_attn(hidden_state, attention_mask=attention_mask)
+ if self.is_gated:
+ hidden_state = self.gate_attn.tanh() * hidden_state
+ hidden_state = residual + hidden_state
+
+ # Feed forward
+ residual = hidden_state
+ hidden_state = self.post_attention_layernorm(hidden_state)
+ hidden_state = self.mlp(hidden_state)
+ if self.is_gated:
+ hidden_state = self.gate_ffn.tanh() * hidden_state
+ hidden_state = residual + hidden_state
+
+ return hidden_state
+
+
+class MllamaVisionEncoder(nn.Module):
+ """
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+ [`MllamaEncoderLayer`].
+
+ Args:
+ config: MllamaConfig
+ """
+
+ def __init__(self, config: MllamaVisionConfig, num_layers=32, is_gated=False):
+ super().__init__()
+ self.config = config
+ self.layers = nn.ModuleList([MllamaVisionEncoderLayer(config, is_gated) for _ in range(num_layers)])
+ self.gradient_checkpointing = False
+ self.config = config
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ ) -> BaseModelOutput:
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ """
+ encoder_states = ()
+ for encoder_layer in self.layers:
+ hidden_states = encoder_layer(
+ hidden_state=hidden_states,
+ attention_mask=attention_mask,
+ )
+ encoder_states = encoder_states + (hidden_states,)
+
+ return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->MllamaText
+class MllamaTextRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ MllamaTextRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class MllamaTextCrossAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ config: Optional[MllamaTextConfig] = None,
+ layer_idx: Optional[int] = None,
+ ):
+ super().__init__()
+ self.config = config
+ self.num_heads = self.config.num_attention_heads
+ self.num_key_value_heads = self.config.num_key_value_heads
+ self.dropout = config.dropout
+ self.hidden_size = config.hidden_size
+ self.head_dim = config.hidden_size // self.num_heads
+ self.layer_idx = layer_idx
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+ self.q_norm = MllamaTextRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+ self.k_norm = MllamaTextRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cross_attention_states: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+ bsz, q_len, _ = hidden_states.size()
+ query_states = self.q_proj(hidden_states)
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ query_states = self.q_norm(query_states)
+
+ if cross_attention_states is not None:
+ key_states = self.k_proj(cross_attention_states)
+ value_states = self.v_proj(cross_attention_states)
+ key_states = key_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ key_states = self.k_norm(key_states)
+ if past_key_values is not None:
+ # if we have a new image + new tokens, we only computed key_states on that new image
+ # we still update the cross key states, past_image, new_image. And use it!
+ key_states, value_states = past_key_values.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ elif cache_position[0] != 0:
+ key_states, value_states = (
+ past_key_values.layers[self.layer_idx].keys,
+ past_key_values.layers[self.layer_idx].values,
+ )
+ else:
+ raise ValueError(
+ "Cross attention layer can't find neither `cross_attn_states` nor cached values for key/values!"
+ )
+
+ attention_interface: Callable = eager_attention_forward
+
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class MllamaTextSelfAttention(nn.Module):
+ def __init__(self, config: MllamaTextConfig, layer_idx: int):
+ super().__init__()
+ self.config = config
+ self.num_heads = config.num_attention_heads
+ self.dropout = config.dropout
+ self.hidden_size = config.hidden_size
+ self.num_key_value_heads = config.num_key_value_heads
+ self.head_dim = config.hidden_size // self.num_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.rope_theta = config.rope_theta
+ self.layer_idx = layer_idx
+
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ position_embeddings: torch.Tensor,
+ use_cache: bool = False,
+ past_key_values=None,
+ cache_position=None,
+ **kwargs,
+ ):
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.gemma2.modeling_gemma2.Gemma2MLP with Gemma2->MllamaText
+class MllamaTextMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ # Ignore copy
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+ return down_proj
+
+
+# Modified from transformers.models.llama.modeling_llama.LlamaDecoderLayer
+class MllamaSelfAttentionDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MllamaTextConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = MllamaTextSelfAttention(config=config, layer_idx=layer_idx)
+
+ self.mlp = MllamaTextMLP(config)
+ self.input_layernorm = MllamaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = MllamaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.layer_idx = layer_idx
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cross_attention_states: Optional[torch.Tensor] = None,
+ cross_attention_mask: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ full_text_row_masked_out_mask: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence
+ position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
+ Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+ with `head_dim` being the embedding dimension of each attention head.
+ kwargs (`dict`, *optional*):
+ Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+ into the model
+ """
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ return hidden_states
+
+
+class MllamaCrossAttentionDecoderLayer(GradientCheckpointingLayer):
+ """Cross-attention transformer block with tanh-gated attention and feedforward."""
+
+ def __init__(self, config: MllamaTextConfig, layer_idx: int) -> None:
+ super().__init__()
+ self.layer_idx = layer_idx
+ self.cross_attn = MllamaTextCrossAttention(config, layer_idx=layer_idx)
+
+ self.input_layernorm = MllamaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.cross_attn_attn_gate = torch.nn.Parameter(torch.zeros(1))
+
+ self.mlp = MllamaTextMLP(config)
+ self.post_attention_layernorm = MllamaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.cross_attn_mlp_gate = torch.nn.Parameter(torch.zeros(1))
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ cross_attention_states: torch.Tensor,
+ cross_attention_mask: torch.Tensor,
+ attention_mask: torch.Tensor,
+ full_text_row_masked_out_mask: tuple[torch.Tensor, torch.Tensor],
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor]:
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+
+ hidden_states, attn_weights = self.cross_attn(
+ hidden_states=hidden_states,
+ attention_mask=cross_attention_mask,
+ cross_attention_states=cross_attention_states,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = residual + self.cross_attn_attn_gate.tanh() * hidden_states
+
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ if full_text_row_masked_out_mask is not None:
+ hidden_states = full_text_row_masked_out_mask[:, 0] * hidden_states # type: ignore
+ hidden_states = residual + self.cross_attn_mlp_gate.tanh() * hidden_states
+
+ return hidden_states
+
+
+class MllamaRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: MllamaTextConfig, device=None):
+ super().__init__()
+ self.rope_type = config.rope_scaling["rope_type"]
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+@auto_docstring
+class MllamaPreTrainedModel(PreTrainedModel):
+ config: MllamaConfig
+ base_model_prefix = ""
+ supports_gradient_checkpointing = True
+ _no_split_modules = [
+ "MllamaVisionEncoderLayer",
+ "MllamaCrossAttentionDecoderLayer",
+ "MllamaSelfAttentionDecoderLayer",
+ ]
+ _can_compile_fullgraph = False # static cache cannot have different shapes for each layer
+ _supports_sdpa = True
+ _supports_flash_attn = True
+ _supports_flex_attn = True
+ _supports_attention_backend = True
+ _can_record_outputs = {
+ "hidden_states": [MllamaSelfAttentionDecoderLayer, MllamaCrossAttentionDecoderLayer],
+ "attentions": [
+ OutputRecorder(MllamaTextSelfAttention, index=1, layer_name="self_attn"),
+ OutputRecorder(MllamaTextSelfAttention, index=1, layer_name="cross_attn"),
+ OutputRecorder(MllamaTextCrossAttention, index=1, layer_name="cross_attn"),
+ ],
+ }
+
+ def _init_weights(self, module):
+ std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range)
+
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+ elif isinstance(module, MllamaTextRMSNorm):
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, MllamaVisionModel):
+ nn.init.normal_(module.class_embedding.data, std=std)
+ elif isinstance(module, MllamaPrecomputedPositionEmbedding):
+ nn.init.normal_(module.embedding.data, std=std)
+ nn.init.zeros_(module.gate.data)
+ elif isinstance(module, MllamaVisionEncoderLayer) and module.is_gated:
+ nn.init.normal_(module.gate_attn.data, std=std)
+ nn.init.normal_(module.gate_ffn.data, std=std)
+ elif isinstance(module, MllamaCrossAttentionDecoderLayer):
+ module.cross_attn_attn_gate.data.zero_()
+ module.cross_attn_mlp_gate.data.zero_()
+ elif isinstance(module, MllamaPrecomputedAspectRatioEmbedding):
+ if module.is_gated:
+ module.gate.data.zero_()
+
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, "BlockMask"],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ output_attentions: bool = False,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and (attention_mask == 0.0).any():
+ return attention_mask
+ return None
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ return attention_mask
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ sequence_length = input_tensor.shape[1]
+ if using_compilable_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ and not output_attentions
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+
+@auto_docstring(
+ custom_intro="""
+ The Mllama Vision Model which consists of two vision encoders.
+ """
+)
+class MllamaVisionModel(MllamaPreTrainedModel):
+ config: MllamaVisionConfig
+ base_model_prefix = "vision_model"
+
+ def __init__(self, config: MllamaVisionConfig):
+ super().__init__(config)
+ self.image_size = config.image_size
+ self.patch_size = config.patch_size
+ self.max_num_tiles = config.max_num_tiles
+ self.hidden_size = config.hidden_size
+ self.num_channels = config.num_channels
+ self.intermediate_layers_indices = config.intermediate_layers_indices
+
+ self.num_patches = (self.image_size // self.patch_size) ** 2 + 1
+ self.scale = config.hidden_size**-0.5
+
+ self.patch_embedding = nn.Conv2d(
+ in_channels=config.num_channels,
+ out_channels=self.hidden_size,
+ kernel_size=self.patch_size,
+ stride=self.patch_size,
+ padding="valid",
+ bias=False,
+ )
+
+ self.class_embedding = nn.Parameter(self.scale * torch.randn(self.hidden_size))
+ self.gated_positional_embedding = MllamaPrecomputedPositionEmbedding(config)
+
+ self.pre_tile_positional_embedding = MllamaPrecomputedAspectRatioEmbedding(config, is_gated=True)
+ self.post_tile_positional_embedding = MllamaPrecomputedAspectRatioEmbedding(config, is_gated=True)
+
+ # layer norms
+ self.layernorm_pre = nn.LayerNorm(self.hidden_size)
+ self.layernorm_post = nn.LayerNorm(self.hidden_size)
+
+ # encoders
+ self.transformer = MllamaVisionEncoder(config, config.num_hidden_layers, is_gated=False)
+ self.global_transformer = MllamaVisionEncoder(config, config.num_global_layers, is_gated=True)
+
+ self.post_init()
+
+ def get_input_embeddings(self):
+ """
+ This function is used to fetch the first embedding layer to activate grads on inputs.
+ """
+ return self.patch_embedding
+
+ def apply_class_embedding(self, hidden_state: torch.Tensor) -> torch.Tensor:
+ batch_size, _, hidden_size = hidden_state.shape
+ class_embedding = self.class_embedding.expand(batch_size, 1, hidden_size)
+ hidden_state = torch.cat([class_embedding, hidden_state], dim=1)
+ return hidden_state
+
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self, pixel_values: torch.Tensor, aspect_ratio_ids: torch.Tensor, aspect_ratio_mask: torch.Tensor, **kwargs
+ ) -> BaseModelOutput:
+ r"""
+ aspect_ratio_ids (`torch.Tensor` of shape `(batch_size, max_num_images)`, *optional*):
+ Aspect ratio ids used to select the appropriate precomputed tile embeddings based on the aspect ratio of each input image.
+ These ids correspond to indices in the model's list of supported aspect ratios, offset by 1.
+
+ For example, if the model supports aspect ratios [[1, 1], [1, 2], [2, 1]]:
+ - An image with aspect ratio [1, 1] would have ID 1
+ - An image with aspect ratio [1, 2] would have ID 2
+ - An image with aspect ratio [2, 1] would have ID 3
+
+ The id 0 is reserved for padding (i.e., no image).
+
+ If an image has aspect ratio [1, 2], that means it was split into 2 tiles horizontally, and its `aspect_ratio_id` would be 2.
+ aspect_ratio_mask (`torch.Tensor` of shape `(batch_size, max_num_images, max_num_tiles)`, *optional*):
+ Mask to avoid performing attention on padding tiles. Mask values selected in `[0, 1]`:
+
+ - 1 for tiles that are **not masked**,
+ - 0 for tiles that are **masked**.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import AutoProcessor, MllamaVisionModel
+
+ >>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
+ >>> model = MllamaVisionModel.from_pretrained(checkpoint)
+ >>> processor = AutoProcessor.from_pretrained(checkpoint)
+
+ >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> inputs = processor(images=image, return_tensors="pt")
+
+ >>> output = model(**inputs)
+
+ >>> print(output.last_hidden_state.shape)
+ torch.Size([1, 1, 4, 1025, 7680])
+ ```
+ """
+ batch_size, num_concurrent_media, num_tiles, num_channels, height, width = pixel_values.shape
+
+ pixel_values = pixel_values.reshape(batch_size * num_concurrent_media * num_tiles, num_channels, height, width)
+ aspect_ratio_ids = aspect_ratio_ids.reshape(batch_size * num_concurrent_media, -1)
+
+ # Patch embedding
+ target_dtype = self.patch_embedding.weight.dtype
+ target_device = self.patch_embedding.weight.device
+ patch_embeds = self.patch_embedding(pixel_values.to(target_device, target_dtype))
+ hidden_state = patch_embeds.flatten(2).transpose(1, 2)
+
+ # Tile embeddings
+ _, num_patches, dim = hidden_state.shape
+ hidden_state = hidden_state.reshape(batch_size * num_concurrent_media, num_tiles, -1, dim)
+ hidden_state = self.pre_tile_positional_embedding(hidden_state, aspect_ratio_ids)
+
+ # Add cls token
+ hidden_state = hidden_state.reshape(batch_size * num_concurrent_media * num_tiles, num_patches, dim)
+ hidden_state = self.apply_class_embedding(hidden_state)
+ num_patches += 1
+
+ # Position embeddings
+ hidden_state = hidden_state.reshape(batch_size * num_concurrent_media, num_tiles, num_patches, dim)
+ hidden_state = self.gated_positional_embedding(hidden_state, aspect_ratio_ids)
+
+ hidden_state = self.layernorm_pre(hidden_state)
+
+ # Compute the number of tokens to pad
+ num_padding_patches = (8 - (hidden_state.shape[-2] % 8)) % 8
+ # Compute padding tuple for pad function
+ padding = (0, 0, 0, num_padding_patches) # (pad_left, pad_right, pad_left for dim -2, pad_right for dim -2)
+ # Pad the tensor
+ hidden_state = F.pad(hidden_state, padding, mode="constant", value=0)
+ slice_index = -num_padding_patches if num_padding_patches > 0 else None
+
+ # Prepare attention mask
+ attention_mask = aspect_ratio_mask.reshape(batch_size * num_concurrent_media, -1)
+ attention_mask = _prepare_aspect_ratio_attention_mask(
+ aspect_ratio_mask=attention_mask,
+ num_patches=self.num_patches,
+ target_length=hidden_state.shape[2],
+ dtype=self.dtype,
+ )
+
+ # Apply encoder
+ hidden_state = hidden_state.view(batch_size * num_concurrent_media, -1, dim)
+ output = self.transformer(
+ hidden_state,
+ attention_mask=attention_mask,
+ )
+ hidden_state = output.last_hidden_state
+
+ hidden_state = self.layernorm_post(hidden_state)
+
+ # Apply global encoder
+ hidden_state = hidden_state.reshape(
+ batch_size * num_concurrent_media, num_tiles, num_patches + num_padding_patches, dim
+ )
+ hidden_state = self.post_tile_positional_embedding(hidden_state, aspect_ratio_ids)
+ hidden_state = hidden_state.reshape(
+ batch_size * num_concurrent_media, num_tiles * (num_patches + num_padding_patches), dim
+ )
+ global_output = self.global_transformer(
+ hidden_state,
+ attention_mask=attention_mask,
+ )
+ hidden_state = global_output.last_hidden_state
+
+ # Remove padding form hidden state
+ hidden_state = hidden_state.reshape(
+ batch_size * num_concurrent_media, num_tiles, num_patches + num_padding_patches, dim
+ )
+ hidden_state = hidden_state[:, :, :slice_index]
+ hidden_state = hidden_state.reshape(batch_size, num_concurrent_media, num_tiles, num_patches, dim)
+
+ # Collect intermediate layer outputs from encoder output
+ all_intermediate_hidden_states = [output.hidden_states[i] for i in self.intermediate_layers_indices]
+ intermediate_hidden_states = torch.stack(all_intermediate_hidden_states, dim=-1)
+
+ # Remove padding from intermediate hidden states
+ intermediate_hidden_states = intermediate_hidden_states.reshape(
+ batch_size * num_concurrent_media, num_tiles, num_patches + num_padding_patches, -1
+ )
+ intermediate_hidden_states = intermediate_hidden_states[:, :, :slice_index]
+ intermediate_hidden_states = intermediate_hidden_states.reshape(
+ batch_size, num_concurrent_media, num_tiles, num_patches, -1
+ )
+
+ # Concatenate final hidden state and intermediate hidden states
+ hidden_state = torch.cat([hidden_state, intermediate_hidden_states], dim=-1)
+
+ return BaseModelOutput(last_hidden_state=hidden_state)
+
+
+@auto_docstring(
+ custom_intro="""
+ The Mllama Text Model which consists of transformer with self and cross attention layers.
+ """
+)
+class MllamaTextModel(MllamaPreTrainedModel):
+ config: MllamaTextConfig
+ base_model_prefix = "language_model.model"
+
+ def __init__(self, config: MllamaTextConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+ self.embed_tokens = nn.Embedding(config.vocab_size + 8, config.hidden_size, self.padding_idx)
+ self.cross_attention_layers = config.cross_attention_layers
+
+ layers = []
+ for layer_idx in range(config.num_hidden_layers):
+ if layer_idx in self.cross_attention_layers:
+ layers.append(MllamaCrossAttentionDecoderLayer(config, layer_idx))
+ else:
+ layers.append(MllamaSelfAttentionDecoderLayer(config, layer_idx))
+
+ self.layers = nn.ModuleList(layers)
+ self.norm = MllamaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.rotary_emb = MllamaRotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+ self.post_init()
+
+ @check_model_inputs
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ cross_attention_states: Optional[torch.FloatTensor] = None,
+ cross_attention_mask: Optional[torch.Tensor] = None,
+ full_text_row_masked_out_mask: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> BaseModelOutputWithPast:
+ r"""
+ cross_attention_states (`torch.FloatTensor`, *optional*):
+ Output of the vision model, used for cross-attention. This tensor contains the processed image features that
+ the language model will attend to.
+ cross_attention_mask (`torch.Tensor` of shape `(batch_size, seq_length, max_num_images, max_num_tiles)`, *optional*):
+ Cross-attention mask to control the interaction between text tokens and image tiles.
+ This 4D tensor defines which image tiles each text token should attend to.
+
+ For each text token (in seq_length):
+ - 1 indicates the token **should attend** to the corresponding image tile
+ - 0 indicates the token **should not attend** to the corresponding image tile
+ full_text_row_masked_out_mask (`tuple[torch.Tensor, torch.Tensor]`, *optional*):
+ A tuple containing two tensors that mask out rows in the cross-attention mechanism:
+ - The first tensor has shape `(batch_size, 1, seq_length, 1)` and contains values of 0 or 1.
+ A value of 0 indicates that the corresponding text token's entire row in the cross-attention
+ matrix should be masked out (all image tokens ignored).
+ - The second tensor has the same shape and is used internally to apply the masking during
+ the forward pass of cross-attention layers.
+ This mask is derived from the cross_attention_mask and is used to handle cases where a text token
+ should not attend to any image token.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoProcessor, MllamaTextModel
+
+ >>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
+ >>> model = MllamaTextModel.from_pretrained(checkpoint)
+ >>> processor = AutoProcessor.from_pretrained(checkpoint)
+
+ >>> text = "<|image|>If I had to write a haiku for this one"
+ >>> inputs = processor(text=text, return_tensors="pt")
+
+ >>> output = model(**inputs)
+
+ >>> print(output.last_hidden_state.shape)
+ torch.Size([1, 13, 4096])
+ ```
+ """
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ hidden_states = inputs_embeds
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_key_values)
+
+ # create position embeddings to be shared across the decoder layers
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ # decoder layers
+ for idx, decoder_layer in enumerate(self.layers):
+ # For text-only path we should skip cross attention layers.
+ # Let's check if the layer is cross attention layer and if we have cross attention states
+ # or cached cross attention states.
+ is_cross_attention_layer = idx in self.cross_attention_layers
+ is_cross_attention_cache_empty = past_key_values is None or (
+ past_key_values is not None and past_key_values.get_seq_length(idx) == 0
+ )
+
+ if is_cross_attention_layer and cross_attention_states is None and is_cross_attention_cache_empty:
+ continue
+
+ hidden_states = decoder_layer(
+ hidden_states,
+ cross_attention_states=cross_attention_states,
+ cross_attention_mask=cross_attention_mask,
+ attention_mask=causal_mask,
+ full_text_row_masked_out_mask=full_text_row_masked_out_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+
+ hidden_states = self.norm(hidden_states)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The Mllama Text Model with a language modeling head on top.
+ """
+)
+class MllamaForCausalLM(MllamaPreTrainedModel, GenerationMixin):
+ config: MllamaTextConfig
+ _can_compile_fullgraph = True # only the LLM without cross attn can do compile
+ base_model_prefix = "language_model"
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config.get_text_config())
+ self.text_config = config.get_text_config()
+ self.vocab_size = self.text_config.vocab_size
+ self.model = MllamaTextModel._from_config(self.text_config)
+ self.lm_head = nn.Linear(self.text_config.hidden_size, self.vocab_size, bias=False)
+
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ cross_attention_states: Optional[torch.LongTensor] = None,
+ cross_attention_mask: Optional[torch.LongTensor] = None,
+ full_text_row_masked_out_mask: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, CausalLMOutputWithPast]:
+ r"""
+ cross_attention_states (`torch.FloatTensor`, *optional*):
+ Output of the vision model, used for cross-attention. This tensor contains the processed image features that
+ the language model will attend to.
+ cross_attention_mask (`torch.Tensor` of shape `(batch_size, seq_length, max_num_images, max_num_tiles)`, *optional*):
+ Cross-attention mask to control the interaction between text tokens and image tiles.
+ This 4D tensor defines which image tiles each text token should attend to.
+
+ For each text token (in seq_length):
+ - 1 indicates the token **should attend** to the corresponding image tile
+ - 0 indicates the token **should not attend** to the corresponding image tile
+ full_text_row_masked_out_mask (`tuple[torch.Tensor, torch.Tensor]`, *optional*):
+ A tuple containing two tensors that mask out rows in the cross-attention mechanism:
+ - The first tensor has shape `(batch_size, 1, seq_length, 1)` and contains values of 0 or 1.
+ A value of 0 indicates that the corresponding text token's entire row in the cross-attention
+ matrix should be masked out (all image tokens ignored).
+ - The second tensor has the same shape and is used internally to apply the masking during
+ the forward pass of cross-attention layers.
+ This mask is derived from the cross_attention_mask and is used to handle cases where a text token
+ should not attend to any image token.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MllamaForCausalLM
+
+ >>> model = MllamaForCausalLM.from_pretrained("Llama-3.2-11B-Vision")
+ >>> tokenizer = AutoTokenizer.from_pretrained("Llama-3.2-11B-Vision")
+
+ >>> prompt = "If I had to write a haiku, it would be:"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=40, do_sample=True, temperature=0.6)
+ >>> result = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ >>> print(result)
+ If I had to write a haiku, it would be: "Snowflakes gently fall" - simple, yet peaceful.
+ I love the idea of snowflakes gently falling, each one
+ ```
+ """
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ cross_attention_states=cross_attention_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ cross_attention_mask=cross_attention_mask,
+ full_text_row_masked_out_mask=full_text_row_masked_out_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :]).float()
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The Mllama model which consists of a vision encoder and a language model without language modeling head.
+ """
+)
+class MllamaModel(MllamaPreTrainedModel):
+ _checkpoint_conversion_mapping = {"language_model.model": "language_model"}
+
+ def __init__(self, config: MllamaConfig):
+ super().__init__(config)
+ self.vocab_size = config.text_config.vocab_size
+ self.hidden_size = config.text_config.hidden_size
+ self.max_num_tiles = config.vision_config.max_num_tiles
+ self.vision_output_dim = config.vision_config.vision_output_dim
+ self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+
+ self.vision_model = MllamaVisionModel._from_config(config.vision_config)
+ self.language_model = MllamaTextModel._from_config(config.text_config)
+ self.multi_modal_projector = nn.Linear(
+ config.vision_config.vision_output_dim,
+ config.text_config.hidden_size,
+ bias=True,
+ )
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def set_decoder(self, decoder):
+ self.language_model = decoder
+
+ def get_decoder(self):
+ return self.language_model
+
+ @check_model_inputs
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ aspect_ratio_mask: Optional[torch.Tensor] = None,
+ aspect_ratio_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ cross_attention_mask: Optional[torch.Tensor] = None,
+ cross_attention_states: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> BaseModelOutputWithPast:
+ r"""
+ aspect_ratio_mask (`torch.Tensor` of shape `(batch_size, max_num_images, max_num_tiles)`, *optional*):
+ Mask to avoid performing attention on padding tiles. Mask values selected in `[0, 1]`:
+
+ - 1 for tiles that are **not masked**,
+ - 0 for tiles that are **masked**.
+ aspect_ratio_ids (`torch.Tensor` of shape `(batch_size, max_num_images)`, *optional*):
+ Aspect ratio ids used to select the appropriate precomputed tile embeddings based on the aspect ratio of each input image.
+ These ids correspond to indices in the model's list of supported aspect ratios, offset by 1.
+
+ For example, if the model supports aspect ratios [[1, 1], [1, 2], [2, 1]]:
+ - An image with aspect ratio [1, 1] would have ID 1
+ - An image with aspect ratio [1, 2] would have ID 2
+ - An image with aspect ratio [2, 1] would have ID 3
+
+ The id 0 is reserved for padding (i.e., no image).
+
+ If an image has aspect ratio [1, 2], that means it was split into 2 tiles horizontally, and its `aspect_ratio_id` would be 2.
+ cross_attention_mask (`torch.Tensor` of shape `(batch_size, seq_length, max_num_images, max_num_tiles)`, *optional*):
+ Cross-attention mask to control the interaction between text tokens and image tiles.
+ This 4D tensor defines which image tiles each text token should attend to.
+
+ For each text token (in seq_length):
+ - 1 indicates the token **should attend** to the corresponding image tile
+ - 0 indicates the token **should not attend** to the corresponding image tile
+ cross_attention_states (`torch.FloatTensor`, *optional*):
+ Output of the vision model, used for cross-attention. This tensor contains the processed image features that
+ the language model will attend to.
+ """
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if pixel_values is not None and cross_attention_states is not None:
+ raise ValueError("`pixel_values` and `cross_attention_states` cannot be provided simultaneously")
+
+ if pixel_values is not None:
+ if aspect_ratio_ids is None:
+ raise ValueError("`aspect_ratio_ids` must be provided if `pixel_values` is provided")
+ # get vision tokens from vision model
+ vision_outputs = self.vision_model(
+ pixel_values=pixel_values,
+ aspect_ratio_ids=aspect_ratio_ids,
+ aspect_ratio_mask=aspect_ratio_mask,
+ )
+ cross_attention_states = vision_outputs.last_hidden_state
+ cross_attention_states = self.multi_modal_projector(cross_attention_states).reshape(
+ -1, cross_attention_states.shape[-2], self.hidden_size
+ )
+
+ if cross_attention_mask is not None:
+ cross_attention_mask, full_text_row_masked_out_mask = _prepare_cross_attention_mask(
+ cross_attention_mask,
+ num_vision_tokens=self.vision_model.num_patches,
+ dtype=self.dtype,
+ )
+ else:
+ full_text_row_masked_out_mask = None
+
+ if cross_attention_mask is not None and cache_position is not None:
+ cross_attention_mask = cross_attention_mask[:, :, cache_position]
+ full_text_row_masked_out_mask = full_text_row_masked_out_mask[:, :, cache_position]
+
+ outputs = self.language_model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ cross_attention_states=cross_attention_states,
+ cross_attention_mask=cross_attention_mask,
+ full_text_row_masked_out_mask=full_text_row_masked_out_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ inputs_embeds=inputs_embeds,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The Mllama model which consists of a vision encoder and a language model.
+ """,
+)
+class MllamaForConditionalGeneration(MllamaPreTrainedModel, GenerationMixin):
+ _checkpoint_conversion_mapping = {
+ "^language_model.model": "model.language_model",
+ "^vision_model": "model.vision_model",
+ "^multi_modal_projector": "model.multi_modal_projector",
+ "^language_model.lm_head": "lm_head",
+ }
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: MllamaConfig):
+ super().__init__(config)
+ self.model = MllamaModel(config)
+ self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ def set_decoder(self, decoder):
+ self.model.set_decoder(decoder)
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ # Make modules available through conditional class for BC
+ @property
+ def language_model(self):
+ return self.model.language_model
+
+ @property
+ def vision_model(self):
+ return self.model.vision_model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ aspect_ratio_mask: Optional[torch.Tensor] = None,
+ aspect_ratio_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ cross_attention_mask: Optional[torch.Tensor] = None,
+ cross_attention_states: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, CausalLMOutputWithPast]:
+ r"""
+ aspect_ratio_mask (`torch.Tensor` of shape `(batch_size, max_num_images, max_num_tiles)`, *optional*):
+ Mask to avoid performing attention on padding tiles. Mask values selected in `[0, 1]`:
+
+ - 1 for tiles that are **not masked**,
+ - 0 for tiles that are **masked**.
+ aspect_ratio_ids (`torch.Tensor` of shape `(batch_size, max_num_images)`, *optional*):
+ Aspect ratio ids used to select the appropriate precomputed tile embeddings based on the aspect ratio of each input image.
+ These ids correspond to indices in the model's list of supported aspect ratios, offset by 1.
+
+ For example, if the model supports aspect ratios [[1, 1], [1, 2], [2, 1]]:
+ - An image with aspect ratio [1, 1] would have ID 1
+ - An image with aspect ratio [1, 2] would have ID 2
+ - An image with aspect ratio [2, 1] would have ID 3
+
+ The id 0 is reserved for padding (i.e., no image).
+
+ If an image has aspect ratio [1, 2], that means it was split into 2 tiles horizontally, and its `aspect_ratio_id` would be 2.
+ cross_attention_mask (`torch.Tensor` of shape `(batch_size, seq_length, max_num_images, max_num_tiles)`, *optional*):
+ Cross-attention mask to control the interaction between text tokens and image tiles.
+ This 4D tensor defines which image tiles each text token should attend to.
+
+ For each text token (in seq_length):
+ - 1 indicates the token **should attend** to the corresponding image tile
+ - 0 indicates the token **should not attend** to the corresponding image tile
+ cross_attention_states (`torch.FloatTensor`, *optional*):
+ Output of the vision model, used for cross-attention. This tensor contains the processed image features that
+ the language model will attend to.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import AutoProcessor, MllamaForConditionalGeneration
+
+ >>> checkpoint = "meta-llama/Llama-3.2-11B-Vision"
+ >>> model = MllamaForConditionalGeneration.from_pretrained(checkpoint)
+ >>> processor = AutoProcessor.from_pretrained(checkpoint)
+
+ >>> prompt = "<|image|>If I had to write a haiku for this one"
+ >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> inputs = processor(text=prompt, images=image, return_tensors="pt")
+
+ >>> # Generate
+ >>> output = model.generate(**inputs, max_new_tokens=15)
+
+ >>> prompt_len = inputs.input_ids.shape[-1]
+ >>> generated_ids = output[:, prompt_len:]
+ >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+ >>> print(generated_text)
+ [', it would be:.\\nA stop sign in Chinatown.\\n']
+ ```
+ """
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ aspect_ratio_mask=aspect_ratio_mask,
+ aspect_ratio_ids=aspect_ratio_ids,
+ cross_attention_mask=cross_attention_mask,
+ cross_attention_states=cross_attention_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits, labels, self.config.text_config.vocab_size, **kwargs)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids=None,
+ inputs_embeds=None,
+ attention_mask=None,
+ position_ids=None,
+ pixel_values=None,
+ aspect_ratio_ids=None,
+ aspect_ratio_mask=None,
+ cross_attention_mask=None,
+ past_key_values=None,
+ use_cache=False,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ inputs_embeds=inputs_embeds,
+ position_ids=position_ids,
+ attention_mask=attention_mask,
+ pixel_values=pixel_values,
+ aspect_ratio_ids=aspect_ratio_ids,
+ aspect_ratio_mask=aspect_ratio_mask,
+ cross_attention_mask=cross_attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ # If we're in pre-fill or cacheless decoding step, then we need pixel_values and aspect ratios
+ # to compute image hidden states, otherwise they are cached within each cross attn layer
+ if cache_position[0] != 0:
+ model_inputs["pixel_values"] = None
+ model_inputs["aspect_ratio_ids"] = None
+ model_inputs["aspect_ratio_mask"] = None
+
+ return model_inputs
+
+ def _update_model_kwargs_for_generation(self, outputs, model_kwargs, is_encoder_decoder, **kwargs):
+ cross_attention_mask_prev = model_kwargs.get("cross_attention_mask", None)
+ model_kwargs = super()._update_model_kwargs_for_generation(
+ outputs=outputs,
+ model_kwargs=model_kwargs,
+ is_encoder_decoder=is_encoder_decoder,
+ **kwargs,
+ )
+
+ # add cross-attn mask for new token
+ if cross_attention_mask_prev is not None:
+ model_kwargs["cross_attention_mask"] = torch.cat(
+ [cross_attention_mask_prev, cross_attention_mask_prev[:, -1:, ...]], dim=1
+ )
+ return model_kwargs
+
+
+__all__ = [
+ "MllamaForConditionalGeneration",
+ "MllamaForCausalLM",
+ "MllamaTextModel",
+ "MllamaVisionModel",
+ "MllamaPreTrainedModel",
+ "MllamaModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/processing_mllama.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/processing_mllama.py
new file mode 100644
index 0000000000000000000000000000000000000000..063f0e8abc34ba215b2141a2a448d8280a157c6f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mllama/processing_mllama.py
@@ -0,0 +1,395 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Processor class for Mllama."""
+
+from typing import Optional, Union
+
+import numpy as np
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_utils import ImageInput, make_nested_list_of_images
+from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+
+
+class MllamaImagesKwargs(ImagesKwargs, total=False):
+ max_image_tiles: Optional[int]
+
+
+class MllamaProcessorKwargs(ProcessingKwargs, total=False):
+ images_kwargs: MllamaImagesKwargs
+
+ _defaults = {
+ "image_kwargs": {
+ "max_image_tiles": 4,
+ },
+ }
+
+
+def get_cross_attention_token_mask(input_ids: list[int], image_token_id: int) -> list[list[int]]:
+ """
+ Generate a cross-attention token mask for image tokens in the input sequence.
+
+ This function identifies the positions of image tokens in the input sequence and creates
+ a mask that defines which subsequent tokens each image token should attend to.
+
+ Args:
+ input_ids (list[int]): A list of token ids representing the input sequence.
+ image_token_id (int): The id of the token used to represent images in the sequence.
+
+ Returns:
+ list[list[int]]: A list of [start, end] pairs, where each pair represents the range
+ of tokens an image token should attend to.
+
+ Notes:
+ - If no image tokens are present, an empty list is returned.
+ - For a single image token, it attends to all subsequent tokens until the end of the sequence.
+ - For multiple image tokens, each attends to tokens up to the next image token or the end of the sequence.
+ - Consecutive image tokens are treated as a group and attend to all subsequent tokens together.
+ """
+
+ image_token_locations = [i for i, token in enumerate(input_ids) if token == image_token_id]
+
+ if len(image_token_locations) == 0:
+ return []
+
+ # only one image present, unmask until end of sequence
+ if len(image_token_locations) == 1:
+ return [[image_token_locations[0], -1]]
+
+ vision_masks = [[loc1, loc2] for loc1, loc2 in zip(image_token_locations[:-1], image_token_locations[1:])]
+
+ # last image will attend to all subsequent text
+ vision_masks.append([image_token_locations[-1], len(input_ids)])
+
+ # if there are two or more consecutive vision tokens,
+ # they should all attend to all subsequent
+ # text present
+ last_mask_end = vision_masks[-1][1]
+ for vision_mask in vision_masks[::-1]:
+ if vision_mask[0] == vision_mask[1] - 1:
+ vision_mask[1] = last_mask_end
+ last_mask_end = vision_mask[1]
+
+ return vision_masks
+
+
+def convert_sparse_cross_attention_mask_to_dense(
+ cross_attention_token_mask: list[list[list[int]]],
+ num_tiles: list[list[int]],
+ max_num_tiles: int,
+ length: int,
+) -> np.ndarray:
+ """
+ Convert the cross attention mask indices to a cross attention mask 4D array.
+
+ This function takes a sparse representation of cross attention masks and converts it to a dense 4D numpy array.
+ The sparse representation is a nested list structure that defines attention ranges for each image in each batch item.
+
+ Args:
+ cross_attention_token_mask (list[list[list[int]]]): A nested list structure where:
+ - The outer list represents the batch dimension.
+ - The middle list represents different images within each batch item.
+ - The inner list contains pairs of integers [start, end] representing token ranges for each image.
+ num_tiles (list[list[int]]): A nested list structure specifying the number of tiles for each image in each batch item.
+ max_num_tiles (int): The maximum possible number of tiles.
+ length (int): The total sequence length of the input.
+
+ Returns:
+ np.ndarray: A 4D numpy array of shape (batch_size, length, max_num_images, max_num_tiles)
+ The array contains `1` where attention is allowed and `0` where it is not.
+
+ Note:
+ - Special handling is done for cases where the end token is -1, which is interpreted as attending to the end of the sequence.
+ """
+
+ batch_size = len(cross_attention_token_mask)
+ max_num_images = max([len(masks) for masks in cross_attention_token_mask])
+
+ cross_attention_mask = np.zeros(
+ shape=(batch_size, length, max_num_images, max_num_tiles),
+ dtype=np.int64,
+ )
+
+ for sample_idx, (sample_masks, sample_num_tiles) in enumerate(zip(cross_attention_token_mask, num_tiles)):
+ for mask_idx, (locations, mask_num_tiles) in enumerate(zip(sample_masks, sample_num_tiles)):
+ if len(locations) == 2:
+ start, end = locations
+ end = min(end, length)
+ if end == -1:
+ end = length
+ cross_attention_mask[sample_idx, start:end, mask_idx, :mask_num_tiles] = 1
+ return cross_attention_mask
+
+
+def build_string_from_input(prompt: str, bos_token: str, image_token: str) -> str:
+ """
+ Builds a string from the input prompt by adding `bos_token` if not already present.
+
+ Args:
+ prompt (`str`):
+ The input prompt string.
+ bos_token (`str`):
+ The beginning of sentence token to be added.
+ image_token (`str`):
+ The image token used to identify the start of an image sequence.
+
+ Returns:
+ str: The modified prompt string with the `bos_token` added if necessary.
+
+ Examples:
+ >>> build_string_from_input("Hello world", "", "<|image|>")
+ 'Hello world'
+
+ >>> build_string_from_input("<|image|>Hello world", "", "<|image|>")
+ '<|image|>Hello world'
+
+ >>> build_string_from_input("Hello world", "", "<|image|>")
+ 'Hello world'
+ """
+
+ if bos_token in prompt:
+ return prompt
+
+ num_image_tokens_on_start = 0
+ while prompt.startswith(image_token):
+ prompt = prompt[len(image_token) :]
+ num_image_tokens_on_start += 1
+
+ return f"{image_token * num_image_tokens_on_start}{bos_token}{prompt}"
+
+
+class MllamaProcessor(ProcessorMixin):
+ r"""
+ Constructs a Mllama processor which wraps [`MllamaImageProcessor`] and
+ [`PretrainedTokenizerFast`] into a single processor that inherits both the image processor and
+ tokenizer functionalities. See the [`~MllamaProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more
+ information.
+ The preferred way of passing kwargs is as a dictionary per modality, see usage example below.
+ ```python
+ from transformers import MllamaProcessor
+ from PIL import Image
+
+ processor = MllamaProcessor.from_pretrained("meta-llama/Llama-3.2-11B-Vision")
+
+ processor(
+ images=your_pil_image,
+ text=["<|image|>If I had to write a haiku for this one"],
+ images_kwargs = {"size": {"height": 448, "width": 448}},
+ text_kwargs = {"padding": "right"},
+ common_kwargs = {"return_tensors": "pt"},
+ )
+ ```
+
+ Args:
+ image_processor ([`MllamaImageProcessor`]):
+ The image processor is a required input.
+ tokenizer ([`PreTrainedTokenizer`, `PreTrainedTokenizerFast`]):
+ The tokenizer is a required input.
+ chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+ in a chat into a tokenizable string.
+
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "MllamaImageProcessor"
+ tokenizer_class = "PreTrainedTokenizerFast"
+
+ def __init__(self, image_processor, tokenizer, chat_template=None):
+ if not hasattr(tokenizer, "image_token"):
+ self.image_token = "<|image|>"
+ self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
+ else:
+ self.image_token = tokenizer.image_token
+ self.image_token_id = tokenizer.image_token_id
+
+ self.python_token = "<|python_tag|>"
+ self.python_token_id = tokenizer.convert_tokens_to_ids(self.python_token)
+ self.bos_token = tokenizer.bos_token
+ super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+ def __call__(
+ self,
+ images: Optional[ImageInput] = None,
+ text: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]] = None,
+ audio=None,
+ videos=None,
+ **kwargs: Unpack[MllamaProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Main method to prepare text(s) and image(s) to be fed as input to the model. This method forwards the `text`
+ arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] if `text` is not `None` to encode
+ the text. To prepare the image(s), this method forwards the `images` arguments to
+ MllamaImageProcessor's [`~MllamaImageProcessor.__call__`] if `images` is not `None`. Please refer
+ to the docstring of the above two methods for more information.
+
+ Args:
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors of a particular framework. Acceptable values are:
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return NumPy `np.ndarray` objects.
+ - `'jax'`: Return JAX `jnp.ndarray` objects.
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+ `None`).
+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+ TODO: add aspect_ratio_ids and aspect_ratio_mask and cross_attention_mask
+ """
+ if text is None and images is None:
+ raise ValueError("You must specify either text or images.")
+
+ output_kwargs = self._merge_kwargs(
+ MllamaProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+
+ text_kwargs = output_kwargs["text_kwargs"]
+ text_kwargs["return_tensors"] = None
+ images_kwargs = output_kwargs["images_kwargs"]
+ common_kwargs = output_kwargs["common_kwargs"]
+
+ data = {}
+ if text is not None:
+ if isinstance(text, str):
+ text = [text]
+ elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
+ raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+ n_images_in_text = [t.count(self.image_token) for t in text]
+ text = [build_string_from_input(text_item, self.bos_token, self.image_token) for text_item in text]
+ _ = text_kwargs.pop("padding_side", None) # hack until padding-side is an accepted kwarg by tokenizers
+ encoding = self.tokenizer(text, **text_kwargs)
+ self._check_special_mm_tokens(text, encoding, modalities=["image"])
+ n_images_in_ids = [token_ids.count(self.image_token_id) for token_ids in encoding["input_ids"]]
+ data.update(encoding)
+
+ n_images_in_images = [0]
+ if images is not None:
+ images = make_nested_list_of_images(images)
+ n_images_in_images = [len(sample) for sample in images]
+
+ if text is not None:
+ if any(batch_img == 0 for batch_img in n_images_in_text) and not all(
+ batch_img == 0 for batch_img in n_images_in_text
+ ):
+ raise ValueError(
+ "If a batch of text is provided, there should be either no images or at least one image per sample"
+ )
+ if sum(n_images_in_text) > 0 and (
+ n_images_in_images != n_images_in_text or n_images_in_ids != n_images_in_images
+ ):
+ if images is None:
+ raise ValueError("No image were provided, but there are image tokens in the prompt")
+ else:
+ add_message = ""
+ if sum(n_images_in_images) == sum(n_images_in_text) and n_images_in_images != n_images_in_text:
+ add_message = "Make sure to pass your images as a nested list, where each sub-list holds images per batch"
+ elif n_images_in_ids != n_images_in_images:
+ add_message = "If you activated truncation with `max_length`, increase the `max_length` so image tokens aren't cropped."
+
+ raise ValueError(
+ f"The number of image tokens in each text ({n_images_in_text}) should be the same as the "
+ f"number of provided images per batch ({n_images_in_images}). {add_message}"
+ )
+
+ if images is not None:
+ image_features = self.image_processor(images, **images_kwargs)
+ num_tiles = image_features.pop("num_tiles")
+ data.update(image_features)
+
+ # Create cross attention mask
+ if images is not None and text is not None:
+ cross_attention_token_mask = [
+ get_cross_attention_token_mask(token_ids, self.image_token_id) for token_ids in encoding["input_ids"]
+ ]
+ cross_attention_mask = convert_sparse_cross_attention_mask_to_dense(
+ cross_attention_token_mask,
+ num_tiles=num_tiles,
+ max_num_tiles=self.image_processor.max_image_tiles,
+ length=max(len(input_ids) for input_ids in encoding["input_ids"]),
+ )
+ data["cross_attention_mask"] = cross_attention_mask
+
+ return_tensors = common_kwargs.pop("return_tensors", None)
+ batch_feature = BatchFeature(data=data, tensor_type=return_tensors)
+
+ return batch_feature
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ def post_process_image_text_to_text(
+ self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
+ ):
+ """
+ Post-process the output of the model to decode the text.
+
+ Args:
+ generated_outputs (`torch.Tensor` or `np.ndarray`):
+ The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+ or `(sequence_length,)`.
+ skip_special_tokens (`bool`, *optional*, defaults to `True`):
+ Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+ clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+ Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
+ **kwargs:
+ Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+ Returns:
+ `list[str]`: The decoded text.
+ """
+ return self.tokenizer.batch_decode(
+ generated_outputs,
+ skip_special_tokens=skip_special_tokens,
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+ **kwargs,
+ )
+
+ @property
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+
+ # Remove `num_tiles`, it is popped and used only when processing. Make a copy of list when remocing
+ # otherwise `self.image_processor.model_input_names` is also modified
+ image_processor_input_names = [name for name in image_processor_input_names if name != "num_tiles"]
+ return list(tokenizer_input_names + image_processor_input_names + ["cross_attention_mask"])
+
+
+__all__ = ["MllamaProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mluke/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mluke/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..521db20088e161bcfed5ca6ad016ebc5d50e18d7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mluke/__init__.py
@@ -0,0 +1,26 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .tokenization_mluke import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mluke/convert_mluke_original_pytorch_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mluke/convert_mluke_original_pytorch_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..be0f52a70ebc9ee3fb76a1231ca3bd9e9d0bc795
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mluke/convert_mluke_original_pytorch_checkpoint_to_pytorch.py
@@ -0,0 +1,229 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert mLUKE checkpoint."""
+
+import argparse
+import json
+import os
+from collections import OrderedDict
+
+import torch
+
+from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
+from transformers.tokenization_utils_base import AddedToken
+
+
+@torch.no_grad()
+def convert_luke_checkpoint(checkpoint_path, metadata_path, entity_vocab_path, pytorch_dump_folder_path, model_size):
+ # Load configuration defined in the metadata file
+ with open(metadata_path) as metadata_file:
+ metadata = json.load(metadata_file)
+ config = LukeConfig(use_entity_aware_attention=True, **metadata["model_config"])
+
+ # Load in the weights from the checkpoint_path
+ state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=True)["module"]
+
+ # Load the entity vocab file
+ entity_vocab = load_original_entity_vocab(entity_vocab_path)
+ # add an entry for [MASK2]
+ entity_vocab["[MASK2]"] = max(entity_vocab.values()) + 1
+ config.entity_vocab_size += 1
+
+ tokenizer = XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"])
+
+ # Add special tokens to the token vocabulary for downstream tasks
+ entity_token_1 = AddedToken("", lstrip=False, rstrip=False)
+ entity_token_2 = AddedToken("", lstrip=False, rstrip=False)
+ tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_1, entity_token_2]})
+ config.vocab_size += 2
+
+ print(f"Saving tokenizer to {pytorch_dump_folder_path}")
+ tokenizer.save_pretrained(pytorch_dump_folder_path)
+ with open(os.path.join(pytorch_dump_folder_path, "tokenizer_config.json"), "r") as f:
+ tokenizer_config = json.load(f)
+ tokenizer_config["tokenizer_class"] = "MLukeTokenizer"
+ with open(os.path.join(pytorch_dump_folder_path, "tokenizer_config.json"), "w") as f:
+ json.dump(tokenizer_config, f)
+
+ with open(os.path.join(pytorch_dump_folder_path, MLukeTokenizer.vocab_files_names["entity_vocab_file"]), "w") as f:
+ json.dump(entity_vocab, f)
+
+ tokenizer = MLukeTokenizer.from_pretrained(pytorch_dump_folder_path)
+
+ # Initialize the embeddings of the special tokens
+ ent_init_index = tokenizer.convert_tokens_to_ids(["@"])[0]
+ ent2_init_index = tokenizer.convert_tokens_to_ids(["#"])[0]
+
+ word_emb = state_dict["embeddings.word_embeddings.weight"]
+ ent_emb = word_emb[ent_init_index].unsqueeze(0)
+ ent2_emb = word_emb[ent2_init_index].unsqueeze(0)
+ state_dict["embeddings.word_embeddings.weight"] = torch.cat([word_emb, ent_emb, ent2_emb])
+ # add special tokens for 'entity_predictions.bias'
+ for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
+ decoder_bias = state_dict[bias_name]
+ ent_decoder_bias = decoder_bias[ent_init_index].unsqueeze(0)
+ ent2_decoder_bias = decoder_bias[ent2_init_index].unsqueeze(0)
+ state_dict[bias_name] = torch.cat([decoder_bias, ent_decoder_bias, ent2_decoder_bias])
+
+ # Initialize the query layers of the entity-aware self-attention mechanism
+ for layer_index in range(config.num_hidden_layers):
+ for matrix_name in ["query.weight", "query.bias"]:
+ prefix = f"encoder.layer.{layer_index}.attention.self."
+ state_dict[prefix + "w2e_" + matrix_name] = state_dict[prefix + matrix_name]
+ state_dict[prefix + "e2w_" + matrix_name] = state_dict[prefix + matrix_name]
+ state_dict[prefix + "e2e_" + matrix_name] = state_dict[prefix + matrix_name]
+
+ # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
+ entity_emb = state_dict["entity_embeddings.entity_embeddings.weight"]
+ entity_mask_emb = entity_emb[entity_vocab["[MASK]"]].unsqueeze(0)
+ state_dict["entity_embeddings.entity_embeddings.weight"] = torch.cat([entity_emb, entity_mask_emb])
+ # add [MASK2] for 'entity_predictions.bias'
+ entity_prediction_bias = state_dict["entity_predictions.bias"]
+ entity_mask_bias = entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0)
+ state_dict["entity_predictions.bias"] = torch.cat([entity_prediction_bias, entity_mask_bias])
+
+ model = LukeForMaskedLM(config=config).eval()
+
+ state_dict.pop("entity_predictions.decoder.weight")
+ state_dict.pop("lm_head.decoder.weight")
+ state_dict.pop("lm_head.decoder.bias")
+ state_dict_for_hugging_face = OrderedDict()
+ for key in state_dict:
+ if not (key.startswith("lm_head") or key.startswith("entity_predictions")):
+ state_dict_for_hugging_face[f"luke.{key}"] = state_dict[key]
+ else:
+ state_dict_for_hugging_face[key] = state_dict[key]
+
+ missing_keys, unexpected_keys = model.load_state_dict(state_dict_for_hugging_face, strict=False)
+
+ if set(unexpected_keys) != {"luke.embeddings.position_ids"}:
+ raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}")
+ if set(missing_keys) != {
+ "lm_head.decoder.weight",
+ "lm_head.decoder.bias",
+ "entity_predictions.decoder.weight",
+ }:
+ raise ValueError(f"Unexpected missing_keys: {missing_keys}")
+
+ model.tie_weights()
+ assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
+ assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
+
+ # Check outputs
+ tokenizer = MLukeTokenizer.from_pretrained(pytorch_dump_folder_path, task="entity_classification")
+
+ text = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
+ span = (0, 9)
+ encoding = tokenizer(text, entity_spans=[span], return_tensors="pt")
+
+ outputs = model(**encoding)
+
+ # Verify word hidden states
+ if model_size == "large":
+ raise NotImplementedError
+ else: # base
+ expected_shape = torch.Size((1, 33, 768))
+ expected_slice = torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]])
+
+ if not (outputs.last_hidden_state.shape == expected_shape):
+ raise ValueError(
+ f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"
+ )
+ if not torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4):
+ raise ValueError
+
+ # Verify entity hidden states
+ if model_size == "large":
+ raise NotImplementedError
+ else: # base
+ expected_shape = torch.Size((1, 1, 768))
+ expected_slice = torch.tensor([[-0.1482, 0.0609, 0.0322]])
+
+ if not (outputs.entity_last_hidden_state.shape == expected_shape):
+ raise ValueError(
+ f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
+ f" {expected_shape}"
+ )
+ if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, atol=1e-4):
+ raise ValueError
+
+ # Verify masked word/entity prediction
+ tokenizer = MLukeTokenizer.from_pretrained(pytorch_dump_folder_path)
+ text = "Tokyo is the capital of ."
+ span = (24, 30)
+ encoding = tokenizer(text, entity_spans=[span], return_tensors="pt")
+
+ outputs = model(**encoding)
+
+ input_ids = encoding["input_ids"][0].tolist()
+ mask_position_id = input_ids.index(tokenizer.convert_tokens_to_ids(""))
+ predicted_id = outputs.logits[0][mask_position_id].argmax(dim=-1)
+ assert "Japan" == tokenizer.decode(predicted_id)
+
+ predicted_entity_id = outputs.entity_logits[0][0].argmax().item()
+ multilingual_predicted_entities = [
+ entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
+ ]
+ assert [e for e in multilingual_predicted_entities if e.startswith("en:")][0] == "en:Japan"
+
+ # Finally, save our PyTorch model and tokenizer
+ print(f"Saving PyTorch model to {pytorch_dump_folder_path}")
+ model.save_pretrained(pytorch_dump_folder_path)
+
+
+def load_original_entity_vocab(entity_vocab_path):
+ SPECIAL_TOKENS = ["[MASK]", "[PAD]", "[UNK]"]
+
+ data = [json.loads(line) for line in open(entity_vocab_path)]
+
+ new_mapping = {}
+ for entry in data:
+ entity_id = entry["id"]
+ for entity_name, language in entry["entities"]:
+ if entity_name in SPECIAL_TOKENS:
+ new_mapping[entity_name] = entity_id
+ break
+ new_entity_name = f"{language}:{entity_name}"
+ new_mapping[new_entity_name] = entity_id
+ return new_mapping
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
+ parser.add_argument(
+ "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
+ )
+ parser.add_argument(
+ "--entity_vocab_path",
+ default=None,
+ type=str,
+ help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
+ )
+ parser.add_argument(
+ "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
+ )
+ args = parser.parse_args()
+ convert_luke_checkpoint(
+ args.checkpoint_path,
+ args.metadata_path,
+ args.entity_vocab_path,
+ args.pytorch_dump_folder_path,
+ args.model_size,
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mluke/tokenization_mluke.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mluke/tokenization_mluke.py
new file mode 100644
index 0000000000000000000000000000000000000000..15f4db53287a7c64d8b156578c63abd9b5feba3e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mluke/tokenization_mluke.py
@@ -0,0 +1,1641 @@
+# coding=utf-8
+# Copyright 2021 Studio Ousia and the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
+"""Tokenization classes for mLUKE."""
+
+import itertools
+import json
+import os
+from collections.abc import Mapping
+from shutil import copyfile
+from typing import Any, Optional, Union
+
+import numpy as np
+import sentencepiece as spm
+
+from ...tokenization_utils import PreTrainedTokenizer
+from ...tokenization_utils_base import (
+ ENCODE_KWARGS_DOCSTRING,
+ AddedToken,
+ BatchEncoding,
+ EncodedInput,
+ PaddingStrategy,
+ TensorType,
+ TextInput,
+ TextInputPair,
+ TruncationStrategy,
+ to_py_obj,
+)
+from ...utils import add_end_docstrings, is_tf_tensor, is_torch_tensor, logging
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+EntitySpan = tuple[int, int]
+EntitySpanInput = list[EntitySpan]
+Entity = str
+EntityInput = list[Entity]
+
+SPIECE_UNDERLINE = "▁"
+
+VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "entity_vocab_file": "entity_vocab.json"}
+
+
+ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
+ return_token_type_ids (`bool`, *optional*):
+ Whether to return token type IDs. If left to the default, will return the token type IDs according to
+ the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ return_attention_mask (`bool`, *optional*):
+ Whether to return the attention mask. If left to the default, will return the attention mask according
+ to the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+ [What are attention masks?](../glossary#attention-mask)
+ return_overflowing_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
+ of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
+ of returning overflowing tokens.
+ return_special_tokens_mask (`bool`, *optional*, defaults to `False`):
+ Whether or not to return special tokens mask information.
+ return_offsets_mapping (`bool`, *optional*, defaults to `False`):
+ Whether or not to return `(char_start, char_end)` for each token.
+
+ This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using
+ Python's tokenizer, this method will raise `NotImplementedError`.
+ return_length (`bool`, *optional*, defaults to `False`):
+ Whether or not to return the lengths of the encoded inputs.
+ verbose (`bool`, *optional*, defaults to `True`):
+ Whether or not to print more information and warnings.
+ **kwargs: passed to the `self.tokenize()` method
+
+ Return:
+ [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or
+ if *"token_type_ids"* is in `self.model_input_names`).
+
+ [What are token type IDs?](../glossary#token-type-ids)
+
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ - **entity_ids** -- List of entity ids to be fed to a model.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ - **entity_position_ids** -- List of entity positions in the input sequence to be fed to a model.
+
+ - **entity_token_type_ids** -- List of entity token type ids to be fed to a model (when
+ `return_token_type_ids=True` or if *"entity_token_type_ids"* is in `self.model_input_names`).
+
+ [What are token type IDs?](../glossary#token-type-ids)
+
+ - **entity_attention_mask** -- List of indices specifying which entities should be attended to by the model
+ (when `return_attention_mask=True` or if *"entity_attention_mask"* is in `self.model_input_names`).
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ - **entity_start_positions** -- List of the start positions of entities in the word token sequence (when
+ `task="entity_span_classification"`).
+ - **entity_end_positions** -- List of the end positions of entities in the word token sequence (when
+ `task="entity_span_classification"`).
+ - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
+ `return_overflowing_tokens=True`).
+ - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
+ `return_overflowing_tokens=True`).
+ - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
+ regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
+ - **length** -- The length of the inputs (when `return_length=True`)
+
+"""
+
+
+@requires(backends=("sentencepiece",))
+class MLukeTokenizer(PreTrainedTokenizer):
+ """
+ Adapted from [`XLMRobertaTokenizer`] and [`LukeTokenizer`]. Based on
+ [SentencePiece](https://github.com/google/sentencepiece).
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ entity_vocab_file (`str`):
+ Path to the entity vocabulary file.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ task (`str`, *optional*):
+ Task for which you want to prepare sequences. One of `"entity_classification"`,
+ `"entity_pair_classification"`, or `"entity_span_classification"`. If you specify this argument, the entity
+ sequence is automatically created based on the given entity span(s).
+ max_entity_length (`int`, *optional*, defaults to 32):
+ The maximum length of `entity_ids`.
+ max_mention_length (`int`, *optional*, defaults to 30):
+ The maximum number of tokens inside an entity span.
+ entity_token_1 (`str`, *optional*, defaults to ``):
+ The special token used to represent an entity span in a word token sequence. This token is only used when
+ `task` is set to `"entity_classification"` or `"entity_pair_classification"`.
+ entity_token_2 (`str`, *optional*, defaults to ``):
+ The special token used to represent an entity span in a word token sequence. This token is only used when
+ `task` is set to `"entity_pair_classification"`.
+ additional_special_tokens (`list[str]`, *optional*, defaults to `["NOTUSED", " NOTUSED"]`):
+ Additional special tokens used by the tokenizer.
+ sp_model_kwargs (`dict`, *optional*):
+ Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+ SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+ to set:
+
+ - `enable_sampling`: Enable subword regularization.
+ - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+ - `nbest_size = {0,1}`: No sampling is performed.
+ - `nbest_size > 1`: samples from the nbest_size results.
+ - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+ using forward-filtering-and-backward-sampling algorithm.
+
+ - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+ BPE-dropout.
+
+ Attributes:
+ sp_model (`SentencePieceProcessor`):
+ The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ def __init__(
+ self,
+ vocab_file,
+ entity_vocab_file,
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ task=None,
+ max_entity_length=32,
+ max_mention_length=30,
+ entity_token_1="",
+ entity_token_2="",
+ entity_unk_token="[UNK]",
+ entity_pad_token="[PAD]",
+ entity_mask_token="[MASK]",
+ entity_mask2_token="[MASK2]",
+ sp_model_kwargs: Optional[dict[str, Any]] = None,
+ **kwargs,
+ ) -> None:
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+ # we add 2 special tokens for downstream tasks
+ # for more information about lstrip and rstrip, see https://github.com/huggingface/transformers/pull/2778
+ entity_token_1 = (
+ AddedToken(entity_token_1, lstrip=False, rstrip=False)
+ if isinstance(entity_token_1, str)
+ else entity_token_1
+ )
+ entity_token_2 = (
+ AddedToken(entity_token_2, lstrip=False, rstrip=False)
+ if isinstance(entity_token_2, str)
+ else entity_token_2
+ )
+ additional_special_tokens = kwargs.pop("additional_special_tokens", [])
+ additional_special_tokens += [entity_token_1, entity_token_2]
+
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.Load(str(vocab_file))
+ self.vocab_file = vocab_file
+
+ # Original fairseq vocab and spm vocab must be "aligned":
+ # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
+ # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
+ # fairseq | '' | '' | ' ' | '' | ',' | '.' | '▁' | 's' | '▁de' | '-'
+ # spm | '' | '' | ' ' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
+
+ # Mimic fairseq token-to-id alignment for the first 4 token
+ self.fairseq_tokens_to_ids = {"": 0, "": 1, " ": 2, "": 3}
+
+ # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
+ self.fairseq_offset = 1
+
+ self.fairseq_tokens_to_ids[""] = len(self.sp_model) + self.fairseq_offset
+ self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
+
+ with open(entity_vocab_file, encoding="utf-8") as entity_vocab_handle:
+ self.entity_vocab = json.load(entity_vocab_handle)
+ for entity_special_token in [entity_unk_token, entity_pad_token, entity_mask_token, entity_mask2_token]:
+ if entity_special_token not in self.entity_vocab:
+ raise ValueError(
+ f"Specified entity special token ``{entity_special_token}`` is not found in entity_vocab. "
+ f"Probably an incorrect entity vocab file is loaded: {entity_vocab_file}."
+ )
+ self.entity_unk_token_id = self.entity_vocab[entity_unk_token]
+ self.entity_pad_token_id = self.entity_vocab[entity_pad_token]
+ self.entity_mask_token_id = self.entity_vocab[entity_mask_token]
+ self.entity_mask2_token_id = self.entity_vocab[entity_mask2_token]
+
+ self.task = task
+ if task is None or task == "entity_span_classification":
+ self.max_entity_length = max_entity_length
+ elif task == "entity_classification":
+ self.max_entity_length = 1
+ elif task == "entity_pair_classification":
+ self.max_entity_length = 2
+ else:
+ raise ValueError(
+ f"Task {task} not supported. Select task from ['entity_classification', 'entity_pair_classification',"
+ " 'entity_span_classification'] only."
+ )
+
+ self.max_mention_length = max_mention_length
+
+ super().__init__(
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ sp_model_kwargs=self.sp_model_kwargs,
+ task=task,
+ max_entity_length=max_entity_length,
+ max_mention_length=max_mention_length,
+ entity_token_1=entity_token_1,
+ entity_token_2=entity_token_2,
+ entity_unk_token=entity_unk_token,
+ entity_pad_token=entity_pad_token,
+ entity_mask_token=entity_mask_token,
+ entity_mask2_token=entity_mask2_token,
+ additional_special_tokens=additional_special_tokens,
+ **kwargs,
+ )
+
+ @property
+ # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.vocab_size
+ def vocab_size(self):
+ return len(self.sp_model) + self.fairseq_offset + 1 # Add the token
+
+ # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.get_vocab
+ def get_vocab(self):
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer._tokenize
+ def _tokenize(self, text: str) -> list[str]:
+ # TODO check if the t5/llama PR also applies here
+ return self.sp_model.encode(text, out_type=str)
+
+ # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer._convert_token_to_id
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ if token in self.fairseq_tokens_to_ids:
+ return self.fairseq_tokens_to_ids[token]
+ spm_id = self.sp_model.PieceToId(token)
+
+ # Need to return unknown token if the SP model returned 0
+ return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ if index in self.fairseq_ids_to_tokens:
+ return self.fairseq_ids_to_tokens[index]
+ return self.sp_model.IdToPiece(index - self.fairseq_offset)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (strings for sub-words) in a single string."""
+ out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
+ return out_string
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+ state["sp_model"] = None
+ state["sp_model_proto"] = self.sp_model.serialized_model_proto()
+ return state
+
+ def __setstate__(self, d):
+ self.__dict__ = d
+
+ # for backward compatibility
+ if not hasattr(self, "sp_model_kwargs"):
+ self.sp_model_kwargs = {}
+
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.__call__
+ def __call__(
+ self,
+ text: Union[TextInput, list[TextInput]],
+ text_pair: Optional[Union[TextInput, list[TextInput]]] = None,
+ entity_spans: Optional[Union[EntitySpanInput, list[EntitySpanInput]]] = None,
+ entity_spans_pair: Optional[Union[EntitySpanInput, list[EntitySpanInput]]] = None,
+ entities: Optional[Union[EntityInput, list[EntityInput]]] = None,
+ entities_pair: Optional[Union[EntityInput, list[EntityInput]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ stride: int = 0,
+ is_split_into_words: Optional[bool] = False,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
+ sequences, depending on the task you want to prepare them for.
+
+ Args:
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
+ tokenizer does not support tokenization based on pretokenized strings.
+ text_pair (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
+ tokenizer does not support tokenization based on pretokenized strings.
+ entity_spans (`list[tuple[int, int]]`, `list[list[tuple[int, int]]]`, *optional*):
+ The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
+ with two integers denoting character-based start and end positions of entities. If you specify
+ `"entity_classification"` or `"entity_pair_classification"` as the `task` argument in the constructor,
+ the length of each sequence must be 1 or 2, respectively. If you specify `entities`, the length of each
+ sequence must be equal to the length of each sequence of `entities`.
+ entity_spans_pair (`list[tuple[int, int]]`, `list[list[tuple[int, int]]]`, *optional*):
+ The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
+ with two integers denoting character-based start and end positions of entities. If you specify the
+ `task` argument in the constructor, this argument is ignored. If you specify `entities_pair`, the
+ length of each sequence must be equal to the length of each sequence of `entities_pair`.
+ entities (`list[str]`, `list[list[str]]`, *optional*):
+ The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
+ representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
+ Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of
+ each sequence must be equal to the length of each sequence of `entity_spans`. If you specify
+ `entity_spans` without specifying this argument, the entity sequence or the batch of entity sequences
+ is automatically constructed by filling it with the [MASK] entity.
+ entities_pair (`list[str]`, `list[list[str]]`, *optional*):
+ The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
+ representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
+ Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of
+ each sequence must be equal to the length of each sequence of `entity_spans_pair`. If you specify
+ `entity_spans_pair` without specifying this argument, the entity sequence or the batch of entity
+ sequences is automatically constructed by filling it with the [MASK] entity.
+ max_entity_length (`int`, *optional*):
+ The maximum length of `entity_ids`.
+ """
+ # Input type checking for clearer error
+ is_valid_single_text = isinstance(text, str)
+ is_valid_batch_text = isinstance(text, (list, tuple)) and (len(text) == 0 or (isinstance(text[0], str)))
+ if not (is_valid_single_text or is_valid_batch_text):
+ raise ValueError("text input must be of type `str` (single example) or `list[str]` (batch).")
+
+ is_valid_single_text_pair = isinstance(text_pair, str)
+ is_valid_batch_text_pair = isinstance(text_pair, (list, tuple)) and (
+ len(text_pair) == 0 or isinstance(text_pair[0], str)
+ )
+ if not (text_pair is None or is_valid_single_text_pair or is_valid_batch_text_pair):
+ raise ValueError("text_pair input must be of type `str` (single example) or `list[str]` (batch).")
+
+ is_batched = bool(isinstance(text, (list, tuple)))
+
+ if is_batched:
+ batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
+ if entities is None:
+ batch_entities_or_entities_pairs = None
+ else:
+ batch_entities_or_entities_pairs = (
+ list(zip(entities, entities_pair)) if entities_pair is not None else entities
+ )
+
+ if entity_spans is None:
+ batch_entity_spans_or_entity_spans_pairs = None
+ else:
+ batch_entity_spans_or_entity_spans_pairs = (
+ list(zip(entity_spans, entity_spans_pair)) if entity_spans_pair is not None else entity_spans
+ )
+
+ return self.batch_encode_plus(
+ batch_text_or_text_pairs=batch_text_or_text_pairs,
+ batch_entity_spans_or_entity_spans_pairs=batch_entity_spans_or_entity_spans_pairs,
+ batch_entities_or_entities_pairs=batch_entities_or_entities_pairs,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ stride=stride,
+ is_split_into_words=is_split_into_words,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+ else:
+ return self.encode_plus(
+ text=text,
+ text_pair=text_pair,
+ entity_spans=entity_spans,
+ entity_spans_pair=entity_spans_pair,
+ entities=entities,
+ entities_pair=entities_pair,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ stride=stride,
+ is_split_into_words=is_split_into_words,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._encode_plus
+ def _encode_plus(
+ self,
+ text: Union[TextInput],
+ text_pair: Optional[Union[TextInput]] = None,
+ entity_spans: Optional[EntitySpanInput] = None,
+ entity_spans_pair: Optional[EntitySpanInput] = None,
+ entities: Optional[EntityInput] = None,
+ entities_pair: Optional[EntityInput] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ stride: int = 0,
+ is_split_into_words: Optional[bool] = False,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast. "
+ "More information on available tokenizers at "
+ "https://github.com/huggingface/transformers/pull/2674"
+ )
+
+ if is_split_into_words:
+ raise NotImplementedError("is_split_into_words is not supported in this tokenizer.")
+
+ (
+ first_ids,
+ second_ids,
+ first_entity_ids,
+ second_entity_ids,
+ first_entity_token_spans,
+ second_entity_token_spans,
+ ) = self._create_input_sequence(
+ text=text,
+ text_pair=text_pair,
+ entities=entities,
+ entities_pair=entities_pair,
+ entity_spans=entity_spans,
+ entity_spans_pair=entity_spans_pair,
+ **kwargs,
+ )
+
+ # prepare_for_model will create the attention_mask and token_type_ids
+ return self.prepare_for_model(
+ first_ids,
+ pair_ids=second_ids,
+ entity_ids=first_entity_ids,
+ pair_entity_ids=second_entity_ids,
+ entity_token_spans=first_entity_token_spans,
+ pair_entity_token_spans=second_entity_token_spans,
+ add_special_tokens=add_special_tokens,
+ padding=padding_strategy.value,
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_tensors=return_tensors,
+ prepend_batch_axis=True,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ verbose=verbose,
+ )
+
+ # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._batch_encode_plus
+ def _batch_encode_plus(
+ self,
+ batch_text_or_text_pairs: Union[list[TextInput], list[TextInputPair]],
+ batch_entity_spans_or_entity_spans_pairs: Optional[
+ Union[list[EntitySpanInput], list[tuple[EntitySpanInput, EntitySpanInput]]]
+ ] = None,
+ batch_entities_or_entities_pairs: Optional[
+ Union[list[EntityInput], list[tuple[EntityInput, EntityInput]]]
+ ] = None,
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ stride: int = 0,
+ is_split_into_words: Optional[bool] = False,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ **kwargs,
+ ) -> BatchEncoding:
+ if return_offsets_mapping:
+ raise NotImplementedError(
+ "return_offset_mapping is not available when using Python tokenizers. "
+ "To use this feature, change your tokenizer to one deriving from "
+ "transformers.PreTrainedTokenizerFast."
+ )
+
+ if is_split_into_words:
+ raise NotImplementedError("is_split_into_words is not supported in this tokenizer.")
+
+ # input_ids is a list of tuples (one for each example in the batch)
+ input_ids = []
+ entity_ids = []
+ entity_token_spans = []
+ for index, text_or_text_pair in enumerate(batch_text_or_text_pairs):
+ if not isinstance(text_or_text_pair, (list, tuple)):
+ text, text_pair = text_or_text_pair, None
+ else:
+ text, text_pair = text_or_text_pair
+
+ entities, entities_pair = None, None
+ if batch_entities_or_entities_pairs is not None:
+ entities_or_entities_pairs = batch_entities_or_entities_pairs[index]
+ if entities_or_entities_pairs:
+ if isinstance(entities_or_entities_pairs[0], str):
+ entities, entities_pair = entities_or_entities_pairs, None
+ else:
+ entities, entities_pair = entities_or_entities_pairs
+
+ entity_spans, entity_spans_pair = None, None
+ if batch_entity_spans_or_entity_spans_pairs is not None:
+ entity_spans_or_entity_spans_pairs = batch_entity_spans_or_entity_spans_pairs[index]
+ if len(entity_spans_or_entity_spans_pairs) > 0 and isinstance(
+ entity_spans_or_entity_spans_pairs[0], list
+ ):
+ entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs
+ else:
+ entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs, None
+
+ (
+ first_ids,
+ second_ids,
+ first_entity_ids,
+ second_entity_ids,
+ first_entity_token_spans,
+ second_entity_token_spans,
+ ) = self._create_input_sequence(
+ text=text,
+ text_pair=text_pair,
+ entities=entities,
+ entities_pair=entities_pair,
+ entity_spans=entity_spans,
+ entity_spans_pair=entity_spans_pair,
+ **kwargs,
+ )
+ input_ids.append((first_ids, second_ids))
+ entity_ids.append((first_entity_ids, second_entity_ids))
+ entity_token_spans.append((first_entity_token_spans, second_entity_token_spans))
+
+ batch_outputs = self._batch_prepare_for_model(
+ input_ids,
+ batch_entity_ids_pairs=entity_ids,
+ batch_entity_token_spans_pairs=entity_token_spans,
+ add_special_tokens=add_special_tokens,
+ padding_strategy=padding_strategy,
+ truncation_strategy=truncation_strategy,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ stride=stride,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=return_tensors,
+ verbose=verbose,
+ )
+
+ return BatchEncoding(batch_outputs)
+
+ # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._check_entity_input_format
+ def _check_entity_input_format(self, entities: Optional[EntityInput], entity_spans: Optional[EntitySpanInput]):
+ if not isinstance(entity_spans, list):
+ raise TypeError("entity_spans should be given as a list")
+ elif len(entity_spans) > 0 and not isinstance(entity_spans[0], tuple):
+ raise ValueError(
+ "entity_spans should be given as a list of tuples containing the start and end character indices"
+ )
+
+ if entities is not None:
+ if not isinstance(entities, list):
+ raise ValueError("If you specify entities, they should be given as a list")
+
+ if len(entities) > 0 and not isinstance(entities[0], str):
+ raise ValueError("If you specify entities, they should be given as a list of entity names")
+
+ if len(entities) != len(entity_spans):
+ raise ValueError("If you specify entities, entities and entity_spans must be the same length")
+
+ # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._create_input_sequence
+ def _create_input_sequence(
+ self,
+ text: Union[TextInput],
+ text_pair: Optional[Union[TextInput]] = None,
+ entities: Optional[EntityInput] = None,
+ entities_pair: Optional[EntityInput] = None,
+ entity_spans: Optional[EntitySpanInput] = None,
+ entity_spans_pair: Optional[EntitySpanInput] = None,
+ **kwargs,
+ ) -> tuple[list, list, list, list, list, list]:
+ def get_input_ids(text):
+ tokens = self.tokenize(text, **kwargs)
+ return self.convert_tokens_to_ids(tokens)
+
+ def get_input_ids_and_entity_token_spans(text, entity_spans):
+ if entity_spans is None:
+ return get_input_ids(text), None
+
+ cur = 0
+ input_ids = []
+ entity_token_spans = [None] * len(entity_spans)
+
+ split_char_positions = sorted(frozenset(itertools.chain(*entity_spans)))
+ char_pos2token_pos = {}
+
+ for split_char_position in split_char_positions:
+ orig_split_char_position = split_char_position
+ if (
+ split_char_position > 0 and text[split_char_position - 1] == " "
+ ): # whitespace should be prepended to the following token
+ split_char_position -= 1
+ if cur != split_char_position:
+ input_ids += get_input_ids(text[cur:split_char_position])
+ cur = split_char_position
+ char_pos2token_pos[orig_split_char_position] = len(input_ids)
+
+ input_ids += get_input_ids(text[cur:])
+
+ entity_token_spans = [
+ (char_pos2token_pos[char_start], char_pos2token_pos[char_end]) for char_start, char_end in entity_spans
+ ]
+
+ return input_ids, entity_token_spans
+
+ first_ids, second_ids = None, None
+ first_entity_ids, second_entity_ids = None, None
+ first_entity_token_spans, second_entity_token_spans = None, None
+
+ if self.task is None:
+ if entity_spans is None:
+ first_ids = get_input_ids(text)
+ else:
+ self._check_entity_input_format(entities, entity_spans)
+
+ first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
+ if entities is None:
+ first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
+ else:
+ first_entity_ids = [self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities]
+
+ if text_pair is not None:
+ if entity_spans_pair is None:
+ second_ids = get_input_ids(text_pair)
+ else:
+ self._check_entity_input_format(entities_pair, entity_spans_pair)
+
+ second_ids, second_entity_token_spans = get_input_ids_and_entity_token_spans(
+ text_pair, entity_spans_pair
+ )
+ if entities_pair is None:
+ second_entity_ids = [self.entity_mask_token_id] * len(entity_spans_pair)
+ else:
+ second_entity_ids = [
+ self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities_pair
+ ]
+
+ elif self.task == "entity_classification":
+ if not (isinstance(entity_spans, list) and len(entity_spans) == 1 and isinstance(entity_spans[0], tuple)):
+ raise ValueError(
+ "Entity spans should be a list containing a single tuple "
+ "containing the start and end character indices of an entity"
+ )
+ first_entity_ids = [self.entity_mask_token_id]
+ first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
+
+ # add special tokens to input ids
+ entity_token_start, entity_token_end = first_entity_token_spans[0]
+ first_ids = (
+ first_ids[:entity_token_end] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_end:]
+ )
+ first_ids = (
+ first_ids[:entity_token_start]
+ + [self.additional_special_tokens_ids[0]]
+ + first_ids[entity_token_start:]
+ )
+ first_entity_token_spans = [(entity_token_start, entity_token_end + 2)]
+
+ elif self.task == "entity_pair_classification":
+ if not (
+ isinstance(entity_spans, list)
+ and len(entity_spans) == 2
+ and isinstance(entity_spans[0], tuple)
+ and isinstance(entity_spans[1], tuple)
+ ):
+ raise ValueError(
+ "Entity spans should be provided as a list of two tuples, "
+ "each tuple containing the start and end character indices of an entity"
+ )
+
+ head_span, tail_span = entity_spans
+ first_entity_ids = [self.entity_mask_token_id, self.entity_mask2_token_id]
+ first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
+
+ head_token_span, tail_token_span = first_entity_token_spans
+ token_span_with_special_token_ids = [
+ (head_token_span, self.additional_special_tokens_ids[0]),
+ (tail_token_span, self.additional_special_tokens_ids[1]),
+ ]
+ if head_token_span[0] < tail_token_span[0]:
+ first_entity_token_spans[0] = (head_token_span[0], head_token_span[1] + 2)
+ first_entity_token_spans[1] = (tail_token_span[0] + 2, tail_token_span[1] + 4)
+ token_span_with_special_token_ids = reversed(token_span_with_special_token_ids)
+ else:
+ first_entity_token_spans[0] = (head_token_span[0] + 2, head_token_span[1] + 4)
+ first_entity_token_spans[1] = (tail_token_span[0], tail_token_span[1] + 2)
+
+ for (entity_token_start, entity_token_end), special_token_id in token_span_with_special_token_ids:
+ first_ids = first_ids[:entity_token_end] + [special_token_id] + first_ids[entity_token_end:]
+ first_ids = first_ids[:entity_token_start] + [special_token_id] + first_ids[entity_token_start:]
+
+ elif self.task == "entity_span_classification":
+ if not (isinstance(entity_spans, list) and len(entity_spans) > 0 and isinstance(entity_spans[0], tuple)):
+ raise ValueError(
+ "Entity spans should be provided as a list of tuples, "
+ "each tuple containing the start and end character indices of an entity"
+ )
+
+ first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
+ first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
+
+ else:
+ raise ValueError(f"Task {self.task} not supported")
+
+ return (
+ first_ids,
+ second_ids,
+ first_entity_ids,
+ second_entity_ids,
+ first_entity_token_spans,
+ second_entity_token_spans,
+ )
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._batch_prepare_for_model
+ def _batch_prepare_for_model(
+ self,
+ batch_ids_pairs: list[tuple[list[int], None]],
+ batch_entity_ids_pairs: list[tuple[Optional[list[int]], Optional[list[int]]]],
+ batch_entity_token_spans_pairs: list[tuple[Optional[list[tuple[int, int]]], Optional[list[tuple[int, int]]]]],
+ add_special_tokens: bool = True,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[str] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
+ adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
+ manages a moving window (with user defined stride) for overflowing tokens
+
+
+ Args:
+ batch_ids_pairs: list of tokenized input ids or input ids pairs
+ batch_entity_ids_pairs: list of entity ids or entity ids pairs
+ batch_entity_token_spans_pairs: list of entity spans or entity spans pairs
+ max_entity_length: The maximum length of the entity sequence.
+ """
+
+ batch_outputs = {}
+ for input_ids, entity_ids, entity_token_span_pairs in zip(
+ batch_ids_pairs, batch_entity_ids_pairs, batch_entity_token_spans_pairs
+ ):
+ first_ids, second_ids = input_ids
+ first_entity_ids, second_entity_ids = entity_ids
+ first_entity_token_spans, second_entity_token_spans = entity_token_span_pairs
+ outputs = self.prepare_for_model(
+ first_ids,
+ second_ids,
+ entity_ids=first_entity_ids,
+ pair_entity_ids=second_entity_ids,
+ entity_token_spans=first_entity_token_spans,
+ pair_entity_token_spans=second_entity_token_spans,
+ add_special_tokens=add_special_tokens,
+ padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
+ truncation=truncation_strategy.value,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ stride=stride,
+ pad_to_multiple_of=None, # we pad in batch afterward
+ padding_side=None, # we pad in batch afterward
+ return_attention_mask=False, # we pad in batch afterward
+ return_token_type_ids=return_token_type_ids,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_length=return_length,
+ return_tensors=None, # We convert the whole batch to tensors at the end
+ prepend_batch_axis=False,
+ verbose=verbose,
+ )
+
+ for key, value in outputs.items():
+ if key not in batch_outputs:
+ batch_outputs[key] = []
+ batch_outputs[key].append(value)
+
+ batch_outputs = self.pad(
+ batch_outputs,
+ padding=padding_strategy.value,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
+
+ return batch_outputs
+
+ @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
+ # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.prepare_for_model
+ def prepare_for_model(
+ self,
+ ids: list[int],
+ pair_ids: Optional[list[int]] = None,
+ entity_ids: Optional[list[int]] = None,
+ pair_entity_ids: Optional[list[int]] = None,
+ entity_token_spans: Optional[list[tuple[int, int]]] = None,
+ pair_entity_token_spans: Optional[list[tuple[int, int]]] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ stride: int = 0,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ prepend_batch_axis: bool = False,
+ **kwargs,
+ ) -> BatchEncoding:
+ """
+ Prepares a sequence of input id, entity id and entity span, or a pair of sequences of inputs ids, entity ids,
+ entity spans so that it can be used by the model. It adds special tokens, truncates sequences if overflowing
+ while taking into account the special tokens and manages a moving window (with user defined stride) for
+ overflowing tokens. Please Note, for *pair_ids* different than `None` and *truncation_strategy = longest_first*
+ or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an
+ error.
+
+ Args:
+ ids (`list[int]`):
+ Tokenized input ids of the first sequence.
+ pair_ids (`list[int]`, *optional*):
+ Tokenized input ids of the second sequence.
+ entity_ids (`list[int]`, *optional*):
+ Entity ids of the first sequence.
+ pair_entity_ids (`list[int]`, *optional*):
+ Entity ids of the second sequence.
+ entity_token_spans (`list[tuple[int, int]]`, *optional*):
+ Entity spans of the first sequence.
+ pair_entity_token_spans (`list[tuple[int, int]]`, *optional*):
+ Entity spans of the second sequence.
+ max_entity_length (`int`, *optional*):
+ The maximum length of the entity sequence.
+ """
+
+ # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+ padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ pad_to_multiple_of=pad_to_multiple_of,
+ verbose=verbose,
+ **kwargs,
+ )
+
+ # Compute lengths
+ pair = bool(pair_ids is not None)
+ len_ids = len(ids)
+ len_pair_ids = len(pair_ids) if pair else 0
+
+ if return_token_type_ids and not add_special_tokens:
+ raise ValueError(
+ "Asking to return token_type_ids while setting add_special_tokens to False "
+ "results in an undefined behavior. Please set add_special_tokens to True or "
+ "set return_token_type_ids to None."
+ )
+ if (
+ return_overflowing_tokens
+ and truncation_strategy == TruncationStrategy.LONGEST_FIRST
+ and pair_ids is not None
+ ):
+ raise ValueError(
+ "Not possible to return overflowing tokens for pair of sequences with the "
+ "`longest_first`. Please select another truncation strategy than `longest_first`, "
+ "for instance `only_second` or `only_first`."
+ )
+
+ # Load from model defaults
+ if return_token_type_ids is None:
+ return_token_type_ids = "token_type_ids" in self.model_input_names
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ encoded_inputs = {}
+
+ # Compute the total size of the returned word encodings
+ total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
+
+ # Truncation: Handle max sequence length and max_entity_length
+ overflowing_tokens = []
+ if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
+ # truncate words up to max_length
+ ids, pair_ids, overflowing_tokens = self.truncate_sequences(
+ ids,
+ pair_ids=pair_ids,
+ num_tokens_to_remove=total_len - max_length,
+ truncation_strategy=truncation_strategy,
+ stride=stride,
+ )
+
+ if return_overflowing_tokens:
+ encoded_inputs["overflowing_tokens"] = overflowing_tokens
+ encoded_inputs["num_truncated_tokens"] = total_len - max_length
+
+ # Add special tokens
+ if add_special_tokens:
+ sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
+ token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
+ entity_token_offset = 1 # 1 * token
+ pair_entity_token_offset = len(ids) + 3 # 1 * token & 2 * tokens
+ else:
+ sequence = ids + pair_ids if pair else ids
+ token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
+ entity_token_offset = 0
+ pair_entity_token_offset = len(ids)
+
+ # Build output dictionary
+ encoded_inputs["input_ids"] = sequence
+ if return_token_type_ids:
+ encoded_inputs["token_type_ids"] = token_type_ids
+ if return_special_tokens_mask:
+ if add_special_tokens:
+ encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
+ else:
+ encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
+
+ # Set max entity length
+ if not max_entity_length:
+ max_entity_length = self.max_entity_length
+
+ if entity_ids is not None:
+ total_entity_len = 0
+ num_invalid_entities = 0
+ valid_entity_ids = [ent_id for ent_id, span in zip(entity_ids, entity_token_spans) if span[1] <= len(ids)]
+ valid_entity_token_spans = [span for span in entity_token_spans if span[1] <= len(ids)]
+
+ total_entity_len += len(valid_entity_ids)
+ num_invalid_entities += len(entity_ids) - len(valid_entity_ids)
+
+ valid_pair_entity_ids, valid_pair_entity_token_spans = None, None
+ if pair_entity_ids is not None:
+ valid_pair_entity_ids = [
+ ent_id
+ for ent_id, span in zip(pair_entity_ids, pair_entity_token_spans)
+ if span[1] <= len(pair_ids)
+ ]
+ valid_pair_entity_token_spans = [span for span in pair_entity_token_spans if span[1] <= len(pair_ids)]
+ total_entity_len += len(valid_pair_entity_ids)
+ num_invalid_entities += len(pair_entity_ids) - len(valid_pair_entity_ids)
+
+ if num_invalid_entities != 0:
+ logger.warning(
+ f"{num_invalid_entities} entities are ignored because their entity spans are invalid due to the"
+ " truncation of input tokens"
+ )
+
+ if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and total_entity_len > max_entity_length:
+ # truncate entities up to max_entity_length
+ valid_entity_ids, valid_pair_entity_ids, overflowing_entities = self.truncate_sequences(
+ valid_entity_ids,
+ pair_ids=valid_pair_entity_ids,
+ num_tokens_to_remove=total_entity_len - max_entity_length,
+ truncation_strategy=truncation_strategy,
+ stride=stride,
+ )
+ valid_entity_token_spans = valid_entity_token_spans[: len(valid_entity_ids)]
+ if valid_pair_entity_token_spans is not None:
+ valid_pair_entity_token_spans = valid_pair_entity_token_spans[: len(valid_pair_entity_ids)]
+
+ if return_overflowing_tokens:
+ encoded_inputs["overflowing_entities"] = overflowing_entities
+ encoded_inputs["num_truncated_entities"] = total_entity_len - max_entity_length
+
+ final_entity_ids = valid_entity_ids + valid_pair_entity_ids if valid_pair_entity_ids else valid_entity_ids
+ encoded_inputs["entity_ids"] = list(final_entity_ids)
+ entity_position_ids = []
+ entity_start_positions = []
+ entity_end_positions = []
+ for token_spans, offset in (
+ (valid_entity_token_spans, entity_token_offset),
+ (valid_pair_entity_token_spans, pair_entity_token_offset),
+ ):
+ if token_spans is not None:
+ for start, end in token_spans:
+ start += offset
+ end += offset
+ position_ids = list(range(start, end))[: self.max_mention_length]
+ position_ids += [-1] * (self.max_mention_length - end + start)
+ entity_position_ids.append(position_ids)
+ entity_start_positions.append(start)
+ entity_end_positions.append(end - 1)
+
+ encoded_inputs["entity_position_ids"] = entity_position_ids
+ if self.task == "entity_span_classification":
+ encoded_inputs["entity_start_positions"] = entity_start_positions
+ encoded_inputs["entity_end_positions"] = entity_end_positions
+
+ if return_token_type_ids:
+ encoded_inputs["entity_token_type_ids"] = [0] * len(encoded_inputs["entity_ids"])
+
+ # Check lengths
+ self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
+
+ # Padding
+ if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
+ encoded_inputs = self.pad(
+ encoded_inputs,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ padding=padding_strategy.value,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ if return_length:
+ encoded_inputs["length"] = len(encoded_inputs["input_ids"])
+
+ batch_outputs = BatchEncoding(
+ encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
+ )
+
+ return batch_outputs
+
+ # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer.pad
+ def pad(
+ self,
+ encoded_inputs: Union[
+ BatchEncoding,
+ list[BatchEncoding],
+ dict[str, EncodedInput],
+ dict[str, list[EncodedInput]],
+ list[dict[str, EncodedInput]],
+ ],
+ padding: Union[bool, str, PaddingStrategy] = True,
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ verbose: bool = True,
+ ) -> BatchEncoding:
+ """
+ Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
+ in the batch. Padding side (left/right) padding token ids are defined at the tokenizer level (with
+ `self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`) .. note:: If the `encoded_inputs` passed
+ are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the result will use the same type unless
+ you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the
+ specific device of your tensors however.
+
+ Args:
+ encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `dict[str, list[int]]`, `dict[str, list[list[int]]` or `list[dict[str, list[int]]]`):
+ Tokenized inputs. Can represent one input ([`BatchEncoding`] or `dict[str, list[int]]`) or a batch of
+ tokenized inputs (list of [`BatchEncoding`], *dict[str, list[list[int]]]* or *list[dict[str,
+ list[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader
+ collate function. Instead of `list[int]` you can have tensors (numpy arrays, PyTorch tensors or
+ TensorFlow tensors), see the note above for the return type.
+ padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
+ Select a strategy to pad the returned sequences (according to the model's padding side and padding
+ index) among:
+
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ max_length (`int`, *optional*):
+ Maximum length of the returned list and optionally padding length (see above).
+ max_entity_length (`int`, *optional*):
+ The maximum length of the entity sequence.
+ pad_to_multiple_of (`int`, *optional*):
+ If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
+ the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
+ padding_side:
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask (`bool`, *optional*):
+ Whether to return the attention mask. If left to the default, will return the attention mask according
+ to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention
+ masks?](../glossary#attention-mask)
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors instead of list of python integers. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return Numpy `np.ndarray` objects.
+ verbose (`bool`, *optional*, defaults to `True`):
+ Whether or not to print more information and warnings.
+ """
+ # If we have a list of dicts, let's convert it in a dict of lists
+ # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
+ if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], Mapping):
+ encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0]}
+
+ # The model's main input name, usually `input_ids`, has be passed for padding
+ if self.model_input_names[0] not in encoded_inputs:
+ raise ValueError(
+ "You should supply an encoding or a list of encodings to this method "
+ f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}"
+ )
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+
+ if not required_input:
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = []
+ return encoded_inputs
+
+ # If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects
+ # and rebuild them afterwards if no return_tensors is specified
+ # Note that we lose the specific device the tensor may be on for PyTorch
+
+ first_element = required_input[0]
+ if isinstance(first_element, (list, tuple)):
+ # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
+ index = 0
+ while len(required_input[index]) == 0:
+ index += 1
+ if index < len(required_input):
+ first_element = required_input[index][0]
+ # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
+ if not isinstance(first_element, (int, list, tuple)):
+ if is_tf_tensor(first_element):
+ return_tensors = "tf" if return_tensors is None else return_tensors
+ elif is_torch_tensor(first_element):
+ return_tensors = "pt" if return_tensors is None else return_tensors
+ elif isinstance(first_element, np.ndarray):
+ return_tensors = "np" if return_tensors is None else return_tensors
+ else:
+ raise ValueError(
+ f"type of {first_element} unknown: {type(first_element)}. "
+ "Should be one of a python, numpy, pytorch or tensorflow object."
+ )
+
+ for key, value in encoded_inputs.items():
+ encoded_inputs[key] = to_py_obj(value)
+
+ # Convert padding_strategy in PaddingStrategy
+ padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
+ padding=padding, max_length=max_length, verbose=verbose
+ )
+
+ if max_entity_length is None:
+ max_entity_length = self.max_entity_length
+
+ required_input = encoded_inputs[self.model_input_names[0]]
+ if required_input and not isinstance(required_input[0], (list, tuple)):
+ encoded_inputs = self._pad(
+ encoded_inputs,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ padding_strategy=padding_strategy,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+ return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
+
+ batch_size = len(required_input)
+ if any(len(v) != batch_size for v in encoded_inputs.values()):
+ raise ValueError("Some items in the output dictionary have a different batch size than others.")
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = max(len(inputs) for inputs in required_input)
+ max_entity_length = (
+ max(len(inputs) for inputs in encoded_inputs["entity_ids"]) if "entity_ids" in encoded_inputs else 0
+ )
+ padding_strategy = PaddingStrategy.MAX_LENGTH
+
+ batch_outputs = {}
+ for i in range(batch_size):
+ inputs = {k: v[i] for k, v in encoded_inputs.items()}
+ outputs = self._pad(
+ inputs,
+ max_length=max_length,
+ max_entity_length=max_entity_length,
+ padding_strategy=padding_strategy,
+ pad_to_multiple_of=pad_to_multiple_of,
+ padding_side=padding_side,
+ return_attention_mask=return_attention_mask,
+ )
+
+ for key, value in outputs.items():
+ if key not in batch_outputs:
+ batch_outputs[key] = []
+ batch_outputs[key].append(value)
+
+ return BatchEncoding(batch_outputs, tensor_type=return_tensors)
+
+ # Copied from transformers.models.luke.tokenization_luke.LukeTokenizer._pad
+ def _pad(
+ self,
+ encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding],
+ max_length: Optional[int] = None,
+ max_entity_length: Optional[int] = None,
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+ pad_to_multiple_of: Optional[int] = None,
+ padding_side: Optional[str] = None,
+ return_attention_mask: Optional[bool] = None,
+ ) -> dict:
+ """
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+
+ Args:
+ encoded_inputs:
+ Dictionary of tokenized inputs (`list[int]`) or batch of tokenized inputs (`list[list[int]]`).
+ max_length: maximum length of the returned list and optionally padding length (see below).
+ Will truncate by taking into account the special tokens.
+ max_entity_length: The maximum length of the entity sequence.
+ padding_strategy: PaddingStrategy to use for padding.
+
+
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
+ The tokenizer padding sides are defined in self.padding_side:
+
+
+ - 'left': pads on the left of the sequences
+ - 'right': pads on the right of the sequences
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta).
+ padding_side:
+ The side on which the model should have padding applied. Should be selected between ['right', 'left'].
+ Default value is picked from the class attribute of the same name.
+ return_attention_mask:
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+ """
+ entities_provided = bool("entity_ids" in encoded_inputs)
+
+ # Load from model defaults
+ if return_attention_mask is None:
+ return_attention_mask = "attention_mask" in self.model_input_names
+
+ if padding_strategy == PaddingStrategy.LONGEST:
+ max_length = len(encoded_inputs["input_ids"])
+ if entities_provided:
+ max_entity_length = len(encoded_inputs["entity_ids"])
+
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ if (
+ entities_provided
+ and max_entity_length is not None
+ and pad_to_multiple_of is not None
+ and (max_entity_length % pad_to_multiple_of != 0)
+ ):
+ max_entity_length = ((max_entity_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and (
+ len(encoded_inputs["input_ids"]) != max_length
+ or (entities_provided and len(encoded_inputs["entity_ids"]) != max_entity_length)
+ )
+
+ # Initialize attention mask if not present.
+ if return_attention_mask and "attention_mask" not in encoded_inputs:
+ encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
+ if entities_provided and return_attention_mask and "entity_attention_mask" not in encoded_inputs:
+ encoded_inputs["entity_attention_mask"] = [1] * len(encoded_inputs["entity_ids"])
+
+ if needs_to_be_padded:
+ difference = max_length - len(encoded_inputs["input_ids"])
+ padding_side = padding_side if padding_side is not None else self.padding_side
+ if entities_provided:
+ entity_difference = max_entity_length - len(encoded_inputs["entity_ids"])
+ if padding_side == "right":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
+ if entities_provided:
+ encoded_inputs["entity_attention_mask"] = (
+ encoded_inputs["entity_attention_mask"] + [0] * entity_difference
+ )
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"] + [0] * difference
+ if entities_provided:
+ encoded_inputs["entity_token_type_ids"] = (
+ encoded_inputs["entity_token_type_ids"] + [0] * entity_difference
+ )
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
+ encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference
+ if entities_provided:
+ encoded_inputs["entity_ids"] = (
+ encoded_inputs["entity_ids"] + [self.entity_pad_token_id] * entity_difference
+ )
+ encoded_inputs["entity_position_ids"] = (
+ encoded_inputs["entity_position_ids"] + [[-1] * self.max_mention_length] * entity_difference
+ )
+ if self.task == "entity_span_classification":
+ encoded_inputs["entity_start_positions"] = (
+ encoded_inputs["entity_start_positions"] + [0] * entity_difference
+ )
+ encoded_inputs["entity_end_positions"] = (
+ encoded_inputs["entity_end_positions"] + [0] * entity_difference
+ )
+
+ elif padding_side == "left":
+ if return_attention_mask:
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+ if entities_provided:
+ encoded_inputs["entity_attention_mask"] = [0] * entity_difference + encoded_inputs[
+ "entity_attention_mask"
+ ]
+ if "token_type_ids" in encoded_inputs:
+ encoded_inputs["token_type_ids"] = [0] * difference + encoded_inputs["token_type_ids"]
+ if entities_provided:
+ encoded_inputs["entity_token_type_ids"] = [0] * entity_difference + encoded_inputs[
+ "entity_token_type_ids"
+ ]
+ if "special_tokens_mask" in encoded_inputs:
+ encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
+ encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
+ if entities_provided:
+ encoded_inputs["entity_ids"] = [self.entity_pad_token_id] * entity_difference + encoded_inputs[
+ "entity_ids"
+ ]
+ encoded_inputs["entity_position_ids"] = [
+ [-1] * self.max_mention_length
+ ] * entity_difference + encoded_inputs["entity_position_ids"]
+ if self.task == "entity_span_classification":
+ encoded_inputs["entity_start_positions"] = [0] * entity_difference + encoded_inputs[
+ "entity_start_positions"
+ ]
+ encoded_inputs["entity_end_positions"] = [0] * entity_difference + encoded_inputs[
+ "entity_end_positions"
+ ]
+ else:
+ raise ValueError("Invalid padding strategy:" + str(padding_side))
+
+ return encoded_inputs
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str, str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+ elif not os.path.isfile(self.vocab_file):
+ with open(out_vocab_file, "wb") as fi:
+ content_spiece_model = self.sp_model.serialized_model_proto()
+ fi.write(content_spiece_model)
+
+ entity_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["entity_vocab_file"]
+ )
+
+ with open(entity_vocab_file, "w", encoding="utf-8") as f:
+ f.write(json.dumps(self.entity_vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+ return out_vocab_file, entity_vocab_file
+
+ # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.build_inputs_with_special_tokens
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. An XLM-RoBERTa sequence has the following format:
+
+ - single sequence: ` X `
+ - pair of sequences: ` A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+ # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.get_special_tokens_mask
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is None:
+ return [1] + ([0] * len(token_ids_0)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+ # Copied from transformers.models.xlm_roberta.tokenization_xlm_roberta.XLMRobertaTokenizer.create_token_type_ids_from_sequences
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does
+ not make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+
+ """
+
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+
+__all__ = ["MLukeTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..72257c2ad40072337696a98ad8ada72dcf05b539
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mm_grounding_dino import *
+ from .modeling_mm_grounding_dino import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/configuration_mm_grounding_dino.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/configuration_mm_grounding_dino.py
new file mode 100644
index 0000000000000000000000000000000000000000..e49ccde7e2d79427a38b9907ab12016ef4c50814
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/configuration_mm_grounding_dino.py
@@ -0,0 +1,303 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/mm_grounding_dino/modular_mm_grounding_dino.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_mm_grounding_dino.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
+from ..auto import CONFIG_MAPPING
+
+
+logger = logging.get_logger(__name__)
+
+
+class MMGroundingDinoConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MMGroundingDinoModel`]. It is used to instantiate a
+ MM Grounding DINO model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the MM Grounding DINO tiny architecture
+ [openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`):
+ The configuration of the backbone model.
+ backbone (`str`, *optional*):
+ Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
+ will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
+ is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
+ use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
+ Whether to use pretrained weights for the backbone.
+ use_timm_backbone (`bool`, *optional*, defaults to `False`):
+ Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
+ library.
+ backbone_kwargs (`dict`, *optional*):
+ Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
+ e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
+ text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `BertConfig`):
+ The config object or dictionary of the text backbone.
+ num_queries (`int`, *optional*, defaults to 900):
+ Number of object queries, i.e. detection slots. This is the maximal number of objects
+ [`MMGroundingDinoModel`] can detect in a single image.
+ encoder_layers (`int`, *optional*, defaults to 6):
+ Number of encoder layers.
+ encoder_ffn_dim (`int`, *optional*, defaults to 2048):
+ Dimension of the "intermediate" (often named feed-forward) layer in decoder.
+ encoder_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ decoder_layers (`int`, *optional*, defaults to 6):
+ Number of decoder layers.
+ decoder_ffn_dim (`int`, *optional*, defaults to 2048):
+ Dimension of the "intermediate" (often named feed-forward) layer in decoder.
+ decoder_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ is_encoder_decoder (`bool`, *optional*, defaults to `True`):
+ Whether the model is used as an encoder/decoder or not.
+ activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ d_model (`int`, *optional*, defaults to 256):
+ Dimension of the layers.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ auxiliary_loss (`bool`, *optional*, defaults to `False`):
+ Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
+ position_embedding_type (`str`, *optional*, defaults to `"sine"`):
+ Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
+ num_feature_levels (`int`, *optional*, defaults to 4):
+ The number of input feature levels.
+ encoder_n_points (`int`, *optional*, defaults to 4):
+ The number of sampled keys in each feature level for each attention head in the encoder.
+ decoder_n_points (`int`, *optional*, defaults to 4):
+ The number of sampled keys in each feature level for each attention head in the decoder.
+ two_stage (`bool`, *optional*, defaults to `True`):
+ Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of
+ Grounding DINO, which are further fed into the decoder for iterative bounding box refinement.
+ class_cost (`float`, *optional*, defaults to 1.0):
+ Relative weight of the classification error in the Hungarian matching cost.
+ bbox_cost (`float`, *optional*, defaults to 5.0):
+ Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
+ giou_cost (`float`, *optional*, defaults to 2.0):
+ Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
+ bbox_loss_coefficient (`float`, *optional*, defaults to 5.0):
+ Relative weight of the L1 bounding box loss in the object detection loss.
+ giou_loss_coefficient (`float`, *optional*, defaults to 2.0):
+ Relative weight of the generalized IoU loss in the object detection loss.
+ focal_alpha (`float`, *optional*, defaults to 0.25):
+ Alpha parameter in the focal loss.
+ disable_custom_kernels (`bool`, *optional*, defaults to `False`):
+ Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom
+ kernels are not supported by PyTorch ONNX export.
+ max_text_len (`int`, *optional*, defaults to 256):
+ The maximum length of the text input.
+ text_enhancer_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the text enhancer.
+ fusion_droppath (`float`, *optional*, defaults to 0.1):
+ The droppath ratio for the fusion module.
+ fusion_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the fusion module.
+ embedding_init_target (`bool`, *optional*, defaults to `True`):
+ Whether to initialize the target with Embedding weights.
+ query_dim (`int`, *optional*, defaults to 4):
+ The dimension of the query vector.
+ positional_embedding_temperature (`float`, *optional*, defaults to 20):
+ The temperature for Sine Positional Embedding that is used together with vision backbone.
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the layer normalization layers.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MMGroundingDinoConfig, MMGroundingDinoModel
+
+ >>> # Initializing a MM Grounding DINO configuration
+ >>> configuration = MMGroundingDinoConfig()
+
+ >>> # Initializing a model (with random weights) from the configuration
+ >>> model = MMGroundingDinoModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mm-grounding-dino"
+ attribute_map = {
+ "hidden_size": "d_model",
+ "num_attention_heads": "encoder_attention_heads",
+ }
+
+ def __init__(
+ self,
+ backbone_config=None,
+ backbone=None,
+ use_pretrained_backbone=False,
+ use_timm_backbone=False,
+ backbone_kwargs=None,
+ text_config=None,
+ num_queries=900,
+ encoder_layers=6,
+ encoder_ffn_dim=2048,
+ encoder_attention_heads=8,
+ decoder_layers=6,
+ decoder_ffn_dim=2048,
+ decoder_attention_heads=8,
+ is_encoder_decoder=True,
+ activation_function="relu",
+ d_model=256,
+ dropout=0.1,
+ attention_dropout=0.0,
+ activation_dropout=0.0,
+ auxiliary_loss=False,
+ position_embedding_type="sine",
+ num_feature_levels=4,
+ encoder_n_points=4,
+ decoder_n_points=4,
+ two_stage=True,
+ class_cost=1.0,
+ bbox_cost=5.0,
+ giou_cost=2.0,
+ bbox_loss_coefficient=5.0,
+ giou_loss_coefficient=2.0,
+ focal_alpha=0.25,
+ disable_custom_kernels=False,
+ # other parameters
+ max_text_len=256,
+ text_enhancer_dropout=0.0,
+ fusion_droppath=0.1,
+ fusion_dropout=0.0,
+ embedding_init_target=True,
+ query_dim=4,
+ positional_embedding_temperature=20,
+ init_std=0.02,
+ layer_norm_eps=1e-5,
+ **kwargs,
+ ):
+ super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
+ if backbone_config is None and backbone is None:
+ logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
+ backbone_config = CONFIG_MAPPING["swin"](
+ window_size=7,
+ image_size=224,
+ embed_dim=96,
+ depths=[2, 2, 6, 2],
+ num_heads=[3, 6, 12, 24],
+ out_indices=[2, 3, 4],
+ )
+ elif isinstance(backbone_config, dict):
+ backbone_model_type = backbone_config.pop("model_type")
+ config_class = CONFIG_MAPPING[backbone_model_type]
+ backbone_config = config_class.from_dict(backbone_config)
+
+ verify_backbone_config_arguments(
+ use_timm_backbone=use_timm_backbone,
+ use_pretrained_backbone=use_pretrained_backbone,
+ backbone=backbone,
+ backbone_config=backbone_config,
+ backbone_kwargs=backbone_kwargs,
+ )
+
+ if text_config is None:
+ text_config = {}
+ logger.info("text_config is None. Initializing the text config with default values (`BertConfig`).")
+
+ self.backbone_config = backbone_config
+ self.backbone = backbone
+ self.use_pretrained_backbone = use_pretrained_backbone
+ self.use_timm_backbone = use_timm_backbone
+ self.backbone_kwargs = backbone_kwargs
+ self.num_queries = num_queries
+ self.d_model = d_model
+ self.encoder_ffn_dim = encoder_ffn_dim
+ self.encoder_layers = encoder_layers
+ self.encoder_attention_heads = encoder_attention_heads
+ self.decoder_ffn_dim = decoder_ffn_dim
+ self.decoder_layers = decoder_layers
+ self.decoder_attention_heads = decoder_attention_heads
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.activation_function = activation_function
+ self.auxiliary_loss = auxiliary_loss
+ self.position_embedding_type = position_embedding_type
+ # deformable attributes
+ self.num_feature_levels = num_feature_levels
+ self.encoder_n_points = encoder_n_points
+ self.decoder_n_points = decoder_n_points
+ self.two_stage = two_stage
+ # Hungarian matcher
+ self.class_cost = class_cost
+ self.bbox_cost = bbox_cost
+ self.giou_cost = giou_cost
+ # Loss coefficients
+ self.bbox_loss_coefficient = bbox_loss_coefficient
+ self.giou_loss_coefficient = giou_loss_coefficient
+ self.focal_alpha = focal_alpha
+ self.disable_custom_kernels = disable_custom_kernels
+ # Text backbone
+ if isinstance(text_config, dict):
+ text_config["model_type"] = text_config.get("model_type", "bert")
+ text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+ elif text_config is None:
+ text_config = CONFIG_MAPPING["bert"]()
+
+ self.text_config = text_config
+ self.max_text_len = max_text_len
+
+ # Text Enhancer
+ self.text_enhancer_dropout = text_enhancer_dropout
+ # Fusion
+ self.fusion_droppath = fusion_droppath
+ self.fusion_dropout = fusion_dropout
+ # Others
+ self.embedding_init_target = embedding_init_target
+ self.query_dim = query_dim
+ self.positional_embedding_temperature = positional_embedding_temperature
+ self.init_std = init_std
+ self.layer_norm_eps = layer_norm_eps
+
+ @property
+ def num_attention_heads(self) -> int:
+ return self.encoder_attention_heads
+
+ @property
+ def hidden_size(self) -> int:
+ return self.d_model
+
+ @property
+ def sub_configs(self):
+ sub_configs = {}
+ backbone_config = getattr(self, "backbone_config", None)
+ text_config = getattr(self, "text_config", None)
+ if isinstance(backbone_config, PretrainedConfig):
+ sub_configs["backbone_config"] = type(backbone_config)
+ if isinstance(text_config, PretrainedConfig):
+ sub_configs["text_config"] = type(self.text_config)
+ return sub_configs
+
+
+__all__ = ["MMGroundingDinoConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/convert_mm_grounding_dino_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/convert_mm_grounding_dino_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..e985fdfef3f7afbe3d8bf38ff1436134ca39471d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/convert_mm_grounding_dino_to_hf.py
@@ -0,0 +1,504 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import re
+
+import requests
+import torch
+from PIL import Image
+
+from transformers.models.bert.tokenization_bert import BertTokenizer
+from transformers.models.grounding_dino.image_processing_grounding_dino import GroundingDinoImageProcessor
+from transformers.models.grounding_dino.processing_grounding_dino import GroundingDinoProcessor
+from transformers.models.mm_grounding_dino.configuration_mm_grounding_dino import MMGroundingDinoConfig
+from transformers.models.mm_grounding_dino.modeling_mm_grounding_dino import MMGroundingDinoForObjectDetection
+from transformers.models.swin.configuration_swin import SwinConfig
+
+
+MODEL_NAME_TO_CHECKPOINT_URL_MAPPING = {
+ "mm_grounding_dino_tiny_o365v1_goldg": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg/grounding_dino_swin-t_pretrain_obj365_goldg_20231122_132602-4ea751ce.pth",
+ "mm_grounding_dino_tiny_o365v1_goldg_grit": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_20231128_200818-169cc352.pth",
+ "mm_grounding_dino_tiny_o365v1_goldg_v3det": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_v3det_20231218_095741-e316e297.pth",
+ "mm_grounding_dino_tiny_o365v1_goldg_grit_v3det": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det/grounding_dino_swin-t_pretrain_obj365_goldg_grit9m_v3det_20231204_095047-b448804b.pth",
+ "mm_grounding_dino_base_o365v1_goldg_v3det": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-b_pretrain_obj365_goldg_v3det/grounding_dino_swin-b_pretrain_obj365_goldg_v3de-f83eef00.pth",
+ "mm_grounding_dino_base_all": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-b_pretrain_all/grounding_dino_swin-b_pretrain_all-f9818a7c.pth",
+ "mm_grounding_dino_large_o365v2_oiv6_goldg": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-l_pretrain_obj365_goldg/grounding_dino_swin-l_pretrain_obj365_goldg-34dcdc53.pth",
+ "mm_grounding_dino_large_all": "https://download.openmmlab.com/mmdetection/v3.0/mm_grounding_dino/grounding_dino_swin-l_pretrain_all/grounding_dino_swin-l_pretrain_all-56d69e78.pth",
+ "llmdet_tiny": "https://huggingface.co/fushh7/LLMDet/resolve/main/tiny.pth?download=true",
+ "llmdet_base": "https://huggingface.co/fushh7/LLMDet/resolve/main/base.pth?download=true",
+ "llmdet_large": "https://huggingface.co/fushh7/LLMDet/resolve/main/large.pth?download=true",
+}
+
+
+MODEL_NAME_TO_EXPECTED_OUTPUT_MAPPING = {
+ "mm_grounding_dino_tiny_o365v1_goldg": {
+ "scores": torch.tensor([0.7722, 0.7584, 0.7984, 0.7163]),
+ "boxes": torch.tensor(
+ [
+ [0.5212, 0.1594, 0.5792, 0.3895],
+ [0.5424, 0.0513, 0.9996, 0.7757],
+ [0.0629, 0.1526, 0.2746, 0.2447],
+ [0.0091, 0.1127, 0.4945, 0.9911],
+ ]
+ ),
+ },
+ "mm_grounding_dino_tiny_o365v1_goldg_grit": {
+ "scores": torch.tensor([0.7865, 0.7180, 0.7665, 0.8177]),
+ "boxes": torch.tensor(
+ [
+ [0.0084, 0.1129, 0.4940, 0.9895],
+ [0.5214, 0.1597, 0.5786, 0.3875],
+ [0.5413, 0.0507, 0.9998, 0.7768],
+ [0.0631, 0.1527, 0.2740, 0.2449],
+ ]
+ ),
+ },
+ "mm_grounding_dino_tiny_o365v1_goldg_v3det": {
+ "scores": torch.tensor([0.5690, 0.5553, 0.6075, 0.5775]),
+ "boxes": torch.tensor(
+ [
+ [0.5393, 0.0502, 0.9989, 0.7763],
+ [0.0090, 0.1125, 0.4950, 0.9895],
+ [0.5207, 0.1589, 0.5794, 0.3889],
+ [0.0625, 0.1519, 0.2750, 0.2446],
+ ]
+ ),
+ },
+ "mm_grounding_dino_tiny_o365v1_goldg_grit_v3det": {
+ "scores": torch.tensor([0.8381, 0.8204, 0.7970, 0.7175]),
+ "boxes": torch.tensor(
+ [
+ [0.0099, 0.1129, 0.4942, 0.9903],
+ [0.5413, 0.0506, 0.9998, 0.7753],
+ [0.0626, 0.1527, 0.2744, 0.2443],
+ [0.5211, 0.1596, 0.5790, 0.3890],
+ ]
+ ),
+ },
+ "mm_grounding_dino_base_o365v1_goldg_v3det": {
+ "scores": torch.tensor([0.8418, 0.8364, 0.8342, 0.7885]),
+ "boxes": torch.tensor(
+ [
+ [0.5427, 0.0502, 0.9996, 0.7770],
+ [0.0628, 0.1529, 0.2747, 0.2448],
+ [0.0085, 0.1132, 0.4947, 0.9898],
+ [0.5208, 0.1597, 0.5787, 0.3910],
+ ]
+ ),
+ },
+ "mm_grounding_dino_base_all": {
+ "scores": torch.tensor([0.4713]),
+ "boxes": torch.tensor([[0.5423, 0.0507, 0.9998, 0.7761]]),
+ },
+ "mm_grounding_dino_large_o365v2_oiv6_goldg": {
+ "scores": torch.tensor([0.7824, 0.8275, 0.7715, 0.8211]),
+ "boxes": torch.tensor(
+ [
+ [0.0082, 0.1133, 0.4945, 0.9889],
+ [0.5410, 0.0508, 0.9998, 0.7771],
+ [0.0632, 0.1526, 0.2740, 0.2439],
+ [0.5205, 0.1599, 0.5787, 0.3906],
+ ]
+ ),
+ },
+ "mm_grounding_dino_large_all": {
+ "scores": torch.tensor([0.7373, 0.6208, 0.6913, 0.4523]),
+ "boxes": torch.tensor(
+ [
+ [0.5424, 0.0509, 0.9997, 0.7765],
+ [0.0632, 0.1529, 0.2744, 0.2447],
+ [0.0121, 0.1125, 0.4947, 0.9884],
+ [0.5206, 0.1597, 0.5789, 0.3933],
+ ]
+ ),
+ },
+ "llmdet_tiny": {
+ "scores": torch.tensor([0.7262, 0.7552, 0.7656, 0.8207]),
+ "boxes": torch.tensor(
+ [
+ [0.0114, 0.1132, 0.4947, 0.9854],
+ [0.5387, 0.0513, 0.9992, 0.7765],
+ [0.5212, 0.1605, 0.5788, 0.3890],
+ [0.0634, 0.1536, 0.2743, 0.2440],
+ ]
+ ),
+ },
+ "llmdet_base": {
+ "scores": torch.tensor([0.8646, 0.7567, 0.6978, 0.8084]),
+ "boxes": torch.tensor(
+ [
+ [0.0632, 0.1529, 0.2745, 0.2438],
+ [0.5420, 0.0512, 0.9989, 0.7774],
+ [0.0110, 0.1134, 0.4950, 0.9875],
+ [0.5209, 0.1602, 0.5789, 0.3908],
+ ]
+ ),
+ },
+ "llmdet_large": {
+ "scores": torch.tensor([0.7107, 0.8626, 0.7458, 0.8166]),
+ "boxes": torch.tensor(
+ [
+ [0.0147, 0.1128, 0.4957, 0.9858],
+ [0.0634, 0.1528, 0.2744, 0.2447],
+ [0.5414, 0.0511, 0.9997, 0.7776],
+ [0.5209, 0.1602, 0.5792, 0.3916],
+ ]
+ ),
+ },
+}
+
+# fmt: off
+ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
+ # vision backbone
+ r"backbone.patch_embed.projection.(weight|bias)": r"model.backbone.conv_encoder.model.embeddings.patch_embeddings.projection.\1",
+ r"backbone.patch_embed.norm.(weight|bias)": r"model.backbone.conv_encoder.model.embeddings.norm.\1",
+ r"backbone.stages.(\d+).blocks.(\d+).attn.w_msa.(relative_position_bias_table|relative_position_index)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.attention.self.\3",
+ r"backbone.stages.(\d+).blocks.(\d+).norm1.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.layernorm_before.\3",
+ r"backbone.stages.(\d+).blocks.(\d+).attn.w_msa.(query|key|value).(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.attention.self.\3.\4",
+ r"backbone.stages.(\d+).blocks.(\d+).attn.w_msa.proj.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.attention.output.dense.\3",
+ r"backbone.stages.(\d+).blocks.(\d+).norm2.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.layernorm_after.\3",
+ r"backbone.stages.(\d+).blocks.(\d+).ffn.layers.0.0.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.intermediate.dense.\3",
+ r"backbone.stages.(\d+).blocks.(\d+).ffn.layers.1.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.blocks.\2.output.dense.\3",
+ r"backbone.stages.(\d+).downsample.reduction.weight": r"model.backbone.conv_encoder.model.encoder.layers.\1.downsample.reduction.weight",
+ r"backbone.stages.(\d+).downsample.norm.(weight|bias)": r"model.backbone.conv_encoder.model.encoder.layers.\1.downsample.norm.\2",
+ r"backbone.norms.(\d+).(weight|bias)": r"model.backbone.conv_encoder.model.hidden_states_norms.stage\1.\2",
+ r"neck.convs.(\d+).conv.(weight|bias)": r"model.input_proj_vision.\1.0.\2",
+ r"neck.convs.(\d+).gn.(weight|bias)": r"model.input_proj_vision.\1.1.\2",
+ r"neck.extra_convs.(\d+).conv.(weight|bias)": r"model.input_proj_vision.\1.0.\2",
+ r"neck.extra_convs.(\d+).gn.(weight|bias)": r"model.input_proj_vision.\1.1.\2",
+ # text backbone
+ r"language_model.language_backbone.body.model.(.*)": r"model.text_backbone.\1",
+ r"text_feat_map.(weight|bias)": r"model.text_projection.\1",
+ # encoder
+ r"encoder.fusion_layers.(\d+).gamma_v": r"model.encoder.layers.\1.fusion_layer.vision_param",
+ r"encoder.fusion_layers.(\d+).gamma_l": r"model.encoder.layers.\1.fusion_layer.text_param",
+ r"encoder.fusion_layers.(\d+).layer_norm_v.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.layer_norm_vision.\2",
+ r"encoder.fusion_layers.(\d+).attn.v_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.vision_proj.\2",
+ r"encoder.fusion_layers.(\d+).attn.values_v_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.values_vision_proj.\2",
+ r"encoder.fusion_layers.(\d+).attn.out_v_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.out_vision_proj.\2",
+ r"encoder.fusion_layers.(\d+).layer_norm_l.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.layer_norm_text.\2",
+ r"encoder.fusion_layers.(\d+).attn.l_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.text_proj.\2",
+ r"encoder.fusion_layers.(\d+).attn.values_l_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.values_text_proj.\2",
+ r"encoder.fusion_layers.(\d+).attn.out_l_proj.(weight|bias)": r"model.encoder.layers.\1.fusion_layer.attn.out_text_proj.\2",
+ r"encoder.layers.(\d+).self_attn.(sampling_offsets|attention_weights|value_proj|output_proj).(weight|bias)": r"model.encoder.layers.\1.deformable_layer.self_attn.\2.\3",
+ r"encoder.layers.(\d+).norms.0.(weight|bias)": r"model.encoder.layers.\1.deformable_layer.self_attn_layer_norm.\2",
+ r"encoder.layers.(\d+).ffn.layers.0.0.(weight|bias)": r"model.encoder.layers.\1.deformable_layer.fc1.\2",
+ r"encoder.layers.(\d+).ffn.layers.1.(weight|bias)": r"model.encoder.layers.\1.deformable_layer.fc2.\2",
+ r"encoder.layers.(\d+).norms.1.(weight|bias)": r"model.encoder.layers.\1.deformable_layer.final_layer_norm.\2",
+ r"encoder.text_layers.(\d+).self_attn.attn.(query|key|value)_proj_(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.self_attn.\2.\3",
+ r"encoder.text_layers.(\d+).self_attn.attn.out_proj.(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.self_attn.out_proj.\2",
+ r"encoder.text_layers.(\d+).norms.0.(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.layer_norm_before.\2",
+ r"encoder.text_layers.(\d+).ffn.layers.0.0.(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.fc1.\2",
+ r"encoder.text_layers.(\d+).ffn.layers.1.(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.fc2.\2",
+ r"encoder.text_layers.(\d+).norms.1.(weight|bias)": r"model.encoder.layers.\1.text_enhancer_layer.layer_norm_after.\2",
+ r"encoder.bbox_head.cls_branch.bias": r"model.encoder_output_class_embed.bias",
+ r"encoder.bbox_head.reg_branch.0.(weight|bias)": r"model.encoder_output_bbox_embed.layers.0.\1",
+ r"encoder.bbox_head.reg_branch.2.(weight|bias)": r"model.encoder_output_bbox_embed.layers.1.\1",
+ r"encoder.bbox_head.reg_branch.4.(weight|bias)": r"model.encoder_output_bbox_embed.layers.2.\1",
+ # decoder
+ r"decoder.norm.(weight|bias)": r"model.decoder.layer_norm.\1",
+ r"decoder.ref_point_head.layers.(\d+).(weight|bias)": r"model.decoder.reference_points_head.layers.\1.\2",
+ r"decoder.layers.(\d+).self_attn.attn.(query|key|value)_proj_(weight|bias)": r"model.decoder.layers.\1.self_attn.\2.\3",
+ r"decoder.layers.(\d+).self_attn.attn.out_proj.(weight|bias)": r"model.decoder.layers.\1.self_attn.out_proj.\2",
+ r"decoder.layers.(\d+).norms.0.(weight|bias)": r"model.decoder.layers.\1.self_attn_layer_norm.\2",
+ r"decoder.layers.(\d+).cross_attn_text.attn.(query|key|value)_proj_(weight|bias)": r"model.decoder.layers.\1.encoder_attn_text.\2.\3",
+ r"decoder.layers.(\d+).cross_attn_text.attn.out_proj.(weight|bias)": r"model.decoder.layers.\1.encoder_attn_text.out_proj.\2",
+ r"decoder.layers.(\d+).norms.1.(weight|bias)": r"model.decoder.layers.\1.encoder_attn_text_layer_norm.\2",
+ r"decoder.layers.(\d+).cross_attn.(sampling_offsets|attention_weights|value_proj|output_proj).(weight|bias)": r"model.decoder.layers.\1.encoder_attn.\2.\3",
+ r"decoder.layers.(\d+).norms.2.(weight|bias)": r"model.decoder.layers.\1.encoder_attn_layer_norm.\2",
+ r"decoder.layers.(\d+).ffn.layers.0.0.(weight|bias)": r"model.decoder.layers.\1.fc1.\2",
+ r"decoder.layers.(\d+).ffn.layers.1.(weight|bias)": r"model.decoder.layers.\1.fc2.\2",
+ r"decoder.layers.(\d+).norms.3.(weight|bias)": r"model.decoder.layers.\1.final_layer_norm.\2",
+ r"decoder.bbox_head.cls_branches.(\d+).bias": r"model.decoder.class_embed.\1.bias",
+ r"decoder.bbox_head.reg_branches.(\d+).0.(weight|bias)": r"model.decoder.bbox_embed.\1.layers.0.\2",
+ r"decoder.bbox_head.reg_branches.(\d+).2.(weight|bias)": r"model.decoder.bbox_embed.\1.layers.1.\2",
+ r"decoder.bbox_head.reg_branches.(\d+).4.(weight|bias)": r"model.decoder.bbox_embed.\1.layers.2.\2",
+ # other
+ r"level_embed": r"model.level_embed",
+ r"query_embedding.weight": r"model.query_position_embeddings.weight",
+ r"memory_trans_fc.(weight|bias)": r"model.enc_output.\1",
+ r"memory_trans_norm.(weight|bias)": r"model.enc_output_norm.\1",
+ r"bbox_head.cls_branches.(\d+).bias": r"class_embed.\1.bias",
+ r"bbox_head.reg_branches.(\d+).0.(weight|bias)": r"bbox_embed.\1.layers.0.\2",
+ r"bbox_head.reg_branches.(\d+).2.(weight|bias)": r"bbox_embed.\1.layers.1.\2",
+ r"bbox_head.reg_branches.(\d+).4.(weight|bias)": r"bbox_embed.\1.layers.2.\2",
+}
+# fmt: on
+
+
+def get_mm_grounding_dino_config(model_name: str) -> MMGroundingDinoConfig:
+ if "tiny" in model_name:
+ swin_image_size = 224
+ swin_window_size = 7
+ swin_embed_dim = 96
+ swin_depths = (2, 2, 6, 2)
+ swin_num_heads = (3, 6, 12, 24)
+ swin_out_features = ["stage2", "stage3", "stage4"]
+ num_feature_levels = 4
+ elif "base" in model_name:
+ swin_image_size = 384
+ swin_window_size = 12
+ swin_embed_dim = 128
+ swin_depths = (2, 2, 18, 2)
+ swin_num_heads = (4, 8, 16, 32)
+ swin_out_features = ["stage2", "stage3", "stage4"]
+ num_feature_levels = 4
+ elif "large" in model_name:
+ swin_image_size = 384
+ swin_window_size = 12
+ swin_embed_dim = 192
+ swin_depths = (2, 2, 18, 2)
+ swin_num_heads = (6, 12, 24, 48)
+ swin_out_features = ["stage1", "stage2", "stage3", "stage4"]
+ num_feature_levels = 5
+ else:
+ raise ValueError(
+ f"Model name: {model_name} is not supported. Only `tiny`, `base` and `large` models are currently supported."
+ )
+
+ backbone_config = SwinConfig(
+ image_size=swin_image_size,
+ window_size=swin_window_size,
+ embed_dim=swin_embed_dim,
+ depths=swin_depths,
+ num_heads=swin_num_heads,
+ out_features=swin_out_features,
+ )
+
+ model_config = MMGroundingDinoConfig(
+ backbone_config=backbone_config,
+ num_feature_levels=num_feature_levels,
+ )
+
+ return model_config
+
+
+def get_mm_grounding_dino_processor() -> GroundingDinoProcessor:
+ img_processor = GroundingDinoImageProcessor()
+ txt_processor = BertTokenizer.from_pretrained("bert-base-uncased")
+ processor = GroundingDinoProcessor(img_processor, txt_processor)
+ return processor
+
+
+# Copied from: https://github.com/iSEE-Laboratory/LLMDet/blob/96ec8c82a9d97b170db759e043afd5b81445d0f1/hf_model/mmdet2groundingdino_swint.py#L8C1-L13C13
+def correct_unfold_reduction_order(x: torch.Tensor) -> torch.Tensor:
+ out_channel, in_channel = x.shape
+ x = x.reshape(out_channel, in_channel // 4, 4).transpose(1, 2)
+ x = x[:, [0, 2, 1, 3], :]
+ x = x.reshape(out_channel, in_channel)
+ return x
+
+
+# Copied from: https://github.com/iSEE-Laboratory/LLMDet/blob/96ec8c82a9d97b170db759e043afd5b81445d0f1/hf_model/mmdet2groundingdino_swint.py#L15C1-L20C13
+def correct_unfold_norm_order(x: torch.Tensor) -> torch.Tensor:
+ in_channel = x.shape[0]
+ x = x.reshape(in_channel // 4, 4).transpose(0, 1)
+ x = x[[0, 2, 1, 3], :]
+ x = x.reshape(in_channel)
+ return x
+
+
+def preprocess_old_state(state_dict: dict, config: MMGroundingDinoConfig) -> dict:
+ """
+ Preprocesses old state dict to enable 1-1 mapping:
+ - split qkv projections in Swin backbone
+ - reorder reduction and norm parameters in Swin backbone
+ - shift output norm indices in Swin backbone
+ - shift output proj indices in neck
+ - split q,k,v projections in text self and cross attentions in encoder and decoder
+ - duplicate detection head parameters for decoder and encoder
+ """
+ new_state_dict = state_dict.copy()
+ for k in state_dict:
+ if k.startswith("backbone"):
+ if "downsample.reduction" in k:
+ new_state_dict[k] = correct_unfold_reduction_order(new_state_dict.pop(k))
+ elif "downsample.norm" in k:
+ new_state_dict[k] = correct_unfold_norm_order(new_state_dict.pop(k))
+ elif "w_msa.qkv" in k:
+ q_param, k_param, v_param = new_state_dict.pop(k).chunk(3)
+ new_state_dict[k.replace("qkv", "query")] = q_param
+ new_state_dict[k.replace("qkv", "key")] = k_param
+ new_state_dict[k.replace("qkv", "value")] = v_param
+ elif "backbone.norm" in k:
+ match = re.match(r"backbone.norm(\d+).(weight|bias)", k)
+ new_state_dict[f"backbone.norms.{int(match.group(1)) + 1}.{match.group(2)}"] = new_state_dict.pop(k)
+ elif k.startswith("neck.extra_convs"):
+ num_normal_convs = len(config.backbone_config.out_indices)
+ if "gn" in k:
+ match = re.match(r"neck.extra_convs.(\d+).gn.(weight|bias)", k)
+ new_state_dict[f"neck.extra_convs.{num_normal_convs + int(match.group(1))}.gn.{match.group(2)}"] = (
+ new_state_dict.pop(k)
+ )
+ elif "conv" in k:
+ match = re.match(r"neck.extra_convs.(\d+).conv.(weight|bias)", k)
+ new_state_dict[f"neck.extra_convs.{num_normal_convs + int(match.group(1))}.conv.{match.group(2)}"] = (
+ new_state_dict.pop(k)
+ )
+ elif k.startswith("encoder"):
+ if "self_attn.attn.in_proj" in k:
+ q_param, k_param, v_param = new_state_dict.pop(k).chunk(3)
+ new_state_dict[k.replace("in", "query")] = q_param
+ new_state_dict[k.replace("in", "key")] = k_param
+ new_state_dict[k.replace("in", "value")] = v_param
+ elif k.startswith("decoder"):
+ if "self_attn.attn.in_proj" in k or "cross_attn_text.attn.in_proj" in k:
+ q_param, k_param, v_param = new_state_dict.pop(k).chunk(3)
+ new_state_dict[k.replace("in", "query")] = q_param
+ new_state_dict[k.replace("in", "key")] = k_param
+ new_state_dict[k.replace("in", "value")] = v_param
+ elif k.startswith("bbox_head"):
+ num_decoder_layers = config.decoder_layers
+ match = re.match(r"bbox_head.(cls|reg)_branches.(\d+).(.*)", k)
+ cls_or_reg = match.group(1)
+ layer_idx = int(match.group(2))
+ suffix = match.group(3)
+ if layer_idx < num_decoder_layers:
+ new_key = f"decoder.bbox_head.{cls_or_reg}_branches.{layer_idx}.{suffix}"
+ new_state_dict[new_key] = new_state_dict[k] # copy
+ else:
+ new_key = f"encoder.bbox_head.{cls_or_reg}_branch.{suffix}"
+ new_state_dict[new_key] = new_state_dict.pop(k) # move
+
+ # remove unused params
+ if (
+ k == "dn_query_generator.label_embedding.weight"
+ or k == "language_model.language_backbone.body.model.embeddings.position_ids"
+ or k == "image_seperate.weight"
+ or k.startswith("lmm")
+ or k.startswith("connector")
+ or k.startswith("region_connector")
+ or k.startswith("ref_point_head")
+ ):
+ new_state_dict.pop(k)
+
+ return new_state_dict
+
+
+# Copied from transformers/models/siglip2/convert_siglip2_to_hf.py
+def convert_old_keys_to_new_keys(state_dict_keys: list) -> dict:
+ """
+ This function should be applied only once, on the concatenated keys to efficiently rename using
+ the key mappings.
+ """
+ output_dict = {}
+ if state_dict_keys is not None:
+ old_text = "\n".join(state_dict_keys)
+ new_text = old_text
+ for pattern, replacement in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items():
+ if replacement is None:
+ new_text = re.sub(pattern, "", new_text) # an empty line
+ continue
+ new_text = re.sub(pattern, replacement, new_text)
+ output_dict = dict(zip(old_text.split("\n"), new_text.split("\n")))
+ return output_dict
+
+
+def convert_mm_to_hf_state(original_state: dict, hf_cfg: MMGroundingDinoConfig) -> dict:
+ original_state = preprocess_old_state(original_state, hf_cfg)
+ original_state_keys = list(original_state.keys())
+ original_to_hf_key_map = convert_old_keys_to_new_keys(original_state_keys)
+
+ hf_state = {}
+ for original_key in original_state_keys:
+ hf_key = original_to_hf_key_map[original_key]
+ hf_state[hf_key] = original_state.pop(original_key)
+
+ return hf_state
+
+
+def prepare_test_inputs():
+ image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ image = Image.open(requests.get(image_url, stream=True).raw)
+ text = [["cat", "remote"]]
+ return image, text
+
+
+@torch.no_grad()
+def convert_mm_grounding_dino_checkpoint(
+ model_name: str,
+ verify_outputs: bool,
+ push_to_hub: bool,
+ hub_user_name: str,
+) -> tuple[MMGroundingDinoConfig, dict]:
+ # Load original state
+ checkpoint_url = MODEL_NAME_TO_CHECKPOINT_URL_MAPPING[model_name]
+ print(f"Loading checkpoint from: {checkpoint_url}")
+ ckpt = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
+ mm_state = ckpt["state_dict"]
+
+ # Create hf model and processor
+ print("Creating model...")
+ hf_cfg = get_mm_grounding_dino_config(model_name)
+ hf_state = convert_mm_to_hf_state(mm_state, hf_cfg)
+ hf_model = MMGroundingDinoForObjectDetection(hf_cfg).eval()
+ hf_model.load_state_dict(hf_state)
+ hf_processor = get_mm_grounding_dino_processor()
+
+ # Verify outputs if needed
+ if verify_outputs:
+ print("Running inference to verify outputs...")
+ image, text = prepare_test_inputs()
+ model_inputs = hf_processor(images=image, text=text, return_tensors="pt")
+ model_outputs = hf_model(**model_inputs)
+ results = hf_processor.post_process_grounded_object_detection(
+ model_outputs,
+ model_inputs.input_ids,
+ box_threshold=0.4,
+ text_threshold=0.3,
+ )
+ result = results[0]
+ print(result)
+ expected = MODEL_NAME_TO_EXPECTED_OUTPUT_MAPPING[model_name]
+ for key in expected:
+ torch.testing.assert_close(result[key], expected[key], atol=1e-3, rtol=1e-3)
+ print("Outputs match.")
+
+ # Push to hub if needed
+ if push_to_hub:
+ print("Pushing to hub...")
+ hub_url = f"{hub_user_name}/{model_name}"
+ hf_model.push_to_hub(hub_url)
+ hf_processor.push_to_hub(hub_url)
+ print(f"Pushed to huggingface hub at: {hub_url}.")
+
+ return hf_cfg, hf_state
+
+
+def parse_args():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--model-name",
+ required=True,
+ type=str,
+ choices=list(MODEL_NAME_TO_CHECKPOINT_URL_MAPPING.keys()),
+ help="URL to the original mm grounding dino checkpoint.",
+ )
+ parser.add_argument("--hub-user-name", type=str, help="User name on the huggingface hub.")
+ parser.add_argument("--push-to-hub", action="store_true", help="Whether to push model to hub or not.")
+ parser.add_argument(
+ "--verify-outputs", action="store_true", help="Whether to verify that model output is correct or not."
+ )
+ return parser.parse_args()
+
+
+if __name__ == "__main__":
+ args = parse_args()
+ convert_mm_grounding_dino_checkpoint(
+ args.model_name,
+ args.verify_outputs,
+ args.push_to_hub,
+ args.hub_user_name,
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/modeling_mm_grounding_dino.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/modeling_mm_grounding_dino.py
new file mode 100644
index 0000000000000000000000000000000000000000..e75456769e8414490d6c3685d137a5982a7317c6
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/modeling_mm_grounding_dino.py
@@ -0,0 +1,2611 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/mm_grounding_dino/modular_mm_grounding_dino.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_mm_grounding_dino.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+from ...activations import ACT2FN
+from ...file_utils import ModelOutput, is_timm_available, requires_backends
+from ...integrations import use_kernel_forward_from_hub
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import meshgrid
+from ...utils import auto_docstring
+from ...utils.backbone_utils import load_backbone
+from ..auto.modeling_auto import AutoModel
+from .configuration_mm_grounding_dino import MMGroundingDinoConfig
+
+
+if is_timm_available():
+ from timm import create_model
+
+
+class MMGroundingDinoContrastiveEmbedding(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.max_text_len = config.max_text_len
+ self.bias = nn.Parameter(torch.tensor(0.0))
+
+ def forward(
+ self,
+ vision_hidden_state: torch.FloatTensor,
+ text_hidden_state: torch.FloatTensor,
+ text_token_mask: torch.BoolTensor,
+ ) -> torch.FloatTensor:
+ res = vision_hidden_state @ text_hidden_state.transpose(-1, -2)
+ res = res / math.sqrt(vision_hidden_state.shape[-1])
+ res = res + self.bias
+ res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
+
+ # padding to max_text_len
+ new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
+ new_res[..., : res.shape[-1]] = res
+
+ return new_res
+
+
+@use_kernel_forward_from_hub("MultiScaleDeformableAttention")
+class MultiScaleDeformableAttention(nn.Module):
+ def forward(
+ self,
+ value: Tensor,
+ value_spatial_shapes: Tensor,
+ value_spatial_shapes_list: list[tuple],
+ level_start_index: Tensor,
+ sampling_locations: Tensor,
+ attention_weights: Tensor,
+ im2col_step: int,
+ ):
+ batch_size, _, num_heads, hidden_dim = value.shape
+ _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
+ value_list = value.split([height * width for height, width in value_spatial_shapes_list], dim=1)
+ sampling_grids = 2 * sampling_locations - 1
+ sampling_value_list = []
+ for level_id, (height, width) in enumerate(value_spatial_shapes_list):
+ # batch_size, height*width, num_heads, hidden_dim
+ # -> batch_size, height*width, num_heads*hidden_dim
+ # -> batch_size, num_heads*hidden_dim, height*width
+ # -> batch_size*num_heads, hidden_dim, height, width
+ value_l_ = (
+ value_list[level_id]
+ .flatten(2)
+ .transpose(1, 2)
+ .reshape(batch_size * num_heads, hidden_dim, height, width)
+ )
+ # batch_size, num_queries, num_heads, num_points, 2
+ # -> batch_size, num_heads, num_queries, num_points, 2
+ # -> batch_size*num_heads, num_queries, num_points, 2
+ sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1)
+ # batch_size*num_heads, hidden_dim, num_queries, num_points
+ sampling_value_l_ = nn.functional.grid_sample(
+ value_l_,
+ sampling_grid_l_,
+ mode="bilinear",
+ padding_mode="zeros",
+ align_corners=False,
+ )
+ sampling_value_list.append(sampling_value_l_)
+ # (batch_size, num_queries, num_heads, num_levels, num_points)
+ # -> (batch_size, num_heads, num_queries, num_levels, num_points)
+ # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points)
+ attention_weights = attention_weights.transpose(1, 2).reshape(
+ batch_size * num_heads, 1, num_queries, num_levels * num_points
+ )
+ output = (
+ (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
+ .sum(-1)
+ .view(batch_size, num_heads * hidden_dim, num_queries)
+ )
+ return output.transpose(1, 2).contiguous()
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of the MMGroundingDinoDecoder. This class adds two attributes to
+ BaseModelOutputWithCrossAttentions, namely:
+ - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer)
+ - a stacked tensor of intermediate reference points.
+ """
+)
+class MMGroundingDinoDecoderOutput(ModelOutput):
+ r"""
+ intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
+ Stacked intermediate hidden states (output of each layer of the decoder).
+ intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`):
+ Stacked intermediate reference points (reference points of each layer of the decoder).
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ intermediate_hidden_states: Optional[torch.FloatTensor] = None
+ intermediate_reference_points: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
+
+
+class MMGroundingDinoLearnedPositionEmbedding(nn.Module):
+ """
+ This module learns positional embeddings up to a fixed maximum size.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+
+ embedding_dim = config.d_model // 2
+ self.row_embeddings = nn.Embedding(50, embedding_dim)
+ self.column_embeddings = nn.Embedding(50, embedding_dim)
+
+ def forward(self, pixel_values, pixel_mask=None):
+ height, width = pixel_values.shape[-2:]
+ width_values = torch.arange(width, device=pixel_values.device)
+ height_values = torch.arange(height, device=pixel_values.device)
+ x_emb = self.column_embeddings(width_values)
+ y_emb = self.row_embeddings(height_values)
+ pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1)
+ pos = pos.permute(2, 0, 1)
+ pos = pos.unsqueeze(0)
+ pos = pos.repeat(pixel_values.shape[0], 1, 1, 1)
+ return pos
+
+
+class MMGroundingDinoMultiscaleDeformableAttention(nn.Module):
+ """
+ Multiscale deformable attention as proposed in Deformable DETR.
+ """
+
+ def __init__(self, config: MMGroundingDinoConfig, num_heads: int, n_points: int):
+ super().__init__()
+
+ self.attn = MultiScaleDeformableAttention()
+
+ if config.d_model % num_heads != 0:
+ raise ValueError(
+ f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}"
+ )
+ dim_per_head = config.d_model // num_heads
+ # check if dim_per_head is power of 2
+ if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0):
+ warnings.warn(
+ "You'd better set embed_dim (d_model) in MMGroundingDinoMultiscaleDeformableAttention to make the"
+ " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA"
+ " implementation."
+ )
+
+ self.im2col_step = 64
+
+ self.d_model = config.d_model
+ self.n_levels = config.num_feature_levels
+ self.n_heads = num_heads
+ self.n_points = n_points
+
+ self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2)
+ self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points)
+ self.value_proj = nn.Linear(config.d_model, config.d_model)
+ self.output_proj = nn.Linear(config.d_model, config.d_model)
+
+ self.disable_custom_kernels = config.disable_custom_kernels
+
+ def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
+ return tensor if position_embeddings is None else tensor + position_embeddings
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ position_embeddings: Optional[torch.Tensor] = None,
+ reference_points=None,
+ spatial_shapes=None,
+ spatial_shapes_list=None,
+ level_start_index=None,
+ output_attentions: bool = False,
+ ):
+ # add position embeddings to the hidden states before projecting to queries and keys
+ if position_embeddings is not None:
+ hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
+
+ batch_size, num_queries, _ = hidden_states.shape
+ batch_size, sequence_length, _ = encoder_hidden_states.shape
+ # Ignore copy
+ if (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() != sequence_length:
+ raise ValueError(
+ "Make sure to align the spatial shapes with the sequence length of the encoder hidden states"
+ )
+
+ value = self.value_proj(encoder_hidden_states)
+ if attention_mask is not None:
+ # we invert the attention_mask
+ value = value.masked_fill(~attention_mask[..., None], float(0))
+ value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads)
+ sampling_offsets = self.sampling_offsets(hidden_states).view(
+ batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2
+ )
+ attention_weights = self.attention_weights(hidden_states).view(
+ batch_size, num_queries, self.n_heads, self.n_levels * self.n_points
+ )
+ attention_weights = F.softmax(attention_weights, -1).view(
+ batch_size, num_queries, self.n_heads, self.n_levels, self.n_points
+ )
+ # batch_size, num_queries, n_heads, n_levels, n_points, 2
+ num_coordinates = reference_points.shape[-1]
+ if num_coordinates == 2:
+ offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
+ sampling_locations = (
+ reference_points[:, :, None, :, None, :]
+ + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+ )
+ elif num_coordinates == 4:
+ sampling_locations = (
+ reference_points[:, :, None, :, None, :2]
+ + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
+ )
+ else:
+ raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")
+
+ output = self.attn(
+ value,
+ spatial_shapes,
+ spatial_shapes_list,
+ level_start_index,
+ sampling_locations,
+ attention_weights,
+ self.im2col_step,
+ )
+
+ output = self.output_proj(output)
+
+ return output, attention_weights
+
+
+class MMGroundingDinoBiMultiHeadAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+
+ vision_dim = text_dim = config.d_model
+ embed_dim = config.encoder_ffn_dim // 2
+ num_heads = config.encoder_attention_heads // 2
+ dropout = config.fusion_dropout
+
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.head_dim = embed_dim // num_heads
+ self.vision_dim = vision_dim
+ self.text_dim = text_dim
+
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f"`embed_dim` must be divisible by `num_heads` (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
+ )
+ self.scale = self.head_dim ** (-0.5)
+ self.dropout = dropout
+
+ self.vision_proj = nn.Linear(self.vision_dim, self.embed_dim)
+ self.text_proj = nn.Linear(self.text_dim, self.embed_dim)
+ self.values_vision_proj = nn.Linear(self.vision_dim, self.embed_dim)
+ self.values_text_proj = nn.Linear(self.text_dim, self.embed_dim)
+
+ self.out_vision_proj = nn.Linear(self.embed_dim, self.vision_dim)
+ self.out_text_proj = nn.Linear(self.embed_dim, self.text_dim)
+
+ def _reshape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
+ return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def forward(
+ self,
+ vision_features: torch.FloatTensor,
+ text_features: torch.FloatTensor,
+ vision_attention_mask: Optional[torch.BoolTensor] = None,
+ text_attention_mask: Optional[torch.BoolTensor] = None,
+ ) -> tuple[tuple[torch.FloatTensor, torch.FloatTensor], tuple[torch.FloatTensor, torch.FloatTensor]]:
+ """Image-to-text and text-to-image cross-attention
+
+ Args:
+ vision_features (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, hidden_dim)`):
+ Projected flattened image features generated by the vision backbone.
+ text_features (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_dim)`):
+ Projected text features generated by the text encoder.
+ vision_attention_mask (`torch.BoolTensor`, **optional**):
+ Attention mask for image-to-text cross-attention. False for real tokens and True for padding tokens.
+ text_attention_mask (`torch.BoolTensor`, **optional**):
+ Attention mask for text-to-image cross-attention. False for real tokens and True for padding tokens.
+
+ Returns:
+ `tuple(tuple(torch.FloatTensor), tuple(torch.FloatTensor))` where each inner tuple comprises an attention
+ output and weights:
+ - **vision_attn_output** (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, hidden_din)`)
+ --
+ Output of the image-to-text cross-attention layer.
+ - **vision_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, vision_sequence_length,
+ vision_sequence_length)`) --
+ Attention weights of the image-to-text cross-attention layer.
+ - **text_attn_output** (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_dim)`) --
+ Output of the text-to-image cross-attention layer.
+ - **text_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, text_sequence_length,
+ text_sequence_length)`) --
+ Attention weights of the text-to-image cross-attention layer.
+ """
+ batch_size, tgt_len, _ = vision_features.size()
+
+ vision_query_states = self.vision_proj(vision_features) * self.scale
+ vision_query_states = self._reshape(vision_query_states, tgt_len, batch_size)
+
+ text_key_states = self.text_proj(text_features)
+ text_key_states = self._reshape(text_key_states, -1, batch_size)
+
+ vision_value_states = self.values_vision_proj(vision_features)
+ vision_value_states = self._reshape(vision_value_states, -1, batch_size)
+
+ text_value_states = self.values_text_proj(text_features)
+ text_value_states = self._reshape(text_value_states, -1, batch_size)
+
+ proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
+
+ vision_query_states = vision_query_states.view(*proj_shape)
+ text_key_states = text_key_states.view(*proj_shape)
+ vision_value_states = vision_value_states.view(*proj_shape)
+ text_value_states = text_value_states.view(*proj_shape)
+
+ src_len = text_key_states.size(1)
+ attn_weights = torch.bmm(vision_query_states, text_key_states.transpose(1, 2)) # bs*nhead, nimg, ntxt
+
+ if attn_weights.size() != (batch_size * self.num_heads, tgt_len, src_len):
+ raise ValueError(
+ f"Attention weights should be of size {(batch_size * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+ )
+
+ attn_weights = attn_weights - attn_weights.max()
+ # Do not increase -50000/50000, data type half has quite limited range
+ attn_weights = torch.clamp(attn_weights, min=-50000, max=50000)
+
+ attn_weights_transposed = attn_weights.transpose(1, 2)
+ text_attn_weights = attn_weights_transposed - torch.max(attn_weights_transposed, dim=-1, keepdim=True)[0]
+
+ # Do not increase -50000/50000, data type half has quite limited range
+ text_attn_weights = torch.clamp(text_attn_weights, min=-50000, max=50000)
+
+ # mask vision for language
+ if vision_attention_mask is not None:
+ vision_attention_mask = (
+ vision_attention_mask[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+ )
+ text_attn_weights.masked_fill_(vision_attention_mask, float("-inf"))
+
+ text_attn_weights = text_attn_weights.softmax(dim=-1)
+
+ # mask language for vision
+ if text_attention_mask is not None:
+ text_attention_mask = text_attention_mask[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+ attn_weights.masked_fill_(text_attention_mask, float("-inf"))
+ vision_attn_weights = attn_weights.softmax(dim=-1)
+
+ vision_attn_probs = F.dropout(vision_attn_weights, p=self.dropout, training=self.training)
+ text_attn_probs = F.dropout(text_attn_weights, p=self.dropout, training=self.training)
+
+ vision_attn_output = torch.bmm(vision_attn_probs, text_value_states)
+ text_attn_output = torch.bmm(text_attn_probs, vision_value_states)
+
+ if vision_attn_output.size() != (batch_size * self.num_heads, tgt_len, self.head_dim):
+ raise ValueError(
+ f"`vision_attn_output` should be of size {(batch_size, self.num_heads, tgt_len, self.head_dim)}, but is {vision_attn_output.size()}"
+ )
+
+ if text_attn_output.size() != (batch_size * self.num_heads, src_len, self.head_dim):
+ raise ValueError(
+ f"`text_attn_output` should be of size {(batch_size, self.num_heads, src_len, self.head_dim)}, but is {text_attn_output.size()}"
+ )
+
+ vision_attn_output = vision_attn_output.view(batch_size, self.num_heads, tgt_len, self.head_dim)
+ vision_attn_output = vision_attn_output.transpose(1, 2)
+ vision_attn_output = vision_attn_output.reshape(batch_size, tgt_len, self.embed_dim)
+
+ text_attn_output = text_attn_output.view(batch_size, self.num_heads, src_len, self.head_dim)
+ text_attn_output = text_attn_output.transpose(1, 2)
+ text_attn_output = text_attn_output.reshape(batch_size, src_len, self.embed_dim)
+
+ vision_attn_output = self.out_vision_proj(vision_attn_output)
+ text_attn_output = self.out_text_proj(text_attn_output)
+
+ return (vision_attn_output, vision_attn_weights), (text_attn_output, text_attn_weights)
+
+
+def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
+ """
+ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+ Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
+ however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+ See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
+ layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
+ argument.
+ """
+ if drop_prob == 0.0 or not training:
+ return input
+ keep_prob = 1 - drop_prob
+ shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
+ random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
+ random_tensor.floor_() # binarize
+ output = input.div(keep_prob) * random_tensor
+ return output
+
+
+class MMGroundingDinoDropPath(nn.Module):
+ """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+ def __init__(self, drop_prob: Optional[float] = None) -> None:
+ super().__init__()
+ self.drop_prob = drop_prob
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ return drop_path(hidden_states, self.drop_prob, self.training)
+
+ def extra_repr(self) -> str:
+ return f"p={self.drop_prob}"
+
+
+class MMGroundingDinoFusionLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ drop_path = config.fusion_droppath
+
+ # pre layer norm
+ self.layer_norm_vision = nn.LayerNorm(config.d_model, config.layer_norm_eps)
+ self.layer_norm_text = nn.LayerNorm(config.d_model, config.layer_norm_eps)
+ self.attn = MMGroundingDinoBiMultiHeadAttention(config)
+
+ # add layer scale for training stability
+ self.drop_path = MMGroundingDinoDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ init_values = 1e-4
+ self.vision_param = nn.Parameter(init_values * torch.ones(config.d_model), requires_grad=True)
+ self.text_param = nn.Parameter(init_values * torch.ones(config.d_model), requires_grad=True)
+
+ def forward(
+ self,
+ vision_features: torch.FloatTensor,
+ text_features: torch.FloatTensor,
+ attention_mask_vision: Optional[torch.BoolTensor] = None,
+ attention_mask_text: Optional[torch.BoolTensor] = None,
+ ) -> tuple[tuple[torch.FloatTensor, torch.FloatTensor], tuple[torch.FloatTensor, torch.FloatTensor]]:
+ """Image and text features fusion
+
+ Args:
+ vision_features (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, hidden_dim)`):
+ Projected flattened image features generated by the vision backbone.
+ text_features (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_dim)`):
+ Projected text features generated by the text encoder.
+ attention_mask_vision (`torch.BoolTensor`, **optional**):
+ Attention mask for image-to-text cross-attention. False for real tokens and True for padding tokens.
+ attention_mask_text (`torch.BoolTensor`, **optional**):
+ Attention mask for text-to-image cross-attention. False for real tokens and True for padding tokens.
+
+ Returns:
+ `tuple(tuple(torch.FloatTensor), tuple(torch.FloatTensor))` where each inner tuple comprises an enhanced
+ feature and attention output and weights:
+ - **vision_features** (`torch.FloatTensor` of shape `(batch_size, vision_sequence_length, vision_dim)`) --
+ Updated vision features with attention output from image-to-text cross-attention layer.
+ - **vision_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, vision_sequence_length,
+ vision_sequence_length)`) --
+ Attention weights of the image-to-text cross-attention layer.
+ - **text_features** (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, text_dim)`) --
+ Updated text features with attention output from text-to-image cross-attention layer.
+ - **text_attn_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, text_sequence_length,
+ text_sequence_length)`) --
+ Attention weights of the text-to-image cross-attention layer.
+ """
+ vision_features = self.layer_norm_vision(vision_features)
+ text_features = self.layer_norm_text(text_features)
+ (delta_v, vision_attn), (delta_t, text_attn) = self.attn(
+ vision_features,
+ text_features,
+ vision_attention_mask=attention_mask_vision,
+ text_attention_mask=attention_mask_text,
+ )
+ vision_features = vision_features + self.drop_path(self.vision_param * delta_v)
+ text_features = text_features + self.drop_path(self.text_param * delta_t)
+
+ return (vision_features, vision_attn), (text_features, text_attn)
+
+
+class MMGroundingDinoMultiheadAttention(nn.Module):
+ """Equivalent implementation of nn.MultiheadAttention with `batch_first=True`."""
+
+ def __init__(self, config, num_attention_heads=None):
+ super().__init__()
+ if config.hidden_size % num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({num_attention_heads})"
+ )
+
+ self.num_attention_heads = num_attention_heads
+ self.attention_head_size = int(config.hidden_size / num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)
+
+ self.dropout = nn.Dropout(config.attention_dropout)
+
+ def forward(
+ self,
+ queries: torch.Tensor,
+ keys: torch.Tensor,
+ values: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor]:
+ batch_size, seq_length, _ = queries.shape
+ query_layer = (
+ self.query(queries)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ key_layer = (
+ self.key(keys).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
+ )
+ value_layer = (
+ self.value(values).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
+ )
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in MMGroundingDinoModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(new_context_layer_shape)
+
+ context_layer = self.out_proj(context_layer)
+
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+ return outputs
+
+
+class MMGroundingDinoDecoderLayer(nn.Module):
+ def __init__(self, config: MMGroundingDinoConfig):
+ super().__init__()
+ self.embed_dim = config.d_model
+
+ # self-attention
+ self.self_attn = MMGroundingDinoMultiheadAttention(config, num_attention_heads=config.decoder_attention_heads)
+
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
+ # cross-attention text
+ self.encoder_attn_text = MMGroundingDinoMultiheadAttention(
+ config, num_attention_heads=config.decoder_attention_heads
+ )
+ self.encoder_attn_text_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
+ # cross-attention
+ self.encoder_attn = MMGroundingDinoMultiscaleDeformableAttention(
+ config,
+ num_heads=config.decoder_attention_heads,
+ n_points=config.decoder_n_points,
+ )
+ self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
+ # feedforward neural networks
+ self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
+ self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
+
+ def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
+ return tensor if position_embeddings is None else tensor + position_embeddings
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: Optional[torch.Tensor] = None,
+ reference_points=None,
+ spatial_shapes=None,
+ spatial_shapes_list=None,
+ level_start_index=None,
+ vision_encoder_hidden_states: Optional[torch.Tensor] = None,
+ vision_encoder_attention_mask: Optional[torch.Tensor] = None,
+ text_encoder_hidden_states: Optional[torch.Tensor] = None,
+ text_encoder_attention_mask: Optional[torch.Tensor] = None,
+ self_attn_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ ):
+ residual = hidden_states
+
+ # Self Attention
+ queries = keys = self.with_pos_embed(hidden_states, position_embeddings)
+ hidden_states, self_attn_weights = self.self_attn(
+ queries=queries,
+ keys=keys,
+ values=hidden_states,
+ attention_mask=self_attn_mask,
+ output_attentions=True,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ second_residual = hidden_states
+
+ # Cross-Attention Text
+ queries = self.with_pos_embed(hidden_states, position_embeddings)
+ hidden_states, text_cross_attn_weights = self.encoder_attn_text(
+ queries=queries,
+ keys=text_encoder_hidden_states,
+ values=text_encoder_hidden_states,
+ attention_mask=text_encoder_attention_mask,
+ output_attentions=True,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = second_residual + hidden_states
+ hidden_states = self.encoder_attn_text_layer_norm(hidden_states)
+
+ third_residual = hidden_states
+
+ # Cross-Attention
+ cross_attn_weights = None
+ hidden_states, cross_attn_weights = self.encoder_attn(
+ hidden_states=hidden_states,
+ attention_mask=vision_encoder_attention_mask,
+ encoder_hidden_states=vision_encoder_hidden_states,
+ encoder_attention_mask=vision_encoder_attention_mask,
+ position_embeddings=position_embeddings,
+ reference_points=reference_points,
+ spatial_shapes=spatial_shapes,
+ spatial_shapes_list=spatial_shapes_list,
+ level_start_index=level_start_index,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = third_residual + hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, text_cross_attn_weights, cross_attn_weights)
+
+ return outputs
+
+
+# Based on https://github.com/IDEA-Research/MMGroundingDino/blob/2b62f419c292ca9c518daae55512fabc3fead4a4/MMGroundingDino/models/MMGroundingDino/utils.py#L24
+def get_sine_pos_embed(
+ pos_tensor: torch.Tensor, num_pos_feats: int = 128, temperature: int = 10000, exchange_xy: bool = True
+) -> Tensor:
+ """
+ Generate sine position embeddings from a position tensor.
+
+ Args:
+ pos_tensor (torch.Tensor):
+ Tensor containing positions. Shape: [..., n].
+ num_pos_feats (`int`, *optional*, defaults to 128):
+ Projected shape for each float in the tensor.
+ temperature (`int`, *optional*, defaults to 10000):
+ Temperature in the sine/cosine function.
+ exchange_xy (`bool`, *optional*, defaults to `True`):
+ Exchange pos x and pos y. For example, input tensor is [x,y], the results will be [pos(y), pos(x)].
+
+ Returns:
+ position_embeddings (torch.Tensor): shape: [..., n * hidden_size].
+ """
+ scale = 2 * math.pi
+ dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
+ dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
+
+ def sine_func(x: torch.Tensor):
+ sin_x = x * scale / dim_t
+ sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
+ return sin_x
+
+ pos_tensor = pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)
+ position_embeddings = [sine_func(x) for x in pos_tensor]
+ if exchange_xy:
+ position_embeddings[0], position_embeddings[1] = position_embeddings[1], position_embeddings[0]
+ position_embeddings = torch.cat(position_embeddings, dim=-1)
+ return position_embeddings
+
+
+@auto_docstring
+class MMGroundingDinoPreTrainedModel(PreTrainedModel):
+ config: MMGroundingDinoConfig
+ base_model_prefix = "model"
+ main_input_name = "pixel_values"
+
+ def _init_weights(self, module):
+ std = self.config.init_std
+
+ if isinstance(module, MMGroundingDinoLearnedPositionEmbedding):
+ nn.init.uniform_(module.row_embeddings.weight)
+ nn.init.uniform_(module.column_embeddings.weight)
+ elif isinstance(module, MMGroundingDinoMultiscaleDeformableAttention):
+ nn.init.constant_(module.sampling_offsets.weight.data, 0.0)
+ default_dtype = torch.get_default_dtype()
+ thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * (
+ 2.0 * math.pi / module.n_heads
+ )
+ grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
+ grid_init = (
+ (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
+ .view(module.n_heads, 1, 1, 2)
+ .repeat(1, module.n_levels, module.n_points, 1)
+ )
+ for i in range(module.n_points):
+ grid_init[:, :, i, :] *= i + 1
+ with torch.no_grad():
+ module.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
+ nn.init.constant_(module.attention_weights.weight.data, 0.0)
+ nn.init.constant_(module.attention_weights.bias.data, 0.0)
+ nn.init.xavier_uniform_(module.value_proj.weight.data)
+ nn.init.constant_(module.value_proj.bias.data, 0.0)
+ nn.init.xavier_uniform_(module.output_proj.weight.data)
+ nn.init.constant_(module.output_proj.bias.data, 0.0)
+ elif isinstance(module, MMGroundingDinoBiMultiHeadAttention):
+ nn.init.xavier_uniform_(module.vision_proj.weight)
+ module.vision_proj.bias.data.fill_(0)
+ nn.init.xavier_uniform_(module.text_proj.weight)
+ module.text_proj.bias.data.fill_(0)
+ nn.init.xavier_uniform_(module.values_vision_proj.weight)
+ module.values_vision_proj.bias.data.fill_(0)
+ nn.init.xavier_uniform_(module.values_text_proj.weight)
+ module.values_text_proj.bias.data.fill_(0)
+ nn.init.xavier_uniform_(module.out_vision_proj.weight)
+ module.out_vision_proj.bias.data.fill_(0)
+ nn.init.xavier_uniform_(module.out_text_proj.weight)
+ module.out_text_proj.bias.data.fill_(0)
+ elif isinstance(module, MMGroundingDinoFusionLayer):
+ module.vision_param.data.fill_(1e-4)
+ module.text_param.data.fill_(1e-4)
+ elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, MMGroundingDinoMLPPredictionHead):
+ nn.init.constant_(module.layers[-1].weight.data, 0)
+ nn.init.constant_(module.layers[-1].bias.data, 0)
+
+ if hasattr(module, "reference_points") and not self.config.two_stage:
+ nn.init.xavier_uniform_(module.reference_points.weight.data, gain=1.0)
+ nn.init.constant_(module.reference_points.bias.data, 0.0)
+ if hasattr(module, "level_embed"):
+ nn.init.normal_(module.level_embed)
+ if isinstance(module, MMGroundingDinoContrastiveEmbedding):
+ nn.init.constant_(module.bias, -math.log((1 - 0.01) / 0.01))
+
+ def _set_gradient_checkpointing(self, module, value=False):
+ if isinstance(module, MMGroundingDinoDecoder):
+ module.gradient_checkpointing = value
+
+
+class MMGroundingDinoFrozenBatchNorm2d(nn.Module):
+ """
+ BatchNorm2d where the batch statistics and the affine parameters are fixed.
+
+ Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
+ torchvision.models.resnet[18,34,50,101] produce nans.
+ """
+
+ def __init__(self, n):
+ super().__init__()
+ self.register_buffer("weight", torch.ones(n))
+ self.register_buffer("bias", torch.zeros(n))
+ self.register_buffer("running_mean", torch.zeros(n))
+ self.register_buffer("running_var", torch.ones(n))
+
+ def _load_from_state_dict(
+ self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+ ):
+ num_batches_tracked_key = prefix + "num_batches_tracked"
+ if num_batches_tracked_key in state_dict:
+ del state_dict[num_batches_tracked_key]
+
+ super()._load_from_state_dict(
+ state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+ )
+
+ def forward(self, x):
+ # move reshapes to the beginning
+ # to make it user-friendly
+ weight = self.weight.reshape(1, -1, 1, 1)
+ bias = self.bias.reshape(1, -1, 1, 1)
+ running_var = self.running_var.reshape(1, -1, 1, 1)
+ running_mean = self.running_mean.reshape(1, -1, 1, 1)
+ epsilon = 1e-5
+ scale = weight * (running_var + epsilon).rsqrt()
+ bias = bias - running_mean * scale
+ return x * scale + bias
+
+
+def replace_batch_norm(model):
+ r"""
+ Recursively replace all `torch.nn.BatchNorm2d` with `MMGroundingDinoFrozenBatchNorm2d`.
+
+ Args:
+ model (torch.nn.Module):
+ input model
+ """
+ for name, module in model.named_children():
+ if isinstance(module, nn.BatchNorm2d):
+ new_module = MMGroundingDinoFrozenBatchNorm2d(module.num_features)
+
+ if module.weight.device != torch.device("meta"):
+ new_module.weight.data.copy_(module.weight)
+ new_module.bias.data.copy_(module.bias)
+ new_module.running_mean.data.copy_(module.running_mean)
+ new_module.running_var.data.copy_(module.running_var)
+
+ model._modules[name] = new_module
+
+ if len(list(module.children())) > 0:
+ replace_batch_norm(module)
+
+
+class MMGroundingDinoConvEncoder(nn.Module):
+ """
+ Convolutional backbone, using either the AutoBackbone API or one from the timm library.
+
+ nn.BatchNorm2d layers are replaced by MMGroundingDinoFrozenBatchNorm2d as defined above.
+
+ """
+
+ def __init__(self, config):
+ super().__init__()
+
+ self.config = config
+
+ if config.use_timm_backbone:
+ requires_backends(self, ["timm"])
+ backbone = create_model(
+ config.backbone,
+ pretrained=config.use_pretrained_backbone,
+ features_only=True,
+ **config.backbone_kwargs,
+ )
+ else:
+ backbone = load_backbone(config)
+
+ # replace batch norm by frozen batch norm
+ with torch.no_grad():
+ replace_batch_norm(backbone)
+ self.model = backbone
+ self.intermediate_channel_sizes = (
+ self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels
+ )
+
+ backbone_model_type = None
+ if config.backbone is not None:
+ backbone_model_type = config.backbone
+ elif config.backbone_config is not None:
+ backbone_model_type = config.backbone_config.model_type
+ else:
+ raise ValueError("Either `backbone` or `backbone_config` should be provided in the config")
+
+ if "resnet" in backbone_model_type:
+ for name, parameter in self.model.named_parameters():
+ if config.use_timm_backbone:
+ if "layer2" not in name and "layer3" not in name and "layer4" not in name:
+ parameter.requires_grad_(False)
+ else:
+ if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name:
+ parameter.requires_grad_(False)
+
+ def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor):
+ # send pixel_values through the model to get list of feature maps
+ features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps
+
+ out = []
+ for feature_map in features:
+ # downsample pixel_mask to match shape of corresponding feature_map
+ mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0]
+ out.append((feature_map, mask))
+ return out
+
+
+class MMGroundingDinoConvModel(nn.Module):
+ """
+ This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder.
+ """
+
+ def __init__(self, conv_encoder, position_embedding):
+ super().__init__()
+ self.conv_encoder = conv_encoder
+ self.position_embedding = position_embedding
+
+ def forward(self, pixel_values, pixel_mask):
+ # send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples
+ out = self.conv_encoder(pixel_values, pixel_mask)
+ pos = []
+ for feature_map, mask in out:
+ # position encoding
+ pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype))
+
+ return out, pos
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of the MMGroundingDinoEncoder. This class extends BaseModelOutput, due to:
+ - vision and text last hidden states
+ - vision and text intermediate hidden states
+ """
+)
+class MMGroundingDinoEncoderOutput(ModelOutput):
+ r"""
+ last_hidden_state_vision (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the vision encoder.
+ last_hidden_state_text (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the text encoder.
+ vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the vision embeddings + one for the output of each
+ layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the vision encoder at the
+ output of each layer plus the initial embedding outputs.
+ text_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the text embeddings + one for the output of each layer)
+ of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the text encoder at the output of
+ each layer plus the initial embedding outputs.
+ """
+
+ last_hidden_state_vision: Optional[torch.FloatTensor] = None
+ last_hidden_state_text: Optional[torch.FloatTensor] = None
+ vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ text_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
+
+
+class MMGroundingDinoTextEnhancerLayer(nn.Module):
+ """Vanilla Transformer with text embeddings as input"""
+
+ def __init__(self, config):
+ super().__init__()
+ self.self_attn = MMGroundingDinoMultiheadAttention(
+ config, num_attention_heads=config.encoder_attention_heads // 2
+ )
+
+ # Implementation of Feedforward model
+ self.fc1 = nn.Linear(config.d_model, config.encoder_ffn_dim // 2)
+ self.fc2 = nn.Linear(config.encoder_ffn_dim // 2, config.d_model)
+
+ self.layer_norm_before = nn.LayerNorm(config.d_model, config.layer_norm_eps)
+ self.layer_norm_after = nn.LayerNorm(config.d_model, config.layer_norm_eps)
+
+ self.activation = ACT2FN[config.activation_function]
+ self.num_heads = config.encoder_attention_heads // 2
+ self.dropout = config.text_enhancer_dropout
+
+ def with_pos_embed(self, hidden_state: Tensor, position_embeddings: Optional[Tensor]):
+ return hidden_state if position_embeddings is None else hidden_state + position_embeddings
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ attention_masks: Optional[torch.BoolTensor] = None,
+ position_embeddings: Optional[torch.FloatTensor] = None,
+ ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
+ """Text self-attention to enhance projection of text features generated by
+ the text encoder (AutoModel based on text_config) within MMGroundingDinoEncoderLayer
+
+ Args:
+ hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_dim)`):
+ Text features generated by the text encoder.
+ attention_masks (`torch.BoolTensor`, *optional*):
+ Attention mask for text self-attention. False for real tokens and True for padding tokens.
+ position_embeddings (`torch.FloatTensor`, *optional*):
+ Position embeddings to be added to the hidden states.
+
+ Returns:
+ `tuple(torch.FloatTensor)` comprising two elements:
+ - **hidden_states** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) --
+ Output of the text self-attention layer.
+ - **attention_weights** (`torch.FloatTensor` of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`) --
+ Attention weights of the text self-attention layer.
+ """
+
+ # repeat attn mask
+ if attention_masks.dim() == 3 and attention_masks.shape[0] == hidden_states.shape[0]:
+ # batch_size, num_queries, num_keys
+ attention_masks = attention_masks[:, None, :, :]
+ attention_masks = attention_masks.repeat(1, self.num_heads, 1, 1)
+
+ dtype = hidden_states.dtype
+ attention_masks = attention_masks.to(dtype=dtype) # fp16 compatibility
+ attention_masks = (1.0 - attention_masks) * torch.finfo(dtype).min
+
+ queries = keys = self.with_pos_embed(hidden_states, position_embeddings)
+ attention_output, attention_weights = self.self_attn(
+ queries=queries,
+ keys=keys,
+ values=hidden_states,
+ attention_mask=attention_masks,
+ output_attentions=True,
+ )
+ attention_output = nn.functional.dropout(attention_output, p=self.dropout, training=self.training)
+ hidden_states = hidden_states + attention_output
+ hidden_states = self.layer_norm_before(hidden_states)
+
+ residual = hidden_states
+ hidden_states = self.activation(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = hidden_states + residual
+ hidden_states = self.layer_norm_after(hidden_states)
+
+ return hidden_states, attention_weights
+
+
+class MMGroundingDinoDeformableLayer(nn.Module):
+ def __init__(self, config: MMGroundingDinoConfig):
+ super().__init__()
+ self.embed_dim = config.d_model
+ self.self_attn = MMGroundingDinoMultiscaleDeformableAttention(
+ config, num_heads=config.encoder_attention_heads, n_points=config.encoder_n_points
+ )
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+ self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
+ self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim, config.layer_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ position_embeddings: Optional[torch.Tensor] = None,
+ reference_points=None,
+ spatial_shapes=None,
+ spatial_shapes_list=None,
+ level_start_index=None,
+ output_attentions: bool = False,
+ ):
+ """
+ Args:
+ hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Input to the layer.
+ attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
+ Attention mask.
+ position_embeddings (`torch.FloatTensor`, *optional*):
+ Position embeddings, to be added to `hidden_states`.
+ reference_points (`torch.FloatTensor`, *optional*):
+ Reference points.
+ spatial_shapes (`torch.LongTensor`, *optional*):
+ Spatial shapes of the backbone feature maps.
+ spatial_shapes_list (`list[tuple[int, int]]`, *optional*):
+ Spatial shapes of the backbone feature maps (but as list for export compatibility).
+ level_start_index (`torch.LongTensor`, *optional*):
+ Level start index.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+
+ # Apply Multi-scale Deformable Attention Module on the multi-scale feature maps.
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ encoder_hidden_states=hidden_states,
+ encoder_attention_mask=attention_mask,
+ position_embeddings=position_embeddings,
+ reference_points=reference_points,
+ spatial_shapes=spatial_shapes,
+ spatial_shapes_list=spatial_shapes_list,
+ level_start_index=level_start_index,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ if self.training:
+ if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ return hidden_states, attn_weights
+
+
+class MMGroundingDinoEncoderLayer(nn.Module):
+ def __init__(self, config) -> None:
+ super().__init__()
+
+ self.d_model = config.d_model
+
+ self.text_enhancer_layer = MMGroundingDinoTextEnhancerLayer(config)
+ self.fusion_layer = MMGroundingDinoFusionLayer(config)
+ self.deformable_layer = MMGroundingDinoDeformableLayer(config)
+
+ def get_text_position_embeddings(
+ self,
+ text_features: Tensor,
+ text_position_embedding: Optional[torch.Tensor],
+ text_position_ids: Optional[torch.Tensor],
+ ) -> Tensor:
+ batch_size, seq_length, _ = text_features.shape
+ if text_position_embedding is None and text_position_ids is None:
+ text_position_embedding = torch.arange(seq_length, device=text_features.device)
+ text_position_embedding = text_position_embedding.float()
+ text_position_embedding = text_position_embedding.unsqueeze(0).unsqueeze(-1)
+ text_position_embedding = text_position_embedding.repeat(batch_size, 1, 1)
+ text_position_embedding = get_sine_pos_embed(
+ text_position_embedding, num_pos_feats=self.d_model, exchange_xy=False
+ )
+ if text_position_ids is not None:
+ text_position_embedding = get_sine_pos_embed(
+ text_position_ids[..., None], num_pos_feats=self.d_model, exchange_xy=False
+ )
+
+ return text_position_embedding
+
+ def forward(
+ self,
+ vision_features: Tensor,
+ vision_position_embedding: Tensor,
+ spatial_shapes: Tensor,
+ spatial_shapes_list: list[tuple[int, int]],
+ level_start_index: Tensor,
+ key_padding_mask: Tensor,
+ reference_points: Tensor,
+ text_features: Optional[Tensor] = None,
+ text_attention_mask: Optional[Tensor] = None,
+ text_position_embedding: Optional[Tensor] = None,
+ text_self_attention_masks: Optional[Tensor] = None,
+ text_position_ids: Optional[Tensor] = None,
+ ):
+ text_position_embedding = self.get_text_position_embeddings(
+ text_features, text_position_embedding, text_position_ids
+ )
+
+ (vision_features, vision_fused_attn), (text_features, text_fused_attn) = self.fusion_layer(
+ vision_features=vision_features,
+ text_features=text_features,
+ attention_mask_vision=key_padding_mask,
+ attention_mask_text=text_attention_mask,
+ )
+
+ (text_features, text_enhanced_attn) = self.text_enhancer_layer(
+ hidden_states=text_features,
+ attention_masks=~text_self_attention_masks, # note we use ~ for mask here
+ position_embeddings=(text_position_embedding if text_position_embedding is not None else None),
+ )
+
+ (vision_features, vision_deformable_attn) = self.deformable_layer(
+ hidden_states=vision_features,
+ attention_mask=~key_padding_mask,
+ position_embeddings=vision_position_embedding,
+ reference_points=reference_points,
+ spatial_shapes=spatial_shapes,
+ spatial_shapes_list=spatial_shapes_list,
+ level_start_index=level_start_index,
+ )
+
+ return (
+ (vision_features, text_features),
+ (vision_fused_attn, text_fused_attn, text_enhanced_attn, vision_deformable_attn),
+ )
+
+
+class MMGroundingDinoEncoder(MMGroundingDinoPreTrainedModel):
+ """
+ Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
+ [`MMGroundingDinoEncoderLayer`].
+
+ The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers.
+
+ Args:
+ config: MMGroundingDinoConfig
+ """
+
+ def __init__(self, config: MMGroundingDinoConfig):
+ super().__init__(config)
+
+ self.dropout = config.dropout
+ self.layers = nn.ModuleList([MMGroundingDinoEncoderLayer(config) for _ in range(config.encoder_layers)])
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @staticmethod
+ def get_reference_points(spatial_shapes, valid_ratios, device):
+ """
+ Get reference points for each feature map.
+
+ Args:
+ spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
+ Spatial shapes of each feature map.
+ valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
+ Valid ratios of each feature map.
+ device (`torch.device`):
+ Device on which to create the tensors.
+ Returns:
+ `torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)`
+ """
+ reference_points_list = []
+ for level, (height, width) in enumerate(spatial_shapes):
+ ref_y, ref_x = meshgrid(
+ torch.linspace(0.5, height - 0.5, height, dtype=torch.float32, device=device),
+ torch.linspace(0.5, width - 0.5, width, dtype=torch.float32, device=device),
+ indexing="ij",
+ )
+ # TODO: valid_ratios could be useless here. check https://github.com/fundamentalvision/Deformable-DETR/issues/36
+ ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, level, 1] * height)
+ ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, level, 0] * width)
+ ref = torch.stack((ref_x, ref_y), -1)
+ reference_points_list.append(ref)
+ reference_points = torch.cat(reference_points_list, 1)
+ reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+ return reference_points
+
+ def forward(
+ self,
+ vision_features: Tensor,
+ vision_attention_mask: Tensor,
+ vision_position_embedding: Tensor,
+ spatial_shapes: Tensor,
+ spatial_shapes_list: list[tuple[int, int]],
+ level_start_index: Tensor,
+ valid_ratios=None,
+ text_features: Optional[Tensor] = None,
+ text_attention_mask: Optional[Tensor] = None,
+ text_position_embedding: Optional[Tensor] = None,
+ text_self_attention_masks: Optional[Tensor] = None,
+ text_position_ids: Optional[Tensor] = None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ r"""
+ Args:
+ vision_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
+ vision_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
+ - 0 for pixel features that are real (i.e. **not masked**),
+ - 1 for pixel features that are padding (i.e. **masked**).
+ [What are attention masks?](../glossary#attention-mask)
+ vision_position_embedding (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Position embeddings that are added to the queries and keys in each self-attention layer.
+ spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
+ Spatial shapes of each feature map.
+ spatial_shapes_list (`list[tuple[int, int]]`):
+ Spatial shapes of each feature map (but as list for export compatibility).
+ level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`):
+ Starting index of each feature map.
+ valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
+ Ratio of valid area in each feature level.
+ text_features (`torch.FloatTensor` of shape `(batch_size, text_seq_len, hidden_size)`):
+ Flattened text features that are passed to the encoder.
+ text_attention_mask (`torch.Tensor` of shape `(batch_size, text_seq_len)`, *optional*):
+ Mask to avoid performing attention on padding text features. Mask values selected in `[0, 1]`:
+ - 0 for text features that are real (i.e. **not masked**),
+ - 1 for text features that are padding (i.e. **masked**).
+ [What are attention masks?](../glossary#attention-mask)
+ text_position_embedding (`torch.FloatTensor` of shape `(batch_size, text_seq_len)`):
+ Position embeddings that are added to the queries and keys in each self-attention layer.
+ text_self_attention_masks (`torch.BoolTensor` of shape `(batch_size, text_seq_len, text_seq_len)`):
+ Masks to avoid performing attention between padding text features. Mask values selected in `[0, 1]`:
+ - 1 for text features that are real (i.e. **not masked**),
+ - 0 for text features that are padding (i.e. **masked**).
+ text_position_ids (`torch.LongTensor` of shape `(batch_size, num_queries)`):
+ Position ids for text features.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=vision_features.device)
+
+ encoder_vision_states = () if output_hidden_states else None
+ encoder_text_states = () if output_hidden_states else None
+ all_attns = () if output_attentions else None
+ all_attn_fused_text = () if output_attentions else None
+ all_attn_fused_vision = () if output_attentions else None
+ all_attn_enhanced_text = () if output_attentions else None
+ all_attn_deformable = () if output_attentions else None
+ for i, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_vision_states += (vision_features,)
+ encoder_text_states += (text_features,)
+
+ (vision_features, text_features), attentions = encoder_layer(
+ vision_features=vision_features,
+ vision_position_embedding=vision_position_embedding,
+ spatial_shapes=spatial_shapes,
+ spatial_shapes_list=spatial_shapes_list,
+ level_start_index=level_start_index,
+ key_padding_mask=vision_attention_mask,
+ reference_points=reference_points,
+ text_features=text_features,
+ text_attention_mask=text_attention_mask,
+ text_position_embedding=text_position_embedding,
+ text_self_attention_masks=text_self_attention_masks,
+ text_position_ids=text_position_ids,
+ )
+
+ if output_attentions:
+ all_attn_fused_vision += (attentions[0],)
+ all_attn_fused_text += (attentions[1],)
+ all_attn_enhanced_text += (attentions[2],)
+ all_attn_deformable += (attentions[3],)
+
+ if output_hidden_states:
+ encoder_vision_states += (vision_features,)
+ encoder_text_states += (text_features,)
+
+ if output_attentions:
+ all_attns = (all_attn_fused_vision, all_attn_fused_text, all_attn_enhanced_text, all_attn_deformable)
+
+ if not return_dict:
+ enc_outputs = [vision_features, text_features, encoder_vision_states, encoder_text_states, all_attns]
+ return tuple(v for v in enc_outputs if v is not None)
+ return MMGroundingDinoEncoderOutput(
+ last_hidden_state_vision=vision_features,
+ last_hidden_state_text=text_features,
+ vision_hidden_states=encoder_vision_states,
+ text_hidden_states=encoder_text_states,
+ attentions=all_attns,
+ )
+
+
+class MMGroundingDinoDecoder(MMGroundingDinoPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MMGroundingDinoDecoderLayer`].
+
+ The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
+
+ Some tweaks for Grounding DINO:
+
+ - `position_embeddings`, `reference_points`, `spatial_shapes` and `valid_ratios` are added to the forward pass.
+ - it also returns a stack of intermediate outputs and reference points from all decoding layers.
+
+ Args:
+ config: MMGroundingDinoConfig
+ """
+
+ def __init__(self, config: MMGroundingDinoConfig):
+ super().__init__(config)
+
+ self.dropout = config.dropout
+ self.layer_norm = nn.LayerNorm(config.d_model, config.layer_norm_eps)
+ self.layers = nn.ModuleList([MMGroundingDinoDecoderLayer(config) for _ in range(config.decoder_layers)])
+ self.reference_points_head = MMGroundingDinoMLPPredictionHead(
+ config.query_dim // 2 * config.d_model, config.d_model, config.d_model, 2
+ )
+ self.gradient_checkpointing = False
+
+ # hack implementation for iterative bounding box refinement as in two-stage Deformable DETR
+ self.bbox_embed = None
+ self.class_embed = None
+ self.query_scale = None
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ inputs_embeds,
+ vision_encoder_hidden_states,
+ vision_encoder_attention_mask=None,
+ text_encoder_hidden_states=None,
+ text_encoder_attention_mask=None,
+ reference_points=None,
+ spatial_shapes=None,
+ spatial_shapes_list=None,
+ level_start_index=None,
+ valid_ratios=None,
+ self_attn_mask=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
+ The query embeddings that are passed into the decoder.
+ vision_encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Last hidden state from encoder related to vision feature map.
+ vision_encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
+ - 1 for pixel features that are real (i.e. **not masked**),
+ - 0 for pixel features that are padding (i.e. **masked**).
+ text_encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, text_seq_len, hidden_size)`):
+ Last hidden state from encoder related to text features.
+ text_encoder_attention_mask (`torch.Tensor` of shape `(batch_size, text_seq_len)`, *optional*):
+ Mask to avoid performing attention on padding text features. Mask values selected in `[0, 1]`:
+ - 0 for text features that are real (i.e. **not masked**),
+ - 1 for text features that are padding (i.e. **masked**).
+ reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*):
+ Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area.
+ spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`):
+ Spatial shapes of the feature maps.
+ spatial_shapes_list (`list[tuple[int, int]]`):
+ Spatial shapes of the feature maps (but as list for export compatibility).
+ level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*):
+ Indexes for the start of each feature level. In range `[0, sequence_length]`.
+ valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`, *optional*):
+ Ratio of valid area in each feature level.
+ self_attn_mask (`torch.BoolTensor` of shape `(batch_size, text_seq_len)`):
+ Masks to avoid performing self-attention between vision hidden state. Mask values selected in `[0, 1]`:
+ - 1 for queries that are real (i.e. **not masked**),
+ - 0 for queries that are padding (i.e. **masked**).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if inputs_embeds is not None:
+ hidden_states = inputs_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_attns = () if output_attentions else None
+ all_cross_attns_vision = () if (output_attentions and vision_encoder_hidden_states is not None) else None
+ all_cross_attns_text = () if (output_attentions and text_encoder_hidden_states is not None) else None
+ intermediate = ()
+ intermediate_reference_points = ()
+
+ if text_encoder_attention_mask is not None:
+ dtype = text_encoder_hidden_states.dtype
+
+ text_encoder_attention_mask = text_encoder_attention_mask[:, None, None, :]
+ text_encoder_attention_mask = text_encoder_attention_mask.repeat(
+ 1, self.config.decoder_attention_heads, self.config.num_queries, 1
+ )
+ text_encoder_attention_mask = text_encoder_attention_mask.to(dtype=dtype)
+ text_encoder_attention_mask = text_encoder_attention_mask * torch.finfo(dtype).min
+
+ for idx, decoder_layer in enumerate(self.layers):
+ num_coordinates = reference_points.shape[-1]
+ if num_coordinates == 4:
+ reference_points_input = (
+ reference_points[:, :, None] * torch.cat([valid_ratios, valid_ratios], -1)[:, None]
+ )
+ elif num_coordinates == 2:
+ reference_points_input = reference_points[:, :, None] * valid_ratios[:, None]
+ else:
+ raise ValueError("Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")
+ query_pos = get_sine_pos_embed(reference_points_input[:, :, 0, :], num_pos_feats=self.config.d_model // 2)
+ query_pos = self.reference_points_head(query_pos)
+
+ # In original implementation they apply layer norm before outputting intermediate hidden states
+ # Though that's not through between layers so the layers use as input the output of the previous layer
+ # without layer norm
+ if output_hidden_states:
+ all_hidden_states += (self.layer_norm(hidden_states),)
+
+ if self.gradient_checkpointing and self.training:
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ return module(*inputs, output_attentions)
+
+ return custom_forward
+
+ layer_outputs = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(decoder_layer),
+ hidden_states,
+ query_pos,
+ reference_points_input,
+ spatial_shapes,
+ level_start_index,
+ vision_encoder_hidden_states,
+ vision_encoder_attention_mask,
+ text_encoder_hidden_states,
+ text_encoder_attention_mask,
+ self_attn_mask,
+ None,
+ )
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states=hidden_states,
+ position_embeddings=query_pos,
+ reference_points=reference_points_input,
+ spatial_shapes=spatial_shapes,
+ spatial_shapes_list=spatial_shapes_list,
+ level_start_index=level_start_index,
+ vision_encoder_hidden_states=vision_encoder_hidden_states,
+ vision_encoder_attention_mask=vision_encoder_attention_mask,
+ text_encoder_hidden_states=text_encoder_hidden_states,
+ text_encoder_attention_mask=text_encoder_attention_mask,
+ self_attn_mask=self_attn_mask,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ # hack implementation for iterative bounding box refinement
+ if self.bbox_embed is not None:
+ tmp = self.bbox_embed[idx](hidden_states)
+ num_coordinates = reference_points.shape[-1]
+ if num_coordinates == 4:
+ new_reference_points = tmp + torch.special.logit(reference_points, eps=1e-5)
+ new_reference_points = new_reference_points.sigmoid()
+ elif num_coordinates == 2:
+ new_reference_points = tmp
+ new_reference_points[..., :2] = tmp[..., :2] + torch.special.logit(reference_points, eps=1e-5)
+ new_reference_points = new_reference_points.sigmoid()
+ else:
+ raise ValueError(
+ f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}"
+ )
+ reference_points = new_reference_points.detach()
+
+ intermediate += (self.layer_norm(hidden_states),)
+ intermediate_reference_points += (reference_points,)
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if text_encoder_hidden_states is not None:
+ all_cross_attns_text += (layer_outputs[2],)
+
+ if vision_encoder_hidden_states is not None:
+ all_cross_attns_vision += (layer_outputs[3],)
+
+ # Keep batch_size as first dimension
+ intermediate = torch.stack(intermediate, dim=1)
+ intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1)
+ hidden_states = self.layer_norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if output_attentions:
+ all_attns += (all_self_attns, all_cross_attns_text, all_cross_attns_vision)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ hidden_states,
+ intermediate,
+ intermediate_reference_points,
+ all_hidden_states,
+ all_attns,
+ ]
+ if v is not None
+ )
+ return MMGroundingDinoDecoderOutput(
+ last_hidden_state=hidden_states,
+ intermediate_hidden_states=intermediate,
+ intermediate_reference_points=intermediate_reference_points,
+ hidden_states=all_hidden_states,
+ attentions=all_attns,
+ )
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of the Grounding DINO encoder-decoder model.
+ """
+)
+class MMGroundingDinoModelOutput(ModelOutput):
+ r"""
+ last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the decoder of the model.
+ init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
+ Initial reference points sent through the Transformer decoder.
+ intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
+ Stacked intermediate hidden states (output of each layer of the decoder).
+ intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
+ Stacked intermediate reference points (reference points of each layer of the decoder).
+ encoder_last_hidden_state_vision (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder of the model.
+ encoder_last_hidden_state_text (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder of the model.
+ encoder_vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the vision embeddings + one for the output of each
+ layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the vision encoder at the
+ output of each layer plus the initial embedding outputs.
+ encoder_text_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the text embeddings + one for the output of each layer)
+ of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the text encoder at the output of
+ each layer plus the initial embedding outputs.
+ encoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of tuples of `torch.FloatTensor` (one for attention for each layer) of shape `(batch_size, num_heads,
+ sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
+ weighted average in the text-vision attention, vision-text attention, text-enhancer (self-attention) and
+ multi-scale deformable attention heads. attention softmax, used to compute the weighted average in the
+ bi-attention heads.
+ enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
+ Predicted bounding boxes scores where the top `config.num_queries` scoring bounding boxes are picked as
+ region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and
+ background).
+ enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
+ Logits of predicted bounding boxes coordinates in the first stage.
+ encoder_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
+ Logits of top `config.num_queries` scoring bounding boxes in the first stage.
+ encoder_pred_boxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
+ Coordinates of top `config.num_queries` scoring bounding boxes in the first stage.
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ init_reference_points: Optional[torch.FloatTensor] = None
+ intermediate_hidden_states: Optional[torch.FloatTensor] = None
+ intermediate_reference_points: Optional[torch.FloatTensor] = None
+ decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ decoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ encoder_last_hidden_state_vision: Optional[torch.FloatTensor] = None
+ encoder_last_hidden_state_text: Optional[torch.FloatTensor] = None
+ encoder_vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ encoder_text_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ encoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ enc_outputs_class: Optional[torch.FloatTensor] = None
+ enc_outputs_coord_logits: Optional[torch.FloatTensor] = None
+ encoder_logits: Optional[torch.FloatTensor] = None
+ encoder_pred_boxes: Optional[torch.FloatTensor] = None
+
+
+class MMGroundingDinoSinePositionEmbedding(nn.Module):
+ """
+ This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
+ need paper, generalized to work on images.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.embedding_dim = config.d_model // 2
+ self.temperature = config.positional_embedding_temperature
+ self.scale = 2 * math.pi
+
+ def forward(self, pixel_values, pixel_mask):
+ y_embed = pixel_mask.cumsum(1, dtype=torch.float32)
+ x_embed = pixel_mask.cumsum(2, dtype=torch.float32)
+ eps = 1e-6
+ y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+ x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+ dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device)
+ dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim)
+
+ pos_x = x_embed[:, :, :, None] / dim_t
+ pos_y = y_embed[:, :, :, None] / dim_t
+ pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
+ pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
+ pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+ return pos
+
+
+def build_position_encoding(config):
+ if config.position_embedding_type == "sine":
+ position_embedding = MMGroundingDinoSinePositionEmbedding(config)
+ elif config.position_embedding_type == "learned":
+ position_embedding = MMGroundingDinoLearnedPositionEmbedding(config)
+ else:
+ raise ValueError(f"Not supported {config.position_embedding_type}")
+
+ return position_embedding
+
+
+# these correspond to [CLS], [SEP], . and ?
+SPECIAL_TOKENS = [101, 102, 1012, 1029]
+
+
+def generate_masks_with_special_tokens_and_transfer_map(input_ids: torch.LongTensor) -> tuple[Tensor, Tensor]:
+ """Generate attention mask between each pair of special tokens and positional ids.
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+ Returns:
+ `tuple(torch.Tensor)` comprising attention mask between each special tokens and position_ids:
+ - **attention_mask** (`torch.BoolTensor` of shape `(batch_size, sequence_length, sequence_length)`)
+ - **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`)
+ """
+ batch_size, num_token = input_ids.shape
+ # special_tokens_mask: batch_size, num_token. 1 for special tokens. 0 for normal tokens
+ special_tokens_mask = torch.zeros((batch_size, num_token), device=input_ids.device).bool()
+ for special_token in SPECIAL_TOKENS:
+ special_tokens_mask |= input_ids == special_token
+
+ # idxs: each row is a list of indices of special tokens
+ idxs = torch.nonzero(special_tokens_mask)
+
+ # generate attention mask and positional ids
+ attention_mask = torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(batch_size, 1, 1)
+ position_ids = torch.zeros((batch_size, num_token), device=input_ids.device)
+ previous_col = 0
+ for i in range(idxs.shape[0]):
+ row, col = idxs[i]
+ if (col == 0) or (col == num_token - 1):
+ attention_mask[row, col, col] = True
+ position_ids[row, col] = 0
+ else:
+ attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
+ position_ids[row, previous_col + 1 : col + 1] = torch.arange(
+ 0, col - previous_col, device=input_ids.device
+ )
+
+ previous_col = col
+
+ return attention_mask, position_ids.to(torch.long)
+
+
+@auto_docstring(
+ custom_intro="""
+ The bare Grounding DINO Model (consisting of a backbone and encoder-decoder Transformer) outputting raw
+ hidden-states without any specific head on top.
+ """
+)
+class MMGroundingDinoModel(MMGroundingDinoPreTrainedModel):
+ def __init__(self, config: MMGroundingDinoConfig):
+ super().__init__(config)
+
+ # Create backbone + positional encoding
+ backbone = MMGroundingDinoConvEncoder(config)
+ position_embeddings = build_position_encoding(config)
+ self.backbone = MMGroundingDinoConvModel(backbone, position_embeddings)
+
+ # Create input projection layers
+ num_backbone_outs = len(backbone.intermediate_channel_sizes)
+ input_proj_list = []
+ for i in range(num_backbone_outs):
+ in_channels = backbone.intermediate_channel_sizes[i]
+ input_proj_list.append(
+ nn.Sequential(
+ nn.Conv2d(in_channels, config.d_model, kernel_size=1),
+ nn.GroupNorm(32, config.d_model),
+ )
+ )
+ for _ in range(config.num_feature_levels - num_backbone_outs):
+ input_proj_list.append(
+ nn.Sequential(
+ nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1),
+ nn.GroupNorm(32, config.d_model),
+ )
+ )
+ in_channels = config.d_model
+ self.input_proj_vision = nn.ModuleList(input_proj_list)
+
+ # Create text backbone
+ self.text_backbone = AutoModel.from_config(config.text_config, add_pooling_layer=False)
+ self.text_projection = nn.Linear(config.text_config.hidden_size, config.d_model)
+
+ if config.embedding_init_target or not config.two_stage:
+ self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model)
+
+ self.encoder = MMGroundingDinoEncoder(config)
+ self.decoder = MMGroundingDinoDecoder(config)
+
+ self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model))
+
+ self.enc_output = nn.Linear(config.d_model, config.d_model)
+ self.enc_output_norm = nn.LayerNorm(config.d_model, config.layer_norm_eps)
+ self.encoder_output_bbox_embed = MMGroundingDinoMLPPredictionHead(
+ input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
+ )
+ self.encoder_output_class_embed = MMGroundingDinoContrastiveEmbedding(config)
+
+ self.post_init()
+
+ def get_encoder(self):
+ return self.encoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ def freeze_backbone(self):
+ for name, param in self.backbone.conv_encoder.model.named_parameters():
+ param.requires_grad_(False)
+
+ def unfreeze_backbone(self):
+ for name, param in self.backbone.conv_encoder.model.named_parameters():
+ param.requires_grad_(True)
+
+ def get_valid_ratio(self, mask):
+ """Get the valid ratio of all feature maps."""
+
+ _, height, width = mask.shape
+ valid_height = torch.sum(mask[:, :, 0], 1)
+ valid_width = torch.sum(mask[:, 0, :], 1)
+ valid_ratio_height = valid_height.float() / height
+ valid_ratio_width = valid_width.float() / width
+ valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1)
+ return valid_ratio
+
+ def generate_encoder_output_proposals(self, enc_output, padding_mask, spatial_shapes):
+ """Generate the encoder output proposals from encoded enc_output.
+
+ Args:
+ enc_output (`torch.Tensor[batch_size, sequence_length, hidden_size]`): Output of the encoder.
+ padding_mask (`torch.Tensor[batch_size, sequence_length]`): Padding mask for `enc_output`.
+ spatial_shapes (`torch.Tensor[num_feature_levels, 2]`): Spatial shapes of the feature maps.
+
+ Returns:
+ `tuple(torch.FloatTensor)`: A tuple of feature map and bbox prediction.
+ - object_query (Tensor[batch_size, sequence_length, hidden_size]): Object query features. Later used to
+ directly predict a bounding box. (without the need of a decoder)
+ - output_proposals (Tensor[batch_size, sequence_length, 4]): Normalized proposals, after an inverse
+ sigmoid.
+ """
+ batch_size = enc_output.shape[0]
+ proposals = []
+ current_position = 0
+ for level, (height, width) in enumerate(spatial_shapes):
+ mask_flatten_ = padding_mask[:, current_position : (current_position + height * width)]
+ mask_flatten_ = mask_flatten_.view(batch_size, height, width, 1)
+ valid_height = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
+ valid_width = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
+
+ grid_y, grid_x = meshgrid(
+ torch.linspace(0, height - 1, height, dtype=torch.float32, device=enc_output.device),
+ torch.linspace(0, width - 1, width, dtype=torch.float32, device=enc_output.device),
+ indexing="ij",
+ )
+ grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)
+
+ scale = torch.cat([valid_width.unsqueeze(-1), valid_height.unsqueeze(-1)], 1).view(batch_size, 1, 1, 2)
+ grid = (grid.unsqueeze(0).expand(batch_size, -1, -1, -1) + 0.5) / scale
+ width_height = torch.ones_like(grid) * 0.05 * (2.0**level)
+ proposal = torch.cat((grid, width_height), -1).view(batch_size, -1, 4)
+ proposals.append(proposal)
+ current_position += height * width
+
+ output_proposals = torch.cat(proposals, 1)
+ output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)
+ output_proposals = torch.log(output_proposals / (1 - output_proposals)) # inverse sigmoid
+ output_proposals = output_proposals.masked_fill(padding_mask.unsqueeze(-1), float("inf"))
+ output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
+
+ # assign each pixel as an object query
+ object_query = enc_output
+ object_query = object_query.masked_fill(padding_mask.unsqueeze(-1), float(0))
+ object_query = object_query.masked_fill(~output_proposals_valid, float(0))
+ object_query = self.enc_output_norm(self.enc_output(object_query))
+ return object_query, output_proposals
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Tensor,
+ input_ids: Tensor,
+ token_type_ids: Optional[Tensor] = None,
+ attention_mask: Optional[Tensor] = None,
+ pixel_mask: Optional[Tensor] = None,
+ encoder_outputs=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`BertTokenizer.__call__`] for details.
+ token_type_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`: 0 corresponds to a `sentence A` token, 1 corresponds to a `sentence B` token
+
+ [What are token type IDs?](../glossary#token-type-ids)
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoProcessor, AutoModel
+ >>> from PIL import Image
+ >>> import requests
+
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> text = "a cat."
+
+ >>> processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-tiny")
+ >>> model = AutoModel.from_pretrained("IDEA-Research/grounding-dino-tiny")
+
+ >>> inputs = processor(images=image, text=text, return_tensors="pt")
+ >>> outputs = model(**inputs)
+
+ >>> last_hidden_states = outputs.last_hidden_state
+ >>> list(last_hidden_states.shape)
+ [1, 900, 256]
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ text_self_attention_masks, position_ids = generate_masks_with_special_tokens_and_transfer_map(input_ids)
+
+ if attention_mask is None:
+ attention_mask = torch.ones_like(input_ids)
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros_like(input_ids)
+
+ text_token_mask = attention_mask.bool() # just to avoid renaming everywhere
+
+ max_text_len = self.config.max_text_len
+ if text_self_attention_masks.shape[1] > max_text_len:
+ text_self_attention_masks = text_self_attention_masks[:, :max_text_len, :max_text_len]
+ position_ids = position_ids[:, :max_text_len]
+ input_ids = input_ids[:, :max_text_len]
+ token_type_ids = token_type_ids[:, :max_text_len]
+ text_token_mask = text_token_mask[:, :max_text_len]
+
+ # Extract text features from text backbone
+ text_outputs = self.text_backbone(
+ input_ids, text_self_attention_masks, token_type_ids, position_ids, return_dict=return_dict
+ )
+ text_features = text_outputs.last_hidden_state if return_dict else text_outputs[0]
+ text_features = self.text_projection(text_features)
+
+ batch_size, num_channels, height, width = pixel_values.shape
+ device = pixel_values.device
+
+ if pixel_mask is None:
+ pixel_mask = torch.ones(((batch_size, height, width)), dtype=torch.long, device=device)
+
+ # Extract multi-scale feature maps of same resolution `config.d_model` (cf Figure 4 in paper)
+ # First, sent pixel_values + pixel_mask through Backbone to obtain the features
+ # which is a list of tuples
+ vision_features, position_embeddings_list = self.backbone(pixel_values, pixel_mask)
+
+ # Then, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
+ feature_maps = []
+ masks = []
+ for level, (source, mask) in enumerate(vision_features):
+ feature_maps.append(self.input_proj_vision[level](source))
+ masks.append(mask)
+
+ # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage
+ if self.config.num_feature_levels > len(feature_maps):
+ _len_sources = len(feature_maps)
+ for level in range(_len_sources, self.config.num_feature_levels):
+ if level == _len_sources:
+ source = self.input_proj_vision[level](vision_features[-1][0])
+ else:
+ source = self.input_proj_vision[level](feature_maps[-1])
+ mask = nn.functional.interpolate(pixel_mask[None].float(), size=source.shape[-2:]).to(torch.bool)[0]
+ pos_l = self.backbone.position_embedding(source, mask).to(source.dtype)
+ feature_maps.append(source)
+ masks.append(mask)
+ position_embeddings_list.append(pos_l)
+
+ # Create queries
+ query_embeds = None
+ if self.config.embedding_init_target or self.config.two_stage:
+ query_embeds = self.query_position_embeddings.weight
+
+ # Prepare encoder inputs (by flattening)
+ source_flatten = []
+ mask_flatten = []
+ lvl_pos_embed_flatten = []
+ spatial_shapes_list = []
+ for level, (source, mask, pos_embed) in enumerate(zip(feature_maps, masks, position_embeddings_list)):
+ batch_size, num_channels, height, width = source.shape
+ spatial_shape = (height, width)
+ spatial_shapes_list.append(spatial_shape)
+ source = source.flatten(2).transpose(1, 2)
+ mask = mask.flatten(1)
+ pos_embed = pos_embed.flatten(2).transpose(1, 2)
+ lvl_pos_embed = pos_embed + self.level_embed[level].view(1, 1, -1)
+ lvl_pos_embed_flatten.append(lvl_pos_embed)
+ source_flatten.append(source)
+ mask_flatten.append(mask)
+ source_flatten = torch.cat(source_flatten, 1)
+ mask_flatten = torch.cat(mask_flatten, 1)
+ lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
+ spatial_shapes = torch.as_tensor(spatial_shapes_list, dtype=torch.long, device=source_flatten.device)
+ level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
+ valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
+ valid_ratios = valid_ratios.float()
+
+ # Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder
+ # Also provide spatial_shapes, level_start_index and valid_ratios
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ vision_features=source_flatten,
+ vision_attention_mask=~mask_flatten,
+ vision_position_embedding=lvl_pos_embed_flatten,
+ spatial_shapes=spatial_shapes,
+ spatial_shapes_list=spatial_shapes_list,
+ level_start_index=level_start_index,
+ valid_ratios=valid_ratios,
+ text_features=text_features,
+ text_attention_mask=~text_token_mask,
+ text_position_embedding=None,
+ text_self_attention_masks=~text_self_attention_masks,
+ text_position_ids=position_ids,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ # If the user passed a tuple for encoder_outputs, we wrap it in a MMGroundingDinoEncoderOutput when return_dict=True
+ elif return_dict and not isinstance(encoder_outputs, MMGroundingDinoEncoderOutput):
+ encoder_outputs = MMGroundingDinoEncoderOutput(
+ last_hidden_state_vision=encoder_outputs[0],
+ last_hidden_state_text=encoder_outputs[1],
+ vision_hidden_states=encoder_outputs[2] if output_hidden_states else None,
+ text_hidden_states=encoder_outputs[3] if output_hidden_states else None,
+ attentions=encoder_outputs[-1] if output_attentions else None,
+ )
+
+ # Fifth, prepare decoder inputs
+ topk_proposals = None
+ enc_outputs_class = None
+ enc_outputs_coord_logits = None
+ encoder_logits = None
+ encoder_pred_boxes = None
+ if self.config.two_stage:
+ object_query_embedding, output_proposals = self.generate_encoder_output_proposals(
+ encoder_outputs[0], ~mask_flatten, spatial_shapes
+ )
+
+ # hack implementation as in two-stage Deformable DETR
+ # apply a detection head to each pixel (A.4 in paper)
+ # linear projection for bounding box binary classification (i.e. foreground and background)
+ enc_outputs_class = self.encoder_output_class_embed(
+ object_query_embedding, encoder_outputs[1], text_token_mask
+ )
+ # 3-layer FFN to predict bounding boxes coordinates (bbox regression branch)
+ delta_bbox = self.encoder_output_bbox_embed(object_query_embedding)
+ enc_outputs_coord_logits = delta_bbox + output_proposals
+
+ # only keep top scoring `config.num_queries` proposals
+ topk = self.config.num_queries
+ topk_logits = enc_outputs_class.max(-1)[0]
+ topk_proposals = torch.topk(topk_logits, topk, dim=1)[1]
+ topk_coords_logits = torch.gather(
+ enc_outputs_coord_logits, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
+ )
+
+ topk_coords_logits = topk_coords_logits.detach()
+ reference_points = topk_coords_logits.sigmoid()
+ init_reference_points = reference_points
+ if query_embeds is not None:
+ target = query_embeds.unsqueeze(0).repeat(batch_size, 1, 1)
+ else:
+ target = torch.gather(
+ object_query_embedding, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
+ ).detach()
+
+ # Set intermediate topk proposals (coords and class) for loss computation
+ encoder_pred_boxes = reference_points
+ encoder_logits = self.encoder_output_class_embed(target, text_features, text_token_mask)
+ else:
+ target = query_embeds.unsqueeze(0).repeat(batch_size, 1, 1)
+ reference_points = self.reference_points.weight.unsqueeze(0).repeat(batch_size, 1, 1).sigmoid()
+ init_reference_points = reference_points
+
+ decoder_outputs = self.decoder(
+ inputs_embeds=target,
+ vision_encoder_hidden_states=encoder_outputs[0],
+ vision_encoder_attention_mask=mask_flatten,
+ text_encoder_hidden_states=encoder_outputs[1],
+ text_encoder_attention_mask=~text_token_mask,
+ reference_points=reference_points,
+ spatial_shapes=spatial_shapes,
+ spatial_shapes_list=spatial_shapes_list,
+ level_start_index=level_start_index,
+ valid_ratios=valid_ratios,
+ self_attn_mask=None,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ if not return_dict:
+ enc_outputs = tuple(
+ value
+ for value in [
+ enc_outputs_class,
+ enc_outputs_coord_logits,
+ encoder_logits,
+ encoder_pred_boxes,
+ ]
+ if value is not None
+ )
+ tuple_outputs = (
+ (decoder_outputs[0], init_reference_points) + decoder_outputs[1:] + encoder_outputs + enc_outputs
+ )
+
+ return tuple_outputs
+
+ return MMGroundingDinoModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ init_reference_points=init_reference_points,
+ intermediate_hidden_states=decoder_outputs.intermediate_hidden_states,
+ intermediate_reference_points=decoder_outputs.intermediate_reference_points,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ encoder_last_hidden_state_vision=encoder_outputs.last_hidden_state_vision,
+ encoder_last_hidden_state_text=encoder_outputs.last_hidden_state_text,
+ encoder_vision_hidden_states=encoder_outputs.vision_hidden_states,
+ encoder_text_hidden_states=encoder_outputs.text_hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ enc_outputs_class=enc_outputs_class,
+ enc_outputs_coord_logits=enc_outputs_coord_logits,
+ encoder_logits=encoder_logits,
+ encoder_pred_boxes=encoder_pred_boxes,
+ )
+
+
+class MMGroundingDinoMLPPredictionHead(nn.Module):
+ """
+ Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
+ height and width of a bounding box w.r.t. an image.
+
+ Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py
+
+ """
+
+ def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+ super().__init__()
+ self.num_layers = num_layers
+ h = [hidden_dim] * (num_layers - 1)
+ self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+
+ def forward(self, x):
+ for i, layer in enumerate(self.layers):
+ x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+ return x
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Output type of [`MMGroundingDinoForObjectDetection`].
+ """
+)
+class MMGroundingDinoObjectDetectionOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
+ Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
+ bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
+ scale-invariant IoU loss.
+ loss_dict (`Dict`, *optional*):
+ A dictionary containing the individual losses. Useful for logging.
+ logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`):
+ Classification logits (including no-object) for all queries.
+ pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
+ Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
+ values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
+ possible padding). You can use [`~MMGroundingDinoProcessor.post_process_grounded_object_detection`] to retrieve the
+ unnormalized bounding boxes.
+ auxiliary_outputs (`list[Dict]`, *optional*):
+ Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`)
+ and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and
+ `pred_boxes`) for each decoder layer.
+ last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the decoder of the model.
+ init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
+ Initial reference points sent through the Transformer decoder.
+ intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
+ Stacked intermediate hidden states (output of each layer of the decoder).
+ intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`):
+ Stacked intermediate reference points (reference points of each layer of the decoder).
+ encoder_last_hidden_state_vision (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder of the model.
+ encoder_last_hidden_state_text (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder of the model.
+ encoder_vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the vision embeddings + one for the output of each
+ layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the vision encoder at the
+ output of each layer plus the initial embedding outputs.
+ encoder_text_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the text embeddings + one for the output of each layer)
+ of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the text encoder at the output of
+ each layer plus the initial embedding outputs.
+ enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
+ Predicted bounding boxes scores where the top `config.num_queries` scoring bounding boxes are picked as
+ region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and
+ background).
+ enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
+ Logits of predicted bounding boxes coordinates in the first stage.
+ encoder_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.two_stage=True`):
+ Logits of top `config.num_queries` scoring bounding boxes in the first stage.
+ encoder_pred_boxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.two_stage=True`):
+ Coordinates of top `config.num_queries` scoring bounding boxes in the first stage.
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Encoded candidate labels sequence. Used in processor to post process object detection result.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ loss_dict: Optional[dict] = None
+ logits: Optional[torch.FloatTensor] = None
+ pred_boxes: Optional[torch.FloatTensor] = None
+ auxiliary_outputs: Optional[list[dict]] = None
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ init_reference_points: Optional[torch.FloatTensor] = None
+ intermediate_hidden_states: Optional[torch.FloatTensor] = None
+ intermediate_reference_points: Optional[torch.FloatTensor] = None
+ decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ decoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ encoder_last_hidden_state_vision: Optional[torch.FloatTensor] = None
+ encoder_last_hidden_state_text: Optional[torch.FloatTensor] = None
+ encoder_vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ encoder_text_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ encoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ enc_outputs_class: Optional[torch.FloatTensor] = None
+ enc_outputs_coord_logits: Optional[torch.FloatTensor] = None
+ encoder_logits: Optional[torch.FloatTensor] = None
+ encoder_pred_boxes: Optional[torch.FloatTensor] = None
+ input_ids: Optional[torch.LongTensor] = None
+
+
+def build_label_maps(logits: torch.FloatTensor, input_ids: torch.LongTensor) -> tuple[torch.FloatTensor]:
+ """
+ Computes a mapping between tokens and their corresponding labels, where `num_labels` is determined by the number of classes in the input prompt.
+ The function identifies segments of tokens between specific delimiter tokens and generates label maps for those segments.
+ Args:
+ logits (`torch.Tensor` of shape `(batch_size, seq_length, hidden_size)`):
+ The output logits from the model, where `hidden_size` corresponds to the dimension of the model's output features.
+
+ input_ids (`torch.Tensor` of shape `(batch_size, seq_length)`):
+ The input token IDs corresponding to the input prompt. For example, given the prompt "fish. shark.",
+ `input_ids` might look like `[101, 3869, 1012, 11420, 1012, 102]` where each number corresponds to a token including special tokens.
+ Returns:
+ tuple: A tuple containing label maps for each instance in the batch.
+ - label_maps (tuple of `torch.Tensor`):
+ A tuple of tensors, where each tensor in the tuple corresponds to an instance in the batch. Each tensor
+ has shape `(num_labels, hidden_size)` and contains binary values (0 or 1), where `1` indicates the tokens
+ that are associated with a specific label (class) between delimiter tokens, and `0` elsewhere.
+ Example:
+ Given an input prompt "fish. shark." and corresponding `input_ids` as `[101, 3869, 1012, 11420, 1012, 102]`:
+ - The function identifies the tokens for "fish" (IDs `[3869]`) and "shark" (IDs `[11420]`).
+ - The function then constructs label maps for these tokens, where each label map indicates which tokens
+ correspond to which label between the delimiter tokens (e.g., between the period `.`).
+ - The output is a tuple of label maps, one for each instance in the batch.
+ Note:
+ - `SPECIAL_TOKENS` should be a predefined list of tokens that are considered special (e.g., `[CLS]`, `[SEP]`, etc.).
+ """
+ max_seq_len = logits.shape[-1]
+ # Add [PAD] token to the list of special tokens
+ delimiter_tokens = torch.tensor(SPECIAL_TOKENS + [0], device=input_ids.device)
+
+ delimiter_token_masks = torch.isin(input_ids, delimiter_tokens)
+ label_groups = torch.cumsum(delimiter_token_masks, dim=1) * (~delimiter_token_masks).to(torch.int32)
+
+ label_maps = ()
+
+ # Iterate over batch dimension as we can have different number of labels
+ for label_group in label_groups:
+ # `label_group` is a tensor of shape `(seq_len,)` with zeros for non-label tokens and integers for label tokens
+ # label tokens with same integer value are part of the same label group
+
+ # Get unique labels and exclude 0 (i.e. non-label tokens)
+ unique_labels = torch.unique(label_group)[1:, None]
+ num_labels = unique_labels.shape[0]
+
+ # Create one-hot encoding for each label group
+ label_map = label_group.unsqueeze(0).repeat(num_labels, 1)
+ label_map = torch.where(label_map == unique_labels, 1, 0)
+
+ # Pad label_map to match `max_seq_len`
+ label_map = F.pad(label_map, (0, max_seq_len - label_map.shape[1]), value=0)
+
+ label_maps += (label_map,)
+
+ return label_maps
+
+
+def build_text_mask(logits, attention_mask):
+ """
+ Create text_mask based on the matching indices
+ """
+ seq_len = attention_mask.shape[1]
+ text_mask = torch.zeros_like(logits, device=logits.device, dtype=attention_mask.dtype)
+ text_mask[:, :, :seq_len] = attention_mask[:, None, :]
+
+ return text_mask.bool()
+
+
+@auto_docstring(
+ custom_intro="""
+ Grounding DINO Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on top,
+ for tasks such as COCO detection.
+ """
+)
+class MMGroundingDinoForObjectDetection(MMGroundingDinoPreTrainedModel):
+ _tied_weights_keys = [
+ r"bbox_embed\.[1-9]\d*",
+ r"model\.decoder\.bbox_embed\.[0-9]\d*",
+ r"class_embed\.[1-9]\d*",
+ r"model\.decoder\.class_embed\.[0-9]\d*",
+ ]
+
+ def __init__(self, config: MMGroundingDinoConfig):
+ super().__init__(config)
+
+ self.model = MMGroundingDinoModel(config)
+
+ self.class_embed = nn.ModuleList(
+ [MMGroundingDinoContrastiveEmbedding(config) for _ in range(config.decoder_layers)]
+ )
+
+ self.bbox_embed = nn.ModuleList(
+ [
+ MMGroundingDinoMLPPredictionHead(
+ input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
+ )
+ for _ in range(config.decoder_layers)
+ ]
+ )
+
+ # hack for box-refinement
+ self.model.decoder.bbox_embed = self.bbox_embed
+ # hack implementation for two-stage
+ self.model.decoder.class_embed = self.class_embed
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: torch.FloatTensor,
+ input_ids: torch.LongTensor,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ pixel_mask: Optional[torch.BoolTensor] = None,
+ encoder_outputs: Optional[Union[MMGroundingDinoEncoderOutput, tuple]] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ labels: Optional[list[dict[str, Union[torch.LongTensor, torch.FloatTensor]]]] = None,
+ ):
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`BertTokenizer.__call__`] for details.
+ token_type_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`: 0 corresponds to a `sentence A` token, 1 corresponds to a `sentence B` token
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ labels (`list[Dict]` of len `(batch_size,)`, *optional*):
+ Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
+ following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch
+ respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes
+ in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`.
+
+ Examples:
+
+ ```python
+ >>> import requests
+
+ >>> import torch
+ >>> from PIL import Image
+ >>> from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
+
+ >>> model_id = "IDEA-Research/grounding-dino-tiny"
+ >>> device = "cuda"
+
+ >>> processor = AutoProcessor.from_pretrained(model_id)
+ >>> model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
+
+ >>> image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(image_url, stream=True).raw)
+ >>> # Check for cats and remote controls
+ >>> text_labels = [["a cat", "a remote control"]]
+
+ >>> inputs = processor(images=image, text=text_labels, return_tensors="pt").to(device)
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+
+ >>> results = processor.post_process_grounded_object_detection(
+ ... outputs,
+ ... threshold=0.4,
+ ... text_threshold=0.3,
+ ... target_sizes=[(image.height, image.width)]
+ ... )
+ >>> # Retrieve the first image result
+ >>> result = results[0]
+ >>> for box, score, text_label in zip(result["boxes"], result["scores"], result["text_labels"]):
+ ... box = [round(x, 2) for x in box.tolist()]
+ ... print(f"Detected {text_label} with confidence {round(score.item(), 3)} at location {box}")
+ Detected a cat with confidence 0.479 at location [344.7, 23.11, 637.18, 374.28]
+ Detected a cat with confidence 0.438 at location [12.27, 51.91, 316.86, 472.44]
+ Detected a remote control with confidence 0.478 at location [38.57, 70.0, 176.78, 118.18]
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if attention_mask is None:
+ attention_mask = torch.ones_like(input_ids)
+
+ # First, sent images through Grounding DINO base model to obtain encoder + decoder outputs
+ outputs = self.model(
+ pixel_values=pixel_values,
+ input_ids=input_ids,
+ token_type_ids=token_type_ids,
+ attention_mask=attention_mask,
+ pixel_mask=pixel_mask,
+ encoder_outputs=encoder_outputs,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ idx = 5 + (1 if output_attentions else 0) + (1 if output_hidden_states else 0)
+ enc_text_hidden_state = outputs.encoder_last_hidden_state_text if return_dict else outputs[idx]
+ hidden_states = outputs.intermediate_hidden_states if return_dict else outputs[2]
+ init_reference_points = outputs.init_reference_points if return_dict else outputs[1]
+ inter_references_points = outputs.intermediate_reference_points if return_dict else outputs[3]
+
+ # class logits + predicted bounding boxes
+ outputs_classes = []
+ outputs_coords = []
+
+ # hidden_states are of shape (batch_size, num_stages, height, width)
+ # predict class and bounding box deltas for each stage
+ num_levels = hidden_states.shape[1]
+ for level in range(num_levels):
+ if level == 0:
+ reference = init_reference_points
+ else:
+ reference = inter_references_points[:, level - 1]
+ reference = torch.special.logit(reference, eps=1e-5)
+ outputs_class = self.class_embed[level](
+ vision_hidden_state=hidden_states[:, level],
+ text_hidden_state=enc_text_hidden_state,
+ text_token_mask=attention_mask.bool(),
+ )
+ delta_bbox = self.bbox_embed[level](hidden_states[:, level])
+
+ reference_coordinates = reference.shape[-1]
+ if reference_coordinates == 4:
+ outputs_coord_logits = delta_bbox + reference
+ elif reference_coordinates == 2:
+ delta_bbox[..., :2] += reference
+ outputs_coord_logits = delta_bbox
+ else:
+ raise ValueError(f"reference.shape[-1] should be 4 or 2, but got {reference.shape[-1]}")
+ outputs_coord = outputs_coord_logits.sigmoid()
+ outputs_classes.append(outputs_class)
+ outputs_coords.append(outputs_coord)
+ outputs_class = torch.stack(outputs_classes)
+ outputs_coord = torch.stack(outputs_coords)
+
+ logits = outputs_class[-1]
+ pred_boxes = outputs_coord[-1]
+
+ loss, loss_dict, auxiliary_outputs = None, None, None
+ if labels is not None:
+ label_maps = build_label_maps(logits, input_ids)
+ text_mask = build_text_mask(logits, attention_mask)
+ loss, loss_dict, auxiliary_outputs = self.loss_function(
+ logits,
+ labels,
+ self.device,
+ pred_boxes,
+ self.config,
+ label_maps,
+ text_mask,
+ outputs_class=outputs_class,
+ outputs_coord=outputs_coord,
+ encoder_logits=outputs[-2],
+ encoder_pred_boxes=outputs[-1],
+ )
+
+ if not return_dict:
+ auxiliary_outputs = auxiliary_outputs if auxiliary_outputs is not None else []
+ output = [loss, loss_dict, logits, pred_boxes, *auxiliary_outputs, *outputs, input_ids]
+ output = tuple(out for out in output if out is not None)
+ return output
+
+ dict_outputs = MMGroundingDinoObjectDetectionOutput(
+ loss=loss,
+ loss_dict=loss_dict,
+ logits=logits,
+ pred_boxes=pred_boxes,
+ last_hidden_state=outputs.last_hidden_state,
+ auxiliary_outputs=auxiliary_outputs,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ encoder_last_hidden_state_vision=outputs.encoder_last_hidden_state_vision,
+ encoder_last_hidden_state_text=outputs.encoder_last_hidden_state_text,
+ encoder_vision_hidden_states=outputs.encoder_vision_hidden_states,
+ encoder_text_hidden_states=outputs.encoder_text_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ intermediate_hidden_states=outputs.intermediate_hidden_states,
+ intermediate_reference_points=outputs.intermediate_reference_points,
+ init_reference_points=outputs.init_reference_points,
+ enc_outputs_class=outputs.enc_outputs_class,
+ enc_outputs_coord_logits=outputs.enc_outputs_coord_logits,
+ encoder_logits=outputs.encoder_logits,
+ encoder_pred_boxes=outputs.encoder_pred_boxes,
+ input_ids=input_ids,
+ )
+
+ return dict_outputs
+
+
+__all__ = ["MMGroundingDinoForObjectDetection", "MMGroundingDinoModel", "MMGroundingDinoPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/modular_mm_grounding_dino.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/modular_mm_grounding_dino.py
new file mode 100644
index 0000000000000000000000000000000000000000..aea644fdd656f46c48dd5fd2102b28e381037f5d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mm_grounding_dino/modular_mm_grounding_dino.py
@@ -0,0 +1,434 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+
+import torch
+from torch import nn
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
+from ..auto import CONFIG_MAPPING
+from ..auto.modeling_auto import AutoModel
+from ..grounding_dino.configuration_grounding_dino import GroundingDinoConfig
+from ..grounding_dino.modeling_grounding_dino import (
+ GroundingDinoContrastiveEmbedding,
+ GroundingDinoConvEncoder,
+ GroundingDinoConvModel,
+ GroundingDinoDecoder,
+ GroundingDinoEncoder,
+ GroundingDinoForObjectDetection,
+ GroundingDinoMLPPredictionHead,
+ GroundingDinoModel,
+ GroundingDinoPreTrainedModel,
+ build_position_encoding,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+class MMGroundingDinoConfig(GroundingDinoConfig, PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MMGroundingDinoModel`]. It is used to instantiate a
+ MM Grounding DINO model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the MM Grounding DINO tiny architecture
+ [openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det](https://huggingface.co/openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`):
+ The configuration of the backbone model.
+ backbone (`str`, *optional*):
+ Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
+ will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
+ is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
+ use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
+ Whether to use pretrained weights for the backbone.
+ use_timm_backbone (`bool`, *optional*, defaults to `False`):
+ Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
+ library.
+ backbone_kwargs (`dict`, *optional*):
+ Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
+ e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
+ text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `BertConfig`):
+ The config object or dictionary of the text backbone.
+ num_queries (`int`, *optional*, defaults to 900):
+ Number of object queries, i.e. detection slots. This is the maximal number of objects
+ [`MMGroundingDinoModel`] can detect in a single image.
+ encoder_layers (`int`, *optional*, defaults to 6):
+ Number of encoder layers.
+ encoder_ffn_dim (`int`, *optional*, defaults to 2048):
+ Dimension of the "intermediate" (often named feed-forward) layer in decoder.
+ encoder_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ decoder_layers (`int`, *optional*, defaults to 6):
+ Number of decoder layers.
+ decoder_ffn_dim (`int`, *optional*, defaults to 2048):
+ Dimension of the "intermediate" (often named feed-forward) layer in decoder.
+ decoder_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ is_encoder_decoder (`bool`, *optional*, defaults to `True`):
+ Whether the model is used as an encoder/decoder or not.
+ activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ d_model (`int`, *optional*, defaults to 256):
+ Dimension of the layers.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ auxiliary_loss (`bool`, *optional*, defaults to `False`):
+ Whether auxiliary decoding losses (loss at each decoder layer) are to be used.
+ position_embedding_type (`str`, *optional*, defaults to `"sine"`):
+ Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`.
+ num_feature_levels (`int`, *optional*, defaults to 4):
+ The number of input feature levels.
+ encoder_n_points (`int`, *optional*, defaults to 4):
+ The number of sampled keys in each feature level for each attention head in the encoder.
+ decoder_n_points (`int`, *optional*, defaults to 4):
+ The number of sampled keys in each feature level for each attention head in the decoder.
+ two_stage (`bool`, *optional*, defaults to `True`):
+ Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of
+ Grounding DINO, which are further fed into the decoder for iterative bounding box refinement.
+ class_cost (`float`, *optional*, defaults to 1.0):
+ Relative weight of the classification error in the Hungarian matching cost.
+ bbox_cost (`float`, *optional*, defaults to 5.0):
+ Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.
+ giou_cost (`float`, *optional*, defaults to 2.0):
+ Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.
+ bbox_loss_coefficient (`float`, *optional*, defaults to 5.0):
+ Relative weight of the L1 bounding box loss in the object detection loss.
+ giou_loss_coefficient (`float`, *optional*, defaults to 2.0):
+ Relative weight of the generalized IoU loss in the object detection loss.
+ focal_alpha (`float`, *optional*, defaults to 0.25):
+ Alpha parameter in the focal loss.
+ disable_custom_kernels (`bool`, *optional*, defaults to `False`):
+ Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom
+ kernels are not supported by PyTorch ONNX export.
+ max_text_len (`int`, *optional*, defaults to 256):
+ The maximum length of the text input.
+ text_enhancer_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the text enhancer.
+ fusion_droppath (`float`, *optional*, defaults to 0.1):
+ The droppath ratio for the fusion module.
+ fusion_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the fusion module.
+ embedding_init_target (`bool`, *optional*, defaults to `True`):
+ Whether to initialize the target with Embedding weights.
+ query_dim (`int`, *optional*, defaults to 4):
+ The dimension of the query vector.
+ positional_embedding_temperature (`float`, *optional*, defaults to 20):
+ The temperature for Sine Positional Embedding that is used together with vision backbone.
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the layer normalization layers.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MMGroundingDinoConfig, MMGroundingDinoModel
+
+ >>> # Initializing a MM Grounding DINO configuration
+ >>> configuration = MMGroundingDinoConfig()
+
+ >>> # Initializing a model (with random weights) from the configuration
+ >>> model = MMGroundingDinoModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mm-grounding-dino"
+
+ def __init__(
+ self,
+ backbone_config=None,
+ backbone=None,
+ use_pretrained_backbone=False,
+ use_timm_backbone=False,
+ backbone_kwargs=None,
+ text_config=None,
+ num_queries=900,
+ encoder_layers=6,
+ encoder_ffn_dim=2048,
+ encoder_attention_heads=8,
+ decoder_layers=6,
+ decoder_ffn_dim=2048,
+ decoder_attention_heads=8,
+ is_encoder_decoder=True,
+ activation_function="relu",
+ d_model=256,
+ dropout=0.1,
+ attention_dropout=0.0,
+ activation_dropout=0.0,
+ auxiliary_loss=False,
+ position_embedding_type="sine",
+ num_feature_levels=4,
+ encoder_n_points=4,
+ decoder_n_points=4,
+ two_stage=True,
+ class_cost=1.0,
+ bbox_cost=5.0,
+ giou_cost=2.0,
+ bbox_loss_coefficient=5.0,
+ giou_loss_coefficient=2.0,
+ focal_alpha=0.25,
+ disable_custom_kernels=False,
+ # other parameters
+ max_text_len=256,
+ text_enhancer_dropout=0.0,
+ fusion_droppath=0.1,
+ fusion_dropout=0.0,
+ embedding_init_target=True,
+ query_dim=4,
+ positional_embedding_temperature=20,
+ init_std=0.02,
+ layer_norm_eps=1e-5,
+ **kwargs,
+ ):
+ PretrainedConfig.__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
+ if backbone_config is None and backbone is None:
+ logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
+ backbone_config = CONFIG_MAPPING["swin"](
+ window_size=7,
+ image_size=224,
+ embed_dim=96,
+ depths=[2, 2, 6, 2],
+ num_heads=[3, 6, 12, 24],
+ out_indices=[2, 3, 4],
+ )
+ elif isinstance(backbone_config, dict):
+ backbone_model_type = backbone_config.pop("model_type")
+ config_class = CONFIG_MAPPING[backbone_model_type]
+ backbone_config = config_class.from_dict(backbone_config)
+
+ verify_backbone_config_arguments(
+ use_timm_backbone=use_timm_backbone,
+ use_pretrained_backbone=use_pretrained_backbone,
+ backbone=backbone,
+ backbone_config=backbone_config,
+ backbone_kwargs=backbone_kwargs,
+ )
+
+ if text_config is None:
+ text_config = {}
+ logger.info("text_config is None. Initializing the text config with default values (`BertConfig`).")
+
+ self.backbone_config = backbone_config
+ self.backbone = backbone
+ self.use_pretrained_backbone = use_pretrained_backbone
+ self.use_timm_backbone = use_timm_backbone
+ self.backbone_kwargs = backbone_kwargs
+ self.num_queries = num_queries
+ self.d_model = d_model
+ self.encoder_ffn_dim = encoder_ffn_dim
+ self.encoder_layers = encoder_layers
+ self.encoder_attention_heads = encoder_attention_heads
+ self.decoder_ffn_dim = decoder_ffn_dim
+ self.decoder_layers = decoder_layers
+ self.decoder_attention_heads = decoder_attention_heads
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.activation_function = activation_function
+ self.auxiliary_loss = auxiliary_loss
+ self.position_embedding_type = position_embedding_type
+ # deformable attributes
+ self.num_feature_levels = num_feature_levels
+ self.encoder_n_points = encoder_n_points
+ self.decoder_n_points = decoder_n_points
+ self.two_stage = two_stage
+ # Hungarian matcher
+ self.class_cost = class_cost
+ self.bbox_cost = bbox_cost
+ self.giou_cost = giou_cost
+ # Loss coefficients
+ self.bbox_loss_coefficient = bbox_loss_coefficient
+ self.giou_loss_coefficient = giou_loss_coefficient
+ self.focal_alpha = focal_alpha
+ self.disable_custom_kernels = disable_custom_kernels
+ # Text backbone
+ if isinstance(text_config, dict):
+ text_config["model_type"] = text_config.get("model_type", "bert")
+ text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
+ elif text_config is None:
+ text_config = CONFIG_MAPPING["bert"]()
+
+ self.text_config = text_config
+ self.max_text_len = max_text_len
+
+ # Text Enhancer
+ self.text_enhancer_dropout = text_enhancer_dropout
+ # Fusion
+ self.fusion_droppath = fusion_droppath
+ self.fusion_dropout = fusion_dropout
+ # Others
+ self.embedding_init_target = embedding_init_target
+ self.query_dim = query_dim
+ self.positional_embedding_temperature = positional_embedding_temperature
+ self.init_std = init_std
+ self.layer_norm_eps = layer_norm_eps
+
+
+class MMGroundingDinoContrastiveEmbedding(GroundingDinoContrastiveEmbedding):
+ def __init__(self, config):
+ super().__init__(config)
+ self.bias = nn.Parameter(torch.tensor(0.0))
+
+ def forward(
+ self,
+ vision_hidden_state: torch.FloatTensor,
+ text_hidden_state: torch.FloatTensor,
+ text_token_mask: torch.BoolTensor,
+ ) -> torch.FloatTensor:
+ res = vision_hidden_state @ text_hidden_state.transpose(-1, -2)
+ res = res / math.sqrt(vision_hidden_state.shape[-1])
+ res = res + self.bias
+ res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
+
+ # padding to max_text_len
+ new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
+ new_res[..., : res.shape[-1]] = res
+
+ return new_res
+
+
+class MMGroundingDinoPreTrainedModel(GroundingDinoPreTrainedModel):
+ def _init_weights(self, module):
+ super()._init_weights(module)
+ if isinstance(module, MMGroundingDinoContrastiveEmbedding):
+ nn.init.constant_(module.bias, -math.log((1 - 0.01) / 0.01))
+
+
+class MMGroundingDinoConvEncoder(GroundingDinoConvEncoder):
+ pass
+
+
+class MMGroundingDinoConvModel(GroundingDinoConvModel):
+ pass
+
+
+class MMGroundingDinoEncoder(GroundingDinoEncoder):
+ pass
+
+
+class MMGroundingDinoDecoder(GroundingDinoDecoder):
+ pass
+
+
+class MMGroundingDinoModel(GroundingDinoModel, MMGroundingDinoPreTrainedModel):
+ def __init__(self, config: MMGroundingDinoConfig):
+ MMGroundingDinoPreTrainedModel.__init__(config)
+
+ # Create backbone + positional encoding
+ backbone = MMGroundingDinoConvEncoder(config)
+ position_embeddings = build_position_encoding(config)
+ self.backbone = MMGroundingDinoConvModel(backbone, position_embeddings)
+
+ # Create input projection layers
+ num_backbone_outs = len(backbone.intermediate_channel_sizes)
+ input_proj_list = []
+ for i in range(num_backbone_outs):
+ in_channels = backbone.intermediate_channel_sizes[i]
+ input_proj_list.append(
+ nn.Sequential(
+ nn.Conv2d(in_channels, config.d_model, kernel_size=1),
+ nn.GroupNorm(32, config.d_model),
+ )
+ )
+ for _ in range(config.num_feature_levels - num_backbone_outs):
+ input_proj_list.append(
+ nn.Sequential(
+ nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1),
+ nn.GroupNorm(32, config.d_model),
+ )
+ )
+ in_channels = config.d_model
+ self.input_proj_vision = nn.ModuleList(input_proj_list)
+
+ # Create text backbone
+ self.text_backbone = AutoModel.from_config(config.text_config, add_pooling_layer=False)
+ self.text_projection = nn.Linear(config.text_config.hidden_size, config.d_model)
+
+ if config.embedding_init_target or not config.two_stage:
+ self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model)
+
+ self.encoder = MMGroundingDinoEncoder(config)
+ self.decoder = MMGroundingDinoDecoder(config)
+
+ self.level_embed = nn.Parameter(torch.Tensor(config.num_feature_levels, config.d_model))
+
+ self.enc_output = nn.Linear(config.d_model, config.d_model)
+ self.enc_output_norm = nn.LayerNorm(config.d_model, config.layer_norm_eps)
+ self.encoder_output_bbox_embed = MMGroundingDinoMLPPredictionHead(
+ input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
+ )
+ self.encoder_output_class_embed = MMGroundingDinoContrastiveEmbedding(config)
+
+ self.post_init()
+
+
+class MMGroundingDinoMLPPredictionHead(GroundingDinoMLPPredictionHead):
+ pass
+
+
+class MMGroundingDinoForObjectDetection(GroundingDinoForObjectDetection, MMGroundingDinoPreTrainedModel):
+ _tied_weights_keys = [
+ r"bbox_embed\.[1-9]\d*",
+ r"model\.decoder\.bbox_embed\.[0-9]\d*",
+ r"class_embed\.[1-9]\d*",
+ r"model\.decoder\.class_embed\.[0-9]\d*",
+ ]
+
+ def __init__(self, config: MMGroundingDinoConfig):
+ MMGroundingDinoPreTrainedModel.__init__(config)
+
+ self.model = MMGroundingDinoModel(config)
+
+ self.class_embed = nn.ModuleList(
+ [MMGroundingDinoContrastiveEmbedding(config) for _ in range(config.decoder_layers)]
+ )
+
+ self.bbox_embed = nn.ModuleList(
+ [
+ MMGroundingDinoMLPPredictionHead(
+ input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3
+ )
+ for _ in range(config.decoder_layers)
+ ]
+ )
+
+ # hack for box-refinement
+ self.model.decoder.bbox_embed = self.bbox_embed
+ # hack implementation for two-stage
+ self.model.decoder.class_embed = self.class_embed
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+
+__all__ = [
+ "MMGroundingDinoConfig",
+ "MMGroundingDinoForObjectDetection",
+ "MMGroundingDinoModel",
+ "MMGroundingDinoPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ea599122ddcbdf7a3c68dd643207a4bf21e57b4
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mobilebert import *
+ from .modeling_mobilebert import *
+ from .modeling_tf_mobilebert import *
+ from .tokenization_mobilebert import *
+ from .tokenization_mobilebert_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/configuration_mobilebert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/configuration_mobilebert.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b37289e070bb2141c60545b8710503a34088f37
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/configuration_mobilebert.py
@@ -0,0 +1,184 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MobileBERT model configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MobileBertConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MobileBertModel`] or a [`TFMobileBertModel`]. It
+ is used to instantiate a MobileBERT model according to the specified arguments, defining the model architecture.
+ Instantiating a configuration with the defaults will yield a similar configuration to that of the MobileBERT
+ [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 30522):
+ Vocabulary size of the MobileBERT model. Defines the number of different tokens that can be represented by
+ the `inputs_ids` passed when calling [`MobileBertModel`] or [`TFMobileBertModel`].
+ hidden_size (`int`, *optional*, defaults to 512):
+ Dimensionality of the encoder layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 24):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 4):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 512):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `function`, *optional*, defaults to `"relu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 2):
+ The vocabulary size of the `token_type_ids` passed when calling [`MobileBertModel`] or
+ [`TFMobileBertModel`].
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+
+ pad_token_id (`int`, *optional*, defaults to 0):
+ The ID of the token in the word embedding to use as padding.
+ embedding_size (`int`, *optional*, defaults to 128):
+ The dimension of the word embedding vectors.
+ trigram_input (`bool`, *optional*, defaults to `True`):
+ Use a convolution of trigram as input.
+ use_bottleneck (`bool`, *optional*, defaults to `True`):
+ Whether to use bottleneck in BERT.
+ intra_bottleneck_size (`int`, *optional*, defaults to 128):
+ Size of bottleneck layer output.
+ use_bottleneck_attention (`bool`, *optional*, defaults to `False`):
+ Whether to use attention inputs from the bottleneck transformation.
+ key_query_shared_bottleneck (`bool`, *optional*, defaults to `True`):
+ Whether to use the same linear transformation for query&key in the bottleneck.
+ num_feedforward_networks (`int`, *optional*, defaults to 4):
+ Number of FFNs in a block.
+ normalization_type (`str`, *optional*, defaults to `"no_norm"`):
+ The normalization type in MobileBERT.
+ classifier_dropout (`float`, *optional*):
+ The dropout ratio for the classification head.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MobileBertConfig, MobileBertModel
+
+ >>> # Initializing a MobileBERT configuration
+ >>> configuration = MobileBertConfig()
+
+ >>> # Initializing a model (with random weights) from the configuration above
+ >>> model = MobileBertModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+ """
+
+ model_type = "mobilebert"
+
+ def __init__(
+ self,
+ vocab_size=30522,
+ hidden_size=512,
+ num_hidden_layers=24,
+ num_attention_heads=4,
+ intermediate_size=512,
+ hidden_act="relu",
+ hidden_dropout_prob=0.0,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ type_vocab_size=2,
+ initializer_range=0.02,
+ layer_norm_eps=1e-12,
+ pad_token_id=0,
+ embedding_size=128,
+ trigram_input=True,
+ use_bottleneck=True,
+ intra_bottleneck_size=128,
+ use_bottleneck_attention=False,
+ key_query_shared_bottleneck=True,
+ num_feedforward_networks=4,
+ normalization_type="no_norm",
+ classifier_activation=True,
+ classifier_dropout=None,
+ **kwargs,
+ ):
+ super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.hidden_act = hidden_act
+ self.intermediate_size = intermediate_size
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.max_position_embeddings = max_position_embeddings
+ self.type_vocab_size = type_vocab_size
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.embedding_size = embedding_size
+ self.trigram_input = trigram_input
+ self.use_bottleneck = use_bottleneck
+ self.intra_bottleneck_size = intra_bottleneck_size
+ self.use_bottleneck_attention = use_bottleneck_attention
+ self.key_query_shared_bottleneck = key_query_shared_bottleneck
+ self.num_feedforward_networks = num_feedforward_networks
+ self.normalization_type = normalization_type
+ self.classifier_activation = classifier_activation
+
+ if self.use_bottleneck:
+ self.true_hidden_size = intra_bottleneck_size
+ else:
+ self.true_hidden_size = hidden_size
+
+ self.classifier_dropout = classifier_dropout
+
+
+# Copied from transformers.models.bert.configuration_bert.BertOnnxConfig with Bert->MobileBert
+class MobileBertOnnxConfig(OnnxConfig):
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ if self.task == "multiple-choice":
+ dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
+ else:
+ dynamic_axis = {0: "batch", 1: "sequence"}
+ return OrderedDict(
+ [
+ ("input_ids", dynamic_axis),
+ ("attention_mask", dynamic_axis),
+ ("token_type_ids", dynamic_axis),
+ ]
+ )
+
+
+__all__ = ["MobileBertConfig", "MobileBertOnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/convert_mobilebert_original_tf_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/convert_mobilebert_original_tf_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..022a9d036cdb24558142222a6aec5fd3ed65afd7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/convert_mobilebert_original_tf_checkpoint_to_pytorch.py
@@ -0,0 +1,58 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+
+import torch
+
+from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+
+
+def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, mobilebert_config_file, pytorch_dump_path):
+ # Initialise PyTorch model
+ config = MobileBertConfig.from_json_file(mobilebert_config_file)
+ print(f"Building PyTorch model from configuration: {config}")
+ model = MobileBertForPreTraining(config)
+ # Load weights from tf checkpoint
+ model = load_tf_weights_in_mobilebert(model, config, tf_checkpoint_path)
+ # Save pytorch-model
+ print(f"Save PyTorch model to {pytorch_dump_path}")
+ torch.save(model.state_dict(), pytorch_dump_path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
+ )
+ parser.add_argument(
+ "--mobilebert_config_file",
+ default=None,
+ type=str,
+ required=True,
+ help=(
+ "The config json file corresponding to the pre-trained MobileBERT model. \n"
+ "This specifies the model architecture."
+ ),
+ )
+ parser.add_argument(
+ "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
+ )
+ args = parser.parse_args()
+ convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/modeling_mobilebert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/modeling_mobilebert.py
new file mode 100644
index 0000000000000000000000000000000000000000..99768685002a2560b492bb8d59e1549d119f475b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/modeling_mobilebert.py
@@ -0,0 +1,1482 @@
+# MIT License
+#
+# Copyright (c) 2020 The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPooling,
+ MaskedLMOutput,
+ MultipleChoiceModelOutput,
+ NextSentencePredictorOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import ModelOutput, auto_docstring, logging
+from .configuration_mobilebert import MobileBertConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def load_tf_weights_in_mobilebert(model, config, tf_checkpoint_path):
+ """Load tf checkpoints in a pytorch model."""
+ try:
+ import re
+
+ import numpy as np
+ import tensorflow as tf
+ except ImportError:
+ logger.error(
+ "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
+ "https://www.tensorflow.org/install/ for installation instructions."
+ )
+ raise
+ tf_path = os.path.abspath(tf_checkpoint_path)
+ logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
+ # Load weights from TF model
+ init_vars = tf.train.list_variables(tf_path)
+ names = []
+ arrays = []
+ for name, shape in init_vars:
+ logger.info(f"Loading TF weight {name} with shape {shape}")
+ array = tf.train.load_variable(tf_path, name)
+ names.append(name)
+ arrays.append(array)
+
+ for name, array in zip(names, arrays):
+ name = name.replace("ffn_layer", "ffn")
+ name = name.replace("FakeLayerNorm", "LayerNorm")
+ name = name.replace("extra_output_weights", "dense/kernel")
+ name = name.replace("bert", "mobilebert")
+ name = name.split("/")
+ # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+ # which are not required for using pretrained model
+ if any(
+ n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
+ for n in name
+ ):
+ logger.info(f"Skipping {'/'.join(name)}")
+ continue
+ pointer = model
+ for m_name in name:
+ if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
+ scope_names = re.split(r"_(\d+)", m_name)
+ else:
+ scope_names = [m_name]
+ if scope_names[0] == "kernel" or scope_names[0] == "gamma":
+ pointer = getattr(pointer, "weight")
+ elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
+ pointer = getattr(pointer, "bias")
+ elif scope_names[0] == "output_weights":
+ pointer = getattr(pointer, "weight")
+ elif scope_names[0] == "squad":
+ pointer = getattr(pointer, "classifier")
+ else:
+ try:
+ pointer = getattr(pointer, scope_names[0])
+ except AttributeError:
+ logger.info(f"Skipping {'/'.join(name)}")
+ continue
+ if len(scope_names) >= 2:
+ num = int(scope_names[1])
+ pointer = pointer[num]
+ if m_name[-11:] == "_embeddings":
+ pointer = getattr(pointer, "weight")
+ elif m_name == "kernel":
+ array = np.transpose(array)
+ try:
+ assert pointer.shape == array.shape, (
+ f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
+ )
+ except AssertionError as e:
+ e.args += (pointer.shape, array.shape)
+ raise
+ logger.info(f"Initialize PyTorch weight {name}")
+ pointer.data = torch.from_numpy(array)
+ return model
+
+
+class NoNorm(nn.Module):
+ def __init__(self, feat_size, eps=None):
+ super().__init__()
+ self.bias = nn.Parameter(torch.zeros(feat_size))
+ self.weight = nn.Parameter(torch.ones(feat_size))
+
+ def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
+ return input_tensor * self.weight + self.bias
+
+
+NORM2FN = {"layer_norm": nn.LayerNorm, "no_norm": NoNorm}
+
+
+class MobileBertEmbeddings(nn.Module):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.trigram_input = config.trigram_input
+ self.embedding_size = config.embedding_size
+ self.hidden_size = config.hidden_size
+
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ embed_dim_multiplier = 3 if self.trigram_input else 1
+ embedded_input_size = self.embedding_size * embed_dim_multiplier
+ self.embedding_transformation = nn.Linear(embedded_input_size, config.hidden_size)
+
+ self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+ self.register_buffer(
+ "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+ )
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ ) -> torch.Tensor:
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+
+ if position_ids is None:
+ position_ids = self.position_ids[:, :seq_length]
+
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+
+ if self.trigram_input:
+ # From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited
+ # Devices (https://huggingface.co/papers/2004.02984)
+ #
+ # The embedding table in BERT models accounts for a substantial proportion of model size. To compress
+ # the embedding layer, we reduce the embedding dimension to 128 in MobileBERT.
+ # Then, we apply a 1D convolution with kernel size 3 on the raw token embedding to produce a 512
+ # dimensional output.
+ inputs_embeds = torch.cat(
+ [
+ nn.functional.pad(inputs_embeds[:, 1:], [0, 0, 0, 1, 0, 0], value=0.0),
+ inputs_embeds,
+ nn.functional.pad(inputs_embeds[:, :-1], [0, 0, 1, 0, 0, 0], value=0.0),
+ ],
+ dim=2,
+ )
+ if self.trigram_input or self.embedding_size != self.hidden_size:
+ inputs_embeds = self.embedding_transformation(inputs_embeds)
+
+ # Add positional embeddings and token type embeddings, then layer
+ # normalize and perform dropout.
+ position_embeddings = self.position_embeddings(position_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+ embeddings = inputs_embeds + position_embeddings + token_type_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+class MobileBertSelfAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.true_hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.true_hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.true_hidden_size, self.all_head_size)
+ self.value = nn.Linear(
+ config.true_hidden_size if config.use_bottleneck_attention else config.hidden_size, self.all_head_size
+ )
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+ def forward(
+ self,
+ query_tensor: torch.Tensor,
+ key_tensor: torch.Tensor,
+ value_tensor: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ ) -> tuple[torch.Tensor]:
+ batch_size, seq_length, _ = query_tensor.shape
+ query_layer = (
+ self.query(query_tensor)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ key_layer = (
+ self.key(key_tensor)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ value_layer = (
+ self.value(value_tensor)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+ attention_scores = attention_scores + attention_mask
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+ context_layer = torch.matmul(attention_probs, value_layer)
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(new_context_layer_shape)
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+ return outputs
+
+
+class MobileBertSelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.use_bottleneck = config.use_bottleneck
+ self.dense = nn.Linear(config.true_hidden_size, config.true_hidden_size)
+ self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)
+ if not self.use_bottleneck:
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
+ layer_outputs = self.dense(hidden_states)
+ if not self.use_bottleneck:
+ layer_outputs = self.dropout(layer_outputs)
+ layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
+ return layer_outputs
+
+
+class MobileBertAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.self = MobileBertSelfAttention(config)
+ self.output = MobileBertSelfOutput(config)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+ )
+
+ # Prune linear layers
+ self.self.query = prune_linear_layer(self.self.query, index)
+ self.self.key = prune_linear_layer(self.self.key, index)
+ self.self.value = prune_linear_layer(self.self.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+ self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(
+ self,
+ query_tensor: torch.Tensor,
+ key_tensor: torch.Tensor,
+ value_tensor: torch.Tensor,
+ layer_input: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ ) -> tuple[torch.Tensor]:
+ self_outputs = self.self(
+ query_tensor,
+ key_tensor,
+ value_tensor,
+ attention_mask,
+ head_mask,
+ output_attentions,
+ )
+ # Run a linear projection of `hidden_size` then add a residual
+ # with `layer_input`.
+ attention_output = self.output(self_outputs[0], layer_input)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+class MobileBertIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.true_hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+class OutputBottleneck(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.true_hidden_size, config.hidden_size)
+ self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
+ layer_outputs = self.dense(hidden_states)
+ layer_outputs = self.dropout(layer_outputs)
+ layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
+ return layer_outputs
+
+
+class MobileBertOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.use_bottleneck = config.use_bottleneck
+ self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
+ self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size)
+ if not self.use_bottleneck:
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ else:
+ self.bottleneck = OutputBottleneck(config)
+
+ def forward(
+ self, intermediate_states: torch.Tensor, residual_tensor_1: torch.Tensor, residual_tensor_2: torch.Tensor
+ ) -> torch.Tensor:
+ layer_output = self.dense(intermediate_states)
+ if not self.use_bottleneck:
+ layer_output = self.dropout(layer_output)
+ layer_output = self.LayerNorm(layer_output + residual_tensor_1)
+ else:
+ layer_output = self.LayerNorm(layer_output + residual_tensor_1)
+ layer_output = self.bottleneck(layer_output, residual_tensor_2)
+ return layer_output
+
+
+class BottleneckLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intra_bottleneck_size)
+ self.LayerNorm = NORM2FN[config.normalization_type](config.intra_bottleneck_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ layer_input = self.dense(hidden_states)
+ layer_input = self.LayerNorm(layer_input)
+ return layer_input
+
+
+class Bottleneck(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.key_query_shared_bottleneck = config.key_query_shared_bottleneck
+ self.use_bottleneck_attention = config.use_bottleneck_attention
+ self.input = BottleneckLayer(config)
+ if self.key_query_shared_bottleneck:
+ self.attention = BottleneckLayer(config)
+
+ def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor]:
+ # This method can return three different tuples of values. These different values make use of bottlenecks,
+ # which are linear layers used to project the hidden states to a lower-dimensional vector, reducing memory
+ # usage. These linear layer have weights that are learned during training.
+ #
+ # If `config.use_bottleneck_attention`, it will return the result of the bottleneck layer four times for the
+ # key, query, value, and "layer input" to be used by the attention layer.
+ # This bottleneck is used to project the hidden. This last layer input will be used as a residual tensor
+ # in the attention self output, after the attention scores have been computed.
+ #
+ # If not `config.use_bottleneck_attention` and `config.key_query_shared_bottleneck`, this will return
+ # four values, three of which have been passed through a bottleneck: the query and key, passed through the same
+ # bottleneck, and the residual layer to be applied in the attention self output, through another bottleneck.
+ #
+ # Finally, in the last case, the values for the query, key and values are the hidden states without bottleneck,
+ # and the residual layer will be this value passed through a bottleneck.
+
+ bottlenecked_hidden_states = self.input(hidden_states)
+ if self.use_bottleneck_attention:
+ return (bottlenecked_hidden_states,) * 4
+ elif self.key_query_shared_bottleneck:
+ shared_attention_input = self.attention(hidden_states)
+ return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states)
+ else:
+ return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states)
+
+
+class FFNOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
+ self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states: torch.Tensor, residual_tensor: torch.Tensor) -> torch.Tensor:
+ layer_outputs = self.dense(hidden_states)
+ layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
+ return layer_outputs
+
+
+class FFNLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.intermediate = MobileBertIntermediate(config)
+ self.output = FFNOutput(config)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ intermediate_output = self.intermediate(hidden_states)
+ layer_outputs = self.output(intermediate_output, hidden_states)
+ return layer_outputs
+
+
+class MobileBertLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.use_bottleneck = config.use_bottleneck
+ self.num_feedforward_networks = config.num_feedforward_networks
+
+ self.attention = MobileBertAttention(config)
+ self.intermediate = MobileBertIntermediate(config)
+ self.output = MobileBertOutput(config)
+ if self.use_bottleneck:
+ self.bottleneck = Bottleneck(config)
+ if config.num_feedforward_networks > 1:
+ self.ffn = nn.ModuleList([FFNLayer(config) for _ in range(config.num_feedforward_networks - 1)])
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ ) -> tuple[torch.Tensor]:
+ if self.use_bottleneck:
+ query_tensor, key_tensor, value_tensor, layer_input = self.bottleneck(hidden_states)
+ else:
+ query_tensor, key_tensor, value_tensor, layer_input = [hidden_states] * 4
+
+ self_attention_outputs = self.attention(
+ query_tensor,
+ key_tensor,
+ value_tensor,
+ layer_input,
+ attention_mask,
+ head_mask,
+ output_attentions=output_attentions,
+ )
+ attention_output = self_attention_outputs[0]
+ s = (attention_output,)
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ if self.num_feedforward_networks != 1:
+ for i, ffn_module in enumerate(self.ffn):
+ attention_output = ffn_module(attention_output)
+ s += (attention_output,)
+
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output, hidden_states)
+ outputs = (
+ (layer_output,)
+ + outputs
+ + (
+ torch.tensor(1000),
+ query_tensor,
+ key_tensor,
+ value_tensor,
+ layer_input,
+ attention_output,
+ intermediate_output,
+ )
+ + s
+ )
+ return outputs
+
+
+class MobileBertEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.layer = nn.ModuleList([MobileBertLayer(config) for _ in range(config.num_hidden_layers)])
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ output_hidden_states: Optional[bool] = False,
+ return_dict: Optional[bool] = True,
+ ) -> Union[tuple, BaseModelOutput]:
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask,
+ head_mask[i],
+ output_attentions,
+ )
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ # Add last layer
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
+ )
+
+
+class MobileBertPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.do_activate = config.classifier_activation
+ if self.do_activate:
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ if not self.do_activate:
+ return first_token_tensor
+ else:
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = torch.tanh(pooled_output)
+ return pooled_output
+
+
+class MobileBertPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ if isinstance(config.hidden_act, str):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = NORM2FN["layer_norm"](config.hidden_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+class MobileBertLMPredictionHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.transform = MobileBertPredictionHeadTransform(config)
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.dense = nn.Linear(config.vocab_size, config.hidden_size - config.embedding_size, bias=False)
+ self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=False)
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+ # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+ self.decoder.bias = self.bias
+
+ def _tie_weights(self) -> None:
+ self.decoder.bias = self.bias
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.transform(hidden_states)
+ hidden_states = hidden_states.matmul(torch.cat([self.decoder.weight.t(), self.dense.weight], dim=0))
+ hidden_states += self.decoder.bias
+ return hidden_states
+
+
+class MobileBertOnlyMLMHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.predictions = MobileBertLMPredictionHead(config)
+
+ def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
+ prediction_scores = self.predictions(sequence_output)
+ return prediction_scores
+
+
+class MobileBertPreTrainingHeads(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.predictions = MobileBertLMPredictionHead(config)
+ self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+ def forward(self, sequence_output: torch.Tensor, pooled_output: torch.Tensor) -> tuple[torch.Tensor]:
+ prediction_scores = self.predictions(sequence_output)
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return prediction_scores, seq_relationship_score
+
+
+@auto_docstring
+class MobileBertPreTrainedModel(PreTrainedModel):
+ config: MobileBertConfig
+ load_tf_weights = load_tf_weights_in_mobilebert
+ base_model_prefix = "mobilebert"
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, nn.Linear):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, (nn.LayerNorm, NoNorm)):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, MobileBertLMPredictionHead):
+ module.bias.data.zero_()
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Output type of [`MobileBertForPreTraining`].
+ """
+)
+class MobileBertForPreTrainingOutput(ModelOutput):
+ r"""
+ loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
+ Total loss as the sum of the masked language modeling loss and the next sequence prediction
+ (classification) loss.
+ prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
+ Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
+ before SoftMax).
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ prediction_logits: Optional[torch.FloatTensor] = None
+ seq_relationship_logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@auto_docstring
+class MobileBertModel(MobileBertPreTrainedModel):
+ """
+ https://huggingface.co/papers/2004.02984
+ """
+
+ def __init__(self, config, add_pooling_layer=True):
+ r"""
+ add_pooling_layer (bool, *optional*, defaults to `True`):
+ Whether to add a pooling layer
+ """
+ super().__init__(config)
+ self.config = config
+ self.embeddings = MobileBertEmbeddings(config)
+ self.encoder = MobileBertEncoder(config)
+
+ self.pooler = MobileBertPooler(config) if add_pooling_layer else None
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPooling]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones(input_shape, device=device)
+ if token_type_ids is None:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
+ )
+ encoder_outputs = self.encoder(
+ embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MobileBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
+ `next sentence prediction (classification)` head.
+ """
+)
+class MobileBertForPreTraining(MobileBertPreTrainedModel):
+ _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.mobilebert = MobileBertModel(config)
+ self.cls = MobileBertPreTrainingHeads(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.cls.predictions.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.cls.predictions.decoder = new_embeddings
+ self.cls.predictions.bias = new_embeddings.bias
+
+ def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
+ # resize dense output embedings at first
+ self.cls.predictions.dense = self._get_resized_lm_head(
+ self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True
+ )
+
+ return super().resize_token_embeddings(new_num_tokens=new_num_tokens)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ next_sentence_label: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[torch.FloatTensor] = None,
+ output_hidden_states: Optional[torch.FloatTensor] = None,
+ return_dict: Optional[torch.FloatTensor] = None,
+ ) -> Union[tuple, MobileBertForPreTrainingOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
+ (see `input_ids` docstring) Indices should be in `[0, 1]`:
+
+ - 0 indicates sequence B is a continuation of sequence A,
+ - 1 indicates sequence B is a random sequence.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MobileBertForPreTraining
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
+ >>> model = MobileBertForPreTraining.from_pretrained("google/mobilebert-uncased")
+
+ >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)
+ >>> # Batch size 1
+ >>> outputs = model(input_ids)
+
+ >>> prediction_logits = outputs.prediction_logits
+ >>> seq_relationship_logits = outputs.seq_relationship_logits
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output, pooled_output = outputs[:2]
+ prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
+
+ total_loss = None
+ if labels is not None and next_sentence_label is not None:
+ loss_fct = CrossEntropyLoss()
+ masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+ next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
+ total_loss = masked_lm_loss + next_sentence_loss
+
+ if not return_dict:
+ output = (prediction_scores, seq_relationship_score) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return MobileBertForPreTrainingOutput(
+ loss=total_loss,
+ prediction_logits=prediction_scores,
+ seq_relationship_logits=seq_relationship_score,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class MobileBertForMaskedLM(MobileBertPreTrainedModel):
+ _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
+ self.cls = MobileBertOnlyMLMHead(config)
+ self.config = config
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.cls.predictions.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.cls.predictions.decoder = new_embeddings
+ self.cls.predictions.bias = new_embeddings.bias
+
+ def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
+ # resize dense output embedings at first
+ self.cls.predictions.dense = self._get_resized_lm_head(
+ self.cls.predictions.dense, new_num_tokens=new_num_tokens, transposed=True
+ )
+ return super().resize_token_embeddings(new_num_tokens=new_num_tokens)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, MaskedLMOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+ prediction_scores = self.cls(sequence_output)
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss() # -100 index = padding token
+ masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return MaskedLMOutput(
+ loss=masked_lm_loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+class MobileBertOnlyNSPHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+ def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return seq_relationship_score
+
+
+@auto_docstring(
+ custom_intro="""
+ MobileBert Model with a `next sentence prediction (classification)` head on top.
+ """
+)
+class MobileBertForNextSentencePrediction(MobileBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.mobilebert = MobileBertModel(config)
+ self.cls = MobileBertOnlyNSPHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple, NextSentencePredictorOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
+ (see `input_ids` docstring) Indices should be in `[0, 1]`.
+
+ - 0 indicates sequence B is a continuation of sequence A,
+ - 1 indicates sequence B is a random sequence.
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MobileBertForNextSentencePrediction
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
+ >>> model = MobileBertForNextSentencePrediction.from_pretrained("google/mobilebert-uncased")
+
+ >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+ >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
+ >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
+
+ >>> outputs = model(**encoding, labels=torch.LongTensor([1]))
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+ ```"""
+
+ if "next_sentence_label" in kwargs:
+ warnings.warn(
+ "The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
+ " `labels` instead.",
+ FutureWarning,
+ )
+ labels = kwargs.pop("next_sentence_label")
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ pooled_output = outputs[1]
+ seq_relationship_score = self.cls(pooled_output)
+
+ next_sentence_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), labels.view(-1))
+
+ if not return_dict:
+ output = (seq_relationship_score,) + outputs[2:]
+ return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
+
+ return NextSentencePredictorOutput(
+ loss=next_sentence_loss,
+ logits=seq_relationship_score,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+ pooled output) e.g. for GLUE tasks.
+ """
+)
+# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification with Bert->MobileBert all-casing
+class MobileBertForSequenceClassification(MobileBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.config = config
+
+ self.mobilebert = MobileBertModel(config)
+ classifier_dropout = (
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.dropout = nn.Dropout(classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ pooled_output = outputs[1]
+
+ pooled_output = self.dropout(pooled_output)
+ logits = self.classifier(pooled_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+# Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering with Bert->MobileBert all-casing
+class MobileBertForQuestionAnswering(MobileBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ start_positions: Optional[torch.Tensor] = None,
+ end_positions: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+# Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice with Bert->MobileBert all-casing
+class MobileBertForMultipleChoice(MobileBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.mobilebert = MobileBertModel(config)
+ classifier_dropout = (
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.dropout = nn.Dropout(classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, 1)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], MultipleChoiceModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+ num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+ `input_ids` above)
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+ input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+ attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+ token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+ position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+ inputs_embeds = (
+ inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+ if inputs_embeds is not None
+ else None
+ )
+
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ pooled_output = outputs[1]
+
+ pooled_output = self.dropout(pooled_output)
+ logits = self.classifier(pooled_output)
+ reshaped_logits = logits.view(-1, num_choices)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(reshaped_logits, labels)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return MultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+# Copied from transformers.models.bert.modeling_bert.BertForTokenClassification with Bert->MobileBert all-casing
+class MobileBertForTokenClassification(MobileBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.mobilebert = MobileBertModel(config, add_pooling_layer=False)
+ classifier_dropout = (
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.dropout = nn.Dropout(classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], TokenClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "MobileBertForMaskedLM",
+ "MobileBertForMultipleChoice",
+ "MobileBertForNextSentencePrediction",
+ "MobileBertForPreTraining",
+ "MobileBertForQuestionAnswering",
+ "MobileBertForSequenceClassification",
+ "MobileBertForTokenClassification",
+ "MobileBertLayer",
+ "MobileBertModel",
+ "MobileBertPreTrainedModel",
+ "load_tf_weights_in_mobilebert",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/modeling_tf_mobilebert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/modeling_tf_mobilebert.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4d148aa76c5a07847a05495569cf5d1942364d0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/modeling_tf_mobilebert.py
@@ -0,0 +1,1979 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TF 2.0 MobileBERT model."""
+
+from __future__ import annotations
+
+import warnings
+from dataclasses import dataclass
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_outputs import (
+ TFBaseModelOutput,
+ TFBaseModelOutputWithPooling,
+ TFMaskedLMOutput,
+ TFMultipleChoiceModelOutput,
+ TFNextSentencePredictorOutput,
+ TFQuestionAnsweringModelOutput,
+ TFSequenceClassifierOutput,
+ TFTokenClassifierOutput,
+)
+from ...modeling_tf_utils import (
+ TFMaskedLanguageModelingLoss,
+ TFModelInputType,
+ TFMultipleChoiceLoss,
+ TFNextSentencePredictionLoss,
+ TFPreTrainedModel,
+ TFQuestionAnsweringLoss,
+ TFSequenceClassificationLoss,
+ TFTokenClassificationLoss,
+ get_initializer,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
+from ...utils import (
+ ModelOutput,
+ add_code_sample_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+ replace_return_docstrings,
+)
+from .configuration_mobilebert import MobileBertConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "google/mobilebert-uncased"
+_CONFIG_FOR_DOC = "MobileBertConfig"
+
+# TokenClassification docstring
+_CHECKPOINT_FOR_TOKEN_CLASSIFICATION = "vumichien/mobilebert-finetuned-ner"
+_TOKEN_CLASS_EXPECTED_OUTPUT = "['I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC']"
+_TOKEN_CLASS_EXPECTED_LOSS = 0.03
+
+# QuestionAnswering docstring
+_CHECKPOINT_FOR_QA = "vumichien/mobilebert-uncased-squad-v2"
+_QA_EXPECTED_OUTPUT = "'a nice puppet'"
+_QA_EXPECTED_LOSS = 3.98
+_QA_TARGET_START_INDEX = 12
+_QA_TARGET_END_INDEX = 13
+
+# SequenceClassification docstring
+_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "vumichien/emo-mobilebert"
+_SEQ_CLASS_EXPECTED_OUTPUT = "'others'"
+_SEQ_CLASS_EXPECTED_LOSS = "4.72"
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertPreTrainingLoss
+class TFMobileBertPreTrainingLoss:
+ """
+ Loss function suitable for BERT-like pretraining, that is, the task of pretraining a language model by combining
+ NSP + MLM. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss
+ computation.
+ """
+
+ def hf_compute_loss(self, labels: tf.Tensor, logits: tf.Tensor) -> tf.Tensor:
+ loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=keras.losses.Reduction.NONE)
+
+ # Clip negative labels to zero here to avoid NaNs and errors - those positions will get masked later anyway
+ unmasked_lm_losses = loss_fn(y_true=tf.nn.relu(labels["labels"]), y_pred=logits[0])
+ # make sure only labels that are not equal to -100
+ # are taken into account for the loss computation
+ lm_loss_mask = tf.cast(labels["labels"] != -100, dtype=unmasked_lm_losses.dtype)
+ masked_lm_losses = unmasked_lm_losses * lm_loss_mask
+ reduced_masked_lm_loss = tf.reduce_sum(masked_lm_losses) / tf.reduce_sum(lm_loss_mask)
+
+ # Clip negative labels to zero here to avoid NaNs and errors - those positions will get masked later anyway
+ unmasked_ns_loss = loss_fn(y_true=tf.nn.relu(labels["next_sentence_label"]), y_pred=logits[1])
+ ns_loss_mask = tf.cast(labels["next_sentence_label"] != -100, dtype=unmasked_ns_loss.dtype)
+ masked_ns_loss = unmasked_ns_loss * ns_loss_mask
+
+ reduced_masked_ns_loss = tf.reduce_sum(masked_ns_loss) / tf.reduce_sum(ns_loss_mask)
+
+ return tf.reshape(reduced_masked_lm_loss + reduced_masked_ns_loss, (1,))
+
+
+class TFMobileBertIntermediate(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(config.intermediate_size, name="dense")
+
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = get_tf_activation(config.hidden_act)
+ else:
+ self.intermediate_act_fn = config.hidden_act
+ self.config = config
+
+ def call(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.true_hidden_size])
+
+
+class TFLayerNorm(keras.layers.LayerNormalization):
+ def __init__(self, feat_size, *args, **kwargs):
+ self.feat_size = feat_size
+ super().__init__(*args, **kwargs)
+
+ def build(self, input_shape=None):
+ super().build([None, None, self.feat_size])
+
+
+class TFNoNorm(keras.layers.Layer):
+ def __init__(self, feat_size, epsilon=None, **kwargs):
+ super().__init__(**kwargs)
+ self.feat_size = feat_size
+
+ def build(self, input_shape):
+ self.bias = self.add_weight("bias", shape=[self.feat_size], initializer="zeros")
+ self.weight = self.add_weight("weight", shape=[self.feat_size], initializer="ones")
+ super().build(input_shape)
+
+ def call(self, inputs: tf.Tensor):
+ return inputs * self.weight + self.bias
+
+
+NORM2FN = {"layer_norm": TFLayerNorm, "no_norm": TFNoNorm}
+
+
+class TFMobileBertEmbeddings(keras.layers.Layer):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.trigram_input = config.trigram_input
+ self.embedding_size = config.embedding_size
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.max_position_embeddings = config.max_position_embeddings
+ self.initializer_range = config.initializer_range
+ self.embedding_transformation = keras.layers.Dense(config.hidden_size, name="embedding_transformation")
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+ self.LayerNorm = NORM2FN[config.normalization_type](
+ config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
+ )
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+ self.embedded_input_size = self.embedding_size * (3 if self.trigram_input else 1)
+
+ def build(self, input_shape=None):
+ with tf.name_scope("word_embeddings"):
+ self.weight = self.add_weight(
+ name="weight",
+ shape=[self.config.vocab_size, self.embedding_size],
+ initializer=get_initializer(initializer_range=self.initializer_range),
+ )
+
+ with tf.name_scope("token_type_embeddings"):
+ self.token_type_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.config.type_vocab_size, self.hidden_size],
+ initializer=get_initializer(initializer_range=self.initializer_range),
+ )
+
+ with tf.name_scope("position_embeddings"):
+ self.position_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.max_position_embeddings, self.hidden_size],
+ initializer=get_initializer(initializer_range=self.initializer_range),
+ )
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embedding_transformation", None) is not None:
+ with tf.name_scope(self.embedding_transformation.name):
+ self.embedding_transformation.build([None, None, self.embedded_input_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build(None)
+
+ def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
+ """
+ Applies embedding based on inputs tensor.
+
+ Returns:
+ final_embeddings (`tf.Tensor`): output embedding tensor.
+ """
+ assert not (input_ids is None and inputs_embeds is None)
+
+ if input_ids is not None:
+ check_embeddings_within_bounds(input_ids, self.config.vocab_size)
+ inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
+
+ input_shape = shape_list(inputs_embeds)[:-1]
+
+ if token_type_ids is None:
+ token_type_ids = tf.fill(dims=input_shape, value=0)
+
+ if self.trigram_input:
+ # From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited
+ # Devices (https://huggingface.co/papers/2004.02984)
+ #
+ # The embedding table in BERT models accounts for a substantial proportion of model size. To compress
+ # the embedding layer, we reduce the embedding dimension to 128 in MobileBERT.
+ # Then, we apply a 1D convolution with kernel size 3 on the raw token embedding to produce a 512
+ # dimensional output.
+ inputs_embeds = tf.concat(
+ [
+ tf.pad(inputs_embeds[:, 1:], ((0, 0), (0, 1), (0, 0))),
+ inputs_embeds,
+ tf.pad(inputs_embeds[:, :-1], ((0, 0), (1, 0), (0, 0))),
+ ],
+ axis=2,
+ )
+
+ if self.trigram_input or self.embedding_size != self.hidden_size:
+ inputs_embeds = self.embedding_transformation(inputs_embeds)
+
+ if position_ids is None:
+ position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
+
+ position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
+ token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
+ final_embeddings = inputs_embeds + position_embeds + token_type_embeds
+ final_embeddings = self.LayerNorm(inputs=final_embeddings)
+ final_embeddings = self.dropout(inputs=final_embeddings, training=training)
+
+ return final_embeddings
+
+
+class TFMobileBertSelfAttention(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads}"
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.output_attentions = config.output_attentions
+ assert config.hidden_size % config.num_attention_heads == 0
+ self.attention_head_size = int(config.true_hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = keras.layers.Dense(
+ self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
+ )
+ self.key = keras.layers.Dense(
+ self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
+ )
+ self.value = keras.layers.Dense(
+ self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
+ )
+
+ self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob)
+ self.config = config
+
+ def transpose_for_scores(self, x, batch_size):
+ # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
+ x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
+ return tf.transpose(x, perm=[0, 2, 1, 3])
+
+ def call(
+ self, query_tensor, key_tensor, value_tensor, attention_mask, head_mask, output_attentions, training=False
+ ):
+ batch_size = shape_list(attention_mask)[0]
+ mixed_query_layer = self.query(query_tensor)
+ mixed_key_layer = self.key(key_tensor)
+ mixed_value_layer = self.value(value_tensor)
+ query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
+ key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
+ value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = tf.matmul(
+ query_layer, key_layer, transpose_b=True
+ ) # (batch size, num_heads, seq_len_q, seq_len_k)
+ dk = tf.cast(shape_list(key_layer)[-1], dtype=attention_scores.dtype) # scale attention_scores
+ attention_scores = attention_scores / tf.math.sqrt(dk)
+
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in TFMobileBertModel call() function)
+ attention_mask = tf.cast(attention_mask, dtype=attention_scores.dtype)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = stable_softmax(attention_scores, axis=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs, training=training)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ context_layer = tf.matmul(attention_probs, value_layer)
+
+ context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
+ context_layer = tf.reshape(
+ context_layer, (batch_size, -1, self.all_head_size)
+ ) # (batch_size, seq_len_q, all_head_size)
+
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "query", None) is not None:
+ with tf.name_scope(self.query.name):
+ self.query.build([None, None, self.config.true_hidden_size])
+ if getattr(self, "key", None) is not None:
+ with tf.name_scope(self.key.name):
+ self.key.build([None, None, self.config.true_hidden_size])
+ if getattr(self, "value", None) is not None:
+ with tf.name_scope(self.value.name):
+ self.value.build(
+ [
+ None,
+ None,
+ self.config.true_hidden_size
+ if self.config.use_bottleneck_attention
+ else self.config.hidden_size,
+ ]
+ )
+
+
+class TFMobileBertSelfOutput(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.use_bottleneck = config.use_bottleneck
+ self.dense = keras.layers.Dense(
+ config.true_hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ self.LayerNorm = NORM2FN[config.normalization_type](
+ config.true_hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
+ )
+ if not self.use_bottleneck:
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.config = config
+
+ def call(self, hidden_states, residual_tensor, training=False):
+ hidden_states = self.dense(hidden_states)
+ if not self.use_bottleneck:
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = self.LayerNorm(hidden_states + residual_tensor)
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.true_hidden_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build(None)
+
+
+class TFMobileBertAttention(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.self = TFMobileBertSelfAttention(config, name="self")
+ self.mobilebert_output = TFMobileBertSelfOutput(config, name="output")
+
+ def prune_heads(self, heads):
+ raise NotImplementedError
+
+ def call(
+ self,
+ query_tensor,
+ key_tensor,
+ value_tensor,
+ layer_input,
+ attention_mask,
+ head_mask,
+ output_attentions,
+ training=False,
+ ):
+ self_outputs = self.self(
+ query_tensor, key_tensor, value_tensor, attention_mask, head_mask, output_attentions, training=training
+ )
+
+ attention_output = self.mobilebert_output(self_outputs[0], layer_input, training=training)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self", None) is not None:
+ with tf.name_scope(self.self.name):
+ self.self.build(None)
+ if getattr(self, "mobilebert_output", None) is not None:
+ with tf.name_scope(self.mobilebert_output.name):
+ self.mobilebert_output.build(None)
+
+
+class TFOutputBottleneck(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(config.hidden_size, name="dense")
+ self.LayerNorm = NORM2FN[config.normalization_type](
+ config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
+ )
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.config = config
+
+ def call(self, hidden_states, residual_tensor, training=False):
+ layer_outputs = self.dense(hidden_states)
+ layer_outputs = self.dropout(layer_outputs, training=training)
+ layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
+ return layer_outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.true_hidden_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build(None)
+
+
+class TFMobileBertOutput(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.use_bottleneck = config.use_bottleneck
+ self.dense = keras.layers.Dense(
+ config.true_hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ self.LayerNorm = NORM2FN[config.normalization_type](
+ config.true_hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
+ )
+ if not self.use_bottleneck:
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ else:
+ self.bottleneck = TFOutputBottleneck(config, name="bottleneck")
+ self.config = config
+
+ def call(self, hidden_states, residual_tensor_1, residual_tensor_2, training=False):
+ hidden_states = self.dense(hidden_states)
+ if not self.use_bottleneck:
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = self.LayerNorm(hidden_states + residual_tensor_1)
+ else:
+ hidden_states = self.LayerNorm(hidden_states + residual_tensor_1)
+ hidden_states = self.bottleneck(hidden_states, residual_tensor_2)
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.intermediate_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build(None)
+ if getattr(self, "bottleneck", None) is not None:
+ with tf.name_scope(self.bottleneck.name):
+ self.bottleneck.build(None)
+
+
+class TFBottleneckLayer(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(config.intra_bottleneck_size, name="dense")
+ self.LayerNorm = NORM2FN[config.normalization_type](
+ config.intra_bottleneck_size, epsilon=config.layer_norm_eps, name="LayerNorm"
+ )
+ self.config = config
+
+ def call(self, inputs):
+ hidden_states = self.dense(inputs)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build(None)
+
+
+class TFBottleneck(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.key_query_shared_bottleneck = config.key_query_shared_bottleneck
+ self.use_bottleneck_attention = config.use_bottleneck_attention
+ self.bottleneck_input = TFBottleneckLayer(config, name="input")
+ if self.key_query_shared_bottleneck:
+ self.attention = TFBottleneckLayer(config, name="attention")
+
+ def call(self, hidden_states):
+ # This method can return three different tuples of values. These different values make use of bottlenecks,
+ # which are linear layers used to project the hidden states to a lower-dimensional vector, reducing memory
+ # usage. These linear layer have weights that are learned during training.
+ #
+ # If `config.use_bottleneck_attention`, it will return the result of the bottleneck layer four times for the
+ # key, query, value, and "layer input" to be used by the attention layer.
+ # This bottleneck is used to project the hidden. This last layer input will be used as a residual tensor
+ # in the attention self output, after the attention scores have been computed.
+ #
+ # If not `config.use_bottleneck_attention` and `config.key_query_shared_bottleneck`, this will return
+ # four values, three of which have been passed through a bottleneck: the query and key, passed through the same
+ # bottleneck, and the residual layer to be applied in the attention self output, through another bottleneck.
+ #
+ # Finally, in the last case, the values for the query, key and values are the hidden states without bottleneck,
+ # and the residual layer will be this value passed through a bottleneck.
+
+ bottlenecked_hidden_states = self.bottleneck_input(hidden_states)
+ if self.use_bottleneck_attention:
+ return (bottlenecked_hidden_states,) * 4
+ elif self.key_query_shared_bottleneck:
+ shared_attention_input = self.attention(hidden_states)
+ return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states)
+ else:
+ return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states)
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "bottleneck_input", None) is not None:
+ with tf.name_scope(self.bottleneck_input.name):
+ self.bottleneck_input.build(None)
+ if getattr(self, "attention", None) is not None:
+ with tf.name_scope(self.attention.name):
+ self.attention.build(None)
+
+
+class TFFFNOutput(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(config.true_hidden_size, name="dense")
+ self.LayerNorm = NORM2FN[config.normalization_type](
+ config.true_hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
+ )
+ self.config = config
+
+ def call(self, hidden_states, residual_tensor):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + residual_tensor)
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.intermediate_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build(None)
+
+
+class TFFFNLayer(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.intermediate = TFMobileBertIntermediate(config, name="intermediate")
+ self.mobilebert_output = TFFFNOutput(config, name="output")
+
+ def call(self, hidden_states):
+ intermediate_output = self.intermediate(hidden_states)
+ layer_outputs = self.mobilebert_output(intermediate_output, hidden_states)
+ return layer_outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "intermediate", None) is not None:
+ with tf.name_scope(self.intermediate.name):
+ self.intermediate.build(None)
+ if getattr(self, "mobilebert_output", None) is not None:
+ with tf.name_scope(self.mobilebert_output.name):
+ self.mobilebert_output.build(None)
+
+
+class TFMobileBertLayer(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.use_bottleneck = config.use_bottleneck
+ self.num_feedforward_networks = config.num_feedforward_networks
+ self.attention = TFMobileBertAttention(config, name="attention")
+ self.intermediate = TFMobileBertIntermediate(config, name="intermediate")
+ self.mobilebert_output = TFMobileBertOutput(config, name="output")
+
+ if self.use_bottleneck:
+ self.bottleneck = TFBottleneck(config, name="bottleneck")
+ if config.num_feedforward_networks > 1:
+ self.ffn = [TFFFNLayer(config, name=f"ffn.{i}") for i in range(config.num_feedforward_networks - 1)]
+
+ def call(self, hidden_states, attention_mask, head_mask, output_attentions, training=False):
+ if self.use_bottleneck:
+ query_tensor, key_tensor, value_tensor, layer_input = self.bottleneck(hidden_states)
+ else:
+ query_tensor, key_tensor, value_tensor, layer_input = [hidden_states] * 4
+
+ attention_outputs = self.attention(
+ query_tensor,
+ key_tensor,
+ value_tensor,
+ layer_input,
+ attention_mask,
+ head_mask,
+ output_attentions,
+ training=training,
+ )
+
+ attention_output = attention_outputs[0]
+ s = (attention_output,)
+
+ if self.num_feedforward_networks != 1:
+ for i, ffn_module in enumerate(self.ffn):
+ attention_output = ffn_module(attention_output)
+ s += (attention_output,)
+
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.mobilebert_output(intermediate_output, attention_output, hidden_states, training=training)
+
+ outputs = (
+ (layer_output,)
+ + attention_outputs[1:]
+ + (
+ tf.constant(0),
+ query_tensor,
+ key_tensor,
+ value_tensor,
+ layer_input,
+ attention_output,
+ intermediate_output,
+ )
+ + s
+ ) # add attentions if we output them
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "attention", None) is not None:
+ with tf.name_scope(self.attention.name):
+ self.attention.build(None)
+ if getattr(self, "intermediate", None) is not None:
+ with tf.name_scope(self.intermediate.name):
+ self.intermediate.build(None)
+ if getattr(self, "mobilebert_output", None) is not None:
+ with tf.name_scope(self.mobilebert_output.name):
+ self.mobilebert_output.build(None)
+ if getattr(self, "bottleneck", None) is not None:
+ with tf.name_scope(self.bottleneck.name):
+ self.bottleneck.build(None)
+ if getattr(self, "ffn", None) is not None:
+ for layer in self.ffn:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+class TFMobileBertEncoder(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.output_attentions = config.output_attentions
+ self.output_hidden_states = config.output_hidden_states
+ self.layer = [TFMobileBertLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
+
+ def call(
+ self,
+ hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions,
+ output_hidden_states,
+ return_dict,
+ training=False,
+ ):
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = layer_module(
+ hidden_states, attention_mask, head_mask[i], output_attentions, training=training
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ # Add last layer
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
+ return TFBaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layer", None) is not None:
+ for layer in self.layer:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+class TFMobileBertPooler(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.do_activate = config.classifier_activation
+ if self.do_activate:
+ self.dense = keras.layers.Dense(
+ config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ activation="tanh",
+ name="dense",
+ )
+ self.config = config
+
+ def call(self, hidden_states):
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ if not self.do_activate:
+ return first_token_tensor
+ else:
+ pooled_output = self.dense(first_token_tensor)
+ return pooled_output
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+
+
+class TFMobileBertPredictionHeadTransform(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(
+ config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ if isinstance(config.hidden_act, str):
+ self.transform_act_fn = get_tf_activation(config.hidden_act)
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = NORM2FN["layer_norm"](config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.config = config
+
+ def call(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build(None)
+
+
+class TFMobileBertLMPredictionHead(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.transform = TFMobileBertPredictionHeadTransform(config, name="transform")
+ self.config = config
+
+ def build(self, input_shape=None):
+ self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
+ self.dense = self.add_weight(
+ shape=(self.config.hidden_size - self.config.embedding_size, self.config.vocab_size),
+ initializer="zeros",
+ trainable=True,
+ name="dense/weight",
+ )
+ self.decoder = self.add_weight(
+ shape=(self.config.vocab_size, self.config.embedding_size),
+ initializer="zeros",
+ trainable=True,
+ name="decoder/weight",
+ )
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "transform", None) is not None:
+ with tf.name_scope(self.transform.name):
+ self.transform.build(None)
+
+ def get_output_embeddings(self):
+ return self
+
+ def set_output_embeddings(self, value):
+ self.decoder = value
+ self.config.vocab_size = shape_list(value)[0]
+
+ def get_bias(self):
+ return {"bias": self.bias}
+
+ def set_bias(self, value):
+ self.bias = value["bias"]
+ self.config.vocab_size = shape_list(value["bias"])[0]
+
+ def call(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = tf.matmul(hidden_states, tf.concat([tf.transpose(self.decoder), self.dense], axis=0))
+ hidden_states = hidden_states + self.bias
+ return hidden_states
+
+
+class TFMobileBertMLMHead(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.predictions = TFMobileBertLMPredictionHead(config, name="predictions")
+
+ def call(self, sequence_output):
+ prediction_scores = self.predictions(sequence_output)
+ return prediction_scores
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "predictions", None) is not None:
+ with tf.name_scope(self.predictions.name):
+ self.predictions.build(None)
+
+
+@keras_serializable
+class TFMobileBertMainLayer(keras.layers.Layer):
+ config_class = MobileBertConfig
+
+ def __init__(self, config, add_pooling_layer=True, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.num_hidden_layers = config.num_hidden_layers
+ self.output_attentions = config.output_attentions
+ self.output_hidden_states = config.output_hidden_states
+ self.return_dict = config.use_return_dict
+
+ self.embeddings = TFMobileBertEmbeddings(config, name="embeddings")
+ self.encoder = TFMobileBertEncoder(config, name="encoder")
+ self.pooler = TFMobileBertPooler(config, name="pooler") if add_pooling_layer else None
+
+ def get_input_embeddings(self):
+ return self.embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.weight = value
+ self.embeddings.vocab_size = shape_list(value)[0]
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ raise NotImplementedError
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ token_type_ids=None,
+ position_ids=None,
+ head_mask=None,
+ inputs_embeds=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ training=False,
+ ):
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if attention_mask is None:
+ attention_mask = tf.fill(input_shape, 1)
+
+ if token_type_ids is None:
+ token_type_ids = tf.fill(input_shape, 0)
+
+ embedding_output = self.embeddings(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
+
+ # We create a 3D attention mask from a 2D tensor mask.
+ # Sizes are [batch_size, 1, 1, to_seq_length]
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+ # this attention mask is more simple than the triangular masking of causal attention
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+ extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1]))
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and -10000.0 for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+ extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
+ one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
+ ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
+ extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ if head_mask is not None:
+ raise NotImplementedError
+ else:
+ head_mask = [None] * self.num_hidden_layers
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ extended_attention_mask,
+ head_mask,
+ output_attentions,
+ output_hidden_states,
+ return_dict,
+ training=training,
+ )
+
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+ if not return_dict:
+ return (
+ sequence_output,
+ pooled_output,
+ ) + encoder_outputs[1:]
+
+ return TFBaseModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embeddings", None) is not None:
+ with tf.name_scope(self.embeddings.name):
+ self.embeddings.build(None)
+ if getattr(self, "encoder", None) is not None:
+ with tf.name_scope(self.encoder.name):
+ self.encoder.build(None)
+ if getattr(self, "pooler", None) is not None:
+ with tf.name_scope(self.pooler.name):
+ self.pooler.build(None)
+
+
+class TFMobileBertPreTrainedModel(TFPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = MobileBertConfig
+ base_model_prefix = "mobilebert"
+
+
+@dataclass
+class TFMobileBertForPreTrainingOutput(ModelOutput):
+ """
+ Output type of [`TFMobileBertForPreTraining`].
+
+ Args:
+ prediction_logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ seq_relationship_logits (`tf.Tensor` of shape `(batch_size, 2)`):
+ Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
+ before SoftMax).
+ hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+ attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`.
+
+ Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+ heads.
+ """
+
+ loss: tf.Tensor | None = None
+ prediction_logits: tf.Tensor | None = None
+ seq_relationship_logits: tf.Tensor | None = None
+ hidden_states: tuple[tf.Tensor] | None = None
+ attentions: tuple[tf.Tensor] | None = None
+
+
+MOBILEBERT_START_DOCSTRING = r"""
+
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Parameters:
+ config ([`MobileBertConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+MOBILEBERT_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
+ [`PreTrainedTokenizer.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+ config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+ used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+ eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+"""
+
+
+@add_start_docstrings(
+ "The bare MobileBert Model transformer outputting raw hidden-states without any specific head on top.",
+ MOBILEBERT_START_DOCSTRING,
+)
+class TFMobileBertModel(TFMobileBertPreTrainedModel):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFBaseModelOutputWithPooling,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFBaseModelOutputWithPooling:
+ outputs = self.mobilebert(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mobilebert", None) is not None:
+ with tf.name_scope(self.mobilebert.name):
+ self.mobilebert.build(None)
+
+
+@add_start_docstrings(
+ """
+ MobileBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
+ `next sentence prediction (classification)` head.
+ """,
+ MOBILEBERT_START_DOCSTRING,
+)
+class TFMobileBertForPreTraining(TFMobileBertPreTrainedModel, TFMobileBertPreTrainingLoss):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
+ self.predictions = TFMobileBertMLMHead(config, name="predictions___cls")
+ self.seq_relationship = TFMobileBertOnlyNSPHead(config, name="seq_relationship___cls")
+
+ def get_lm_head(self):
+ return self.predictions.predictions
+
+ def get_prefix_bias_name(self):
+ warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
+ return self.name + "/" + self.predictions.name + "/" + self.predictions.predictions.name
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @replace_return_docstrings(output_type=TFMobileBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ next_sentence_label: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFMobileBertForPreTrainingOutput:
+ r"""
+ Return:
+
+ Examples:
+
+ ```python
+ >>> import tensorflow as tf
+ >>> from transformers import AutoTokenizer, TFMobileBertForPreTraining
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
+ >>> model = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased")
+ >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
+ >>> outputs = model(input_ids)
+ >>> prediction_scores, seq_relationship_scores = outputs[:2]
+ ```"""
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ sequence_output, pooled_output = outputs[:2]
+ prediction_scores = self.predictions(sequence_output)
+ seq_relationship_score = self.seq_relationship(pooled_output)
+
+ total_loss = None
+ if labels is not None and next_sentence_label is not None:
+ d_labels = {"labels": labels}
+ d_labels["next_sentence_label"] = next_sentence_label
+ total_loss = self.hf_compute_loss(labels=d_labels, logits=(prediction_scores, seq_relationship_score))
+
+ if not return_dict:
+ output = (prediction_scores, seq_relationship_score) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return TFMobileBertForPreTrainingOutput(
+ loss=total_loss,
+ prediction_logits=prediction_scores,
+ seq_relationship_logits=seq_relationship_score,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mobilebert", None) is not None:
+ with tf.name_scope(self.mobilebert.name):
+ self.mobilebert.build(None)
+ if getattr(self, "predictions", None) is not None:
+ with tf.name_scope(self.predictions.name):
+ self.predictions.build(None)
+ if getattr(self, "seq_relationship", None) is not None:
+ with tf.name_scope(self.seq_relationship.name):
+ self.seq_relationship.build(None)
+
+ def tf_to_pt_weight_rename(self, tf_weight):
+ if tf_weight == "cls.predictions.decoder.weight":
+ return tf_weight, "mobilebert.embeddings.word_embeddings.weight"
+ else:
+ return (tf_weight,)
+
+
+@add_start_docstrings("""MobileBert Model with a `language modeling` head on top.""", MOBILEBERT_START_DOCSTRING)
+class TFMobileBertForMaskedLM(TFMobileBertPreTrainedModel, TFMaskedLanguageModelingLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [
+ r"pooler",
+ r"seq_relationship___cls",
+ r"cls.seq_relationship",
+ ]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.mobilebert = TFMobileBertMainLayer(config, add_pooling_layer=False, name="mobilebert")
+ self.predictions = TFMobileBertMLMHead(config, name="predictions___cls")
+
+ def get_lm_head(self):
+ return self.predictions.predictions
+
+ def get_prefix_bias_name(self):
+ warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
+ return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFMaskedLMOutput,
+ config_class=_CONFIG_FOR_DOC,
+ expected_output="'paris'",
+ expected_loss=0.57,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFMaskedLMOutput:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels
+ """
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+ prediction_scores = self.predictions(sequence_output, training=training)
+
+ loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores)
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFMaskedLMOutput(
+ loss=loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mobilebert", None) is not None:
+ with tf.name_scope(self.mobilebert.name):
+ self.mobilebert.build(None)
+ if getattr(self, "predictions", None) is not None:
+ with tf.name_scope(self.predictions.name):
+ self.predictions.build(None)
+
+ def tf_to_pt_weight_rename(self, tf_weight):
+ if tf_weight == "cls.predictions.decoder.weight":
+ return tf_weight, "mobilebert.embeddings.word_embeddings.weight"
+ else:
+ return (tf_weight,)
+
+
+class TFMobileBertOnlyNSPHead(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.seq_relationship = keras.layers.Dense(2, name="seq_relationship")
+ self.config = config
+
+ def call(self, pooled_output):
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return seq_relationship_score
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "seq_relationship", None) is not None:
+ with tf.name_scope(self.seq_relationship.name):
+ self.seq_relationship.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """MobileBert Model with a `next sentence prediction (classification)` head on top.""",
+ MOBILEBERT_START_DOCSTRING,
+)
+class TFMobileBertForNextSentencePrediction(TFMobileBertPreTrainedModel, TFNextSentencePredictionLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [r"predictions___cls", r"cls.predictions"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
+ self.cls = TFMobileBertOnlyNSPHead(config, name="seq_relationship___cls")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @replace_return_docstrings(output_type=TFNextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ next_sentence_label: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFNextSentencePredictorOutput:
+ r"""
+ Return:
+
+ Examples:
+
+ ```python
+ >>> import tensorflow as tf
+ >>> from transformers import AutoTokenizer, TFMobileBertForNextSentencePrediction
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
+ >>> model = TFMobileBertForNextSentencePrediction.from_pretrained("google/mobilebert-uncased")
+
+ >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+ >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
+ >>> encoding = tokenizer(prompt, next_sentence, return_tensors="tf")
+
+ >>> logits = model(encoding["input_ids"], token_type_ids=encoding["token_type_ids"])[0]
+ ```"""
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ pooled_output = outputs[1]
+ seq_relationship_scores = self.cls(pooled_output)
+
+ next_sentence_loss = (
+ None
+ if next_sentence_label is None
+ else self.hf_compute_loss(labels=next_sentence_label, logits=seq_relationship_scores)
+ )
+
+ if not return_dict:
+ output = (seq_relationship_scores,) + outputs[2:]
+ return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
+
+ return TFNextSentencePredictorOutput(
+ loss=next_sentence_loss,
+ logits=seq_relationship_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mobilebert", None) is not None:
+ with tf.name_scope(self.mobilebert.name):
+ self.mobilebert.build(None)
+ if getattr(self, "cls", None) is not None:
+ with tf.name_scope(self.cls.name):
+ self.cls.build(None)
+
+
+@add_start_docstrings(
+ """
+ MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+ pooled output) e.g. for GLUE tasks.
+ """,
+ MOBILEBERT_START_DOCSTRING,
+)
+class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSequenceClassificationLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [
+ r"predictions___cls",
+ r"seq_relationship___cls",
+ r"cls.predictions",
+ r"cls.seq_relationship",
+ ]
+ _keys_to_ignore_on_load_missing = [r"dropout"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.num_labels = config.num_labels
+
+ self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
+ classifier_dropout = (
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.dropout = keras.layers.Dropout(classifier_dropout)
+ self.classifier = keras.layers.Dense(
+ config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
+ output_type=TFSequenceClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
+ expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFSequenceClassifierOutput:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ pooled_output = outputs[1]
+
+ pooled_output = self.dropout(pooled_output, training=training)
+ logits = self.classifier(pooled_output)
+
+ loss = None if labels is None else self.hf_compute_loss(labels, logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFSequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mobilebert", None) is not None:
+ with tf.name_scope(self.mobilebert.name):
+ self.mobilebert.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ MobileBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
+ linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
+ """,
+ MOBILEBERT_START_DOCSTRING,
+)
+class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAnsweringLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [
+ r"pooler",
+ r"predictions___cls",
+ r"seq_relationship___cls",
+ r"cls.predictions",
+ r"cls.seq_relationship",
+ ]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.num_labels = config.num_labels
+
+ self.mobilebert = TFMobileBertMainLayer(config, add_pooling_layer=False, name="mobilebert")
+ self.qa_outputs = keras.layers.Dense(
+ config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_QA,
+ output_type=TFQuestionAnsweringModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ qa_target_start_index=_QA_TARGET_START_INDEX,
+ qa_target_end_index=_QA_TARGET_END_INDEX,
+ expected_output=_QA_EXPECTED_OUTPUT,
+ expected_loss=_QA_EXPECTED_LOSS,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ start_positions: np.ndarray | tf.Tensor | None = None,
+ end_positions: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFQuestionAnsweringModelOutput:
+ r"""
+ start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the start of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+ are not taken into account for computing the loss.
+ end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the end of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+ are not taken into account for computing the loss.
+ """
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = tf.split(logits, 2, axis=-1)
+ start_logits = tf.squeeze(start_logits, axis=-1)
+ end_logits = tf.squeeze(end_logits, axis=-1)
+
+ loss = None
+ if start_positions is not None and end_positions is not None:
+ labels = {"start_position": start_positions, "end_position": end_positions}
+ loss = self.hf_compute_loss(labels, (start_logits, end_logits))
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFQuestionAnsweringModelOutput(
+ loss=loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mobilebert", None) is not None:
+ with tf.name_scope(self.mobilebert.name):
+ self.mobilebert.build(None)
+ if getattr(self, "qa_outputs", None) is not None:
+ with tf.name_scope(self.qa_outputs.name):
+ self.qa_outputs.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ MobileBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
+ a softmax) e.g. for RocStories/SWAG tasks.
+ """,
+ MOBILEBERT_START_DOCSTRING,
+)
+class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoiceLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [
+ r"predictions___cls",
+ r"seq_relationship___cls",
+ r"cls.predictions",
+ r"cls.seq_relationship",
+ ]
+ _keys_to_ignore_on_load_missing = [r"dropout"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.classifier = keras.layers.Dense(
+ 1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(
+ MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
+ )
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFMultipleChoiceModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFMultipleChoiceModelOutput:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
+ where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)
+ """
+ if input_ids is not None:
+ num_choices = shape_list(input_ids)[1]
+ seq_length = shape_list(input_ids)[2]
+ else:
+ num_choices = shape_list(inputs_embeds)[1]
+ seq_length = shape_list(inputs_embeds)[2]
+
+ flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
+ flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
+ flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
+ flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
+ flat_inputs_embeds = (
+ tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
+ if inputs_embeds is not None
+ else None
+ )
+ outputs = self.mobilebert(
+ flat_input_ids,
+ flat_attention_mask,
+ flat_token_type_ids,
+ flat_position_ids,
+ head_mask,
+ flat_inputs_embeds,
+ output_attentions,
+ output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ pooled_output = outputs[1]
+ pooled_output = self.dropout(pooled_output, training=training)
+ logits = self.classifier(pooled_output)
+ reshaped_logits = tf.reshape(logits, (-1, num_choices))
+
+ loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFMultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mobilebert", None) is not None:
+ with tf.name_scope(self.mobilebert.name):
+ self.mobilebert.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ MobileBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
+ for Named-Entity-Recognition (NER) tasks.
+ """,
+ MOBILEBERT_START_DOCSTRING,
+)
+class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenClassificationLoss):
+ # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+ _keys_to_ignore_on_load_unexpected = [
+ r"pooler",
+ r"predictions___cls",
+ r"seq_relationship___cls",
+ r"cls.predictions",
+ r"cls.seq_relationship",
+ ]
+ _keys_to_ignore_on_load_missing = [r"dropout"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.num_labels = config.num_labels
+
+ self.mobilebert = TFMobileBertMainLayer(config, add_pooling_layer=False, name="mobilebert")
+ classifier_dropout = (
+ config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
+ )
+ self.dropout = keras.layers.Dropout(classifier_dropout)
+ self.classifier = keras.layers.Dense(
+ config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION,
+ output_type=TFTokenClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT,
+ expected_loss=_TOKEN_CLASS_EXPECTED_LOSS,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFTokenClassifierOutput:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+ outputs = self.mobilebert(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output, training=training)
+ logits = self.classifier(sequence_output)
+
+ loss = None if labels is None else self.hf_compute_loss(labels, logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFTokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mobilebert", None) is not None:
+ with tf.name_scope(self.mobilebert.name):
+ self.mobilebert.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build([None, None, self.config.hidden_size])
+
+
+__all__ = [
+ "TFMobileBertForMaskedLM",
+ "TFMobileBertForMultipleChoice",
+ "TFMobileBertForNextSentencePrediction",
+ "TFMobileBertForPreTraining",
+ "TFMobileBertForQuestionAnswering",
+ "TFMobileBertForSequenceClassification",
+ "TFMobileBertForTokenClassification",
+ "TFMobileBertMainLayer",
+ "TFMobileBertModel",
+ "TFMobileBertPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/tokenization_mobilebert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/tokenization_mobilebert.py
new file mode 100644
index 0000000000000000000000000000000000000000..88628400ca49079a0689ecf5978c1256fd82234c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/tokenization_mobilebert.py
@@ -0,0 +1,484 @@
+# coding=utf-8
+#
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for MobileBERT."""
+
+import collections
+import os
+import unicodedata
+from typing import Optional
+
+from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+
+# Copied from transformers.models.bert.tokenization_bert.load_vocab
+def load_vocab(vocab_file):
+ """Loads a vocabulary file into a dictionary."""
+ vocab = collections.OrderedDict()
+ with open(vocab_file, "r", encoding="utf-8") as reader:
+ tokens = reader.readlines()
+ for index, token in enumerate(tokens):
+ token = token.rstrip("\n")
+ vocab[token] = index
+ return vocab
+
+
+# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
+def whitespace_tokenize(text):
+ """Runs basic whitespace cleaning and splitting on a piece of text."""
+ text = text.strip()
+ if not text:
+ return []
+ tokens = text.split()
+ return tokens
+
+
+# Copied from transformers.models.bert.tokenization_bert.BertTokenizer with BERT->MobileBERT,Bert->MobileBert
+class MobileBertTokenizer(PreTrainedTokenizer):
+ r"""
+ Construct a MobileBERT tokenizer. Based on WordPiece.
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ File containing the vocabulary.
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ do_basic_tokenize (`bool`, *optional*, defaults to `True`):
+ Whether or not to do basic tokenization before WordPiece.
+ never_split (`Iterable`, *optional*):
+ Collection of tokens which will never be split during tokenization. Only has an effect when
+ `do_basic_tokenize=True`
+ unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ sep_token (`str`, *optional*, defaults to `"[SEP]"`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ pad_token (`str`, *optional*, defaults to `"[PAD]"`):
+ The token used for padding, for example when batching sequences of different lengths.
+ cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ mask_token (`str`, *optional*, defaults to `"[MASK]"`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters.
+
+ This should likely be deactivated for Japanese (see this
+ [issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original MobileBERT).
+ clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
+ Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+ extra spaces.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+
+ def __init__(
+ self,
+ vocab_file,
+ do_lower_case=True,
+ do_basic_tokenize=True,
+ never_split=None,
+ unk_token="[UNK]",
+ sep_token="[SEP]",
+ pad_token="[PAD]",
+ cls_token="[CLS]",
+ mask_token="[MASK]",
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ clean_up_tokenization_spaces=True,
+ **kwargs,
+ ):
+ if not os.path.isfile(vocab_file):
+ raise ValueError(
+ f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
+ " model use `tokenizer = MobileBertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+ )
+ self.vocab = load_vocab(vocab_file)
+ self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
+ self.do_basic_tokenize = do_basic_tokenize
+ if do_basic_tokenize:
+ self.basic_tokenizer = BasicTokenizer(
+ do_lower_case=do_lower_case,
+ never_split=never_split,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ )
+
+ self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
+
+ super().__init__(
+ do_lower_case=do_lower_case,
+ do_basic_tokenize=do_basic_tokenize,
+ never_split=never_split,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ pad_token=pad_token,
+ cls_token=cls_token,
+ mask_token=mask_token,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+ **kwargs,
+ )
+
+ @property
+ def do_lower_case(self):
+ return self.basic_tokenizer.do_lower_case
+
+ @property
+ def vocab_size(self):
+ return len(self.vocab)
+
+ def get_vocab(self):
+ return dict(self.vocab, **self.added_tokens_encoder)
+
+ def _tokenize(self, text, split_special_tokens=False):
+ split_tokens = []
+ if self.do_basic_tokenize:
+ for token in self.basic_tokenizer.tokenize(
+ text, never_split=self.all_special_tokens if not split_special_tokens else None
+ ):
+ # If the token is part of the never_split set
+ if token in self.basic_tokenizer.never_split:
+ split_tokens.append(token)
+ else:
+ split_tokens += self.wordpiece_tokenizer.tokenize(token)
+ else:
+ split_tokens = self.wordpiece_tokenizer.tokenize(text)
+ return split_tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.ids_to_tokens.get(index, self.unk_token)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ out_string = " ".join(tokens).replace(" ##", "").strip()
+ return out_string
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A MobileBERT sequence has the following format:
+
+ - single sequence: `[CLS] X [SEP]`
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + token_ids_1 + sep
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is not None:
+ return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1]
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ index = 0
+ if os.path.isdir(save_directory):
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ else:
+ vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
+ with open(vocab_file, "w", encoding="utf-8") as writer:
+ for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+ " Please check that the vocabulary is not corrupted!"
+ )
+ index = token_index
+ writer.write(token + "\n")
+ index += 1
+ return (vocab_file,)
+
+
+# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
+class BasicTokenizer:
+ """
+ Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
+
+ Args:
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ never_split (`Iterable`, *optional*):
+ Collection of tokens which will never be split during tokenization. Only has an effect when
+ `do_basic_tokenize=True`
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters.
+
+ This should likely be deactivated for Japanese (see this
+ [issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original BERT).
+ do_split_on_punc (`bool`, *optional*, defaults to `True`):
+ In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
+ the full context of the words, such as contractions.
+ """
+
+ def __init__(
+ self,
+ do_lower_case=True,
+ never_split=None,
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ do_split_on_punc=True,
+ ):
+ if never_split is None:
+ never_split = []
+ self.do_lower_case = do_lower_case
+ self.never_split = set(never_split)
+ self.tokenize_chinese_chars = tokenize_chinese_chars
+ self.strip_accents = strip_accents
+ self.do_split_on_punc = do_split_on_punc
+
+ def tokenize(self, text, never_split=None):
+ """
+ Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
+
+ Args:
+ never_split (`List[str]`, *optional*)
+ Kept for backward compatibility purposes. Now implemented directly at the base class level (see
+ [`PreTrainedTokenizer.tokenize`]) List of token not to split.
+ """
+ # union() returns a new set by concatenating the two sets.
+ never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
+ text = self._clean_text(text)
+
+ # This was added on November 1st, 2018 for the multilingual and Chinese
+ # models. This is also applied to the English models now, but it doesn't
+ # matter since the English models were not trained on any Chinese data
+ # and generally don't have any Chinese data in them (there are Chinese
+ # characters in the vocabulary because Wikipedia does have some Chinese
+ # words in the English Wikipedia.).
+ if self.tokenize_chinese_chars:
+ text = self._tokenize_chinese_chars(text)
+ # prevents treating the same character with different unicode codepoints as different characters
+ unicode_normalized_text = unicodedata.normalize("NFC", text)
+ orig_tokens = whitespace_tokenize(unicode_normalized_text)
+ split_tokens = []
+ for token in orig_tokens:
+ if token not in never_split:
+ if self.do_lower_case:
+ token = token.lower()
+ if self.strip_accents is not False:
+ token = self._run_strip_accents(token)
+ elif self.strip_accents:
+ token = self._run_strip_accents(token)
+ split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+ output_tokens = whitespace_tokenize(" ".join(split_tokens))
+ return output_tokens
+
+ def _run_strip_accents(self, text):
+ """Strips accents from a piece of text."""
+ text = unicodedata.normalize("NFD", text)
+ output = []
+ for char in text:
+ cat = unicodedata.category(char)
+ if cat == "Mn":
+ continue
+ output.append(char)
+ return "".join(output)
+
+ def _run_split_on_punc(self, text, never_split=None):
+ """Splits punctuation on a piece of text."""
+ if not self.do_split_on_punc or (never_split is not None and text in never_split):
+ return [text]
+ chars = list(text)
+ i = 0
+ start_new_word = True
+ output = []
+ while i < len(chars):
+ char = chars[i]
+ if _is_punctuation(char):
+ output.append([char])
+ start_new_word = True
+ else:
+ if start_new_word:
+ output.append([])
+ start_new_word = False
+ output[-1].append(char)
+ i += 1
+
+ return ["".join(x) for x in output]
+
+ def _tokenize_chinese_chars(self, text):
+ """Adds whitespace around any CJK character."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if self._is_chinese_char(cp):
+ output.append(" ")
+ output.append(char)
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+ def _is_chinese_char(self, cp):
+ """Checks whether CP is the codepoint of a CJK character."""
+ # This defines a "chinese character" as anything in the CJK Unicode block:
+ # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+ #
+ # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+ # despite its name. The modern Korean Hangul alphabet is a different block,
+ # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+ # space-separated words, so they are not treated specially and handled
+ # like the all of the other languages.
+ if (
+ (cp >= 0x4E00 and cp <= 0x9FFF)
+ or (cp >= 0x3400 and cp <= 0x4DBF)
+ or (cp >= 0x20000 and cp <= 0x2A6DF)
+ or (cp >= 0x2A700 and cp <= 0x2B73F)
+ or (cp >= 0x2B740 and cp <= 0x2B81F)
+ or (cp >= 0x2B820 and cp <= 0x2CEAF)
+ or (cp >= 0xF900 and cp <= 0xFAFF)
+ or (cp >= 0x2F800 and cp <= 0x2FA1F)
+ ):
+ return True
+
+ return False
+
+ def _clean_text(self, text):
+ """Performs invalid character removal and whitespace cleanup on text."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if cp == 0 or cp == 0xFFFD or _is_control(char):
+ continue
+ if _is_whitespace(char):
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+
+# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
+class WordpieceTokenizer:
+ """Runs WordPiece tokenization."""
+
+ def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+ self.vocab = vocab
+ self.unk_token = unk_token
+ self.max_input_chars_per_word = max_input_chars_per_word
+
+ def tokenize(self, text):
+ """
+ Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
+ tokenization using the given vocabulary.
+
+ For example, `input = "unaffable"` will return as output `["un", "##aff", "##able"]`.
+
+ Args:
+ text: A single token or whitespace separated tokens. This should have
+ already been passed through *BasicTokenizer*.
+
+ Returns:
+ A list of wordpiece tokens.
+ """
+
+ output_tokens = []
+ for token in whitespace_tokenize(text):
+ chars = list(token)
+ if len(chars) > self.max_input_chars_per_word:
+ output_tokens.append(self.unk_token)
+ continue
+
+ is_bad = False
+ start = 0
+ sub_tokens = []
+ while start < len(chars):
+ end = len(chars)
+ cur_substr = None
+ while start < end:
+ substr = "".join(chars[start:end])
+ if start > 0:
+ substr = "##" + substr
+ if substr in self.vocab:
+ cur_substr = substr
+ break
+ end -= 1
+ if cur_substr is None:
+ is_bad = True
+ break
+ sub_tokens.append(cur_substr)
+ start = end
+
+ if is_bad:
+ output_tokens.append(self.unk_token)
+ else:
+ output_tokens.extend(sub_tokens)
+ return output_tokens
+
+
+__all__ = ["MobileBertTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/tokenization_mobilebert_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/tokenization_mobilebert_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f97ddbbb7031c14aba617780973ba101d791307
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilebert/tokenization_mobilebert_fast.py
@@ -0,0 +1,148 @@
+# coding=utf-8
+#
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for MobileBERT."""
+
+import json
+from typing import Optional
+
+from tokenizers import normalizers
+
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import logging
+from .tokenization_mobilebert import MobileBertTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
+
+
+# Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast with BERT->MobileBERT,Bert->MobileBert
+class MobileBertTokenizerFast(PreTrainedTokenizerFast):
+ r"""
+ Construct a "fast" MobileBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ File containing the vocabulary.
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ sep_token (`str`, *optional*, defaults to `"[SEP]"`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ pad_token (`str`, *optional*, defaults to `"[PAD]"`):
+ The token used for padding, for example when batching sequences of different lengths.
+ cls_token (`str`, *optional*, defaults to `"[CLS]"`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ mask_token (`str`, *optional*, defaults to `"[MASK]"`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ clean_text (`bool`, *optional*, defaults to `True`):
+ Whether or not to clean the text before tokenization by removing any control characters and replacing all
+ whitespaces by the classic one.
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
+ issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original MobileBERT).
+ wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
+ The prefix for subwords.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ slow_tokenizer_class = MobileBertTokenizer
+
+ def __init__(
+ self,
+ vocab_file=None,
+ tokenizer_file=None,
+ do_lower_case=True,
+ unk_token="[UNK]",
+ sep_token="[SEP]",
+ pad_token="[PAD]",
+ cls_token="[CLS]",
+ mask_token="[MASK]",
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ **kwargs,
+ ):
+ super().__init__(
+ vocab_file,
+ tokenizer_file=tokenizer_file,
+ do_lower_case=do_lower_case,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ pad_token=pad_token,
+ cls_token=cls_token,
+ mask_token=mask_token,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ **kwargs,
+ )
+
+ normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
+ if (
+ normalizer_state.get("lowercase", do_lower_case) != do_lower_case
+ or normalizer_state.get("strip_accents", strip_accents) != strip_accents
+ or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars
+ ):
+ normalizer_class = getattr(normalizers, normalizer_state.pop("type"))
+ normalizer_state["lowercase"] = do_lower_case
+ normalizer_state["strip_accents"] = strip_accents
+ normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars
+ self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state)
+
+ self.do_lower_case = do_lower_case
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A MobileBERT sequence has the following format:
+
+ - single sequence: `[CLS] X [SEP]`
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
+
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+
+ if token_ids_1 is not None:
+ output += token_ids_1 + [self.sep_token_id]
+
+ return output
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+ return tuple(files)
+
+
+__all__ = ["MobileBertTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bce83216c35546fe01cf96c738cbe9c2f3582486
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mobilenet_v1 import *
+ from .feature_extraction_mobilenet_v1 import *
+ from .image_processing_mobilenet_v1 import *
+ from .image_processing_mobilenet_v1_fast import *
+ from .modeling_mobilenet_v1 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py
new file mode 100644
index 0000000000000000000000000000000000000000..c18d72d55003dc88e8fbcfe2ee80a18975d23dbc
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py
@@ -0,0 +1,126 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MobileNetV1 model configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+
+from packaging import version
+
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MobileNetV1Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MobileNetV1Model`]. It is used to instantiate a
+ MobileNetV1 model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the MobileNetV1
+ [google/mobilenet_v1_1.0_224](https://huggingface.co/google/mobilenet_v1_1.0_224) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ num_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ image_size (`int`, *optional*, defaults to 224):
+ The size (resolution) of each image.
+ depth_multiplier (`float`, *optional*, defaults to 1.0):
+ Shrinks or expands the number of channels in each layer. Default is 1.0, which starts the network with 32
+ channels. This is sometimes also called "alpha" or "width multiplier".
+ min_depth (`int`, *optional*, defaults to 8):
+ All layers will have at least this many channels.
+ hidden_act (`str` or `function`, *optional*, defaults to `"relu6"`):
+ The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
+ tf_padding (`bool`, *optional*, defaults to `True`):
+ Whether to use TensorFlow padding rules on the convolution layers.
+ classifier_dropout_prob (`float`, *optional*, defaults to 0.999):
+ The dropout ratio for attached classifiers.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 0.001):
+ The epsilon used by the layer normalization layers.
+
+ Example:
+
+ ```python
+ >>> from transformers import MobileNetV1Config, MobileNetV1Model
+
+ >>> # Initializing a "mobilenet_v1_1.0_224" style configuration
+ >>> configuration = MobileNetV1Config()
+
+ >>> # Initializing a model from the "mobilenet_v1_1.0_224" style configuration
+ >>> model = MobileNetV1Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mobilenet_v1"
+
+ def __init__(
+ self,
+ num_channels=3,
+ image_size=224,
+ depth_multiplier=1.0,
+ min_depth=8,
+ hidden_act="relu6",
+ tf_padding=True,
+ classifier_dropout_prob=0.999,
+ initializer_range=0.02,
+ layer_norm_eps=0.001,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ if depth_multiplier <= 0:
+ raise ValueError("depth_multiplier must be greater than zero.")
+
+ self.num_channels = num_channels
+ self.image_size = image_size
+ self.depth_multiplier = depth_multiplier
+ self.min_depth = min_depth
+ self.hidden_act = hidden_act
+ self.tf_padding = tf_padding
+ self.classifier_dropout_prob = classifier_dropout_prob
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+
+
+class MobileNetV1OnnxConfig(OnnxConfig):
+ torch_onnx_minimum_version = version.parse("1.11")
+
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ return OrderedDict([("pixel_values", {0: "batch"})])
+
+ @property
+ def outputs(self) -> Mapping[str, Mapping[int, str]]:
+ if self.task == "image-classification":
+ return OrderedDict([("logits", {0: "batch"})])
+ else:
+ return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})])
+
+ @property
+ def atol_for_validation(self) -> float:
+ return 1e-4
+
+
+__all__ = ["MobileNetV1Config", "MobileNetV1OnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/convert_original_tf_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/convert_original_tf_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b53bbeab475c036f8e917bf65b2d7411b141d2a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/convert_original_tf_checkpoint_to_pytorch.py
@@ -0,0 +1,141 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert MobileNetV1 checkpoints from the tensorflow/models library."""
+
+import argparse
+import json
+import re
+from pathlib import Path
+
+import requests
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from transformers import (
+ MobileNetV1Config,
+ MobileNetV1ForImageClassification,
+ MobileNetV1ImageProcessor,
+ load_tf_weights_in_mobilenet_v1,
+)
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def get_mobilenet_v1_config(model_name):
+ config = MobileNetV1Config(layer_norm_eps=0.001)
+
+ if "_quant" in model_name:
+ raise ValueError("Quantized models are not supported.")
+
+ matches = re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$", model_name)
+ if matches:
+ config.depth_multiplier = float(matches[1])
+ config.image_size = int(matches[2])
+
+ # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
+ # the usual 1000. The first class (index 0) is "background".
+ config.num_labels = 1001
+ filename = "imagenet-1k-id2label.json"
+ repo_id = "huggingface/label-files"
+ id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
+ id2label = {int(k) + 1: v for k, v in id2label.items()}
+ id2label[0] = "background"
+ config.id2label = id2label
+ config.label2id = {v: k for k, v in id2label.items()}
+
+ return config
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ im = Image.open(requests.get(url, stream=True).raw)
+ return im
+
+
+@torch.no_grad()
+def convert_movilevit_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub=False):
+ """
+ Copy/paste/tweak model's weights to our MobileNetV1 structure.
+ """
+ config = get_mobilenet_v1_config(model_name)
+
+ # Load 🤗 model
+ model = MobileNetV1ForImageClassification(config).eval()
+
+ # Load weights from TensorFlow checkpoint
+ load_tf_weights_in_mobilenet_v1(model, config, checkpoint_path)
+
+ # Check outputs on an image, prepared by MobileNetV1ImageProcessor
+ image_processor = MobileNetV1ImageProcessor(
+ crop_size={"width": config.image_size, "height": config.image_size},
+ size={"shortest_edge": config.image_size + 32},
+ )
+ encoding = image_processor(images=prepare_img(), return_tensors="pt")
+ outputs = model(**encoding)
+ logits = outputs.logits
+
+ assert logits.shape == (1, 1001)
+
+ if model_name == "mobilenet_v1_1.0_224":
+ expected_logits = torch.tensor([-4.1739, -1.1233, 3.1205])
+ elif model_name == "mobilenet_v1_0.75_192":
+ expected_logits = torch.tensor([-3.9440, -2.3141, -0.3333])
+ else:
+ expected_logits = None
+
+ if expected_logits is not None:
+ assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4)
+
+ Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+ print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
+ model.save_pretrained(pytorch_dump_folder_path)
+ print(f"Saving image processor to {pytorch_dump_folder_path}")
+ image_processor.save_pretrained(pytorch_dump_folder_path)
+
+ if push_to_hub:
+ print("Pushing to the hub...")
+ repo_id = "google/" + model_name
+ image_processor.push_to_hub(repo_id)
+ model.push_to_hub(repo_id)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--model_name",
+ default="mobilenet_v1_1.0_224",
+ type=str,
+ help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1__'.",
+ )
+ parser.add_argument(
+ "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
+ )
+ parser.add_argument(
+ "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
+ )
+
+ args = parser.parse_args()
+ convert_movilevit_checkpoint(
+ args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/feature_extraction_mobilenet_v1.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/feature_extraction_mobilenet_v1.py
new file mode 100644
index 0000000000000000000000000000000000000000..02a5401bc145996d1126641ee656180a48c92e20
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/feature_extraction_mobilenet_v1.py
@@ -0,0 +1,38 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Feature extractor class for MobileNetV1."""
+
+import warnings
+
+from ...utils import logging
+from ...utils.import_utils import requires
+from .image_processing_mobilenet_v1 import MobileNetV1ImageProcessor
+
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("vision",))
+class MobileNetV1FeatureExtractor(MobileNetV1ImageProcessor):
+ def __init__(self, *args, **kwargs) -> None:
+ warnings.warn(
+ "The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
+ " Please use MobileNetV1ImageProcessor instead.",
+ FutureWarning,
+ )
+ super().__init__(*args, **kwargs)
+
+
+__all__ = ["MobileNetV1FeatureExtractor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py
new file mode 100644
index 0000000000000000000000000000000000000000..449b0b6f7bcd96b26c919b82d6dbc56db70c5ba5
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py
@@ -0,0 +1,307 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for MobileNetV1."""
+
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import (
+ get_resize_output_image_size,
+ resize,
+ to_channel_dimension_format,
+)
+from ...image_utils import (
+ IMAGENET_STANDARD_MEAN,
+ IMAGENET_STANDARD_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import TensorType, filter_out_non_signature_kwargs, logging
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("vision",))
+class MobileNetV1ImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a MobileNetV1 image processor.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
+ `do_resize` in the `preprocess` method.
+ size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 256}`):
+ Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
+ method.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+ Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
+ `preprocess` method.
+ do_center_crop (`bool`, *optional*, defaults to `True`):
+ Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
+ is padded with 0's and then center cropped. Can be overridden by the `do_center_crop` parameter in the
+ `preprocess` method.
+ crop_size (`dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
+ Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`.
+ Can be overridden by the `crop_size` parameter in the `preprocess` method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+ parameter in the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
+ `preprocess` method.
+ do_normalize:
+ Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+ method.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
+ Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+ number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+ """
+
+ model_input_names = ["pixel_values"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ do_center_crop: bool = True,
+ crop_size: Optional[dict[str, int]] = None,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"shortest_edge": 256}
+ size = get_size_dict(size, default_to_square=False)
+ crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
+ crop_size = get_size_dict(crop_size)
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.do_center_crop = do_center_crop
+ self.crop_size = crop_size
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
+ self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
+
+ # Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
+ resized to keep the input aspect ratio.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Size of the output image.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ Resampling filter to use when resiizing the image.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ default_to_square = True
+ if "shortest_edge" in size:
+ size = size["shortest_edge"]
+ default_to_square = False
+ elif "height" in size and "width" in size:
+ size = (size["height"], size["width"])
+ else:
+ raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
+ output_size = get_resize_output_image_size(
+ image,
+ size=size,
+ default_to_square=default_to_square,
+ input_data_format=input_data_format,
+ )
+ return resize(
+ image,
+ size=output_size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[dict[str, int]] = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ """
+ Preprocess an image or batch of images.
+
+ Args:
+ images (`ImageInput`):
+ Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio.
+ resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
+ `PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has
+ an effect if `do_resize` is set to `True`.
+ do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
+ Whether to center crop the image.
+ crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`):
+ Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image values between [0 - 1].
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to use if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to use if `do_normalize` is set to `True`.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ size = get_size_dict(size, default_to_square=False)
+ resample = resample if resample is not None else self.resample
+ do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
+ crop_size = crop_size if crop_size is not None else self.crop_size
+ crop_size = get_size_dict(crop_size)
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+
+ images = make_list_of_images(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if do_rescale and is_scaled_image(images[0]):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ all_images = []
+ for image in images:
+ if do_resize:
+ image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+
+ if do_center_crop:
+ image = self.center_crop(image=image, size=crop_size, input_data_format=input_data_format)
+
+ if do_rescale:
+ image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+
+ if do_normalize:
+ image = self.normalize(
+ image=image, mean=image_mean, std=image_std, input_data_format=input_data_format
+ )
+
+ all_images.append(image)
+ images = [
+ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+ for image in all_images
+ ]
+
+ data = {"pixel_values": images}
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+
+__all__ = ["MobileNetV1ImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..e716553a6d102fcd5d32af50f9e28c5397812287
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1_fast.py
@@ -0,0 +1,43 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for MobileNetV1."""
+
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ Unpack,
+)
+from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, PILImageResampling
+from ...utils import auto_docstring
+
+
+@auto_docstring
+class MobileNetV1ImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BILINEAR
+ image_mean = IMAGENET_STANDARD_MEAN
+ image_std = IMAGENET_STANDARD_STD
+ size = {"shortest_edge": 256}
+ default_to_square = False
+ crop_size = {"height": 224, "width": 224}
+ do_resize = True
+ do_center_crop = True
+ do_rescale = True
+ do_normalize = True
+
+ def __init__(self, **kwargs: Unpack[DefaultFastImageProcessorKwargs]) -> None:
+ super().__init__(**kwargs)
+
+
+__all__ = ["MobileNetV1ImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py
new file mode 100644
index 0000000000000000000000000000000000000000..47b9f43e8a85c24c1d6e7bd9e93a0d8fe19fe198
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py
@@ -0,0 +1,434 @@
+# coding=utf-8
+# Copyright 2022 Apple Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MobileNetV1 model."""
+
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, logging
+from .configuration_mobilenet_v1 import MobileNetV1Config
+
+
+logger = logging.get_logger(__name__)
+
+
+def _build_tf_to_pytorch_map(model, config, tf_weights=None):
+ """
+ A map of modules from TF to PyTorch.
+ """
+
+ tf_to_pt_map = {}
+
+ if isinstance(model, MobileNetV1ForImageClassification):
+ backbone = model.mobilenet_v1
+ else:
+ backbone = model
+
+ prefix = "MobilenetV1/Conv2d_0/"
+ tf_to_pt_map[prefix + "weights"] = backbone.conv_stem.convolution.weight
+ tf_to_pt_map[prefix + "BatchNorm/beta"] = backbone.conv_stem.normalization.bias
+ tf_to_pt_map[prefix + "BatchNorm/gamma"] = backbone.conv_stem.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.normalization.running_var
+
+ for i in range(13):
+ tf_index = i + 1
+ pt_index = i * 2
+
+ pointer = backbone.layer[pt_index]
+ prefix = f"MobilenetV1/Conv2d_{tf_index}_depthwise/"
+ tf_to_pt_map[prefix + "depthwise_weights"] = pointer.convolution.weight
+ tf_to_pt_map[prefix + "BatchNorm/beta"] = pointer.normalization.bias
+ tf_to_pt_map[prefix + "BatchNorm/gamma"] = pointer.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.normalization.running_var
+
+ pointer = backbone.layer[pt_index + 1]
+ prefix = f"MobilenetV1/Conv2d_{tf_index}_pointwise/"
+ tf_to_pt_map[prefix + "weights"] = pointer.convolution.weight
+ tf_to_pt_map[prefix + "BatchNorm/beta"] = pointer.normalization.bias
+ tf_to_pt_map[prefix + "BatchNorm/gamma"] = pointer.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.normalization.running_var
+
+ if isinstance(model, MobileNetV1ForImageClassification):
+ prefix = "MobilenetV1/Logits/Conv2d_1c_1x1/"
+ tf_to_pt_map[prefix + "weights"] = model.classifier.weight
+ tf_to_pt_map[prefix + "biases"] = model.classifier.bias
+
+ return tf_to_pt_map
+
+
+def load_tf_weights_in_mobilenet_v1(model, config, tf_checkpoint_path):
+ """Load TensorFlow checkpoints in a PyTorch model."""
+ try:
+ import numpy as np
+ import tensorflow as tf
+ except ImportError:
+ logger.error(
+ "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
+ "https://www.tensorflow.org/install/ for installation instructions."
+ )
+ raise
+
+ # Load weights from TF model
+ init_vars = tf.train.list_variables(tf_checkpoint_path)
+ tf_weights = {}
+ for name, shape in init_vars:
+ logger.info(f"Loading TF weight {name} with shape {shape}")
+ array = tf.train.load_variable(tf_checkpoint_path, name)
+ tf_weights[name] = array
+
+ # Build TF to PyTorch weights loading map
+ tf_to_pt_map = _build_tf_to_pytorch_map(model, config, tf_weights)
+
+ for name, pointer in tf_to_pt_map.items():
+ logger.info(f"Importing {name}")
+ if name not in tf_weights:
+ logger.info(f"{name} not in tf pre-trained weights, skipping")
+ continue
+
+ array = tf_weights[name]
+
+ if "depthwise_weights" in name:
+ logger.info("Transposing depthwise")
+ array = np.transpose(array, (2, 3, 0, 1))
+ elif "weights" in name:
+ logger.info("Transposing")
+ if len(pointer.shape) == 2: # copying into linear layer
+ array = array.squeeze().transpose()
+ else:
+ array = np.transpose(array, (3, 2, 0, 1))
+
+ if pointer.shape != array.shape:
+ raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
+
+ logger.info(f"Initialize PyTorch weight {name} {array.shape}")
+ pointer.data = torch.from_numpy(array)
+
+ tf_weights.pop(name, None)
+ tf_weights.pop(name + "/RMSProp", None)
+ tf_weights.pop(name + "/RMSProp_1", None)
+ tf_weights.pop(name + "/ExponentialMovingAverage", None)
+
+ logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}")
+ return model
+
+
+def apply_tf_padding(features: torch.Tensor, conv_layer: nn.Conv2d) -> torch.Tensor:
+ """
+ Apply TensorFlow-style "SAME" padding to a convolution layer. See the notes at:
+ https://www.tensorflow.org/api_docs/python/tf/nn#notes_on_padding_2
+ """
+ in_height, in_width = features.shape[-2:]
+ stride_height, stride_width = conv_layer.stride
+ kernel_height, kernel_width = conv_layer.kernel_size
+
+ if in_height % stride_height == 0:
+ pad_along_height = max(kernel_height - stride_height, 0)
+ else:
+ pad_along_height = max(kernel_height - (in_height % stride_height), 0)
+
+ if in_width % stride_width == 0:
+ pad_along_width = max(kernel_width - stride_width, 0)
+ else:
+ pad_along_width = max(kernel_width - (in_width % stride_width), 0)
+
+ pad_left = pad_along_width // 2
+ pad_right = pad_along_width - pad_left
+ pad_top = pad_along_height // 2
+ pad_bottom = pad_along_height - pad_top
+
+ padding = (pad_left, pad_right, pad_top, pad_bottom)
+ return nn.functional.pad(features, padding, "constant", 0.0)
+
+
+class MobileNetV1ConvLayer(nn.Module):
+ def __init__(
+ self,
+ config: MobileNetV1Config,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ stride: Optional[int] = 1,
+ groups: Optional[int] = 1,
+ bias: bool = False,
+ use_normalization: Optional[bool] = True,
+ use_activation: Optional[bool or str] = True,
+ ) -> None:
+ super().__init__()
+ self.config = config
+
+ if in_channels % groups != 0:
+ raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
+ if out_channels % groups != 0:
+ raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")
+
+ padding = 0 if config.tf_padding else int((kernel_size - 1) / 2)
+
+ self.convolution = nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ groups=groups,
+ bias=bias,
+ padding_mode="zeros",
+ )
+
+ if use_normalization:
+ self.normalization = nn.BatchNorm2d(
+ num_features=out_channels,
+ eps=config.layer_norm_eps,
+ momentum=0.9997,
+ affine=True,
+ track_running_stats=True,
+ )
+ else:
+ self.normalization = None
+
+ if use_activation:
+ if isinstance(use_activation, str):
+ self.activation = ACT2FN[use_activation]
+ elif isinstance(config.hidden_act, str):
+ self.activation = ACT2FN[config.hidden_act]
+ else:
+ self.activation = config.hidden_act
+ else:
+ self.activation = None
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ if self.config.tf_padding:
+ features = apply_tf_padding(features, self.convolution)
+ features = self.convolution(features)
+ if self.normalization is not None:
+ features = self.normalization(features)
+ if self.activation is not None:
+ features = self.activation(features)
+ return features
+
+
+@auto_docstring
+class MobileNetV1PreTrainedModel(PreTrainedModel):
+ config: MobileNetV1Config
+ load_tf_weights = load_tf_weights_in_mobilenet_v1
+ base_model_prefix = "mobilenet_v1"
+ main_input_name = "pixel_values"
+ supports_gradient_checkpointing = False
+ _no_split_modules = []
+
+ def _init_weights(self, module: Union[nn.Linear, nn.Conv2d]) -> None:
+ """Initialize the weights"""
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.BatchNorm2d):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class MobileNetV1Model(MobileNetV1PreTrainedModel):
+ def __init__(self, config: MobileNetV1Config, add_pooling_layer: bool = True):
+ r"""
+ add_pooling_layer (bool, *optional*, defaults to `True`):
+ Whether to add a pooling layer
+ """
+ super().__init__(config)
+ self.config = config
+
+ depth = 32
+ out_channels = max(int(depth * config.depth_multiplier), config.min_depth)
+
+ self.conv_stem = MobileNetV1ConvLayer(
+ config,
+ in_channels=config.num_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ stride=2,
+ )
+
+ strides = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
+
+ self.layer = nn.ModuleList()
+ for i in range(13):
+ in_channels = out_channels
+
+ if strides[i] == 2 or i == 0:
+ depth *= 2
+ out_channels = max(int(depth * config.depth_multiplier), config.min_depth)
+
+ self.layer.append(
+ MobileNetV1ConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=in_channels,
+ kernel_size=3,
+ stride=strides[i],
+ groups=in_channels,
+ )
+ )
+
+ self.layer.append(
+ MobileNetV1ConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ )
+ )
+
+ self.pooler = nn.AdaptiveAvgPool2d((1, 1)) if add_pooling_layer else None
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def _prune_heads(self, heads_to_prune):
+ raise NotImplementedError
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ hidden_states = self.conv_stem(pixel_values)
+
+ all_hidden_states = () if output_hidden_states else None
+
+ for i, layer_module in enumerate(self.layer):
+ hidden_states = layer_module(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ last_hidden_state = hidden_states
+
+ if self.pooler is not None:
+ pooled_output = torch.flatten(self.pooler(last_hidden_state), start_dim=1)
+ else:
+ pooled_output = None
+
+ if not return_dict:
+ return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)
+
+ return BaseModelOutputWithPoolingAndNoAttention(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=all_hidden_states,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
+ ImageNet.
+ """
+)
+class MobileNetV1ForImageClassification(MobileNetV1PreTrainedModel):
+ def __init__(self, config: MobileNetV1Config) -> None:
+ super().__init__(config)
+
+ self.num_labels = config.num_labels
+ self.mobilenet_v1 = MobileNetV1Model(config)
+
+ last_hidden_size = self.mobilenet_v1.layer[-1].convolution.out_channels
+
+ # Classifier head
+ self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True)
+ self.classifier = nn.Linear(last_hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ labels: Optional[torch.Tensor] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mobilenet_v1(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
+
+ pooled_output = outputs.pooler_output if return_dict else outputs[1]
+
+ logits = self.classifier(self.dropout(pooled_output))
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return ImageClassifierOutputWithNoAttention(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ )
+
+
+__all__ = [
+ "MobileNetV1ForImageClassification",
+ "MobileNetV1Model",
+ "MobileNetV1PreTrainedModel",
+ "load_tf_weights_in_mobilenet_v1",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a5dbc3ce4c84e179d028b297ba1d3b75c249c3c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mobilenet_v2 import *
+ from .feature_extraction_mobilenet_v2 import *
+ from .image_processing_mobilenet_v2 import *
+ from .image_processing_mobilenet_v2_fast import *
+ from .modeling_mobilenet_v2 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/configuration_mobilenet_v2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/configuration_mobilenet_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..b344593bffd9b2db742af3d60d96055db20f61d0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/configuration_mobilenet_v2.py
@@ -0,0 +1,154 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MobileNetV2 model configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+
+from packaging import version
+
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MobileNetV2Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MobileNetV2Model`]. It is used to instantiate a
+ MobileNetV2 model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the MobileNetV2
+ [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ num_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ image_size (`int`, *optional*, defaults to 224):
+ The size (resolution) of each image.
+ depth_multiplier (`float`, *optional*, defaults to 1.0):
+ Shrinks or expands the number of channels in each layer. Default is 1.0, which starts the network with 32
+ channels. This is sometimes also called "alpha" or "width multiplier".
+ depth_divisible_by (`int`, *optional*, defaults to 8):
+ The number of channels in each layer will always be a multiple of this number.
+ min_depth (`int`, *optional*, defaults to 8):
+ All layers will have at least this many channels.
+ expand_ratio (`float`, *optional*, defaults to 6.0):
+ The number of output channels of the first layer in each block is input channels times expansion ratio.
+ output_stride (`int`, *optional*, defaults to 32):
+ The ratio between the spatial resolution of the input and output feature maps. By default the model reduces
+ the input dimensions by a factor of 32. If `output_stride` is 8 or 16, the model uses dilated convolutions
+ on the depthwise layers instead of regular convolutions, so that the feature maps never become more than 8x
+ or 16x smaller than the input image.
+ first_layer_is_expansion (`bool`, *optional*, defaults to `True`):
+ True if the very first convolution layer is also the expansion layer for the first expansion block.
+ finegrained_output (`bool`, *optional*, defaults to `True`):
+ If true, the number of output channels in the final convolution layer will stay large (1280) even if
+ `depth_multiplier` is less than 1.
+ hidden_act (`str` or `function`, *optional*, defaults to `"relu6"`):
+ The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
+ tf_padding (`bool`, *optional*, defaults to `True`):
+ Whether to use TensorFlow padding rules on the convolution layers.
+ classifier_dropout_prob (`float`, *optional*, defaults to 0.8):
+ The dropout ratio for attached classifiers.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 0.001):
+ The epsilon used by the layer normalization layers.
+ semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
+ The index that is ignored by the loss function of the semantic segmentation model.
+
+ Example:
+
+ ```python
+ >>> from transformers import MobileNetV2Config, MobileNetV2Model
+
+ >>> # Initializing a "mobilenet_v2_1.0_224" style configuration
+ >>> configuration = MobileNetV2Config()
+
+ >>> # Initializing a model from the "mobilenet_v2_1.0_224" style configuration
+ >>> model = MobileNetV2Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mobilenet_v2"
+
+ def __init__(
+ self,
+ num_channels=3,
+ image_size=224,
+ depth_multiplier=1.0,
+ depth_divisible_by=8,
+ min_depth=8,
+ expand_ratio=6.0,
+ output_stride=32,
+ first_layer_is_expansion=True,
+ finegrained_output=True,
+ hidden_act="relu6",
+ tf_padding=True,
+ classifier_dropout_prob=0.8,
+ initializer_range=0.02,
+ layer_norm_eps=0.001,
+ semantic_loss_ignore_index=255,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ if depth_multiplier <= 0:
+ raise ValueError("depth_multiplier must be greater than zero.")
+
+ self.num_channels = num_channels
+ self.image_size = image_size
+ self.depth_multiplier = depth_multiplier
+ self.depth_divisible_by = depth_divisible_by
+ self.min_depth = min_depth
+ self.expand_ratio = expand_ratio
+ self.output_stride = output_stride
+ self.first_layer_is_expansion = first_layer_is_expansion
+ self.finegrained_output = finegrained_output
+ self.hidden_act = hidden_act
+ self.tf_padding = tf_padding
+ self.classifier_dropout_prob = classifier_dropout_prob
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.semantic_loss_ignore_index = semantic_loss_ignore_index
+
+
+class MobileNetV2OnnxConfig(OnnxConfig):
+ torch_onnx_minimum_version = version.parse("1.11")
+
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ return OrderedDict([("pixel_values", {0: "batch"})])
+
+ @property
+ def outputs(self) -> Mapping[str, Mapping[int, str]]:
+ if self.task == "image-classification":
+ return OrderedDict([("logits", {0: "batch"})])
+ else:
+ return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})])
+
+ @property
+ def atol_for_validation(self) -> float:
+ return 1e-4
+
+
+__all__ = ["MobileNetV2Config", "MobileNetV2OnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/convert_original_tf_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/convert_original_tf_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fdb9783ccf0f444fb7314813727412334d3a9d5
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/convert_original_tf_checkpoint_to_pytorch.py
@@ -0,0 +1,177 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert MobileNetV2 checkpoints from the tensorflow/models library."""
+
+import argparse
+import json
+import re
+from pathlib import Path
+
+import requests
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from transformers import (
+ MobileNetV2Config,
+ MobileNetV2ForImageClassification,
+ MobileNetV2ForSemanticSegmentation,
+ MobileNetV2ImageProcessor,
+ load_tf_weights_in_mobilenet_v2,
+)
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def get_mobilenet_v2_config(model_name):
+ config = MobileNetV2Config(layer_norm_eps=0.001)
+
+ if "quant" in model_name:
+ raise ValueError("Quantized models are not supported.")
+
+ matches = re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$", model_name)
+ if matches:
+ config.depth_multiplier = float(matches[1])
+ config.image_size = int(matches[2])
+
+ if model_name.startswith("deeplabv3_"):
+ config.output_stride = 8
+ config.num_labels = 21
+ filename = "pascal-voc-id2label.json"
+ else:
+ # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
+ # of the usual 1000. The first class (index 0) is "background".
+ config.num_labels = 1001
+ filename = "imagenet-1k-id2label.json"
+
+ repo_id = "huggingface/label-files"
+ id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
+
+ if config.num_labels == 1001:
+ id2label = {int(k) + 1: v for k, v in id2label.items()}
+ id2label[0] = "background"
+ else:
+ id2label = {int(k): v for k, v in id2label.items()}
+
+ config.id2label = id2label
+ config.label2id = {v: k for k, v in id2label.items()}
+
+ return config
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ im = Image.open(requests.get(url, stream=True).raw)
+ return im
+
+
+@torch.no_grad()
+def convert_movilevit_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub=False):
+ """
+ Copy/paste/tweak model's weights to our MobileNetV2 structure.
+ """
+ config = get_mobilenet_v2_config(model_name)
+
+ # Load 🤗 model
+ if model_name.startswith("deeplabv3_"):
+ model = MobileNetV2ForSemanticSegmentation(config).eval()
+ else:
+ model = MobileNetV2ForImageClassification(config).eval()
+
+ # Load weights from TensorFlow checkpoint
+ load_tf_weights_in_mobilenet_v2(model, config, checkpoint_path)
+
+ # Check outputs on an image, prepared by MobileNetV2ImageProcessor
+ image_processor = MobileNetV2ImageProcessor(
+ crop_size={"width": config.image_size, "height": config.image_size},
+ size={"shortest_edge": config.image_size + 32},
+ )
+ encoding = image_processor(images=prepare_img(), return_tensors="pt")
+ outputs = model(**encoding)
+ logits = outputs.logits
+
+ if model_name.startswith("deeplabv3_"):
+ assert logits.shape == (1, 21, 65, 65)
+
+ if model_name == "deeplabv3_mobilenet_v2_1.0_513":
+ expected_logits = torch.tensor(
+ [
+ [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
+ [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
+ [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
+ ]
+ )
+
+ else:
+ raise ValueError(f"Unknown model name: {model_name}")
+
+ assert torch.allclose(logits[0, :3, :3, :3], expected_logits, atol=1e-4)
+ else:
+ assert logits.shape == (1, 1001)
+
+ if model_name == "mobilenet_v2_1.4_224":
+ expected_logits = torch.tensor([0.0181, -1.0015, 0.4688])
+ elif model_name == "mobilenet_v2_1.0_224":
+ expected_logits = torch.tensor([0.2445, -1.1993, 0.1905])
+ elif model_name == "mobilenet_v2_0.75_160":
+ expected_logits = torch.tensor([0.2482, 0.4136, 0.6669])
+ elif model_name == "mobilenet_v2_0.35_96":
+ expected_logits = torch.tensor([0.1451, -0.4624, 0.7192])
+ else:
+ expected_logits = None
+
+ if expected_logits is not None:
+ assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4)
+
+ Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+ print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
+ model.save_pretrained(pytorch_dump_folder_path)
+ print(f"Saving image processor to {pytorch_dump_folder_path}")
+ image_processor.save_pretrained(pytorch_dump_folder_path)
+
+ if push_to_hub:
+ print("Pushing to the hub...")
+ repo_id = "google/" + model_name
+ image_processor.push_to_hub(repo_id)
+ model.push_to_hub(repo_id)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--model_name",
+ default="mobilenet_v2_1.0_224",
+ type=str,
+ help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2__'.",
+ )
+ parser.add_argument(
+ "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
+ )
+ parser.add_argument(
+ "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
+ )
+
+ args = parser.parse_args()
+ convert_movilevit_checkpoint(
+ args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/feature_extraction_mobilenet_v2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/feature_extraction_mobilenet_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e36b50cffaada3293d1dada34f06022f3ecf8163
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/feature_extraction_mobilenet_v2.py
@@ -0,0 +1,38 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Feature extractor class for MobileNetV2."""
+
+import warnings
+
+from ...utils import logging
+from ...utils.import_utils import requires
+from .image_processing_mobilenet_v2 import MobileNetV2ImageProcessor
+
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("vision",))
+class MobileNetV2FeatureExtractor(MobileNetV2ImageProcessor):
+ def __init__(self, *args, **kwargs) -> None:
+ warnings.warn(
+ "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
+ " Please use MobileNetV2ImageProcessor instead.",
+ FutureWarning,
+ )
+ super().__init__(*args, **kwargs)
+
+
+__all__ = ["MobileNetV2FeatureExtractor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..41d765ea314aa9ad8f413a1babe7422de9259c5e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py
@@ -0,0 +1,523 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for MobileNetV2."""
+
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import (
+ get_resize_output_image_size,
+ resize,
+ to_channel_dimension_format,
+)
+from ...image_utils import (
+ IMAGENET_STANDARD_MEAN,
+ IMAGENET_STANDARD_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import TensorType, filter_out_non_signature_kwargs, is_torch_available, is_torch_tensor, logging
+
+
+if is_torch_available():
+ import torch
+
+
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("vision",))
+class MobileNetV2ImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a MobileNetV2 image processor.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
+ `do_resize` in the `preprocess` method.
+ size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 256}`):
+ Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
+ method.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+ Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
+ `preprocess` method.
+ do_center_crop (`bool`, *optional*, defaults to `True`):
+ Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
+ is padded with 0's and then center cropped. Can be overridden by the `do_center_crop` parameter in the
+ `preprocess` method.
+ crop_size (`dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
+ Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`.
+ Can be overridden by the `crop_size` parameter in the `preprocess` method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+ parameter in the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
+ `preprocess` method.
+ do_normalize:
+ Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+ method.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
+ Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+ number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+ do_reduce_labels (`bool`, *optional*, defaults to `False`):
+ Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is
+ used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The
+ background label will be replaced by 255. Can be overridden by the `do_reduce_labels` parameter in the
+ `preprocess` method.
+ """
+
+ model_input_names = ["pixel_values"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ do_center_crop: bool = True,
+ crop_size: Optional[dict[str, int]] = None,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_reduce_labels: bool = False,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"shortest_edge": 256}
+ size = get_size_dict(size, default_to_square=False)
+ crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
+ crop_size = get_size_dict(crop_size, param_name="crop_size")
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.do_center_crop = do_center_crop
+ self.crop_size = crop_size
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
+ self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
+ self.do_reduce_labels = do_reduce_labels
+
+ # Copied from transformers.models.mobilenet_v1.image_processing_mobilenet_v1.MobileNetV1ImageProcessor.resize
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
+ resized to keep the input aspect ratio.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Size of the output image.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ Resampling filter to use when resiizing the image.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ default_to_square = True
+ if "shortest_edge" in size:
+ size = size["shortest_edge"]
+ default_to_square = False
+ elif "height" in size and "width" in size:
+ size = (size["height"], size["width"])
+ else:
+ raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
+ output_size = get_resize_output_image_size(
+ image,
+ size=size,
+ default_to_square=default_to_square,
+ input_data_format=input_data_format,
+ )
+ return resize(
+ image,
+ size=output_size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.reduce_label
+ def reduce_label(self, label: ImageInput) -> np.ndarray:
+ label = to_numpy_array(label)
+ # Avoid using underflow conversion
+ label[label == 0] = 255
+ label = label - 1
+ label[label == 254] = 255
+ return label
+
+ def __call__(self, images, segmentation_maps=None, **kwargs):
+ """
+ Preprocesses a batch of images and optionally segmentation maps.
+
+ Overrides the `__call__` method of the `Preprocessor` class so that both images and segmentation maps can be
+ passed in as positional arguments.
+ """
+ return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs)
+
+ def _preprocess(
+ self,
+ image: ImageInput,
+ do_reduce_labels: bool,
+ do_resize: bool,
+ do_rescale: bool,
+ do_center_crop: bool,
+ do_normalize: bool,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ rescale_factor: Optional[float] = None,
+ crop_size: Optional[dict[str, int]] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ if do_reduce_labels:
+ image = self.reduce_label(image)
+
+ if do_resize:
+ image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+
+ if do_center_crop:
+ image = self.center_crop(image=image, size=crop_size, input_data_format=input_data_format)
+
+ if do_rescale:
+ image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+
+ if do_normalize:
+ image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+
+ return image
+
+ def _preprocess_image(
+ self,
+ image: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[dict[str, int]] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """Preprocesses a single image."""
+ # All transformations expect numpy arrays.
+ image = to_numpy_array(image)
+ if do_rescale and is_scaled_image(image):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(image)
+
+ image = self._preprocess(
+ image=image,
+ do_reduce_labels=False,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ input_data_format=input_data_format,
+ )
+
+ image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+
+ return image
+
+ def _preprocess_mask(
+ self,
+ segmentation_map: ImageInput,
+ do_reduce_labels: Optional[bool] = None,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[dict[str, int]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """Preprocesses a single mask."""
+ segmentation_map = to_numpy_array(segmentation_map)
+ # Add channel dimension if missing - needed for certain transformations
+ if segmentation_map.ndim == 2:
+ added_channel_dim = True
+ segmentation_map = segmentation_map[None, ...]
+ input_data_format = ChannelDimension.FIRST
+ else:
+ added_channel_dim = False
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(segmentation_map, num_channels=1)
+
+ segmentation_map = self._preprocess(
+ image=segmentation_map,
+ do_reduce_labels=do_reduce_labels,
+ do_resize=do_resize,
+ size=size,
+ resample=PILImageResampling.NEAREST,
+ do_rescale=False,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_normalize=False,
+ image_mean=None,
+ image_std=None,
+ input_data_format=input_data_format,
+ )
+ # Remove extra channel dimension if added for processing
+ if added_channel_dim:
+ segmentation_map = segmentation_map.squeeze(0)
+ segmentation_map = segmentation_map.astype(np.int64)
+ return segmentation_map
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ segmentation_maps: Optional[ImageInput] = None,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[dict[str, int]] = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_reduce_labels: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ """
+ Preprocess an image or batch of images.
+
+ Args:
+ images (`ImageInput`):
+ Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ segmentation_maps (`ImageInput`, *optional*):
+ Segmentation map to preprocess.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
+ the longest edge resized to keep the input aspect ratio.
+ resample (`PILImageResampling` filter, *optional*, defaults to `self.resample`):
+ `PILImageResampling` filter to use if resizing the image e.g. `PILImageResampling.BILINEAR`. Only has
+ an effect if `do_resize` is set to `True`.
+ do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
+ Whether to center crop the image.
+ crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`):
+ Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image values between [0 - 1].
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to use if `do_normalize` is set to `True`.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to use if `do_normalize` is set to `True`.
+ do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
+ Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
+ is used for background, and background itself is not included in all classes of a dataset (e.g.
+ ADE20k). The background label will be replaced by 255.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ size = get_size_dict(size, default_to_square=False)
+ do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
+ resample = resample if resample is not None else self.resample
+ do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
+ crop_size = crop_size if crop_size is not None else self.crop_size
+ crop_size = get_size_dict(crop_size, param_name="crop_size")
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+
+ images = make_list_of_images(images)
+
+ if segmentation_maps is not None:
+ segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ if segmentation_maps is not None and not valid_images(segmentation_maps):
+ raise ValueError(
+ "Invalid segmentation map type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ images = [
+ self._preprocess_image(
+ image=img,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ for img in images
+ ]
+
+ data = {"pixel_values": images}
+
+ if segmentation_maps is not None:
+ segmentation_maps = [
+ self._preprocess_mask(
+ segmentation_map=segmentation_map,
+ do_reduce_labels=do_reduce_labels,
+ do_resize=do_resize,
+ size=size,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ input_data_format=input_data_format,
+ )
+ for segmentation_map in segmentation_maps
+ ]
+ data["labels"] = segmentation_maps
+
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+ # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->MobileNetV2
+ def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[list[tuple]] = None):
+ """
+ Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
+
+ Args:
+ outputs ([`MobileNetV2ForSemanticSegmentation`]):
+ Raw outputs of the model.
+ target_sizes (`list[Tuple]` of length `batch_size`, *optional*):
+ List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
+ predictions will not be resized.
+
+ Returns:
+ semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic
+ segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
+ specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
+ """
+ # TODO: add support for other frameworks
+ logits = outputs.logits
+
+ # Resize logits and compute semantic segmentation maps
+ if target_sizes is not None:
+ if len(logits) != len(target_sizes):
+ raise ValueError(
+ "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+ )
+
+ if is_torch_tensor(target_sizes):
+ target_sizes = target_sizes.numpy()
+
+ semantic_segmentation = []
+
+ for idx in range(len(logits)):
+ resized_logits = torch.nn.functional.interpolate(
+ logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+ )
+ semantic_map = resized_logits[0].argmax(dim=0)
+ semantic_segmentation.append(semantic_map)
+ else:
+ semantic_segmentation = logits.argmax(dim=1)
+ semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+ return semantic_segmentation
+
+
+__all__ = ["MobileNetV2ImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..e50d71025d545d59685910ee23a259f7138b8abd
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2_fast.py
@@ -0,0 +1,255 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for MobileNetV2."""
+
+from typing import Optional, Union
+
+from ...image_processing_utils import BatchFeature
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_utils import (
+ IMAGENET_STANDARD_MEAN,
+ IMAGENET_STANDARD_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ SizeDict,
+ is_torch_tensor,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+)
+
+
+if is_torch_available():
+ import torch
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+class MobileNetV2FastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ """
+ do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
+ Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
+ is used for background, and background itself is not included in all classes of a dataset (e.g.
+ ADE20k). The background label will be replaced by 255.
+ """
+
+ do_reduce_labels: Optional[bool]
+
+
+@auto_docstring
+class MobileNetV2ImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BILINEAR
+ image_mean = IMAGENET_STANDARD_MEAN
+ image_std = IMAGENET_STANDARD_STD
+ size = {"shortest_edge": 256}
+ default_to_square = False
+ crop_size = {"height": 224, "width": 224}
+ do_resize = True
+ do_center_crop = True
+ do_rescale = True
+ do_normalize = True
+ do_reduce_labels = False
+ valid_kwargs = MobileNetV2FastImageProcessorKwargs
+
+ def __init__(self, **kwargs: Unpack[MobileNetV2FastImageProcessorKwargs]):
+ super().__init__(**kwargs)
+
+ # Copied from transformers.models.beit.image_processing_beit_fast.BeitImageProcessorFast.reduce_label
+ def reduce_label(self, labels: list["torch.Tensor"]):
+ for idx in range(len(labels)):
+ label = labels[idx]
+ label = torch.where(label == 0, torch.tensor(255, dtype=label.dtype), label)
+ label = label - 1
+ label = torch.where(label == 254, torch.tensor(255, dtype=label.dtype), label)
+ labels[idx] = label
+
+ return label
+
+ @auto_docstring
+ def preprocess(
+ self,
+ images: ImageInput,
+ segmentation_maps: Optional[ImageInput] = None,
+ **kwargs: Unpack[MobileNetV2FastImageProcessorKwargs],
+ ) -> BatchFeature:
+ r"""
+ segmentation_maps (`ImageInput`, *optional*):
+ The segmentation maps to preprocess.
+ """
+ return super().preprocess(images, segmentation_maps, **kwargs)
+
+ def _preprocess_image_like_inputs(
+ self,
+ images: ImageInput,
+ segmentation_maps: Optional[ImageInput],
+ do_convert_rgb: bool,
+ input_data_format: ChannelDimension,
+ device: Optional[Union[str, "torch.device"]] = None,
+ **kwargs: Unpack[MobileNetV2FastImageProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Preprocess image-like inputs.
+ """
+ images = self._prepare_image_like_inputs(
+ images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
+ )
+ images_kwargs = kwargs.copy()
+ images_kwargs["do_reduce_labels"] = False
+ batch_feature = self._preprocess(images, **images_kwargs)
+
+ if segmentation_maps is not None:
+ processed_segmentation_maps = self._prepare_image_like_inputs(
+ images=segmentation_maps,
+ expected_ndims=2,
+ do_convert_rgb=False,
+ input_data_format=ChannelDimension.FIRST,
+ )
+
+ segmentation_maps_kwargs = kwargs.copy()
+ segmentation_maps_kwargs.update(
+ {
+ "do_normalize": False,
+ "do_rescale": False,
+ # Nearest interpolation is used for segmentation maps instead of BILINEAR.
+ "interpolation": F.InterpolationMode.NEAREST_EXACT
+ if is_torchvision_v2_available()
+ else F.InterpolationMode.NEAREST,
+ }
+ )
+
+ processed_segmentation_maps = self._preprocess(
+ images=processed_segmentation_maps, **segmentation_maps_kwargs
+ ).pixel_values
+ batch_feature["labels"] = processed_segmentation_maps.squeeze(1).to(torch.int64)
+
+ return batch_feature
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ do_reduce_labels: bool,
+ do_resize: bool,
+ do_rescale: bool,
+ do_center_crop: bool,
+ do_normalize: bool,
+ size: Optional[SizeDict],
+ interpolation: Optional["F.InterpolationMode"],
+ rescale_factor: Optional[float],
+ crop_size: Optional[SizeDict],
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ disable_grouping: bool,
+ return_tensors: Optional[Union[str, TensorType]],
+ **kwargs,
+ ) -> BatchFeature:
+ processed_images = []
+
+ if do_reduce_labels:
+ images = self.reduce_label(images)
+
+ # Group images by shape for more efficient batch processing
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ resized_images_grouped = {}
+
+ # Process each group of images with the same shape
+ for shape, stacked_images in grouped_images.items():
+ if do_resize:
+ stacked_images = self.resize(image=stacked_images, size=size, interpolation=interpolation)
+ resized_images_grouped[shape] = stacked_images
+
+ # Reorder images to original sequence
+ resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+ # Group again after resizing (in case resize produced different sizes)
+ grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+ processed_images_grouped = {}
+
+ for shape, stacked_images in grouped_images.items():
+ if do_center_crop:
+ stacked_images = self.center_crop(stacked_images, crop_size)
+ # Fused rescale and normalize
+ stacked_images = self.rescale_and_normalize(
+ stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ processed_images_grouped[shape] = stacked_images
+
+ processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+
+ # Stack all processed images if return_tensors is specified
+ processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+
+ return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
+
+ # Copied from transformers.models.beit.image_processing_beit_fast.BeitImageProcessorFast.post_process_semantic_segmentation with Beit->MobileNetV2
+ def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[list[tuple]] = None):
+ """
+ Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
+
+ Args:
+ outputs ([`MobileNetV2ForSemanticSegmentation`]):
+ Raw outputs of the model.
+ target_sizes (`list[Tuple]` of length `batch_size`, *optional*):
+ List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
+ predictions will not be resized.
+
+ Returns:
+ semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic
+ segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
+ specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
+ """
+ # TODO: add support for other frameworks
+ logits = outputs.logits
+
+ # Resize logits and compute semantic segmentation maps
+ if target_sizes is not None:
+ if len(logits) != len(target_sizes):
+ raise ValueError(
+ "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+ )
+
+ if is_torch_tensor(target_sizes):
+ target_sizes = target_sizes.numpy()
+
+ semantic_segmentation = []
+
+ for idx in range(len(logits)):
+ resized_logits = torch.nn.functional.interpolate(
+ logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+ )
+ semantic_map = resized_logits[0].argmax(dim=0)
+ semantic_segmentation.append(semantic_map)
+ else:
+ semantic_segmentation = logits.argmax(dim=1)
+ semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+ return semantic_segmentation
+
+
+__all__ = ["MobileNetV2ImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa213ab9d98d89983733321728dd0442bf300fd7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py
@@ -0,0 +1,805 @@
+# coding=utf-8
+# Copyright 2022 Apple Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MobileNetV2 model."""
+
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_outputs import (
+ BaseModelOutputWithPoolingAndNoAttention,
+ ImageClassifierOutputWithNoAttention,
+ SemanticSegmenterOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, logging
+from .configuration_mobilenet_v2 import MobileNetV2Config
+
+
+logger = logging.get_logger(__name__)
+
+
+def _build_tf_to_pytorch_map(model, config, tf_weights=None):
+ """
+ A map of modules from TF to PyTorch.
+ """
+
+ tf_to_pt_map = {}
+
+ if isinstance(model, (MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation)):
+ backbone = model.mobilenet_v2
+ else:
+ backbone = model
+
+ # Use the EMA weights if available
+ def ema(x):
+ return x + "/ExponentialMovingAverage" if x + "/ExponentialMovingAverage" in tf_weights else x
+
+ prefix = "MobilenetV2/Conv/"
+ tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_stem.first_conv.convolution.weight
+ tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.first_conv.normalization.bias
+ tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.first_conv.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.first_conv.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.first_conv.normalization.running_var
+
+ prefix = "MobilenetV2/expanded_conv/depthwise/"
+ tf_to_pt_map[ema(prefix + "depthwise_weights")] = backbone.conv_stem.conv_3x3.convolution.weight
+ tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.conv_3x3.normalization.bias
+ tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.conv_3x3.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.conv_3x3.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.conv_3x3.normalization.running_var
+
+ prefix = "MobilenetV2/expanded_conv/project/"
+ tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_stem.reduce_1x1.convolution.weight
+ tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.reduce_1x1.normalization.bias
+ tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.reduce_1x1.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.reduce_1x1.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.reduce_1x1.normalization.running_var
+
+ for i in range(16):
+ tf_index = i + 1
+ pt_index = i
+ pointer = backbone.layer[pt_index]
+
+ prefix = f"MobilenetV2/expanded_conv_{tf_index}/expand/"
+ tf_to_pt_map[ema(prefix + "weights")] = pointer.expand_1x1.convolution.weight
+ tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.expand_1x1.normalization.bias
+ tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.expand_1x1.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.expand_1x1.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.expand_1x1.normalization.running_var
+
+ prefix = f"MobilenetV2/expanded_conv_{tf_index}/depthwise/"
+ tf_to_pt_map[ema(prefix + "depthwise_weights")] = pointer.conv_3x3.convolution.weight
+ tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.conv_3x3.normalization.bias
+ tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.conv_3x3.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.conv_3x3.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.conv_3x3.normalization.running_var
+
+ prefix = f"MobilenetV2/expanded_conv_{tf_index}/project/"
+ tf_to_pt_map[ema(prefix + "weights")] = pointer.reduce_1x1.convolution.weight
+ tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.reduce_1x1.normalization.bias
+ tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.reduce_1x1.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.reduce_1x1.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.reduce_1x1.normalization.running_var
+
+ prefix = "MobilenetV2/Conv_1/"
+ tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_1x1.convolution.weight
+ tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_1x1.normalization.bias
+ tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_1x1.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_1x1.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_1x1.normalization.running_var
+
+ if isinstance(model, MobileNetV2ForImageClassification):
+ prefix = "MobilenetV2/Logits/Conv2d_1c_1x1/"
+ tf_to_pt_map[ema(prefix + "weights")] = model.classifier.weight
+ tf_to_pt_map[ema(prefix + "biases")] = model.classifier.bias
+
+ if isinstance(model, MobileNetV2ForSemanticSegmentation):
+ prefix = "image_pooling/"
+ tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_pool.convolution.weight
+ tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_pool.normalization.bias
+ tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_pool.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = model.segmentation_head.conv_pool.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = (
+ model.segmentation_head.conv_pool.normalization.running_var
+ )
+
+ prefix = "aspp0/"
+ tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_aspp.convolution.weight
+ tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_aspp.normalization.bias
+ tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_aspp.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = model.segmentation_head.conv_aspp.normalization.running_mean
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = (
+ model.segmentation_head.conv_aspp.normalization.running_var
+ )
+
+ prefix = "concat_projection/"
+ tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_projection.convolution.weight
+ tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_projection.normalization.bias
+ tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_projection.normalization.weight
+ tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = (
+ model.segmentation_head.conv_projection.normalization.running_mean
+ )
+ tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = (
+ model.segmentation_head.conv_projection.normalization.running_var
+ )
+
+ prefix = "logits/semantic/"
+ tf_to_pt_map[ema(prefix + "weights")] = model.segmentation_head.classifier.convolution.weight
+ tf_to_pt_map[ema(prefix + "biases")] = model.segmentation_head.classifier.convolution.bias
+
+ return tf_to_pt_map
+
+
+def load_tf_weights_in_mobilenet_v2(model, config, tf_checkpoint_path):
+ """Load TensorFlow checkpoints in a PyTorch model."""
+ try:
+ import numpy as np
+ import tensorflow as tf
+ except ImportError:
+ logger.error(
+ "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
+ "https://www.tensorflow.org/install/ for installation instructions."
+ )
+ raise
+
+ # Load weights from TF model
+ init_vars = tf.train.list_variables(tf_checkpoint_path)
+ tf_weights = {}
+ for name, shape in init_vars:
+ logger.info(f"Loading TF weight {name} with shape {shape}")
+ array = tf.train.load_variable(tf_checkpoint_path, name)
+ tf_weights[name] = array
+
+ # Build TF to PyTorch weights loading map
+ tf_to_pt_map = _build_tf_to_pytorch_map(model, config, tf_weights)
+
+ for name, pointer in tf_to_pt_map.items():
+ logger.info(f"Importing {name}")
+ if name not in tf_weights:
+ logger.info(f"{name} not in tf pre-trained weights, skipping")
+ continue
+
+ array = tf_weights[name]
+
+ if "depthwise_weights" in name:
+ logger.info("Transposing depthwise")
+ array = np.transpose(array, (2, 3, 0, 1))
+ elif "weights" in name:
+ logger.info("Transposing")
+ if len(pointer.shape) == 2: # copying into linear layer
+ array = array.squeeze().transpose()
+ else:
+ array = np.transpose(array, (3, 2, 0, 1))
+
+ if pointer.shape != array.shape:
+ raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
+
+ logger.info(f"Initialize PyTorch weight {name} {array.shape}")
+ pointer.data = torch.from_numpy(array)
+
+ tf_weights.pop(name, None)
+ tf_weights.pop(name + "/RMSProp", None)
+ tf_weights.pop(name + "/RMSProp_1", None)
+ tf_weights.pop(name + "/ExponentialMovingAverage", None)
+ tf_weights.pop(name + "/Momentum", None)
+
+ logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}")
+ return model
+
+
+def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int:
+ """
+ Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
+ original TensorFlow repo. It can be seen here:
+ https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+ """
+ if min_value is None:
+ min_value = divisor
+ new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
+ # Make sure that round down does not go down by more than 10%.
+ if new_value < 0.9 * value:
+ new_value += divisor
+ return int(new_value)
+
+
+def apply_depth_multiplier(config: MobileNetV2Config, channels: int) -> int:
+ return make_divisible(int(round(channels * config.depth_multiplier)), config.depth_divisible_by, config.min_depth)
+
+
+def apply_tf_padding(features: torch.Tensor, conv_layer: nn.Conv2d) -> torch.Tensor:
+ """
+ Apply TensorFlow-style "SAME" padding to a convolution layer. See the notes at:
+ https://www.tensorflow.org/api_docs/python/tf/nn#notes_on_padding_2
+ """
+ in_height = int(features.shape[-2])
+ in_width = int(features.shape[-1])
+ stride_height, stride_width = conv_layer.stride
+ kernel_height, kernel_width = conv_layer.kernel_size
+ dilation_height, dilation_width = conv_layer.dilation
+
+ if in_height % stride_height == 0:
+ pad_along_height = max(kernel_height - stride_height, 0)
+ else:
+ pad_along_height = max(kernel_height - (in_height % stride_height), 0)
+
+ if in_width % stride_width == 0:
+ pad_along_width = max(kernel_width - stride_width, 0)
+ else:
+ pad_along_width = max(kernel_width - (in_width % stride_width), 0)
+
+ pad_left = pad_along_width // 2
+ pad_right = pad_along_width - pad_left
+ pad_top = pad_along_height // 2
+ pad_bottom = pad_along_height - pad_top
+
+ padding = (
+ pad_left * dilation_width,
+ pad_right * dilation_width,
+ pad_top * dilation_height,
+ pad_bottom * dilation_height,
+ )
+ return nn.functional.pad(features, padding, "constant", 0.0)
+
+
+class MobileNetV2ConvLayer(nn.Module):
+ def __init__(
+ self,
+ config: MobileNetV2Config,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ stride: int = 1,
+ groups: int = 1,
+ bias: bool = False,
+ dilation: int = 1,
+ use_normalization: bool = True,
+ use_activation: Union[bool, str] = True,
+ layer_norm_eps: Optional[float] = None,
+ ) -> None:
+ super().__init__()
+ self.config = config
+
+ if in_channels % groups != 0:
+ raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
+ if out_channels % groups != 0:
+ raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")
+
+ padding = 0 if config.tf_padding else int((kernel_size - 1) / 2) * dilation
+
+ self.convolution = nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ bias=bias,
+ padding_mode="zeros",
+ )
+
+ if use_normalization:
+ self.normalization = nn.BatchNorm2d(
+ num_features=out_channels,
+ eps=config.layer_norm_eps if layer_norm_eps is None else layer_norm_eps,
+ momentum=0.997,
+ affine=True,
+ track_running_stats=True,
+ )
+ else:
+ self.normalization = None
+
+ if use_activation:
+ if isinstance(use_activation, str):
+ self.activation = ACT2FN[use_activation]
+ elif isinstance(config.hidden_act, str):
+ self.activation = ACT2FN[config.hidden_act]
+ else:
+ self.activation = config.hidden_act
+ else:
+ self.activation = None
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ if self.config.tf_padding:
+ features = apply_tf_padding(features, self.convolution)
+ features = self.convolution(features)
+ if self.normalization is not None:
+ features = self.normalization(features)
+ if self.activation is not None:
+ features = self.activation(features)
+ return features
+
+
+class MobileNetV2InvertedResidual(nn.Module):
+ def __init__(
+ self, config: MobileNetV2Config, in_channels: int, out_channels: int, stride: int, dilation: int = 1
+ ) -> None:
+ super().__init__()
+
+ expanded_channels = make_divisible(
+ int(round(in_channels * config.expand_ratio)), config.depth_divisible_by, config.min_depth
+ )
+
+ if stride not in [1, 2]:
+ raise ValueError(f"Invalid stride {stride}.")
+
+ self.use_residual = (stride == 1) and (in_channels == out_channels)
+
+ self.expand_1x1 = MobileNetV2ConvLayer(
+ config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1
+ )
+
+ self.conv_3x3 = MobileNetV2ConvLayer(
+ config,
+ in_channels=expanded_channels,
+ out_channels=expanded_channels,
+ kernel_size=3,
+ stride=stride,
+ groups=expanded_channels,
+ dilation=dilation,
+ )
+
+ self.reduce_1x1 = MobileNetV2ConvLayer(
+ config,
+ in_channels=expanded_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ use_activation=False,
+ )
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ residual = features
+
+ features = self.expand_1x1(features)
+ features = self.conv_3x3(features)
+ features = self.reduce_1x1(features)
+
+ return residual + features if self.use_residual else features
+
+
+class MobileNetV2Stem(nn.Module):
+ def __init__(self, config: MobileNetV2Config, in_channels: int, expanded_channels: int, out_channels: int) -> None:
+ super().__init__()
+
+ # The very first layer is a regular 3x3 convolution with stride 2 that expands to 32 channels.
+ # All other expansion layers use the expansion factor to compute the number of output channels.
+ self.first_conv = MobileNetV2ConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=expanded_channels,
+ kernel_size=3,
+ stride=2,
+ )
+
+ if config.first_layer_is_expansion:
+ self.expand_1x1 = None
+ else:
+ self.expand_1x1 = MobileNetV2ConvLayer(
+ config, in_channels=expanded_channels, out_channels=expanded_channels, kernel_size=1
+ )
+
+ self.conv_3x3 = MobileNetV2ConvLayer(
+ config,
+ in_channels=expanded_channels,
+ out_channels=expanded_channels,
+ kernel_size=3,
+ stride=1,
+ groups=expanded_channels,
+ )
+
+ self.reduce_1x1 = MobileNetV2ConvLayer(
+ config,
+ in_channels=expanded_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ use_activation=False,
+ )
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ features = self.first_conv(features)
+ if self.expand_1x1 is not None:
+ features = self.expand_1x1(features)
+ features = self.conv_3x3(features)
+ features = self.reduce_1x1(features)
+ return features
+
+
+@auto_docstring
+class MobileNetV2PreTrainedModel(PreTrainedModel):
+ config: MobileNetV2Config
+ load_tf_weights = load_tf_weights_in_mobilenet_v2
+ base_model_prefix = "mobilenet_v2"
+ main_input_name = "pixel_values"
+ supports_gradient_checkpointing = False
+ _no_split_modules = []
+
+ def _init_weights(self, module: Union[nn.Linear, nn.Conv2d]) -> None:
+ """Initialize the weights"""
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.BatchNorm2d):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class MobileNetV2Model(MobileNetV2PreTrainedModel):
+ def __init__(self, config: MobileNetV2Config, add_pooling_layer: bool = True):
+ r"""
+ add_pooling_layer (bool, *optional*, defaults to `True`):
+ Whether to add a pooling layer
+ """
+ super().__init__(config)
+ self.config = config
+
+ # Output channels for the projection layers
+ channels = [16, 24, 24, 32, 32, 32, 64, 64, 64, 64, 96, 96, 96, 160, 160, 160, 320]
+ channels = [apply_depth_multiplier(config, x) for x in channels]
+
+ # Strides for the depthwise layers
+ strides = [2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1]
+
+ self.conv_stem = MobileNetV2Stem(
+ config,
+ in_channels=config.num_channels,
+ expanded_channels=apply_depth_multiplier(config, 32),
+ out_channels=channels[0],
+ )
+
+ current_stride = 2 # first conv layer has stride 2
+ dilation = 1
+
+ self.layer = nn.ModuleList()
+ for i in range(16):
+ # Keep making the feature maps smaller or use dilated convolution?
+ if current_stride == config.output_stride:
+ layer_stride = 1
+ layer_dilation = dilation
+ dilation *= strides[i] # larger dilation starts in next block
+ else:
+ layer_stride = strides[i]
+ layer_dilation = 1
+ current_stride *= layer_stride
+
+ self.layer.append(
+ MobileNetV2InvertedResidual(
+ config,
+ in_channels=channels[i],
+ out_channels=channels[i + 1],
+ stride=layer_stride,
+ dilation=layer_dilation,
+ )
+ )
+
+ if config.finegrained_output and config.depth_multiplier < 1.0:
+ output_channels = 1280
+ else:
+ output_channels = apply_depth_multiplier(config, 1280)
+
+ self.conv_1x1 = MobileNetV2ConvLayer(
+ config,
+ in_channels=channels[-1],
+ out_channels=output_channels,
+ kernel_size=1,
+ )
+
+ self.pooler = nn.AdaptiveAvgPool2d((1, 1)) if add_pooling_layer else None
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def _prune_heads(self, heads_to_prune):
+ raise NotImplementedError
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ hidden_states = self.conv_stem(pixel_values)
+
+ all_hidden_states = () if output_hidden_states else None
+
+ for i, layer_module in enumerate(self.layer):
+ hidden_states = layer_module(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ last_hidden_state = self.conv_1x1(hidden_states)
+
+ if self.pooler is not None:
+ pooled_output = torch.flatten(self.pooler(last_hidden_state), start_dim=1)
+ else:
+ pooled_output = None
+
+ if not return_dict:
+ return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)
+
+ return BaseModelOutputWithPoolingAndNoAttention(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=all_hidden_states,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MobileNetV2 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
+ ImageNet.
+ """
+)
+class MobileNetV2ForImageClassification(MobileNetV2PreTrainedModel):
+ def __init__(self, config: MobileNetV2Config) -> None:
+ super().__init__(config)
+
+ self.num_labels = config.num_labels
+ self.mobilenet_v2 = MobileNetV2Model(config)
+
+ last_hidden_size = self.mobilenet_v2.conv_1x1.convolution.out_channels
+
+ # Classifier head
+ self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True)
+ self.classifier = nn.Linear(last_hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ labels: Optional[torch.Tensor] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mobilenet_v2(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
+
+ pooled_output = outputs.pooler_output if return_dict else outputs[1]
+
+ logits = self.classifier(self.dropout(pooled_output))
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return ImageClassifierOutputWithNoAttention(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ )
+
+
+class MobileNetV2DeepLabV3Plus(nn.Module):
+ """
+ The neural network from the paper "Encoder-Decoder with Atrous Separable Convolution for Semantic Image
+ Segmentation" https://huggingface.co/papers/1802.02611
+ """
+
+ def __init__(self, config: MobileNetV2Config) -> None:
+ super().__init__()
+
+ self.avg_pool = nn.AdaptiveAvgPool2d(output_size=1)
+
+ self.conv_pool = MobileNetV2ConvLayer(
+ config,
+ in_channels=apply_depth_multiplier(config, 320),
+ out_channels=256,
+ kernel_size=1,
+ stride=1,
+ use_normalization=True,
+ use_activation="relu",
+ layer_norm_eps=1e-5,
+ )
+
+ self.conv_aspp = MobileNetV2ConvLayer(
+ config,
+ in_channels=apply_depth_multiplier(config, 320),
+ out_channels=256,
+ kernel_size=1,
+ stride=1,
+ use_normalization=True,
+ use_activation="relu",
+ layer_norm_eps=1e-5,
+ )
+
+ self.conv_projection = MobileNetV2ConvLayer(
+ config,
+ in_channels=512,
+ out_channels=256,
+ kernel_size=1,
+ stride=1,
+ use_normalization=True,
+ use_activation="relu",
+ layer_norm_eps=1e-5,
+ )
+
+ self.dropout = nn.Dropout2d(config.classifier_dropout_prob)
+
+ self.classifier = MobileNetV2ConvLayer(
+ config,
+ in_channels=256,
+ out_channels=config.num_labels,
+ kernel_size=1,
+ use_normalization=False,
+ use_activation=False,
+ bias=True,
+ )
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ spatial_size = features.shape[-2:]
+
+ features_pool = self.avg_pool(features)
+ features_pool = self.conv_pool(features_pool)
+ features_pool = nn.functional.interpolate(
+ features_pool, size=spatial_size, mode="bilinear", align_corners=True
+ )
+
+ features_aspp = self.conv_aspp(features)
+
+ features = torch.cat([features_pool, features_aspp], dim=1)
+
+ features = self.conv_projection(features)
+ features = self.dropout(features)
+ features = self.classifier(features)
+ return features
+
+
+@auto_docstring(
+ custom_intro="""
+ MobileNetV2 model with a semantic segmentation head on top, e.g. for Pascal VOC.
+ """
+)
+class MobileNetV2ForSemanticSegmentation(MobileNetV2PreTrainedModel):
+ def __init__(self, config: MobileNetV2Config) -> None:
+ super().__init__(config)
+
+ self.num_labels = config.num_labels
+ self.mobilenet_v2 = MobileNetV2Model(config, add_pooling_layer=False)
+ self.segmentation_head = MobileNetV2DeepLabV3Plus(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, SemanticSegmenterOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
+ Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoImageProcessor, MobileNetV2ForSemanticSegmentation
+ >>> from PIL import Image
+ >>> import requests
+
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> image_processor = AutoImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
+ >>> model = MobileNetV2ForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513")
+
+ >>> inputs = image_processor(images=image, return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+
+ >>> # logits are of shape (batch_size, num_labels, height, width)
+ >>> logits = outputs.logits
+ ```"""
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if labels is not None and self.config.num_labels == 1:
+ raise ValueError("The number of labels should be greater than one")
+
+ outputs = self.mobilenet_v2(
+ pixel_values,
+ output_hidden_states=True, # we need the intermediate hidden states
+ return_dict=return_dict,
+ )
+
+ encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
+
+ logits = self.segmentation_head(encoder_hidden_states[-1])
+
+ loss = None
+ if labels is not None:
+ # upsample logits to the images' original size
+ upsampled_logits = nn.functional.interpolate(
+ logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
+ )
+ loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
+ loss = loss_fct(upsampled_logits, labels)
+
+ if not return_dict:
+ if output_hidden_states:
+ output = (logits,) + outputs[1:]
+ else:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SemanticSegmenterOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states if output_hidden_states else None,
+ attentions=None,
+ )
+
+
+__all__ = [
+ "MobileNetV2ForImageClassification",
+ "MobileNetV2ForSemanticSegmentation",
+ "MobileNetV2Model",
+ "MobileNetV2PreTrainedModel",
+ "load_tf_weights_in_mobilenet_v2",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6750449a3eae900890eeaffca5c766ba9cba3339
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/__init__.py
@@ -0,0 +1,31 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mobilevit import *
+ from .feature_extraction_mobilevit import *
+ from .image_processing_mobilevit import *
+ from .image_processing_mobilevit_fast import *
+ from .modeling_mobilevit import *
+ from .modeling_tf_mobilevit import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/configuration_mobilevit.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/configuration_mobilevit.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bb22e1590b2d0de857c1e5c801311900fa58b7f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/configuration_mobilevit.py
@@ -0,0 +1,172 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MobileViT model configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+
+from packaging import version
+
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MobileViTConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MobileViTModel`]. It is used to instantiate a
+ MobileViT model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the MobileViT
+ [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ num_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ image_size (`int`, *optional*, defaults to 256):
+ The size (resolution) of each image.
+ patch_size (`int`, *optional*, defaults to 2):
+ The size (resolution) of each patch.
+ hidden_sizes (`list[int]`, *optional*, defaults to `[144, 192, 240]`):
+ Dimensionality (hidden size) of the Transformer encoders at each stage.
+ neck_hidden_sizes (`list[int]`, *optional*, defaults to `[16, 32, 64, 96, 128, 160, 640]`):
+ The number of channels for the feature maps of the backbone.
+ num_attention_heads (`int`, *optional*, defaults to 4):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ mlp_ratio (`float`, *optional*, defaults to 2.0):
+ The ratio of the number of channels in the output of the MLP to the number of channels in the input.
+ expand_ratio (`float`, *optional*, defaults to 4.0):
+ Expansion factor for the MobileNetv2 layers.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
+ conv_kernel_size (`int`, *optional*, defaults to 3):
+ The size of the convolutional kernel in the MobileViT layer.
+ output_stride (`int`, *optional*, defaults to 32):
+ The ratio of the spatial resolution of the output to the resolution of the input image.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the Transformer encoder.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for attached classifiers.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the layer normalization layers.
+ qkv_bias (`bool`, *optional*, defaults to `True`):
+ Whether to add a bias to the queries, keys and values.
+ aspp_out_channels (`int`, *optional*, defaults to 256):
+ Number of output channels used in the ASPP layer for semantic segmentation.
+ atrous_rates (`list[int]`, *optional*, defaults to `[6, 12, 18]`):
+ Dilation (atrous) factors used in the ASPP layer for semantic segmentation.
+ aspp_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the ASPP layer for semantic segmentation.
+ semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
+ The index that is ignored by the loss function of the semantic segmentation model.
+
+ Example:
+
+ ```python
+ >>> from transformers import MobileViTConfig, MobileViTModel
+
+ >>> # Initializing a mobilevit-small style configuration
+ >>> configuration = MobileViTConfig()
+
+ >>> # Initializing a model from the mobilevit-small style configuration
+ >>> model = MobileViTModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mobilevit"
+
+ def __init__(
+ self,
+ num_channels=3,
+ image_size=256,
+ patch_size=2,
+ hidden_sizes=[144, 192, 240],
+ neck_hidden_sizes=[16, 32, 64, 96, 128, 160, 640],
+ num_attention_heads=4,
+ mlp_ratio=2.0,
+ expand_ratio=4.0,
+ hidden_act="silu",
+ conv_kernel_size=3,
+ output_stride=32,
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.0,
+ classifier_dropout_prob=0.1,
+ initializer_range=0.02,
+ layer_norm_eps=1e-5,
+ qkv_bias=True,
+ aspp_out_channels=256,
+ atrous_rates=[6, 12, 18],
+ aspp_dropout_prob=0.1,
+ semantic_loss_ignore_index=255,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.num_channels = num_channels
+ self.image_size = image_size
+ self.patch_size = patch_size
+ self.hidden_sizes = hidden_sizes
+ self.neck_hidden_sizes = neck_hidden_sizes
+ self.num_attention_heads = num_attention_heads
+ self.mlp_ratio = mlp_ratio
+ self.expand_ratio = expand_ratio
+ self.hidden_act = hidden_act
+ self.conv_kernel_size = conv_kernel_size
+ self.output_stride = output_stride
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.classifier_dropout_prob = classifier_dropout_prob
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.qkv_bias = qkv_bias
+
+ # decode head attributes for semantic segmentation
+ self.aspp_out_channels = aspp_out_channels
+ self.atrous_rates = atrous_rates
+ self.aspp_dropout_prob = aspp_dropout_prob
+ self.semantic_loss_ignore_index = semantic_loss_ignore_index
+
+
+class MobileViTOnnxConfig(OnnxConfig):
+ torch_onnx_minimum_version = version.parse("1.11")
+
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})])
+
+ @property
+ def outputs(self) -> Mapping[str, Mapping[int, str]]:
+ if self.task == "image-classification":
+ return OrderedDict([("logits", {0: "batch"})])
+ else:
+ return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})])
+
+ @property
+ def atol_for_validation(self) -> float:
+ return 1e-4
+
+
+__all__ = ["MobileViTConfig", "MobileViTOnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/convert_mlcvnets_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/convert_mlcvnets_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8159b446f1e290040c9e3878ccf33203a439e9e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/convert_mlcvnets_to_pytorch.py
@@ -0,0 +1,311 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert MobileViT checkpoints from the ml-cvnets library."""
+
+import argparse
+import json
+from pathlib import Path
+
+import requests
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from transformers import (
+ MobileViTConfig,
+ MobileViTForImageClassification,
+ MobileViTForSemanticSegmentation,
+ MobileViTImageProcessor,
+)
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def get_mobilevit_config(mobilevit_name):
+ config = MobileViTConfig()
+
+ # size of the architecture
+ if "mobilevit_s" in mobilevit_name:
+ config.hidden_sizes = [144, 192, 240]
+ config.neck_hidden_sizes = [16, 32, 64, 96, 128, 160, 640]
+ elif "mobilevit_xs" in mobilevit_name:
+ config.hidden_sizes = [96, 120, 144]
+ config.neck_hidden_sizes = [16, 32, 48, 64, 80, 96, 384]
+ elif "mobilevit_xxs" in mobilevit_name:
+ config.hidden_sizes = [64, 80, 96]
+ config.neck_hidden_sizes = [16, 16, 24, 48, 64, 80, 320]
+ config.hidden_dropout_prob = 0.05
+ config.expand_ratio = 2.0
+
+ if mobilevit_name.startswith("deeplabv3_"):
+ config.image_size = 512
+ config.output_stride = 16
+ config.num_labels = 21
+ filename = "pascal-voc-id2label.json"
+ else:
+ config.num_labels = 1000
+ filename = "imagenet-1k-id2label.json"
+
+ repo_id = "huggingface/label-files"
+ id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
+ id2label = {int(k): v for k, v in id2label.items()}
+ config.id2label = id2label
+ config.label2id = {v: k for k, v in id2label.items()}
+
+ return config
+
+
+def rename_key(name, base_model=False):
+ for i in range(1, 6):
+ if f"layer_{i}." in name:
+ name = name.replace(f"layer_{i}.", f"encoder.layer.{i - 1}.")
+
+ if "conv_1." in name:
+ name = name.replace("conv_1.", "conv_stem.")
+ if ".block." in name:
+ name = name.replace(".block.", ".")
+
+ if "exp_1x1" in name:
+ name = name.replace("exp_1x1", "expand_1x1")
+ if "red_1x1" in name:
+ name = name.replace("red_1x1", "reduce_1x1")
+ if ".local_rep.conv_3x3." in name:
+ name = name.replace(".local_rep.conv_3x3.", ".conv_kxk.")
+ if ".local_rep.conv_1x1." in name:
+ name = name.replace(".local_rep.conv_1x1.", ".conv_1x1.")
+ if ".norm." in name:
+ name = name.replace(".norm.", ".normalization.")
+ if ".conv." in name:
+ name = name.replace(".conv.", ".convolution.")
+ if ".conv_proj." in name:
+ name = name.replace(".conv_proj.", ".conv_projection.")
+
+ for i in range(0, 2):
+ for j in range(0, 4):
+ if f".{i}.{j}." in name:
+ name = name.replace(f".{i}.{j}.", f".{i}.layer.{j}.")
+
+ for i in range(2, 6):
+ for j in range(0, 4):
+ if f".{i}.{j}." in name:
+ name = name.replace(f".{i}.{j}.", f".{i}.")
+ if "expand_1x1" in name:
+ name = name.replace("expand_1x1", "downsampling_layer.expand_1x1")
+ if "conv_3x3" in name:
+ name = name.replace("conv_3x3", "downsampling_layer.conv_3x3")
+ if "reduce_1x1" in name:
+ name = name.replace("reduce_1x1", "downsampling_layer.reduce_1x1")
+
+ for i in range(2, 5):
+ if f".global_rep.{i}.weight" in name:
+ name = name.replace(f".global_rep.{i}.weight", ".layernorm.weight")
+ if f".global_rep.{i}.bias" in name:
+ name = name.replace(f".global_rep.{i}.bias", ".layernorm.bias")
+
+ if ".global_rep." in name:
+ name = name.replace(".global_rep.", ".transformer.")
+ if ".pre_norm_mha.0." in name:
+ name = name.replace(".pre_norm_mha.0.", ".layernorm_before.")
+ if ".pre_norm_mha.1.out_proj." in name:
+ name = name.replace(".pre_norm_mha.1.out_proj.", ".attention.output.dense.")
+ if ".pre_norm_ffn.0." in name:
+ name = name.replace(".pre_norm_ffn.0.", ".layernorm_after.")
+ if ".pre_norm_ffn.1." in name:
+ name = name.replace(".pre_norm_ffn.1.", ".intermediate.dense.")
+ if ".pre_norm_ffn.4." in name:
+ name = name.replace(".pre_norm_ffn.4.", ".output.dense.")
+ if ".transformer." in name:
+ name = name.replace(".transformer.", ".transformer.layer.")
+
+ if ".aspp_layer." in name:
+ name = name.replace(".aspp_layer.", ".")
+ if ".aspp_pool." in name:
+ name = name.replace(".aspp_pool.", ".")
+ if "seg_head." in name:
+ name = name.replace("seg_head.", "segmentation_head.")
+ if "segmentation_head.classifier.classifier." in name:
+ name = name.replace("segmentation_head.classifier.classifier.", "segmentation_head.classifier.")
+
+ if "classifier.fc." in name:
+ name = name.replace("classifier.fc.", "classifier.")
+ elif (not base_model) and ("segmentation_head." not in name):
+ name = "mobilevit." + name
+
+ return name
+
+
+def convert_state_dict(orig_state_dict, model, base_model=False):
+ if base_model:
+ model_prefix = ""
+ else:
+ model_prefix = "mobilevit."
+
+ for key in orig_state_dict.copy():
+ val = orig_state_dict.pop(key)
+
+ if key[:8] == "encoder.":
+ key = key[8:]
+
+ if "qkv" in key:
+ key_split = key.split(".")
+ layer_num = int(key_split[0][6:]) - 1
+ transformer_num = int(key_split[3])
+ layer = model.get_submodule(f"{model_prefix}encoder.layer.{layer_num}")
+ dim = layer.transformer.layer[transformer_num].attention.attention.all_head_size
+ prefix = (
+ f"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
+ )
+ if "weight" in key:
+ orig_state_dict[prefix + "query.weight"] = val[:dim, :]
+ orig_state_dict[prefix + "key.weight"] = val[dim : dim * 2, :]
+ orig_state_dict[prefix + "value.weight"] = val[-dim:, :]
+ else:
+ orig_state_dict[prefix + "query.bias"] = val[:dim]
+ orig_state_dict[prefix + "key.bias"] = val[dim : dim * 2]
+ orig_state_dict[prefix + "value.bias"] = val[-dim:]
+ else:
+ orig_state_dict[rename_key(key, base_model)] = val
+
+ return orig_state_dict
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ im = Image.open(requests.get(url, stream=True).raw)
+ return im
+
+
+@torch.no_grad()
+def convert_movilevit_checkpoint(mobilevit_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub=False):
+ """
+ Copy/paste/tweak model's weights to our MobileViT structure.
+ """
+ config = get_mobilevit_config(mobilevit_name)
+
+ # load original state_dict
+ state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
+
+ # load 🤗 model
+ if mobilevit_name.startswith("deeplabv3_"):
+ model = MobileViTForSemanticSegmentation(config).eval()
+ else:
+ model = MobileViTForImageClassification(config).eval()
+
+ new_state_dict = convert_state_dict(state_dict, model)
+ model.load_state_dict(new_state_dict)
+
+ # Check outputs on an image, prepared by MobileViTImageProcessor
+ image_processor = MobileViTImageProcessor(crop_size=config.image_size, size=config.image_size + 32)
+ encoding = image_processor(images=prepare_img(), return_tensors="pt")
+ outputs = model(**encoding)
+ logits = outputs.logits
+
+ if mobilevit_name.startswith("deeplabv3_"):
+ assert logits.shape == (1, 21, 32, 32)
+
+ if mobilevit_name == "deeplabv3_mobilevit_s":
+ expected_logits = torch.tensor(
+ [
+ [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
+ [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
+ [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
+ ]
+ )
+ elif mobilevit_name == "deeplabv3_mobilevit_xs":
+ expected_logits = torch.tensor(
+ [
+ [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
+ [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
+ [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
+ ]
+ )
+ elif mobilevit_name == "deeplabv3_mobilevit_xxs":
+ expected_logits = torch.tensor(
+ [
+ [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
+ [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
+ [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
+ ]
+ )
+ else:
+ raise ValueError(f"Unknown mobilevit_name: {mobilevit_name}")
+
+ assert torch.allclose(logits[0, :3, :3, :3], expected_logits, atol=1e-4)
+ else:
+ assert logits.shape == (1, 1000)
+
+ if mobilevit_name == "mobilevit_s":
+ expected_logits = torch.tensor([-0.9866, 0.2392, -1.1241])
+ elif mobilevit_name == "mobilevit_xs":
+ expected_logits = torch.tensor([-2.4761, -0.9399, -1.9587])
+ elif mobilevit_name == "mobilevit_xxs":
+ expected_logits = torch.tensor([-1.9364, -1.2327, -0.4653])
+ else:
+ raise ValueError(f"Unknown mobilevit_name: {mobilevit_name}")
+
+ assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4)
+
+ Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+ print(f"Saving model {mobilevit_name} to {pytorch_dump_folder_path}")
+ model.save_pretrained(pytorch_dump_folder_path)
+ print(f"Saving image processor to {pytorch_dump_folder_path}")
+ image_processor.save_pretrained(pytorch_dump_folder_path)
+
+ if push_to_hub:
+ model_mapping = {
+ "mobilevit_s": "mobilevit-small",
+ "mobilevit_xs": "mobilevit-x-small",
+ "mobilevit_xxs": "mobilevit-xx-small",
+ "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
+ "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
+ "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
+ }
+
+ print("Pushing to the hub...")
+ model_name = model_mapping[mobilevit_name]
+ image_processor.push_to_hub(model_name, organization="apple")
+ model.push_to_hub(model_name, organization="apple")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--mobilevit_name",
+ default="mobilevit_s",
+ type=str,
+ help=(
+ "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
+ " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
+ ),
+ )
+ parser.add_argument(
+ "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
+ )
+ parser.add_argument(
+ "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
+ )
+
+ args = parser.parse_args()
+ convert_movilevit_checkpoint(
+ args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/feature_extraction_mobilevit.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/feature_extraction_mobilevit.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c220df918647341a289e39ef0f885b80dcd9df3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/feature_extraction_mobilevit.py
@@ -0,0 +1,38 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Feature extractor class for MobileViT."""
+
+import warnings
+
+from ...utils import logging
+from ...utils.import_utils import requires
+from .image_processing_mobilevit import MobileViTImageProcessor
+
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("vision",))
+class MobileViTFeatureExtractor(MobileViTImageProcessor):
+ def __init__(self, *args, **kwargs) -> None:
+ warnings.warn(
+ "The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
+ " Please use MobileViTImageProcessor instead.",
+ FutureWarning,
+ )
+ super().__init__(*args, **kwargs)
+
+
+__all__ = ["MobileViTFeatureExtractor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/image_processing_mobilevit.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/image_processing_mobilevit.py
new file mode 100644
index 0000000000000000000000000000000000000000..86707613764f4202f0899cb3043dfe5f5f880506
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/image_processing_mobilevit.py
@@ -0,0 +1,518 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for MobileViT."""
+
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import flip_channel_order, get_resize_output_image_size, resize, to_channel_dimension_format
+from ...image_utils import (
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import (
+ TensorType,
+ filter_out_non_signature_kwargs,
+ is_torch_available,
+ is_torch_tensor,
+ is_vision_available,
+ logging,
+)
+from ...utils.import_utils import requires
+
+
+if is_vision_available():
+ import PIL
+
+if is_torch_available():
+ import torch
+
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("vision",))
+class MobileViTImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a MobileViT image processor.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
+ `do_resize` parameter in the `preprocess` method.
+ size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
+ Controls the size of the output image after resizing. Can be overridden by the `size` parameter in the
+ `preprocess` method.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
+ Defines the resampling filter to use if resizing the image. Can be overridden by the `resample` parameter
+ in the `preprocess` method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+ parameter in the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
+ `preprocess` method.
+ do_center_crop (`bool`, *optional*, defaults to `True`):
+ Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the
+ image is padded with 0's and then center cropped. Can be overridden by the `do_center_crop` parameter in
+ the `preprocess` method.
+ crop_size (`dict[str, int]`, *optional*, defaults to `{"height": 256, "width": 256}`):
+ Desired output size `(size["height"], size["width"])` when applying center-cropping. Can be overridden by
+ the `crop_size` parameter in the `preprocess` method.
+ do_flip_channel_order (`bool`, *optional*, defaults to `True`):
+ Whether to flip the color channels from RGB to BGR. Can be overridden by the `do_flip_channel_order`
+ parameter in the `preprocess` method.
+ do_reduce_labels (`bool`, *optional*, defaults to `False`):
+ Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is
+ used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The
+ background label will be replaced by 255. Can be overridden by the `do_reduce_labels` parameter in the
+ `preprocess` method.
+ """
+
+ model_input_names = ["pixel_values"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_center_crop: bool = True,
+ crop_size: Optional[dict[str, int]] = None,
+ do_flip_channel_order: bool = True,
+ do_reduce_labels: bool = False,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"shortest_edge": 224}
+ size = get_size_dict(size, default_to_square=False)
+ crop_size = crop_size if crop_size is not None else {"height": 256, "width": 256}
+ crop_size = get_size_dict(crop_size, param_name="crop_size")
+
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_center_crop = do_center_crop
+ self.crop_size = crop_size
+ self.do_flip_channel_order = do_flip_channel_order
+ self.do_reduce_labels = do_reduce_labels
+
+ # Copied from transformers.models.mobilenet_v1.image_processing_mobilenet_v1.MobileNetV1ImageProcessor.resize with PILImageResampling.BICUBIC->PILImageResampling.BILINEAR
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
+ resized to keep the input aspect ratio.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Size of the output image.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
+ Resampling filter to use when resiizing the image.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ default_to_square = True
+ if "shortest_edge" in size:
+ size = size["shortest_edge"]
+ default_to_square = False
+ elif "height" in size and "width" in size:
+ size = (size["height"], size["width"])
+ else:
+ raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+
+ output_size = get_resize_output_image_size(
+ image,
+ size=size,
+ default_to_square=default_to_square,
+ input_data_format=input_data_format,
+ )
+ return resize(
+ image,
+ size=output_size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ def flip_channel_order(
+ self,
+ image: np.ndarray,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Flip the color channels from RGB to BGR or vice versa.
+
+ Args:
+ image (`np.ndarray`):
+ The image, represented as a numpy array.
+ data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ return flip_channel_order(image, data_format=data_format, input_data_format=input_data_format)
+
+ # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.reduce_label
+ def reduce_label(self, label: ImageInput) -> np.ndarray:
+ label = to_numpy_array(label)
+ # Avoid using underflow conversion
+ label[label == 0] = 255
+ label = label - 1
+ label[label == 254] = 255
+ return label
+
+ def __call__(self, images, segmentation_maps=None, **kwargs):
+ """
+ Preprocesses a batch of images and optionally segmentation maps.
+
+ Overrides the `__call__` method of the `Preprocessor` class so that both images and segmentation maps can be
+ passed in as positional arguments.
+ """
+ return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs)
+
+ def _preprocess(
+ self,
+ image: ImageInput,
+ do_reduce_labels: bool,
+ do_resize: bool,
+ do_rescale: bool,
+ do_center_crop: bool,
+ do_flip_channel_order: bool,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ rescale_factor: Optional[float] = None,
+ crop_size: Optional[dict[str, int]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ if do_reduce_labels:
+ image = self.reduce_label(image)
+
+ if do_resize:
+ image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+
+ if do_rescale:
+ image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+
+ if do_center_crop:
+ image = self.center_crop(image=image, size=crop_size, input_data_format=input_data_format)
+
+ if do_flip_channel_order:
+ image = self.flip_channel_order(image, input_data_format=input_data_format)
+
+ return image
+
+ def _preprocess_image(
+ self,
+ image: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[dict[str, int]] = None,
+ do_flip_channel_order: Optional[bool] = None,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """Preprocesses a single image."""
+ # All transformations expect numpy arrays.
+ image = to_numpy_array(image)
+ if do_rescale and is_scaled_image(image):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(image)
+
+ image = self._preprocess(
+ image=image,
+ do_reduce_labels=False,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_flip_channel_order=do_flip_channel_order,
+ input_data_format=input_data_format,
+ )
+
+ image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+
+ return image
+
+ def _preprocess_mask(
+ self,
+ segmentation_map: ImageInput,
+ do_reduce_labels: Optional[bool] = None,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[dict[str, int]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """Preprocesses a single mask."""
+ segmentation_map = to_numpy_array(segmentation_map)
+ # Add channel dimension if missing - needed for certain transformations
+ if segmentation_map.ndim == 2:
+ added_channel_dim = True
+ segmentation_map = segmentation_map[None, ...]
+ input_data_format = ChannelDimension.FIRST
+ else:
+ added_channel_dim = False
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(segmentation_map, num_channels=1)
+
+ segmentation_map = self._preprocess(
+ image=segmentation_map,
+ do_reduce_labels=do_reduce_labels,
+ do_resize=do_resize,
+ size=size,
+ resample=PILImageResampling.NEAREST,
+ do_rescale=False,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_flip_channel_order=False,
+ input_data_format=input_data_format,
+ )
+ # Remove extra channel dimension if added for processing
+ if added_channel_dim:
+ segmentation_map = segmentation_map.squeeze(0)
+ segmentation_map = segmentation_map.astype(np.int64)
+ return segmentation_map
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ segmentation_maps: Optional[ImageInput] = None,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_center_crop: Optional[bool] = None,
+ crop_size: Optional[dict[str, int]] = None,
+ do_flip_channel_order: Optional[bool] = None,
+ do_reduce_labels: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: ChannelDimension = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> PIL.Image.Image:
+ """
+ Preprocess an image or batch of images.
+
+ Args:
+ images (`ImageInput`):
+ Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ segmentation_maps (`ImageInput`, *optional*):
+ Segmentation map to preprocess.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image after resizing.
+ resample (`int`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
+ has an effect if `do_resize` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image by rescale factor.
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
+ Whether to center crop the image.
+ crop_size (`dict[str, int]`, *optional*, defaults to `self.crop_size`):
+ Size of the center crop if `do_center_crop` is set to `True`.
+ do_flip_channel_order (`bool`, *optional*, defaults to `self.do_flip_channel_order`):
+ Whether to flip the channel order of the image.
+ do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
+ Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
+ is used for background, and background itself is not included in all classes of a dataset (e.g.
+ ADE20k). The background label will be replaced by 255.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ resample = resample if resample is not None else self.resample
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
+ do_flip_channel_order = (
+ do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
+ )
+
+ size = size if size is not None else self.size
+ size = get_size_dict(size, default_to_square=False)
+ crop_size = crop_size if crop_size is not None else self.crop_size
+ crop_size = get_size_dict(crop_size, param_name="crop_size")
+
+ do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
+
+ images = make_list_of_images(images)
+
+ if segmentation_maps is not None:
+ segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)
+
+ images = make_list_of_images(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ if segmentation_maps is not None and not valid_images(segmentation_maps):
+ raise ValueError(
+ "Invalid segmentation map type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ images = [
+ self._preprocess_image(
+ image=img,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ do_flip_channel_order=do_flip_channel_order,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ for img in images
+ ]
+
+ data = {"pixel_values": images}
+
+ if segmentation_maps is not None:
+ segmentation_maps = [
+ self._preprocess_mask(
+ segmentation_map=segmentation_map,
+ do_reduce_labels=do_reduce_labels,
+ do_resize=do_resize,
+ size=size,
+ do_center_crop=do_center_crop,
+ crop_size=crop_size,
+ input_data_format=input_data_format,
+ )
+ for segmentation_map in segmentation_maps
+ ]
+
+ data["labels"] = segmentation_maps
+
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+ # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->MobileViT
+ def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[list[tuple]] = None):
+ """
+ Converts the output of [`MobileViTForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
+
+ Args:
+ outputs ([`MobileViTForSemanticSegmentation`]):
+ Raw outputs of the model.
+ target_sizes (`list[Tuple]` of length `batch_size`, *optional*):
+ List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
+ predictions will not be resized.
+
+ Returns:
+ semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic
+ segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
+ specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
+ """
+ # TODO: add support for other frameworks
+ logits = outputs.logits
+
+ # Resize logits and compute semantic segmentation maps
+ if target_sizes is not None:
+ if len(logits) != len(target_sizes):
+ raise ValueError(
+ "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+ )
+
+ if is_torch_tensor(target_sizes):
+ target_sizes = target_sizes.numpy()
+
+ semantic_segmentation = []
+
+ for idx in range(len(logits)):
+ resized_logits = torch.nn.functional.interpolate(
+ logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+ )
+ semantic_map = resized_logits[0].argmax(dim=0)
+ semantic_segmentation.append(semantic_map)
+ else:
+ semantic_segmentation = logits.argmax(dim=1)
+ semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+ return semantic_segmentation
+
+
+__all__ = ["MobileViTImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/image_processing_mobilevit_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/image_processing_mobilevit_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..442f88a3a8484a7cd41c790a20692dedd3013770
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/image_processing_mobilevit_fast.py
@@ -0,0 +1,258 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for MobileViT."""
+
+from typing import Optional, Union
+
+from ...image_processing_utils import BatchFeature
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_utils import (
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ SizeDict,
+ is_torch_tensor,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+)
+
+
+if is_torch_available():
+ import torch
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+class MobileVitFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ """
+ do_flip_channel_order (`bool`, *optional*, defaults to `self.do_flip_channel_order`):
+ Whether to flip the color channels from RGB to BGR or vice versa.
+ do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
+ Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
+ is used for background, and background itself is not included in all classes of a dataset (e.g.
+ ADE20k). The background label will be replaced by 255.
+ """
+
+ do_flip_channel_order: Optional[bool]
+ do_reduce_labels: Optional[bool]
+
+
+@auto_docstring
+class MobileViTImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BILINEAR
+ size = {"shortest_edge": 224}
+ default_to_square = False
+ crop_size = {"height": 256, "width": 256}
+ do_resize = True
+ do_center_crop = True
+ do_rescale = True
+ do_normalize = None
+ do_convert_rgb = None
+ do_flip_channel_order = True
+ do_reduce_labels = False
+ valid_kwargs = MobileVitFastImageProcessorKwargs
+
+ def __init__(self, **kwargs: Unpack[MobileVitFastImageProcessorKwargs]):
+ super().__init__(**kwargs)
+
+ # Copied from transformers.models.beit.image_processing_beit_fast.BeitImageProcessorFast.reduce_label
+ def reduce_label(self, labels: list["torch.Tensor"]):
+ for idx in range(len(labels)):
+ label = labels[idx]
+ label = torch.where(label == 0, torch.tensor(255, dtype=label.dtype), label)
+ label = label - 1
+ label = torch.where(label == 254, torch.tensor(255, dtype=label.dtype), label)
+ labels[idx] = label
+
+ return label
+
+ @auto_docstring
+ def preprocess(
+ self,
+ images: ImageInput,
+ segmentation_maps: Optional[ImageInput] = None,
+ **kwargs: Unpack[MobileVitFastImageProcessorKwargs],
+ ) -> BatchFeature:
+ r"""
+ segmentation_maps (`ImageInput`, *optional*):
+ The segmentation maps to preprocess.
+ """
+ return super().preprocess(images, segmentation_maps, **kwargs)
+
+ def _preprocess_image_like_inputs(
+ self,
+ images: ImageInput,
+ segmentation_maps: Optional[ImageInput],
+ do_convert_rgb: bool,
+ input_data_format: ChannelDimension,
+ device: Optional[Union[str, "torch.device"]] = None,
+ **kwargs: Unpack[MobileVitFastImageProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Preprocess image-like inputs.
+ """
+ images = self._prepare_image_like_inputs(
+ images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
+ )
+ images_kwargs = kwargs.copy()
+ images_kwargs["do_reduce_labels"] = False
+ batch_feature = self._preprocess(images, **images_kwargs)
+
+ if segmentation_maps is not None:
+ processed_segmentation_maps = self._prepare_image_like_inputs(
+ images=segmentation_maps,
+ expected_ndims=2,
+ do_convert_rgb=False,
+ input_data_format=ChannelDimension.FIRST,
+ )
+
+ segmentation_maps_kwargs = kwargs.copy()
+ segmentation_maps_kwargs.update(
+ {
+ "do_rescale": False,
+ "do_flip_channel_order": False,
+ # Nearest interpolation is used for segmentation maps instead of BILINEAR.
+ "interpolation": F.InterpolationMode.NEAREST_EXACT
+ if is_torchvision_v2_available()
+ else F.InterpolationMode.NEAREST,
+ }
+ )
+
+ processed_segmentation_maps = self._preprocess(
+ images=processed_segmentation_maps, **segmentation_maps_kwargs
+ ).pixel_values
+ batch_feature["labels"] = processed_segmentation_maps.squeeze(1).to(torch.int64)
+
+ return batch_feature
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ do_reduce_labels: bool,
+ do_resize: bool,
+ size: Optional[SizeDict],
+ interpolation: Optional["F.InterpolationMode"],
+ do_rescale: bool,
+ rescale_factor: Optional[float],
+ do_center_crop: bool,
+ crop_size: Optional[SizeDict],
+ do_flip_channel_order: bool,
+ disable_grouping: bool,
+ return_tensors: Optional[Union[str, TensorType]],
+ **kwargs,
+ ) -> BatchFeature:
+ processed_images = []
+
+ if do_reduce_labels:
+ images = self.reduce_label(images)
+
+ # Group images by shape for more efficient batch processing
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ resized_images_grouped = {}
+
+ # Process each group of images with the same shape
+ for shape, stacked_images in grouped_images.items():
+ if do_resize:
+ stacked_images = self.resize(image=stacked_images, size=size, interpolation=interpolation)
+ resized_images_grouped[shape] = stacked_images
+
+ # Reorder images to original sequence
+ resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+ # Group again after resizing (in case resize produced different sizes)
+ grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+ processed_images_grouped = {}
+
+ for shape, stacked_images in grouped_images.items():
+ if do_center_crop:
+ stacked_images = self.center_crop(image=stacked_images, size=crop_size)
+ if do_rescale:
+ stacked_images = self.rescale(image=stacked_images, scale=rescale_factor)
+ if do_flip_channel_order:
+ # For batched images, we need to handle them all at once
+ if stacked_images.ndim > 3 and stacked_images.shape[1] >= 3:
+ # Flip RGB → BGR for batched images
+ flipped = stacked_images.clone()
+ flipped[:, 0:3] = stacked_images[:, [2, 1, 0], ...]
+ stacked_images = flipped
+
+ processed_images_grouped[shape] = stacked_images
+
+ processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+
+ # Stack all processed images if return_tensors is specified
+ processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+
+ return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
+
+ def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[list[tuple]] = None):
+ """
+ Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
+
+ Args:
+ outputs ([`MobileNetV2ForSemanticSegmentation`]):
+ Raw outputs of the model.
+ target_sizes (`list[Tuple]` of length `batch_size`, *optional*):
+ List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
+ predictions will not be resized.
+
+ Returns:
+ semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic
+ segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
+ specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
+ """
+ logits = outputs.logits
+
+ # Resize logits and compute semantic segmentation maps
+ if target_sizes is not None:
+ if len(logits) != len(target_sizes):
+ raise ValueError(
+ "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+ )
+
+ if is_torch_tensor(target_sizes):
+ target_sizes = target_sizes.numpy()
+
+ semantic_segmentation = []
+
+ for idx in range(len(logits)):
+ resized_logits = torch.nn.functional.interpolate(
+ logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+ )
+ semantic_map = resized_logits[0].argmax(dim=0)
+ semantic_segmentation.append(semantic_map)
+ else:
+ semantic_segmentation = logits.argmax(dim=1)
+ semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+ return semantic_segmentation
+
+
+__all__ = ["MobileViTImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/modeling_mobilevit.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/modeling_mobilevit.py
new file mode 100644
index 0000000000000000000000000000000000000000..adfa133c510e0c9ea78cdd0336ca428e3856b79a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/modeling_mobilevit.py
@@ -0,0 +1,1018 @@
+# coding=utf-8
+# Copyright 2022 Apple Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE
+"""PyTorch MobileViT model."""
+
+import math
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutputWithNoAttention,
+ BaseModelOutputWithPoolingAndNoAttention,
+ ImageClassifierOutputWithNoAttention,
+ SemanticSegmenterOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import auto_docstring, logging, torch_int
+from .configuration_mobilevit import MobileViTConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int:
+ """
+ Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
+ original TensorFlow repo. It can be seen here:
+ https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+ """
+ if min_value is None:
+ min_value = divisor
+ new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
+ # Make sure that round down does not go down by more than 10%.
+ if new_value < 0.9 * value:
+ new_value += divisor
+ return int(new_value)
+
+
+class MobileViTConvLayer(nn.Module):
+ def __init__(
+ self,
+ config: MobileViTConfig,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ stride: int = 1,
+ groups: int = 1,
+ bias: bool = False,
+ dilation: int = 1,
+ use_normalization: bool = True,
+ use_activation: Union[bool, str] = True,
+ ) -> None:
+ super().__init__()
+ padding = int((kernel_size - 1) / 2) * dilation
+
+ if in_channels % groups != 0:
+ raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
+ if out_channels % groups != 0:
+ raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")
+
+ self.convolution = nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ bias=bias,
+ padding_mode="zeros",
+ )
+
+ if use_normalization:
+ self.normalization = nn.BatchNorm2d(
+ num_features=out_channels,
+ eps=1e-5,
+ momentum=0.1,
+ affine=True,
+ track_running_stats=True,
+ )
+ else:
+ self.normalization = None
+
+ if use_activation:
+ if isinstance(use_activation, str):
+ self.activation = ACT2FN[use_activation]
+ elif isinstance(config.hidden_act, str):
+ self.activation = ACT2FN[config.hidden_act]
+ else:
+ self.activation = config.hidden_act
+ else:
+ self.activation = None
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ features = self.convolution(features)
+ if self.normalization is not None:
+ features = self.normalization(features)
+ if self.activation is not None:
+ features = self.activation(features)
+ return features
+
+
+class MobileViTInvertedResidual(nn.Module):
+ """
+ Inverted residual block (MobileNetv2): https://huggingface.co/papers/1801.04381
+ """
+
+ def __init__(
+ self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, dilation: int = 1
+ ) -> None:
+ super().__init__()
+ expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8)
+
+ if stride not in [1, 2]:
+ raise ValueError(f"Invalid stride {stride}.")
+
+ self.use_residual = (stride == 1) and (in_channels == out_channels)
+
+ self.expand_1x1 = MobileViTConvLayer(
+ config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1
+ )
+
+ self.conv_3x3 = MobileViTConvLayer(
+ config,
+ in_channels=expanded_channels,
+ out_channels=expanded_channels,
+ kernel_size=3,
+ stride=stride,
+ groups=expanded_channels,
+ dilation=dilation,
+ )
+
+ self.reduce_1x1 = MobileViTConvLayer(
+ config,
+ in_channels=expanded_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ use_activation=False,
+ )
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ residual = features
+
+ features = self.expand_1x1(features)
+ features = self.conv_3x3(features)
+ features = self.reduce_1x1(features)
+
+ return residual + features if self.use_residual else features
+
+
+class MobileViTMobileNetLayer(nn.Module):
+ def __init__(
+ self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1
+ ) -> None:
+ super().__init__()
+
+ self.layer = nn.ModuleList()
+ for i in range(num_stages):
+ layer = MobileViTInvertedResidual(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ stride=stride if i == 0 else 1,
+ )
+ self.layer.append(layer)
+ in_channels = out_channels
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ for layer_module in self.layer:
+ features = layer_module(features)
+ return features
+
+
+class MobileViTSelfAttention(nn.Module):
+ def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
+ super().__init__()
+
+ if hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size {hidden_size} is not a multiple of the number of attention "
+ f"heads {config.num_attention_heads}."
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)
+ self.key = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)
+ self.value = nn.Linear(hidden_size, self.all_head_size, bias=config.qkv_bias)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ batch_size, seq_length, _ = hidden_states.shape
+ query_layer = (
+ self.query(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ key_layer = (
+ self.key(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ value_layer = (
+ self.value(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+ return context_layer
+
+
+class MobileViTSelfOutput(nn.Module):
+ def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
+ super().__init__()
+ self.dense = nn.Linear(hidden_size, hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ return hidden_states
+
+
+class MobileViTAttention(nn.Module):
+ def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
+ super().__init__()
+ self.attention = MobileViTSelfAttention(config, hidden_size)
+ self.output = MobileViTSelfOutput(config, hidden_size)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads: set[int]) -> None:
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
+ )
+
+ # Prune linear layers
+ self.attention.query = prune_linear_layer(self.attention.query, index)
+ self.attention.key = prune_linear_layer(self.attention.key, index)
+ self.attention.value = prune_linear_layer(self.attention.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
+ self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ self_outputs = self.attention(hidden_states)
+ attention_output = self.output(self_outputs)
+ return attention_output
+
+
+class MobileViTIntermediate(nn.Module):
+ def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
+ super().__init__()
+ self.dense = nn.Linear(hidden_size, intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+class MobileViTOutput(nn.Module):
+ def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
+ super().__init__()
+ self.dense = nn.Linear(intermediate_size, hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = hidden_states + input_tensor
+ return hidden_states
+
+
+class MobileViTTransformerLayer(nn.Module):
+ def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
+ super().__init__()
+ self.attention = MobileViTAttention(config, hidden_size)
+ self.intermediate = MobileViTIntermediate(config, hidden_size, intermediate_size)
+ self.output = MobileViTOutput(config, hidden_size, intermediate_size)
+ self.layernorm_before = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
+ self.layernorm_after = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ attention_output = self.attention(self.layernorm_before(hidden_states))
+ hidden_states = attention_output + hidden_states
+
+ layer_output = self.layernorm_after(hidden_states)
+ layer_output = self.intermediate(layer_output)
+ layer_output = self.output(layer_output, hidden_states)
+ return layer_output
+
+
+class MobileViTTransformer(nn.Module):
+ def __init__(self, config: MobileViTConfig, hidden_size: int, num_stages: int) -> None:
+ super().__init__()
+
+ self.layer = nn.ModuleList()
+ for _ in range(num_stages):
+ transformer_layer = MobileViTTransformerLayer(
+ config,
+ hidden_size=hidden_size,
+ intermediate_size=int(hidden_size * config.mlp_ratio),
+ )
+ self.layer.append(transformer_layer)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ for layer_module in self.layer:
+ hidden_states = layer_module(hidden_states)
+ return hidden_states
+
+
+class MobileViTLayer(GradientCheckpointingLayer):
+ """
+ MobileViT block: https://huggingface.co/papers/2110.02178
+ """
+
+ def __init__(
+ self,
+ config: MobileViTConfig,
+ in_channels: int,
+ out_channels: int,
+ stride: int,
+ hidden_size: int,
+ num_stages: int,
+ dilation: int = 1,
+ ) -> None:
+ super().__init__()
+ self.patch_width = config.patch_size
+ self.patch_height = config.patch_size
+
+ if stride == 2:
+ self.downsampling_layer = MobileViTInvertedResidual(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ stride=stride if dilation == 1 else 1,
+ dilation=dilation // 2 if dilation > 1 else 1,
+ )
+ in_channels = out_channels
+ else:
+ self.downsampling_layer = None
+
+ self.conv_kxk = MobileViTConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=in_channels,
+ kernel_size=config.conv_kernel_size,
+ )
+
+ self.conv_1x1 = MobileViTConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=hidden_size,
+ kernel_size=1,
+ use_normalization=False,
+ use_activation=False,
+ )
+
+ self.transformer = MobileViTTransformer(
+ config,
+ hidden_size=hidden_size,
+ num_stages=num_stages,
+ )
+
+ self.layernorm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
+
+ self.conv_projection = MobileViTConvLayer(
+ config, in_channels=hidden_size, out_channels=in_channels, kernel_size=1
+ )
+
+ self.fusion = MobileViTConvLayer(
+ config, in_channels=2 * in_channels, out_channels=in_channels, kernel_size=config.conv_kernel_size
+ )
+
+ def unfolding(self, features: torch.Tensor) -> tuple[torch.Tensor, dict]:
+ patch_width, patch_height = self.patch_width, self.patch_height
+ patch_area = int(patch_width * patch_height)
+
+ batch_size, channels, orig_height, orig_width = features.shape
+
+ new_height = (
+ torch_int(torch.ceil(orig_height / patch_height) * patch_height)
+ if torch.jit.is_tracing()
+ else int(math.ceil(orig_height / patch_height) * patch_height)
+ )
+ new_width = (
+ torch_int(torch.ceil(orig_width / patch_width) * patch_width)
+ if torch.jit.is_tracing()
+ else int(math.ceil(orig_width / patch_width) * patch_width)
+ )
+
+ interpolate = False
+ if new_width != orig_width or new_height != orig_height:
+ # Note: Padding can be done, but then it needs to be handled in attention function.
+ features = nn.functional.interpolate(
+ features, size=(new_height, new_width), mode="bilinear", align_corners=False
+ )
+ interpolate = True
+
+ # number of patches along width and height
+ num_patch_width = new_width // patch_width
+ num_patch_height = new_height // patch_height
+ num_patches = num_patch_height * num_patch_width
+
+ # convert from shape (batch_size, channels, orig_height, orig_width)
+ # to the shape (batch_size * patch_area, num_patches, channels)
+ patches = features.reshape(
+ batch_size * channels * num_patch_height, patch_height, num_patch_width, patch_width
+ )
+ patches = patches.transpose(1, 2)
+ patches = patches.reshape(batch_size, channels, num_patches, patch_area)
+ patches = patches.transpose(1, 3)
+ patches = patches.reshape(batch_size * patch_area, num_patches, -1)
+
+ info_dict = {
+ "orig_size": (orig_height, orig_width),
+ "batch_size": batch_size,
+ "channels": channels,
+ "interpolate": interpolate,
+ "num_patches": num_patches,
+ "num_patches_width": num_patch_width,
+ "num_patches_height": num_patch_height,
+ }
+ return patches, info_dict
+
+ def folding(self, patches: torch.Tensor, info_dict: dict) -> torch.Tensor:
+ patch_width, patch_height = self.patch_width, self.patch_height
+ patch_area = int(patch_width * patch_height)
+
+ batch_size = info_dict["batch_size"]
+ channels = info_dict["channels"]
+ num_patches = info_dict["num_patches"]
+ num_patch_height = info_dict["num_patches_height"]
+ num_patch_width = info_dict["num_patches_width"]
+
+ # convert from shape (batch_size * patch_area, num_patches, channels)
+ # back to shape (batch_size, channels, orig_height, orig_width)
+ features = patches.contiguous().view(batch_size, patch_area, num_patches, -1)
+ features = features.transpose(1, 3)
+ features = features.reshape(
+ batch_size * channels * num_patch_height, num_patch_width, patch_height, patch_width
+ )
+ features = features.transpose(1, 2)
+ features = features.reshape(
+ batch_size, channels, num_patch_height * patch_height, num_patch_width * patch_width
+ )
+
+ if info_dict["interpolate"]:
+ features = nn.functional.interpolate(
+ features, size=info_dict["orig_size"], mode="bilinear", align_corners=False
+ )
+
+ return features
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ # reduce spatial dimensions if needed
+ if self.downsampling_layer:
+ features = self.downsampling_layer(features)
+
+ residual = features
+
+ # local representation
+ features = self.conv_kxk(features)
+ features = self.conv_1x1(features)
+
+ # convert feature map to patches
+ patches, info_dict = self.unfolding(features)
+
+ # learn global representations
+ patches = self.transformer(patches)
+ patches = self.layernorm(patches)
+
+ # convert patches back to feature maps
+ features = self.folding(patches, info_dict)
+
+ features = self.conv_projection(features)
+ features = self.fusion(torch.cat((residual, features), dim=1))
+ return features
+
+
+class MobileViTEncoder(nn.Module):
+ def __init__(self, config: MobileViTConfig) -> None:
+ super().__init__()
+ self.config = config
+
+ self.layer = nn.ModuleList()
+ self.gradient_checkpointing = False
+
+ # segmentation architectures like DeepLab and PSPNet modify the strides
+ # of the classification backbones
+ dilate_layer_4 = dilate_layer_5 = False
+ if config.output_stride == 8:
+ dilate_layer_4 = True
+ dilate_layer_5 = True
+ elif config.output_stride == 16:
+ dilate_layer_5 = True
+
+ dilation = 1
+
+ layer_1 = MobileViTMobileNetLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[0],
+ out_channels=config.neck_hidden_sizes[1],
+ stride=1,
+ num_stages=1,
+ )
+ self.layer.append(layer_1)
+
+ layer_2 = MobileViTMobileNetLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[1],
+ out_channels=config.neck_hidden_sizes[2],
+ stride=2,
+ num_stages=3,
+ )
+ self.layer.append(layer_2)
+
+ layer_3 = MobileViTLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[2],
+ out_channels=config.neck_hidden_sizes[3],
+ stride=2,
+ hidden_size=config.hidden_sizes[0],
+ num_stages=2,
+ )
+ self.layer.append(layer_3)
+
+ if dilate_layer_4:
+ dilation *= 2
+
+ layer_4 = MobileViTLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[3],
+ out_channels=config.neck_hidden_sizes[4],
+ stride=2,
+ hidden_size=config.hidden_sizes[1],
+ num_stages=4,
+ dilation=dilation,
+ )
+ self.layer.append(layer_4)
+
+ if dilate_layer_5:
+ dilation *= 2
+
+ layer_5 = MobileViTLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[4],
+ out_channels=config.neck_hidden_sizes[5],
+ stride=2,
+ hidden_size=config.hidden_sizes[2],
+ num_stages=3,
+ dilation=dilation,
+ )
+ self.layer.append(layer_5)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ) -> Union[tuple, BaseModelOutputWithNoAttention]:
+ all_hidden_states = () if output_hidden_states else None
+
+ for i, layer_module in enumerate(self.layer):
+ hidden_states = layer_module(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
+
+ return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
+
+
+@auto_docstring
+class MobileViTPreTrainedModel(PreTrainedModel):
+ config: MobileViTConfig
+ base_model_prefix = "mobilevit"
+ main_input_name = "pixel_values"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MobileViTLayer"]
+
+ def _init_weights(self, module: nn.Module) -> None:
+ """Initialize the weights"""
+ if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class MobileViTModel(MobileViTPreTrainedModel):
+ def __init__(self, config: MobileViTConfig, expand_output: bool = True):
+ r"""
+ expand_output (`bool`, *optional*, defaults to `True`):
+ Whether to expand the output of the model using a 1x1 convolution. If `True`, the model will apply an additional
+ 1x1 convolution to expand the output channels from `config.neck_hidden_sizes[5]` to `config.neck_hidden_sizes[6]`.
+ """
+ super().__init__(config)
+ self.config = config
+ self.expand_output = expand_output
+
+ self.conv_stem = MobileViTConvLayer(
+ config,
+ in_channels=config.num_channels,
+ out_channels=config.neck_hidden_sizes[0],
+ kernel_size=3,
+ stride=2,
+ )
+
+ self.encoder = MobileViTEncoder(config)
+
+ if self.expand_output:
+ self.conv_1x1_exp = MobileViTConvLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[5],
+ out_channels=config.neck_hidden_sizes[6],
+ kernel_size=1,
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def _prune_heads(self, heads_to_prune):
+ """Prunes heads of the model.
+ heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel
+ """
+ for layer_index, heads in heads_to_prune.items():
+ mobilevit_layer = self.encoder.layer[layer_index]
+ if isinstance(mobilevit_layer, MobileViTLayer):
+ for transformer_layer in mobilevit_layer.transformer.layer:
+ transformer_layer.attention.prune_heads(heads)
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ embedding_output = self.conv_stem(pixel_values)
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ if self.expand_output:
+ last_hidden_state = self.conv_1x1_exp(encoder_outputs[0])
+
+ # global average pooling: (batch_size, channels, height, width) -> (batch_size, channels)
+ pooled_output = torch.mean(last_hidden_state, dim=[-2, -1], keepdim=False)
+ else:
+ last_hidden_state = encoder_outputs[0]
+ pooled_output = None
+
+ if not return_dict:
+ output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,)
+ return output + encoder_outputs[1:]
+
+ return BaseModelOutputWithPoolingAndNoAttention(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MobileViT model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
+ ImageNet.
+ """
+)
+class MobileViTForImageClassification(MobileViTPreTrainedModel):
+ def __init__(self, config: MobileViTConfig) -> None:
+ super().__init__(config)
+
+ self.num_labels = config.num_labels
+ self.mobilevit = MobileViTModel(config)
+
+ # Classifier head
+ self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True)
+ self.classifier = (
+ nn.Linear(config.neck_hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ labels: Optional[torch.Tensor] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mobilevit(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
+
+ pooled_output = outputs.pooler_output if return_dict else outputs[1]
+
+ logits = self.classifier(self.dropout(pooled_output))
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return ImageClassifierOutputWithNoAttention(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ )
+
+
+class MobileViTASPPPooling(nn.Module):
+ def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int) -> None:
+ super().__init__()
+
+ self.global_pool = nn.AdaptiveAvgPool2d(output_size=1)
+
+ self.conv_1x1 = MobileViTConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ stride=1,
+ use_normalization=True,
+ use_activation="relu",
+ )
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ spatial_size = features.shape[-2:]
+ features = self.global_pool(features)
+ features = self.conv_1x1(features)
+ features = nn.functional.interpolate(features, size=spatial_size, mode="bilinear", align_corners=False)
+ return features
+
+
+class MobileViTASPP(nn.Module):
+ """
+ ASPP module defined in DeepLab papers: https://huggingface.co/papers/1606.00915, https://huggingface.co/papers/1706.05587
+ """
+
+ def __init__(self, config: MobileViTConfig) -> None:
+ super().__init__()
+
+ in_channels = config.neck_hidden_sizes[-2]
+ out_channels = config.aspp_out_channels
+
+ if len(config.atrous_rates) != 3:
+ raise ValueError("Expected 3 values for atrous_rates")
+
+ self.convs = nn.ModuleList()
+
+ in_projection = MobileViTConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ use_activation="relu",
+ )
+ self.convs.append(in_projection)
+
+ self.convs.extend(
+ [
+ MobileViTConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ dilation=rate,
+ use_activation="relu",
+ )
+ for rate in config.atrous_rates
+ ]
+ )
+
+ pool_layer = MobileViTASPPPooling(config, in_channels, out_channels)
+ self.convs.append(pool_layer)
+
+ self.project = MobileViTConvLayer(
+ config, in_channels=5 * out_channels, out_channels=out_channels, kernel_size=1, use_activation="relu"
+ )
+
+ self.dropout = nn.Dropout(p=config.aspp_dropout_prob)
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ pyramid = []
+ for conv in self.convs:
+ pyramid.append(conv(features))
+ pyramid = torch.cat(pyramid, dim=1)
+
+ pooled_features = self.project(pyramid)
+ pooled_features = self.dropout(pooled_features)
+ return pooled_features
+
+
+class MobileViTDeepLabV3(nn.Module):
+ """
+ DeepLabv3 architecture: https://huggingface.co/papers/1706.05587
+ """
+
+ def __init__(self, config: MobileViTConfig) -> None:
+ super().__init__()
+ self.aspp = MobileViTASPP(config)
+
+ self.dropout = nn.Dropout2d(config.classifier_dropout_prob)
+
+ self.classifier = MobileViTConvLayer(
+ config,
+ in_channels=config.aspp_out_channels,
+ out_channels=config.num_labels,
+ kernel_size=1,
+ use_normalization=False,
+ use_activation=False,
+ bias=True,
+ )
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ features = self.aspp(hidden_states[-1])
+ features = self.dropout(features)
+ features = self.classifier(features)
+ return features
+
+
+@auto_docstring(
+ custom_intro="""
+ MobileViT model with a semantic segmentation head on top, e.g. for Pascal VOC.
+ """
+)
+class MobileViTForSemanticSegmentation(MobileViTPreTrainedModel):
+ def __init__(self, config: MobileViTConfig) -> None:
+ super().__init__(config)
+
+ self.num_labels = config.num_labels
+ self.mobilevit = MobileViTModel(config, expand_output=False)
+ self.segmentation_head = MobileViTDeepLabV3(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, SemanticSegmenterOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
+ Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
+
+ Examples:
+
+ ```python
+ >>> import requests
+ >>> import torch
+ >>> from PIL import Image
+ >>> from transformers import AutoImageProcessor, MobileViTForSemanticSegmentation
+
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> image_processor = AutoImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-small")
+ >>> model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small")
+
+ >>> inputs = image_processor(images=image, return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+
+ >>> # logits are of shape (batch_size, num_labels, height, width)
+ >>> logits = outputs.logits
+ ```"""
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if labels is not None and self.config.num_labels == 1:
+ raise ValueError("The number of labels should be greater than one")
+
+ outputs = self.mobilevit(
+ pixel_values,
+ output_hidden_states=True, # we need the intermediate hidden states
+ return_dict=return_dict,
+ )
+
+ encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
+
+ logits = self.segmentation_head(encoder_hidden_states)
+
+ loss = None
+ if labels is not None:
+ # upsample logits to the images' original size
+ upsampled_logits = nn.functional.interpolate(
+ logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
+ )
+ loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
+ loss = loss_fct(upsampled_logits, labels)
+
+ if not return_dict:
+ if output_hidden_states:
+ output = (logits,) + outputs[1:]
+ else:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SemanticSegmenterOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states if output_hidden_states else None,
+ attentions=None,
+ )
+
+
+__all__ = [
+ "MobileViTForImageClassification",
+ "MobileViTForSemanticSegmentation",
+ "MobileViTModel",
+ "MobileViTPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/modeling_tf_mobilevit.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/modeling_tf_mobilevit.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcad0f302a8ebfdc0679f140eb6dd7e139f215c2
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevit/modeling_tf_mobilevit.py
@@ -0,0 +1,1376 @@
+# coding=utf-8
+# Copyright 2022 Apple Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE
+"""TensorFlow 2.0 MobileViT model."""
+
+from __future__ import annotations
+
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...file_utils import (
+ add_code_sample_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ replace_return_docstrings,
+)
+from ...modeling_tf_outputs import (
+ TFBaseModelOutput,
+ TFBaseModelOutputWithPooling,
+ TFImageClassifierOutputWithNoAttention,
+ TFSemanticSegmenterOutputWithNoAttention,
+)
+from ...modeling_tf_utils import (
+ TFPreTrainedModel,
+ TFSequenceClassificationLoss,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import shape_list, stable_softmax
+from ...utils import logging
+from .configuration_mobilevit import MobileViTConfig
+
+
+logger = logging.get_logger(__name__)
+
+# General docstring
+_CONFIG_FOR_DOC = "MobileViTConfig"
+
+# Base docstring
+_CHECKPOINT_FOR_DOC = "apple/mobilevit-small"
+_EXPECTED_OUTPUT_SHAPE = [1, 640, 8, 8]
+
+# Image classification docstring
+_IMAGE_CLASS_CHECKPOINT = "apple/mobilevit-small"
+_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
+
+
+def make_divisible(value: int, divisor: int = 8, min_value: int | None = None) -> int:
+ """
+ Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
+ original TensorFlow repo. It can be seen here:
+ https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+ """
+ if min_value is None:
+ min_value = divisor
+ new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
+ # Make sure that round down does not go down by more than 10%.
+ if new_value < 0.9 * value:
+ new_value += divisor
+ return int(new_value)
+
+
+class TFMobileViTConvLayer(keras.layers.Layer):
+ def __init__(
+ self,
+ config: MobileViTConfig,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ stride: int = 1,
+ groups: int = 1,
+ bias: bool = False,
+ dilation: int = 1,
+ use_normalization: bool = True,
+ use_activation: bool | str = True,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ logger.warning(
+ f"\n{self.__class__.__name__} has backpropagation operations that are NOT supported on CPU. If you wish "
+ "to train/fine-tune this model, you need a GPU or a TPU"
+ )
+
+ padding = int((kernel_size - 1) / 2) * dilation
+ self.padding = keras.layers.ZeroPadding2D(padding)
+
+ if out_channels % groups != 0:
+ raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")
+
+ self.convolution = keras.layers.Conv2D(
+ filters=out_channels,
+ kernel_size=kernel_size,
+ strides=stride,
+ padding="VALID",
+ dilation_rate=dilation,
+ groups=groups,
+ use_bias=bias,
+ name="convolution",
+ )
+
+ if use_normalization:
+ self.normalization = keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.1, name="normalization")
+ else:
+ self.normalization = None
+
+ if use_activation:
+ if isinstance(use_activation, str):
+ self.activation = get_tf_activation(use_activation)
+ elif isinstance(config.hidden_act, str):
+ self.activation = get_tf_activation(config.hidden_act)
+ else:
+ self.activation = config.hidden_act
+ else:
+ self.activation = None
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
+ padded_features = self.padding(features)
+ features = self.convolution(padded_features)
+ if self.normalization is not None:
+ features = self.normalization(features, training=training)
+ if self.activation is not None:
+ features = self.activation(features)
+ return features
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "convolution", None) is not None:
+ with tf.name_scope(self.convolution.name):
+ self.convolution.build([None, None, None, self.in_channels])
+ if getattr(self, "normalization", None) is not None:
+ if hasattr(self.normalization, "name"):
+ with tf.name_scope(self.normalization.name):
+ self.normalization.build([None, None, None, self.out_channels])
+
+
+class TFMobileViTInvertedResidual(keras.layers.Layer):
+ """
+ Inverted residual block (MobileNetv2): https://huggingface.co/papers/1801.04381
+ """
+
+ def __init__(
+ self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, dilation: int = 1, **kwargs
+ ) -> None:
+ super().__init__(**kwargs)
+ expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8)
+
+ if stride not in [1, 2]:
+ raise ValueError(f"Invalid stride {stride}.")
+
+ self.use_residual = (stride == 1) and (in_channels == out_channels)
+
+ self.expand_1x1 = TFMobileViTConvLayer(
+ config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1, name="expand_1x1"
+ )
+
+ self.conv_3x3 = TFMobileViTConvLayer(
+ config,
+ in_channels=expanded_channels,
+ out_channels=expanded_channels,
+ kernel_size=3,
+ stride=stride,
+ groups=expanded_channels,
+ dilation=dilation,
+ name="conv_3x3",
+ )
+
+ self.reduce_1x1 = TFMobileViTConvLayer(
+ config,
+ in_channels=expanded_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ use_activation=False,
+ name="reduce_1x1",
+ )
+
+ def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
+ residual = features
+
+ features = self.expand_1x1(features, training=training)
+ features = self.conv_3x3(features, training=training)
+ features = self.reduce_1x1(features, training=training)
+
+ return residual + features if self.use_residual else features
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "expand_1x1", None) is not None:
+ with tf.name_scope(self.expand_1x1.name):
+ self.expand_1x1.build(None)
+ if getattr(self, "conv_3x3", None) is not None:
+ with tf.name_scope(self.conv_3x3.name):
+ self.conv_3x3.build(None)
+ if getattr(self, "reduce_1x1", None) is not None:
+ with tf.name_scope(self.reduce_1x1.name):
+ self.reduce_1x1.build(None)
+
+
+class TFMobileViTMobileNetLayer(keras.layers.Layer):
+ def __init__(
+ self,
+ config: MobileViTConfig,
+ in_channels: int,
+ out_channels: int,
+ stride: int = 1,
+ num_stages: int = 1,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+
+ self.layers = []
+ for i in range(num_stages):
+ layer = TFMobileViTInvertedResidual(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ stride=stride if i == 0 else 1,
+ name=f"layer.{i}",
+ )
+ self.layers.append(layer)
+ in_channels = out_channels
+
+ def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
+ for layer_module in self.layers:
+ features = layer_module(features, training=training)
+ return features
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layers", None) is not None:
+ for layer_module in self.layers:
+ with tf.name_scope(layer_module.name):
+ layer_module.build(None)
+
+
+class TFMobileViTSelfAttention(keras.layers.Layer):
+ def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None:
+ super().__init__(**kwargs)
+
+ if hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size {hidden_size} is not a multiple of the number of attention "
+ f"heads {config.num_attention_heads}."
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+ scale = tf.cast(self.attention_head_size, dtype=tf.float32)
+ self.scale = tf.math.sqrt(scale)
+
+ self.query = keras.layers.Dense(self.all_head_size, use_bias=config.qkv_bias, name="query")
+ self.key = keras.layers.Dense(self.all_head_size, use_bias=config.qkv_bias, name="key")
+ self.value = keras.layers.Dense(self.all_head_size, use_bias=config.qkv_bias, name="value")
+
+ self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob)
+ self.hidden_size = hidden_size
+
+ def transpose_for_scores(self, x: tf.Tensor) -> tf.Tensor:
+ batch_size = tf.shape(x)[0]
+ x = tf.reshape(x, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
+ return tf.transpose(x, perm=[0, 2, 1, 3])
+
+ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
+ batch_size = tf.shape(hidden_states)[0]
+
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
+ query_layer = self.transpose_for_scores(self.query(hidden_states))
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
+ attention_scores = attention_scores / self.scale
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = stable_softmax(attention_scores, axis=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs, training=training)
+
+ context_layer = tf.matmul(attention_probs, value_layer)
+
+ context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
+ context_layer = tf.reshape(context_layer, shape=(batch_size, -1, self.all_head_size))
+ return context_layer
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "query", None) is not None:
+ with tf.name_scope(self.query.name):
+ self.query.build([None, None, self.hidden_size])
+ if getattr(self, "key", None) is not None:
+ with tf.name_scope(self.key.name):
+ self.key.build([None, None, self.hidden_size])
+ if getattr(self, "value", None) is not None:
+ with tf.name_scope(self.value.name):
+ self.value.build([None, None, self.hidden_size])
+
+
+class TFMobileViTSelfOutput(keras.layers.Layer):
+ def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None:
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(hidden_size, name="dense")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.hidden_size = hidden_size
+
+ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.hidden_size])
+
+
+class TFMobileViTAttention(keras.layers.Layer):
+ def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None:
+ super().__init__(**kwargs)
+ self.attention = TFMobileViTSelfAttention(config, hidden_size, name="attention")
+ self.dense_output = TFMobileViTSelfOutput(config, hidden_size, name="output")
+
+ def prune_heads(self, heads):
+ raise NotImplementedError
+
+ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
+ self_outputs = self.attention(hidden_states, training=training)
+ attention_output = self.dense_output(self_outputs, training=training)
+ return attention_output
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "attention", None) is not None:
+ with tf.name_scope(self.attention.name):
+ self.attention.build(None)
+ if getattr(self, "dense_output", None) is not None:
+ with tf.name_scope(self.dense_output.name):
+ self.dense_output.build(None)
+
+
+class TFMobileViTIntermediate(keras.layers.Layer):
+ def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None:
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(intermediate_size, name="dense")
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = get_tf_activation(config.hidden_act)
+ else:
+ self.intermediate_act_fn = config.hidden_act
+ self.hidden_size = hidden_size
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.hidden_size])
+
+
+class TFMobileViTOutput(keras.layers.Layer):
+ def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None:
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(hidden_size, name="dense")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.intermediate_size = intermediate_size
+
+ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = hidden_states + input_tensor
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.intermediate_size])
+
+
+class TFMobileViTTransformerLayer(keras.layers.Layer):
+ def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None:
+ super().__init__(**kwargs)
+ self.attention = TFMobileViTAttention(config, hidden_size, name="attention")
+ self.intermediate = TFMobileViTIntermediate(config, hidden_size, intermediate_size, name="intermediate")
+ self.mobilevit_output = TFMobileViTOutput(config, hidden_size, intermediate_size, name="output")
+ self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before")
+ self.layernorm_after = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_after")
+ self.hidden_size = hidden_size
+
+ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
+ attention_output = self.attention(self.layernorm_before(hidden_states), training=training)
+ hidden_states = attention_output + hidden_states
+
+ layer_output = self.layernorm_after(hidden_states)
+ layer_output = self.intermediate(layer_output)
+ layer_output = self.mobilevit_output(layer_output, hidden_states, training=training)
+ return layer_output
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "attention", None) is not None:
+ with tf.name_scope(self.attention.name):
+ self.attention.build(None)
+ if getattr(self, "intermediate", None) is not None:
+ with tf.name_scope(self.intermediate.name):
+ self.intermediate.build(None)
+ if getattr(self, "mobilevit_output", None) is not None:
+ with tf.name_scope(self.mobilevit_output.name):
+ self.mobilevit_output.build(None)
+ if getattr(self, "layernorm_before", None) is not None:
+ with tf.name_scope(self.layernorm_before.name):
+ self.layernorm_before.build([None, None, self.hidden_size])
+ if getattr(self, "layernorm_after", None) is not None:
+ with tf.name_scope(self.layernorm_after.name):
+ self.layernorm_after.build([None, None, self.hidden_size])
+
+
+class TFMobileViTTransformer(keras.layers.Layer):
+ def __init__(self, config: MobileViTConfig, hidden_size: int, num_stages: int, **kwargs) -> None:
+ super().__init__(**kwargs)
+
+ self.layers = []
+ for i in range(num_stages):
+ transformer_layer = TFMobileViTTransformerLayer(
+ config,
+ hidden_size=hidden_size,
+ intermediate_size=int(hidden_size * config.mlp_ratio),
+ name=f"layer.{i}",
+ )
+ self.layers.append(transformer_layer)
+
+ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
+ for layer_module in self.layers:
+ hidden_states = layer_module(hidden_states, training=training)
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layers", None) is not None:
+ for layer_module in self.layers:
+ with tf.name_scope(layer_module.name):
+ layer_module.build(None)
+
+
+class TFMobileViTLayer(keras.layers.Layer):
+ """
+ MobileViT block: https://huggingface.co/papers/2110.02178
+ """
+
+ def __init__(
+ self,
+ config: MobileViTConfig,
+ in_channels: int,
+ out_channels: int,
+ stride: int,
+ hidden_size: int,
+ num_stages: int,
+ dilation: int = 1,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ self.patch_width = config.patch_size
+ self.patch_height = config.patch_size
+
+ if stride == 2:
+ self.downsampling_layer = TFMobileViTInvertedResidual(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ stride=stride if dilation == 1 else 1,
+ dilation=dilation // 2 if dilation > 1 else 1,
+ name="downsampling_layer",
+ )
+ in_channels = out_channels
+ else:
+ self.downsampling_layer = None
+
+ self.conv_kxk = TFMobileViTConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=in_channels,
+ kernel_size=config.conv_kernel_size,
+ name="conv_kxk",
+ )
+
+ self.conv_1x1 = TFMobileViTConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=hidden_size,
+ kernel_size=1,
+ use_normalization=False,
+ use_activation=False,
+ name="conv_1x1",
+ )
+
+ self.transformer = TFMobileViTTransformer(
+ config, hidden_size=hidden_size, num_stages=num_stages, name="transformer"
+ )
+
+ self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
+
+ self.conv_projection = TFMobileViTConvLayer(
+ config, in_channels=hidden_size, out_channels=in_channels, kernel_size=1, name="conv_projection"
+ )
+
+ self.fusion = TFMobileViTConvLayer(
+ config,
+ in_channels=2 * in_channels,
+ out_channels=in_channels,
+ kernel_size=config.conv_kernel_size,
+ name="fusion",
+ )
+ self.hidden_size = hidden_size
+
+ def unfolding(self, features: tf.Tensor) -> tuple[tf.Tensor, dict]:
+ patch_width, patch_height = self.patch_width, self.patch_height
+ patch_area = tf.cast(patch_width * patch_height, "int32")
+
+ batch_size = tf.shape(features)[0]
+ orig_height = tf.shape(features)[1]
+ orig_width = tf.shape(features)[2]
+ channels = tf.shape(features)[3]
+
+ new_height = tf.cast(tf.math.ceil(orig_height / patch_height) * patch_height, "int32")
+ new_width = tf.cast(tf.math.ceil(orig_width / patch_width) * patch_width, "int32")
+
+ interpolate = new_width != orig_width or new_height != orig_height
+ if interpolate:
+ # Note: Padding can be done, but then it needs to be handled in attention function.
+ features = tf.image.resize(features, size=(new_height, new_width), method="bilinear")
+
+ # number of patches along width and height
+ num_patch_width = new_width // patch_width
+ num_patch_height = new_height // patch_height
+ num_patches = num_patch_height * num_patch_width
+
+ # convert from shape (batch_size, orig_height, orig_width, channels)
+ # to the shape (batch_size * patch_area, num_patches, channels)
+ features = tf.transpose(features, [0, 3, 1, 2])
+ patches = tf.reshape(
+ features, (batch_size * channels * num_patch_height, patch_height, num_patch_width, patch_width)
+ )
+ patches = tf.transpose(patches, [0, 2, 1, 3])
+ patches = tf.reshape(patches, (batch_size, channels, num_patches, patch_area))
+ patches = tf.transpose(patches, [0, 3, 2, 1])
+ patches = tf.reshape(patches, (batch_size * patch_area, num_patches, channels))
+
+ info_dict = {
+ "orig_size": (orig_height, orig_width),
+ "batch_size": batch_size,
+ "channels": channels,
+ "interpolate": interpolate,
+ "num_patches": num_patches,
+ "num_patches_width": num_patch_width,
+ "num_patches_height": num_patch_height,
+ }
+ return patches, info_dict
+
+ def folding(self, patches: tf.Tensor, info_dict: dict) -> tf.Tensor:
+ patch_width, patch_height = self.patch_width, self.patch_height
+ patch_area = int(patch_width * patch_height)
+
+ batch_size = info_dict["batch_size"]
+ channels = info_dict["channels"]
+ num_patches = info_dict["num_patches"]
+ num_patch_height = info_dict["num_patches_height"]
+ num_patch_width = info_dict["num_patches_width"]
+
+ # convert from shape (batch_size * patch_area, num_patches, channels)
+ # back to shape (batch_size, channels, orig_height, orig_width)
+ features = tf.reshape(patches, (batch_size, patch_area, num_patches, -1))
+ features = tf.transpose(features, perm=(0, 3, 2, 1))
+ features = tf.reshape(
+ features, (batch_size * channels * num_patch_height, num_patch_width, patch_height, patch_width)
+ )
+ features = tf.transpose(features, perm=(0, 2, 1, 3))
+ features = tf.reshape(
+ features, (batch_size, channels, num_patch_height * patch_height, num_patch_width * patch_width)
+ )
+ features = tf.transpose(features, perm=(0, 2, 3, 1))
+
+ if info_dict["interpolate"]:
+ features = tf.image.resize(features, size=info_dict["orig_size"], method="bilinear")
+
+ return features
+
+ def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
+ # reduce spatial dimensions if needed
+ if self.downsampling_layer:
+ features = self.downsampling_layer(features, training=training)
+
+ residual = features
+
+ # local representation
+ features = self.conv_kxk(features, training=training)
+ features = self.conv_1x1(features, training=training)
+
+ # convert feature map to patches
+ patches, info_dict = self.unfolding(features)
+
+ # learn global representations
+ patches = self.transformer(patches, training=training)
+ patches = self.layernorm(patches)
+
+ # convert patches back to feature maps
+ features = self.folding(patches, info_dict)
+
+ features = self.conv_projection(features, training=training)
+ features = self.fusion(tf.concat([residual, features], axis=-1), training=training)
+ return features
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "conv_kxk", None) is not None:
+ with tf.name_scope(self.conv_kxk.name):
+ self.conv_kxk.build(None)
+ if getattr(self, "conv_1x1", None) is not None:
+ with tf.name_scope(self.conv_1x1.name):
+ self.conv_1x1.build(None)
+ if getattr(self, "transformer", None) is not None:
+ with tf.name_scope(self.transformer.name):
+ self.transformer.build(None)
+ if getattr(self, "layernorm", None) is not None:
+ with tf.name_scope(self.layernorm.name):
+ self.layernorm.build([None, None, self.hidden_size])
+ if getattr(self, "conv_projection", None) is not None:
+ with tf.name_scope(self.conv_projection.name):
+ self.conv_projection.build(None)
+ if getattr(self, "fusion", None) is not None:
+ with tf.name_scope(self.fusion.name):
+ self.fusion.build(None)
+ if getattr(self, "downsampling_layer", None) is not None:
+ with tf.name_scope(self.downsampling_layer.name):
+ self.downsampling_layer.build(None)
+
+
+class TFMobileViTEncoder(keras.layers.Layer):
+ def __init__(self, config: MobileViTConfig, **kwargs) -> None:
+ super().__init__(**kwargs)
+ self.config = config
+
+ self.layers = []
+
+ # segmentation architectures like DeepLab and PSPNet modify the strides
+ # of the classification backbones
+ dilate_layer_4 = dilate_layer_5 = False
+ if config.output_stride == 8:
+ dilate_layer_4 = True
+ dilate_layer_5 = True
+ elif config.output_stride == 16:
+ dilate_layer_5 = True
+
+ dilation = 1
+
+ layer_1 = TFMobileViTMobileNetLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[0],
+ out_channels=config.neck_hidden_sizes[1],
+ stride=1,
+ num_stages=1,
+ name="layer.0",
+ )
+ self.layers.append(layer_1)
+
+ layer_2 = TFMobileViTMobileNetLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[1],
+ out_channels=config.neck_hidden_sizes[2],
+ stride=2,
+ num_stages=3,
+ name="layer.1",
+ )
+ self.layers.append(layer_2)
+
+ layer_3 = TFMobileViTLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[2],
+ out_channels=config.neck_hidden_sizes[3],
+ stride=2,
+ hidden_size=config.hidden_sizes[0],
+ num_stages=2,
+ name="layer.2",
+ )
+ self.layers.append(layer_3)
+
+ if dilate_layer_4:
+ dilation *= 2
+
+ layer_4 = TFMobileViTLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[3],
+ out_channels=config.neck_hidden_sizes[4],
+ stride=2,
+ hidden_size=config.hidden_sizes[1],
+ num_stages=4,
+ dilation=dilation,
+ name="layer.3",
+ )
+ self.layers.append(layer_4)
+
+ if dilate_layer_5:
+ dilation *= 2
+
+ layer_5 = TFMobileViTLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[4],
+ out_channels=config.neck_hidden_sizes[5],
+ stride=2,
+ hidden_size=config.hidden_sizes[2],
+ num_stages=3,
+ dilation=dilation,
+ name="layer.4",
+ )
+ self.layers.append(layer_5)
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ training: bool = False,
+ ) -> tuple | TFBaseModelOutput:
+ all_hidden_states = () if output_hidden_states else None
+
+ for i, layer_module in enumerate(self.layers):
+ hidden_states = layer_module(hidden_states, training=training)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
+
+ return TFBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "layers", None) is not None:
+ for layer_module in self.layers:
+ with tf.name_scope(layer_module.name):
+ layer_module.build(None)
+
+
+@keras_serializable
+class TFMobileViTMainLayer(keras.layers.Layer):
+ config_class = MobileViTConfig
+
+ def __init__(self, config: MobileViTConfig, expand_output: bool = True, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.expand_output = expand_output
+
+ self.conv_stem = TFMobileViTConvLayer(
+ config,
+ in_channels=config.num_channels,
+ out_channels=config.neck_hidden_sizes[0],
+ kernel_size=3,
+ stride=2,
+ name="conv_stem",
+ )
+
+ self.encoder = TFMobileViTEncoder(config, name="encoder")
+
+ if self.expand_output:
+ self.conv_1x1_exp = TFMobileViTConvLayer(
+ config,
+ in_channels=config.neck_hidden_sizes[5],
+ out_channels=config.neck_hidden_sizes[6],
+ kernel_size=1,
+ name="conv_1x1_exp",
+ )
+
+ self.pooler = keras.layers.GlobalAveragePooling2D(data_format="channels_first", name="pooler")
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ raise NotImplementedError
+
+ @unpack_inputs
+ def call(
+ self,
+ pixel_values: tf.Tensor | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ) -> tuple[tf.Tensor] | TFBaseModelOutputWithPooling:
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format.
+ # So change the input format from `NCHW` to `NHWC`.
+ # shape = (batch_size, in_height, in_width, in_channels=num_channels)
+ pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
+
+ embedding_output = self.conv_stem(pixel_values, training=training)
+
+ encoder_outputs = self.encoder(
+ embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training
+ )
+
+ if self.expand_output:
+ last_hidden_state = self.conv_1x1_exp(encoder_outputs[0])
+
+ # Change to NCHW output format to have uniformity in the modules
+ last_hidden_state = tf.transpose(last_hidden_state, perm=[0, 3, 1, 2])
+
+ # global average pooling: (batch_size, channels, height, width) -> (batch_size, channels)
+ pooled_output = self.pooler(last_hidden_state)
+ else:
+ last_hidden_state = encoder_outputs[0]
+ # Change to NCHW output format to have uniformity in the modules
+ last_hidden_state = tf.transpose(last_hidden_state, perm=[0, 3, 1, 2])
+ pooled_output = None
+
+ if not return_dict:
+ output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,)
+
+ # Change to NCHW output format to have uniformity in the modules
+ if not self.expand_output:
+ remaining_encoder_outputs = encoder_outputs[1:]
+ remaining_encoder_outputs = tuple(
+ tf.transpose(h, perm=(0, 3, 1, 2)) for h in remaining_encoder_outputs[0]
+ )
+ remaining_encoder_outputs = (remaining_encoder_outputs,)
+ return output + remaining_encoder_outputs
+ else:
+ return output + encoder_outputs[1:]
+
+ # Change the other hidden state outputs to NCHW as well
+ if output_hidden_states:
+ hidden_states = tuple(tf.transpose(h, perm=(0, 3, 1, 2)) for h in encoder_outputs[1])
+
+ return TFBaseModelOutputWithPooling(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "conv_stem", None) is not None:
+ with tf.name_scope(self.conv_stem.name):
+ self.conv_stem.build(None)
+ if getattr(self, "encoder", None) is not None:
+ with tf.name_scope(self.encoder.name):
+ self.encoder.build(None)
+ if getattr(self, "pooler", None) is not None:
+ with tf.name_scope(self.pooler.name):
+ self.pooler.build([None, None, None, None])
+ if getattr(self, "conv_1x1_exp", None) is not None:
+ with tf.name_scope(self.conv_1x1_exp.name):
+ self.conv_1x1_exp.build(None)
+
+
+class TFMobileViTPreTrainedModel(TFPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = MobileViTConfig
+ base_model_prefix = "mobilevit"
+ main_input_name = "pixel_values"
+
+
+MOBILEVIT_START_DOCSTRING = r"""
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Parameters:
+ config ([`MobileViTConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+MOBILEVIT_INPUTS_DOCSTRING = r"""
+ Args:
+ pixel_values (`np.ndarray`, `tf.Tensor`, `list[tf.Tensor]`, `dict[str, tf.Tensor]` or `dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
+ Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
+ [`MobileViTImageProcessor.__call__`] for details.
+
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+ used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+ eager mode, in graph mode the value will always be set to True.
+"""
+
+
+@add_start_docstrings(
+ "The bare MobileViT model outputting raw hidden-states without any specific head on top.",
+ MOBILEVIT_START_DOCSTRING,
+)
+class TFMobileViTModel(TFMobileViTPreTrainedModel):
+ def __init__(self, config: MobileViTConfig, expand_output: bool = True, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.config = config
+ self.expand_output = expand_output
+
+ self.mobilevit = TFMobileViTMainLayer(config, expand_output=expand_output, name="mobilevit")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING)
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFBaseModelOutputWithPooling,
+ config_class=_CONFIG_FOR_DOC,
+ modality="vision",
+ expected_output=_EXPECTED_OUTPUT_SHAPE,
+ )
+ def call(
+ self,
+ pixel_values: tf.Tensor | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ) -> tuple[tf.Tensor] | TFBaseModelOutputWithPooling:
+ output = self.mobilevit(pixel_values, output_hidden_states, return_dict, training=training)
+ return output
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mobilevit", None) is not None:
+ with tf.name_scope(self.mobilevit.name):
+ self.mobilevit.build(None)
+
+
+@add_start_docstrings(
+ """
+ MobileViT model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
+ ImageNet.
+ """,
+ MOBILEVIT_START_DOCSTRING,
+)
+class TFMobileViTForImageClassification(TFMobileViTPreTrainedModel, TFSequenceClassificationLoss):
+ def __init__(self, config: MobileViTConfig, *inputs, **kwargs) -> None:
+ super().__init__(config, *inputs, **kwargs)
+
+ self.num_labels = config.num_labels
+ self.mobilevit = TFMobileViTMainLayer(config, name="mobilevit")
+
+ # Classifier head
+ self.dropout = keras.layers.Dropout(config.classifier_dropout_prob)
+ self.classifier = (
+ keras.layers.Dense(config.num_labels, name="classifier") if config.num_labels > 0 else tf.identity
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING)
+ @add_code_sample_docstrings(
+ checkpoint=_IMAGE_CLASS_CHECKPOINT,
+ output_type=TFImageClassifierOutputWithNoAttention,
+ config_class=_CONFIG_FOR_DOC,
+ expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
+ )
+ def call(
+ self,
+ pixel_values: tf.Tensor | None = None,
+ output_hidden_states: bool | None = None,
+ labels: tf.Tensor | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFImageClassifierOutputWithNoAttention:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mobilevit(
+ pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training
+ )
+
+ pooled_output = outputs.pooler_output if return_dict else outputs[1]
+
+ logits = self.classifier(self.dropout(pooled_output, training=training))
+ loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mobilevit", None) is not None:
+ with tf.name_scope(self.mobilevit.name):
+ self.mobilevit.build(None)
+ if getattr(self, "classifier", None) is not None:
+ if hasattr(self.classifier, "name"):
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build([None, None, self.config.neck_hidden_sizes[-1]])
+
+
+class TFMobileViTASPPPooling(keras.layers.Layer):
+ def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int, **kwargs) -> None:
+ super().__init__(**kwargs)
+
+ self.global_pool = keras.layers.GlobalAveragePooling2D(keepdims=True, name="global_pool")
+
+ self.conv_1x1 = TFMobileViTConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ stride=1,
+ use_normalization=True,
+ use_activation="relu",
+ name="conv_1x1",
+ )
+
+ def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
+ spatial_size = shape_list(features)[1:-1]
+ features = self.global_pool(features)
+ features = self.conv_1x1(features, training=training)
+ features = tf.image.resize(features, size=spatial_size, method="bilinear")
+ return features
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "global_pool", None) is not None:
+ with tf.name_scope(self.global_pool.name):
+ self.global_pool.build([None, None, None, None])
+ if getattr(self, "conv_1x1", None) is not None:
+ with tf.name_scope(self.conv_1x1.name):
+ self.conv_1x1.build(None)
+
+
+class TFMobileViTASPP(keras.layers.Layer):
+ """
+ ASPP module defined in DeepLab papers: https://huggingface.co/papers/1606.00915, https://huggingface.co/papers/1706.05587
+ """
+
+ def __init__(self, config: MobileViTConfig, **kwargs) -> None:
+ super().__init__(**kwargs)
+
+ in_channels = config.neck_hidden_sizes[-2]
+ out_channels = config.aspp_out_channels
+
+ if len(config.atrous_rates) != 3:
+ raise ValueError("Expected 3 values for atrous_rates")
+
+ self.convs = []
+
+ in_projection = TFMobileViTConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ use_activation="relu",
+ name="convs.0",
+ )
+ self.convs.append(in_projection)
+
+ self.convs.extend(
+ [
+ TFMobileViTConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ dilation=rate,
+ use_activation="relu",
+ name=f"convs.{i + 1}",
+ )
+ for i, rate in enumerate(config.atrous_rates)
+ ]
+ )
+
+ pool_layer = TFMobileViTASPPPooling(
+ config, in_channels, out_channels, name=f"convs.{len(config.atrous_rates) + 1}"
+ )
+ self.convs.append(pool_layer)
+
+ self.project = TFMobileViTConvLayer(
+ config,
+ in_channels=5 * out_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ use_activation="relu",
+ name="project",
+ )
+
+ self.dropout = keras.layers.Dropout(config.aspp_dropout_prob)
+
+ def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
+ # since the hidden states were transposed to have `(batch_size, channels, height, width)`
+ # layout we transpose them back to have `(batch_size, height, width, channels)` layout.
+ features = tf.transpose(features, perm=[0, 2, 3, 1])
+ pyramid = []
+ for conv in self.convs:
+ pyramid.append(conv(features, training=training))
+ pyramid = tf.concat(pyramid, axis=-1)
+
+ pooled_features = self.project(pyramid, training=training)
+ pooled_features = self.dropout(pooled_features, training=training)
+ return pooled_features
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "project", None) is not None:
+ with tf.name_scope(self.project.name):
+ self.project.build(None)
+ if getattr(self, "convs", None) is not None:
+ for conv in self.convs:
+ with tf.name_scope(conv.name):
+ conv.build(None)
+
+
+class TFMobileViTDeepLabV3(keras.layers.Layer):
+ """
+ DeepLabv3 architecture: https://huggingface.co/papers/1706.05587
+ """
+
+ def __init__(self, config: MobileViTConfig, **kwargs) -> None:
+ super().__init__(**kwargs)
+ self.aspp = TFMobileViTASPP(config, name="aspp")
+
+ self.dropout = keras.layers.Dropout(config.classifier_dropout_prob)
+
+ self.classifier = TFMobileViTConvLayer(
+ config,
+ in_channels=config.aspp_out_channels,
+ out_channels=config.num_labels,
+ kernel_size=1,
+ use_normalization=False,
+ use_activation=False,
+ bias=True,
+ name="classifier",
+ )
+
+ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
+ features = self.aspp(hidden_states[-1], training=training)
+ features = self.dropout(features, training=training)
+ features = self.classifier(features, training=training)
+ return features
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "aspp", None) is not None:
+ with tf.name_scope(self.aspp.name):
+ self.aspp.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build(None)
+
+
+@add_start_docstrings(
+ """
+ MobileViT model with a semantic segmentation head on top, e.g. for Pascal VOC.
+ """,
+ MOBILEVIT_START_DOCSTRING,
+)
+class TFMobileViTForSemanticSegmentation(TFMobileViTPreTrainedModel):
+ def __init__(self, config: MobileViTConfig, **kwargs) -> None:
+ super().__init__(config, **kwargs)
+
+ self.num_labels = config.num_labels
+ self.mobilevit = TFMobileViTMainLayer(config, expand_output=False, name="mobilevit")
+ self.segmentation_head = TFMobileViTDeepLabV3(config, name="segmentation_head")
+
+ def hf_compute_loss(self, logits, labels):
+ # upsample logits to the images' original size
+ # `labels` is of shape (batch_size, height, width)
+ label_interp_shape = shape_list(labels)[1:]
+
+ upsampled_logits = tf.image.resize(logits, size=label_interp_shape, method="bilinear")
+ # compute weighted loss
+ loss_fct = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction="none")
+
+ def masked_loss(real, pred):
+ unmasked_loss = loss_fct(real, pred)
+ mask = tf.cast(real != self.config.semantic_loss_ignore_index, dtype=unmasked_loss.dtype)
+ masked_loss = unmasked_loss * mask
+ # Reduction strategy in the similar spirit with
+ # https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_tf_utils.py#L210
+ reduced_masked_loss = tf.reduce_sum(masked_loss) / tf.reduce_sum(mask)
+ return tf.reshape(reduced_masked_loss, (1,))
+
+ return masked_loss(labels, upsampled_logits)
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MOBILEVIT_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=TFSemanticSegmenterOutputWithNoAttention, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ pixel_values: tf.Tensor | None = None,
+ labels: tf.Tensor | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ) -> tuple | TFSemanticSegmenterOutputWithNoAttention:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, height, width)`, *optional*):
+ Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
+
+ Returns:
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoImageProcessor, TFMobileViTForSemanticSegmentation
+ >>> from PIL import Image
+ >>> import requests
+
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> image_processor = AutoImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-small")
+ >>> model = TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-small")
+
+ >>> inputs = image_processor(images=image, return_tensors="tf")
+
+ >>> outputs = model(**inputs)
+
+ >>> # logits are of shape (batch_size, num_labels, height, width)
+ >>> logits = outputs.logits
+ ```"""
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if labels is not None and not self.config.num_labels > 1:
+ raise ValueError("The number of labels should be greater than one")
+
+ outputs = self.mobilevit(
+ pixel_values,
+ output_hidden_states=True, # we need the intermediate hidden states
+ return_dict=return_dict,
+ training=training,
+ )
+
+ encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
+
+ logits = self.segmentation_head(encoder_hidden_states, training=training)
+
+ loss = None
+ if labels is not None:
+ loss = self.hf_compute_loss(logits=logits, labels=labels)
+
+ # make logits of shape (batch_size, num_labels, height, width) to
+ # keep them consistent across APIs
+ logits = tf.transpose(logits, perm=[0, 3, 1, 2])
+
+ if not return_dict:
+ if output_hidden_states:
+ output = (logits,) + outputs[1:]
+ else:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFSemanticSegmenterOutputWithNoAttention(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states if output_hidden_states else None,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mobilevit", None) is not None:
+ with tf.name_scope(self.mobilevit.name):
+ self.mobilevit.build(None)
+ if getattr(self, "segmentation_head", None) is not None:
+ with tf.name_scope(self.segmentation_head.name):
+ self.segmentation_head.build(None)
+
+
+__all__ = [
+ "TFMobileViTForImageClassification",
+ "TFMobileViTForSemanticSegmentation",
+ "TFMobileViTModel",
+ "TFMobileViTPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..15e10a1d9815935629fadf9405effdae5a16fbb2
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mobilevitv2 import *
+ from .modeling_mobilevitv2 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/configuration_mobilevitv2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/configuration_mobilevitv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc9db247602c77daa35c3c3ad97dbda10e881cc8
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/configuration_mobilevitv2.py
@@ -0,0 +1,168 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MobileViTV2 model configuration"""
+
+from collections import OrderedDict
+from collections.abc import Mapping
+
+from packaging import version
+
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MobileViTV2Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MobileViTV2Model`]. It is used to instantiate a
+ MobileViTV2 model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the MobileViTV2
+ [apple/mobilevitv2-1.0](https://huggingface.co/apple/mobilevitv2-1.0) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ num_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ image_size (`int`, *optional*, defaults to 256):
+ The size (resolution) of each image.
+ patch_size (`int`, *optional*, defaults to 2):
+ The size (resolution) of each patch.
+ expand_ratio (`float`, *optional*, defaults to 2.0):
+ Expansion factor for the MobileNetv2 layers.
+ hidden_act (`str` or `function`, *optional*, defaults to `"swish"`):
+ The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
+ conv_kernel_size (`int`, *optional*, defaults to 3):
+ The size of the convolutional kernel in the MobileViTV2 layer.
+ output_stride (`int`, *optional*, defaults to 32):
+ The ratio of the spatial resolution of the output to the resolution of the input image.
+ classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for attached classifiers.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the layer normalization layers.
+ aspp_out_channels (`int`, *optional*, defaults to 512):
+ Number of output channels used in the ASPP layer for semantic segmentation.
+ atrous_rates (`list[int]`, *optional*, defaults to `[6, 12, 18]`):
+ Dilation (atrous) factors used in the ASPP layer for semantic segmentation.
+ aspp_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the ASPP layer for semantic segmentation.
+ semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
+ The index that is ignored by the loss function of the semantic segmentation model.
+ n_attn_blocks (`list[int]`, *optional*, defaults to `[2, 4, 3]`):
+ The number of attention blocks in each MobileViTV2Layer
+ base_attn_unit_dims (`list[int]`, *optional*, defaults to `[128, 192, 256]`):
+ The base multiplier for dimensions of attention blocks in each MobileViTV2Layer
+ width_multiplier (`float`, *optional*, defaults to 1.0):
+ The width multiplier for MobileViTV2.
+ ffn_multiplier (`int`, *optional*, defaults to 2):
+ The FFN multiplier for MobileViTV2.
+ attn_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout in the attention layer.
+ ffn_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout between FFN layers.
+
+ Example:
+
+ ```python
+ >>> from transformers import MobileViTV2Config, MobileViTV2Model
+
+ >>> # Initializing a mobilevitv2-small style configuration
+ >>> configuration = MobileViTV2Config()
+
+ >>> # Initializing a model from the mobilevitv2-small style configuration
+ >>> model = MobileViTV2Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mobilevitv2"
+
+ def __init__(
+ self,
+ num_channels=3,
+ image_size=256,
+ patch_size=2,
+ expand_ratio=2.0,
+ hidden_act="swish",
+ conv_kernel_size=3,
+ output_stride=32,
+ classifier_dropout_prob=0.1,
+ initializer_range=0.02,
+ layer_norm_eps=1e-5,
+ aspp_out_channels=512,
+ atrous_rates=[6, 12, 18],
+ aspp_dropout_prob=0.1,
+ semantic_loss_ignore_index=255,
+ n_attn_blocks=[2, 4, 3],
+ base_attn_unit_dims=[128, 192, 256],
+ width_multiplier=1.0,
+ ffn_multiplier=2,
+ attn_dropout=0.0,
+ ffn_dropout=0.0,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.num_channels = num_channels
+ self.image_size = image_size
+ self.patch_size = patch_size
+ self.expand_ratio = expand_ratio
+ self.hidden_act = hidden_act
+ self.conv_kernel_size = conv_kernel_size
+ self.output_stride = output_stride
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.n_attn_blocks = n_attn_blocks
+ self.base_attn_unit_dims = base_attn_unit_dims
+ self.width_multiplier = width_multiplier
+ self.ffn_multiplier = ffn_multiplier
+ self.ffn_dropout = ffn_dropout
+ self.attn_dropout = attn_dropout
+ self.classifier_dropout_prob = classifier_dropout_prob
+
+ # decode head attributes for semantic segmentation
+ self.aspp_out_channels = aspp_out_channels
+ self.atrous_rates = atrous_rates
+ self.aspp_dropout_prob = aspp_dropout_prob
+ self.semantic_loss_ignore_index = semantic_loss_ignore_index
+
+
+class MobileViTV2OnnxConfig(OnnxConfig):
+ torch_onnx_minimum_version = version.parse("1.11")
+
+ @property
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})])
+
+ @property
+ def outputs(self) -> Mapping[str, Mapping[int, str]]:
+ if self.task == "image-classification":
+ return OrderedDict([("logits", {0: "batch"})])
+ else:
+ return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})])
+
+ @property
+ def atol_for_validation(self) -> float:
+ return 1e-4
+
+
+__all__ = ["MobileViTV2Config", "MobileViTV2OnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/convert_mlcvnets_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/convert_mlcvnets_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d462c7dd49f58e77aac0232b0c36d54ca942731
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/convert_mlcvnets_to_pytorch.py
@@ -0,0 +1,332 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert MobileViTV2 checkpoints from the ml-cvnets library."""
+
+import argparse
+import collections
+import json
+from pathlib import Path
+
+import requests
+import torch
+import yaml
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from transformers import (
+ MobileViTImageProcessor,
+ MobileViTV2Config,
+ MobileViTV2ForImageClassification,
+ MobileViTV2ForSemanticSegmentation,
+)
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def load_orig_config_file(orig_cfg_file):
+ print("Loading config file...")
+
+ def flatten_yaml_as_dict(d, parent_key="", sep="."):
+ items = []
+ for k, v in d.items():
+ new_key = parent_key + sep + k if parent_key else k
+ if isinstance(v, collections.abc.MutableMapping):
+ items.extend(flatten_yaml_as_dict(v, new_key, sep=sep).items())
+ else:
+ items.append((new_key, v))
+ return dict(items)
+
+ config = argparse.Namespace()
+ with open(orig_cfg_file, "r") as yaml_file:
+ try:
+ cfg = yaml.load(yaml_file, Loader=yaml.FullLoader)
+
+ flat_cfg = flatten_yaml_as_dict(cfg)
+ for k, v in flat_cfg.items():
+ setattr(config, k, v)
+ except yaml.YAMLError as exc:
+ logger.error(f"Error while loading config file: {orig_cfg_file}. Error message: {str(exc)}")
+ return config
+
+
+def get_mobilevitv2_config(task_name, orig_cfg_file):
+ config = MobileViTV2Config()
+
+ is_segmentation_model = False
+
+ # dataset
+ if task_name.startswith("imagenet1k_"):
+ config.num_labels = 1000
+ if int(task_name.strip().split("_")[-1]) == 384:
+ config.image_size = 384
+ else:
+ config.image_size = 256
+ filename = "imagenet-1k-id2label.json"
+ elif task_name.startswith("imagenet21k_to_1k_"):
+ config.num_labels = 21000
+ if int(task_name.strip().split("_")[-1]) == 384:
+ config.image_size = 384
+ else:
+ config.image_size = 256
+ filename = "imagenet-22k-id2label.json"
+ elif task_name.startswith("ade20k_"):
+ config.num_labels = 151
+ config.image_size = 512
+ filename = "ade20k-id2label.json"
+ is_segmentation_model = True
+ elif task_name.startswith("voc_"):
+ config.num_labels = 21
+ config.image_size = 512
+ filename = "pascal-voc-id2label.json"
+ is_segmentation_model = True
+
+ # orig_config
+ orig_config = load_orig_config_file(orig_cfg_file)
+ assert getattr(orig_config, "model.classification.name", -1) == "mobilevit_v2", "Invalid model"
+ config.width_multiplier = getattr(orig_config, "model.classification.mitv2.width_multiplier", 1.0)
+ assert getattr(orig_config, "model.classification.mitv2.attn_norm_layer", -1) == "layer_norm_2d", (
+ "Norm layers other than layer_norm_2d is not supported"
+ )
+ config.hidden_act = getattr(orig_config, "model.classification.activation.name", "swish")
+ # config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
+
+ if is_segmentation_model:
+ config.output_stride = getattr(orig_config, "model.segmentation.output_stride", 16)
+ if "_deeplabv3" in task_name:
+ config.atrous_rates = getattr(orig_config, "model.segmentation.deeplabv3.aspp_rates", [12, 24, 36])
+ config.aspp_out_channels = getattr(orig_config, "model.segmentation.deeplabv3.aspp_out_channels", 512)
+ config.aspp_dropout_prob = getattr(orig_config, "model.segmentation.deeplabv3.aspp_dropout", 0.1)
+
+ # id2label
+ repo_id = "huggingface/label-files"
+ id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
+ id2label = {int(k): v for k, v in id2label.items()}
+ config.id2label = id2label
+ config.label2id = {v: k for k, v in id2label.items()}
+
+ return config
+
+
+def rename_key(dct, old, new):
+ val = dct.pop(old)
+ dct[new] = val
+
+
+def create_rename_keys(state_dict, base_model=False):
+ if base_model:
+ model_prefix = ""
+ else:
+ model_prefix = "mobilevitv2."
+
+ rename_keys = []
+ for k in state_dict:
+ if k[:8] == "encoder.":
+ k_new = k[8:]
+ else:
+ k_new = k
+
+ if ".block." in k:
+ k_new = k_new.replace(".block.", ".")
+ if ".conv." in k:
+ k_new = k_new.replace(".conv.", ".convolution.")
+ if ".norm." in k:
+ k_new = k_new.replace(".norm.", ".normalization.")
+
+ if "conv_1." in k:
+ k_new = k_new.replace("conv_1.", f"{model_prefix}conv_stem.")
+ for i in [1, 2]:
+ if f"layer_{i}." in k:
+ k_new = k_new.replace(f"layer_{i}.", f"{model_prefix}encoder.layer.{i - 1}.layer.")
+ if ".exp_1x1." in k:
+ k_new = k_new.replace(".exp_1x1.", ".expand_1x1.")
+ if ".red_1x1." in k:
+ k_new = k_new.replace(".red_1x1.", ".reduce_1x1.")
+
+ for i in [3, 4, 5]:
+ if f"layer_{i}.0." in k:
+ k_new = k_new.replace(f"layer_{i}.0.", f"{model_prefix}encoder.layer.{i - 1}.downsampling_layer.")
+ if f"layer_{i}.1.local_rep.0." in k:
+ k_new = k_new.replace(f"layer_{i}.1.local_rep.0.", f"{model_prefix}encoder.layer.{i - 1}.conv_kxk.")
+ if f"layer_{i}.1.local_rep.1." in k:
+ k_new = k_new.replace(f"layer_{i}.1.local_rep.1.", f"{model_prefix}encoder.layer.{i - 1}.conv_1x1.")
+
+ for i in [3, 4, 5]:
+ if i == 3:
+ j_in = [0, 1]
+ elif i == 4:
+ j_in = [0, 1, 2, 3]
+ elif i == 5:
+ j_in = [0, 1, 2]
+
+ for j in j_in:
+ if f"layer_{i}.1.global_rep.{j}." in k:
+ k_new = k_new.replace(
+ f"layer_{i}.1.global_rep.{j}.", f"{model_prefix}encoder.layer.{i - 1}.transformer.layer.{j}."
+ )
+ if f"layer_{i}.1.global_rep.{j + 1}." in k:
+ k_new = k_new.replace(
+ f"layer_{i}.1.global_rep.{j + 1}.", f"{model_prefix}encoder.layer.{i - 1}.layernorm."
+ )
+
+ if f"layer_{i}.1.conv_proj." in k:
+ k_new = k_new.replace(
+ f"layer_{i}.1.conv_proj.", f"{model_prefix}encoder.layer.{i - 1}.conv_projection."
+ )
+
+ if "pre_norm_attn.0." in k:
+ k_new = k_new.replace("pre_norm_attn.0.", "layernorm_before.")
+ if "pre_norm_attn.1." in k:
+ k_new = k_new.replace("pre_norm_attn.1.", "attention.")
+ if "pre_norm_ffn.0." in k:
+ k_new = k_new.replace("pre_norm_ffn.0.", "layernorm_after.")
+ if "pre_norm_ffn.1." in k:
+ k_new = k_new.replace("pre_norm_ffn.1.", "ffn.conv1.")
+ if "pre_norm_ffn.3." in k:
+ k_new = k_new.replace("pre_norm_ffn.3.", "ffn.conv2.")
+
+ if "classifier.1." in k:
+ k_new = k_new.replace("classifier.1.", "classifier.")
+
+ if "seg_head." in k:
+ k_new = k_new.replace("seg_head.", "segmentation_head.")
+ if ".aspp_layer." in k:
+ k_new = k_new.replace(".aspp_layer.", ".")
+ if ".aspp_pool." in k:
+ k_new = k_new.replace(".aspp_pool.", ".")
+
+ rename_keys.append((k, k_new))
+ return rename_keys
+
+
+def remove_unused_keys(state_dict):
+ """remove unused keys (e.g.: seg_head.aux_head)"""
+ keys_to_ignore = []
+ for k in state_dict:
+ if k.startswith("seg_head.aux_head."):
+ keys_to_ignore.append(k)
+ for k in keys_to_ignore:
+ state_dict.pop(k, None)
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
+ im = Image.open(requests.get(url, stream=True).raw)
+ return im
+
+
+@torch.no_grad()
+def convert_mobilevitv2_checkpoint(task_name, checkpoint_path, orig_config_path, pytorch_dump_folder_path):
+ """
+ Copy/paste/tweak model's weights to our MobileViTV2 structure.
+ """
+ config = get_mobilevitv2_config(task_name, orig_config_path)
+
+ # load original state_dict
+ checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
+
+ # load huggingface model
+ if task_name.startswith("ade20k_") or task_name.startswith("voc_"):
+ model = MobileViTV2ForSemanticSegmentation(config).eval()
+ base_model = False
+ else:
+ model = MobileViTV2ForImageClassification(config).eval()
+ base_model = False
+
+ # remove and rename some keys of load the original model
+ state_dict = checkpoint
+ remove_unused_keys(state_dict)
+ rename_keys = create_rename_keys(state_dict, base_model=base_model)
+ for rename_key_src, rename_key_dest in rename_keys:
+ rename_key(state_dict, rename_key_src, rename_key_dest)
+
+ # load modified state_dict
+ model.load_state_dict(state_dict)
+
+ # Check outputs on an image, prepared by MobileViTImageProcessor
+ image_processor = MobileViTImageProcessor(crop_size=config.image_size, size=config.image_size + 32)
+ encoding = image_processor(images=prepare_img(), return_tensors="pt")
+ outputs = model(**encoding)
+
+ # verify classification model
+ if task_name.startswith("imagenet"):
+ logits = outputs.logits
+ predicted_class_idx = logits.argmax(-1).item()
+ print("Predicted class:", model.config.id2label[predicted_class_idx])
+ if task_name.startswith("imagenet1k_256") and config.width_multiplier == 1.0:
+ # expected_logits for base variant
+ expected_logits = torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01])
+ assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4)
+
+ Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+ print(f"Saving model {task_name} to {pytorch_dump_folder_path}")
+ model.save_pretrained(pytorch_dump_folder_path)
+ print(f"Saving image processor to {pytorch_dump_folder_path}")
+ image_processor.save_pretrained(pytorch_dump_folder_path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--task",
+ default="imagenet1k_256",
+ type=str,
+ help=(
+ "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
+ """
+ Classification (ImageNet-1k)
+ - MobileViTV2 (256x256) : imagenet1k_256
+ - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
+ - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
+ imagenet21k_to_1k_256
+ - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
+ ImageNet-1k 384x384) : imagenet21k_to_1k_384
+ Segmentation
+ - ADE20K Dataset : ade20k_deeplabv3
+ - Pascal VOC 2012 Dataset: voc_deeplabv3
+ """
+ ),
+ choices=[
+ "imagenet1k_256",
+ "imagenet1k_384",
+ "imagenet21k_to_1k_256",
+ "imagenet21k_to_1k_384",
+ "ade20k_deeplabv3",
+ "voc_deeplabv3",
+ ],
+ )
+
+ parser.add_argument(
+ "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
+ )
+ parser.add_argument(
+ "--orig_config_path",
+ required=True,
+ type=str,
+ help="Path to the original config file. yaml.load will be used to load the file, please be wary of which file you're loading.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
+ )
+
+ args = parser.parse_args()
+ convert_mobilevitv2_checkpoint(
+ args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/modeling_mobilevitv2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/modeling_mobilevitv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..450c871ca9f04b653032aedf98193d154866fb7a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mobilevitv2/modeling_mobilevitv2.py
@@ -0,0 +1,967 @@
+# coding=utf-8
+# Copyright 2023 Apple Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Original license: https://github.com/apple/ml-cvnets/blob/main/LICENSE
+"""PyTorch MobileViTV2 model."""
+
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutputWithNoAttention,
+ BaseModelOutputWithPoolingAndNoAttention,
+ ImageClassifierOutputWithNoAttention,
+ SemanticSegmenterOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, logging
+from .configuration_mobilevitv2 import MobileViTV2Config
+
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.mobilevit.modeling_mobilevit.make_divisible
+def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int:
+ """
+ Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
+ original TensorFlow repo. It can be seen here:
+ https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+ """
+ if min_value is None:
+ min_value = divisor
+ new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
+ # Make sure that round down does not go down by more than 10%.
+ if new_value < 0.9 * value:
+ new_value += divisor
+ return int(new_value)
+
+
+def clip(value: float, min_val: float = float("-inf"), max_val: float = float("inf")) -> float:
+ return max(min_val, min(max_val, value))
+
+
+# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTConvLayer with MobileViT->MobileViTV2
+class MobileViTV2ConvLayer(nn.Module):
+ def __init__(
+ self,
+ config: MobileViTV2Config,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ stride: int = 1,
+ groups: int = 1,
+ bias: bool = False,
+ dilation: int = 1,
+ use_normalization: bool = True,
+ use_activation: Union[bool, str] = True,
+ ) -> None:
+ super().__init__()
+ padding = int((kernel_size - 1) / 2) * dilation
+
+ if in_channels % groups != 0:
+ raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
+ if out_channels % groups != 0:
+ raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")
+
+ self.convolution = nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ bias=bias,
+ padding_mode="zeros",
+ )
+
+ if use_normalization:
+ self.normalization = nn.BatchNorm2d(
+ num_features=out_channels,
+ eps=1e-5,
+ momentum=0.1,
+ affine=True,
+ track_running_stats=True,
+ )
+ else:
+ self.normalization = None
+
+ if use_activation:
+ if isinstance(use_activation, str):
+ self.activation = ACT2FN[use_activation]
+ elif isinstance(config.hidden_act, str):
+ self.activation = ACT2FN[config.hidden_act]
+ else:
+ self.activation = config.hidden_act
+ else:
+ self.activation = None
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ features = self.convolution(features)
+ if self.normalization is not None:
+ features = self.normalization(features)
+ if self.activation is not None:
+ features = self.activation(features)
+ return features
+
+
+# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTInvertedResidual with MobileViT->MobileViTV2
+class MobileViTV2InvertedResidual(nn.Module):
+ """
+ Inverted residual block (MobileNetv2): https://huggingface.co/papers/1801.04381
+ """
+
+ def __init__(
+ self, config: MobileViTV2Config, in_channels: int, out_channels: int, stride: int, dilation: int = 1
+ ) -> None:
+ super().__init__()
+ expanded_channels = make_divisible(int(round(in_channels * config.expand_ratio)), 8)
+
+ if stride not in [1, 2]:
+ raise ValueError(f"Invalid stride {stride}.")
+
+ self.use_residual = (stride == 1) and (in_channels == out_channels)
+
+ self.expand_1x1 = MobileViTV2ConvLayer(
+ config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1
+ )
+
+ self.conv_3x3 = MobileViTV2ConvLayer(
+ config,
+ in_channels=expanded_channels,
+ out_channels=expanded_channels,
+ kernel_size=3,
+ stride=stride,
+ groups=expanded_channels,
+ dilation=dilation,
+ )
+
+ self.reduce_1x1 = MobileViTV2ConvLayer(
+ config,
+ in_channels=expanded_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ use_activation=False,
+ )
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ residual = features
+
+ features = self.expand_1x1(features)
+ features = self.conv_3x3(features)
+ features = self.reduce_1x1(features)
+
+ return residual + features if self.use_residual else features
+
+
+# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTMobileNetLayer with MobileViT->MobileViTV2
+class MobileViTV2MobileNetLayer(nn.Module):
+ def __init__(
+ self, config: MobileViTV2Config, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1
+ ) -> None:
+ super().__init__()
+
+ self.layer = nn.ModuleList()
+ for i in range(num_stages):
+ layer = MobileViTV2InvertedResidual(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ stride=stride if i == 0 else 1,
+ )
+ self.layer.append(layer)
+ in_channels = out_channels
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ for layer_module in self.layer:
+ features = layer_module(features)
+ return features
+
+
+class MobileViTV2LinearSelfAttention(nn.Module):
+ """
+ This layer applies a self-attention with linear complexity, as described in MobileViTV2 paper:
+ https://huggingface.co/papers/2206.02680
+
+ Args:
+ config (`MobileVitv2Config`):
+ Model configuration object
+ embed_dim (`int`):
+ `input_channels` from an expected input of size :math:`(batch_size, input_channels, height, width)`
+ """
+
+ def __init__(self, config: MobileViTV2Config, embed_dim: int) -> None:
+ super().__init__()
+
+ self.qkv_proj = MobileViTV2ConvLayer(
+ config=config,
+ in_channels=embed_dim,
+ out_channels=1 + (2 * embed_dim),
+ bias=True,
+ kernel_size=1,
+ use_normalization=False,
+ use_activation=False,
+ )
+
+ self.attn_dropout = nn.Dropout(p=config.attn_dropout)
+ self.out_proj = MobileViTV2ConvLayer(
+ config=config,
+ in_channels=embed_dim,
+ out_channels=embed_dim,
+ bias=True,
+ kernel_size=1,
+ use_normalization=False,
+ use_activation=False,
+ )
+ self.embed_dim = embed_dim
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ # (batch_size, embed_dim, num_pixels_in_patch, num_patches) --> (batch_size, 1+2*embed_dim, num_pixels_in_patch, num_patches)
+ qkv = self.qkv_proj(hidden_states)
+
+ # Project hidden_states into query, key and value
+ # Query --> [batch_size, 1, num_pixels_in_patch, num_patches]
+ # value, key --> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
+ query, key, value = torch.split(qkv, split_size_or_sections=[1, self.embed_dim, self.embed_dim], dim=1)
+
+ # apply softmax along num_patches dimension
+ context_scores = torch.nn.functional.softmax(query, dim=-1)
+ context_scores = self.attn_dropout(context_scores)
+
+ # Compute context vector
+ # [batch_size, embed_dim, num_pixels_in_patch, num_patches] x [batch_size, 1, num_pixels_in_patch, num_patches] -> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
+ context_vector = key * context_scores
+ # [batch_size, embed_dim, num_pixels_in_patch, num_patches] --> [batch_size, embed_dim, num_pixels_in_patch, 1]
+ context_vector = torch.sum(context_vector, dim=-1, keepdim=True)
+
+ # combine context vector with values
+ # [batch_size, embed_dim, num_pixels_in_patch, num_patches] * [batch_size, embed_dim, num_pixels_in_patch, 1] --> [batch_size, embed_dim, num_pixels_in_patch, num_patches]
+ out = torch.nn.functional.relu(value) * context_vector.expand_as(value)
+ out = self.out_proj(out)
+ return out
+
+
+class MobileViTV2FFN(nn.Module):
+ def __init__(
+ self,
+ config: MobileViTV2Config,
+ embed_dim: int,
+ ffn_latent_dim: int,
+ ffn_dropout: float = 0.0,
+ ) -> None:
+ super().__init__()
+ self.conv1 = MobileViTV2ConvLayer(
+ config=config,
+ in_channels=embed_dim,
+ out_channels=ffn_latent_dim,
+ kernel_size=1,
+ stride=1,
+ bias=True,
+ use_normalization=False,
+ use_activation=True,
+ )
+ self.dropout1 = nn.Dropout(ffn_dropout)
+
+ self.conv2 = MobileViTV2ConvLayer(
+ config=config,
+ in_channels=ffn_latent_dim,
+ out_channels=embed_dim,
+ kernel_size=1,
+ stride=1,
+ bias=True,
+ use_normalization=False,
+ use_activation=False,
+ )
+ self.dropout2 = nn.Dropout(ffn_dropout)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.conv1(hidden_states)
+ hidden_states = self.dropout1(hidden_states)
+ hidden_states = self.conv2(hidden_states)
+ hidden_states = self.dropout2(hidden_states)
+ return hidden_states
+
+
+class MobileViTV2TransformerLayer(nn.Module):
+ def __init__(
+ self,
+ config: MobileViTV2Config,
+ embed_dim: int,
+ ffn_latent_dim: int,
+ dropout: float = 0.0,
+ ) -> None:
+ super().__init__()
+ self.layernorm_before = nn.GroupNorm(num_groups=1, num_channels=embed_dim, eps=config.layer_norm_eps)
+ self.attention = MobileViTV2LinearSelfAttention(config, embed_dim)
+ self.dropout1 = nn.Dropout(p=dropout)
+ self.layernorm_after = nn.GroupNorm(num_groups=1, num_channels=embed_dim, eps=config.layer_norm_eps)
+ self.ffn = MobileViTV2FFN(config, embed_dim, ffn_latent_dim, config.ffn_dropout)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ layernorm_1_out = self.layernorm_before(hidden_states)
+ attention_output = self.attention(layernorm_1_out)
+ hidden_states = attention_output + hidden_states
+
+ layer_output = self.layernorm_after(hidden_states)
+ layer_output = self.ffn(layer_output)
+
+ layer_output = layer_output + hidden_states
+ return layer_output
+
+
+class MobileViTV2Transformer(nn.Module):
+ def __init__(self, config: MobileViTV2Config, n_layers: int, d_model: int) -> None:
+ super().__init__()
+
+ ffn_multiplier = config.ffn_multiplier
+
+ ffn_dims = [ffn_multiplier * d_model] * n_layers
+
+ # ensure that dims are multiple of 16
+ ffn_dims = [int((d // 16) * 16) for d in ffn_dims]
+
+ self.layer = nn.ModuleList()
+ for block_idx in range(n_layers):
+ transformer_layer = MobileViTV2TransformerLayer(
+ config, embed_dim=d_model, ffn_latent_dim=ffn_dims[block_idx]
+ )
+ self.layer.append(transformer_layer)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ for layer_module in self.layer:
+ hidden_states = layer_module(hidden_states)
+ return hidden_states
+
+
+class MobileViTV2Layer(GradientCheckpointingLayer):
+ """
+ MobileViTV2 layer: https://huggingface.co/papers/2206.02680
+ """
+
+ def __init__(
+ self,
+ config: MobileViTV2Config,
+ in_channels: int,
+ out_channels: int,
+ attn_unit_dim: int,
+ n_attn_blocks: int = 2,
+ dilation: int = 1,
+ stride: int = 2,
+ ) -> None:
+ super().__init__()
+ self.patch_width = config.patch_size
+ self.patch_height = config.patch_size
+
+ cnn_out_dim = attn_unit_dim
+
+ if stride == 2:
+ self.downsampling_layer = MobileViTV2InvertedResidual(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ stride=stride if dilation == 1 else 1,
+ dilation=dilation // 2 if dilation > 1 else 1,
+ )
+ in_channels = out_channels
+ else:
+ self.downsampling_layer = None
+
+ # Local representations
+ self.conv_kxk = MobileViTV2ConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=in_channels,
+ kernel_size=config.conv_kernel_size,
+ groups=in_channels,
+ )
+ self.conv_1x1 = MobileViTV2ConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=cnn_out_dim,
+ kernel_size=1,
+ use_normalization=False,
+ use_activation=False,
+ )
+
+ # Global representations
+ self.transformer = MobileViTV2Transformer(config, d_model=attn_unit_dim, n_layers=n_attn_blocks)
+
+ # self.layernorm = MobileViTV2LayerNorm2D(attn_unit_dim, eps=config.layer_norm_eps)
+ self.layernorm = nn.GroupNorm(num_groups=1, num_channels=attn_unit_dim, eps=config.layer_norm_eps)
+
+ # Fusion
+ self.conv_projection = MobileViTV2ConvLayer(
+ config,
+ in_channels=cnn_out_dim,
+ out_channels=in_channels,
+ kernel_size=1,
+ use_normalization=True,
+ use_activation=False,
+ )
+
+ def unfolding(self, feature_map: torch.Tensor) -> tuple[torch.Tensor, tuple[int, int]]:
+ batch_size, in_channels, img_height, img_width = feature_map.shape
+ patches = nn.functional.unfold(
+ feature_map,
+ kernel_size=(self.patch_height, self.patch_width),
+ stride=(self.patch_height, self.patch_width),
+ )
+ patches = patches.reshape(batch_size, in_channels, self.patch_height * self.patch_width, -1)
+
+ return patches, (img_height, img_width)
+
+ def folding(self, patches: torch.Tensor, output_size: tuple[int, int]) -> torch.Tensor:
+ batch_size, in_dim, patch_size, n_patches = patches.shape
+ patches = patches.reshape(batch_size, in_dim * patch_size, n_patches)
+
+ feature_map = nn.functional.fold(
+ patches,
+ output_size=output_size,
+ kernel_size=(self.patch_height, self.patch_width),
+ stride=(self.patch_height, self.patch_width),
+ )
+
+ return feature_map
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ # reduce spatial dimensions if needed
+ if self.downsampling_layer:
+ features = self.downsampling_layer(features)
+
+ # local representation
+ features = self.conv_kxk(features)
+ features = self.conv_1x1(features)
+
+ # convert feature map to patches
+ patches, output_size = self.unfolding(features)
+
+ # learn global representations
+ patches = self.transformer(patches)
+ patches = self.layernorm(patches)
+
+ # convert patches back to feature maps
+ # [batch_size, patch_height, patch_width, input_dim] --> [batch_size, input_dim, patch_height, patch_width]
+ features = self.folding(patches, output_size)
+
+ features = self.conv_projection(features)
+ return features
+
+
+class MobileViTV2Encoder(nn.Module):
+ def __init__(self, config: MobileViTV2Config) -> None:
+ super().__init__()
+ self.config = config
+
+ self.layer = nn.ModuleList()
+ self.gradient_checkpointing = False
+
+ # segmentation architectures like DeepLab and PSPNet modify the strides
+ # of the classification backbones
+ dilate_layer_4 = dilate_layer_5 = False
+ if config.output_stride == 8:
+ dilate_layer_4 = True
+ dilate_layer_5 = True
+ elif config.output_stride == 16:
+ dilate_layer_5 = True
+
+ dilation = 1
+
+ layer_0_dim = make_divisible(
+ clip(value=32 * config.width_multiplier, min_val=16, max_val=64), divisor=8, min_value=16
+ )
+
+ layer_1_dim = make_divisible(64 * config.width_multiplier, divisor=16)
+ layer_2_dim = make_divisible(128 * config.width_multiplier, divisor=8)
+ layer_3_dim = make_divisible(256 * config.width_multiplier, divisor=8)
+ layer_4_dim = make_divisible(384 * config.width_multiplier, divisor=8)
+ layer_5_dim = make_divisible(512 * config.width_multiplier, divisor=8)
+
+ layer_1 = MobileViTV2MobileNetLayer(
+ config,
+ in_channels=layer_0_dim,
+ out_channels=layer_1_dim,
+ stride=1,
+ num_stages=1,
+ )
+ self.layer.append(layer_1)
+
+ layer_2 = MobileViTV2MobileNetLayer(
+ config,
+ in_channels=layer_1_dim,
+ out_channels=layer_2_dim,
+ stride=2,
+ num_stages=2,
+ )
+ self.layer.append(layer_2)
+
+ layer_3 = MobileViTV2Layer(
+ config,
+ in_channels=layer_2_dim,
+ out_channels=layer_3_dim,
+ attn_unit_dim=make_divisible(config.base_attn_unit_dims[0] * config.width_multiplier, divisor=8),
+ n_attn_blocks=config.n_attn_blocks[0],
+ )
+ self.layer.append(layer_3)
+
+ if dilate_layer_4:
+ dilation *= 2
+
+ layer_4 = MobileViTV2Layer(
+ config,
+ in_channels=layer_3_dim,
+ out_channels=layer_4_dim,
+ attn_unit_dim=make_divisible(config.base_attn_unit_dims[1] * config.width_multiplier, divisor=8),
+ n_attn_blocks=config.n_attn_blocks[1],
+ dilation=dilation,
+ )
+ self.layer.append(layer_4)
+
+ if dilate_layer_5:
+ dilation *= 2
+
+ layer_5 = MobileViTV2Layer(
+ config,
+ in_channels=layer_4_dim,
+ out_channels=layer_5_dim,
+ attn_unit_dim=make_divisible(config.base_attn_unit_dims[2] * config.width_multiplier, divisor=8),
+ n_attn_blocks=config.n_attn_blocks[2],
+ dilation=dilation,
+ )
+ self.layer.append(layer_5)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ) -> Union[tuple, BaseModelOutputWithNoAttention]:
+ all_hidden_states = () if output_hidden_states else None
+
+ for i, layer_module in enumerate(self.layer):
+ hidden_states = layer_module(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
+
+ return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
+
+
+@auto_docstring
+class MobileViTV2PreTrainedModel(PreTrainedModel):
+ config: MobileViTV2Config
+ base_model_prefix = "mobilevitv2"
+ main_input_name = "pixel_values"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MobileViTV2Layer"]
+
+ def _init_weights(self, module: nn.Module) -> None:
+ """Initialize the weights"""
+ if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.GroupNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class MobileViTV2Model(MobileViTV2PreTrainedModel):
+ def __init__(self, config: MobileViTV2Config, expand_output: bool = True):
+ r"""
+ expand_output (`bool`, *optional*, defaults to `True`):
+ Whether to expand the output of the model. If `True`, the model will output pooled features in addition to
+ hidden states. If `False`, only the hidden states will be returned.
+ """
+ super().__init__(config)
+ self.config = config
+ self.expand_output = expand_output
+
+ layer_0_dim = make_divisible(
+ clip(value=32 * config.width_multiplier, min_val=16, max_val=64), divisor=8, min_value=16
+ )
+
+ self.conv_stem = MobileViTV2ConvLayer(
+ config,
+ in_channels=config.num_channels,
+ out_channels=layer_0_dim,
+ kernel_size=3,
+ stride=2,
+ use_normalization=True,
+ use_activation=True,
+ )
+ self.encoder = MobileViTV2Encoder(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def _prune_heads(self, heads_to_prune):
+ """Prunes heads of the model.
+ heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel
+ """
+ for layer_index, heads in heads_to_prune.items():
+ mobilevitv2_layer = self.encoder.layer[layer_index]
+ if isinstance(mobilevitv2_layer, MobileViTV2Layer):
+ for transformer_layer in mobilevitv2_layer.transformer.layer:
+ transformer_layer.attention.prune_heads(heads)
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ embedding_output = self.conv_stem(pixel_values)
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ if self.expand_output:
+ last_hidden_state = encoder_outputs[0]
+
+ # global average pooling: (batch_size, channels, height, width) -> (batch_size, channels)
+ pooled_output = torch.mean(last_hidden_state, dim=[-2, -1], keepdim=False)
+ else:
+ last_hidden_state = encoder_outputs[0]
+ pooled_output = None
+
+ if not return_dict:
+ output = (last_hidden_state, pooled_output) if pooled_output is not None else (last_hidden_state,)
+ return output + encoder_outputs[1:]
+
+ return BaseModelOutputWithPoolingAndNoAttention(
+ last_hidden_state=last_hidden_state,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MobileViTV2 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
+ ImageNet.
+ """
+)
+class MobileViTV2ForImageClassification(MobileViTV2PreTrainedModel):
+ def __init__(self, config: MobileViTV2Config) -> None:
+ super().__init__(config)
+
+ self.num_labels = config.num_labels
+ self.mobilevitv2 = MobileViTV2Model(config)
+
+ out_channels = make_divisible(512 * config.width_multiplier, divisor=8) # layer 5 output dimension
+ # Classifier head
+ self.classifier = (
+ nn.Linear(in_features=out_channels, out_features=config.num_labels)
+ if config.num_labels > 0
+ else nn.Identity()
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ labels: Optional[torch.Tensor] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mobilevitv2(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
+
+ pooled_output = outputs.pooler_output if return_dict else outputs[1]
+
+ logits = self.classifier(pooled_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return ImageClassifierOutputWithNoAttention(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ )
+
+
+# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTASPPPooling with MobileViT->MobileViTV2
+class MobileViTV2ASPPPooling(nn.Module):
+ def __init__(self, config: MobileViTV2Config, in_channels: int, out_channels: int) -> None:
+ super().__init__()
+
+ self.global_pool = nn.AdaptiveAvgPool2d(output_size=1)
+
+ self.conv_1x1 = MobileViTV2ConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ stride=1,
+ use_normalization=True,
+ use_activation="relu",
+ )
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ spatial_size = features.shape[-2:]
+ features = self.global_pool(features)
+ features = self.conv_1x1(features)
+ features = nn.functional.interpolate(features, size=spatial_size, mode="bilinear", align_corners=False)
+ return features
+
+
+class MobileViTV2ASPP(nn.Module):
+ """
+ ASPP module defined in DeepLab papers: https://huggingface.co/papers/1606.00915, https://huggingface.co/papers/1706.05587
+ """
+
+ def __init__(self, config: MobileViTV2Config) -> None:
+ super().__init__()
+
+ encoder_out_channels = make_divisible(512 * config.width_multiplier, divisor=8) # layer 5 output dimension
+ in_channels = encoder_out_channels
+ out_channels = config.aspp_out_channels
+
+ if len(config.atrous_rates) != 3:
+ raise ValueError("Expected 3 values for atrous_rates")
+
+ self.convs = nn.ModuleList()
+
+ in_projection = MobileViTV2ConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ use_activation="relu",
+ )
+ self.convs.append(in_projection)
+
+ self.convs.extend(
+ [
+ MobileViTV2ConvLayer(
+ config,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ dilation=rate,
+ use_activation="relu",
+ )
+ for rate in config.atrous_rates
+ ]
+ )
+
+ pool_layer = MobileViTV2ASPPPooling(config, in_channels, out_channels)
+ self.convs.append(pool_layer)
+
+ self.project = MobileViTV2ConvLayer(
+ config, in_channels=5 * out_channels, out_channels=out_channels, kernel_size=1, use_activation="relu"
+ )
+
+ self.dropout = nn.Dropout(p=config.aspp_dropout_prob)
+
+ def forward(self, features: torch.Tensor) -> torch.Tensor:
+ pyramid = []
+ for conv in self.convs:
+ pyramid.append(conv(features))
+ pyramid = torch.cat(pyramid, dim=1)
+
+ pooled_features = self.project(pyramid)
+ pooled_features = self.dropout(pooled_features)
+ return pooled_features
+
+
+# Copied from transformers.models.mobilevit.modeling_mobilevit.MobileViTDeepLabV3 with MobileViT->MobileViTV2
+class MobileViTV2DeepLabV3(nn.Module):
+ """
+ DeepLabv3 architecture: https://huggingface.co/papers/1706.05587
+ """
+
+ def __init__(self, config: MobileViTV2Config) -> None:
+ super().__init__()
+ self.aspp = MobileViTV2ASPP(config)
+
+ self.dropout = nn.Dropout2d(config.classifier_dropout_prob)
+
+ self.classifier = MobileViTV2ConvLayer(
+ config,
+ in_channels=config.aspp_out_channels,
+ out_channels=config.num_labels,
+ kernel_size=1,
+ use_normalization=False,
+ use_activation=False,
+ bias=True,
+ )
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ features = self.aspp(hidden_states[-1])
+ features = self.dropout(features)
+ features = self.classifier(features)
+ return features
+
+
+@auto_docstring(
+ custom_intro="""
+ MobileViTV2 model with a semantic segmentation head on top, e.g. for Pascal VOC.
+ """
+)
+class MobileViTV2ForSemanticSegmentation(MobileViTV2PreTrainedModel):
+ def __init__(self, config: MobileViTV2Config) -> None:
+ super().__init__(config)
+
+ self.num_labels = config.num_labels
+ self.mobilevitv2 = MobileViTV2Model(config, expand_output=False)
+ self.segmentation_head = MobileViTV2DeepLabV3(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, SemanticSegmenterOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
+ Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
+
+ Examples:
+
+ ```python
+ >>> import requests
+ >>> import torch
+ >>> from PIL import Image
+ >>> from transformers import AutoImageProcessor, MobileViTV2ForSemanticSegmentation
+
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> image_processor = AutoImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
+ >>> model = MobileViTV2ForSemanticSegmentation.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
+
+ >>> inputs = image_processor(images=image, return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+
+ >>> # logits are of shape (batch_size, num_labels, height, width)
+ >>> logits = outputs.logits
+ ```"""
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if labels is not None and self.config.num_labels == 1:
+ raise ValueError("The number of labels should be greater than one")
+
+ outputs = self.mobilevitv2(
+ pixel_values,
+ output_hidden_states=True, # we need the intermediate hidden states
+ return_dict=return_dict,
+ )
+
+ encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
+
+ logits = self.segmentation_head(encoder_hidden_states)
+
+ loss = None
+ if labels is not None:
+ # upsample logits to the images' original size
+ upsampled_logits = nn.functional.interpolate(
+ logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
+ )
+ loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
+ loss = loss_fct(upsampled_logits, labels)
+
+ if not return_dict:
+ if output_hidden_states:
+ output = (logits,) + outputs[1:]
+ else:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SemanticSegmenterOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states if output_hidden_states else None,
+ attentions=None,
+ )
+
+
+__all__ = [
+ "MobileViTV2ForImageClassification",
+ "MobileViTV2ForSemanticSegmentation",
+ "MobileViTV2Model",
+ "MobileViTV2PreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..18317742981909460973138da806584ddfc4a390
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_modernbert import *
+ from .modeling_modernbert import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/configuration_modernbert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/configuration_modernbert.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b0da20ad203cb02375f40e14ea818b642c26c91
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/configuration_modernbert.py
@@ -0,0 +1,224 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/modernbert/modular_modernbert.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_modernbert.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2024 Answer.AI, LightOn, and contributors, and the HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Literal
+
+from ...configuration_utils import PretrainedConfig
+
+
+class ModernBertConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`ModernBertModel`]. It is used to instantiate an ModernBert
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the ModernBERT-base.
+ e.g. [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50368):
+ Vocabulary size of the ModernBert model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`ModernBertModel`]
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 1152):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 22):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ hidden_activation (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the decoder. Will default to `"gelu"`
+ if not specified.
+ max_position_embeddings (`int`, *optional*, defaults to 8192):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ initializer_cutoff_factor (`float`, *optional*, defaults to 2.0):
+ The cutoff factor for the truncated_normal_initializer for initializing all weight matrices.
+ norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+ norm_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the normalization layers.
+ pad_token_id (`int`, *optional*, defaults to 50283):
+ Padding token id.
+ eos_token_id (`int`, *optional*, defaults to 50282):
+ End of stream token id.
+ bos_token_id (`int`, *optional*, defaults to 50281):
+ Beginning of stream token id.
+ cls_token_id (`int`, *optional*, defaults to 50281):
+ Classification token id.
+ sep_token_id (`int`, *optional*, defaults to 50282):
+ Separation token id.
+ global_rope_theta (`float`, *optional*, defaults to 160000.0):
+ The base period of the global RoPE embeddings.
+ attention_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ global_attn_every_n_layers (`int`, *optional*, defaults to 3):
+ The number of layers between global attention layers.
+ local_attention (`int`, *optional*, defaults to 128):
+ The window size for local attention.
+ local_rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the local RoPE embeddings.
+ embedding_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the embeddings.
+ mlp_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the MLP layers.
+ mlp_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the MLP layers.
+ decoder_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use bias in the decoder layers.
+ classifier_pooling (`str`, *optional*, defaults to `"cls"`):
+ The pooling method for the classifier. Should be either `"cls"` or `"mean"`. In local attention layers, the
+ CLS token doesn't attend to all tokens on long sequences.
+ classifier_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the classifier.
+ classifier_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the classifier.
+ classifier_activation (`str`, *optional*, defaults to `"gelu"`):
+ The activation function for the classifier.
+ deterministic_flash_attn (`bool`, *optional*, defaults to `False`):
+ Whether to use deterministic flash attention. If `False`, inference will be faster but not deterministic.
+ sparse_prediction (`bool`, *optional*, defaults to `False`):
+ Whether to use sparse prediction for the masked language model instead of returning the full dense logits.
+ sparse_pred_ignore_index (`int`, *optional*, defaults to -100):
+ The index to ignore for the sparse prediction.
+ reference_compile (`bool`, *optional*):
+ Whether to compile the layers of the model which were compiled during pretraining. If `None`, then parts of
+ the model will be compiled if 1) `triton` is installed, 2) the model is not on MPS, 3) the model is not
+ shared between devices, and 4) the model is not resized after initialization. If `True`, then the model may
+ be faster in some scenarios.
+ repad_logits_with_grad (`bool`, *optional*, defaults to `False`):
+ When True, ModernBertForMaskedLM keeps track of the logits' gradient when repadding for output. This only
+ applies when using Flash Attention 2 with passed labels. Otherwise output logits always have a gradient.
+
+ Examples:
+
+ ```python
+ >>> from transformers import ModernBertModel, ModernBertConfig
+
+ >>> # Initializing a ModernBert style configuration
+ >>> configuration = ModernBertConfig()
+
+ >>> # Initializing a model from the modernbert-base style configuration
+ >>> model = ModernBertModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "modernbert"
+ attribute_map = {"rope_theta": "global_rope_theta"}
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=50368,
+ hidden_size=768,
+ intermediate_size=1152,
+ num_hidden_layers=22,
+ num_attention_heads=12,
+ hidden_activation="gelu",
+ max_position_embeddings=8192,
+ initializer_range=0.02,
+ initializer_cutoff_factor=2.0,
+ norm_eps=1e-5,
+ norm_bias=False,
+ pad_token_id=50283,
+ eos_token_id=50282,
+ bos_token_id=50281,
+ cls_token_id=50281,
+ sep_token_id=50282,
+ global_rope_theta=160000.0,
+ attention_bias=False,
+ attention_dropout=0.0,
+ global_attn_every_n_layers=3,
+ local_attention=128,
+ local_rope_theta=10000.0,
+ embedding_dropout=0.0,
+ mlp_bias=False,
+ mlp_dropout=0.0,
+ decoder_bias=True,
+ classifier_pooling: Literal["cls", "mean"] = "cls",
+ classifier_dropout=0.0,
+ classifier_bias=False,
+ classifier_activation="gelu",
+ deterministic_flash_attn=False,
+ sparse_prediction=False,
+ sparse_pred_ignore_index=-100,
+ reference_compile=None,
+ repad_logits_with_grad=False,
+ **kwargs,
+ ):
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ cls_token_id=cls_token_id,
+ sep_token_id=sep_token_id,
+ **kwargs,
+ )
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.initializer_range = initializer_range
+ self.initializer_cutoff_factor = initializer_cutoff_factor
+ self.norm_eps = norm_eps
+ self.norm_bias = norm_bias
+ self.global_rope_theta = global_rope_theta
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.hidden_activation = hidden_activation
+ self.global_attn_every_n_layers = global_attn_every_n_layers
+ self.local_attention = local_attention
+ self.local_rope_theta = local_rope_theta
+ self.embedding_dropout = embedding_dropout
+ self.mlp_bias = mlp_bias
+ self.mlp_dropout = mlp_dropout
+ self.decoder_bias = decoder_bias
+ self.classifier_pooling = classifier_pooling
+ self.classifier_dropout = classifier_dropout
+ self.classifier_bias = classifier_bias
+ self.classifier_activation = classifier_activation
+ self.deterministic_flash_attn = deterministic_flash_attn
+ self.sparse_prediction = sparse_prediction
+ self.sparse_pred_ignore_index = sparse_pred_ignore_index
+ self.reference_compile = reference_compile
+ self.repad_logits_with_grad = repad_logits_with_grad
+
+ if self.classifier_pooling not in ["cls", "mean"]:
+ raise ValueError(
+ f'Invalid value for `classifier_pooling`, should be either "cls" or "mean", but is {self.classifier_pooling}.'
+ )
+
+ def to_dict(self):
+ output = super().to_dict()
+ output.pop("reference_compile", None)
+ return output
+
+
+__all__ = ["ModernBertConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/modeling_modernbert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/modeling_modernbert.py
new file mode 100644
index 0000000000000000000000000000000000000000..48e1e823950bf69192a3a34ae7d63e2cfba29d21
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/modeling_modernbert.py
@@ -0,0 +1,1529 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/modernbert/modular_modernbert.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_modernbert.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2024 Answer.AI, LightOn, and contributors, and the HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import math
+from contextlib import nullcontext
+from typing import Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ MaskedLMOutput,
+ MultipleChoiceModelOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, is_flash_attn_2_available, logging
+from ...utils.import_utils import is_triton_available
+from .configuration_modernbert import ModernBertConfig
+
+
+if is_flash_attn_2_available():
+ from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
+ from flash_attn.layers.rotary import RotaryEmbedding
+ from flash_attn.ops.triton.rotary import apply_rotary
+else:
+ RotaryEmbedding = object
+
+
+logger = logging.get_logger(__name__)
+
+
+class ApplyRotaryEmbUnpad(torch.autograd.Function):
+ @staticmethod
+ def forward(
+ ctx,
+ qkv,
+ cos,
+ sin,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ ):
+ # (total_nnz, 3, nheads, headdim)
+ qkv = qkv.contiguous()
+ total_nnz, _three, _nheads, headdim = qkv.shape
+ # We need qkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
+ # we get the same tensor
+ # qk = rearrange(qkv[:, :2], "b_s t h d -> b_s (t h) d")
+ qk = qkv[:, :2].view(total_nnz, -1, headdim)
+ apply_rotary(
+ qk,
+ cos,
+ sin,
+ seqlen_offsets=0,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ interleaved=False,
+ inplace=True,
+ )
+
+ ctx.save_for_backward(cos, sin, cu_seqlens)
+ ctx.max_seqlen = max_seqlen
+ return qkv
+
+ @staticmethod
+ def backward(ctx, do):
+ cos, sin, cu_seqlens = ctx.saved_tensors
+ do = do.contiguous()
+ total_nnz, _three, _nheads, headdim = do.shape
+ # We need dqkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
+ # we get the same tensor
+ dqk = do[:, :2].view(total_nnz, -1, headdim)
+ apply_rotary(
+ dqk,
+ cos,
+ sin,
+ seqlen_offsets=0,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=ctx.max_seqlen,
+ interleaved=False,
+ inplace=True,
+ conjugate=True,
+ )
+
+ return do, None, None, None, None, None, None
+
+
+def apply_rotary_unpadded(
+ qkv,
+ cos,
+ sin,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+):
+ """
+ Arguments:
+ qkv: (total_nnz, 3, nheads, headdim) - input tensor for packed QKV.
+ cos, sin: (seqlen_rotary, rotary_dim / 2)
+ interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
+ of 1st half and 2nd half (GPT-NeoX style).
+ inplace: if True, apply rotary embedding in-place.
+ seqlen_offsets: (batch_size,) or int. Each sequence in x is shifted by this amount.
+ Most commonly used in inference when we have KV cache.
+ cu_seqlens: (batch + 1,) or None
+ max_seqlen: int
+ Return:
+ out: (total_nnz, dim)
+ rotary_dim must be <= headdim
+ Apply rotary embedding to the first rotary_dim of x.
+ """
+ return ApplyRotaryEmbUnpad.apply(qkv, cos, sin, cu_seqlens, max_seqlen)
+
+
+class ModernBertUnpaddedRotaryEmbedding(RotaryEmbedding):
+ """
+ The rotary position embeddings applied directly to unpadded sequences.
+ """
+
+ def __init__(
+ self,
+ dim: int,
+ base: float = 10000.0,
+ max_seqlen: Optional[int] = None,
+ device: Optional[torch.device] = None,
+ dtype: Optional[torch.dtype] = None,
+ ):
+ """
+ max_seqlen: if max_seqlen, device, and dtype are provided, we precompute the cos_sin_cache
+ up to max_seqlen. If the max_seqlen, device, or dtype during training/inference differ,
+ the cos_sin_cache will be recomputed during the forward pass.
+ """
+ super().__init__(dim=dim, base=base, device=device, interleaved=False)
+ self.max_seqlen = max_seqlen
+
+ if max_seqlen is not None and device is not None and dtype is not None:
+ self._update_cos_sin_cache(max_seqlen, device=device, dtype=dtype)
+
+ def forward(
+ self,
+ qkv: torch.Tensor,
+ cu_seqlens: torch.Tensor,
+ max_seqlen: Optional[int] = None,
+ ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+ """
+ Apply rotary embedding *inplace* to qkv.
+ qkv: (total_nnz, 3, nheads, headdim)
+ cu_seqlens: (batch + 1,) cumulative sequence lengths
+ max_seqlen: int max seq length in the batch
+ """
+ if max_seqlen is not None:
+ self._update_cos_sin_cache(max_seqlen, device=qkv.device, dtype=qkv.dtype)
+
+ qkv = apply_rotary_unpadded(
+ qkv,
+ self._cos_cached,
+ self._sin_cached,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ )
+
+ return qkv
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, base={self.base}, scale_base={self.scale_base}"
+
+
+class ModernBertEmbeddings(nn.Module):
+ """
+ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+ """
+
+ def __init__(self, config: ModernBertConfig):
+ super().__init__()
+ self.config = config
+ self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.drop = nn.Dropout(config.embedding_dropout)
+
+ @torch.compile(dynamic=True)
+ def compiled_embeddings(self, input_ids: torch.LongTensor) -> torch.Tensor:
+ return self.drop(self.norm(self.tok_embeddings(input_ids)))
+
+ def forward(
+ self, input_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.Tensor] = None
+ ) -> torch.Tensor:
+ if inputs_embeds is not None:
+ hidden_states = self.drop(self.norm(inputs_embeds))
+ else:
+ hidden_states = (
+ self.compiled_embeddings(input_ids)
+ if self.config.reference_compile
+ else self.drop(self.norm(self.tok_embeddings(input_ids)))
+ )
+ return hidden_states
+
+
+class ModernBertMLP(nn.Module):
+ """Applies the GLU at the end of each ModernBERT layer.
+
+ Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
+ and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality.
+ """
+
+ def __init__(self, config: ModernBertConfig):
+ super().__init__()
+ self.config = config
+ self.Wi = nn.Linear(config.hidden_size, int(config.intermediate_size) * 2, bias=config.mlp_bias)
+ self.act = ACT2FN[config.hidden_activation]
+ self.drop = nn.Dropout(config.mlp_dropout)
+ self.Wo = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_bias)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ input, gate = self.Wi(hidden_states).chunk(2, dim=-1)
+ return self.Wo(self.drop(self.act(input) * gate))
+
+
+class ModernBertRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: ModernBertConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+def eager_attention_forward(
+ module: "ModernBertAttention",
+ qkv: torch.Tensor,
+ attention_mask: torch.Tensor,
+ sliding_window_mask: torch.Tensor,
+ position_ids: Optional[torch.LongTensor],
+ local_attention: tuple[int, int],
+ bs: int,
+ dim: int,
+ output_attentions: Optional[bool] = False,
+ **_kwargs,
+) -> Union[tuple[torch.Tensor, torch.Tensor], tuple[torch.Tensor]]:
+ # qkv: [batch_size, seqlen, 3, nheads, headdim]
+ cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
+ query, key, value = qkv.transpose(3, 1).unbind(dim=2)
+ # query, key, value: [batch_size, heads, seq_len, head_dim]
+ query, key = apply_rotary_pos_emb(query, key, cos, sin)
+
+ scale = module.head_dim**-0.5
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scale
+
+ if local_attention != (-1, -1):
+ attention_mask = sliding_window_mask
+
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=module.attention_dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.view(bs, -1, dim)
+ if output_attentions:
+ return (attn_output, attn_weights)
+ return (attn_output,)
+
+
+def flash_attention_forward(
+ module: "ModernBertAttention",
+ qkv: torch.Tensor,
+ rotary_emb: ModernBertUnpaddedRotaryEmbedding,
+ cu_seqlens: torch.Tensor,
+ max_seqlen: int,
+ local_attention: tuple[int, int],
+ bs: int,
+ dim: int,
+ target_dtype: torch.dtype = torch.bfloat16,
+ **_kwargs,
+) -> tuple[torch.Tensor]:
+ # (total_seqlen, 3, nheads, headdim)
+ qkv = rotary_emb(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
+
+ convert_dtype = qkv.dtype not in (torch.float16, torch.bfloat16)
+ if convert_dtype:
+ # FA2 implementation only supports fp16 and bf16. If FA2 is supported,
+ # bfloat16 must be supported as of FA2 2.5.7. (Turing GPUs not supported)
+ orig_dtype = qkv.dtype
+ qkv = qkv.to(target_dtype)
+
+ attn = flash_attn_varlen_qkvpacked_func(
+ qkv,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ dropout_p=module.attention_dropout if module.training else 0.0,
+ deterministic=module.deterministic_flash_attn,
+ window_size=local_attention,
+ )
+ attn = attn.to(orig_dtype) # type: ignore
+ else:
+ attn = flash_attn_varlen_qkvpacked_func(
+ qkv,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ dropout_p=module.attention_dropout if module.training else 0.0,
+ deterministic=module.deterministic_flash_attn,
+ window_size=local_attention,
+ )
+ return (attn.view(bs, dim),)
+
+
+def sdpa_attention_forward(
+ module: "ModernBertAttention",
+ qkv: torch.Tensor,
+ attention_mask: torch.Tensor,
+ sliding_window_mask: torch.Tensor,
+ position_ids: Optional[torch.LongTensor],
+ local_attention: tuple[int, int],
+ bs: int,
+ dim: int,
+ **_kwargs,
+) -> tuple[torch.Tensor]:
+ # qkv: [batch_size, seqlen, 3, nheads, headdim]
+ cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
+ query, key, value = qkv.transpose(3, 1).unbind(dim=2)
+ # query, key, value: [batch_size, heads, seq_len, head_dim]
+ query, key = apply_rotary_pos_emb(query, key, cos, sin)
+
+ if local_attention != (-1, -1):
+ attention_mask = sliding_window_mask
+
+ attn_output = (
+ F.scaled_dot_product_attention(
+ query,
+ key,
+ value,
+ dropout_p=module.attention_dropout if module.training else 0.0,
+ attn_mask=attention_mask,
+ )
+ .transpose(1, 2)
+ .contiguous()
+ )
+ attn_output = attn_output.view(bs, -1, dim)
+ return (attn_output,)
+
+
+MODERNBERT_ATTENTION_FUNCTION = {
+ "flash_attention_2": flash_attention_forward,
+ "eager": eager_attention_forward,
+ "sdpa": sdpa_attention_forward,
+}
+
+
+class ModernBertAttention(nn.Module):
+ """Performs multi-headed self attention on a batch of unpadded sequences.
+
+ If Flash Attention 2 is installed, this module uses Flash Attention to improve throughput.
+ If Flash Attention 2 is not installed, the implementation will use PyTorch's SDPA kernel,
+ which requires padding and unpadding inputs, adding some overhead.
+
+ See `forward` method for additional details.
+ """
+
+ def __init__(self, config: ModernBertConfig, layer_id: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_id = layer_id
+
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})"
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.deterministic_flash_attn = config.deterministic_flash_attn
+ self.num_heads = config.num_attention_heads
+ self.head_dim = config.hidden_size // config.num_attention_heads
+ self.all_head_size = self.head_dim * self.num_heads
+ self.Wqkv = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=config.attention_bias)
+
+ if layer_id % config.global_attn_every_n_layers != 0:
+ self.local_attention = (config.local_attention // 2, config.local_attention // 2)
+ rope_theta = config.local_rope_theta if config.local_rope_theta is not None else config.global_rope_theta
+ max_position_embeddings = config.local_attention
+ else:
+ self.local_attention = (-1, -1)
+ max_position_embeddings = config.max_position_embeddings
+ rope_theta = config.global_rope_theta
+
+ if config._attn_implementation == "flash_attention_2":
+ self.rotary_emb = ModernBertUnpaddedRotaryEmbedding(
+ dim=self.head_dim, max_seqlen=max_position_embeddings, base=rope_theta
+ )
+ else:
+ config_copy = copy.deepcopy(config)
+ config_copy.rope_theta = rope_theta
+ self.rotary_emb = ModernBertRotaryEmbedding(config=config_copy)
+
+ self.Wo = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
+ self.out_drop = nn.Dropout(config.attention_dropout) if config.attention_dropout > 0.0 else nn.Identity()
+ self.pruned_heads = set()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ output_attentions: Optional[bool] = False,
+ **kwargs,
+ ) -> torch.Tensor:
+ qkv = self.Wqkv(hidden_states)
+
+ bs = hidden_states.shape[0]
+ if self.config._attn_implementation == "flash_attention_2":
+ qkv = qkv.view(-1, 3, self.num_heads, self.head_dim)
+ else:
+ qkv = qkv.view(bs, -1, 3, self.num_heads, self.head_dim)
+
+ attn_outputs = MODERNBERT_ATTENTION_FUNCTION[self.config._attn_implementation](
+ self,
+ qkv=qkv,
+ rotary_emb=self.rotary_emb,
+ local_attention=self.local_attention,
+ bs=bs,
+ dim=self.all_head_size,
+ output_attentions=output_attentions,
+ **kwargs,
+ )
+ hidden_states = attn_outputs[0]
+ hidden_states = self.out_drop(self.Wo(hidden_states))
+
+ return (hidden_states,) + attn_outputs[1:] # add attentions if outputted
+
+
+class ModernBertEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: ModernBertConfig, layer_id: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ if layer_id == 0:
+ self.attn_norm = nn.Identity()
+ else:
+ self.attn_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.attn = ModernBertAttention(config=config, layer_id=layer_id)
+ self.mlp_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.mlp = ModernBertMLP(config)
+
+ @torch.compile(dynamic=True)
+ def compiled_mlp(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ return self.mlp(self.mlp_norm(hidden_states))
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> torch.Tensor:
+ attn_outputs = self.attn(
+ self.attn_norm(hidden_states),
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ output_attentions=output_attentions,
+ )
+ hidden_states = hidden_states + attn_outputs[0]
+ mlp_output = (
+ self.compiled_mlp(hidden_states)
+ if self.config.reference_compile
+ else self.mlp(self.mlp_norm(hidden_states))
+ )
+ hidden_states = hidden_states + mlp_output
+
+ return (hidden_states,) + attn_outputs[1:] # add attentions if outputted
+
+
+@auto_docstring
+class ModernBertPreTrainedModel(PreTrainedModel):
+ config: ModernBertConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["ModernBertEmbeddings", "ModernBertEncoderLayer"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = False
+
+ def _init_weights(self, module: nn.Module):
+ cutoff_factor = self.config.initializer_cutoff_factor
+ if cutoff_factor is None:
+ cutoff_factor = 3
+
+ def init_weight(module: nn.Module, std: float):
+ nn.init.trunc_normal_(
+ module.weight,
+ mean=0.0,
+ std=std,
+ a=-cutoff_factor * std,
+ b=cutoff_factor * std,
+ )
+
+ if isinstance(module, nn.Linear):
+ if module.bias is not None:
+ nn.init.zeros_(module.bias)
+
+ stds = {
+ "in": self.config.initializer_range,
+ "out": self.config.initializer_range / math.sqrt(2.0 * self.config.num_hidden_layers),
+ "embedding": self.config.initializer_range,
+ "final_out": self.config.hidden_size**-0.5,
+ }
+
+ if isinstance(module, ModernBertEmbeddings):
+ init_weight(module.tok_embeddings, stds["embedding"])
+ elif isinstance(module, ModernBertMLP):
+ init_weight(module.Wi, stds["in"])
+ init_weight(module.Wo, stds["out"])
+ elif isinstance(module, ModernBertAttention):
+ init_weight(module.Wqkv, stds["in"])
+ init_weight(module.Wo, stds["out"])
+ elif isinstance(module, ModernBertPredictionHead):
+ init_weight(module.dense, stds["out"])
+ elif isinstance(module, ModernBertForMaskedLM):
+ init_weight(module.decoder, stds["out"])
+ elif isinstance(
+ module,
+ (
+ ModernBertForSequenceClassification,
+ ModernBertForMultipleChoice,
+ ModernBertForTokenClassification,
+ ModernBertForQuestionAnswering,
+ ),
+ ):
+ init_weight(module.classifier, stds["final_out"])
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ if module.bias is not None:
+ module.bias.data.zero_()
+
+ def _check_and_adjust_attn_implementation(
+ self, attn_implementation: Optional[str], is_init_check: bool = False
+ ) -> str:
+ """
+ Checks and dispatches to hhe requested attention implementation.
+ """
+ # If the user didn't specify anything, try to use flash_attention_2 if available.
+ # Otherwise we fall back to the default SDPA -> Eager from the super() method.
+ # ModernBert's FA2 implementation correctly handles non-fp16/bf16 dtypes, we don't
+ # need the FA2 warning for non-fp16/bf16 dtypes so we set fp16 for the FA2 check.
+
+ try:
+ attn_implementation = (
+ "flash_attention_2"
+ if attn_implementation is None and self._flash_attn_2_can_dispatch()
+ else attn_implementation
+ )
+ except (ValueError, ImportError):
+ pass
+ return super()._check_and_adjust_attn_implementation(
+ attn_implementation=attn_implementation, is_init_check=is_init_check
+ )
+
+ def _maybe_set_compile(self):
+ if self.config.reference_compile is False:
+ return
+
+ if hasattr(self, "hf_device_map") and len(self.hf_device_map) > 1:
+ if self.config.reference_compile:
+ logger.warning_once(
+ "If `accelerate` split the model across devices, `torch.compile` will not work. "
+ "Falling back to non-compiled mode."
+ )
+ self.config.reference_compile = False
+
+ if self.device.type == "mps":
+ if self.config.reference_compile:
+ logger.warning_once(
+ "Compiling the model with `torch.compile` and using a `torch.mps` device is not supported. "
+ "Falling back to non-compiled mode."
+ )
+ self.config.reference_compile = False
+
+ if self.device.type == "cpu":
+ if self.config.reference_compile:
+ logger.warning_once(
+ "Compiling the model with `torch.compile` and using a `torch.cpu` device is not supported. "
+ "Falling back to non-compiled mode."
+ )
+ self.config.reference_compile = False
+
+ if self.config.reference_compile is None:
+ self.config.reference_compile = is_triton_available()
+
+ def resize_token_embeddings(self, *args, **kwargs):
+ model_embeds = super().resize_token_embeddings(*args, **kwargs)
+
+ if self.config.reference_compile in {True, None}:
+ if self.config.reference_compile:
+ logger.warning_once(
+ "Resizing token embeddings with `torch.compile` is not supported. Falling back to non-compiled mode."
+ )
+ self.config.reference_compile = False
+
+ return model_embeds
+
+
+def _unpad_modernbert_input(
+ inputs: torch.Tensor,
+ attention_mask: torch.Tensor,
+ position_ids: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, Optional[torch.Tensor], Optional[torch.Tensor]]:
+ """
+ Remove padding from input sequences.
+
+ Args:
+ inputs: (batch, seqlen, ...) or (batch, seqlen)
+ attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
+ position_ids: (batch, seqlen), int, position ids
+ labels: (batch, seqlen), int, labels
+
+ Returns:
+ unpadded_inputs: (total_nnz, ...), where total_nnz = number of tokens selected in attention_mask.
+ indices: (total_nnz)
+ cu_seqlens: (batch + 1), the cumulative sequence lengths
+ max_seqlen_in_batch: int
+ unpadded_position_ids: (total_nnz) or None
+ unpadded_labels: (total_nnz) or None
+ """
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+ max_seqlen_in_batch = int(seqlens_in_batch.max().item())
+ cu_seqlens = torch.nn.functional.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+
+ if inputs.dim() == 2:
+ unpadded_inputs = inputs.flatten()[indices]
+ else:
+ batch, seqlen, *rest = inputs.shape
+ shape = batch * seqlen
+ unpadded_inputs = inputs.view(shape, *rest)[indices]
+
+ unpadded_position_ids = position_ids.flatten()[indices] if position_ids is not None else None
+ unpadded_labels = labels.flatten()[indices] if labels is not None else None
+
+ return unpadded_inputs, indices, cu_seqlens, max_seqlen_in_batch, unpadded_position_ids, unpadded_labels
+
+
+def _pad_modernbert_output(
+ inputs: torch.Tensor,
+ indices: torch.Tensor,
+ batch: int,
+ seqlen: int,
+) -> torch.Tensor:
+ """
+ Add padding to sequences.
+
+ Args:
+ inputs: (total_nnz, ...) or (total_nnz,), where total_nnz = number of tokens selected in attention_mask.
+ indices: (total_nnz)
+ batch: int, batch size
+ seqlen: int, max sequence length
+
+ Returns:
+ padded_inputs: (batch, seqlen, ...) or (batch, seqlen)
+ """
+ if inputs.dim() == 1:
+ output = torch.zeros(batch * seqlen, dtype=inputs.dtype, device=inputs.device)
+ output[indices] = inputs
+ padded_inputs = output.view(batch, seqlen)
+ else:
+ _, *rest = inputs.shape
+ output = torch.zeros(batch * seqlen, *rest, dtype=inputs.dtype, device=inputs.device)
+ output[indices] = inputs
+ padded_inputs = output.view(batch, seqlen, *rest)
+
+ return padded_inputs
+
+
+@auto_docstring
+class ModernBertModel(ModernBertPreTrainedModel):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.config = config
+ self.embeddings = ModernBertEmbeddings(config)
+ self.layers = nn.ModuleList(
+ [ModernBertEncoderLayer(config, layer_id) for layer_id in range(config.num_hidden_layers)]
+ )
+ self.final_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.gradient_checkpointing = False
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.tok_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.tok_embeddings = value
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor, ...], BaseModelOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+
+ self._maybe_set_compile()
+
+ if input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+
+ if batch_size is None and seq_len is None:
+ if inputs_embeds is not None:
+ batch_size, seq_len = inputs_embeds.shape[:2]
+ else:
+ batch_size, seq_len = input_ids.shape[:2]
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+ repad = False
+ if self.config._attn_implementation == "flash_attention_2":
+ if indices is None and cu_seqlens is None and max_seqlen is None:
+ repad = True
+ if inputs_embeds is None:
+ with torch.no_grad():
+ input_ids, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+ inputs=input_ids, attention_mask=attention_mask
+ )
+ else:
+ inputs_embeds, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+ inputs=inputs_embeds, attention_mask=attention_mask
+ )
+ else:
+ if position_ids is None:
+ position_ids = torch.arange(seq_len, device=device).unsqueeze(0)
+
+ attention_mask, sliding_window_mask = self._update_attention_mask(
+ attention_mask, output_attentions=output_attentions
+ )
+
+ hidden_states = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)
+
+ for encoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ output_attentions=output_attentions,
+ )
+ hidden_states = layer_outputs[0]
+ if output_attentions and len(layer_outputs) > 1:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ hidden_states = self.final_norm(hidden_states)
+
+ if repad:
+ hidden_states = _pad_modernbert_output(
+ inputs=hidden_states, indices=indices, batch=batch_size, seqlen=seq_len
+ )
+ if all_hidden_states is not None:
+ all_hidden_states = tuple(
+ _pad_modernbert_output(inputs=hs, indices=indices, batch=batch_size, seqlen=seq_len)
+ for hs in all_hidden_states
+ )
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+
+ def _update_attention_mask(self, attention_mask: torch.Tensor, output_attentions: bool) -> torch.Tensor:
+ if output_attentions:
+ if self.config._attn_implementation == "sdpa":
+ logger.warning_once(
+ "Outputting attentions is only supported with the 'eager' attention implementation, "
+ 'not with "sdpa". Falling back to `attn_implementation="eager"`.'
+ )
+ self.config._attn_implementation = "eager"
+ elif self.config._attn_implementation != "eager":
+ logger.warning_once(
+ "Outputting attentions is only supported with the eager attention implementation, "
+ f'not with {self.config._attn_implementation}. Consider setting `attn_implementation="eager"`.'
+ " Setting `output_attentions=False`."
+ )
+
+ global_attention_mask = _prepare_4d_attention_mask(attention_mask, self.dtype)
+
+ # Create position indices
+ rows = torch.arange(global_attention_mask.shape[2]).unsqueeze(0)
+ # Calculate distance between positions
+ distance = torch.abs(rows - rows.T)
+
+ # Create sliding window mask (1 for positions within window, 0 outside)
+ window_mask = (
+ (distance <= self.config.local_attention // 2).unsqueeze(0).unsqueeze(0).to(attention_mask.device)
+ )
+ # Combine with existing mask
+ sliding_window_mask = global_attention_mask.masked_fill(window_mask.logical_not(), torch.finfo(self.dtype).min)
+
+ return global_attention_mask, sliding_window_mask
+
+
+class ModernBertPredictionHead(nn.Module):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__()
+ self.config = config
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size, config.classifier_bias)
+ self.act = ACT2FN[config.classifier_activation]
+ self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ return self.norm(self.act(self.dense(hidden_states)))
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Model with a decoder head on top that is used for masked language modeling.
+ """
+)
+class ModernBertForMaskedLM(ModernBertPreTrainedModel):
+ _tied_weights_keys = ["decoder.weight"]
+
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.config = config
+ self.model = ModernBertModel(config)
+ self.head = ModernBertPredictionHead(config)
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=config.decoder_bias)
+
+ self.sparse_prediction = self.config.sparse_prediction
+ self.sparse_pred_ignore_index = self.config.sparse_pred_ignore_index
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.decoder
+
+ def set_output_embeddings(self, new_embeddings: nn.Linear):
+ self.decoder = new_embeddings
+
+ @torch.compile(dynamic=True)
+ def compiled_head(self, output: torch.Tensor) -> torch.Tensor:
+ return self.decoder(self.head(output))
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], MaskedLMOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ self._maybe_set_compile()
+
+ if self.config._attn_implementation == "flash_attention_2":
+ if indices is None and cu_seqlens is None and max_seqlen is None:
+ if batch_size is None and seq_len is None:
+ if inputs_embeds is not None:
+ batch_size, seq_len = inputs_embeds.shape[:2]
+ else:
+ batch_size, seq_len = input_ids.shape[:2]
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+ if inputs_embeds is None:
+ with torch.no_grad():
+ input_ids, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
+ inputs=input_ids, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+ )
+ else:
+ inputs_embeds, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
+ inputs=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+ )
+
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ indices=indices,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ batch_size=batch_size,
+ seq_len=seq_len,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ last_hidden_state = outputs[0]
+
+ if self.sparse_prediction and labels is not None:
+ # flatten labels and output first
+ labels = labels.view(-1)
+ last_hidden_state = last_hidden_state.view(labels.shape[0], -1)
+
+ # then filter out the non-masked tokens
+ mask_tokens = labels != self.sparse_pred_ignore_index
+ last_hidden_state = last_hidden_state[mask_tokens]
+ labels = labels[mask_tokens]
+
+ logits = (
+ self.compiled_head(last_hidden_state)
+ if self.config.reference_compile
+ else self.decoder(self.head(last_hidden_state))
+ )
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits, labels, vocab_size=self.config.vocab_size, **kwargs)
+
+ if self.config._attn_implementation == "flash_attention_2":
+ with nullcontext() if self.config.repad_logits_with_grad or labels is None else torch.no_grad():
+ logits = _pad_modernbert_output(inputs=logits, indices=indices, batch=batch_size, seqlen=seq_len)
+
+ if not return_dict:
+ output = (logits,)
+ return ((loss,) + output) if loss is not None else output
+
+ return MaskedLMOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Model with a sequence classification head on top that performs pooling.
+ """
+)
+class ModernBertForSequenceClassification(ModernBertPreTrainedModel):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.config = config
+
+ self.model = ModernBertModel(config)
+ self.head = ModernBertPredictionHead(config)
+ self.drop = torch.nn.Dropout(config.classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ self._maybe_set_compile()
+
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ indices=indices,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ batch_size=batch_size,
+ seq_len=seq_len,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ last_hidden_state = outputs[0]
+
+ if self.config.classifier_pooling == "cls":
+ last_hidden_state = last_hidden_state[:, 0]
+ elif self.config.classifier_pooling == "mean":
+ last_hidden_state = (last_hidden_state * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(
+ dim=1, keepdim=True
+ )
+
+ pooled_output = self.head(last_hidden_state)
+ pooled_output = self.drop(pooled_output)
+ logits = self.classifier(pooled_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ if not return_dict:
+ output = (logits,)
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Model with a token classification head on top, e.g. for Named Entity Recognition (NER) tasks.
+ """
+)
+class ModernBertForTokenClassification(ModernBertPreTrainedModel):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.model = ModernBertModel(config)
+ self.head = ModernBertPredictionHead(config)
+ self.drop = torch.nn.Dropout(config.classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], TokenClassifierOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ self._maybe_set_compile()
+
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ indices=indices,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ batch_size=batch_size,
+ seq_len=seq_len,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ last_hidden_state = outputs[0]
+
+ last_hidden_state = self.head(last_hidden_state)
+ last_hidden_state = self.drop(last_hidden_state)
+ logits = self.classifier(last_hidden_state)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class ModernBertForQuestionAnswering(ModernBertPreTrainedModel):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.model = ModernBertModel(config)
+ self.head = ModernBertPredictionHead(config)
+ self.drop = torch.nn.Dropout(config.classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ start_positions: Optional[torch.Tensor] = None,
+ end_positions: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ self._maybe_set_compile()
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ indices=indices,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ batch_size=batch_size,
+ seq_len=seq_len,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ last_hidden_state = outputs[0]
+
+ last_hidden_state = self.head(last_hidden_state)
+ last_hidden_state = self.drop(last_hidden_state)
+ logits = self.classifier(last_hidden_state)
+
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ loss = None
+ if start_positions is not None and end_positions is not None:
+ loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.
+ """
+)
+class ModernBertForMultipleChoice(ModernBertPreTrainedModel):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.config = config
+
+ self.model = ModernBertModel(config)
+ self.head = ModernBertPredictionHead(config)
+ self.drop = torch.nn.Dropout(config.classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, 1)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], MultipleChoiceModelOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+ num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors.
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+ input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+ attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+ position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+ inputs_embeds = (
+ inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+ if inputs_embeds is not None
+ else None
+ )
+
+ self._maybe_set_compile()
+
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ indices=indices,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ batch_size=batch_size,
+ seq_len=seq_len,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ last_hidden_state = outputs[0]
+
+ if self.config.classifier_pooling == "cls":
+ last_hidden_state = last_hidden_state[:, 0]
+ elif self.config.classifier_pooling == "mean":
+ last_hidden_state = (last_hidden_state * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(
+ dim=1, keepdim=True
+ )
+
+ pooled_output = self.head(last_hidden_state)
+ pooled_output = self.drop(pooled_output)
+ logits = self.classifier(pooled_output)
+
+ reshaped_logits = logits.view(-1, num_choices)
+
+ loss = None
+ if labels is not None:
+ loss_fct = nn.CrossEntropyLoss()
+ loss = loss_fct(reshaped_logits, labels)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return MultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "ModernBertModel",
+ "ModernBertPreTrainedModel",
+ "ModernBertForMaskedLM",
+ "ModernBertForSequenceClassification",
+ "ModernBertForTokenClassification",
+ "ModernBertForQuestionAnswering",
+ "ModernBertForMultipleChoice",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/modular_modernbert.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/modular_modernbert.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e1d5fbfb168339d4c6d718fb8579ace087200c5
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert/modular_modernbert.py
@@ -0,0 +1,1656 @@
+# Copyright 2024 Answer.AI, LightOn, and contributors, and the HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import math
+from contextlib import nullcontext
+from typing import Literal, Optional, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...configuration_utils import PretrainedConfig
+from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ MaskedLMOutput,
+ MultipleChoiceModelOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, is_flash_attn_2_available, logging
+from ...utils.import_utils import is_triton_available
+from ..gemma.modeling_gemma import GemmaRotaryEmbedding, apply_rotary_pos_emb
+
+
+if is_flash_attn_2_available():
+ from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
+ from flash_attn.layers.rotary import RotaryEmbedding
+ from flash_attn.ops.triton.rotary import apply_rotary
+else:
+ RotaryEmbedding = object
+
+
+logger = logging.get_logger(__name__)
+
+
+class ModernBertConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`ModernBertModel`]. It is used to instantiate an ModernBert
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the ModernBERT-base.
+ e.g. [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50368):
+ Vocabulary size of the ModernBert model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`ModernBertModel`]
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 1152):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 22):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ hidden_activation (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the decoder. Will default to `"gelu"`
+ if not specified.
+ max_position_embeddings (`int`, *optional*, defaults to 8192):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ initializer_cutoff_factor (`float`, *optional*, defaults to 2.0):
+ The cutoff factor for the truncated_normal_initializer for initializing all weight matrices.
+ norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+ norm_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the normalization layers.
+ pad_token_id (`int`, *optional*, defaults to 50283):
+ Padding token id.
+ eos_token_id (`int`, *optional*, defaults to 50282):
+ End of stream token id.
+ bos_token_id (`int`, *optional*, defaults to 50281):
+ Beginning of stream token id.
+ cls_token_id (`int`, *optional*, defaults to 50281):
+ Classification token id.
+ sep_token_id (`int`, *optional*, defaults to 50282):
+ Separation token id.
+ global_rope_theta (`float`, *optional*, defaults to 160000.0):
+ The base period of the global RoPE embeddings.
+ attention_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ global_attn_every_n_layers (`int`, *optional*, defaults to 3):
+ The number of layers between global attention layers.
+ local_attention (`int`, *optional*, defaults to 128):
+ The window size for local attention.
+ local_rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the local RoPE embeddings.
+ embedding_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the embeddings.
+ mlp_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the MLP layers.
+ mlp_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the MLP layers.
+ decoder_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use bias in the decoder layers.
+ classifier_pooling (`str`, *optional*, defaults to `"cls"`):
+ The pooling method for the classifier. Should be either `"cls"` or `"mean"`. In local attention layers, the
+ CLS token doesn't attend to all tokens on long sequences.
+ classifier_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the classifier.
+ classifier_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the classifier.
+ classifier_activation (`str`, *optional*, defaults to `"gelu"`):
+ The activation function for the classifier.
+ deterministic_flash_attn (`bool`, *optional*, defaults to `False`):
+ Whether to use deterministic flash attention. If `False`, inference will be faster but not deterministic.
+ sparse_prediction (`bool`, *optional*, defaults to `False`):
+ Whether to use sparse prediction for the masked language model instead of returning the full dense logits.
+ sparse_pred_ignore_index (`int`, *optional*, defaults to -100):
+ The index to ignore for the sparse prediction.
+ reference_compile (`bool`, *optional*):
+ Whether to compile the layers of the model which were compiled during pretraining. If `None`, then parts of
+ the model will be compiled if 1) `triton` is installed, 2) the model is not on MPS, 3) the model is not
+ shared between devices, and 4) the model is not resized after initialization. If `True`, then the model may
+ be faster in some scenarios.
+ repad_logits_with_grad (`bool`, *optional*, defaults to `False`):
+ When True, ModernBertForMaskedLM keeps track of the logits' gradient when repadding for output. This only
+ applies when using Flash Attention 2 with passed labels. Otherwise output logits always have a gradient.
+
+ Examples:
+
+ ```python
+ >>> from transformers import ModernBertModel, ModernBertConfig
+
+ >>> # Initializing a ModernBert style configuration
+ >>> configuration = ModernBertConfig()
+
+ >>> # Initializing a model from the modernbert-base style configuration
+ >>> model = ModernBertModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "modernbert"
+ attribute_map = {"rope_theta": "global_rope_theta"}
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=50368,
+ hidden_size=768,
+ intermediate_size=1152,
+ num_hidden_layers=22,
+ num_attention_heads=12,
+ hidden_activation="gelu",
+ max_position_embeddings=8192,
+ initializer_range=0.02,
+ initializer_cutoff_factor=2.0,
+ norm_eps=1e-5,
+ norm_bias=False,
+ pad_token_id=50283,
+ eos_token_id=50282,
+ bos_token_id=50281,
+ cls_token_id=50281,
+ sep_token_id=50282,
+ global_rope_theta=160000.0,
+ attention_bias=False,
+ attention_dropout=0.0,
+ global_attn_every_n_layers=3,
+ local_attention=128,
+ local_rope_theta=10000.0,
+ embedding_dropout=0.0,
+ mlp_bias=False,
+ mlp_dropout=0.0,
+ decoder_bias=True,
+ classifier_pooling: Literal["cls", "mean"] = "cls",
+ classifier_dropout=0.0,
+ classifier_bias=False,
+ classifier_activation="gelu",
+ deterministic_flash_attn=False,
+ sparse_prediction=False,
+ sparse_pred_ignore_index=-100,
+ reference_compile=None,
+ repad_logits_with_grad=False,
+ **kwargs,
+ ):
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ cls_token_id=cls_token_id,
+ sep_token_id=sep_token_id,
+ **kwargs,
+ )
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.initializer_range = initializer_range
+ self.initializer_cutoff_factor = initializer_cutoff_factor
+ self.norm_eps = norm_eps
+ self.norm_bias = norm_bias
+ self.global_rope_theta = global_rope_theta
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.hidden_activation = hidden_activation
+ self.global_attn_every_n_layers = global_attn_every_n_layers
+ self.local_attention = local_attention
+ self.local_rope_theta = local_rope_theta
+ self.embedding_dropout = embedding_dropout
+ self.mlp_bias = mlp_bias
+ self.mlp_dropout = mlp_dropout
+ self.decoder_bias = decoder_bias
+ self.classifier_pooling = classifier_pooling
+ self.classifier_dropout = classifier_dropout
+ self.classifier_bias = classifier_bias
+ self.classifier_activation = classifier_activation
+ self.deterministic_flash_attn = deterministic_flash_attn
+ self.sparse_prediction = sparse_prediction
+ self.sparse_pred_ignore_index = sparse_pred_ignore_index
+ self.reference_compile = reference_compile
+ self.repad_logits_with_grad = repad_logits_with_grad
+
+ if self.classifier_pooling not in ["cls", "mean"]:
+ raise ValueError(
+ f'Invalid value for `classifier_pooling`, should be either "cls" or "mean", but is {self.classifier_pooling}.'
+ )
+
+ def to_dict(self):
+ output = super().to_dict()
+ output.pop("reference_compile", None)
+ return output
+
+
+def _unpad_modernbert_input(
+ inputs: torch.Tensor,
+ attention_mask: torch.Tensor,
+ position_ids: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, Optional[torch.Tensor], Optional[torch.Tensor]]:
+ """
+ Remove padding from input sequences.
+
+ Args:
+ inputs: (batch, seqlen, ...) or (batch, seqlen)
+ attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
+ position_ids: (batch, seqlen), int, position ids
+ labels: (batch, seqlen), int, labels
+
+ Returns:
+ unpadded_inputs: (total_nnz, ...), where total_nnz = number of tokens selected in attention_mask.
+ indices: (total_nnz)
+ cu_seqlens: (batch + 1), the cumulative sequence lengths
+ max_seqlen_in_batch: int
+ unpadded_position_ids: (total_nnz) or None
+ unpadded_labels: (total_nnz) or None
+ """
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+ max_seqlen_in_batch = int(seqlens_in_batch.max().item())
+ cu_seqlens = torch.nn.functional.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+
+ if inputs.dim() == 2:
+ unpadded_inputs = inputs.flatten()[indices]
+ else:
+ batch, seqlen, *rest = inputs.shape
+ shape = batch * seqlen
+ unpadded_inputs = inputs.view(shape, *rest)[indices]
+
+ unpadded_position_ids = position_ids.flatten()[indices] if position_ids is not None else None
+ unpadded_labels = labels.flatten()[indices] if labels is not None else None
+
+ return unpadded_inputs, indices, cu_seqlens, max_seqlen_in_batch, unpadded_position_ids, unpadded_labels
+
+
+def _pad_modernbert_output(
+ inputs: torch.Tensor,
+ indices: torch.Tensor,
+ batch: int,
+ seqlen: int,
+) -> torch.Tensor:
+ """
+ Add padding to sequences.
+
+ Args:
+ inputs: (total_nnz, ...) or (total_nnz,), where total_nnz = number of tokens selected in attention_mask.
+ indices: (total_nnz)
+ batch: int, batch size
+ seqlen: int, max sequence length
+
+ Returns:
+ padded_inputs: (batch, seqlen, ...) or (batch, seqlen)
+ """
+ if inputs.dim() == 1:
+ output = torch.zeros(batch * seqlen, dtype=inputs.dtype, device=inputs.device)
+ output[indices] = inputs
+ padded_inputs = output.view(batch, seqlen)
+ else:
+ _, *rest = inputs.shape
+ output = torch.zeros(batch * seqlen, *rest, dtype=inputs.dtype, device=inputs.device)
+ output[indices] = inputs
+ padded_inputs = output.view(batch, seqlen, *rest)
+
+ return padded_inputs
+
+
+class ApplyRotaryEmbUnpad(torch.autograd.Function):
+ @staticmethod
+ def forward(
+ ctx,
+ qkv,
+ cos,
+ sin,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ ):
+ # (total_nnz, 3, nheads, headdim)
+ qkv = qkv.contiguous()
+ total_nnz, _three, _nheads, headdim = qkv.shape
+ # We need qkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
+ # we get the same tensor
+ # qk = rearrange(qkv[:, :2], "b_s t h d -> b_s (t h) d")
+ qk = qkv[:, :2].view(total_nnz, -1, headdim)
+ apply_rotary(
+ qk,
+ cos,
+ sin,
+ seqlen_offsets=0,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ interleaved=False,
+ inplace=True,
+ )
+
+ ctx.save_for_backward(cos, sin, cu_seqlens)
+ ctx.max_seqlen = max_seqlen
+ return qkv
+
+ @staticmethod
+ def backward(ctx, do):
+ cos, sin, cu_seqlens = ctx.saved_tensors
+ do = do.contiguous()
+ total_nnz, _three, _nheads, headdim = do.shape
+ # We need dqkv to be contiguous so that when we reshape to combine (3, nheads) dimensions,
+ # we get the same tensor
+ dqk = do[:, :2].view(total_nnz, -1, headdim)
+ apply_rotary(
+ dqk,
+ cos,
+ sin,
+ seqlen_offsets=0,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=ctx.max_seqlen,
+ interleaved=False,
+ inplace=True,
+ conjugate=True,
+ )
+
+ return do, None, None, None, None, None, None
+
+
+def apply_rotary_unpadded(
+ qkv,
+ cos,
+ sin,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+):
+ """
+ Arguments:
+ qkv: (total_nnz, 3, nheads, headdim) - input tensor for packed QKV.
+ cos, sin: (seqlen_rotary, rotary_dim / 2)
+ interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
+ of 1st half and 2nd half (GPT-NeoX style).
+ inplace: if True, apply rotary embedding in-place.
+ seqlen_offsets: (batch_size,) or int. Each sequence in x is shifted by this amount.
+ Most commonly used in inference when we have KV cache.
+ cu_seqlens: (batch + 1,) or None
+ max_seqlen: int
+ Return:
+ out: (total_nnz, dim)
+ rotary_dim must be <= headdim
+ Apply rotary embedding to the first rotary_dim of x.
+ """
+ return ApplyRotaryEmbUnpad.apply(qkv, cos, sin, cu_seqlens, max_seqlen)
+
+
+class ModernBertUnpaddedRotaryEmbedding(RotaryEmbedding):
+ """
+ The rotary position embeddings applied directly to unpadded sequences.
+ """
+
+ def __init__(
+ self,
+ dim: int,
+ base: float = 10000.0,
+ max_seqlen: Optional[int] = None,
+ device: Optional[torch.device] = None,
+ dtype: Optional[torch.dtype] = None,
+ ):
+ """
+ max_seqlen: if max_seqlen, device, and dtype are provided, we precompute the cos_sin_cache
+ up to max_seqlen. If the max_seqlen, device, or dtype during training/inference differ,
+ the cos_sin_cache will be recomputed during the forward pass.
+ """
+ super().__init__(dim=dim, base=base, device=device, interleaved=False)
+ self.max_seqlen = max_seqlen
+
+ if max_seqlen is not None and device is not None and dtype is not None:
+ self._update_cos_sin_cache(max_seqlen, device=device, dtype=dtype)
+
+ def forward(
+ self,
+ qkv: torch.Tensor,
+ cu_seqlens: torch.Tensor,
+ max_seqlen: Optional[int] = None,
+ ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+ """
+ Apply rotary embedding *inplace* to qkv.
+ qkv: (total_nnz, 3, nheads, headdim)
+ cu_seqlens: (batch + 1,) cumulative sequence lengths
+ max_seqlen: int max seq length in the batch
+ """
+ if max_seqlen is not None:
+ self._update_cos_sin_cache(max_seqlen, device=qkv.device, dtype=qkv.dtype)
+
+ qkv = apply_rotary_unpadded(
+ qkv,
+ self._cos_cached,
+ self._sin_cached,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ )
+
+ return qkv
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, base={self.base}, scale_base={self.scale_base}"
+
+
+class ModernBertEmbeddings(nn.Module):
+ """
+ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+ """
+
+ def __init__(self, config: ModernBertConfig):
+ super().__init__()
+ self.config = config
+ self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.drop = nn.Dropout(config.embedding_dropout)
+
+ @torch.compile(dynamic=True)
+ def compiled_embeddings(self, input_ids: torch.LongTensor) -> torch.Tensor:
+ return self.drop(self.norm(self.tok_embeddings(input_ids)))
+
+ def forward(
+ self, input_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.Tensor] = None
+ ) -> torch.Tensor:
+ if inputs_embeds is not None:
+ hidden_states = self.drop(self.norm(inputs_embeds))
+ else:
+ hidden_states = (
+ self.compiled_embeddings(input_ids)
+ if self.config.reference_compile
+ else self.drop(self.norm(self.tok_embeddings(input_ids)))
+ )
+ return hidden_states
+
+
+class ModernBertMLP(nn.Module):
+ """Applies the GLU at the end of each ModernBERT layer.
+
+ Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
+ and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality.
+ """
+
+ def __init__(self, config: ModernBertConfig):
+ super().__init__()
+ self.config = config
+ self.Wi = nn.Linear(config.hidden_size, int(config.intermediate_size) * 2, bias=config.mlp_bias)
+ self.act = ACT2FN[config.hidden_activation]
+ self.drop = nn.Dropout(config.mlp_dropout)
+ self.Wo = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.mlp_bias)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ input, gate = self.Wi(hidden_states).chunk(2, dim=-1)
+ return self.Wo(self.drop(self.act(input) * gate))
+
+
+class ModernBertRotaryEmbedding(GemmaRotaryEmbedding):
+ pass
+
+
+def eager_attention_forward(
+ module: "ModernBertAttention",
+ qkv: torch.Tensor,
+ attention_mask: torch.Tensor,
+ sliding_window_mask: torch.Tensor,
+ position_ids: Optional[torch.LongTensor],
+ local_attention: tuple[int, int],
+ bs: int,
+ dim: int,
+ output_attentions: Optional[bool] = False,
+ **_kwargs,
+) -> Union[tuple[torch.Tensor, torch.Tensor], tuple[torch.Tensor]]:
+ # qkv: [batch_size, seqlen, 3, nheads, headdim]
+ cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
+ query, key, value = qkv.transpose(3, 1).unbind(dim=2)
+ # query, key, value: [batch_size, heads, seq_len, head_dim]
+ query, key = apply_rotary_pos_emb(query, key, cos, sin)
+
+ scale = module.head_dim**-0.5
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scale
+
+ if local_attention != (-1, -1):
+ attention_mask = sliding_window_mask
+
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=module.attention_dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.view(bs, -1, dim)
+ if output_attentions:
+ return (attn_output, attn_weights)
+ return (attn_output,)
+
+
+def flash_attention_forward(
+ module: "ModernBertAttention",
+ qkv: torch.Tensor,
+ rotary_emb: ModernBertUnpaddedRotaryEmbedding,
+ cu_seqlens: torch.Tensor,
+ max_seqlen: int,
+ local_attention: tuple[int, int],
+ bs: int,
+ dim: int,
+ target_dtype: torch.dtype = torch.bfloat16,
+ **_kwargs,
+) -> tuple[torch.Tensor]:
+ # (total_seqlen, 3, nheads, headdim)
+ qkv = rotary_emb(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen)
+
+ convert_dtype = qkv.dtype not in (torch.float16, torch.bfloat16)
+ if convert_dtype:
+ # FA2 implementation only supports fp16 and bf16. If FA2 is supported,
+ # bfloat16 must be supported as of FA2 2.5.7. (Turing GPUs not supported)
+ orig_dtype = qkv.dtype
+ qkv = qkv.to(target_dtype)
+
+ attn = flash_attn_varlen_qkvpacked_func(
+ qkv,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ dropout_p=module.attention_dropout if module.training else 0.0,
+ deterministic=module.deterministic_flash_attn,
+ window_size=local_attention,
+ )
+ attn = attn.to(orig_dtype) # type: ignore
+ else:
+ attn = flash_attn_varlen_qkvpacked_func(
+ qkv,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ dropout_p=module.attention_dropout if module.training else 0.0,
+ deterministic=module.deterministic_flash_attn,
+ window_size=local_attention,
+ )
+ return (attn.view(bs, dim),)
+
+
+def sdpa_attention_forward(
+ module: "ModernBertAttention",
+ qkv: torch.Tensor,
+ attention_mask: torch.Tensor,
+ sliding_window_mask: torch.Tensor,
+ position_ids: Optional[torch.LongTensor],
+ local_attention: tuple[int, int],
+ bs: int,
+ dim: int,
+ **_kwargs,
+) -> tuple[torch.Tensor]:
+ # qkv: [batch_size, seqlen, 3, nheads, headdim]
+ cos, sin = module.rotary_emb(qkv, position_ids=position_ids)
+ query, key, value = qkv.transpose(3, 1).unbind(dim=2)
+ # query, key, value: [batch_size, heads, seq_len, head_dim]
+ query, key = apply_rotary_pos_emb(query, key, cos, sin)
+
+ if local_attention != (-1, -1):
+ attention_mask = sliding_window_mask
+
+ attn_output = (
+ F.scaled_dot_product_attention(
+ query,
+ key,
+ value,
+ dropout_p=module.attention_dropout if module.training else 0.0,
+ attn_mask=attention_mask,
+ )
+ .transpose(1, 2)
+ .contiguous()
+ )
+ attn_output = attn_output.view(bs, -1, dim)
+ return (attn_output,)
+
+
+MODERNBERT_ATTENTION_FUNCTION = {
+ "flash_attention_2": flash_attention_forward,
+ "eager": eager_attention_forward,
+ "sdpa": sdpa_attention_forward,
+}
+
+
+class ModernBertAttention(nn.Module):
+ """Performs multi-headed self attention on a batch of unpadded sequences.
+
+ If Flash Attention 2 is installed, this module uses Flash Attention to improve throughput.
+ If Flash Attention 2 is not installed, the implementation will use PyTorch's SDPA kernel,
+ which requires padding and unpadding inputs, adding some overhead.
+
+ See `forward` method for additional details.
+ """
+
+ def __init__(self, config: ModernBertConfig, layer_id: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_id = layer_id
+
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})"
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.deterministic_flash_attn = config.deterministic_flash_attn
+ self.num_heads = config.num_attention_heads
+ self.head_dim = config.hidden_size // config.num_attention_heads
+ self.all_head_size = self.head_dim * self.num_heads
+ self.Wqkv = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=config.attention_bias)
+
+ if layer_id % config.global_attn_every_n_layers != 0:
+ self.local_attention = (config.local_attention // 2, config.local_attention // 2)
+ rope_theta = config.local_rope_theta if config.local_rope_theta is not None else config.global_rope_theta
+ max_position_embeddings = config.local_attention
+ else:
+ self.local_attention = (-1, -1)
+ max_position_embeddings = config.max_position_embeddings
+ rope_theta = config.global_rope_theta
+
+ if config._attn_implementation == "flash_attention_2":
+ self.rotary_emb = ModernBertUnpaddedRotaryEmbedding(
+ dim=self.head_dim, max_seqlen=max_position_embeddings, base=rope_theta
+ )
+ else:
+ config_copy = copy.deepcopy(config)
+ config_copy.rope_theta = rope_theta
+ self.rotary_emb = ModernBertRotaryEmbedding(config=config_copy)
+
+ self.Wo = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
+ self.out_drop = nn.Dropout(config.attention_dropout) if config.attention_dropout > 0.0 else nn.Identity()
+ self.pruned_heads = set()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ output_attentions: Optional[bool] = False,
+ **kwargs,
+ ) -> torch.Tensor:
+ qkv = self.Wqkv(hidden_states)
+
+ bs = hidden_states.shape[0]
+ if self.config._attn_implementation == "flash_attention_2":
+ qkv = qkv.view(-1, 3, self.num_heads, self.head_dim)
+ else:
+ qkv = qkv.view(bs, -1, 3, self.num_heads, self.head_dim)
+
+ attn_outputs = MODERNBERT_ATTENTION_FUNCTION[self.config._attn_implementation](
+ self,
+ qkv=qkv,
+ rotary_emb=self.rotary_emb,
+ local_attention=self.local_attention,
+ bs=bs,
+ dim=self.all_head_size,
+ output_attentions=output_attentions,
+ **kwargs,
+ )
+ hidden_states = attn_outputs[0]
+ hidden_states = self.out_drop(self.Wo(hidden_states))
+
+ return (hidden_states,) + attn_outputs[1:] # add attentions if outputted
+
+
+class ModernBertEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: ModernBertConfig, layer_id: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ if layer_id == 0:
+ self.attn_norm = nn.Identity()
+ else:
+ self.attn_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.attn = ModernBertAttention(config=config, layer_id=layer_id)
+ self.mlp_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.mlp = ModernBertMLP(config)
+
+ @torch.compile(dynamic=True)
+ def compiled_mlp(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ return self.mlp(self.mlp_norm(hidden_states))
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> torch.Tensor:
+ attn_outputs = self.attn(
+ self.attn_norm(hidden_states),
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ output_attentions=output_attentions,
+ )
+ hidden_states = hidden_states + attn_outputs[0]
+ mlp_output = (
+ self.compiled_mlp(hidden_states)
+ if self.config.reference_compile
+ else self.mlp(self.mlp_norm(hidden_states))
+ )
+ hidden_states = hidden_states + mlp_output
+
+ return (hidden_states,) + attn_outputs[1:] # add attentions if outputted
+
+
+@auto_docstring
+class ModernBertPreTrainedModel(PreTrainedModel):
+ config: ModernBertConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["ModernBertEmbeddings", "ModernBertEncoderLayer"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = False
+
+ def _init_weights(self, module: nn.Module):
+ cutoff_factor = self.config.initializer_cutoff_factor
+ if cutoff_factor is None:
+ cutoff_factor = 3
+
+ def init_weight(module: nn.Module, std: float):
+ nn.init.trunc_normal_(
+ module.weight,
+ mean=0.0,
+ std=std,
+ a=-cutoff_factor * std,
+ b=cutoff_factor * std,
+ )
+
+ if isinstance(module, nn.Linear):
+ if module.bias is not None:
+ nn.init.zeros_(module.bias)
+
+ stds = {
+ "in": self.config.initializer_range,
+ "out": self.config.initializer_range / math.sqrt(2.0 * self.config.num_hidden_layers),
+ "embedding": self.config.initializer_range,
+ "final_out": self.config.hidden_size**-0.5,
+ }
+
+ if isinstance(module, ModernBertEmbeddings):
+ init_weight(module.tok_embeddings, stds["embedding"])
+ elif isinstance(module, ModernBertMLP):
+ init_weight(module.Wi, stds["in"])
+ init_weight(module.Wo, stds["out"])
+ elif isinstance(module, ModernBertAttention):
+ init_weight(module.Wqkv, stds["in"])
+ init_weight(module.Wo, stds["out"])
+ elif isinstance(module, ModernBertPredictionHead):
+ init_weight(module.dense, stds["out"])
+ elif isinstance(module, ModernBertForMaskedLM):
+ init_weight(module.decoder, stds["out"])
+ elif isinstance(
+ module,
+ (
+ ModernBertForSequenceClassification,
+ ModernBertForMultipleChoice,
+ ModernBertForTokenClassification,
+ ModernBertForQuestionAnswering,
+ ),
+ ):
+ init_weight(module.classifier, stds["final_out"])
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ if module.bias is not None:
+ module.bias.data.zero_()
+
+ def _check_and_adjust_attn_implementation(
+ self, attn_implementation: Optional[str], is_init_check: bool = False
+ ) -> str:
+ """
+ Checks and dispatches to hhe requested attention implementation.
+ """
+ # If the user didn't specify anything, try to use flash_attention_2 if available.
+ # Otherwise we fall back to the default SDPA -> Eager from the super() method.
+ # ModernBert's FA2 implementation correctly handles non-fp16/bf16 dtypes, we don't
+ # need the FA2 warning for non-fp16/bf16 dtypes so we set fp16 for the FA2 check.
+
+ try:
+ attn_implementation = (
+ "flash_attention_2"
+ if attn_implementation is None and self._flash_attn_2_can_dispatch()
+ else attn_implementation
+ )
+ except (ValueError, ImportError):
+ pass
+ return super()._check_and_adjust_attn_implementation(
+ attn_implementation=attn_implementation, is_init_check=is_init_check
+ )
+
+ def _maybe_set_compile(self):
+ if self.config.reference_compile is False:
+ return
+
+ if hasattr(self, "hf_device_map") and len(self.hf_device_map) > 1:
+ if self.config.reference_compile:
+ logger.warning_once(
+ "If `accelerate` split the model across devices, `torch.compile` will not work. "
+ "Falling back to non-compiled mode."
+ )
+ self.config.reference_compile = False
+
+ if self.device.type == "mps":
+ if self.config.reference_compile:
+ logger.warning_once(
+ "Compiling the model with `torch.compile` and using a `torch.mps` device is not supported. "
+ "Falling back to non-compiled mode."
+ )
+ self.config.reference_compile = False
+
+ if self.device.type == "cpu":
+ if self.config.reference_compile:
+ logger.warning_once(
+ "Compiling the model with `torch.compile` and using a `torch.cpu` device is not supported. "
+ "Falling back to non-compiled mode."
+ )
+ self.config.reference_compile = False
+
+ if self.config.reference_compile is None:
+ self.config.reference_compile = is_triton_available()
+
+ def resize_token_embeddings(self, *args, **kwargs):
+ model_embeds = super().resize_token_embeddings(*args, **kwargs)
+
+ if self.config.reference_compile in {True, None}:
+ if self.config.reference_compile:
+ logger.warning_once(
+ "Resizing token embeddings with `torch.compile` is not supported. Falling back to non-compiled mode."
+ )
+ self.config.reference_compile = False
+
+ return model_embeds
+
+
+@auto_docstring
+class ModernBertModel(ModernBertPreTrainedModel):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.config = config
+ self.embeddings = ModernBertEmbeddings(config)
+ self.layers = nn.ModuleList(
+ [ModernBertEncoderLayer(config, layer_id) for layer_id in range(config.num_hidden_layers)]
+ )
+ self.final_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.gradient_checkpointing = False
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.tok_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.tok_embeddings = value
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor, ...], BaseModelOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+
+ self._maybe_set_compile()
+
+ if input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+
+ if batch_size is None and seq_len is None:
+ if inputs_embeds is not None:
+ batch_size, seq_len = inputs_embeds.shape[:2]
+ else:
+ batch_size, seq_len = input_ids.shape[:2]
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+ repad = False
+ if self.config._attn_implementation == "flash_attention_2":
+ if indices is None and cu_seqlens is None and max_seqlen is None:
+ repad = True
+ if inputs_embeds is None:
+ with torch.no_grad():
+ input_ids, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+ inputs=input_ids, attention_mask=attention_mask
+ )
+ else:
+ inputs_embeds, indices, cu_seqlens, max_seqlen, *_ = _unpad_modernbert_input(
+ inputs=inputs_embeds, attention_mask=attention_mask
+ )
+ else:
+ if position_ids is None:
+ position_ids = torch.arange(seq_len, device=device).unsqueeze(0)
+
+ attention_mask, sliding_window_mask = self._update_attention_mask(
+ attention_mask, output_attentions=output_attentions
+ )
+
+ hidden_states = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)
+
+ for encoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ output_attentions=output_attentions,
+ )
+ hidden_states = layer_outputs[0]
+ if output_attentions and len(layer_outputs) > 1:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ hidden_states = self.final_norm(hidden_states)
+
+ if repad:
+ hidden_states = _pad_modernbert_output(
+ inputs=hidden_states, indices=indices, batch=batch_size, seqlen=seq_len
+ )
+ if all_hidden_states is not None:
+ all_hidden_states = tuple(
+ _pad_modernbert_output(inputs=hs, indices=indices, batch=batch_size, seqlen=seq_len)
+ for hs in all_hidden_states
+ )
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+
+ def _update_attention_mask(self, attention_mask: torch.Tensor, output_attentions: bool) -> torch.Tensor:
+ if output_attentions:
+ if self.config._attn_implementation == "sdpa":
+ logger.warning_once(
+ "Outputting attentions is only supported with the 'eager' attention implementation, "
+ 'not with "sdpa". Falling back to `attn_implementation="eager"`.'
+ )
+ self.config._attn_implementation = "eager"
+ elif self.config._attn_implementation != "eager":
+ logger.warning_once(
+ "Outputting attentions is only supported with the eager attention implementation, "
+ f'not with {self.config._attn_implementation}. Consider setting `attn_implementation="eager"`.'
+ " Setting `output_attentions=False`."
+ )
+
+ global_attention_mask = _prepare_4d_attention_mask(attention_mask, self.dtype)
+
+ # Create position indices
+ rows = torch.arange(global_attention_mask.shape[2]).unsqueeze(0)
+ # Calculate distance between positions
+ distance = torch.abs(rows - rows.T)
+
+ # Create sliding window mask (1 for positions within window, 0 outside)
+ window_mask = (
+ (distance <= self.config.local_attention // 2).unsqueeze(0).unsqueeze(0).to(attention_mask.device)
+ )
+ # Combine with existing mask
+ sliding_window_mask = global_attention_mask.masked_fill(window_mask.logical_not(), torch.finfo(self.dtype).min)
+
+ return global_attention_mask, sliding_window_mask
+
+
+class ModernBertPredictionHead(nn.Module):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__()
+ self.config = config
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size, config.classifier_bias)
+ self.act = ACT2FN[config.classifier_activation]
+ self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ return self.norm(self.act(self.dense(hidden_states)))
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Model with a decoder head on top that is used for masked language modeling.
+ """
+)
+class ModernBertForMaskedLM(ModernBertPreTrainedModel):
+ _tied_weights_keys = ["decoder.weight"]
+
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.config = config
+ self.model = ModernBertModel(config)
+ self.head = ModernBertPredictionHead(config)
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=config.decoder_bias)
+
+ self.sparse_prediction = self.config.sparse_prediction
+ self.sparse_pred_ignore_index = self.config.sparse_pred_ignore_index
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.decoder
+
+ def set_output_embeddings(self, new_embeddings: nn.Linear):
+ self.decoder = new_embeddings
+
+ @torch.compile(dynamic=True)
+ def compiled_head(self, output: torch.Tensor) -> torch.Tensor:
+ return self.decoder(self.head(output))
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], MaskedLMOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ self._maybe_set_compile()
+
+ if self.config._attn_implementation == "flash_attention_2":
+ if indices is None and cu_seqlens is None and max_seqlen is None:
+ if batch_size is None and seq_len is None:
+ if inputs_embeds is not None:
+ batch_size, seq_len = inputs_embeds.shape[:2]
+ else:
+ batch_size, seq_len = input_ids.shape[:2]
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.bool)
+
+ if inputs_embeds is None:
+ with torch.no_grad():
+ input_ids, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
+ inputs=input_ids, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+ )
+ else:
+ inputs_embeds, indices, cu_seqlens, max_seqlen, position_ids, labels = _unpad_modernbert_input(
+ inputs=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, labels=labels
+ )
+
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ indices=indices,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ batch_size=batch_size,
+ seq_len=seq_len,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ last_hidden_state = outputs[0]
+
+ if self.sparse_prediction and labels is not None:
+ # flatten labels and output first
+ labels = labels.view(-1)
+ last_hidden_state = last_hidden_state.view(labels.shape[0], -1)
+
+ # then filter out the non-masked tokens
+ mask_tokens = labels != self.sparse_pred_ignore_index
+ last_hidden_state = last_hidden_state[mask_tokens]
+ labels = labels[mask_tokens]
+
+ logits = (
+ self.compiled_head(last_hidden_state)
+ if self.config.reference_compile
+ else self.decoder(self.head(last_hidden_state))
+ )
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits, labels, vocab_size=self.config.vocab_size, **kwargs)
+
+ if self.config._attn_implementation == "flash_attention_2":
+ with nullcontext() if self.config.repad_logits_with_grad or labels is None else torch.no_grad():
+ logits = _pad_modernbert_output(inputs=logits, indices=indices, batch=batch_size, seqlen=seq_len)
+
+ if not return_dict:
+ output = (logits,)
+ return ((loss,) + output) if loss is not None else output
+
+ return MaskedLMOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Model with a sequence classification head on top that performs pooling.
+ """
+)
+class ModernBertForSequenceClassification(ModernBertPreTrainedModel):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.config = config
+
+ self.model = ModernBertModel(config)
+ self.head = ModernBertPredictionHead(config)
+ self.drop = torch.nn.Dropout(config.classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ self._maybe_set_compile()
+
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ indices=indices,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ batch_size=batch_size,
+ seq_len=seq_len,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ last_hidden_state = outputs[0]
+
+ if self.config.classifier_pooling == "cls":
+ last_hidden_state = last_hidden_state[:, 0]
+ elif self.config.classifier_pooling == "mean":
+ last_hidden_state = (last_hidden_state * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(
+ dim=1, keepdim=True
+ )
+
+ pooled_output = self.head(last_hidden_state)
+ pooled_output = self.drop(pooled_output)
+ logits = self.classifier(pooled_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+
+ if not return_dict:
+ output = (logits,)
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Model with a token classification head on top, e.g. for Named Entity Recognition (NER) tasks.
+ """
+)
+class ModernBertForTokenClassification(ModernBertPreTrainedModel):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.model = ModernBertModel(config)
+ self.head = ModernBertPredictionHead(config)
+ self.drop = torch.nn.Dropout(config.classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], TokenClassifierOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ self._maybe_set_compile()
+
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ indices=indices,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ batch_size=batch_size,
+ seq_len=seq_len,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ last_hidden_state = outputs[0]
+
+ last_hidden_state = self.head(last_hidden_state)
+ last_hidden_state = self.drop(last_hidden_state)
+ logits = self.classifier(last_hidden_state)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class ModernBertForQuestionAnswering(ModernBertPreTrainedModel):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.model = ModernBertModel(config)
+ self.head = ModernBertPredictionHead(config)
+ self.drop = torch.nn.Dropout(config.classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ start_positions: Optional[torch.Tensor] = None,
+ end_positions: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ self._maybe_set_compile()
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ indices=indices,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ batch_size=batch_size,
+ seq_len=seq_len,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ last_hidden_state = outputs[0]
+
+ last_hidden_state = self.head(last_hidden_state)
+ last_hidden_state = self.drop(last_hidden_state)
+ logits = self.classifier(last_hidden_state)
+
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ loss = None
+ if start_positions is not None and end_positions is not None:
+ loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.
+ """
+)
+class ModernBertForMultipleChoice(ModernBertPreTrainedModel):
+ def __init__(self, config: ModernBertConfig):
+ super().__init__(config)
+ self.config = config
+
+ self.model = ModernBertModel(config)
+ self.head = ModernBertPredictionHead(config)
+ self.drop = torch.nn.Dropout(config.classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, 1)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ sliding_window_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ indices: Optional[torch.Tensor] = None,
+ cu_seqlens: Optional[torch.Tensor] = None,
+ max_seqlen: Optional[int] = None,
+ batch_size: Optional[int] = None,
+ seq_len: Optional[int] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], MultipleChoiceModelOutput]:
+ r"""
+ sliding_window_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding or far-away tokens. In ModernBert, only every few layers
+ perform global attention, while the rest perform local attention. This mask is used to avoid attending to
+ far-away tokens in the local attention layers when not using Flash Attention.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+ num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors.
+ indices (`torch.Tensor` of shape `(total_unpadded_tokens,)`, *optional*):
+ Indices of the non-padding tokens in the input sequence. Used for unpadding the output.
+ cu_seqlens (`torch.Tensor` of shape `(batch + 1,)`, *optional*):
+ Cumulative sequence lengths of the input sequences. Used to index the unpadded tensors.
+ max_seqlen (`int`, *optional*):
+ Maximum sequence length in the batch excluding padding tokens. Used to unpad input_ids and pad output tensors.
+ batch_size (`int`, *optional*):
+ Batch size of the input sequences. Used to pad the output tensors.
+ seq_len (`int`, *optional*):
+ Sequence length of the input sequences including padding tokens. Used to pad the output tensors.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+ input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+ attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+ position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+ inputs_embeds = (
+ inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+ if inputs_embeds is not None
+ else None
+ )
+
+ self._maybe_set_compile()
+
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ sliding_window_mask=sliding_window_mask,
+ position_ids=position_ids,
+ inputs_embeds=inputs_embeds,
+ indices=indices,
+ cu_seqlens=cu_seqlens,
+ max_seqlen=max_seqlen,
+ batch_size=batch_size,
+ seq_len=seq_len,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ last_hidden_state = outputs[0]
+
+ if self.config.classifier_pooling == "cls":
+ last_hidden_state = last_hidden_state[:, 0]
+ elif self.config.classifier_pooling == "mean":
+ last_hidden_state = (last_hidden_state * attention_mask.unsqueeze(-1)).sum(dim=1) / attention_mask.sum(
+ dim=1, keepdim=True
+ )
+
+ pooled_output = self.head(last_hidden_state)
+ pooled_output = self.drop(pooled_output)
+ logits = self.classifier(pooled_output)
+
+ reshaped_logits = logits.view(-1, num_choices)
+
+ loss = None
+ if labels is not None:
+ loss_fct = nn.CrossEntropyLoss()
+ loss = loss_fct(reshaped_logits, labels)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return MultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "ModernBertConfig",
+ "ModernBertModel",
+ "ModernBertPreTrainedModel",
+ "ModernBertForMaskedLM",
+ "ModernBertForSequenceClassification",
+ "ModernBertForTokenClassification",
+ "ModernBertForQuestionAnswering",
+ "ModernBertForMultipleChoice",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f29efca0bf52a68509d3b58ad3ca456f216a042
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_modernbert_decoder import *
+ from .modeling_modernbert_decoder import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/configuration_modernbert_decoder.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/configuration_modernbert_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a065a53dfc4b27d1f82cd0385c0c67636018c63
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/configuration_modernbert_decoder.py
@@ -0,0 +1,208 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/modernbert_decoder/modular_modernbert_decoder.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_modernbert_decoder.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 Johns Hopkins University, LightOn, and the HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ...configuration_utils import PretrainedConfig
+
+
+class ModernBertDecoderConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`ModernBertDecoderModel`]. It is used to instantiate a ModernBert
+ decoder model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the ModernBERT-base decoder.
+ e.g. [blab-jhu/test-32m-dec](https://huggingface.co/blab-jhu/test-32m-dec)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50368):
+ Vocabulary size of the ModernBert decoder model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`ModernBertDecoderModel`]
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 1152):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 22):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ hidden_activation (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the decoder. Will default to `"gelu"`
+ if not specified.
+ max_position_embeddings (`int`, *optional*, defaults to 8192):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ initializer_cutoff_factor (`float`, *optional*, defaults to 2.0):
+ The cutoff factor for the truncated_normal_initializer for initializing all weight matrices.
+ norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+ norm_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the normalization layers.
+ pad_token_id (`int`, *optional*, defaults to 50283):
+ Padding token id.
+ eos_token_id (`int`, *optional*, defaults to 50282):
+ End of stream token id.
+ bos_token_id (`int`, *optional*, defaults to 50281):
+ Beginning of stream token id.
+ cls_token_id (`int`, *optional*, defaults to 50281):
+ Classification token id.
+ sep_token_id (`int`, *optional*, defaults to 50282):
+ Separation token id.
+ global_rope_theta (`float`, *optional*, defaults to 160000.0):
+ The base period of the global RoPE embeddings.
+ attention_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ embedding_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the embeddings.
+ mlp_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the MLP layers.
+ mlp_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the MLP layers.
+ decoder_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use bias in the decoder layers.
+ classifier_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the classifier.
+ classifier_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the classifier.
+ classifier_activation (`str`, *optional*, defaults to `"gelu"`):
+ The activation function for the classifier.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ local_attention (`int`, *optional*, defaults to 128):
+ The sliding window size for local attention. Only used for layers that use local attention. Note that for
+ the decoder to match ModernBERT this is actually half of the sliding window size, so 128 => 64.
+ global_attn_every_n_layers (`int`, *optional*, defaults to 3):
+ Every `global_attn_every_n_layers` layers will use global attention instead of local attention.
+ local_rope_theta (`float`, *optional*, defaults to 160000.0):
+ The base period of the local RoPE embeddings. If not specified, defaults to 160000.0
+ layer_types (`list`, *optional*):
+ List of layer types, one for each layer. If not specified, will be automatically generated based on
+ `global_attn_every_n_layers`. Should contain "full_attention" or "sliding_attention".
+
+ Examples:
+
+ ```python
+ >>> from transformers import ModernBertDecoderModel, ModernBertDecoderConfig
+
+ >>> # Initializing a ModernBert decoder style configuration
+ >>> configuration = ModernBertDecoderConfig()
+
+ >>> # Initializing a model from the modernbert-base decoder style configuration
+ >>> model = ModernBertDecoderModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "modernbert-decoder"
+ attribute_map = {"rope_theta": "global_rope_theta"}
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=50368,
+ hidden_size=768,
+ intermediate_size=1152,
+ num_hidden_layers=22,
+ num_attention_heads=12,
+ hidden_activation="gelu",
+ max_position_embeddings=8192,
+ initializer_range=0.02,
+ initializer_cutoff_factor=2.0,
+ norm_eps=1e-5,
+ norm_bias=False,
+ pad_token_id=50283,
+ eos_token_id=50282,
+ bos_token_id=50281,
+ cls_token_id=50281,
+ sep_token_id=50282,
+ global_rope_theta=160000.0,
+ attention_bias=False,
+ attention_dropout=0.0,
+ embedding_dropout=0.0,
+ mlp_bias=False,
+ mlp_dropout=0.0,
+ decoder_bias=True,
+ classifier_dropout=0.0,
+ classifier_bias=False,
+ classifier_activation="gelu",
+ use_cache=True,
+ local_attention=128,
+ global_attn_every_n_layers=3,
+ local_rope_theta=160000.0,
+ layer_types=None,
+ **kwargs,
+ ):
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ cls_token_id=cls_token_id,
+ sep_token_id=sep_token_id,
+ **kwargs,
+ )
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.initializer_range = initializer_range
+ self.initializer_cutoff_factor = initializer_cutoff_factor
+ self.norm_eps = norm_eps
+ self.norm_bias = norm_bias
+ self.global_rope_theta = global_rope_theta
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.hidden_activation = hidden_activation
+ self.embedding_dropout = embedding_dropout
+ self.mlp_bias = mlp_bias
+ self.mlp_dropout = mlp_dropout
+ self.decoder_bias = decoder_bias
+ self.classifier_dropout = classifier_dropout
+ self.classifier_bias = classifier_bias
+ self.classifier_activation = classifier_activation
+ self.use_cache = use_cache
+ self.global_attn_every_n_layers = global_attn_every_n_layers
+ self.local_rope_theta = local_rope_theta
+ # for consistency with ModernBert
+ self.reference_compile = False
+
+ # Set up layer_types for standardized layer type detection
+ self.layer_types = layer_types
+ if self.layer_types is None:
+ # Create layer_types based on the alternating pattern
+ self.layer_types = []
+ for layer_id in range(num_hidden_layers):
+ if layer_id % global_attn_every_n_layers != 0:
+ self.layer_types.append("sliding_attention")
+ else:
+ self.layer_types.append("full_attention")
+
+ # NOTE: sliding window numbers matches ModernBERT but is only half of it
+ self.sliding_window = local_attention // 2 if local_attention else -1
+
+
+__all__ = ["ModernBertDecoderConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/modeling_modernbert_decoder.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/modeling_modernbert_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..e254a298a738663e339e3d3c8b3a3f4045d215c7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/modeling_modernbert_decoder.py
@@ -0,0 +1,607 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/modernbert_decoder/modular_modernbert_decoder.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_modernbert_decoder.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 Johns Hopkins University, LightOn, and the HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from collections.abc import Callable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
+from ...models.modernbert.modeling_modernbert import (
+ ModernBertEmbeddings,
+ ModernBertMLP,
+ ModernBertPredictionHead,
+ ModernBertPreTrainedModel,
+ ModernBertRotaryEmbedding,
+ apply_rotary_pos_emb,
+)
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import check_model_inputs
+from .configuration_modernbert_decoder import ModernBertDecoderConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def eager_attention_forward(
+ module: "ModernBertDecoderAttention",
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ dropout: float = 0.0,
+ scaling: Optional[float] = None,
+ sliding_window: Optional[int] = None,
+ **kwargs,
+) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ """A simple eager attention implementation for ModernBERT decoder."""
+ if scaling is None:
+ scaling = module.head_dim**-0.5
+
+ # Compute attention scores
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+
+ # Use the pre-computed attention mask
+ causal_mask = attention_mask[:, :, :, : key.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ return attn_output, attn_weights
+
+
+class ModernBertDecoderAttention(nn.Module):
+ """Performs causal multi-headed self attention for ModernBERT decoder.
+
+ It supports both local attention (sliding window) and global attention patterns.
+ """
+
+ def __init__(self, config: ModernBertDecoderConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.is_sliding = config.layer_types[layer_idx] == "sliding_attention"
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = config.hidden_size // config.num_attention_heads
+ self.num_heads = config.num_attention_heads
+ self.all_head_size = self.head_dim * self.num_heads
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = self.config.attention_dropout
+ self.is_causal = True
+
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})"
+ )
+
+ # NOTE: this is different than ModernBERT (separated QKV) so be sure to adapt to this
+ self.q_proj = nn.Linear(self.config.hidden_size, self.all_head_size, bias=self.config.attention_bias)
+ self.k_proj = nn.Linear(self.config.hidden_size, self.all_head_size, bias=self.config.attention_bias)
+ self.v_proj = nn.Linear(self.config.hidden_size, self.all_head_size, bias=self.config.attention_bias)
+
+ self.Wo = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
+ self.out_drop = nn.Dropout(config.attention_dropout)
+
+ self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=self.attention_dropout if self.training else 0.0,
+ scaling=self.scaling,
+ sliding_window=self.sliding_window,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.out_drop(self.Wo(attn_output))
+ return attn_output, attn_weights
+
+
+class ModernBertDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: ModernBertDecoderConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.attention_type = config.layer_types[layer_idx]
+ self.attn_norm = (
+ nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ if layer_idx != 0
+ else nn.Identity()
+ )
+ self.attn = ModernBertDecoderAttention(config=config, layer_idx=layer_idx)
+ self.mlp_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.mlp = ModernBertMLP(config)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings_global: torch.Tensor,
+ position_embeddings_local: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ residual = hidden_states
+ hidden_states = self.attn_norm(hidden_states)
+
+ # apply global RoPE to non-sliding layer only
+ if self.attn.is_sliding:
+ position_embeddings = position_embeddings_local
+ else:
+ position_embeddings = position_embeddings_global
+
+ # Self Attention
+ attn_outputs = self.attn(
+ hidden_states=hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = attn_outputs[0]
+
+ # Add residual connection
+ hidden_states = residual + hidden_states
+
+ # MLP
+ residual = hidden_states
+ hidden_states = self.mlp_norm(hidden_states)
+ mlp_output = self.mlp(hidden_states)
+ hidden_states = residual + mlp_output
+ return hidden_states
+
+
+@auto_docstring
+class ModernBertDecoderPreTrainedModel(ModernBertPreTrainedModel):
+ config: ModernBertDecoderConfig
+ base_model_prefix = "model"
+ _skip_keys_device_placement = ["past_key_values"]
+ _no_split_modules = ["ModernBertDecoderLayer"]
+ _supports_flash_attn = True
+ _supports_sdpa = False
+ _supports_gradient_checkpointing = True
+ _can_compile_fullgraph = False
+ _supports_attention_backend = True
+ _can_record_outputs = {
+ "hidden_states": ModernBertDecoderLayer,
+ "attentions": ModernBertDecoderAttention,
+ }
+
+ def _init_weights(self, module: nn.Module):
+ cutoff_factor = self.config.initializer_cutoff_factor
+ if cutoff_factor is None:
+ cutoff_factor = 3
+
+ def init_weight(module: nn.Module, std: float):
+ nn.init.trunc_normal_(
+ module.weight,
+ mean=0.0,
+ std=std,
+ a=-cutoff_factor * std,
+ b=cutoff_factor * std,
+ )
+
+ if isinstance(module, nn.Linear):
+ if module.bias is not None:
+ nn.init.zeros_(module.bias)
+
+ stds = {
+ "in": self.config.initializer_range,
+ "out": self.config.initializer_range / math.sqrt(2.0 * self.config.num_hidden_layers),
+ "embedding": self.config.initializer_range,
+ "final_out": self.config.hidden_size**-0.5,
+ }
+
+ if isinstance(module, ModernBertEmbeddings):
+ init_weight(module.tok_embeddings, stds["embedding"])
+ elif isinstance(module, ModernBertMLP):
+ init_weight(module.Wi, stds["in"])
+ init_weight(module.Wo, stds["out"])
+ elif isinstance(module, ModernBertDecoderAttention):
+ init_weight(module.q_proj, stds["in"])
+ init_weight(module.k_proj, stds["in"])
+ init_weight(module.v_proj, stds["in"])
+ init_weight(module.Wo, stds["out"])
+ elif isinstance(module, ModernBertPredictionHead):
+ init_weight(module.dense, stds["out"])
+ elif isinstance(module, ModernBertDecoderForSequenceClassification):
+ init_weight(module.classifier, stds["final_out"])
+ elif isinstance(module, ModernBertDecoderForCausalLM):
+ init_weight(module.decoder, stds["out"])
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ if module.bias is not None:
+ module.bias.data.zero_()
+
+
+@auto_docstring
+class ModernBertDecoderModel(ModernBertDecoderPreTrainedModel):
+ def __init__(self, config: ModernBertDecoderConfig):
+ super().__init__(config)
+ self.config = config
+ self.embeddings = ModernBertEmbeddings(config)
+ self.layers = nn.ModuleList(
+ [ModernBertDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.final_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.gradient_checkpointing = False
+
+ self.global_rotary_emb = ModernBertRotaryEmbedding(config=config)
+ self.local_rotary_emb = ModernBertRotaryEmbedding(config=config)
+
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.tok_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.tok_embeddings = value
+
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor, ...], BaseModelOutputWithPast]:
+ if (input_ids is None) == (inputs_embeds is None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ batch_size, seq_length = input_ids.shape[:2]
+ else:
+ batch_size, seq_length = inputs_embeds.shape[:2]
+
+ # Handle past_key_values and cache setup
+ if use_cache and past_key_values is None and not self.training:
+ past_key_values = DynamicCache(config=self.config)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens,
+ past_seen_tokens + seq_length,
+ device=input_ids.device if input_ids is not None else inputs_embeds.device,
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0).expand(batch_size, -1)
+
+ # Calculate embeddings
+ hidden_states = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)
+
+ # It may already have been prepared by e.g. `generate`
+ if not isinstance(causal_mask_mapping := attention_mask, dict):
+ # Prepare mask arguments
+ mask_kwargs = {
+ "config": self.config,
+ "input_embeds": hidden_states,
+ "attention_mask": attention_mask,
+ "cache_position": cache_position,
+ "past_key_values": past_key_values,
+ "position_ids": position_ids,
+ }
+
+ causal_mask_mapping = {
+ "full_attention": create_causal_mask(**mask_kwargs),
+ "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
+ }
+
+ # create position embeddings to be shared across the decoder layers
+ position_embeddings_global = self.global_rotary_emb(hidden_states, position_ids)
+ position_embeddings_local = self.local_rotary_emb(hidden_states, position_ids)
+
+ for idx, decoder_layer in enumerate(self.layers):
+ hidden_states = decoder_layer(
+ hidden_states,
+ position_embeddings_global=position_embeddings_global,
+ position_embeddings_local=position_embeddings_local,
+ attention_mask=causal_mask_mapping[decoder_layer.attention_type],
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = self.final_norm(hidden_states)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Decoder Model with a language modeling head on top for causal language modeling (CLM).
+ """
+)
+class ModernBertDecoderForCausalLM(ModernBertDecoderPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["decoder.weight"]
+
+ def __init__(self, config: ModernBertDecoderConfig):
+ super().__init__(config)
+ self.config = config
+ self.model = ModernBertDecoderModel(config)
+ self.lm_head = ModernBertPredictionHead(config)
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=config.decoder_bias)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.decoder = new_embeddings
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple, CausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Returns:
+ [`~modeling_outputs.CausalLMOutputWithPast`]
+ comprising various elements depending on the configuration and inputs.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, ModernBertDecoderForCausalLM
+
+ >>> model = ModernBertDecoderForCausalLM.from_pretrained("blab-jhu/test-32m-dec")
+ >>> tokenizer = AutoTokenizer.from_pretrained("blab-jhu/test-32m-dec")
+
+ >>> prompt = "The capital of France is"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=1)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "The capital of France is Paris"
+ ```
+ """
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ logits = self.decoder(self.lm_head(hidden_states))
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Decoder Model with a sequence classification head on top (linear layer).
+
+ [`ModernBertDecoderForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-1, GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """
+)
+class ModernBertDecoderForSequenceClassification(ModernBertDecoderPreTrainedModel):
+ def __init__(self, config: ModernBertDecoderConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.model = ModernBertDecoderModel(config)
+
+ self.head = ModernBertPredictionHead(config)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels, bias=config.classifier_bias)
+ self.drop = torch.nn.Dropout(config.classifier_dropout)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @can_return_tuple
+ @auto_docstring(checkpoint="blab-jhu/test-32m-dec")
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ transformer_outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ **kwargs,
+ )
+ hidden_states = transformer_outputs[0]
+ hidden_states = self.drop(self.head(hidden_states))
+ logits = self.classifier(hidden_states)
+
+ if input_ids is not None:
+ batch_size, sequence_length = input_ids.shape[:2]
+ else:
+ batch_size, sequence_length = inputs_embeds.shape[:2]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ last_non_pad_token = -1
+ elif input_ids is not None:
+ # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+ non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+ token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
+ last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+ else:
+ last_non_pad_token = -1
+ logger.warning_once(
+ f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+ "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+ )
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+
+__all__ = [
+ "ModernBertDecoderModel",
+ "ModernBertDecoderPreTrainedModel",
+ "ModernBertDecoderForCausalLM",
+ "ModernBertDecoderForSequenceClassification",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/modular_modernbert_decoder.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/modular_modernbert_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..475ee8bb381055b07e72ad7425e58c78d8602101
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/modernbert_decoder/modular_modernbert_decoder.py
@@ -0,0 +1,785 @@
+# Copyright 2025 Johns Hopkins University, LightOn, and the HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from collections.abc import Callable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...cache_utils import Cache, DynamicCache
+from ...configuration_utils import PretrainedConfig
+from ...generation import GenerationMixin
+from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
+from ...models.modernbert.modeling_modernbert import (
+ ModernBertEmbeddings,
+ ModernBertMLP,
+ ModernBertPredictionHead,
+ ModernBertPreTrainedModel,
+ ModernBertRotaryEmbedding,
+ apply_rotary_pos_emb,
+)
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import check_model_inputs
+
+
+logger = logging.get_logger(__name__)
+
+
+class ModernBertDecoderConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`ModernBertDecoderModel`]. It is used to instantiate a ModernBert
+ decoder model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the ModernBERT-base decoder.
+ e.g. [blab-jhu/test-32m-dec](https://huggingface.co/blab-jhu/test-32m-dec)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50368):
+ Vocabulary size of the ModernBert decoder model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`ModernBertDecoderModel`]
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 1152):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 22):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ hidden_activation (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the decoder. Will default to `"gelu"`
+ if not specified.
+ max_position_embeddings (`int`, *optional*, defaults to 8192):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ initializer_cutoff_factor (`float`, *optional*, defaults to 2.0):
+ The cutoff factor for the truncated_normal_initializer for initializing all weight matrices.
+ norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+ norm_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the normalization layers.
+ pad_token_id (`int`, *optional*, defaults to 50283):
+ Padding token id.
+ eos_token_id (`int`, *optional*, defaults to 50282):
+ End of stream token id.
+ bos_token_id (`int`, *optional*, defaults to 50281):
+ Beginning of stream token id.
+ cls_token_id (`int`, *optional*, defaults to 50281):
+ Classification token id.
+ sep_token_id (`int`, *optional*, defaults to 50282):
+ Separation token id.
+ global_rope_theta (`float`, *optional*, defaults to 160000.0):
+ The base period of the global RoPE embeddings.
+ attention_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ embedding_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the embeddings.
+ mlp_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the MLP layers.
+ mlp_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the MLP layers.
+ decoder_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use bias in the decoder layers.
+ classifier_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the classifier.
+ classifier_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use bias in the classifier.
+ classifier_activation (`str`, *optional*, defaults to `"gelu"`):
+ The activation function for the classifier.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ local_attention (`int`, *optional*, defaults to 128):
+ The sliding window size for local attention. Only used for layers that use local attention. Note that for
+ the decoder to match ModernBERT this is actually half of the sliding window size, so 128 => 64.
+ global_attn_every_n_layers (`int`, *optional*, defaults to 3):
+ Every `global_attn_every_n_layers` layers will use global attention instead of local attention.
+ local_rope_theta (`float`, *optional*, defaults to 160000.0):
+ The base period of the local RoPE embeddings. If not specified, defaults to 160000.0
+ layer_types (`list`, *optional*):
+ List of layer types, one for each layer. If not specified, will be automatically generated based on
+ `global_attn_every_n_layers`. Should contain "full_attention" or "sliding_attention".
+
+ Examples:
+
+ ```python
+ >>> from transformers import ModernBertDecoderModel, ModernBertDecoderConfig
+
+ >>> # Initializing a ModernBert decoder style configuration
+ >>> configuration = ModernBertDecoderConfig()
+
+ >>> # Initializing a model from the modernbert-base decoder style configuration
+ >>> model = ModernBertDecoderModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "modernbert-decoder"
+ attribute_map = {"rope_theta": "global_rope_theta"}
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=50368,
+ hidden_size=768,
+ intermediate_size=1152,
+ num_hidden_layers=22,
+ num_attention_heads=12,
+ hidden_activation="gelu",
+ max_position_embeddings=8192,
+ initializer_range=0.02,
+ initializer_cutoff_factor=2.0,
+ norm_eps=1e-5,
+ norm_bias=False,
+ pad_token_id=50283,
+ eos_token_id=50282,
+ bos_token_id=50281,
+ cls_token_id=50281,
+ sep_token_id=50282,
+ global_rope_theta=160000.0,
+ attention_bias=False,
+ attention_dropout=0.0,
+ embedding_dropout=0.0,
+ mlp_bias=False,
+ mlp_dropout=0.0,
+ decoder_bias=True,
+ classifier_dropout=0.0,
+ classifier_bias=False,
+ classifier_activation="gelu",
+ use_cache=True,
+ local_attention=128,
+ global_attn_every_n_layers=3,
+ local_rope_theta=160000.0,
+ layer_types=None,
+ **kwargs,
+ ):
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ cls_token_id=cls_token_id,
+ sep_token_id=sep_token_id,
+ **kwargs,
+ )
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.initializer_range = initializer_range
+ self.initializer_cutoff_factor = initializer_cutoff_factor
+ self.norm_eps = norm_eps
+ self.norm_bias = norm_bias
+ self.global_rope_theta = global_rope_theta
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.hidden_activation = hidden_activation
+ self.embedding_dropout = embedding_dropout
+ self.mlp_bias = mlp_bias
+ self.mlp_dropout = mlp_dropout
+ self.decoder_bias = decoder_bias
+ self.classifier_dropout = classifier_dropout
+ self.classifier_bias = classifier_bias
+ self.classifier_activation = classifier_activation
+ self.use_cache = use_cache
+ self.global_attn_every_n_layers = global_attn_every_n_layers
+ self.local_rope_theta = local_rope_theta
+ # for consistency with ModernBert
+ self.reference_compile = False
+
+ # Set up layer_types for standardized layer type detection
+ self.layer_types = layer_types
+ if self.layer_types is None:
+ # Create layer_types based on the alternating pattern
+ self.layer_types = []
+ for layer_id in range(num_hidden_layers):
+ if layer_id % global_attn_every_n_layers != 0:
+ self.layer_types.append("sliding_attention")
+ else:
+ self.layer_types.append("full_attention")
+
+ # NOTE: sliding window numbers matches ModernBERT but is only half of it
+ self.sliding_window = local_attention // 2 if local_attention else -1
+
+
+def eager_attention_forward(
+ module: "ModernBertDecoderAttention",
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ dropout: float = 0.0,
+ scaling: Optional[float] = None,
+ sliding_window: Optional[int] = None,
+ **kwargs,
+) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ """A simple eager attention implementation for ModernBERT decoder."""
+ if scaling is None:
+ scaling = module.head_dim**-0.5
+
+ # Compute attention scores
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+
+ # Use the pre-computed attention mask
+ causal_mask = attention_mask[:, :, :, : key.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ return attn_output, attn_weights
+
+
+class ModernBertDecoderAttention(nn.Module):
+ """Performs causal multi-headed self attention for ModernBERT decoder.
+
+ It supports both local attention (sliding window) and global attention patterns.
+ """
+
+ def __init__(self, config: ModernBertDecoderConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.is_sliding = config.layer_types[layer_idx] == "sliding_attention"
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = config.hidden_size // config.num_attention_heads
+ self.num_heads = config.num_attention_heads
+ self.all_head_size = self.head_dim * self.num_heads
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = self.config.attention_dropout
+ self.is_causal = True
+
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})"
+ )
+
+ # NOTE: this is different than ModernBERT (separated QKV) so be sure to adapt to this
+ self.q_proj = nn.Linear(self.config.hidden_size, self.all_head_size, bias=self.config.attention_bias)
+ self.k_proj = nn.Linear(self.config.hidden_size, self.all_head_size, bias=self.config.attention_bias)
+ self.v_proj = nn.Linear(self.config.hidden_size, self.all_head_size, bias=self.config.attention_bias)
+
+ self.Wo = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias)
+ self.out_drop = nn.Dropout(config.attention_dropout)
+
+ self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+ value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=self.attention_dropout if self.training else 0.0,
+ scaling=self.scaling,
+ sliding_window=self.sliding_window,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.out_drop(self.Wo(attn_output))
+ return attn_output, attn_weights
+
+
+class ModernBertDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: ModernBertDecoderConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.attention_type = config.layer_types[layer_idx]
+ self.attn_norm = (
+ nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ if layer_idx != 0
+ else nn.Identity()
+ )
+ self.attn = ModernBertDecoderAttention(config=config, layer_idx=layer_idx)
+ self.mlp_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.mlp = ModernBertMLP(config)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings_global: torch.Tensor,
+ position_embeddings_local: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ residual = hidden_states
+ hidden_states = self.attn_norm(hidden_states)
+
+ # apply global RoPE to non-sliding layer only
+ if self.attn.is_sliding:
+ position_embeddings = position_embeddings_local
+ else:
+ position_embeddings = position_embeddings_global
+
+ # Self Attention
+ attn_outputs = self.attn(
+ hidden_states=hidden_states,
+ position_embeddings=position_embeddings,
+ attention_mask=attention_mask,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = attn_outputs[0]
+
+ # Add residual connection
+ hidden_states = residual + hidden_states
+
+ # MLP
+ residual = hidden_states
+ hidden_states = self.mlp_norm(hidden_states)
+ mlp_output = self.mlp(hidden_states)
+ hidden_states = residual + mlp_output
+ return hidden_states
+
+
+@auto_docstring
+class ModernBertDecoderPreTrainedModel(ModernBertPreTrainedModel):
+ config: ModernBertDecoderConfig
+ base_model_prefix = "model"
+ _skip_keys_device_placement = ["past_key_values"]
+ _no_split_modules = ["ModernBertDecoderLayer"]
+ _supports_flash_attn = True
+ _supports_sdpa = False
+ _supports_gradient_checkpointing = True
+ _can_compile_fullgraph = False
+ _supports_attention_backend = True
+ _can_record_outputs = {
+ "hidden_states": ModernBertDecoderLayer,
+ "attentions": ModernBertDecoderAttention,
+ }
+
+ def _init_weights(self, module: nn.Module):
+ cutoff_factor = self.config.initializer_cutoff_factor
+ if cutoff_factor is None:
+ cutoff_factor = 3
+
+ def init_weight(module: nn.Module, std: float):
+ nn.init.trunc_normal_(
+ module.weight,
+ mean=0.0,
+ std=std,
+ a=-cutoff_factor * std,
+ b=cutoff_factor * std,
+ )
+
+ if isinstance(module, nn.Linear):
+ if module.bias is not None:
+ nn.init.zeros_(module.bias)
+
+ stds = {
+ "in": self.config.initializer_range,
+ "out": self.config.initializer_range / math.sqrt(2.0 * self.config.num_hidden_layers),
+ "embedding": self.config.initializer_range,
+ "final_out": self.config.hidden_size**-0.5,
+ }
+
+ if isinstance(module, ModernBertEmbeddings):
+ init_weight(module.tok_embeddings, stds["embedding"])
+ elif isinstance(module, ModernBertMLP):
+ init_weight(module.Wi, stds["in"])
+ init_weight(module.Wo, stds["out"])
+ elif isinstance(module, ModernBertDecoderAttention):
+ init_weight(module.q_proj, stds["in"])
+ init_weight(module.k_proj, stds["in"])
+ init_weight(module.v_proj, stds["in"])
+ init_weight(module.Wo, stds["out"])
+ elif isinstance(module, ModernBertPredictionHead):
+ init_weight(module.dense, stds["out"])
+ elif isinstance(module, ModernBertDecoderForSequenceClassification):
+ init_weight(module.classifier, stds["final_out"])
+ elif isinstance(module, ModernBertDecoderForCausalLM):
+ init_weight(module.decoder, stds["out"])
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ if module.bias is not None:
+ module.bias.data.zero_()
+
+
+@auto_docstring
+class ModernBertDecoderModel(ModernBertDecoderPreTrainedModel):
+ def __init__(self, config: ModernBertDecoderConfig):
+ super().__init__(config)
+ self.config = config
+ self.embeddings = ModernBertEmbeddings(config)
+ self.layers = nn.ModuleList(
+ [ModernBertDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.final_norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps, bias=config.norm_bias)
+ self.gradient_checkpointing = False
+
+ self.global_rotary_emb = ModernBertRotaryEmbedding(config=config)
+ self.local_rotary_emb = ModernBertRotaryEmbedding(config=config)
+
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.tok_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.tok_embeddings = value
+
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor, ...], BaseModelOutputWithPast]:
+ if (input_ids is None) == (inputs_embeds is None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ batch_size, seq_length = input_ids.shape[:2]
+ else:
+ batch_size, seq_length = inputs_embeds.shape[:2]
+
+ # Handle past_key_values and cache setup
+ if use_cache and past_key_values is None and not self.training:
+ past_key_values = DynamicCache(config=self.config)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens,
+ past_seen_tokens + seq_length,
+ device=input_ids.device if input_ids is not None else inputs_embeds.device,
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0).expand(batch_size, -1)
+
+ # Calculate embeddings
+ hidden_states = self.embeddings(input_ids=input_ids, inputs_embeds=inputs_embeds)
+
+ # It may already have been prepared by e.g. `generate`
+ if not isinstance(causal_mask_mapping := attention_mask, dict):
+ # Prepare mask arguments
+ mask_kwargs = {
+ "config": self.config,
+ "input_embeds": hidden_states,
+ "attention_mask": attention_mask,
+ "cache_position": cache_position,
+ "past_key_values": past_key_values,
+ "position_ids": position_ids,
+ }
+
+ causal_mask_mapping = {
+ "full_attention": create_causal_mask(**mask_kwargs),
+ "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
+ }
+
+ # create position embeddings to be shared across the decoder layers
+ position_embeddings_global = self.global_rotary_emb(hidden_states, position_ids)
+ position_embeddings_local = self.local_rotary_emb(hidden_states, position_ids)
+
+ for idx, decoder_layer in enumerate(self.layers):
+ hidden_states = decoder_layer(
+ hidden_states,
+ position_embeddings_global=position_embeddings_global,
+ position_embeddings_local=position_embeddings_local,
+ attention_mask=causal_mask_mapping[decoder_layer.attention_type],
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = self.final_norm(hidden_states)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Decoder Model with a language modeling head on top for causal language modeling (CLM).
+ """
+)
+class ModernBertDecoderForCausalLM(ModernBertDecoderPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["decoder.weight"]
+
+ def __init__(self, config: ModernBertDecoderConfig):
+ super().__init__(config)
+ self.config = config
+ self.model = ModernBertDecoderModel(config)
+ self.lm_head = ModernBertPredictionHead(config)
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=config.decoder_bias)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.decoder = new_embeddings
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple, CausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Returns:
+ [`~modeling_outputs.CausalLMOutputWithPast`]
+ comprising various elements depending on the configuration and inputs.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, ModernBertDecoderForCausalLM
+
+ >>> model = ModernBertDecoderForCausalLM.from_pretrained("blab-jhu/test-32m-dec")
+ >>> tokenizer = AutoTokenizer.from_pretrained("blab-jhu/test-32m-dec")
+
+ >>> prompt = "The capital of France is"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=1)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "The capital of France is Paris"
+ ```
+ """
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ logits = self.decoder(self.lm_head(hidden_states))
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The ModernBert Decoder Model with a sequence classification head on top (linear layer).
+
+ [`ModernBertDecoderForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-1, GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """
+)
+class ModernBertDecoderForSequenceClassification(ModernBertDecoderPreTrainedModel):
+ def __init__(self, config: ModernBertDecoderConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.model = ModernBertDecoderModel(config)
+
+ self.head = ModernBertPredictionHead(config)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels, bias=config.classifier_bias)
+ self.drop = torch.nn.Dropout(config.classifier_dropout)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @can_return_tuple
+ @auto_docstring(checkpoint="blab-jhu/test-32m-dec")
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ transformer_outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ **kwargs,
+ )
+ hidden_states = transformer_outputs[0]
+ hidden_states = self.drop(self.head(hidden_states))
+ logits = self.classifier(hidden_states)
+
+ if input_ids is not None:
+ batch_size, sequence_length = input_ids.shape[:2]
+ else:
+ batch_size, sequence_length = inputs_embeds.shape[:2]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ last_non_pad_token = -1
+ elif input_ids is not None:
+ # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+ non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+ token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
+ last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+ else:
+ last_non_pad_token = -1
+ logger.warning_once(
+ f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+ "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+ )
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+
+__all__ = [
+ "ModernBertDecoderConfig",
+ "ModernBertDecoderModel",
+ "ModernBertDecoderPreTrainedModel",
+ "ModernBertDecoderForCausalLM",
+ "ModernBertDecoderForSequenceClassification",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa3c1870c2a1d6f5d089df5a396d4b049e4a9d4e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_moonshine import *
+ from .modeling_moonshine import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/configuration_moonshine.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/configuration_moonshine.py
new file mode 100644
index 0000000000000000000000000000000000000000..270a2e3e484023a5b7422e222905e9f08d01d94f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/configuration_moonshine.py
@@ -0,0 +1,230 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/moonshine/modular_moonshine.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_moonshine.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ...configuration_utils import PretrainedConfig
+from ...modeling_rope_utils import rope_config_validation
+
+
+class MoonshineConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MoonshineModel`]. It is used to instantiate a Moonshine
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the Moonshine
+ [UsefulSensors/moonshine-tiny](https://huggingface.co/UsefulSensors/moonshine-tiny).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32768):
+ Vocabulary size of the Moonshine model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MoonshineModel`].
+ hidden_size (`int`, *optional*, defaults to 288):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 1152):
+ Dimension of the MLP representations.
+ encoder_num_hidden_layers (`int`, *optional*, defaults to 6):
+ Number of hidden layers in the Transformer encoder.
+ decoder_num_hidden_layers (`int`, *optional*, defaults to 6):
+ Number of hidden layers in the Transformer decoder.
+ encoder_num_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ decoder_num_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ encoder_num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `encoder_num_key_value_heads=encoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `encoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+ `num_attention_heads`.
+ decoder_num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `decoder_num_key_value_heads=decoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `decoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+ `decoder_num_attention_heads`.
+ pad_head_dim_to_multiple_of (`int`, *optional*):
+ Pad head dimension in encoder and decoder to the next multiple of this value. Necessary for using certain
+ optimized attention implementations.
+ encoder_hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder.
+ decoder_hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ decoder_start_token_id (`int`, *optional*, defaults to 1):
+ Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids`
+ are provided to the `generate` function. It is used to guide the model`s generation process depending on
+ the task.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models).
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+ and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+ accordingly.
+ Expected contents:
+ `rope_type` (`str`):
+ The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+ 'llama3'], with 'default' being the original RoPE implementation.
+ `factor` (`float`, *optional*):
+ Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+ most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+ original maximum pre-trained length.
+ `original_max_position_embeddings` (`int`, *optional*):
+ Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+ pretraining.
+ `attention_factor` (`float`, *optional*):
+ Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+ computation. If unspecified, it defaults to value recommended by the implementation, using the
+ `factor` field to infer the suggested value.
+ `beta_fast` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+ ramp function. If unspecified, it defaults to 32.
+ `beta_slow` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+ ramp function. If unspecified, it defaults to 1.
+ `short_factor` (`list[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+ size divided by the number of attention heads divided by 2
+ `long_factor` (`list[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+ size divided by the number of attention heads divided by 2
+ `low_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+ `high_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+ partial_rotary_factor (`float`, *optional*, defaults to 0.9):
+ Percentage of the query and keys which will have rotary embedding.
+ is_encoder_decoder (`bool`, *optional*, defaults to `True`):
+ Whether the model is used as an encoder/decoder or not.
+ attention_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ Denotes beginning of sequences token id.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ Denotes end of sequences token id.
+
+ Example:
+
+ ```python
+ >>> from transformers import MoonshineModel, MoonshineConfig
+
+ >>> # Initializing a Moonshine style configuration
+ >>> configuration = MoonshineConfig().from_pretrained("UsefulSensors/moonshine-tiny")
+
+ >>> # Initializing a model from the configuration
+ >>> model = MoonshineModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "moonshine"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {
+ "num_key_value_heads": "encoder_num_key_value_heads",
+ "num_attention_heads": "encoder_num_attention_heads",
+ "num_hidden_layers": "encoder_num_hidden_layers",
+ }
+
+ def __init__(
+ self,
+ vocab_size=32768,
+ hidden_size=288,
+ intermediate_size=1152,
+ encoder_num_hidden_layers=6,
+ decoder_num_hidden_layers=6,
+ encoder_num_attention_heads=8,
+ decoder_num_attention_heads=8,
+ encoder_num_key_value_heads=None,
+ decoder_num_key_value_heads=None,
+ pad_head_dim_to_multiple_of=None,
+ encoder_hidden_act="gelu",
+ decoder_hidden_act="silu",
+ max_position_embeddings=512,
+ initializer_range=0.02,
+ decoder_start_token_id=1,
+ use_cache=True,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ partial_rotary_factor=0.9,
+ is_encoder_decoder=True,
+ attention_bias=False,
+ attention_dropout=0.0,
+ bos_token_id=1,
+ eos_token_id=2,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.encoder_num_hidden_layers = encoder_num_hidden_layers
+ self.decoder_num_hidden_layers = decoder_num_hidden_layers
+ self.encoder_num_attention_heads = encoder_num_attention_heads
+ self.decoder_num_attention_heads = decoder_num_attention_heads
+
+ if encoder_num_key_value_heads is None:
+ encoder_num_key_value_heads = encoder_num_attention_heads
+ self.encoder_num_key_value_heads = encoder_num_key_value_heads
+
+ if decoder_num_key_value_heads is None:
+ decoder_num_key_value_heads = decoder_num_attention_heads
+ self.decoder_num_key_value_heads = decoder_num_key_value_heads
+
+ self.pad_head_dim_to_multiple_of = pad_head_dim_to_multiple_of
+
+ self.encoder_hidden_act = encoder_hidden_act
+ self.decoder_hidden_act = decoder_hidden_act
+ self.max_position_embeddings = max_position_embeddings
+ self.initializer_range = initializer_range
+ self.decoder_start_token_id = decoder_start_token_id
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ self.partial_rotary_factor = partial_rotary_factor
+ self.is_encoder_decoder = is_encoder_decoder
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+
+ # Validate the correctness of rotary position embeddings parameters
+ rope_config_validation(self)
+
+ super().__init__(
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ is_encoder_decoder=is_encoder_decoder,
+ decoder_start_token_id=decoder_start_token_id,
+ **kwargs,
+ )
+
+
+__all__ = ["MoonshineConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/convert_usefulsensors_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/convert_usefulsensors_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..f29da8c8e21665484ce78ee6797ea5853047c945
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/convert_usefulsensors_to_hf.py
@@ -0,0 +1,169 @@
+# Copyright 2025 Useful Sensors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import re
+
+import h5py
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+
+from transformers.models.moonshine.modeling_moonshine import MoonshineConfig, MoonshineForConditionalGeneration
+
+
+# Copied from https://github.com/usefulsensors/moonshine/blob/a1d77cc573b0471ac4602b86f67b3f48d67df1a9/moonshine/model.py
+def _get_weights(model_name):
+ repo = "UsefulSensors/moonshine"
+
+ return (
+ hf_hub_download(repo, f"{x}.weights.h5", subfolder=model_name) for x in ("preprocessor", "encoder", "decoder")
+ )
+
+
+def _read_h5_weights(group, current_key="", weights={}):
+ for key in group:
+ full_key = f"{current_key}.{key}" if current_key else key
+ if isinstance(group[key], h5py.Dataset):
+ w = np.array(group[key])
+ w = torch.from_numpy(w)
+ if len(w.shape) > 1:
+ if len(w.shape) == 3:
+ hidden_size = max(list(w.shape))
+ try:
+ w = w.reshape(hidden_size, hidden_size)
+ except RuntimeError:
+ # meaning its a conv layers
+ pass
+ w = w.transpose(0, -1)
+ weights[full_key] = w
+ else:
+ _read_h5_weights(group[key], full_key, weights)
+ return weights
+
+
+def _convert_layer_names(name, gated_mlp=False):
+ name = re.sub(
+ r"layers\.functional(?:_(\d+))?\.layers",
+ lambda m: f"layers.{m.group(1) if m.group(1) else '0'}",
+ name,
+ count=1,
+ )
+ if gated_mlp:
+ name = re.sub(r"functional\.layers\.dense\.", "mlp.fc1.", name)
+ name = re.sub(r"functional\.layers\.dense_1\.", "mlp.fc2.", name)
+ else:
+ name = re.sub(r"functional\.layers\.sequential\.layers\.dense\.", "mlp.fc1.", name)
+ name = re.sub(r"functional\.layers\.sequential\.layers\.dense_1\.", "mlp.fc2.", name)
+ name = re.sub(r"layers\.sequential\.layers\.conv1d\.", "conv1.", name)
+ name = re.sub(r"layers\.sequential\.layers\.conv1d_1\.", "conv2.", name)
+ name = re.sub(r"layers\.sequential\.layers\.conv1d_2\.", "conv3.", name)
+ name = re.sub(r"layers\.sequential\.layers\.group_normalization\.", "groupnorm.", name)
+ name = re.sub(r"mha_with_rope\.key_dense", "self_attn.k_proj", name)
+ name = re.sub(r"mha_with_rope\.query_dense", "self_attn.q_proj", name)
+ name = re.sub(r"mha_with_rope\.value_dense", "self_attn.v_proj", name)
+ name = re.sub(r"mha_with_rope\.output_dense", "self_attn.o_proj", name)
+ name = re.sub(r"mha_precomputed_kv\.key_dense", "encoder_attn.k_proj", name)
+ name = re.sub(r"mha_precomputed_kv\.query_dense", "encoder_attn.q_proj", name)
+ name = re.sub(r"mha_precomputed_kv\.value_dense", "encoder_attn.v_proj", name)
+ name = re.sub(r"mha_precomputed_kv\.output_dense", "encoder_attn.o_proj", name)
+ name = re.sub(r"mha_causal_with_rope\.key_dense", "self_attn.k_proj", name)
+ name = re.sub(r"mha_causal_with_rope\.query_dense", "self_attn.q_proj", name)
+ name = re.sub(r"mha_causal_with_rope\.value_dense", "self_attn.v_proj", name)
+ name = re.sub(r"mha_causal_with_rope\.output_dense", "self_attn.o_proj", name)
+ name = re.sub(r"layer_normalization\.", "input_layernorm.", name)
+ name = re.sub(r"layer_normalization_1\.", "post_attention_layernorm.", name)
+ name = re.sub(r"layer_normalization_2\.", "final_layernorm.", name)
+ name = re.sub(r"vars\.0", "weight", name)
+ name = re.sub(r"vars\.1", "bias", name)
+ name = re.sub(r"layers\.reversible_embedding", "embed_tokens", name)
+
+ return name
+
+
+def _convert_weights(weights, encoder=True):
+ if "layers.rotary_embedding.vars.0" in weights:
+ weights.pop("layers.rotary_embedding.vars.0")
+
+ converted_weights = {}
+ if encoder:
+ converted_weights["layer_norm.weight"] = weights.pop("layers.layer_normalization.vars.0")
+ else:
+ converted_weights["norm.weight"] = weights.pop("layers.layer_normalization.vars.0")
+
+ for name, w in weights.items():
+ if encoder:
+ new_name = _convert_layer_names(name)
+ else:
+ new_name = _convert_layer_names(name, gated_mlp=True)
+ converted_weights[new_name] = w
+
+ return converted_weights
+
+
+def convert_usefulsensors_moonshine_to_hf(model_name, pytorch_dump_folder_path):
+ preprocessor_weights_path, encoder_weights_path, decoder_weights_path = _get_weights(model_name)
+
+ with h5py.File(preprocessor_weights_path, "r") as f:
+ loaded_preprocessor_weights = _read_h5_weights(f, weights={})
+
+ with h5py.File(encoder_weights_path, "r") as f:
+ loaded_encoder_weights = _read_h5_weights(f, weights={})
+
+ with h5py.File(decoder_weights_path, "r") as f:
+ loaded_decoder_weights = _read_h5_weights(f, weights={})
+
+ encoder_state_dict = {**loaded_encoder_weights, **loaded_preprocessor_weights}
+ converted_encoder_state_dict = _convert_weights(encoder_state_dict)
+
+ converted_decoder_state_dict = _convert_weights(loaded_decoder_weights, encoder=False)
+ converted_decoder_state_dict["embed_tokens.weight"] = converted_decoder_state_dict["embed_tokens.weight"].T
+
+ final_weights = {}
+ for k, v in converted_encoder_state_dict.items():
+ final_weights[f"model.encoder.{k}"] = v
+
+ for k, v in converted_decoder_state_dict.items():
+ final_weights[f"model.decoder.{k}"] = v
+
+ if model_name == "tiny":
+ config = MoonshineConfig()
+ elif model_name == "base":
+ config = MoonshineConfig(
+ hidden_size=416,
+ intermediate_size=1664,
+ encoder_num_hidden_layers=8,
+ decoder_num_hidden_layers=8,
+ encoder_num_attention_heads=8,
+ decoder_num_attention_heads=8,
+ partial_rotary_factor=0.62,
+ )
+ else:
+ raise ValueError(f"Unknown model name {model_name}")
+
+ final_weights["proj_out.weight"] = converted_decoder_state_dict["embed_tokens.weight"]
+
+ model = MoonshineForConditionalGeneration(config)
+ model.load_state_dict(final_weights)
+ model.save_pretrained(pytorch_dump_folder_path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # # Required parameters
+ parser.add_argument("--model_name", type=str, help="Path to the downloaded checkpoints")
+ parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
+ args = parser.parse_args()
+
+ convert_usefulsensors_moonshine_to_hf(args.model_name, args.pytorch_dump_folder_path)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/modeling_moonshine.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/modeling_moonshine.py
new file mode 100644
index 0000000000000000000000000000000000000000..45db56a9ee117e3709a29ee34e32750b04f68e1c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/modeling_moonshine.py
@@ -0,0 +1,1100 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/moonshine/modular_moonshine.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_moonshine.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Callable, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from transformers.utils.generic import OutputRecorder, check_model_inputs
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...masking_utils import create_causal_mask
+from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPast,
+ BaseModelOutputWithPastAndCrossAttentions,
+ Seq2SeqLMOutput,
+ Seq2SeqModelOutput,
+)
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_moonshine import MoonshineConfig
+
+
+class MoonshineEncoderMLP(nn.Module):
+ def __init__(self, config, hidden_act):
+ super().__init__()
+ self.config = config
+ self.activation_fn = ACT2FN[hidden_act]
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+class MoonshineDecoderMLP(nn.Module):
+ def __init__(self, config, hidden_act):
+ super().__init__()
+ self.config = config
+ self.activation_fn = ACT2FN[hidden_act]
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size * 2)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states, gate = hidden_states.chunk(2, dim=-1)
+ hidden_states = self.activation_fn(gate) * hidden_states
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., 0::2]
+ x2 = x[..., 1::2]
+ return torch.stack((-x2, x1), dim=-1).flatten(-2)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+
+ # Interleave them instead of usual shape
+ cos = cos[..., : cos.shape[-1] // 2].repeat_interleave(2, dim=-1)
+ sin = sin[..., : sin.shape[-1] // 2].repeat_interleave(2, dim=-1)
+
+ # Keep half or full tensor for later concatenation
+ rotary_dim = cos.shape[-1]
+ q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
+ k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
+
+ # Apply rotary embeddings on the first half or full tensor
+ q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
+ k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
+
+ # Concatenate back to full shape
+ q_embed = torch.cat([q_embed, q_pass], dim=-1)
+ k_embed = torch.cat([k_embed, k_pass], dim=-1)
+ return q_embed, k_embed
+
+
+class MoonshineAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ config: MoonshineConfig,
+ layer_idx: int,
+ is_causal: bool,
+ num_attention_heads: int,
+ num_key_value_heads: int,
+ ):
+ super().__init__()
+ config.update({"num_attention_heads": num_attention_heads, "num_key_value_heads": num_key_value_heads})
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = config.attention_dropout
+ self.is_causal = is_causal
+
+ self.q_proj = nn.Linear(
+ config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.k_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.v_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+
+ # Pad head dimension to the next specified multiple.
+ if self.config.pad_head_dim_to_multiple_of is not None:
+ target_multiple = self.config.pad_head_dim_to_multiple_of
+ target_head_dim = target_multiple * ((self.head_dim + target_multiple - 1) // target_multiple)
+ self.head_dim_padding = target_head_dim - self.head_dim
+ else:
+ self.head_dim_padding = 0
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ key_value_states: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ bsz, q_len = hidden_states.shape[:-1]
+
+ query_states = (
+ self.q_proj(hidden_states).view(bsz, q_len, self.config.num_key_value_heads, self.head_dim).transpose(1, 2)
+ )
+
+ is_cross_attention = key_value_states is not None
+ if past_key_values is not None:
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_states from cache
+ past_key_values.is_updated[self.layer_idx] = True
+ past_key_values = past_key_values.cross_attention_cache
+ else:
+ past_key_values = past_key_values.self_attention_cache
+
+ # use key_value_states if cross attention
+ current_states = key_value_states if key_value_states is not None else hidden_states
+ if is_cross_attention and past_key_values and is_updated:
+ key_states = past_key_values.layers[self.layer_idx].keys
+ value_states = past_key_values.layers[self.layer_idx].values
+ else:
+ key_states = (
+ self.k_proj(current_states)
+ .view(bsz, -1, self.config.num_key_value_heads, self.head_dim)
+ .transpose(1, 2)
+ )
+ value_states = (
+ self.v_proj(current_states)
+ .view(bsz, -1, self.config.num_key_value_heads, self.head_dim)
+ .transpose(1, 2)
+ )
+ if is_cross_attention and past_key_values is not None:
+ key_states, value_states = past_key_values.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+
+ if not is_cross_attention:
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(
+ key_states, value_states, self.layer_idx, cache_kwargs
+ )
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ is_causal = self.is_causal and attention_mask is None and q_len > 1
+
+ if self.head_dim_padding > 0:
+ query_states = torch.nn.functional.pad(query_states, (0, self.head_dim_padding))
+ key_states = torch.nn.functional.pad(key_states, (0, self.head_dim_padding))
+ value_states = torch.nn.functional.pad(value_states, (0, self.head_dim_padding))
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ is_causal=is_causal,
+ **kwargs,
+ )
+
+ if self.head_dim_padding > 0:
+ attn_output = attn_output[..., : -self.head_dim_padding]
+
+ attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class MoonshineRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: MoonshineConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class MoonshineEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MoonshineConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = MoonshineAttention(
+ config=config,
+ layer_idx=layer_idx,
+ is_causal=False,
+ num_attention_heads=config.encoder_num_attention_heads,
+ num_key_value_heads=config.encoder_num_key_value_heads,
+ )
+
+ self.mlp = MoonshineEncoderMLP(config, config.encoder_hidden_act)
+ self.input_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
+ self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> torch.Tensor:
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+ # Self Attention
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+ return hidden_states
+
+
+class MoonshineDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MoonshineConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = MoonshineAttention(
+ config=config,
+ layer_idx=layer_idx,
+ is_causal=True,
+ num_attention_heads=config.decoder_num_attention_heads,
+ num_key_value_heads=config.decoder_num_key_value_heads,
+ )
+ self.encoder_attn = MoonshineAttention(
+ config=config,
+ layer_idx=layer_idx,
+ is_causal=False,
+ num_attention_heads=config.decoder_num_attention_heads,
+ num_key_value_heads=config.decoder_num_key_value_heads,
+ )
+
+ self.mlp = MoonshineDecoderMLP(config, config.decoder_hidden_act)
+ self.input_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
+ self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
+ self.final_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ encoder_position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ encoder_position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states, _ = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ )
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+ hidden_states = self.final_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+ return hidden_states
+
+
+@auto_docstring
+class MoonshinePreTrainedModel(PreTrainedModel):
+ config: MoonshineConfig
+ base_model_prefix = "model"
+ main_input_name = "input_values"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MoonshineEncoderLayer", "MoonshineDecoderLayer"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+ # TODO arthur, how do we separate when it cross / self coming from different layer?
+
+ def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
+ """
+ Computes the output length of the convolutional layers
+ """
+ output_conv1_length = int((input_lengths - 127) / 64 + 1)
+ output_conv2_length = int((output_conv1_length - 7) / 3 + 1)
+ output_conv3_length = int((output_conv2_length - 3) / 2 + 1)
+
+ return output_conv3_length
+
+
+class MoonshineEncoder(MoonshinePreTrainedModel):
+ """
+ Transformer encoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MoonshineEncoderLayer`]
+
+ Args:
+ config: MoonshineConfig
+ """
+
+ main_input_name = "input_values"
+ _can_record_outputs = {
+ "attentions": MoonshineAttention,
+ "hidden_states": MoonshineEncoderLayer,
+ }
+
+ def __init__(self, config: MoonshineConfig):
+ super().__init__(config)
+ self.config = config
+ embed_dim = config.hidden_size
+
+ self.conv1 = nn.Conv1d(1, embed_dim, kernel_size=127, stride=64, bias=False)
+ self.conv2 = nn.Conv1d(embed_dim, 2 * embed_dim, kernel_size=7, stride=3)
+ self.conv3 = nn.Conv1d(2 * embed_dim, embed_dim, kernel_size=3, stride=2)
+ self.groupnorm = nn.GroupNorm(num_groups=1, num_channels=embed_dim, eps=1e-5)
+ self.rotary_emb = MoonshineRotaryEmbedding(config=config)
+
+ self.layers = nn.ModuleList(
+ [MoonshineEncoderLayer(config, idx) for idx in range(config.encoder_num_hidden_layers)]
+ )
+ self.layer_norm = nn.LayerNorm(embed_dim, bias=False)
+ self.gradient_checkpointing = False
+ self.post_init()
+
+ def get_input_embeddings(self) -> nn.Module:
+ return self.conv1
+
+ def set_input_embeddings(self, value: nn.Module):
+ self.conv1 = value
+
+ @check_model_inputs
+ def forward(
+ self,
+ input_values: torch.FloatTensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> BaseModelOutputWithPast:
+ r"""
+ Args:
+ input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
+ Float values of the raw speech waveform. Raw speech waveform can be
+ obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]`, a
+ `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+ the soundfile library (`pip install soundfile`). To prepare the array into
+ `input_values`, the [`AutoFeatureExtractor`] should be used for padding
+ and conversion into a tensor of type `torch.FloatTensor`.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding indices in `input_values`. Mask values selected in `[0, 1]`:
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ [What are attention masks?](../glossary#attention-mask)
+ """
+ input_values = input_values.unsqueeze(1)
+ hidden_states = nn.functional.tanh(self.conv1(input_values))
+ hidden_states = self.groupnorm(hidden_states)
+ hidden_states = nn.functional.gelu(self.conv2(hidden_states))
+ hidden_states = nn.functional.gelu(self.conv3(hidden_states))
+ hidden_states = hidden_states.permute(0, 2, 1)
+
+ # attention mask downsampling
+ if attention_mask is not None:
+ mask_len = self._get_feat_extract_output_lengths(attention_mask.shape[-1])
+ downsample_stride = 64 * 3 * 2 # conv strides
+ attention_mask = attention_mask[..., ::downsample_stride][..., :mask_len]
+ if self.config._attn_implementation == "flash_attention_2":
+ attention_mask = attention_mask if (attention_mask == 0.0).any() else None
+ elif self.config._attn_implementation == "sdpa":
+ attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, hidden_states.dtype)
+ else:
+ attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)
+
+ position_ids = torch.arange(0, hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ for encoder_layer in self.layers:
+ hidden_states = encoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+
+ hidden_states = self.layer_norm(hidden_states)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ )
+
+
+@auto_docstring
+class MoonshineDecoder(MoonshinePreTrainedModel):
+ main_input_name = "input_ids"
+ _can_record_outputs = {
+ "attentions": OutputRecorder(MoonshineAttention, index=1, layer_name="self_attn"),
+ "hidden_states": MoonshineDecoderLayer,
+ "cross_attentions": OutputRecorder(MoonshineAttention, index=1, layer_name="encoder_attn"),
+ }
+
+ def __init__(self, config: MoonshineConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [MoonshineDecoderLayer(config, idx) for idx in range(config.decoder_num_hidden_layers)]
+ )
+ self.norm = nn.LayerNorm(config.hidden_size, bias=False)
+ self.rotary_emb = MoonshineRotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @check_model_inputs
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ r"""
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding indices in `encoder_hidden_states`. Mask values selected in `[0, 1]`:
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ [What are attention masks?](../glossary#attention-mask)
+ """
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if use_cache and past_key_values is None:
+ past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = create_causal_mask(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ if encoder_attention_mask is not None:
+ mask_len = encoder_hidden_states.shape[-2]
+ downsample_stride = 64 * 3 * 2 # conv strides
+ encoder_attention_mask = encoder_attention_mask[..., ::downsample_stride][..., :mask_len]
+ if self.config._attn_implementation == "flash_attention_2":
+ encoder_attention_mask = encoder_attention_mask if (encoder_attention_mask == 0.0).any() else None
+ elif self.config._attn_implementation == "sdpa":
+ encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
+ encoder_attention_mask, hidden_states.dtype, hidden_states.shape[-2]
+ )
+ else:
+ encoder_attention_mask = _prepare_4d_attention_mask(
+ encoder_attention_mask, hidden_states.dtype, hidden_states.shape[-2]
+ )
+
+ for decoder_layer in self.layers:
+ hidden_states = decoder_layer(
+ hidden_states,
+ causal_mask,
+ encoder_hidden_states, # as a positional argument for gradient checkpointing
+ encoder_attention_mask=encoder_attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+
+ hidden_states = self.norm(hidden_states)
+
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values if use_cache else None,
+ )
+
+
+def _compute_mask_indices(
+ shape: tuple[int, int],
+ mask_prob: float,
+ mask_length: int,
+ attention_mask: Optional[torch.LongTensor] = None,
+ min_masks: int = 0,
+) -> np.ndarray:
+ """
+ Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for
+ ASR](https://huggingface.co/papers/1904.08779). Note that this method is not optimized to run on TPU and should be run on
+ CPU as part of the preprocessing during training.
+
+ Args:
+ shape: The shape for which to compute masks. This should be of a tuple of size 2 where
+ the first element is the batch size and the second element is the length of the axis to span.
+ mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of
+ independently generated mask spans of length `mask_length` is computed by
+ `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the
+ actual percentage will be smaller.
+ mask_length: size of the mask
+ min_masks: minimum number of masked spans
+ attention_mask: A (right-padded) attention mask which independently shortens the feature axis of
+ each batch dimension.
+ """
+ batch_size, sequence_length = shape
+
+ if mask_length < 1:
+ raise ValueError("`mask_length` has to be bigger than 0.")
+
+ if mask_length > sequence_length:
+ raise ValueError(
+ f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}"
+ f" and `sequence_length`: {sequence_length}`"
+ )
+
+ # epsilon is used for probabilistic rounding
+ epsilon = np.random.rand(1).item()
+
+ def compute_num_masked_span(input_length):
+ """Given input length, compute how many spans should be masked"""
+ num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
+ num_masked_span = max(num_masked_span, min_masks)
+
+ # make sure num masked span <= sequence_length
+ if num_masked_span * mask_length > sequence_length:
+ num_masked_span = sequence_length // mask_length
+
+ # make sure num_masked span is also <= input_length - (mask_length - 1)
+ if input_length - (mask_length - 1) < num_masked_span:
+ num_masked_span = max(input_length - (mask_length - 1), 0)
+
+ return num_masked_span
+
+ # compute number of masked spans in batch
+ input_lengths = (
+ attention_mask.detach().sum(-1).tolist()
+ if attention_mask is not None
+ else [sequence_length for _ in range(batch_size)]
+ )
+
+ # SpecAugment mask to fill
+ spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
+ spec_aug_mask_idxs = []
+
+ max_num_masked_span = compute_num_masked_span(sequence_length)
+
+ if max_num_masked_span == 0:
+ return spec_aug_mask
+
+ for input_length in input_lengths:
+ # compute num of masked spans for this input
+ num_masked_span = compute_num_masked_span(input_length)
+
+ # get random indices to mask
+ spec_aug_mask_idx = np.random.choice(
+ np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False
+ )
+
+ # pick first sampled index that will serve as a dummy index to pad vector
+ # to ensure same dimension for all batches due to probabilistic rounding
+ # Picking first sample just pads those vectors twice.
+ if len(spec_aug_mask_idx) == 0:
+ # this case can only happen if `input_length` is strictly smaller then
+ # `sequence_length` in which case the last token has to be a padding
+ # token which we can use as a dummy mask id
+ dummy_mask_idx = sequence_length - 1
+ else:
+ dummy_mask_idx = spec_aug_mask_idx[0]
+
+ spec_aug_mask_idx = np.concatenate(
+ [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
+ )
+ spec_aug_mask_idxs.append(spec_aug_mask_idx)
+
+ spec_aug_mask_idxs = np.array(spec_aug_mask_idxs)
+
+ # expand masked indices to masked spans
+ spec_aug_mask_idxs = np.broadcast_to(
+ spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length)
+ )
+ spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length)
+
+ # add offset to the starting indexes so that indexes now create a span
+ offsets = np.arange(mask_length)[None, None, :]
+ offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape(
+ batch_size, max_num_masked_span * mask_length
+ )
+ spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
+
+ # ensure that we cannot have indices larger than sequence_length
+ if spec_aug_mask_idxs.max() > sequence_length - 1:
+ spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
+
+ # scatter indices to mask
+ np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
+
+ return spec_aug_mask
+
+
+@auto_docstring
+class MoonshineModel(MoonshinePreTrainedModel):
+ def __init__(self, config: MoonshineConfig):
+ super().__init__(config)
+
+ self.encoder = MoonshineEncoder(config)
+ self.decoder = MoonshineDecoder(config)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.decoder.embed_tokens = value
+
+ def get_encoder(self):
+ return self.encoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ def freeze_encoder(self):
+ """
+ Calling this function will disable the gradient computation for the Moonshine encoder so that its parameters will
+ not be updated during training.
+ """
+ self.encoder._freeze_parameters()
+
+ def _mask_input_features(
+ self,
+ input_features: torch.FloatTensor,
+ attention_mask: Optional[torch.LongTensor] = None,
+ ):
+ """
+ Masks extracted features along time axis and/or along feature axis according to
+ [SpecAugment](https://huggingface.co/papers/1904.08779).
+ """
+
+ # `config.apply_spec_augment` can set masking to False
+ if not getattr(self.config, "apply_spec_augment", True):
+ return input_features
+
+ # generate indices & apply SpecAugment along time axis
+ batch_size, hidden_size, sequence_length = input_features.size()
+
+ if self.config.mask_time_prob > 0 and self.training:
+ # generate indices & apply SpecAugment along time axis
+ mask_time_indices = _compute_mask_indices(
+ (batch_size, sequence_length),
+ mask_prob=self.config.mask_time_prob,
+ mask_length=self.config.mask_time_length,
+ attention_mask=attention_mask,
+ min_masks=self.config.mask_time_min_masks,
+ )
+ mask_time_indices = torch.tensor(mask_time_indices, device=input_features.device, dtype=torch.bool)
+ mask_time_indices = mask_time_indices[:, None].expand(-1, hidden_size, -1)
+ input_features[mask_time_indices] = 0
+
+ if self.config.mask_feature_prob > 0 and self.training:
+ # generate indices & apply SpecAugment along feature axis
+ mask_feature_indices = _compute_mask_indices(
+ (batch_size, hidden_size),
+ mask_prob=self.config.mask_feature_prob,
+ mask_length=self.config.mask_feature_length,
+ min_masks=self.config.mask_feature_min_masks,
+ )
+ mask_feature_indices = torch.tensor(mask_feature_indices, device=input_features.device, dtype=torch.bool)
+ input_features[mask_feature_indices] = 0
+
+ return input_features
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_values: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[Union[EncoderDecoderCache, tuple[torch.FloatTensor]]] = None,
+ decoder_inputs_embeds: Optional[tuple[torch.FloatTensor]] = None,
+ decoder_position_ids: Optional[tuple[torch.LongTensor]] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Seq2SeqModelOutput:
+ r"""
+ input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
+ Float values of the raw speech waveform. Raw speech waveform can be
+ obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]`, a
+ `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+ the soundfile library (`pip install soundfile`). To prepare the array into
+ `input_values`, the [`AutoFeatureExtractor`] should be used for padding
+ and conversion into a tensor of type `torch.FloatTensor`.
+ decoder_position_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`):
+ Indices of positions of each input sequence tokens in the position embeddings.
+ Used to calculate the position embeddings up to `config.decoder_config.max_position_embeddings`
+
+ Example:
+
+ ```python
+ >>> import torch
+ >>> from transformers import AutoFeatureExtractor, MoonshineModel
+ >>> from datasets import load_dataset
+
+ >>> model = MoonshineModel.from_pretrained("UsefulSensors/moonshine-tiny")
+ >>> feature_extractor = AutoFeatureExtractor.from_pretrained("UsefulSensors/moonshine-tiny")
+ >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+ >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
+ >>> input_values = inputs.input_values
+ >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
+ >>> last_hidden_state = model(input_values, decoder_input_ids=decoder_input_ids).last_hidden_state
+ >>> list(last_hidden_state.shape)
+ [1, 2, 288]
+ ```
+ """
+ if encoder_outputs is None:
+ encoder_outputs: BaseModelOutput = self.encoder(input_values, attention_mask=attention_mask, **kwargs)
+
+ decoder_outputs: BaseModelOutputWithPastAndCrossAttentions = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_attention_mask=attention_mask,
+ encoder_hidden_states=encoder_outputs.last_hidden_state,
+ past_key_values=past_key_values,
+ inputs_embeds=decoder_inputs_embeds,
+ position_ids=decoder_position_ids,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return Seq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+ """
+ Shift input ids one token to the right.
+ """
+ shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+ shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
+ shifted_input_ids[:, 0] = decoder_start_token_id
+
+ if pad_token_id is None:
+ raise ValueError("self.model.config.pad_token_id has to be defined.")
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+ return shifted_input_ids
+
+
+@auto_docstring(
+ custom_intro="""
+ The Moonshine Model with a language modeling head. Can be used for automatic speech recognition.
+ """
+)
+class MoonshineForConditionalGeneration(MoonshinePreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["proj_out.weight"]
+
+ def __init__(self, config: MoonshineConfig):
+ super().__init__(config)
+ self.model = MoonshineModel(config)
+ self.proj_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_encoder(self):
+ return self.model.get_encoder()
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def get_output_embeddings(self):
+ return self.proj_out
+
+ def set_output_embeddings(self, new_embeddings):
+ self.proj_out = new_embeddings
+
+ def get_input_embeddings(self) -> nn.Module:
+ return self.model.get_input_embeddings()
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_values: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[Union[EncoderDecoderCache, tuple[torch.FloatTensor]]] = None,
+ decoder_inputs_embeds: Optional[tuple[torch.FloatTensor]] = None,
+ decoder_position_ids: Optional[tuple[torch.LongTensor]] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Seq2SeqLMOutput:
+ r"""
+ input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
+ Float values of the raw speech waveform. Raw speech waveform can be
+ obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]`, a
+ `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+ the soundfile library (`pip install soundfile`). To prepare the array into
+ `input_values`, the [`AutoFeatureExtractor`] should be used for padding
+ and conversion into a tensor of type `torch.FloatTensor`.
+ decoder_position_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`):
+ Indices of positions of each input sequence tokens in the position embeddings.
+ Used to calculate the position embeddings up to `config.decoder_config.max_position_embeddings`
+
+ Example:
+
+ ```python
+ >>> import torch
+ >>> from transformers import AutoProcessor, MoonshineForConditionalGeneration
+ >>> from datasets import load_dataset
+
+ >>> processor = AutoProcessor.from_pretrained("UsefulSensors/moonshine-tiny")
+ >>> model = MoonshineForConditionalGeneration.from_pretrained("UsefulSensors/moonshine-tiny")
+
+ >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+
+ >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
+ >>> input_values = inputs.input_values
+
+ >>> generated_ids = model.generate(input_values, max_new_tokens=100)
+
+ >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+ >>> transcription
+ 'Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
+ ```"""
+
+ if labels is not None:
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ decoder_input_ids = shift_tokens_right(
+ labels, self.config.pad_token_id, self.config.decoder_start_token_id
+ )
+
+ outputs: Seq2SeqModelOutput = self.model(
+ input_values,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ encoder_outputs=encoder_outputs,
+ decoder_attention_mask=decoder_attention_mask,
+ past_key_values=past_key_values,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ decoder_position_ids=decoder_position_ids,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ logits = self.proj_out(outputs.last_hidden_state)
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size)
+
+ return Seq2SeqLMOutput(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+__all__ = ["MoonshineModel", "MoonshinePreTrainedModel", "MoonshineForConditionalGeneration"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/modular_moonshine.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/modular_moonshine.py
new file mode 100644
index 0000000000000000000000000000000000000000..c095abdefed5f4dff9e170e48e18069089af7ce3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moonshine/modular_moonshine.py
@@ -0,0 +1,921 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn as nn
+
+from transformers.utils.generic import OutputRecorder, check_model_inputs
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...configuration_utils import PretrainedConfig
+from ...generation import GenerationMixin
+from ...masking_utils import create_causal_mask
+from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPast,
+ BaseModelOutputWithPastAndCrossAttentions,
+ Seq2SeqLMOutput,
+ Seq2SeqModelOutput,
+)
+from ...modeling_rope_utils import rope_config_validation
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils.deprecation import deprecate_kwarg
+from ..glm.modeling_glm import GlmAttention, GlmRotaryEmbedding, apply_rotary_pos_emb
+from ..llama.modeling_llama import LlamaDecoderLayer, LlamaModel, eager_attention_forward
+from ..whisper.modeling_whisper import WhisperModel, shift_tokens_right
+
+
+logger = logging.get_logger(__name__)
+
+
+class MoonshineConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MoonshineModel`]. It is used to instantiate a Moonshine
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the Moonshine
+ [UsefulSensors/moonshine-tiny](https://huggingface.co/UsefulSensors/moonshine-tiny).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32768):
+ Vocabulary size of the Moonshine model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MoonshineModel`].
+ hidden_size (`int`, *optional*, defaults to 288):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 1152):
+ Dimension of the MLP representations.
+ encoder_num_hidden_layers (`int`, *optional*, defaults to 6):
+ Number of hidden layers in the Transformer encoder.
+ decoder_num_hidden_layers (`int`, *optional*, defaults to 6):
+ Number of hidden layers in the Transformer decoder.
+ encoder_num_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ decoder_num_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ encoder_num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `encoder_num_key_value_heads=encoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `encoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+ `num_attention_heads`.
+ decoder_num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `decoder_num_key_value_heads=decoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `decoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+ `decoder_num_attention_heads`.
+ pad_head_dim_to_multiple_of (`int`, *optional*):
+ Pad head dimension in encoder and decoder to the next multiple of this value. Necessary for using certain
+ optimized attention implementations.
+ encoder_hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder.
+ decoder_hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ decoder_start_token_id (`int`, *optional*, defaults to 1):
+ Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids`
+ are provided to the `generate` function. It is used to guide the model`s generation process depending on
+ the task.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models).
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+ and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+ accordingly.
+ Expected contents:
+ `rope_type` (`str`):
+ The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+ 'llama3'], with 'default' being the original RoPE implementation.
+ `factor` (`float`, *optional*):
+ Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+ most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+ original maximum pre-trained length.
+ `original_max_position_embeddings` (`int`, *optional*):
+ Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+ pretraining.
+ `attention_factor` (`float`, *optional*):
+ Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+ computation. If unspecified, it defaults to value recommended by the implementation, using the
+ `factor` field to infer the suggested value.
+ `beta_fast` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+ ramp function. If unspecified, it defaults to 32.
+ `beta_slow` (`float`, *optional*):
+ Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+ ramp function. If unspecified, it defaults to 1.
+ `short_factor` (`list[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+ size divided by the number of attention heads divided by 2
+ `long_factor` (`list[float]`, *optional*):
+ Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+ `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+ size divided by the number of attention heads divided by 2
+ `low_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+ `high_freq_factor` (`float`, *optional*):
+ Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+ partial_rotary_factor (`float`, *optional*, defaults to 0.9):
+ Percentage of the query and keys which will have rotary embedding.
+ is_encoder_decoder (`bool`, *optional*, defaults to `True`):
+ Whether the model is used as an encoder/decoder or not.
+ attention_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ Denotes beginning of sequences token id.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ Denotes end of sequences token id.
+
+ Example:
+
+ ```python
+ >>> from transformers import MoonshineModel, MoonshineConfig
+
+ >>> # Initializing a Moonshine style configuration
+ >>> configuration = MoonshineConfig().from_pretrained("UsefulSensors/moonshine-tiny")
+
+ >>> # Initializing a model from the configuration
+ >>> model = MoonshineModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "moonshine"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {
+ "num_key_value_heads": "encoder_num_key_value_heads",
+ "num_attention_heads": "encoder_num_attention_heads",
+ "num_hidden_layers": "encoder_num_hidden_layers",
+ }
+
+ def __init__(
+ self,
+ vocab_size=32768,
+ hidden_size=288,
+ intermediate_size=1152,
+ encoder_num_hidden_layers=6,
+ decoder_num_hidden_layers=6,
+ encoder_num_attention_heads=8,
+ decoder_num_attention_heads=8,
+ encoder_num_key_value_heads=None,
+ decoder_num_key_value_heads=None,
+ pad_head_dim_to_multiple_of=None,
+ encoder_hidden_act="gelu",
+ decoder_hidden_act="silu",
+ max_position_embeddings=512,
+ initializer_range=0.02,
+ decoder_start_token_id=1,
+ use_cache=True,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ partial_rotary_factor=0.9,
+ is_encoder_decoder=True,
+ attention_bias=False,
+ attention_dropout=0.0,
+ bos_token_id=1,
+ eos_token_id=2,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.encoder_num_hidden_layers = encoder_num_hidden_layers
+ self.decoder_num_hidden_layers = decoder_num_hidden_layers
+ self.encoder_num_attention_heads = encoder_num_attention_heads
+ self.decoder_num_attention_heads = decoder_num_attention_heads
+
+ if encoder_num_key_value_heads is None:
+ encoder_num_key_value_heads = encoder_num_attention_heads
+ self.encoder_num_key_value_heads = encoder_num_key_value_heads
+
+ if decoder_num_key_value_heads is None:
+ decoder_num_key_value_heads = decoder_num_attention_heads
+ self.decoder_num_key_value_heads = decoder_num_key_value_heads
+
+ self.pad_head_dim_to_multiple_of = pad_head_dim_to_multiple_of
+
+ self.encoder_hidden_act = encoder_hidden_act
+ self.decoder_hidden_act = decoder_hidden_act
+ self.max_position_embeddings = max_position_embeddings
+ self.initializer_range = initializer_range
+ self.decoder_start_token_id = decoder_start_token_id
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ self.partial_rotary_factor = partial_rotary_factor
+ self.is_encoder_decoder = is_encoder_decoder
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+
+ # Validate the correctness of rotary position embeddings parameters
+ rope_config_validation(self)
+
+ super().__init__(
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ is_encoder_decoder=is_encoder_decoder,
+ decoder_start_token_id=decoder_start_token_id,
+ **kwargs,
+ )
+
+
+class MoonshineEncoderMLP(nn.Module):
+ def __init__(self, config, hidden_act):
+ super().__init__()
+ self.config = config
+ self.activation_fn = ACT2FN[hidden_act]
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+class MoonshineDecoderMLP(nn.Module):
+ def __init__(self, config, hidden_act):
+ super().__init__()
+ self.config = config
+ self.activation_fn = ACT2FN[hidden_act]
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size * 2)
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states, gate = hidden_states.chunk(2, dim=-1)
+ hidden_states = self.activation_fn(gate) * hidden_states
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+class MoonshineAttention(GlmAttention):
+ def __init__(
+ self,
+ config: MoonshineConfig,
+ layer_idx: int,
+ is_causal: bool,
+ num_attention_heads: int,
+ num_key_value_heads: int,
+ ):
+ config.update({"num_attention_heads": num_attention_heads, "num_key_value_heads": num_key_value_heads})
+ super().__init__(config, layer_idx)
+ self.is_causal = is_causal
+ self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+
+ # Pad head dimension to the next specified multiple.
+ if self.config.pad_head_dim_to_multiple_of is not None:
+ target_multiple = self.config.pad_head_dim_to_multiple_of
+ target_head_dim = target_multiple * ((self.head_dim + target_multiple - 1) // target_multiple)
+ self.head_dim_padding = target_head_dim - self.head_dim
+ else:
+ self.head_dim_padding = 0
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ key_value_states: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ bsz, q_len = hidden_states.shape[:-1]
+
+ query_states = (
+ self.q_proj(hidden_states).view(bsz, q_len, self.config.num_key_value_heads, self.head_dim).transpose(1, 2)
+ )
+
+ is_cross_attention = key_value_states is not None
+ if past_key_values is not None:
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_states from cache
+ past_key_values.is_updated[self.layer_idx] = True
+ past_key_values = past_key_values.cross_attention_cache
+ else:
+ past_key_values = past_key_values.self_attention_cache
+
+ # use key_value_states if cross attention
+ current_states = key_value_states if key_value_states is not None else hidden_states
+ if is_cross_attention and past_key_values and is_updated:
+ key_states = past_key_values.layers[self.layer_idx].keys
+ value_states = past_key_values.layers[self.layer_idx].values
+ else:
+ key_states = (
+ self.k_proj(current_states)
+ .view(bsz, -1, self.config.num_key_value_heads, self.head_dim)
+ .transpose(1, 2)
+ )
+ value_states = (
+ self.v_proj(current_states)
+ .view(bsz, -1, self.config.num_key_value_heads, self.head_dim)
+ .transpose(1, 2)
+ )
+ if is_cross_attention and past_key_values is not None:
+ key_states, value_states = past_key_values.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+
+ if not is_cross_attention:
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(
+ key_states, value_states, self.layer_idx, cache_kwargs
+ )
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ is_causal = self.is_causal and attention_mask is None and q_len > 1
+
+ if self.head_dim_padding > 0:
+ query_states = torch.nn.functional.pad(query_states, (0, self.head_dim_padding))
+ key_states = torch.nn.functional.pad(key_states, (0, self.head_dim_padding))
+ value_states = torch.nn.functional.pad(value_states, (0, self.head_dim_padding))
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ is_causal=is_causal,
+ **kwargs,
+ )
+
+ if self.head_dim_padding > 0:
+ attn_output = attn_output[..., : -self.head_dim_padding]
+
+ attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class MoonshineRotaryEmbedding(GlmRotaryEmbedding):
+ pass
+
+
+class MoonshineEncoderLayer(LlamaDecoderLayer):
+ def __init__(self, config: MoonshineConfig, layer_idx: int):
+ super().__init__(config, layer_idx)
+
+ self.self_attn = MoonshineAttention(
+ config=config,
+ layer_idx=layer_idx,
+ is_causal=False,
+ num_attention_heads=config.encoder_num_attention_heads,
+ num_key_value_heads=config.encoder_num_key_value_heads,
+ )
+
+ self.mlp = MoonshineEncoderMLP(config, config.encoder_hidden_act)
+ self.input_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
+ self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
+
+
+class MoonshineDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MoonshineConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = MoonshineAttention(
+ config=config,
+ layer_idx=layer_idx,
+ is_causal=True,
+ num_attention_heads=config.decoder_num_attention_heads,
+ num_key_value_heads=config.decoder_num_key_value_heads,
+ )
+ self.encoder_attn = MoonshineAttention(
+ config=config,
+ layer_idx=layer_idx,
+ is_causal=False,
+ num_attention_heads=config.decoder_num_attention_heads,
+ num_key_value_heads=config.decoder_num_key_value_heads,
+ )
+
+ self.mlp = MoonshineDecoderMLP(config, config.decoder_hidden_act)
+ self.input_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
+ self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
+ self.final_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ encoder_position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ encoder_position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states, _ = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ )
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+ hidden_states = self.final_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+ return hidden_states
+
+
+@auto_docstring
+class MoonshinePreTrainedModel(PreTrainedModel):
+ config: MoonshineConfig
+ base_model_prefix = "model"
+ main_input_name = "input_values"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MoonshineEncoderLayer", "MoonshineDecoderLayer"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+ # TODO arthur, how do we separate when it cross / self coming from different layer?
+
+ def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
+ """
+ Computes the output length of the convolutional layers
+ """
+ output_conv1_length = int((input_lengths - 127) / 64 + 1)
+ output_conv2_length = int((output_conv1_length - 7) / 3 + 1)
+ output_conv3_length = int((output_conv2_length - 3) / 2 + 1)
+
+ return output_conv3_length
+
+
+class MoonshineEncoder(MoonshinePreTrainedModel):
+ """
+ Transformer encoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MoonshineEncoderLayer`]
+
+ Args:
+ config: MoonshineConfig
+ """
+
+ main_input_name = "input_values"
+ _can_record_outputs = {
+ "attentions": MoonshineAttention,
+ "hidden_states": MoonshineEncoderLayer,
+ }
+
+ def __init__(self, config: MoonshineConfig):
+ super().__init__(config)
+ self.config = config
+ embed_dim = config.hidden_size
+
+ self.conv1 = nn.Conv1d(1, embed_dim, kernel_size=127, stride=64, bias=False)
+ self.conv2 = nn.Conv1d(embed_dim, 2 * embed_dim, kernel_size=7, stride=3)
+ self.conv3 = nn.Conv1d(2 * embed_dim, embed_dim, kernel_size=3, stride=2)
+ self.groupnorm = nn.GroupNorm(num_groups=1, num_channels=embed_dim, eps=1e-5)
+ self.rotary_emb = MoonshineRotaryEmbedding(config=config)
+
+ self.layers = nn.ModuleList(
+ [MoonshineEncoderLayer(config, idx) for idx in range(config.encoder_num_hidden_layers)]
+ )
+ self.layer_norm = nn.LayerNorm(embed_dim, bias=False)
+ self.gradient_checkpointing = False
+ self.post_init()
+
+ def get_input_embeddings(self) -> nn.Module:
+ return self.conv1
+
+ def set_input_embeddings(self, value: nn.Module):
+ self.conv1 = value
+
+ @check_model_inputs
+ def forward(
+ self,
+ input_values: torch.FloatTensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> BaseModelOutputWithPast:
+ r"""
+ Args:
+ input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
+ Float values of the raw speech waveform. Raw speech waveform can be
+ obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]`, a
+ `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+ the soundfile library (`pip install soundfile`). To prepare the array into
+ `input_values`, the [`AutoFeatureExtractor`] should be used for padding
+ and conversion into a tensor of type `torch.FloatTensor`.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding indices in `input_values`. Mask values selected in `[0, 1]`:
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ [What are attention masks?](../glossary#attention-mask)
+ """
+ input_values = input_values.unsqueeze(1)
+ hidden_states = nn.functional.tanh(self.conv1(input_values))
+ hidden_states = self.groupnorm(hidden_states)
+ hidden_states = nn.functional.gelu(self.conv2(hidden_states))
+ hidden_states = nn.functional.gelu(self.conv3(hidden_states))
+ hidden_states = hidden_states.permute(0, 2, 1)
+
+ # attention mask downsampling
+ if attention_mask is not None:
+ mask_len = self._get_feat_extract_output_lengths(attention_mask.shape[-1])
+ downsample_stride = 64 * 3 * 2 # conv strides
+ attention_mask = attention_mask[..., ::downsample_stride][..., :mask_len]
+ if self.config._attn_implementation == "flash_attention_2":
+ attention_mask = attention_mask if (attention_mask == 0.0).any() else None
+ elif self.config._attn_implementation == "sdpa":
+ attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, hidden_states.dtype)
+ else:
+ attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)
+
+ position_ids = torch.arange(0, hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ for encoder_layer in self.layers:
+ hidden_states = encoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+
+ hidden_states = self.layer_norm(hidden_states)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ )
+
+
+class MoonshineDecoder(LlamaModel):
+ main_input_name = "input_ids"
+ _can_record_outputs = {
+ "attentions": OutputRecorder(MoonshineAttention, index=1, layer_name="self_attn"),
+ "hidden_states": MoonshineDecoderLayer,
+ "cross_attentions": OutputRecorder(MoonshineAttention, index=1, layer_name="encoder_attn"),
+ }
+
+ def __init__(self, config: MoonshineConfig):
+ super().__init__(config)
+ self.norm = nn.LayerNorm(config.hidden_size, bias=False)
+ self.layers = nn.ModuleList(
+ [MoonshineDecoderLayer(config, idx) for idx in range(config.decoder_num_hidden_layers)]
+ )
+
+ @check_model_inputs
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ r"""
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding indices in `encoder_hidden_states`. Mask values selected in `[0, 1]`:
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ [What are attention masks?](../glossary#attention-mask)
+ """
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if use_cache and past_key_values is None:
+ past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = create_causal_mask(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ if encoder_attention_mask is not None:
+ mask_len = encoder_hidden_states.shape[-2]
+ downsample_stride = 64 * 3 * 2 # conv strides
+ encoder_attention_mask = encoder_attention_mask[..., ::downsample_stride][..., :mask_len]
+ if self.config._attn_implementation == "flash_attention_2":
+ encoder_attention_mask = encoder_attention_mask if (encoder_attention_mask == 0.0).any() else None
+ elif self.config._attn_implementation == "sdpa":
+ encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
+ encoder_attention_mask, hidden_states.dtype, hidden_states.shape[-2]
+ )
+ else:
+ encoder_attention_mask = _prepare_4d_attention_mask(
+ encoder_attention_mask, hidden_states.dtype, hidden_states.shape[-2]
+ )
+
+ for decoder_layer in self.layers:
+ hidden_states = decoder_layer(
+ hidden_states,
+ causal_mask,
+ encoder_hidden_states, # as a positional argument for gradient checkpointing
+ encoder_attention_mask=encoder_attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+
+ hidden_states = self.norm(hidden_states)
+
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values if use_cache else None,
+ )
+
+
+class MoonshineModel(WhisperModel):
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_values: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[Union[EncoderDecoderCache, tuple[torch.FloatTensor]]] = None,
+ decoder_inputs_embeds: Optional[tuple[torch.FloatTensor]] = None,
+ decoder_position_ids: Optional[tuple[torch.LongTensor]] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Seq2SeqModelOutput:
+ r"""
+ input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
+ Float values of the raw speech waveform. Raw speech waveform can be
+ obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]`, a
+ `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+ the soundfile library (`pip install soundfile`). To prepare the array into
+ `input_values`, the [`AutoFeatureExtractor`] should be used for padding
+ and conversion into a tensor of type `torch.FloatTensor`.
+ decoder_position_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`):
+ Indices of positions of each input sequence tokens in the position embeddings.
+ Used to calculate the position embeddings up to `config.decoder_config.max_position_embeddings`
+
+ Example:
+
+ ```python
+ >>> import torch
+ >>> from transformers import AutoFeatureExtractor, MoonshineModel
+ >>> from datasets import load_dataset
+
+ >>> model = MoonshineModel.from_pretrained("UsefulSensors/moonshine-tiny")
+ >>> feature_extractor = AutoFeatureExtractor.from_pretrained("UsefulSensors/moonshine-tiny")
+ >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+ >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
+ >>> input_values = inputs.input_values
+ >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
+ >>> last_hidden_state = model(input_values, decoder_input_ids=decoder_input_ids).last_hidden_state
+ >>> list(last_hidden_state.shape)
+ [1, 2, 288]
+ ```
+ """
+ if encoder_outputs is None:
+ encoder_outputs: BaseModelOutput = self.encoder(input_values, attention_mask=attention_mask, **kwargs)
+
+ decoder_outputs: BaseModelOutputWithPastAndCrossAttentions = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_attention_mask=attention_mask,
+ encoder_hidden_states=encoder_outputs.last_hidden_state,
+ past_key_values=past_key_values,
+ inputs_embeds=decoder_inputs_embeds,
+ position_ids=decoder_position_ids,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return Seq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The Moonshine Model with a language modeling head. Can be used for automatic speech recognition.
+ """
+)
+class MoonshineForConditionalGeneration(MoonshinePreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["proj_out.weight"]
+
+ def __init__(self, config: MoonshineConfig):
+ super().__init__(config)
+ self.model = MoonshineModel(config)
+ self.proj_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_encoder(self):
+ return self.model.get_encoder()
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def get_output_embeddings(self):
+ return self.proj_out
+
+ def set_output_embeddings(self, new_embeddings):
+ self.proj_out = new_embeddings
+
+ def get_input_embeddings(self) -> nn.Module:
+ return self.model.get_input_embeddings()
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_values: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.LongTensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[Union[EncoderDecoderCache, tuple[torch.FloatTensor]]] = None,
+ decoder_inputs_embeds: Optional[tuple[torch.FloatTensor]] = None,
+ decoder_position_ids: Optional[tuple[torch.LongTensor]] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Seq2SeqLMOutput:
+ r"""
+ input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`):
+ Float values of the raw speech waveform. Raw speech waveform can be
+ obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]`, a
+ `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
+ the soundfile library (`pip install soundfile`). To prepare the array into
+ `input_values`, the [`AutoFeatureExtractor`] should be used for padding
+ and conversion into a tensor of type `torch.FloatTensor`.
+ decoder_position_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`):
+ Indices of positions of each input sequence tokens in the position embeddings.
+ Used to calculate the position embeddings up to `config.decoder_config.max_position_embeddings`
+
+ Example:
+
+ ```python
+ >>> import torch
+ >>> from transformers import AutoProcessor, MoonshineForConditionalGeneration
+ >>> from datasets import load_dataset
+
+ >>> processor = AutoProcessor.from_pretrained("UsefulSensors/moonshine-tiny")
+ >>> model = MoonshineForConditionalGeneration.from_pretrained("UsefulSensors/moonshine-tiny")
+
+ >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+
+ >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
+ >>> input_values = inputs.input_values
+
+ >>> generated_ids = model.generate(input_values, max_new_tokens=100)
+
+ >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+ >>> transcription
+ 'Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
+ ```"""
+
+ if labels is not None:
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ decoder_input_ids = shift_tokens_right(
+ labels, self.config.pad_token_id, self.config.decoder_start_token_id
+ )
+
+ outputs: Seq2SeqModelOutput = self.model(
+ input_values,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ encoder_outputs=encoder_outputs,
+ decoder_attention_mask=decoder_attention_mask,
+ past_key_values=past_key_values,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ decoder_position_ids=decoder_position_ids,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ logits = self.proj_out(outputs.last_hidden_state)
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size)
+
+ return Seq2SeqLMOutput(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+__all__ = [
+ "MoonshineConfig",
+ "MoonshineModel",
+ "MoonshinePreTrainedModel",
+ "MoonshineForConditionalGeneration",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..69da6e940ea643a7a8c024a795e3845e5d89730b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_moshi import *
+ from .modeling_moshi import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/configuration_moshi.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/configuration_moshi.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca2837017b861a6e3ad9123f0e7880ba0fcdd6c0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/configuration_moshi.py
@@ -0,0 +1,333 @@
+# coding=utf-8
+# Copyright 2024 Meta AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Moshi model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ..auto.configuration_auto import AutoConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class MoshiDepthConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MoshiDepthDecoder`]. It is used to instantiate a
+ Moshi depth decoder model according to the specified arguments, defining the Moshi depth decoder config.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the MoshiDepthDecoder model. Defines the number of different tokens that can be
+ represented by the `inputs_ids` passed when calling [`MoshiDepthDecoder`].
+ hidden_size (`int`, *optional*, defaults to 1024):
+ Dimensionality of the layers and the pooler layer of the depth decoder.
+ input_size (`int`, *optional*, defaults to 4096):
+ Dimensionality of the input hidden states. Used to connect the main decoder to the depth decoder.
+ num_hidden_layers (`int`, *optional*, defaults to 6):
+ Number of depth decoder layers.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the depth decoder block.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`.
+ audio_vocab_size (`int`, *optional*, defaults to 2048):
+ Vocabulary size of the audio part of model. Defines the number of different tokens that can be
+ represented by the `audio_codes` passed when calling the Moshi models.
+ max_position_embeddings (`int`, *optional*, defaults to 9):
+ The maximum sequence length that this model might ever be used with. Typically, set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the depth decoder.
+ head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
+ The attention head dimension.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ sliding_window (`int`, *optional*, defaults to 8):
+ Sliding window attention window size. If not specified, will default to `8`.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ ffn_dim (`int`, *optional*, defaults to 5632):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the depth decoder block. Must be even.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-08):
+ The epsilon used by the rms normalization layers.
+ num_codebooks (`int`, *optional*, defaults to 8):
+ The number of audio codebooks for each audio channels.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ kwargs (*optional*):
+ Dictionary of keyword arguments. Notably:
+ - **audio_encoder_config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
+ defines the audio encoder config.
+
+ Example:
+
+ ```python
+ >>> from transformers import (
+ ... MoshiDepthConfig,
+ ... MoshiDepthDecoder,
+ ... )
+
+ >>> configuration = MoshiDepthConfig()
+
+ >>> # Initializing a MoshiDepthDecoder (with random weights) from the kmhf/hf-moshiko style configuration
+ >>> model = MoshiDepthDecoder(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "moshi_depth"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=32000,
+ hidden_size=1024,
+ input_size=4096,
+ num_hidden_layers=6,
+ num_attention_heads=16,
+ num_key_value_heads=None,
+ audio_vocab_size=2048,
+ max_position_embeddings=9,
+ hidden_act="silu",
+ head_dim=None,
+ initializer_range=0.02,
+ use_cache=True,
+ sliding_window=8,
+ attention_dropout=0.0,
+ ffn_dim=5632,
+ rms_norm_eps=1e-8,
+ num_codebooks=8,
+ tie_word_embeddings=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.input_size = input_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.num_key_value_heads = num_key_value_heads if num_key_value_heads is not None else num_attention_heads
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_act = hidden_act
+ self.head_dim = head_dim or hidden_size // num_attention_heads
+ self.initializer_range = initializer_range
+ self.use_cache = use_cache
+ self.sliding_window = sliding_window
+ self.attention_dropout = attention_dropout
+ if ffn_dim % 2 == 1:
+ raise ValueError(f"`ffn_dim={ffn_dim}` must be even.")
+ self.ffn_dim = ffn_dim
+ self.rms_norm_eps = rms_norm_eps
+ self.num_codebooks = num_codebooks
+ self.audio_vocab_size = audio_vocab_size
+
+ super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+class MoshiConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MoshiModel`]. It is used to instantiate a
+ Moshi model according to the specified arguments, defining the audio encoder, Moshi depth decoder and Moshi decoder
+ configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the Moshiko model,
+ e.g. [kmhf/hf-moshiko](https://huggingface.co/kmhf/hf-moshiko)
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the MoshiDecoder model. Defines the number of different tokens that can be
+ represented by the `inputs_ids` passed when calling [`MoshiDecoder`].
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimensionality of the layers and the pooler layer of the main decoder.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of decoder layers.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the main decoder block.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`.
+ audio_vocab_size (`int`, *optional*):
+ Vocabulary size of the audio part of model. Defines the number of different tokens that can be
+ represented by the `audio_codes` passed when calling the Moshi models.
+ max_position_embeddings (`int`, *optional*, defaults to 3000):
+ The maximum sequence length that this model might ever be used with. Typically, set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
+ The attention head dimension.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ sliding_window (`int`, *optional*, defaults to 3000):
+ Sliding window attention window size. If not specified, will default to `3000`.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ ffn_dim (`int`, *optional*, defaults to 22528):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the main decoder block. Must be even.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-08):
+ The epsilon used by the rms normalization layers.
+ num_codebooks (`int`, *optional*, defaults to 8):
+ The number of audio codebooks for each audio channels.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ kwargs (*optional*):
+ Dictionary of keyword arguments. Notably:
+ - **audio_encoder_config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
+ defines the audio encoder config.
+ - **depth__config** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
+ defines the depth decoder config.
+
+
+ Example:
+
+ ```python
+ >>> from transformers import (
+ ... MoshiConfig,
+ ... MoshiForConditionalGeneration,
+ ... )
+
+ >>> configuration = MoshiConfig()
+
+ >>> # Initializing a MoshiForConditionalGeneration (with random weights) from the kmhf/hf-moshiko style configuration
+ >>> model = MoshiForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+
+ >>> # Saving the model, including its configuration
+ >>> model.save_pretrained("kmhf/hf-moshiko")
+
+ >>> # loading model and config from pretrained folder
+ >>> moshi_config = MoshiConfig.from_pretrained("kmhf/hf-moshiko")
+ >>> model = MoshiForConditionalGeneration.from_pretrained("kmhf/hf-moshiko", config=moshi_config)
+ ```"""
+
+ model_type = "moshi"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ sub_configs = {"audio_encoder_config": AutoConfig, "depth_decoder_config": MoshiDepthConfig}
+
+ def __init__(
+ self,
+ vocab_size=32000,
+ hidden_size=4096,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=None,
+ audio_vocab_size=None,
+ max_position_embeddings=3000,
+ rope_theta=10000.0,
+ hidden_act="silu",
+ head_dim=None,
+ initializer_range=0.02,
+ use_cache=True,
+ sliding_window=3000,
+ attention_dropout=0.0,
+ ffn_dim=22528,
+ rms_norm_eps=1e-8,
+ num_codebooks=8,
+ tie_word_embeddings=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.num_key_value_heads = num_key_value_heads if num_key_value_heads is not None else num_attention_heads
+ self.max_position_embeddings = max_position_embeddings
+ self.rope_theta = rope_theta
+ self.hidden_act = hidden_act
+ self.head_dim = head_dim or hidden_size // num_attention_heads
+ self.initializer_range = initializer_range
+ self.use_cache = use_cache
+ self.sliding_window = sliding_window
+ self.attention_dropout = attention_dropout
+ if ffn_dim % 2 == 1:
+ raise ValueError(f"`ffn_dim={ffn_dim}` must be even.")
+ self.ffn_dim = ffn_dim
+ self.rms_norm_eps = rms_norm_eps
+ self.num_codebooks = num_codebooks
+
+ audio_encoder_config = kwargs.pop("audio_encoder_config", {})
+ audio_encoder_model_type = audio_encoder_config.pop("model_type", "mimi")
+
+ self.audio_encoder_config = AutoConfig.for_model(audio_encoder_model_type, **audio_encoder_config)
+
+ if self.num_codebooks > self.audio_encoder_config.num_codebooks:
+ raise ValueError(
+ f"`num_codebooks={num_codebooks}` is greater than the maximum number of codebooks that the audio encoder can deal with ({self.audio_encoder_config.num_codebooks}). Please lower it."
+ )
+
+ self.audio_vocab_size = (
+ self.audio_encoder_config.codebook_size if audio_vocab_size is None else audio_vocab_size
+ )
+
+ depth_decoder_config = kwargs.pop("depth_decoder_config", {})
+ depth_decoder_config.update(
+ {
+ "audio_vocab_size": self.audio_vocab_size,
+ "input_size": hidden_size,
+ "vocab_size": vocab_size,
+ "num_codebooks": num_codebooks,
+ }
+ )
+
+ self.depth_decoder_config = MoshiDepthConfig(**depth_decoder_config)
+
+ super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+ @property
+ def sampling_rate(self):
+ return self.audio_encoder_config.sampling_rate
+
+ @classmethod
+ def from_audio_encoder_config(
+ cls,
+ audio_encoder_config: PretrainedConfig,
+ **kwargs,
+ ):
+ r"""
+ Instantiate a [`MoshiConfig`] (or a derived class) from an audio encoder configuration.
+
+ Returns:
+ [`MoshiConfig`]: An instance of a configuration object
+ """
+
+ return cls(
+ audio_encoder_config=audio_encoder_config.to_dict(),
+ **kwargs,
+ )
+
+
+__all__ = ["MoshiConfig", "MoshiDepthConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/convert_moshi_transformers.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/convert_moshi_transformers.py
new file mode 100644
index 0000000000000000000000000000000000000000..55d8f77ad045e0f1c4967231115481e682c748cb
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/convert_moshi_transformers.py
@@ -0,0 +1,311 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert Moshi checkpoints."""
+
+import argparse
+
+import safetensors
+import sentencepiece
+import torch
+
+from transformers import (
+ AutoFeatureExtractor,
+ GenerationConfig,
+ MimiModel, # initial audio encoder
+ MoshiConfig,
+ MoshiForConditionalGeneration,
+ PreTrainedTokenizerFast,
+ logging,
+)
+from transformers.convert_slow_tokenizer import MoshiConverter
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger("transformers.models.mimi")
+
+
+def assert_param_count(model_1, model_2):
+ count_1 = sum(p[1].numel() for p in model_1.named_parameters() if "final_proj" not in p[0])
+ count_2 = sum(p[1].numel() for p in model_2.named_parameters() if "final_proj" not in p[0])
+ assert count_1 == count_2, f"{model_1.__class__}: {count_1} != {model_2.__class__}: {count_2}"
+
+
+def param_count(model):
+ return sum(p[1].numel() for p in model.named_parameters() if "final_proj" not in p[0])
+
+
+def _grab_best_device(use_gpu=True):
+ if torch.cuda.device_count() > 0 and use_gpu:
+ device = "cuda"
+ else:
+ device = "cpu"
+ return torch.device(device)
+
+
+convert_list = [
+ # GENERAL
+ ("out_norm", "decoder.model.norm"),
+ ("depformer_emb", "depth_decoder.emb"),
+ ("depformer_text_emb", "depth_decoder.text_emb"),
+ ("text_emb", "decoder.model.emb"),
+ ("emb", "embed_tokens"),
+ ("text_linear", "decoder.lm_head"),
+ ("depformer", "depth_decoder"),
+ ("transformer", "decoder.model"),
+ # TRANSFORMERS PART
+ ("gating.linear_in", "mlp.fc1"),
+ ("gating.linear_out", "mlp.fc2"),
+ ("self_attn.out_proj", "self_attn.o_proj.linear"),
+ ("norm1", "input_layernorm"),
+ ("norm2", "post_attention_layernorm"),
+ ("layer_scale_1", "self_attn_layer_scale"),
+ ("layer_scale_2", "mlp_layer_scale"),
+ ("alpha", "weight"),
+]
+
+
+def _preprocess_state_dict(state_dict, config):
+ # Moshi original weights are using a gating mechanism
+
+ # pattern for depth transformer:
+ # stack(gating.{i}.linear_in)->mlp.fc1
+ # stack(gating.{i}.linear_out)->mlp.fc2
+
+ for layer_idx in range(config.depth_decoder_config.num_hidden_layers):
+ linear_layers_in = [
+ state_dict.pop(f"depformer.layers.{layer_idx}.gating.{i}.linear_in.weight")
+ for i in range(config.num_codebooks)
+ ]
+ linear_layers_out = [
+ state_dict.pop(f"depformer.layers.{layer_idx}.gating.{i}.linear_out.weight")
+ for i in range(config.num_codebooks)
+ ]
+
+ state_dict[f"depth_decoder.layers.{layer_idx}.mlp.fc1.weight"] = torch.stack(linear_layers_in)
+ state_dict[f"depth_decoder.layers.{layer_idx}.mlp.fc2.weight"] = torch.stack(linear_layers_out)
+
+ input_projections = []
+ lm_heads = []
+ for codebook_idx in range(config.num_codebooks):
+ input_projections.append(state_dict.pop(f"depformer_in.{codebook_idx}.weight"))
+ lm_heads.append(state_dict.pop(f"linears.{codebook_idx}.weight"))
+
+ state_dict["depth_decoder.input_projections.weight"] = torch.stack(input_projections, dim=0)
+ state_dict["depth_decoder.lm_heads.weight"] = torch.stack(lm_heads, dim=0)
+
+ return state_dict
+
+
+def _convert_model(
+ state_dict,
+ hf_model,
+ convert_list,
+ device,
+ config,
+ unwanted_prefix=None,
+):
+ hidden_size = config.hidden_size
+ head_dim = config.head_dim
+ num_heads = int(config.hidden_size // config.head_dim)
+ num_key_value_heads = config.num_key_value_heads
+ key_value_head_dim = config.num_key_value_heads * head_dim
+
+ state_dict = _preprocess_state_dict(state_dict, config)
+
+ # permute for sliced rotary
+ def permute(w, n_heads, dim1=hidden_size, dim2=hidden_size):
+ return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
+
+ for k, v in list(state_dict.items()):
+ if "audio_encoder" not in k:
+ new_k = k if unwanted_prefix is None else k[len(unwanted_prefix) :]
+ for old_layer_name, new_layer_name in convert_list:
+ if old_layer_name in new_k:
+ new_k = new_k.replace(old_layer_name, new_layer_name)
+
+ if "alpha" in k:
+ state_dict[k] = state_dict[k].squeeze()
+
+ if "in_proj_weight" in new_k:
+ # split qkv into query key and value
+ mixed_qkv = state_dict.pop(k)
+ if "depth_decoder" in new_k:
+ mixed_qkv = mixed_qkv.view(config.num_codebooks, -1, mixed_qkv.shape[-1])
+
+ qkv_dim = mixed_qkv.size(1) // 3
+
+ query_layer = mixed_qkv[:, :qkv_dim]
+ key_layer = mixed_qkv[:, qkv_dim : qkv_dim * 2]
+ value_layer = mixed_qkv[:, qkv_dim * 2 :]
+ state_dict[new_k.replace("in_proj_weight", "q_proj.linear.weight")] = query_layer
+ state_dict[new_k.replace("in_proj_weight", "k_proj.linear.weight")] = key_layer
+
+ else:
+ qkv_dim = mixed_qkv.size(0) // 3
+
+ query_layer = mixed_qkv[:qkv_dim]
+ key_layer = mixed_qkv[qkv_dim : qkv_dim * 2]
+ value_layer = mixed_qkv[qkv_dim * 2 :]
+ state_dict[new_k.replace("in_proj_weight", "q_proj.linear.weight")] = permute(
+ query_layer, num_heads, hidden_size, hidden_size
+ )
+ state_dict[new_k.replace("in_proj_weight", "k_proj.linear.weight")] = permute(
+ key_layer, num_key_value_heads, key_value_head_dim, hidden_size
+ )
+
+ state_dict[new_k.replace("in_proj_weight", "v_proj.linear.weight")] = value_layer
+ elif "o_proj" in new_k and "depth_decoder" in new_k:
+ output_layer = state_dict.pop(k)
+ state_dict[new_k] = output_layer.view(config.num_codebooks, -1, output_layer.shape[-1])
+ else:
+ state_dict[new_k] = state_dict.pop(k)
+
+ # Do the last one by hand
+ state_dict["depth_decoder.text_embed_tokens.weight"] = state_dict.pop(
+ "depth_decoder.decoder.model.embed_tokens.weight"
+ )
+
+ extra_keys = set(state_dict.keys()) - set(hf_model.state_dict().keys())
+ missing_keys = set(hf_model.state_dict().keys()) - set(state_dict.keys())
+ if len(extra_keys) != 0:
+ raise ValueError(f"extra keys found: {extra_keys}")
+ if len(missing_keys) != 0:
+ raise ValueError(f"missing keys: {missing_keys}")
+ hf_model.load_state_dict(state_dict, strict=True)
+ n_params = param_count(hf_model)
+
+ logger.info(f"model loaded: {round(n_params / 1e6, 1)}M params")
+
+ hf_model.eval()
+ hf_model.to(device)
+ del state_dict
+
+ return hf_model
+
+
+@torch.no_grad()
+def convert_checkpoint(
+ checkpoint_path,
+ pytorch_dump_folder_path,
+ mimi_repo_id,
+ config_path=None,
+ repo_id=None,
+):
+ """
+ Copy/paste/tweak model's weights to transformers design.
+ """
+ device = _grab_best_device()
+
+ mimi_model = MimiModel.from_pretrained(mimi_repo_id, torch_dtype=torch.bfloat16)
+
+ if config_path is not None:
+ config = MoshiConfig.from_pretrained(config_path)
+ else:
+ audio_encoder_config = mimi_model.config
+ config = MoshiConfig.from_audio_encoder_config(audio_encoder_config)
+
+ model = MoshiForConditionalGeneration(config).to(torch.bfloat16)
+
+ depth_decoder_generation_config = GenerationConfig(
+ do_sample=True,
+ temperature=0.8,
+ top_k=250,
+ min_length=config.num_codebooks + 1,
+ max_length=config.num_codebooks + 1,
+ cache_implementation="sliding_window",
+ )
+
+ generation_config = GenerationConfig(
+ do_sample=True,
+ temp=0.7,
+ top_k=25,
+ cache_implementation="sliding_window",
+ pad_token_id=config.vocab_size,
+ bos_token_id=config.vocab_size,
+ )
+ generation_config.depth_decoder_config = depth_decoder_generation_config.to_diff_dict()
+
+ model.generation_config = generation_config
+
+ original_checkpoint = safetensors.torch.load_file(checkpoint_path)
+ if "best_state" in original_checkpoint:
+ # we might have a training state saved, in which case discard the yaml results and just retain the weights
+ original_checkpoint = original_checkpoint["best_state"]
+
+ audio_checkpoint = mimi_model.state_dict()
+ original_checkpoint.update({f"audio_encoder.{key}": value for (key, value) in audio_checkpoint.items()})
+
+ model = _convert_model(original_checkpoint, model, convert_list, device, config)
+
+ model.save_pretrained(pytorch_dump_folder_path)
+
+ if repo_id:
+ print("Pushing to the hub...")
+ model.push_to_hub(repo_id)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
+ parser.add_argument(
+ "--tokenizer_vocab_path", required=False, default=None, type=str, help="Path to original tokenizer vocab file"
+ )
+ parser.add_argument("--mimi_repo_id", required=True, default=None, type=str, help="Repository id to HF Mimi.")
+ parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
+ parser.add_argument(
+ "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
+ )
+ parser.add_argument(
+ "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
+ )
+
+ args = parser.parse_args()
+
+ # convert tokenizer
+ if args.tokenizer_vocab_path:
+ original_tokenizer = sentencepiece.SentencePieceProcessor(args.tokenizer_vocab_path)
+ tokenizer = MoshiConverter(args.tokenizer_vocab_path).converted()
+ tokenizer = PreTrainedTokenizerFast(
+ tokenizer_object=tokenizer,
+ chat_template=None,
+ unk_token="",
+ model_input_names=["input_ids", "attention_mask"],
+ clean_up_tokenization_spaces=False,
+ bos_token_id=original_tokenizer.bos_id(),
+ eos_token_id=original_tokenizer.eos_id(),
+ pad_token_id=original_tokenizer.pad_id(),
+ )
+
+ tokenizer.save_pretrained(args.pytorch_dump_folder_path)
+
+ if args.push_to_hub:
+ print("Pushing the tokenizer to the hub...")
+ tokenizer.push_to_hub(args.push_to_hub)
+
+ # upload feature extractor
+ feature_extractor = AutoFeatureExtractor.from_pretrained(args.mimi_repo_id)
+ feature_extractor.save_pretrained(args.pytorch_dump_folder_path)
+
+ if args.push_to_hub:
+ print("Pushing the feature extractor to the hub...")
+ feature_extractor.push_to_hub(args.push_to_hub)
+
+ convert_checkpoint(
+ args.checkpoint_path,
+ args.pytorch_dump_folder_path,
+ args.mimi_repo_id,
+ args.config_path,
+ args.push_to_hub,
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/modeling_moshi.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/modeling_moshi.py
new file mode 100644
index 0000000000000000000000000000000000000000..de8f8c1087ee35d179730b544936b46b4e23c1c9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/moshi/modeling_moshi.py
@@ -0,0 +1,2517 @@
+# coding=utf-8
+# Copyright 2024 Kyutai and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Moshi model."""
+
+import math
+from dataclasses import dataclass
+from typing import Any, Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from torch.nn import CrossEntropyLoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
+from ...generation import GenerationConfig, GenerationMixin
+from ...modeling_attn_mask_utils import AttentionMaskConverter
+from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, ModelOutput, Seq2SeqLMOutput
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, is_torch_flex_attn_available, logging
+from ...utils.deprecation import deprecate_kwarg
+from ..auto.modeling_auto import AutoModel
+from .configuration_moshi import MoshiConfig, MoshiDepthConfig
+
+
+if is_flash_attn_available():
+ from ...modeling_flash_attention_utils import _flash_attention_forward
+
+if is_torch_flex_attn_available():
+ from torch.nn.attention.flex_attention import BlockMask
+
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Outputs of [`MoshiForConditionalConditionalGeneration.generate`].
+ """
+)
+class MoshiConditionalGenerationGenerateOutput(ModelOutput):
+ r"""
+ audio_sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, 1, sequence_length)`, *optional*):
+ The generated audio waveforms.
+ sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
+ The generated text sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
+ if all batches finished early due to the `eos_token_id`.
+ sequences_scores (`torch.FloatTensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True`):
+ Final beam scores of the generated `sequences`.
+ scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True`):
+ Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
+ of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
+ Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
+ with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
+ logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`):
+ Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
+ at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
+ each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
+ beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True`):
+ Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
+ `(batch_size*num_return_sequences, sequence_length)`.
+ attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
+ Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+ `torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
+ hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`):
+ Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+ `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
+ past_key_values (`tuple(tuple(torch.FloatTensor)))`, *optional*, returned when `use_cache=True`):
+ Contains the model cache, used to speed up decoding. Different models have a different cache format, check
+ the model's documentation. Usually, a [`~cache_utils.Cache`] instance.
+ audio_codes (`torch.LongTensor` of shape `(batch_size*num_return_sequences, num_codeooks, sequence_length)`, *optional*):
+ The generated audio codes. Returned if `return_audio_codes=True`. Intermediate audio "tokens" which transforms to `audio_sequences` once passed through the audio decoder.
+ """
+
+ audio_sequences: Optional[torch.Tensor] = None
+ sequences: Optional[torch.LongTensor] = None
+ sequences_scores: Optional[torch.FloatTensor] = None
+ scores: Optional[tuple[torch.FloatTensor]] = None
+ logits: Optional[tuple[torch.FloatTensor]] = None
+ beam_indices: Optional[torch.LongTensor] = None
+ attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ hidden_states: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ past_key_values: Optional[tuple[tuple[tuple[torch.FloatTensor]]]] = None
+ audio_codes: Optional[torch.LongTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ `MoshiForCausalLM` outputs.
+ """
+)
+class MoshiCausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ `MoshiForConditionalGeneration` outputs.
+ """
+)
+class MoshiConditionalGenerationOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `text_labels` is provided):
+ Text language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the text language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ depth_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `audio_labels` is provided):
+ Audio language modeling loss (for next-token prediction).
+ audio_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the audio language modeling heads.
+ depth_past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Past key-values of the depth decoder.
+ depth_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Hidden states of the depth decoder
+ depth_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Depth decoder's Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+ heads.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+ depth_loss: Optional[torch.FloatTensor] = None
+ audio_logits: Optional[torch.FloatTensor] = None
+ depth_past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ depth_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+ depth_attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+@dataclass
+@auto_docstring
+class MoshiUnconditionalInput(ModelOutput):
+ r"""
+ input_ids (`torch.Tensor `of shape `(batch_size, sequence_length), *optional*):
+ The sequence used as a text prompt for the generation.
+ user_audio_codes (`torch.Tensor `of shape `(batch_size, num_codebooks, sequence_length), *optional*):
+ The audio codes used as audio user prompt for the generation. Has priority over `user_input_values` and represents the audio "tokens" of `user_input_values` once passed through the audio encoder.
+ moshi_audio_codes (`torch.Tensor `of shape `(batch_size, num_codebooks, sequence_length), *optional*):
+ The audio codes used as audio Moshi prompt for the generation. Has priority over `moshi_input_values` and represents the audio "tokens" of `moshi_input_values` once passed through the audio encoder.
+ attention_mask (`torch.LongTensor`) of shape `(batch_size, sequence_length)`, *optional*):
+ Attention mask to avoid performing attention on padding token indices. Mask values selected in `[0,
+ 1]`: 1 for tokens that are **not masked**, 0 for tokens that are **masked**.
+ """
+
+ input_ids: Optional[torch.LongTensor] = None
+ user_audio_codes: Optional[torch.Tensor] = None
+ moshi_audio_codes: Optional[torch.Tensor] = None
+ attention_mask: Optional[torch.LongTensor] = None
+
+
+# Copied from transformers.models.gemma.modeling_gemma.GemmaRMSNorm with Gemma->Moshi
+class MoshiRMSNorm(nn.Module):
+ def __init__(self, dim: int, eps: float = 1e-6):
+ super().__init__()
+ self.eps = eps
+ self.weight = nn.Parameter(torch.ones(dim)) # Ignore copy
+
+ def _norm(self, x):
+ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+ # Ignore copy
+ def forward(self, x):
+ output = self._norm(x.float())
+ output = output * self.weight.float()
+ return output.type_as(x)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.eps}"
+
+
+class MoshiFlexibleLinear(nn.Module):
+ def __init__(self, input_size, output_size, num_layers):
+ super().__init__()
+ # Stack the weights for N layers into a single tensor (num_layers, output_size, input_size)
+ self.weight = nn.Parameter(torch.randn(num_layers, output_size, input_size))
+
+ def forward(self, x, layer_idx=None):
+ """
+ `MoshiFlexibleLinear` creates one linear layer per codebook. There's multiple ways to use it.
+ In the default case, `sequence_length=num_layers`, so each element of the sequence will be matmul to the weights corresponding to its index on the sequence.
+
+ For more advanced cases, one can specify which codebook's layer(s) to use with `layer_idx`.
+ If `layer_idx` indicates a single integer, all of the element of the sequence will be matmul to this single codebook's layer.
+ But if `layer_idx` is a tensor of shape `(seq_length,)`, it will matmul each i-th element of the input sequence to the corresponding layer `weight[i]`.
+
+
+ Args:
+ x (`torch.FloatTensor): input to the layer of shape `(batch, num_layers, embed_dim)` or of shape `(batch, seq_length, embed_dim)`
+ layer_idx (`torch.Tensor`, *optional*):
+ Can be used to specify which codebook's layers(s) to use.
+ If it's a tensor of shape `(seq_length,)`, will matmul each element of the sequence to the corresponding weights.
+ But if `layer_idx` is a tensor of shape `(seq_length,)`, it will matmul each i-th element of the input sequence to the corresponding layer `weight[i]`.
+ """
+
+ # Use torch.gather to select the corresponding weights for each sample
+ # (codebooks, output_size, hidden_size)
+ selected_weights = torch.index_select(self.weight, 0, layer_idx) if layer_idx is not None else self.weight
+
+ # (1, codebooks, hidden_size, output_size)
+ selected_weights = selected_weights.transpose(1, 2)[None, :, :, :]
+
+ # (batch_size, codebooks, 1, hidden_size) x (1, codebooks, hidden_size, output_size)
+ # -> (batch_size, codebooks, 1, output_size)
+ x = torch.matmul(x[:, :, None, :], selected_weights)
+
+ # (batch_size, codebooks, output_size)
+ return x.squeeze(2)
+
+
+class MoshiLinear(nn.Module):
+ def __init__(self, input_dim, output_dim, num_codebooks, use_flexible_linear=False):
+ super().__init__()
+
+ self.use_flexible_linear = use_flexible_linear
+
+ if not use_flexible_linear:
+ self.linear = nn.Linear(input_dim, output_dim, bias=False)
+ else:
+ self.linear = MoshiFlexibleLinear(input_dim, output_dim, num_layers=num_codebooks)
+
+ def forward(self, x, layer_idx=None):
+ if self.use_flexible_linear:
+ return self.linear(x, layer_idx)
+ else:
+ return self.linear(x)
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Moshi
+class MoshiRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: MoshiConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class MoshiGatingMLP(nn.Module):
+ def __init__(self, config, use_flexible_linear=False):
+ super().__init__()
+
+ self.activation_fn = ACT2FN[config.hidden_act]
+ ffn_dim = config.ffn_dim
+ hidden_size = config.hidden_size
+ num_layers = config.num_codebooks if use_flexible_linear else 1
+ if num_layers == 1:
+ self.fc1 = nn.Linear(hidden_size, ffn_dim, bias=False)
+ self.fc2 = nn.Linear(ffn_dim // 2, hidden_size, bias=False)
+ else:
+ self.fc1 = MoshiFlexibleLinear(hidden_size, ffn_dim, num_layers)
+ self.fc2 = MoshiFlexibleLinear(ffn_dim // 2, hidden_size, num_layers)
+
+ def forward(self, hidden_states: torch.Tensor, layer_idx: Optional[int] = None) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states) if layer_idx is None else self.fc1(hidden_states, layer_idx)
+
+ batch_size, sequence_length, _ = hidden_states.shape
+ hidden_states = hidden_states.view(batch_size, sequence_length, 2, -1)
+ hidden_states = self.activation_fn(hidden_states[..., 0, :]) * hidden_states[..., 1, :]
+ hidden_states = self.fc2(hidden_states) if layer_idx is None else self.fc2(hidden_states, layer_idx)
+ return hidden_states
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class MoshiAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: MoshiConfig, layer_idx: Optional[int] = None, use_flexible_linear=False, use_rope=True):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = config.head_dim
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.is_causal = True
+ self.scaling = 1 / math.sqrt(self.head_dim)
+
+ if self.hidden_size % self.num_heads != 0:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ self.q_proj = MoshiLinear(
+ self.hidden_size, self.num_heads * self.head_dim, config.num_codebooks, use_flexible_linear
+ )
+ self.k_proj = MoshiLinear(
+ self.hidden_size, self.num_key_value_heads * self.head_dim, config.num_codebooks, use_flexible_linear
+ )
+ self.v_proj = MoshiLinear(
+ self.hidden_size, self.num_key_value_heads * self.head_dim, config.num_codebooks, use_flexible_linear
+ )
+ self.o_proj = MoshiLinear(
+ self.num_heads * self.head_dim, self.hidden_size, config.num_codebooks, use_flexible_linear
+ )
+
+ # rotary embeddings are not used in the depth decoder
+ self.rotary_emb = None
+ if use_rope:
+ self.rope_theta = config.rope_theta
+ self.rotary_emb = MoshiRotaryEmbedding(config)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states, cache_position) # Ignore copy
+ key_states = self.k_proj(hidden_states, cache_position) # Ignore copy
+ value_states = self.v_proj(hidden_states, cache_position) # Ignore copy
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ if self.rotary_emb is not None: # Ignore copy
+ cos, sin = self.rotary_emb(value_states, position_ids) # Ignore copy
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) # Ignore copy
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = (
+ {"sin": sin, "cos": cos, "cache_position": cache_position}
+ if self.rotary_emb is not None
+ else {"cache_position": cache_position}
+ ) # Ignore copy
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling
+
+ if attention_mask is not None: # no matter the length, we just slice it
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.view(bsz, q_len, -1)
+ attn_output = self.o_proj(attn_output, cache_position) # Ignore copy
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights
+
+
+# NO LONGER EXIST Copied from transformers.models.gemma.modeling_gemma.GemmaFlashAttention2 with Gemma->Moshi
+# TODO cyril: modular
+class MoshiFlashAttention2(MoshiAttention):
+ """
+ Moshi flash attention module. This module inherits from `MoshiAttention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+ self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ if isinstance(past_key_values, StaticCache):
+ raise ValueError(
+ "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
+ "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
+ )
+
+ output_attentions = False
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states, cache_position) # Ignore copy
+ key_states = self.k_proj(hidden_states, cache_position) # Ignore copy
+ value_states = self.v_proj(hidden_states, cache_position) # Ignore copy
+
+ # Flash attention requires the input to have the shape
+ # batch_size x seq_length x head_dim x hidden_dim
+ # therefore we just need to keep the original shape
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ if self.rotary_emb is not None: # Ignore copy
+ cos, sin = self.rotary_emb(value_states, position_ids) # Ignore copy
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) # Ignore copy
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = (
+ {"sin": sin, "cos": cos, "cache_position": cache_position}
+ if self.rotary_emb is not None
+ else {"cache_position": cache_position}
+ ) # Ignore copy
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+ # to be able to avoid many of these transpose/reshape/view.
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ dropout_rate = self.attention_dropout if self.training else 0.0
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in the correct dtype just to be sure everything works as expected.
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+ # in fp32. (MoshiRMSNorm handles it correctly)
+
+ input_dtype = query_states.dtype
+ device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
+ if input_dtype == torch.float32:
+ if torch.is_autocast_enabled():
+ target_dtype = (
+ torch.get_autocast_dtype(device_type)
+ if hasattr(torch, "get_autocast_dtype")
+ else torch.get_autocast_gpu_dtype()
+ )
+ # Handle the case where the model is quantized
+ elif hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ else:
+ target_dtype = self.q_proj.weight.dtype
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ attn_output = _flash_attention_forward(
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ q_len,
+ position_ids=position_ids,
+ dropout=dropout_rate,
+ sliding_window=getattr(self, "sliding_window", None),
+ is_causal=self.is_causal,
+ use_top_left_mask=self._flash_attn_uses_top_left_mask,
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+ attn_output = self.o_proj(attn_output, cache_position) # Ignore copy
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights
+
+
+# NO LONGER EXIST Copied from transformers.models.gemma.modeling_gemma.GemmaSdpaAttention with Gemma->Moshi
+# TODO cyril: modular
+class MoshiSdpaAttention(MoshiAttention):
+ """
+ Moshi attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `MoshiAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+ SDPA API.
+ """
+
+ # Adapted from MoshiAttention.forward
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ if output_attentions:
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+ logger.warning_once(
+ "MoshiModel is using MoshiSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ return super().forward(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states, cache_position) # Ignore copy
+ key_states = self.k_proj(hidden_states, cache_position) # Ignore copy
+ value_states = self.v_proj(hidden_states, cache_position) # Ignore copy
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ if self.rotary_emb is not None: # Ignore copy
+ cos, sin = self.rotary_emb(value_states, position_ids) # Ignore copy
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) # Ignore copy
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = (
+ {"sin": sin, "cos": cos, "cache_position": cache_position}
+ if self.rotary_emb is not None
+ else {"cache_position": cache_position}
+ ) # Ignore copy
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ causal_mask = attention_mask
+ if attention_mask is not None:
+ causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ if query_states.device.type == "cuda" and causal_mask is not None:
+ query_states = query_states.contiguous()
+ key_states = key_states.contiguous()
+ value_states = value_states.contiguous()
+
+ # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+ # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+ is_causal = causal_mask is None and q_len > 1
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=causal_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ is_causal=is_causal,
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.view(bsz, q_len, -1)
+
+ attn_output = self.o_proj(attn_output, cache_position) # Ignore copy
+
+ return attn_output, None
+
+
+MOSHI_ATTENTION_CLASSES = {
+ "eager": MoshiAttention,
+ "flash_attention_2": MoshiFlashAttention2,
+ "sdpa": MoshiSdpaAttention,
+}
+
+
+class MoshiDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MoshiConfig, layer_idx: int, use_flexible_linear: bool, use_rope=True):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.use_flexible_linear = use_flexible_linear
+
+ self.self_attn = MOSHI_ATTENTION_CLASSES[config._attn_implementation](
+ config=config, layer_idx=layer_idx, use_flexible_linear=use_flexible_linear, use_rope=use_rope
+ )
+
+ self.mlp = MoshiGatingMLP(config, use_flexible_linear)
+ self.input_layernorm = MoshiRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = MoshiRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
+ self.sliding_window = config.sliding_window
+
+ self._attn_implementation = config._attn_implementation
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence
+ kwargs (`dict`, *optional*):
+ Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+ into the model
+ """
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = (
+ self.mlp(hidden_states) if not self.use_flexible_linear else self.mlp(hidden_states, cache_position)
+ )
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ return outputs
+
+
+@auto_docstring
+class MoshiPreTrainedModel(PreTrainedModel):
+ config: MoshiConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MoshiDecoderLayer", "MimiTransformerLayer"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ main_input_name = "input_ids"
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, MoshiFlexibleLinear):
+ module.weight.data.normal_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, MoshiRMSNorm):
+ module.weight.data.fill_(1.0)
+
+
+class MoshiDepthDecoder(MoshiPreTrainedModel, GenerationMixin):
+ """
+ Transformer depth decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MoshiTransformerLayer`]
+
+ Args:
+ config: MoshiConfig
+ """
+
+ config: MoshiDepthConfig
+
+ def __init__(self, config: MoshiDepthConfig):
+ super().__init__(config)
+
+ self.text_embed_tokens = nn.Embedding(config.vocab_size + 1, config.hidden_size)
+
+ # the last codebook is never used as input
+ self.embed_tokens = nn.ModuleList(
+ [nn.Embedding(config.audio_vocab_size + 1, config.hidden_size) for _ in range(config.num_codebooks - 1)]
+ )
+
+ self.input_projections = MoshiFlexibleLinear(config.input_size, config.hidden_size, config.num_codebooks)
+
+ self.layers = nn.ModuleList(
+ [
+ MoshiDecoderLayer(config, layer_idx, use_flexible_linear=True, use_rope=False)
+ for layer_idx in range(config.num_hidden_layers)
+ ]
+ )
+
+ self.lm_heads = MoshiFlexibleLinear(config.hidden_size, config.audio_vocab_size, config.num_codebooks)
+ self._attn_implementation = config._attn_implementation
+ self.gradient_checkpointing = False
+ self.config = config
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ last_hidden_state: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.BoolTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ """
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens. The first element of the sequence must the text token associated to the audio codebooks.
+ The rest of the elements must be flatten audio codebooks. The `cache_position` argument can be used to indicate to which index is associated each token.
+ last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the main decoder. Used to contextualize `input_ids`
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ Two formats are allowed:
+ - a [`~cache_utils.Cache`] instance;
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+ cache format.
+
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+ legacy cache format will be returned.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+ of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert the inputs into associated vectors than the
+ model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+ )
+ use_cache = False
+
+ if use_cache and past_key_values is None and not self.training:
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+
+ past_seen_tokens = 0 if past_key_values is None else past_key_values.get_seq_length()
+ if cache_position is None:
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + input_ids.shape[1], device=input_ids.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ # If inputs_embeds is provided, it has the priority over input_ids, which won't be used
+ if inputs_embeds is None:
+ inputs_embeds = []
+ for position_idx in cache_position:
+ position_idx = position_idx.item()
+ if position_idx == 0:
+ inputs_embeds.append(self.text_embed_tokens(input_ids[:, [position_idx]]))
+ else:
+ inputs_embeds.append(
+ self.embed_tokens[(position_idx - 1)](input_ids[:, [position_idx - past_seen_tokens]])
+ )
+
+ inputs_embeds = torch.cat(inputs_embeds, dim=1)
+
+ inputs_embeds += self.input_projections(last_hidden_state, cache_position)
+
+ causal_mask = None
+ if attention_mask is not None:
+ causal_mask = self._update_causal_mask(
+ attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+ )
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ hidden_states = inputs_embeds
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ logits = self.lm_heads(hidden_states, cache_position)
+
+ loss = None
+ if labels is not None:
+ # Upcast to float if we need to compute the loss to avoid potential precision issues
+ logits = logits.float()
+ loss_fct = CrossEntropyLoss()
+
+ labels = labels.masked_fill(labels == self.config.audio_vocab_size, -100).reshape(-1)
+ # Enable model parallelism
+ labels = labels.to(logits.device)
+ loss = loss_fct(logits.reshape(-1, self.config.audio_vocab_size), labels)
+
+ if not return_dict:
+ return tuple(
+ v for v in [loss, logits, past_key_values, all_hidden_states, all_self_attns] if v is not None
+ )
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=past_key_values,
+ hidden_states=past_key_values,
+ attentions=all_self_attns,
+ )
+
+ # Copied from transformers.models.phimoe.modeling_phimoe.PhimoeModel._update_causal_mask with Phimoe->Moshi
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, "BlockMask"],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ output_attentions: bool = False,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and past_key_values is not None:
+ is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
+ if is_padding_right:
+ raise ValueError(
+ "You are attempting to perform batched generation with padding_side='right'"
+ " this may lead to unexpected behaviour for Flash Attention version of Moshi. Make sure to "
+ " call `tokenizer.padding_side = 'left'` before tokenizing the input. "
+ )
+ if attention_mask is not None and 0.0 in attention_mask:
+ return attention_mask
+ return None
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ return attention_mask
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_static_cache = isinstance(past_key_values, StaticCache)
+ using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if (
+ self.config._attn_implementation == "sdpa"
+ and not (using_static_cache or using_sliding_window_cache)
+ and not output_attentions
+ ):
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ sliding_window=self.config.sliding_window,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ min_dtype = torch.finfo(dtype).min
+ sequence_length = input_tensor.shape[1]
+ # SlidingWindowCache or StaticCache
+ if using_sliding_window_cache or using_static_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ # DynamicCache or no cache
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ config=self.config,
+ past_key_values=past_key_values,
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ and not output_attentions
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ # Copied from transformers.models.phimoe.modeling_phimoe.PhimoeModel._prepare_4d_causal_attention_mask_with_cache_position with Phimoe->MoshiDepth
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ config: MoshiDepthConfig,
+ past_key_values: Cache,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ config (`MoshiDepthConfig`):
+ The model's configuration class
+ past_key_values (`Cache`):
+ The cache class that is being used currently to generate
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
+ -1, 1
+ )
+ text_config = config.get_text_config()
+ if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None:
+ # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
+ # the check is needed to verify is current checkpoint was trained with sliding window or not
+ if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
+ sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= (
+ cache_position.reshape(-1, 1) - text_config.sliding_window
+ )
+ diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
+ causal_mask *= diagonal_attend_mask
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ if attention_mask.shape[-1] > target_length:
+ attention_mask = attention_mask[:, :target_length]
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+ return causal_mask
+
+
+@auto_docstring
+class MoshiModel(MoshiPreTrainedModel):
+ def __init__(self, config: MoshiConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size + 1, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [
+ MoshiDecoderLayer(config, layer_idx, use_flexible_linear=False)
+ for layer_idx in range(config.num_hidden_layers)
+ ]
+ )
+ self.norm = MoshiRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+ )
+ use_cache = False
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = None
+ if attention_mask is not None:
+ causal_mask = self._update_causal_mask(
+ attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+ )
+
+ # embed positions
+ hidden_states = inputs_embeds
+
+ # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
+ if not isinstance(past_key_values, (type(None), Cache)):
+ raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None
+ )
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+ # Copied from transformers.models.phimoe.modeling_phimoe.PhimoeModel._update_causal_mask with Phimoe->Moshi
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, "BlockMask"],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ output_attentions: bool = False,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and past_key_values is not None:
+ is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
+ if is_padding_right:
+ raise ValueError(
+ "You are attempting to perform batched generation with padding_side='right'"
+ " this may lead to unexpected behaviour for Flash Attention version of Moshi. Make sure to "
+ " call `tokenizer.padding_side = 'left'` before tokenizing the input. "
+ )
+ if attention_mask is not None and 0.0 in attention_mask:
+ return attention_mask
+ return None
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ return attention_mask
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_static_cache = isinstance(past_key_values, StaticCache)
+ using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if (
+ self.config._attn_implementation == "sdpa"
+ and not (using_static_cache or using_sliding_window_cache)
+ and not output_attentions
+ ):
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ sliding_window=self.config.sliding_window,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ min_dtype = torch.finfo(dtype).min
+ sequence_length = input_tensor.shape[1]
+ # SlidingWindowCache or StaticCache
+ if using_sliding_window_cache or using_static_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ # DynamicCache or no cache
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ config=self.config,
+ past_key_values=past_key_values,
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ and not output_attentions
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ # Copied from transformers.models.phimoe.modeling_phimoe.PhimoeModel._prepare_4d_causal_attention_mask_with_cache_position with Phimoe->Moshi
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ config: MoshiConfig,
+ past_key_values: Cache,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ config (`MoshiConfig`):
+ The model's configuration class
+ past_key_values (`Cache`):
+ The cache class that is being used currently to generate
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
+ -1, 1
+ )
+ text_config = config.get_text_config()
+ if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None:
+ # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
+ # the check is needed to verify is current checkpoint was trained with sliding window or not
+ if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
+ sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= (
+ cache_position.reshape(-1, 1) - text_config.sliding_window
+ )
+ diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
+ causal_mask *= diagonal_attend_mask
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ if attention_mask.shape[-1] > target_length:
+ attention_mask = attention_mask[:, :target_length]
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+ return causal_mask
+
+
+@auto_docstring(
+ custom_intro="""
+ The Moshi decoder model with a text language modelling head on top. Only usable for text.
+ """
+)
+class MoshiForCausalLM(MoshiPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["model.embed_tokens.weight", "lm_head.weight"]
+
+ # Copied from transformers.models.gemma.modeling_gemma.GemmaForCausalLM.__init__ with Gemma->Moshi
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = MoshiModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs,
+ ) -> Union[tuple, MoshiCausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MoshiForCausalLM
+
+ >>> model = MoshiForCausalLM.from_pretrained("kmhf/hf-moshiko")
+ >>> tokenizer = AutoTokenizer.from_pretrained("kmhf/hf-moshiko")
+
+ >>> prompt = "What is your favorite condiment?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "What is your favorite condiment?"
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ # Upcast to float if we need to compute the loss to avoid potential precision issues
+ logits = logits.float()
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = self.loss_function(
+ shift_logits,
+ shift_labels,
+ vocab_size=self.config.vocab_size,
+ **kwargs,
+ )
+
+ if not return_dict:
+ output = (
+ logits,
+ hidden_states,
+ ) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return MoshiCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ last_hidden_state=hidden_states, # Ignore copy
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The original Moshi model with an audio encoder, a Moshi depth decoder and a Moshi decoder, for speech-to-speech.
+ """
+)
+class MoshiForConditionalGeneration(MoshiPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["decoder.model.embed_tokens.weight", "decoder.lm_head.weight"]
+ config: MoshiConfig
+ main_input_name = "input_ids"
+ supports_gradient_checkpointing = True
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ def __init__(self, config: MoshiConfig):
+ super().__init__(config)
+ # We have 2 * num_codebooks audio embedding layers because we have the user input channel and the model output channel.
+ self.embed_tokens = nn.ModuleList(
+ [nn.Embedding(config.audio_vocab_size + 1, config.hidden_size) for _ in range(2 * config.num_codebooks)]
+ )
+ self.audio_encoder = AutoModel.from_config(config.audio_encoder_config)
+ self.decoder = MoshiForCausalLM(config)
+
+ self.depth_decoder = MoshiDepthDecoder._from_config(config.depth_decoder_config)
+
+ self.num_codebooks = config.num_codebooks
+ self.post_init()
+
+ def get_audio_encoder(self):
+ return self.audio_encoder
+
+ def get_depth_decoder(self):
+ return self.depth_decoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.BoolTensor] = None,
+ user_input_values: Optional[torch.FloatTensor] = None,
+ user_audio_codes: Optional[torch.Tensor] = None,
+ moshi_input_values: Optional[torch.FloatTensor] = None,
+ moshi_audio_codes: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ text_labels: Optional[torch.LongTensor] = None,
+ audio_labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple, Seq2SeqLMOutput]:
+ r"""
+ user_input_values (`torch.Tensor `of shape `(batch_size, 1, audio_sequence_length), *optional*):
+ The audio waveforms used as audio user prompt for the generation.
+ user_audio_codes (`torch.Tensor `of shape `(batch_size, num_codebooks, sequence_length), *optional*):
+ The audio codes used as audio user prompt for the generation. Has priority over `user_input_values` and represents the audio "tokens" of `user_input_values` once passed through the audio encoder.
+ moshi_input_values (`torch.Tensor `of shape `(batch_size, 1, audio_sequence_length), *optional*):
+ The audio waveforms used as audio Moshi prompt for the generation.
+ moshi_audio_codes (`torch.Tensor `of shape `(batch_size, num_codebooks, sequence_length), *optional*):
+ The audio codes used as audio Moshi prompt for the generation. Has priority over `moshi_input_values` and represents the audio "tokens" of `moshi_input_values` once passed through the audio encoder.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded
+ representation. If `past_key_values` is used, optionally only the last `inputs_embeds` have to be
+ input (see `past_key_values`). This is useful if you want more control over how to convert
+ `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
+
+ If `input_ids` and `inputs_embeds` are both unset, `inputs_embeds` takes the value
+ of `inputs_embeds`.
+ text_labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for text language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+ audio_labels (`torch.LongTensor` of shape `(batch_size, num_codebooks, sequence_length)`, *optional*):
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.audio_vocab_size]`
+
+ Examples:
+ ```python
+ >>> from transformers import MoshiForConditionalGeneration
+ >>> import torch
+
+ >>> model = MoshiForConditionalGeneration.from_pretrained("kmhf/hf-moshiko")
+ >>> inputs = moshi.get_unconditional_inputs()
+
+ >>> logits = model(**inputs, ).logits
+ >>> logits.shape # (bsz, seq_len, text_vocab_size)
+ torch.Size([1, 1, 32000])
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ kwargs_audio_encoder = {
+ argument[len("audio_encoder_")]: value
+ for argument, value in kwargs.items()
+ if argument.startswith("audio_encoder_")
+ }
+
+ kwargs_decoder = {
+ argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
+ }
+
+ kwargs_depth_decoder = {
+ argument[len("depth_decoder_") :]: value
+ for argument, value in kwargs.items()
+ if argument.startswith("depth_decoder_")
+ }
+
+ # If inputs_embeds is provided, it has the priority over input_ids and audio_codes, which won't be used
+ if inputs_embeds is None:
+ if user_input_values is not None and user_audio_codes is None:
+ user_audio_codes = self.audio_encoder.encode(
+ user_input_values, num_quantizers=self.num_codebooks, **kwargs_audio_encoder
+ )[0]
+
+ if moshi_input_values is not None and moshi_audio_codes is None:
+ moshi_audio_codes = self.audio_encoder.encode(
+ moshi_input_values, num_quantizers=self.num_codebooks, **kwargs_audio_encoder
+ )[0]
+
+ audio_codes = torch.cat([moshi_audio_codes, user_audio_codes], dim=1)
+
+ if input_ids is None and audio_codes is None:
+ raise ValueError(
+ "You must provide at least one of `input_ids`, `inputs_embeds`, `input_values` and `audio_codes`."
+ )
+
+ if input_ids is not None:
+ inputs_embeds = self.decoder.model.embed_tokens(input_ids)
+
+ if audio_codes is not None:
+ audio_inputs_embeds = sum(
+ [self.embed_tokens[codebook](audio_codes[:, codebook]) for codebook in range(audio_codes.shape[1])]
+ )
+ inputs_embeds = (
+ audio_inputs_embeds
+ if inputs_embeds is None
+ else audio_inputs_embeds + inputs_embeds.to(audio_inputs_embeds.device)
+ )
+
+ # Decode
+ decoder_outputs = self.decoder(
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ use_cache=use_cache,
+ past_key_values=past_key_values,
+ return_dict=True,
+ labels=text_labels,
+ **kwargs_decoder,
+ )
+
+ decoder_last_hidden_state = decoder_outputs.last_hidden_state
+
+ depth_decoder_outputs = None
+ final_loss = decoder_outputs.loss
+ if text_labels is not None and audio_labels is not None:
+ # To use depth decoder forward here, we actually need oracle input ids since we're supposed to pass the true input ids
+
+ audio_labels = self.build_delay_pattern_mask(
+ audio_labels,
+ bos_token_id=self.config.audio_vocab_size,
+ pad_token_id=self.config.audio_vocab_size,
+ max_length=audio_labels.shape[-1] + 1,
+ )[0]
+
+ # (batch_size, sequence_length) -> (batch_size * sequence_length, 1)
+ text_labels = text_labels.view(-1, 1)
+
+ # (batch_size, num_codebooks, sequence_length) -> (batch_size * sequence_length, num_codebooks)
+ audio_labels = audio_labels.transpose(1, 2).reshape(-1, audio_labels.shape[1])
+
+ depth_input_ids = torch.cat([text_labels, audio_labels], dim=1)
+ # keep the last codebook out of input_ids
+ depth_input_ids = depth_input_ids[:, :-1]
+
+ # (batch_size, sequence_length, dim) -> (batch_size * sequence_length, 1, dim)
+ decoder_last_hidden_state = decoder_last_hidden_state.view(-1, 1, decoder_last_hidden_state.shape[-1])
+
+ depth_decoder_outputs = self.depth_decoder(
+ last_hidden_state=decoder_last_hidden_state,
+ input_ids=depth_input_ids,
+ attention_mask=attention_mask,
+ labels=audio_labels,
+ **kwargs_depth_decoder,
+ )
+
+ final_loss += depth_decoder_outputs.loss
+
+ if not return_dict:
+ outputs = decoder_outputs.to_tuple()
+ if depth_decoder_outputs is not None:
+ outputs += depth_decoder_outputs.to_tuple()
+ return outputs
+
+ return MoshiConditionalGenerationOutputWithPast(
+ loss=decoder_outputs.loss,
+ logits=decoder_outputs.logits,
+ last_hidden_state=decoder_last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ hidden_states=decoder_outputs.hidden_states,
+ attentions=decoder_outputs.attentions,
+ depth_loss=None if depth_decoder_outputs is None else depth_decoder_outputs.loss,
+ audio_logits=None if depth_decoder_outputs is None else depth_decoder_outputs.logits,
+ depth_past_key_values=None if decoder_outputs is None else decoder_outputs.past_key_values,
+ depth_hidden_states=None if decoder_outputs is None else decoder_outputs.hidden_states,
+ depth_attentions=None if decoder_outputs is None else decoder_outputs.attentions,
+ )
+
+ def _prepare_attention_mask_for_generation(
+ self,
+ input_ids: torch.LongTensor,
+ generation_config: GenerationConfig,
+ kwargs: dict[str, Any],
+ ) -> torch.LongTensor:
+ pad_token_id = generation_config.pad_token_id
+ eos_token_id = generation_config.eos_token_id
+
+ default_attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device)
+ if pad_token_id is None:
+ return default_attention_mask
+
+ is_pad_token_in_inputs = (pad_token_id is not None) and torch.isin(input_ids, pad_token_id).any()
+ is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or ~torch.isin(
+ eos_token_id, pad_token_id
+ ).any()
+ can_infer_attention_mask = is_pad_token_in_inputs * is_pad_token_not_equal_to_eos_token_id
+ attention_mask_from_padding = input_ids.ne(pad_token_id).long()
+
+ attention_mask = (
+ attention_mask_from_padding * can_infer_attention_mask + default_attention_mask * ~can_infer_attention_mask
+ )
+ return attention_mask
+
+ def _prepare_inputs_embeds_for_generation(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ user_input_values: Optional[torch.FloatTensor] = None,
+ user_audio_codes: Optional[torch.Tensor] = None,
+ moshi_input_values: Optional[torch.FloatTensor] = None,
+ moshi_audio_codes: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ generation_config: Optional[GenerationConfig] = None,
+ apply_delay_pattern_mask: bool = False,
+ concat_unconditional_inputs: bool = False,
+ ):
+ user_delay_pattern_mask = None
+ moshi_delay_pattern_mask = None
+
+ if (
+ inputs_embeds is None
+ and input_ids is None
+ and user_input_values is None
+ and user_audio_codes is None
+ and moshi_input_values is None
+ and moshi_audio_codes is None
+ ):
+ raise ValueError(
+ "You must provide at least one of `input_ids`, `user_input_values`, `moshi_input_values`, `user_audio_codes`, `moshi_audio_codes` or `inputs_embeds`."
+ )
+
+ # in case inputs_embeds is passed, we might still need to create delay pattern masks
+ if inputs_embeds is None or apply_delay_pattern_mask:
+ if user_input_values is not None and user_audio_codes is None:
+ user_audio_codes = self.audio_encoder.encode(user_input_values, num_quantizers=self.num_codebooks)[0]
+
+ if moshi_input_values is not None and moshi_audio_codes is None:
+ moshi_audio_codes = self.audio_encoder.encode(moshi_input_values, num_quantizers=self.num_codebooks)[0]
+
+ if inputs_embeds is None and concat_unconditional_inputs:
+ unconditional_inputs = self.get_unconditional_inputs(num_samples=user_audio_codes.shape[0])
+ moshi_audio_codes = torch.cat([unconditional_inputs.moshi_audio_codes, moshi_audio_codes], dim=2)
+ user_audio_codes = torch.cat([unconditional_inputs.user_audio_codes, user_audio_codes], dim=2)
+ input_ids = torch.cat([unconditional_inputs.input_ids, input_ids], dim=1)
+ if attention_mask is not None:
+ attention_mask = torch.cat([unconditional_inputs.attention_mask, attention_mask], dim=1)
+
+ if inputs_embeds is None or apply_delay_pattern_mask:
+ if apply_delay_pattern_mask and user_audio_codes is not None:
+ user_audio_codes, user_delay_pattern_mask = self.build_delay_pattern_mask(
+ user_audio_codes,
+ bos_token_id=self.config.audio_vocab_size,
+ pad_token_id=self.config.audio_vocab_size,
+ max_length=generation_config.max_length,
+ )
+
+ if apply_delay_pattern_mask and moshi_audio_codes is not None:
+ moshi_audio_codes, moshi_delay_pattern_mask = self.build_delay_pattern_mask(
+ moshi_audio_codes,
+ bos_token_id=self.config.audio_vocab_size,
+ pad_token_id=self.config.audio_vocab_size,
+ max_length=generation_config.max_length,
+ )
+
+ # If inputs_embeds is provided, it has the priority over input_ids and audio_codes, which won't be used
+ if inputs_embeds is None:
+ audio_inputs_embeds = None
+ if user_audio_codes is not None and moshi_audio_codes is not None:
+ audio_codes = torch.cat([moshi_audio_codes, user_audio_codes], dim=1)
+ audio_inputs_embeds = sum(
+ [self.embed_tokens[codebook](audio_codes[:, codebook]) for codebook in range(audio_codes.shape[1])]
+ )
+ elif moshi_audio_codes is not None:
+ audio_codes = moshi_audio_codes
+ audio_inputs_embeds = sum(
+ [self.embed_tokens[codebook](audio_codes[:, codebook]) for codebook in range(audio_codes.shape[1])]
+ )
+ elif user_audio_codes is not None:
+ audio_codes = user_audio_codes
+ audio_inputs_embeds = sum(
+ [
+ self.embed_tokens[codebook](audio_codes[:, codebook + self.num_codebooks])
+ for codebook in range(audio_codes.shape[1])
+ ]
+ )
+
+ if input_ids is not None:
+ inputs_embeds = self.decoder.model.embed_tokens(input_ids)
+
+ if audio_inputs_embeds is not None:
+ inputs_embeds = (
+ audio_inputs_embeds
+ if inputs_embeds is None
+ else audio_inputs_embeds + inputs_embeds.to(audio_inputs_embeds.device)
+ )
+
+ return (
+ inputs_embeds,
+ input_ids,
+ user_audio_codes,
+ moshi_audio_codes,
+ user_delay_pattern_mask,
+ moshi_delay_pattern_mask,
+ attention_mask,
+ )
+
+ @torch.no_grad()
+ def generate(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ user_input_values: Optional[torch.FloatTensor] = None,
+ user_audio_codes: Optional[torch.Tensor] = None,
+ moshi_input_values: Optional[torch.FloatTensor] = None,
+ moshi_audio_codes: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ return_audio_waveforms: Optional[bool] = True,
+ return_audio_codes: Optional[bool] = None,
+ concat_unconditional_inputs: Optional[bool] = True,
+ **kwargs,
+ ) -> torch.LongTensor:
+ """
+ Generates sequences of text token ids and audio tokens ids.
+
+ Parameters:
+ input_ids (`torch.Tensor `of shape `(batch_size, sequence_length), *optional*):
+ The sequence used as a text prompt for the generation.
+ user_input_values (`torch.Tensor `of shape `(batch_size, 1, audio_sequence_length), *optional*):
+ The audio waveforms used as audio user prompt for the generation.
+ user_audio_codes (`torch.Tensor `of shape `(batch_size, num_codebooks, sequence_length), *optional*):
+ The audio codes used as audio user prompt for the generation. Has priority over `user_input_values` and represents the audio "tokens" of `user_input_values` once passed through the audio encoder.
+ moshi_input_values (`torch.Tensor `of shape `(batch_size, 1, audio_sequence_length), *optional*):
+ The audio waveforms used as audio Moshi prompt for the generation.
+ moshi_audio_codes (`torch.Tensor `of shape `(batch_size, num_codebooks, sequence_length), *optional*):
+ The audio codes used as audio Moshi prompt for the generation. Has priority over `moshi_input_values` and represents the audio "tokens" of `moshi_input_values` once passed through the audio encoder.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` and the audio inputs you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert the inputs into associated vectors than the
+ model's internal embedding lookup matrix.
+ return_audio_waveforms (`bool`, *optional*, defaults to `True`):
+ If `False`, won't generate the audio waveforms.
+ return_audio_codes (`bool`, *optional*):
+ If `True`, will also returns the generated audio codes, i.e the intermediate audio "tokens" which transforms to `audio_sequences` once passed through the audio decoder.
+ concat_unconditional_inputs (`bool`, *optional*, defaults to `True`):
+ If `False`, won't concatenate initial audio and text tokens.
+ kwargs (`dict[str, Any]`, *optional*):
+ Remaining dictionary of keyword arguments that are passed to the `generate` method. Refers to the
+ original [`generate` docstrings](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate)
+ for more information on how to use them.
+ Note that keywords with a *depth_* prefix will be input for the `generate` method of the
+ depth decoder. Otherwise, the latter will use its default generation config.
+ Return:
+ [`MoshiConditionalGenerationGenerateOutput`]
+ """
+ # multiple generate -> need to create/update device map
+ if hasattr(self, "hf_device_map") and not hasattr(self.depth_decoder, "hf_device_map"):
+ self.depth_decoder.hf_device_map = {}
+ if "" in self.hf_device_map:
+ self.depth_decoder.hf_device_map = self.hf_device_map
+ else:
+ main_device = [d for d in self.hf_device_map.values() if d not in ["cpu", "disk"]][0]
+ self.depth_decoder.hf_device_map = {
+ key[len("depth_decoder") :]: main_device if value in ["cpu", "disk"] else value
+ for key, value in self.hf_device_map.items()
+ if key.startswith("depth_decoder")
+ }
+ # need to remove depth_decoder from the top device_map so that we assign correctly the device for each layer idx in the cache
+ self.hf_device_map = {
+ key: value for key, value in self.hf_device_map.items() if not key.startswith("depth_decoder")
+ }
+ # retrieve depth decoder kwargs
+ depth_decoder_kwargs_keys = {argument for argument in kwargs if argument.startswith("depth_decoder_")}
+ kwargs_depth_decoder = {
+ argument[len("depth_decoder_") :]: kwargs.pop(argument) for argument in depth_decoder_kwargs_keys
+ }
+
+ # needs to prepare generation config, even though it'll be done again in `generate`
+ generation_config, kwargs = self._prepare_generation_config(kwargs.pop("generation_config", None), **kwargs)
+
+ input_ids, user_audio_codes, moshi_audio_codes, concat_unconditional_inputs = (
+ self._check_and_maybe_initialize_inputs(
+ input_ids=input_ids,
+ user_input_values=user_input_values,
+ user_audio_codes=user_audio_codes,
+ moshi_input_values=moshi_input_values,
+ moshi_audio_codes=moshi_audio_codes,
+ inputs_embeds=inputs_embeds,
+ concat_unconditional_inputs=concat_unconditional_inputs,
+ )
+ )
+
+ inputs = inputs_embeds if input_ids is None else input_ids
+
+ input_ids_length = inputs.shape[-1] + 1 if concat_unconditional_inputs else inputs.shape[-1]
+ has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+ has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
+ generation_config = self._prepare_generated_length(
+ generation_config=generation_config,
+ has_default_max_length=has_default_max_length,
+ has_default_min_length=has_default_min_length,
+ model_input_name="inputs_embeds" if input_ids is None else "input_ids",
+ inputs_tensor=inputs,
+ input_ids_length=input_ids_length,
+ )
+
+ # retrieve depth decoder generation config if it exists
+ if hasattr(generation_config, "depth_decoder_config"):
+ depth_decoder_generation_config = generation_config.depth_decoder_config
+ else:
+ # we need to control the number of tokens generated by the depth decoder
+ depth_decoder_generation_config = {
+ "min_length": self.num_codebooks + 1,
+ "max_length": self.num_codebooks + 1,
+ "cache_implementation": "sliding_window",
+ }
+ # update kwargs_depth_decoder: kwargs_depth_decoder have priority over depth_decoder_generation_config
+ depth_decoder_generation_config.update(kwargs_depth_decoder)
+ kwargs_depth_decoder = depth_decoder_generation_config
+
+ attention_mask = kwargs.pop("attention_mask", None)
+ if attention_mask is None:
+ attention_mask = self._prepare_attention_mask_for_generation(
+ input_ids=input_ids,
+ generation_config=generation_config,
+ kwargs=kwargs,
+ )
+ (
+ inputs_embeds,
+ input_ids,
+ user_audio_codes,
+ moshi_audio_codes,
+ user_delay_pattern_mask,
+ moshi_delay_pattern_mask,
+ attention_mask,
+ ) = self._prepare_inputs_embeds_for_generation(
+ input_ids=input_ids,
+ user_input_values=user_input_values,
+ user_audio_codes=user_audio_codes,
+ moshi_input_values=moshi_input_values,
+ moshi_audio_codes=moshi_audio_codes,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ generation_config=generation_config,
+ apply_delay_pattern_mask=True,
+ concat_unconditional_inputs=concat_unconditional_inputs,
+ )
+
+ # create blank user inputs - moshi needs a constant stream of user inputs
+ blank_input_values = torch.zeros(
+ (inputs_embeds.shape[0], 1, int(self.config.sampling_rate / self.config.audio_encoder_config.frame_rate)),
+ dtype=self.dtype,
+ device=self.device,
+ )
+ blank_user_audio_codes = self.audio_encoder.encode(blank_input_values, num_quantizers=self.num_codebooks)[0]
+
+ # set delay pattern mask for the rest of the generation
+ kwargs["user_delay_pattern_mask"] = (
+ user_delay_pattern_mask if user_delay_pattern_mask is not None else kwargs.get("user_delay_pattern_mask")
+ )
+ kwargs["moshi_delay_pattern_mask"] = (
+ moshi_delay_pattern_mask
+ if moshi_delay_pattern_mask is not None
+ else kwargs.get("moshi_delay_pattern_mask")
+ )
+
+ self.generated_audio_codes = torch.repeat_interleave(
+ moshi_audio_codes, max(generation_config.num_beams, generation_config.num_return_sequences), dim=0
+ )
+
+ return_dict_in_generate = generation_config.num_beams > 1 or generation_config.return_dict_in_generate
+ output_scores = generation_config.num_beams > 1 or generation_config.output_scores
+ outputs = super().generate(
+ inputs_embeds=inputs_embeds,
+ input_ids=input_ids,
+ generation_config=generation_config,
+ blank_user_audio_codes=blank_user_audio_codes,
+ kwargs_depth_decoder=kwargs_depth_decoder,
+ return_dict_in_generate=return_dict_in_generate,
+ output_scores=output_scores,
+ attention_mask=attention_mask,
+ **kwargs,
+ )
+
+ if not return_audio_waveforms and not return_audio_codes:
+ if return_dict_in_generate and not generation_config.return_dict_in_generate:
+ return outputs.sequences
+ return outputs
+
+ # check if outputs is a dict or tokens
+ if not return_dict_in_generate:
+ output_text_ids = outputs
+ else:
+ output_text_ids = outputs.sequences
+
+ if generation_config.num_return_sequences > 1:
+ moshi_delay_pattern_mask = torch.repeat_interleave(
+ moshi_delay_pattern_mask, generation_config.num_return_sequences, dim=0
+ )
+
+ if generation_config.num_beams > 1:
+ # we need to reorganize self.last_hidden_states and generated audio codes according to the beam_indices
+
+ # Beam indices are of shape `input_length + number_generated_tokens` but actually starts
+ # indexing indices at index 0 instead of index `input_length-1`.
+ # We thus discard the last `input_length` indices that are never used.
+ beam_indices = outputs.beam_indices[:, : -moshi_audio_codes.shape[-1]]
+
+ generated_audio_codes = self.generated_audio_codes[:, :, moshi_audio_codes.shape[-1] :]
+
+ # we've generated audio tokens `number_generated_tokens-1` times, so we use the corresponding beam indices to
+ # retrieve the right audio tokens
+ expanded_beam_indices = beam_indices[:, :-1].unsqueeze(1).expand(-1, self.num_codebooks, -1)
+ generated_audio_codes = torch.gather(generated_audio_codes, dim=0, index=expanded_beam_indices)
+
+ # now, rebuild generated audio codes, this time with the right beam tracking
+ moshi_audio_codes = torch.repeat_interleave(
+ moshi_audio_codes, generation_config.num_return_sequences, dim=0
+ )
+ self.generated_audio_codes = torch.cat((moshi_audio_codes, generated_audio_codes), dim=2)
+
+ # use the last beam indice to retrieve the right self.last_hidden_state
+ self.last_hidden_state = torch.index_select(self.last_hidden_state, dim=0, index=beam_indices[:, -1])
+
+ # we need to make a last generation with the latest generated tokens
+ last_hidden_state = self.last_hidden_state.view(-1, 1, self.last_hidden_state.shape[-1])
+
+ last_generated_audio_codes = self.depth_decoder.generate(
+ last_hidden_state=last_hidden_state,
+ input_ids=output_text_ids[:, -1:].view(-1, 1),
+ **kwargs_depth_decoder,
+ )
+
+ last_generated_audio_codes = last_generated_audio_codes[:, 1:].unsqueeze(2)
+
+ self.generated_audio_codes = torch.cat([self.generated_audio_codes, last_generated_audio_codes], dim=2)
+
+ # apply the pattern mask to the final audio ids
+ output_audio_codes = self.apply_delay_pattern_mask(self.generated_audio_codes, moshi_delay_pattern_mask)
+
+ # revert the pattern delay mask by filtering the pad token id and bos token ids
+ mask = moshi_delay_pattern_mask != self.config.audio_vocab_size
+
+ output_audio_codes = output_audio_codes[mask].reshape(mask.shape[0], self.num_codebooks, -1)
+
+ output_values = None
+ if return_audio_waveforms:
+ output_values = self.audio_encoder.decode(
+ output_audio_codes,
+ ).audio_values
+
+ output_audio_codes = output_audio_codes if return_audio_codes else None
+
+ if generation_config.return_dict_in_generate:
+ return MoshiConditionalGenerationGenerateOutput(
+ audio_sequences=output_values, audio_codes=output_audio_codes, **outputs
+ )
+
+ return MoshiConditionalGenerationGenerateOutput(
+ audio_sequences=output_values, sequences=output_text_ids, audio_codes=output_audio_codes
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ cache_position=None,
+ position_ids=None,
+ use_cache=True,
+ logits_to_keep=None,
+ user_delay_pattern_mask=None,
+ moshi_delay_pattern_mask=None,
+ kwargs_depth_decoder=None,
+ blank_user_audio_codes: Optional[torch.FloatTensor] = None,
+ **kwargs,
+ ):
+ # Overwritten -- Moshi has custom post-processing on the prepared inputs.
+
+ # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+ # Exception 1: when passing input_embeds, input_ids may be missing entries
+ # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
+ # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
+ # (we can't check exception 3 while compiling)
+
+ if past_key_values is not None:
+ if (
+ inputs_embeds is not None # Exception 1
+ or cache_position[-1] >= input_ids.shape[1] # Exception 3
+ ):
+ input_ids = input_ids[:, -cache_position.shape[0] :]
+ elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2)
+ input_ids = input_ids[:, cache_position]
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and cache_position[0] == 0:
+ model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
+ else:
+ model_inputs = {"input_ids": input_ids, "inputs_embeds": None}
+
+ if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
+ if model_inputs["inputs_embeds"] is not None:
+ batch_size, sequence_length, _ = inputs_embeds.shape
+ device = inputs_embeds.device
+ else:
+ batch_size, sequence_length = input_ids.shape
+ device = input_ids.device
+
+ attention_mask = self.decoder.model._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=past_key_values.get_max_cache_shape(),
+ dtype=self.decoder.lm_head.weight.dtype,
+ device=device,
+ cache_position=cache_position,
+ batch_size=batch_size,
+ config=self.config,
+ past_key_values=past_key_values,
+ )
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": use_cache,
+ "attention_mask": attention_mask,
+ "cache_position": cache_position,
+ }
+ )
+
+ # 2. Now that everything is prepared, generate audio_codes using the depth decoder
+
+ # we want to do it after a first token has been generated
+ if model_inputs["input_ids"] is not None:
+ last_hidden_state = kwargs.get("last_hidden_state")
+ # (batch_size, sequence_length, dim) -> (batch_size * sequence_length, 1, dim)
+ last_hidden_state = last_hidden_state.view(-1, 1, last_hidden_state.shape[-1])
+
+ input_ids = model_inputs.pop("input_ids")
+
+ generated_audio_codes = self.depth_decoder.generate(
+ last_hidden_state=last_hidden_state,
+ input_ids=input_ids.view(-1, 1),
+ **kwargs_depth_decoder,
+ )
+
+ # the first tokens are text tokens
+ generated_audio_codes = generated_audio_codes[:, 1:].unsqueeze(2)
+
+ user_audio_codes = self.apply_delay_pattern_mask(
+ torch.cat(
+ [self.generated_audio_codes, blank_user_audio_codes.to(self.generated_audio_codes.device)], dim=2
+ ),
+ user_delay_pattern_mask,
+ )[:, :, -1:]
+ self.generated_audio_codes = self.apply_delay_pattern_mask(
+ torch.cat([self.generated_audio_codes, generated_audio_codes], dim=2), moshi_delay_pattern_mask
+ )
+
+ inputs_embeds, _, _, _, _, _, _ = self._prepare_inputs_embeds_for_generation(
+ input_ids, moshi_audio_codes=self.generated_audio_codes[:, :, -1:], user_audio_codes=user_audio_codes
+ )
+
+ model_inputs["input_ids"] = None
+ model_inputs["inputs_embeds"] = inputs_embeds
+
+ return model_inputs
+
+ def _update_model_kwargs_for_generation(
+ self,
+ outputs: ModelOutput,
+ model_kwargs: dict[str, Any],
+ is_encoder_decoder: bool = False,
+ num_new_tokens: int = 1,
+ ) -> dict[str, Any]:
+ model_kwargs = super()._update_model_kwargs_for_generation(
+ outputs, model_kwargs, is_encoder_decoder, num_new_tokens
+ )
+
+ # update last_hidden_state that'll be used in the depth decoder
+ model_kwargs["last_hidden_state"] = outputs.get("last_hidden_state")[:, -1:]
+
+ # dirty, but we need to make a last depth_decoder.generate
+ self.last_hidden_state = outputs.get("last_hidden_state")[:, -1:]
+ return model_kwargs
+
+ def get_input_embeddings(self):
+ return self.decoder.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.decoder.set_input_embeddings(value)
+
+ def get_output_embeddings(self):
+ return self.decoder.get_output_embeddings()
+
+ def set_output_embeddings(self, new_embeddings):
+ self.decoder.set_output_embeddings(new_embeddings)
+
+ def freeze_audio_encoder(self):
+ """
+ Freeze the audio encoder weights.
+ """
+ for param in self.audio_encoder.parameters():
+ param.requires_grad = False
+ self.audio_encoder._requires_grad = False
+
+ def freeze_depth_decoder(self):
+ """
+ Freeze the depth encoder weights.
+ """
+ for param in self.depth_decoder.parameters():
+ param.requires_grad = False
+ self.depth_decoder._requires_grad = False
+
+ @staticmethod
+ # Copied from transformers.models.musicgen.modeling_musicgen.MusicgenForCausalLM.apply_delay_pattern_mask
+ def apply_delay_pattern_mask(input_ids, decoder_pad_token_mask):
+ """Apply a delay pattern mask to the decoder input ids, only preserving predictions where
+ the mask is set to -1, and otherwise setting to the value detailed in the mask."""
+ seq_len = input_ids.shape[-1]
+ decoder_pad_token_mask = decoder_pad_token_mask[..., :seq_len]
+ input_ids = torch.where(decoder_pad_token_mask == -1, input_ids, decoder_pad_token_mask)
+ return input_ids
+
+ def build_delay_pattern_mask(
+ self, input_ids: torch.LongTensor, bos_token_id: int, pad_token_id: int, max_length: Optional[int] = None
+ ):
+ """Build a delayed pattern mask to the input_ids. Each codebook, except the first one, is offset by
+ one, giving a delayed pattern mask at the start of sequence and end of sequence. Take the example where there
+ are 4 codebooks and a max sequence length of 6, we have the delayed pattern mask of shape `(codebooks,
+ seq_len)`:
+ - [-1, -1, -1, -1, -1, P]
+ - [ B, -1, -1, -1, -1, -1]
+ - [ B, -1, -1, -1, -1, -1]
+ - [ B, -1, -1, -1, -1, -1]
+ where B is the beginning-of-sentence token, P is the special padding token id and -1 indicates that the token is valid for prediction. If we include
+ a prompt (input ids), the -1 positions indicate where new tokens should be predicted. Otherwise, the
+ mask is set to the value in the prompt:
+ - [ a0, a1, -1, -1, -1, P]
+ - [ B, b0, b1, -1, -1, -1]
+ - [ B, c0, c1, -1, -1, -1]
+ - [ B, d0, d1, -1, -1, -1]
+ where a-d indicate the codebook channel and 0/1 indicates the temporality. Now, we only override the -1
+ tokens in our prediction.
+ """
+ bsz, num_codebooks, seq_len = input_ids.shape
+
+ max_length = max_length if max_length is not None else self.generation_config.max_length
+ input_ids_shifted = (
+ torch.ones((bsz, num_codebooks, max_length), dtype=torch.long, device=input_ids.device) * -1
+ )
+
+ # the first codebook channel is not shifted
+ seq_len_to_keep = min(seq_len, max_length - 1)
+ input_ids_shifted[:, 0, :seq_len_to_keep] = input_ids[:, 0, :seq_len_to_keep]
+
+ # fill the shifted ids with the prompt entries
+ input_ids_shifted[:, 1:, 1 : seq_len_to_keep + 1] = input_ids[:, 1:, :seq_len_to_keep]
+
+ # fill with BOS and PAD
+ input_ids_shifted[:, 1:, 0] = bos_token_id
+ input_ids_shifted[:, 0, -1] = pad_token_id
+
+ # construct a pattern mask that indicates the positions of BOS and PAD tokens for each codebook
+ pattern_mask = input_ids_shifted
+
+ input_ids = input_ids_shifted[..., :seq_len_to_keep]
+ return input_ids, pattern_mask
+
+ def get_unconditional_inputs(self, num_samples=1):
+ """
+ Helper function to get null inputs for unconditional generation, enabling the model to be used without the
+ feature extractor or tokenizer.
+
+ Args:
+ num_samples (int, *optional*):
+ Number of audio samples to unconditionally generate.
+ max_new_tokens (int, *optional*):
+ Number of tokens to generate for each sample. More tokens means longer audio samples, at the expense of
+ longer inference (since more audio tokens need to be generated per sample).
+
+ Example:
+ ```python
+ >>> from transformers import MoshiForConditionalGeneration
+
+ >>> model = MoshiForConditionalGeneration.from_pretrained("kmhf/hf-moshiko-pytorch-bf16")
+
+ >>> # get the unconditional (or 'null') inputs for the model
+ >>> unconditional_inputs = model.get_unconditional_inputs(num_samples=1)
+ >>> audio_samples = model.generate(**unconditional_inputs, max_new_tokens=256)
+ ```"""
+
+ input_ids = torch.ones((num_samples, 1), device=self.device, dtype=torch.int64) * self.config.vocab_size
+ user_audio_codes = (
+ torch.ones((num_samples, self.num_codebooks, 1), device=self.device, dtype=torch.int64)
+ * self.config.audio_vocab_size
+ )
+ moshi_audio_codes = (
+ torch.ones((num_samples, self.num_codebooks, 1), device=self.device, dtype=torch.int64)
+ * self.config.audio_vocab_size
+ )
+ attention_mask = torch.ones((num_samples, 1), device=self.device, dtype=torch.long)
+
+ return MoshiUnconditionalInput(
+ input_ids=input_ids,
+ user_audio_codes=user_audio_codes,
+ moshi_audio_codes=moshi_audio_codes,
+ attention_mask=attention_mask,
+ )
+
+ def _check_and_maybe_initialize_inputs(
+ self,
+ input_ids=None,
+ user_input_values=None,
+ user_audio_codes=None,
+ moshi_input_values=None,
+ moshi_audio_codes=None,
+ inputs_embeds=None,
+ concat_unconditional_inputs=None,
+ ):
+ inputs = input_ids if inputs_embeds is None else inputs_embeds
+ user_input = user_audio_codes if user_input_values is None else user_input_values
+ moshi_input = moshi_audio_codes if moshi_input_values is None else moshi_input_values
+
+ one_input_has_been_passed = (user_input is not None) or (moshi_input is not None) or (inputs is not None)
+
+ # concat_unconditional_inputs will be False if inputs_embeds is used
+ concat_unconditional_inputs = concat_unconditional_inputs and not (
+ inputs_embeds is not None and input_ids is None
+ )
+
+ # if one or two of the three required inputs have been passed, throws an error
+ if one_input_has_been_passed and (user_input is None):
+ raise ValueError(
+ "No user audio inputs have been passed alongside the other inputs. Make sure either `user_input_values` or `user_audio_codes` is passed or use `MoshiForConditionalGeneration.get_unconditional_inputs`. Check the `MoshiForConditionalGeneration` docstrings for more information."
+ )
+ elif one_input_has_been_passed and (moshi_input is None):
+ raise ValueError(
+ "No Moshi audio inputs have been passed alongside the other inputs. Make sure either `moshi_input_values` or `moshi_audio_codes` is passed or use `MoshiForConditionalGeneration.get_unconditional_inputs`. Check the `MoshiForConditionalGeneration` docstrings for more information."
+ )
+ elif one_input_has_been_passed and (inputs is None):
+ raise ValueError(
+ "No `input_ids` or `inputs_embeds` have been passed alongside the other inputs. Make sure `input_ids` is passed or use `MoshiForConditionalGeneration.get_unconditional_inputs`. Check the `MoshiForConditionalGeneration` docstrings for more information."
+ )
+ elif not one_input_has_been_passed:
+ # if no inputs have been passed, use default values
+ unconditional_inputs = self.get_unconditional_inputs()
+ input_ids = unconditional_inputs.input_ids
+ user_audio_codes = unconditional_inputs.user_audio_codes
+ moshi_audio_codes = unconditional_inputs.moshi_audio_codes
+
+ # in that case, no need to concat unconditional inputs
+ concat_unconditional_inputs = False
+ else:
+ # check if same sequence length
+ user_seq_length = user_input.shape[-1]
+ moshi_seq_length = moshi_input.shape[-1]
+ tokens_seq_length = inputs.shape[1]
+
+ ratio = self.config.audio_encoder_config.frame_rate / self.config.sampling_rate
+ moshi_seq_length = math.ceil(moshi_seq_length * ratio) if moshi_audio_codes is None else moshi_seq_length
+ user_seq_length = math.ceil(user_seq_length * ratio) if user_audio_codes is None else user_seq_length
+
+ if tokens_seq_length != moshi_seq_length or tokens_seq_length != user_seq_length:
+ raise ValueError(
+ "At least one of the 3 inputs of `MoshiForConditionalGeneration` doesn't have the same sequence length as the others."
+ "Make sure that they all have the same sequence length. Check the `MoshiForConditionalGeneration` docstrings for more information."
+ )
+
+ return input_ids, user_audio_codes, moshi_audio_codes, concat_unconditional_inputs
+
+
+__all__ = ["MoshiForCausalLM", "MoshiForConditionalGeneration", "MoshiModel", "MoshiPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b7abc8357cc18e9d0dcc75fe79faa98e294dd77
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mpnet import *
+ from .modeling_mpnet import *
+ from .modeling_tf_mpnet import *
+ from .tokenization_mpnet import *
+ from .tokenization_mpnet_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/configuration_mpnet.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/configuration_mpnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..e80d6a0c30301f72bde911026489f9c89c0759cd
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/configuration_mpnet.py
@@ -0,0 +1,116 @@
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MPNet model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MPNetConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MPNetModel`] or a [`TFMPNetModel`]. It is used to
+ instantiate a MPNet model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the MPNet
+ [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 30527):
+ Vocabulary size of the MPNet model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MPNetModel`] or [`TFMPNetModel`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimensionality of the encoder layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+ relative_attention_num_buckets (`int`, *optional*, defaults to 32):
+ The number of buckets to use for each attention layer.
+
+ Examples:
+
+ ```python
+ >>> from transformers import MPNetModel, MPNetConfig
+
+ >>> # Initializing a MPNet mpnet-base style configuration
+ >>> configuration = MPNetConfig()
+
+ >>> # Initializing a model from the mpnet-base style configuration
+ >>> model = MPNetModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mpnet"
+
+ def __init__(
+ self,
+ vocab_size=30527,
+ hidden_size=768,
+ num_hidden_layers=12,
+ num_attention_heads=12,
+ intermediate_size=3072,
+ hidden_act="gelu",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ initializer_range=0.02,
+ layer_norm_eps=1e-12,
+ relative_attention_num_buckets=32,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ **kwargs,
+ ):
+ super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.hidden_act = hidden_act
+ self.intermediate_size = intermediate_size
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.max_position_embeddings = max_position_embeddings
+ self.initializer_range = initializer_range
+ self.layer_norm_eps = layer_norm_eps
+ self.relative_attention_num_buckets = relative_attention_num_buckets
+
+
+__all__ = ["MPNetConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/modeling_mpnet.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/modeling_mpnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..b25e5491738b7b5e9270fdfa96f27f7e0f3ce712
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/modeling_mpnet.py
@@ -0,0 +1,967 @@
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MPNet model."""
+
+import math
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN, gelu
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPooling,
+ MaskedLMOutput,
+ MultipleChoiceModelOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import auto_docstring, logging
+from .configuration_mpnet import MPNetConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring
+class MPNetPreTrainedModel(PreTrainedModel):
+ config: MPNetConfig
+ base_model_prefix = "mpnet"
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, nn.Linear):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, MPNetLMHead):
+ module.bias.data.zero_()
+
+
+class MPNetEmbeddings(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.padding_idx = 1
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.padding_idx)
+ self.position_embeddings = nn.Embedding(
+ config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
+ )
+
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.register_buffer(
+ "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+ )
+
+ def forward(self, input_ids=None, position_ids=None, inputs_embeds=None, **kwargs):
+ if position_ids is None:
+ if input_ids is not None:
+ position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx)
+ else:
+ position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
+
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+
+ if position_ids is None:
+ position_ids = self.position_ids[:, :seq_length]
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+ position_embeddings = self.position_embeddings(position_ids)
+
+ embeddings = inputs_embeds + position_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds):
+ """
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+
+ Args:
+ inputs_embeds: torch.Tensor
+
+ Returns: torch.Tensor
+ """
+ input_shape = inputs_embeds.size()[:-1]
+ sequence_length = input_shape[1]
+
+ position_ids = torch.arange(
+ self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+ )
+ return position_ids.unsqueeze(0).expand(input_shape)
+
+
+class MPNetSelfAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.q = nn.Linear(config.hidden_size, self.all_head_size)
+ self.k = nn.Linear(config.hidden_size, self.all_head_size)
+ self.v = nn.Linear(config.hidden_size, self.all_head_size)
+ self.o = nn.Linear(config.hidden_size, config.hidden_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ position_bias=None,
+ output_attentions=False,
+ **kwargs,
+ ):
+ batch_size, seq_length, _ = hidden_states.shape
+ q = (
+ self.q(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ k = (
+ self.k(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ v = (
+ self.v(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(q, k.transpose(-1, -2))
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+
+ # Apply relative position embedding (precomputed in MPNetEncoder) if provided.
+ if position_bias is not None:
+ attention_scores += position_bias
+
+ if attention_mask is not None:
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+ attention_probs = self.dropout(attention_probs)
+
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ c = torch.matmul(attention_probs, v)
+
+ c = c.permute(0, 2, 1, 3).contiguous()
+ new_c_shape = c.size()[:-2] + (self.all_head_size,)
+ c = c.view(*new_c_shape)
+
+ o = self.o(c)
+
+ outputs = (o, attention_probs) if output_attentions else (o,)
+ return outputs
+
+
+class MPNetAttention(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.attn = MPNetSelfAttention(config)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.attn.num_attention_heads, self.attn.attention_head_size, self.pruned_heads
+ )
+
+ self.attn.q = prune_linear_layer(self.attn.q, index)
+ self.attn.k = prune_linear_layer(self.attn.k, index)
+ self.attn.v = prune_linear_layer(self.attn.v, index)
+ self.attn.o = prune_linear_layer(self.attn.o, index, dim=1)
+
+ self.attn.num_attention_heads = self.attn.num_attention_heads - len(heads)
+ self.attn.all_head_size = self.attn.attention_head_size * self.attn.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ position_bias=None,
+ output_attentions=False,
+ **kwargs,
+ ):
+ self_outputs = self.attn(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ position_bias,
+ output_attentions=output_attentions,
+ )
+ attention_output = self.LayerNorm(self.dropout(self_outputs[0]) + hidden_states)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate
+class MPNetIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOutput
+class MPNetOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class MPNetLayer(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.attention = MPNetAttention(config)
+ self.intermediate = MPNetIntermediate(config)
+ self.output = MPNetOutput(config)
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ position_bias=None,
+ output_attentions=False,
+ **kwargs,
+ ):
+ self_attention_outputs = self.attention(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ position_bias=position_bias,
+ output_attentions=output_attentions,
+ )
+ attention_output = self_attention_outputs[0]
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ outputs = (layer_output,) + outputs
+ return outputs
+
+
+class MPNetEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.n_heads = config.num_attention_heads
+ self.layer = nn.ModuleList([MPNetLayer(config) for _ in range(config.num_hidden_layers)])
+ self.relative_attention_bias = nn.Embedding(config.relative_attention_num_buckets, self.n_heads)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = False,
+ **kwargs,
+ ):
+ position_bias = self.compute_position_bias(hidden_states)
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask,
+ head_mask[i],
+ position_bias,
+ output_attentions=output_attentions,
+ **kwargs,
+ )
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ # Add last layer
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_attentions,
+ )
+
+ def compute_position_bias(self, x, position_ids=None, num_buckets=32):
+ bsz, qlen, klen = x.size(0), x.size(1), x.size(1)
+ if position_ids is not None:
+ context_position = position_ids[:, :, None]
+ memory_position = position_ids[:, None, :]
+ else:
+ context_position = torch.arange(qlen, dtype=torch.long)[:, None]
+ memory_position = torch.arange(klen, dtype=torch.long)[None, :]
+
+ relative_position = memory_position - context_position
+
+ rp_bucket = self.relative_position_bucket(relative_position, num_buckets=num_buckets)
+ rp_bucket = rp_bucket.to(x.device)
+ values = self.relative_attention_bias(rp_bucket)
+ values = values.permute([2, 0, 1]).unsqueeze(0)
+ values = values.expand((bsz, -1, qlen, klen)).contiguous()
+ return values
+
+ @staticmethod
+ def relative_position_bucket(relative_position, num_buckets=32, max_distance=128):
+ ret = 0
+ n = -relative_position
+
+ num_buckets //= 2
+ ret += (n < 0).to(torch.long) * num_buckets
+ n = torch.abs(n)
+
+ max_exact = num_buckets // 2
+ is_small = n < max_exact
+
+ val_if_large = max_exact + (
+ torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
+ ).to(torch.long)
+
+ val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
+ ret += torch.where(is_small, n, val_if_large)
+ return ret
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler
+class MPNetPooler(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.activation = nn.Tanh()
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+@auto_docstring
+class MPNetModel(MPNetPreTrainedModel):
+ def __init__(self, config, add_pooling_layer=True):
+ r"""
+ add_pooling_layer (bool, *optional*, defaults to `True`):
+ Whether to add a pooling layer
+ """
+ super().__init__(config)
+ self.config = config
+
+ self.embeddings = MPNetEmbeddings(config)
+ self.encoder = MPNetEncoder(config)
+ self.pooler = MPNetPooler(config) if add_pooling_layer else None
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], BaseModelOutputWithPooling]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones(input_shape, device=device)
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+ embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, inputs_embeds=inputs_embeds)
+ encoder_outputs = self.encoder(
+ embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+class MPNetForMaskedLM(MPNetPreTrainedModel):
+ _tied_weights_keys = ["lm_head.decoder"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.mpnet = MPNetModel(config, add_pooling_layer=False)
+ self.lm_head = MPNetLMHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.lm_head.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head.decoder = new_embeddings
+ self.lm_head.bias = new_embeddings.bias
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], MaskedLMOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mpnet(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+ prediction_scores = self.lm_head(sequence_output)
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return MaskedLMOutput(
+ loss=masked_lm_loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+class MPNetLMHead(nn.Module):
+ """MPNet Head for masked and permuted language modeling."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+ # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+ self.decoder.bias = self.bias
+
+ def _tie_weights(self):
+ self.decoder.bias = self.bias
+
+ def forward(self, features, **kwargs):
+ x = self.dense(features)
+ x = gelu(x)
+ x = self.layer_norm(x)
+
+ # project back to size of vocabulary with bias
+ x = self.decoder(x)
+
+ return x
+
+
+@auto_docstring(
+ custom_intro="""
+ MPNet Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
+ output) e.g. for GLUE tasks.
+ """
+)
+class MPNetForSequenceClassification(MPNetPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.num_labels = config.num_labels
+ self.mpnet = MPNetModel(config, add_pooling_layer=False)
+ self.classifier = MPNetClassificationHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mpnet(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = outputs[0]
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class MPNetForMultipleChoice(MPNetPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.mpnet = MPNetModel(config)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, 1)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], MultipleChoiceModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+ num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+ `input_ids` above)
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+ flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+ flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+ flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+ flat_inputs_embeds = (
+ inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+ if inputs_embeds is not None
+ else None
+ )
+
+ outputs = self.mpnet(
+ flat_input_ids,
+ position_ids=flat_position_ids,
+ attention_mask=flat_attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=flat_inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ pooled_output = outputs[1]
+
+ pooled_output = self.dropout(pooled_output)
+ logits = self.classifier(pooled_output)
+ reshaped_logits = logits.view(-1, num_choices)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(reshaped_logits, labels)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return MultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class MPNetForTokenClassification(MPNetPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.mpnet = MPNetModel(config, add_pooling_layer=False)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], TokenClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mpnet(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+class MPNetClassificationHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+ def forward(self, features, **kwargs):
+ x = features[:, 0, :] # take token (equiv. to BERT's [CLS] token)
+ x = self.dropout(x)
+ x = self.dense(x)
+ x = torch.tanh(x)
+ x = self.dropout(x)
+ x = self.out_proj(x)
+ return x
+
+
+@auto_docstring
+class MPNetForQuestionAnswering(MPNetPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.num_labels = config.num_labels
+ self.mpnet = MPNetModel(config, add_pooling_layer=False)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mpnet(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+def create_position_ids_from_input_ids(input_ids, padding_idx):
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+ are ignored. This is modified from fairseq's `utils.make_positions`. :param torch.Tensor x: :return torch.Tensor:
+ """
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+ mask = input_ids.ne(padding_idx).int()
+ incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
+ return incremental_indices.long() + padding_idx
+
+
+__all__ = [
+ "MPNetForMaskedLM",
+ "MPNetForMultipleChoice",
+ "MPNetForQuestionAnswering",
+ "MPNetForSequenceClassification",
+ "MPNetForTokenClassification",
+ "MPNetLayer",
+ "MPNetModel",
+ "MPNetPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/modeling_tf_mpnet.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/modeling_tf_mpnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..1afea867df35413e13616b25309fa62b336b2712
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/modeling_tf_mpnet.py
@@ -0,0 +1,1353 @@
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TF 2.0 MPNet model."""
+
+from __future__ import annotations
+
+import math
+import warnings
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_outputs import (
+ TFBaseModelOutput,
+ TFBaseModelOutputWithPooling,
+ TFMaskedLMOutput,
+ TFMultipleChoiceModelOutput,
+ TFQuestionAnsweringModelOutput,
+ TFSequenceClassifierOutput,
+ TFTokenClassifierOutput,
+)
+from ...modeling_tf_utils import (
+ TFMaskedLanguageModelingLoss,
+ TFModelInputType,
+ TFMultipleChoiceLoss,
+ TFPreTrainedModel,
+ TFQuestionAnsweringLoss,
+ TFSequenceClassificationLoss,
+ TFTokenClassificationLoss,
+ get_initializer,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
+from ...utils import (
+ add_code_sample_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+)
+from .configuration_mpnet import MPNetConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "microsoft/mpnet-base"
+_CONFIG_FOR_DOC = "MPNetConfig"
+
+
+class TFMPNetPreTrainedModel(TFPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = MPNetConfig
+ base_model_prefix = "mpnet"
+
+
+class TFMPNetEmbeddings(keras.layers.Layer):
+ """Construct the embeddings from word, position embeddings."""
+
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.padding_idx = 1
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.max_position_embeddings = config.max_position_embeddings
+ self.initializer_range = config.initializer_range
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+
+ def build(self, input_shape=None):
+ with tf.name_scope("word_embeddings"):
+ self.weight = self.add_weight(
+ name="weight",
+ shape=[self.config.vocab_size, self.hidden_size],
+ initializer=get_initializer(initializer_range=self.initializer_range),
+ )
+
+ with tf.name_scope("position_embeddings"):
+ self.position_embeddings = self.add_weight(
+ name="embeddings",
+ shape=[self.max_position_embeddings, self.hidden_size],
+ initializer=get_initializer(initializer_range=self.initializer_range),
+ )
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+ def create_position_ids_from_input_ids(self, input_ids):
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
+ symbols are ignored. This is modified from fairseq's `utils.make_positions`.
+
+ Args:
+ input_ids: tf.Tensor
+ Returns: tf.Tensor
+ """
+ mask = tf.cast(tf.math.not_equal(input_ids, self.padding_idx), dtype=input_ids.dtype)
+ incremental_indices = tf.math.cumsum(mask, axis=1) * mask
+
+ return incremental_indices + self.padding_idx
+
+ def call(self, input_ids=None, position_ids=None, inputs_embeds=None, training=False):
+ """
+ Applies embedding based on inputs tensor.
+
+ Returns:
+ final_embeddings (`tf.Tensor`): output embedding tensor.
+ """
+ assert not (input_ids is None and inputs_embeds is None)
+
+ if input_ids is not None:
+ check_embeddings_within_bounds(input_ids, self.config.vocab_size)
+ inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
+
+ input_shape = shape_list(inputs_embeds)[:-1]
+
+ if position_ids is None:
+ if input_ids is not None:
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
+ position_ids = self.create_position_ids_from_input_ids(input_ids=input_ids)
+ else:
+ position_ids = tf.expand_dims(
+ tf.range(start=self.padding_idx + 1, limit=input_shape[-1] + self.padding_idx + 1), axis=0
+ )
+
+ position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
+ final_embeddings = inputs_embeds + position_embeds
+ final_embeddings = self.LayerNorm(inputs=final_embeddings)
+ final_embeddings = self.dropout(inputs=final_embeddings, training=training)
+
+ return final_embeddings
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->MPNet
+class TFMPNetPooler(keras.layers.Layer):
+ def __init__(self, config: MPNetConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ activation="tanh",
+ name="dense",
+ )
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(inputs=first_token_tensor)
+
+ return pooled_output
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+
+
+class TFMPNetSelfAttention(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ if config.hidden_size % config.num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads}"
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ assert config.hidden_size % config.num_attention_heads == 0
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.q = keras.layers.Dense(
+ self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="q"
+ )
+ self.k = keras.layers.Dense(
+ self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="k"
+ )
+ self.v = keras.layers.Dense(
+ self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="v"
+ )
+ self.o = keras.layers.Dense(
+ config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="o"
+ )
+ self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob)
+ self.config = config
+
+ def transpose_for_scores(self, x, batch_size):
+ # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
+ x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
+
+ return tf.transpose(x, perm=[0, 2, 1, 3])
+
+ def call(self, hidden_states, attention_mask, head_mask, output_attentions, position_bias=None, training=False):
+ batch_size = shape_list(hidden_states)[0]
+
+ q = self.q(hidden_states)
+ k = self.k(hidden_states)
+ v = self.v(hidden_states)
+
+ q = self.transpose_for_scores(q, batch_size)
+ k = self.transpose_for_scores(k, batch_size)
+ v = self.transpose_for_scores(v, batch_size)
+
+ attention_scores = tf.matmul(q, k, transpose_b=True)
+ dk = tf.cast(shape_list(k)[-1], attention_scores.dtype)
+ attention_scores = attention_scores / tf.math.sqrt(dk)
+
+ # Apply relative position embedding (precomputed in MPNetEncoder) if provided.
+ if position_bias is not None:
+ attention_scores += position_bias
+
+ if attention_mask is not None:
+ attention_scores = attention_scores + attention_mask
+
+ attention_probs = stable_softmax(attention_scores, axis=-1)
+
+ attention_probs = self.dropout(attention_probs, training=training)
+
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ c = tf.matmul(attention_probs, v)
+ c = tf.transpose(c, perm=[0, 2, 1, 3])
+ c = tf.reshape(c, (batch_size, -1, self.all_head_size))
+ o = self.o(c)
+
+ outputs = (o, attention_probs) if output_attentions else (o,)
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "q", None) is not None:
+ with tf.name_scope(self.q.name):
+ self.q.build([None, None, self.config.hidden_size])
+ if getattr(self, "k", None) is not None:
+ with tf.name_scope(self.k.name):
+ self.k.build([None, None, self.config.hidden_size])
+ if getattr(self, "v", None) is not None:
+ with tf.name_scope(self.v.name):
+ self.v.build([None, None, self.config.hidden_size])
+ if getattr(self, "o", None) is not None:
+ with tf.name_scope(self.o.name):
+ self.o.build([None, None, self.config.hidden_size])
+
+
+class TFMPNetAttention(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.attn = TFMPNetSelfAttention(config, name="attn")
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.config = config
+
+ def prune_heads(self, heads):
+ raise NotImplementedError
+
+ def call(self, input_tensor, attention_mask, head_mask, output_attentions, position_bias=None, training=False):
+ self_outputs = self.attn(
+ input_tensor, attention_mask, head_mask, output_attentions, position_bias=position_bias, training=training
+ )
+ attention_output = self.LayerNorm(self.dropout(self_outputs[0]) + input_tensor)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "attn", None) is not None:
+ with tf.name_scope(self.attn.name):
+ self.attn.build(None)
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->MPNet
+class TFMPNetIntermediate(keras.layers.Layer):
+ def __init__(self, config: MPNetConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = get_tf_activation(config.hidden_act)
+ else:
+ self.intermediate_act_fn = config.hidden_act
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+
+
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->MPNet
+class TFMPNetOutput(keras.layers.Layer):
+ def __init__(self, config: MPNetConfig, **kwargs):
+ super().__init__(**kwargs)
+
+ self.dense = keras.layers.Dense(
+ units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+ self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob)
+ self.config = config
+
+ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
+ hidden_states = self.dense(inputs=hidden_states)
+ hidden_states = self.dropout(inputs=hidden_states, training=training)
+ hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
+
+ return hidden_states
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.intermediate_size])
+ if getattr(self, "LayerNorm", None) is not None:
+ with tf.name_scope(self.LayerNorm.name):
+ self.LayerNorm.build([None, None, self.config.hidden_size])
+
+
+class TFMPNetLayer(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.attention = TFMPNetAttention(config, name="attention")
+ self.intermediate = TFMPNetIntermediate(config, name="intermediate")
+ self.out = TFMPNetOutput(config, name="output")
+
+ def call(self, hidden_states, attention_mask, head_mask, output_attentions, position_bias=None, training=False):
+ self_attention_outputs = self.attention(
+ hidden_states, attention_mask, head_mask, output_attentions, position_bias=position_bias, training=training
+ )
+ attention_output = self_attention_outputs[0]
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.out(intermediate_output, attention_output, training=training)
+ outputs = (layer_output,) + outputs # add attentions if we output them
+
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "attention", None) is not None:
+ with tf.name_scope(self.attention.name):
+ self.attention.build(None)
+ if getattr(self, "intermediate", None) is not None:
+ with tf.name_scope(self.intermediate.name):
+ self.intermediate.build(None)
+ if getattr(self, "out", None) is not None:
+ with tf.name_scope(self.out.name):
+ self.out.build(None)
+
+
+class TFMPNetEncoder(keras.layers.Layer):
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.n_heads = config.num_attention_heads
+ self.output_attentions = config.output_attentions
+ self.output_hidden_states = config.output_hidden_states
+ self.relative_attention_num_buckets = config.relative_attention_num_buckets
+ self.initializer_range = config.initializer_range
+
+ self.layer = [TFMPNetLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
+ self.relative_attention_num_buckets = config.relative_attention_num_buckets
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ with tf.name_scope("relative_attention_bias"):
+ self.relative_attention_bias = self.add_weight(
+ name="embeddings",
+ shape=[self.relative_attention_num_buckets, self.n_heads],
+ initializer=get_initializer(self.initializer_range),
+ )
+ if getattr(self, "layer", None) is not None:
+ for layer in self.layer:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+ def call(
+ self,
+ hidden_states,
+ attention_mask,
+ head_mask,
+ output_attentions,
+ output_hidden_states,
+ return_dict,
+ training=False,
+ ):
+ position_bias = self.compute_position_bias(hidden_states)
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask,
+ head_mask[i],
+ output_attentions,
+ position_bias=position_bias,
+ training=training,
+ )
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ # Add last layer
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
+
+ return TFBaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
+ )
+
+ @staticmethod
+ def _relative_position_bucket(relative_position, num_buckets=32, max_distance=128):
+ ret = 0
+ n = -relative_position
+
+ num_buckets //= 2
+ ret += tf.cast(tf.math.less(n, 0), dtype=relative_position.dtype) * num_buckets
+ n = tf.math.abs(n)
+
+ # now n is in the range [0, inf)
+ max_exact = num_buckets // 2
+ is_small = tf.math.less(n, max_exact)
+
+ val_if_large = max_exact + tf.cast(
+ tf.math.log(n / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact),
+ dtype=relative_position.dtype,
+ )
+
+ val_if_large = tf.math.minimum(val_if_large, num_buckets - 1)
+ ret += tf.where(is_small, n, val_if_large)
+ return ret
+
+ def compute_position_bias(self, x, position_ids=None):
+ """Compute binned relative position bias"""
+ input_shape = shape_list(x)
+ qlen, klen = input_shape[1], input_shape[1]
+
+ if position_ids is not None:
+ context_position = position_ids[:, :, None]
+ memory_position = position_ids[:, None, :]
+ else:
+ context_position = tf.range(qlen)[:, None]
+ memory_position = tf.range(klen)[None, :]
+
+ relative_position = memory_position - context_position # shape (qlen, klen)
+
+ rp_bucket = self._relative_position_bucket(
+ relative_position,
+ num_buckets=self.relative_attention_num_buckets,
+ )
+ values = tf.gather(self.relative_attention_bias, rp_bucket) # shape (qlen, klen, num_heads)
+ values = tf.expand_dims(tf.transpose(values, [2, 0, 1]), axis=0) # shape (1, num_heads, qlen, klen)
+ return values
+
+
+@keras_serializable
+class TFMPNetMainLayer(keras.layers.Layer):
+ config_class = MPNetConfig
+
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.num_hidden_layers = config.num_hidden_layers
+ self.initializer_range = config.initializer_range
+ self.output_attentions = config.output_attentions
+ self.output_hidden_states = config.output_hidden_states
+ self.return_dict = config.use_return_dict
+ self.encoder = TFMPNetEncoder(config, name="encoder")
+ self.pooler = TFMPNetPooler(config, name="pooler")
+ # The embeddings must be the last declaration in order to follow the weights order
+ self.embeddings = TFMPNetEmbeddings(config, name="embeddings")
+
+ # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings
+ def get_input_embeddings(self) -> keras.layers.Layer:
+ return self.embeddings
+
+ # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings
+ def set_input_embeddings(self, value: tf.Variable):
+ self.embeddings.weight = value
+ self.embeddings.vocab_size = shape_list(value)[0]
+
+ # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ raise NotImplementedError
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ position_ids=None,
+ head_mask=None,
+ inputs_embeds=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ training=False,
+ ):
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if attention_mask is None:
+ attention_mask = tf.fill(input_shape, 1)
+
+ embedding_output = self.embeddings(
+ input_ids,
+ position_ids,
+ inputs_embeds,
+ training=training,
+ )
+
+ # We create a 3D attention mask from a 2D tensor mask.
+ # Sizes are [batch_size, 1, 1, to_seq_length]
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+ # this attention mask is more simple than the triangular masking of causal attention
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+ extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1]))
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and -10000.0 for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+ extended_attention_mask = tf.cast(extended_attention_mask, embedding_output.dtype)
+ one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
+ ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
+ extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ if head_mask is not None:
+ raise NotImplementedError
+ else:
+ head_mask = [None] * self.num_hidden_layers
+
+ encoder_outputs = self.encoder(
+ embedding_output,
+ extended_attention_mask,
+ head_mask,
+ output_attentions,
+ output_hidden_states,
+ return_dict,
+ training=training,
+ )
+
+ sequence_output = encoder_outputs[0]
+ pooled_output = self.pooler(sequence_output)
+
+ if not return_dict:
+ return (
+ sequence_output,
+ pooled_output,
+ ) + encoder_outputs[1:]
+
+ return TFBaseModelOutputWithPooling(
+ last_hidden_state=sequence_output,
+ pooler_output=pooled_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "encoder", None) is not None:
+ with tf.name_scope(self.encoder.name):
+ self.encoder.build(None)
+ if getattr(self, "pooler", None) is not None:
+ with tf.name_scope(self.pooler.name):
+ self.pooler.build(None)
+ if getattr(self, "embeddings", None) is not None:
+ with tf.name_scope(self.embeddings.name):
+ self.embeddings.build(None)
+
+
+MPNET_START_DOCSTRING = r"""
+
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Args:
+ config ([`MPNetConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+MPNET_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
+ [`PreTrainedTokenizer.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+ config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+ used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+ eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+"""
+
+
+@add_start_docstrings(
+ "The bare MPNet Model transformer outputting raw hidden-states without any specific head on top.",
+ MPNET_START_DOCSTRING,
+)
+class TFMPNetModel(TFMPNetPreTrainedModel):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.mpnet = TFMPNetMainLayer(config, name="mpnet")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFBaseModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.array | tf.Tensor | None = None,
+ position_ids: np.array | tf.Tensor | None = None,
+ head_mask: np.array | tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool = False,
+ ) -> TFBaseModelOutput | tuple[tf.Tensor]:
+ outputs = self.mpnet(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mpnet", None) is not None:
+ with tf.name_scope(self.mpnet.name):
+ self.mpnet.build(None)
+
+
+class TFMPNetLMHead(keras.layers.Layer):
+ """MPNet head for masked and permuted language modeling"""
+
+ def __init__(self, config, input_embeddings, **kwargs):
+ super().__init__(**kwargs)
+
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.dense = keras.layers.Dense(
+ config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+ )
+ self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
+ self.act = get_tf_activation("gelu")
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = input_embeddings
+
+ def build(self, input_shape=None):
+ self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "layer_norm", None) is not None:
+ with tf.name_scope(self.layer_norm.name):
+ self.layer_norm.build([None, None, self.config.hidden_size])
+
+ def get_output_embeddings(self):
+ return self.decoder
+
+ def set_output_embeddings(self, value):
+ self.decoder.weight = value
+ self.decoder.vocab_size = shape_list(value)[0]
+
+ def get_bias(self):
+ return {"bias": self.bias}
+
+ def set_bias(self, value):
+ self.bias = value["bias"]
+ self.config.vocab_size = shape_list(value["bias"])[0]
+
+ def call(self, hidden_states):
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.layer_norm(hidden_states)
+
+ # project back to size of vocabulary with bias
+ seq_length = shape_list(tensor=hidden_states)[1]
+ hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
+ hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True)
+ hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size])
+ hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
+
+ return hidden_states
+
+
+@add_start_docstrings("""MPNet Model with a `language modeling` head on top.""", MPNET_START_DOCSTRING)
+class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
+ _keys_to_ignore_on_load_missing = [r"pooler"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.mpnet = TFMPNetMainLayer(config, name="mpnet")
+ self.lm_head = TFMPNetLMHead(config, self.mpnet.embeddings, name="lm_head")
+
+ def get_lm_head(self):
+ return self.lm_head
+
+ def get_prefix_bias_name(self):
+ warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
+ return self.name + "/" + self.lm_head.name
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFMaskedLMOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> TFMaskedLMOutput | tuple[tf.Tensor]:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+ """
+ outputs = self.mpnet(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+ prediction_scores = self.lm_head(sequence_output)
+
+ loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores)
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFMaskedLMOutput(
+ loss=loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mpnet", None) is not None:
+ with tf.name_scope(self.mpnet.name):
+ self.mpnet.build(None)
+ if getattr(self, "lm_head", None) is not None:
+ with tf.name_scope(self.lm_head.name):
+ self.lm_head.build(None)
+
+
+class TFMPNetClassificationHead(keras.layers.Layer):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(self, config, **kwargs):
+ super().__init__(**kwargs)
+ self.dense = keras.layers.Dense(
+ config.hidden_size,
+ kernel_initializer=get_initializer(config.initializer_range),
+ activation="tanh",
+ name="dense",
+ )
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.out_proj = keras.layers.Dense(
+ config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
+ )
+ self.config = config
+
+ def call(self, features, training=False):
+ x = features[:, 0, :] # take token (equiv. to [CLS])
+ x = self.dropout(x, training=training)
+ x = self.dense(x)
+ x = self.dropout(x, training=training)
+ x = self.out_proj(x)
+ return x
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "dense", None) is not None:
+ with tf.name_scope(self.dense.name):
+ self.dense.build([None, None, self.config.hidden_size])
+ if getattr(self, "out_proj", None) is not None:
+ with tf.name_scope(self.out_proj.name):
+ self.out_proj.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ MPNet Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
+ output) e.g. for GLUE tasks.
+ """,
+ MPNET_START_DOCSTRING,
+)
+class TFMPNetForSequenceClassification(TFMPNetPreTrainedModel, TFSequenceClassificationLoss):
+ _keys_to_ignore_on_load_missing = [r"pooler"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.num_labels = config.num_labels
+
+ self.mpnet = TFMPNetMainLayer(config, name="mpnet")
+ self.classifier = TFMPNetClassificationHead(config, name="classifier")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFSequenceClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.array | tf.Tensor | None = None,
+ position_ids: np.array | tf.Tensor | None = None,
+ head_mask: np.array | tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> TFSequenceClassifierOutput | tuple[tf.Tensor]:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ outputs = self.mpnet(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ sequence_output = outputs[0]
+ logits = self.classifier(sequence_output, training=training)
+
+ loss = None if labels is None else self.hf_compute_loss(labels, logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFSequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mpnet", None) is not None:
+ with tf.name_scope(self.mpnet.name):
+ self.mpnet.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build(None)
+
+
+@add_start_docstrings(
+ """
+ MPNet Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
+ softmax) e.g. for RocStories/SWAG tasks.
+ """,
+ MPNET_START_DOCSTRING,
+)
+class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, TFMultipleChoiceLoss):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.mpnet = TFMPNetMainLayer(config, name="mpnet")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.classifier = keras.layers.Dense(
+ 1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFMultipleChoiceModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> TFMultipleChoiceModelOutput | tuple[tf.Tensor]:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
+ where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)
+ """
+ if input_ids is not None:
+ num_choices = shape_list(input_ids)[1]
+ seq_length = shape_list(input_ids)[2]
+ else:
+ num_choices = shape_list(inputs_embeds)[1]
+ seq_length = shape_list(inputs_embeds)[2]
+
+ flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
+ flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
+ flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
+ flat_inputs_embeds = (
+ tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
+ if inputs_embeds is not None
+ else None
+ )
+ outputs = self.mpnet(
+ flat_input_ids,
+ flat_attention_mask,
+ flat_position_ids,
+ head_mask,
+ flat_inputs_embeds,
+ output_attentions,
+ output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ pooled_output = outputs[1]
+ pooled_output = self.dropout(pooled_output, training=training)
+ logits = self.classifier(pooled_output)
+ reshaped_logits = tf.reshape(logits, (-1, num_choices))
+ loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFMultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mpnet", None) is not None:
+ with tf.name_scope(self.mpnet.name):
+ self.mpnet.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ MPNet Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+ Named-Entity-Recognition (NER) tasks.
+ """,
+ MPNET_START_DOCSTRING,
+)
+class TFMPNetForTokenClassification(TFMPNetPreTrainedModel, TFTokenClassificationLoss):
+ _keys_to_ignore_on_load_missing = [r"pooler"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+
+ self.num_labels = config.num_labels
+ self.mpnet = TFMPNetMainLayer(config, name="mpnet")
+ self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
+ self.classifier = keras.layers.Dense(
+ config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFTokenClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: tf.Tensor | None = None,
+ training: bool = False,
+ ) -> TFTokenClassifierOutput | tuple[tf.Tensor]:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+ outputs = self.mpnet(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output, training=training)
+ logits = self.classifier(sequence_output)
+
+ loss = None if labels is None else self.hf_compute_loss(labels, logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFTokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mpnet", None) is not None:
+ with tf.name_scope(self.mpnet.name):
+ self.mpnet.build(None)
+ if getattr(self, "classifier", None) is not None:
+ with tf.name_scope(self.classifier.name):
+ self.classifier.build([None, None, self.config.hidden_size])
+
+
+@add_start_docstrings(
+ """
+ MPNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
+ layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
+ """,
+ MPNET_START_DOCSTRING,
+)
+class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLoss):
+ _keys_to_ignore_on_load_missing = [r"pooler"]
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.num_labels = config.num_labels
+
+ self.mpnet = TFMPNetMainLayer(config, name="mpnet")
+ self.qa_outputs = keras.layers.Dense(
+ config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
+ )
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFQuestionAnsweringModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.array | tf.Tensor | None = None,
+ position_ids: np.array | tf.Tensor | None = None,
+ head_mask: np.array | tf.Tensor | None = None,
+ inputs_embeds: tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ start_positions: tf.Tensor | None = None,
+ end_positions: tf.Tensor | None = None,
+ training: bool = False,
+ **kwargs,
+ ) -> TFQuestionAnsweringModelOutput | tuple[tf.Tensor]:
+ r"""
+ start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the start of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+ are not taken into account for computing the loss.
+ end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the end of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+ are not taken into account for computing the loss.
+ """
+ outputs = self.mpnet(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = tf.split(logits, 2, axis=-1)
+ start_logits = tf.squeeze(start_logits, axis=-1)
+ end_logits = tf.squeeze(end_logits, axis=-1)
+ loss = None
+
+ if start_positions is not None and end_positions is not None:
+ labels = {"start_position": start_positions, "end_position": end_positions}
+ loss = self.hf_compute_loss(labels, (start_logits, end_logits))
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFQuestionAnsweringModelOutput(
+ loss=loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "mpnet", None) is not None:
+ with tf.name_scope(self.mpnet.name):
+ self.mpnet.build(None)
+ if getattr(self, "qa_outputs", None) is not None:
+ with tf.name_scope(self.qa_outputs.name):
+ self.qa_outputs.build([None, None, self.config.hidden_size])
+
+
+__all__ = [
+ "TFMPNetEmbeddings",
+ "TFMPNetForMaskedLM",
+ "TFMPNetForMultipleChoice",
+ "TFMPNetForQuestionAnswering",
+ "TFMPNetForSequenceClassification",
+ "TFMPNetForTokenClassification",
+ "TFMPNetMainLayer",
+ "TFMPNetModel",
+ "TFMPNetPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/tokenization_mpnet.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/tokenization_mpnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf035cf8e4bd490023890a0d690bba32a123124b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/tokenization_mpnet.py
@@ -0,0 +1,537 @@
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for MPNet."""
+
+import collections
+import os
+import unicodedata
+from typing import Optional
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+
+def load_vocab(vocab_file):
+ """Loads a vocabulary file into a dictionary."""
+ vocab = collections.OrderedDict()
+ with open(vocab_file, "r", encoding="utf-8") as reader:
+ tokens = reader.readlines()
+ for index, token in enumerate(tokens):
+ token = token.rstrip("\n")
+ vocab[token] = index
+ return vocab
+
+
+def whitespace_tokenize(text):
+ """Runs basic whitespace cleaning and splitting on a piece of text."""
+ text = text.strip()
+ if not text:
+ return []
+ tokens = text.split()
+ return tokens
+
+
+class MPNetTokenizer(PreTrainedTokenizer):
+ """
+
+ This tokenizer inherits from [`BertTokenizer`] which contains most of the methods. Users should refer to the
+ superclass for more information regarding methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ do_basic_tokenize (`bool`, *optional*, defaults to `True`):
+ Whether or not to do basic tokenization before WordPiece.
+ never_split (`Iterable`, *optional*):
+ Collection of tokens which will never be split during tokenization. Only has an effect when
+ `do_basic_tokenize=True`
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters.
+
+ This should likely be deactivated for Japanese (see this
+ [issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original BERT).
+ clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
+ Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+ extra spaces.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ def __init__(
+ self,
+ vocab_file,
+ do_lower_case=True,
+ do_basic_tokenize=True,
+ never_split=None,
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="[UNK]",
+ pad_token="",
+ mask_token="",
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ clean_up_tokenization_spaces=True,
+ **kwargs,
+ ):
+ bos_token = AddedToken(bos_token, special=True) if isinstance(bos_token, str) else bos_token
+ eos_token = AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token
+ sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token
+ cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token
+ unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
+
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
+
+ if not os.path.isfile(vocab_file):
+ raise ValueError(
+ f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
+ " model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+ )
+ self.vocab = load_vocab(vocab_file)
+ self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
+ self.do_basic_tokenize = do_basic_tokenize
+ if do_basic_tokenize:
+ self.basic_tokenizer = BasicTokenizer(
+ do_lower_case=do_lower_case,
+ never_split=never_split,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ )
+ self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
+
+ super().__init__(
+ do_lower_case=do_lower_case,
+ do_basic_tokenize=do_basic_tokenize,
+ never_split=never_split,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+ **kwargs,
+ )
+
+ @property
+ def do_lower_case(self):
+ return self.basic_tokenizer.do_lower_case
+
+ @property
+ def vocab_size(self):
+ return len(self.vocab)
+
+ def get_vocab(self):
+ # "" is part of the vocab, but was wrongfully added at a wrong index in the fast saved version
+ vocab = self.added_tokens_encoder.copy()
+ vocab.update(self.vocab)
+ return vocab
+
+ def _tokenize(self, text):
+ split_tokens = []
+ if self.do_basic_tokenize:
+ for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
+ # If the token is part of the never_split set
+ if token in self.basic_tokenizer.never_split:
+ split_tokens.append(token)
+ else:
+ split_tokens += self.wordpiece_tokenizer.tokenize(token)
+ else:
+ split_tokens = self.wordpiece_tokenizer.tokenize(text)
+ return split_tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.ids_to_tokens.get(index, self.unk_token)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ out_string = " ".join(tokens).replace(" ##", "").strip()
+ return out_string
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A MPNet sequence has the following format:
+
+ - single sequence: ` X `
+ - pair of sequences: ` A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` methods.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of ids.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Set to True if the token list is already formatted with special tokens for the model
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is None:
+ return [1] + ([0] * len(token_ids_0)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. MPNet does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of ids.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ index = 0
+ if os.path.isdir(save_directory):
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ else:
+ vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
+ with open(vocab_file, "w", encoding="utf-8") as writer:
+ for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+ " Please check that the vocabulary is not corrupted!"
+ )
+ index = token_index
+ writer.write(token + "\n")
+ index += 1
+ return (vocab_file,)
+
+
+# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
+class BasicTokenizer:
+ """
+ Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
+
+ Args:
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ never_split (`Iterable`, *optional*):
+ Collection of tokens which will never be split during tokenization. Only has an effect when
+ `do_basic_tokenize=True`
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters.
+
+ This should likely be deactivated for Japanese (see this
+ [issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original BERT).
+ do_split_on_punc (`bool`, *optional*, defaults to `True`):
+ In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
+ the full context of the words, such as contractions.
+ """
+
+ def __init__(
+ self,
+ do_lower_case=True,
+ never_split=None,
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ do_split_on_punc=True,
+ ):
+ if never_split is None:
+ never_split = []
+ self.do_lower_case = do_lower_case
+ self.never_split = set(never_split)
+ self.tokenize_chinese_chars = tokenize_chinese_chars
+ self.strip_accents = strip_accents
+ self.do_split_on_punc = do_split_on_punc
+
+ def tokenize(self, text, never_split=None):
+ """
+ Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
+
+ Args:
+ never_split (`List[str]`, *optional*)
+ Kept for backward compatibility purposes. Now implemented directly at the base class level (see
+ [`PreTrainedTokenizer.tokenize`]) List of token not to split.
+ """
+ # union() returns a new set by concatenating the two sets.
+ never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
+ text = self._clean_text(text)
+
+ # This was added on November 1st, 2018 for the multilingual and Chinese
+ # models. This is also applied to the English models now, but it doesn't
+ # matter since the English models were not trained on any Chinese data
+ # and generally don't have any Chinese data in them (there are Chinese
+ # characters in the vocabulary because Wikipedia does have some Chinese
+ # words in the English Wikipedia.).
+ if self.tokenize_chinese_chars:
+ text = self._tokenize_chinese_chars(text)
+ # prevents treating the same character with different unicode codepoints as different characters
+ unicode_normalized_text = unicodedata.normalize("NFC", text)
+ orig_tokens = whitespace_tokenize(unicode_normalized_text)
+ split_tokens = []
+ for token in orig_tokens:
+ if token not in never_split:
+ if self.do_lower_case:
+ token = token.lower()
+ if self.strip_accents is not False:
+ token = self._run_strip_accents(token)
+ elif self.strip_accents:
+ token = self._run_strip_accents(token)
+ split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+ output_tokens = whitespace_tokenize(" ".join(split_tokens))
+ return output_tokens
+
+ def _run_strip_accents(self, text):
+ """Strips accents from a piece of text."""
+ text = unicodedata.normalize("NFD", text)
+ output = []
+ for char in text:
+ cat = unicodedata.category(char)
+ if cat == "Mn":
+ continue
+ output.append(char)
+ return "".join(output)
+
+ def _run_split_on_punc(self, text, never_split=None):
+ """Splits punctuation on a piece of text."""
+ if not self.do_split_on_punc or (never_split is not None and text in never_split):
+ return [text]
+ chars = list(text)
+ i = 0
+ start_new_word = True
+ output = []
+ while i < len(chars):
+ char = chars[i]
+ if _is_punctuation(char):
+ output.append([char])
+ start_new_word = True
+ else:
+ if start_new_word:
+ output.append([])
+ start_new_word = False
+ output[-1].append(char)
+ i += 1
+
+ return ["".join(x) for x in output]
+
+ def _tokenize_chinese_chars(self, text):
+ """Adds whitespace around any CJK character."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if self._is_chinese_char(cp):
+ output.append(" ")
+ output.append(char)
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+ def _is_chinese_char(self, cp):
+ """Checks whether CP is the codepoint of a CJK character."""
+ # This defines a "chinese character" as anything in the CJK Unicode block:
+ # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+ #
+ # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+ # despite its name. The modern Korean Hangul alphabet is a different block,
+ # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+ # space-separated words, so they are not treated specially and handled
+ # like the all of the other languages.
+ if (
+ (cp >= 0x4E00 and cp <= 0x9FFF)
+ or (cp >= 0x3400 and cp <= 0x4DBF)
+ or (cp >= 0x20000 and cp <= 0x2A6DF)
+ or (cp >= 0x2A700 and cp <= 0x2B73F)
+ or (cp >= 0x2B740 and cp <= 0x2B81F)
+ or (cp >= 0x2B820 and cp <= 0x2CEAF)
+ or (cp >= 0xF900 and cp <= 0xFAFF)
+ or (cp >= 0x2F800 and cp <= 0x2FA1F)
+ ):
+ return True
+
+ return False
+
+ def _clean_text(self, text):
+ """Performs invalid character removal and whitespace cleanup on text."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if cp == 0 or cp == 0xFFFD or _is_control(char):
+ continue
+ if _is_whitespace(char):
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+
+# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
+class WordpieceTokenizer:
+ """Runs WordPiece tokenization."""
+
+ def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
+ self.vocab = vocab
+ self.unk_token = unk_token
+ self.max_input_chars_per_word = max_input_chars_per_word
+
+ def tokenize(self, text):
+ """
+ Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
+ tokenization using the given vocabulary.
+
+ For example, `input = "unaffable"` will return as output `["un", "##aff", "##able"]`.
+
+ Args:
+ text: A single token or whitespace separated tokens. This should have
+ already been passed through *BasicTokenizer*.
+
+ Returns:
+ A list of wordpiece tokens.
+ """
+
+ output_tokens = []
+ for token in whitespace_tokenize(text):
+ chars = list(token)
+ if len(chars) > self.max_input_chars_per_word:
+ output_tokens.append(self.unk_token)
+ continue
+
+ is_bad = False
+ start = 0
+ sub_tokens = []
+ while start < len(chars):
+ end = len(chars)
+ cur_substr = None
+ while start < end:
+ substr = "".join(chars[start:end])
+ if start > 0:
+ substr = "##" + substr
+ if substr in self.vocab:
+ cur_substr = substr
+ break
+ end -= 1
+ if cur_substr is None:
+ is_bad = True
+ break
+ sub_tokens.append(cur_substr)
+ start = end
+
+ if is_bad:
+ output_tokens.append(self.unk_token)
+ else:
+ output_tokens.extend(sub_tokens)
+ return output_tokens
+
+
+__all__ = ["MPNetTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/tokenization_mpnet_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/tokenization_mpnet_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a470565a8452e765a509d0ffffb41dc6d6cd5e9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpnet/tokenization_mpnet_fast.py
@@ -0,0 +1,209 @@
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Tokenization classes for MPNet."""
+
+import json
+from typing import Optional
+
+from tokenizers import normalizers
+
+from ...tokenization_utils import AddedToken
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import logging
+from .tokenization_mpnet import MPNetTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
+
+
+class MPNetTokenizerFast(PreTrainedTokenizerFast):
+ r"""
+ Construct a "fast" MPNet tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ File containing the vocabulary.
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `"[UNK]"`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
+ issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original BERT).
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ slow_tokenizer_class = MPNetTokenizer
+ model_input_names = ["input_ids", "attention_mask"]
+
+ def __init__(
+ self,
+ vocab_file=None,
+ tokenizer_file=None,
+ do_lower_case=True,
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="[UNK]",
+ pad_token="",
+ mask_token="",
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ **kwargs,
+ ):
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+ sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
+ cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+ super().__init__(
+ vocab_file,
+ tokenizer_file=tokenizer_file,
+ do_lower_case=do_lower_case,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ tokenize_chinese_chars=tokenize_chinese_chars,
+ strip_accents=strip_accents,
+ **kwargs,
+ )
+
+ pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
+ if (
+ pre_tok_state.get("lowercase", do_lower_case) != do_lower_case
+ or pre_tok_state.get("strip_accents", strip_accents) != strip_accents
+ ):
+ pre_tok_class = getattr(normalizers, pre_tok_state.pop("type"))
+ pre_tok_state["lowercase"] = do_lower_case
+ pre_tok_state["strip_accents"] = strip_accents
+ self.backend_tokenizer.normalizer = pre_tok_class(**pre_tok_state)
+
+ self.do_lower_case = do_lower_case
+
+ @property
+ def mask_token(self) -> str:
+ """
+ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
+ having been set.
+
+ MPNet tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
+ comprise the space before the **.
+ """
+ if self._mask_token is None:
+ if self.verbose:
+ logger.error("Using mask_token, but it is not set yet.")
+ return None
+ return str(self._mask_token)
+
+ @mask_token.setter
+ def mask_token(self, value):
+ """
+ Overriding the default behavior of the mask token to have it eat the space before it.
+
+ This is needed to preserve backward compatibility with all the previously used models based on MPNet.
+ """
+ # Mask token behave like a normal word, i.e. include the space before it
+ # So we set lstrip to True
+ value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value
+ self._mask_token = value
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
+ if token_ids_1 is None:
+ return output
+
+ return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. MPNet does not
+ make use of token type ids, therefore a list of zeros is returned
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of ids.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs
+
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+ return tuple(files)
+
+
+__all__ = ["MPNetTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpt/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpt/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..20bf8c5ebaf9871204c64eedc5990498ecff1ddc
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpt/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mpt import *
+ from .modeling_mpt import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpt/configuration_mpt.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpt/configuration_mpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3468ca8fac4e0eb69cb378f2e762bba9b9e12d8
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpt/configuration_mpt.py
@@ -0,0 +1,229 @@
+# coding=utf-8
+# Copyright 2023 HuggingFace Inc. team and MosaicML NLP team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Mpt configuration"""
+
+from typing import Optional, Union
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MptAttentionConfig(PretrainedConfig):
+ """
+ This is the configuration class to store the configuration of a [`MptAttention`] class. It is used to instantiate
+ attention layers according to the specified arguments, defining the layers architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the MPT
+ [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b) architecture. Most of the arguments are kept for backward
+ compatibility with previous MPT models that are hosted on the Hub (previously with `trust_remote_code=True`).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ attn_type (`str`, *optional*, defaults to `"multihead_attention"`):
+ type of attention to use. Options: `"multihead_attention"`, `"multiquery_attention"`.
+ attn_pdrop (`float`, *optional*, defaults to `0.0`):
+ The dropout probability for the attention layers.
+ attn_impl (`str`, *optional*, defaults to `"torch"`):
+ The attention implementation to use. One of `"torch"`, `"flash"`, or `"triton"`.
+ clip_qkv (`float`, *optional*):
+ If not `None`, clip the queries, keys, and values in the attention layer to this value.
+ softmax_scale (`float`, *optional*):
+ If not `None`, scale the softmax in the attention layer by this value. If `None`, will default to
+ `1/sqrt(hidden_size)`.
+ prefix_lm (`bool`, *optional*, defaults to `False`):
+ Whether the model should operate as a Prefix LM. This requires passing an extra `prefix_mask` argument
+ which indicates which tokens belong to the prefix. Tokens in the prefix can attend to one another
+ bi-directionally. Tokens outside the prefix use causal attention.
+ qk_ln (`bool`, *optional*, defaults to `False`):
+ Whether to apply layer normalization to the queries and keys in the attention layer.
+ attn_uses_sequence_id (`bool`, *optional*, defaults to `False`):
+ Whether to restrict attention to tokens that have the same token_type_ids. When the model is in `train`
+ mode, this requires passing an extra *token_type_ids* argument which indicates which sub-sequence each
+ token belongs to. Defaults to `False` meaning any provided *token_type_ids* will be ignored.
+ alibi (`bool`, *optional*, defaults to `True`):
+ Whether or not to use the alibi bias instead of positional embedding.
+ alibi_bias_max (`int`, *optional*, defaults to 8):
+ The maximum value of the alibi bias.
+ """
+
+ base_config_key = "attn_config"
+
+ def __init__(
+ self,
+ attn_type="multihead_attention",
+ attn_pdrop=0,
+ attn_impl="torch",
+ clip_qkv=None,
+ softmax_scale=None,
+ prefix_lm=False,
+ qk_ln=False,
+ attn_uses_sequence_id=False,
+ alibi=True,
+ alibi_bias_max=8,
+ **kwargs,
+ ):
+ super().__init__()
+ self.attn_type = attn_type
+ self.attn_pdrop = attn_pdrop
+ self.attn_impl = attn_impl
+ self.clip_qkv = clip_qkv
+ self.softmax_scale = softmax_scale
+ self.prefix_lm = prefix_lm
+ self.attn_uses_sequence_id = attn_uses_sequence_id
+ self.alibi = alibi
+ self.qk_ln = qk_ln
+ self.alibi_bias_max = alibi_bias_max
+
+ if attn_type not in ["multihead_attention", "multiquery_attention"]:
+ raise ValueError(
+ f"`attn_type` has to be either `multihead_attention` or `multiquery_attention`. Received: {attn_type}"
+ )
+
+
+class MptConfig(PretrainedConfig):
+ """
+ This is the configuration class to store the configuration of a [`MptModel`]. It is used to instantiate a Mpt model
+ according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to the Mpt-7b architecture
+ [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ d_model (`int`, *optional*, defaults to 2048):
+ Dimensionality of the embeddings and hidden states.
+ n_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ n_layers (`int`, *optional*, defaults to 24):
+ Number of hidden layers in the Transformer encoder.
+ expansion_ratio (`int`, *optional*, defaults to 4):
+ The ratio of the up/down scale in the MLP.
+ max_seq_len (`int`, *optional*, defaults to 2048):
+ The maximum sequence length of the model.
+ vocab_size (`int`, *optional*, defaults to 50368):
+ Vocabulary size of the Mpt model. Defines the maximum number of different tokens that can be represented by
+ the `inputs_ids` passed when calling [`MptModel`]. Check [this
+ discussion](https://huggingface.co/bigscience/mpt/discussions/120#633d28389addb8530b406c2a) on how the
+ `vocab_size` has been defined.
+ resid_pdrop (`float`, *optional*, defaults to 0.0):
+ The dropout probability applied to the attention output before combining with residual.
+ layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
+ The epsilon to use in the layer normalization layers.
+ emb_pdrop (`float`, *optional*, defaults to 0.0):
+ The dropout probability for the embedding layer.
+ learned_pos_emb (`bool`, *optional*, defaults to `True`):
+ Whether to use learned positional embeddings.
+ attn_config (`dict`, *optional*):
+ A dictionary used to configure the model's attention module.
+ init_device (`str`, *optional*, defaults to `"cpu"`):
+ The device to use for parameter initialization. Defined for backward compatibility
+ logit_scale (`float`, *optional*):
+ If not None, scale the logits by this value.
+ no_bias (`bool`, *optional*, defaults to `True`):
+ Whether to use bias in all linear layers.
+ verbose (`int`, *optional*, defaults to 0):
+ The verbosity level to use for logging. Used in the previous versions of MPT models for logging. This
+ argument is deprecated.
+ embedding_fraction (`float`, *optional*, defaults to 1.0):
+ The fraction to scale the gradients of the embedding layer by.
+ norm_type (`str`, *optional*, defaults to `"low_precision_layernorm"`):
+ Type of layer norm to use. All MPT models uses the same layer norm implementation. Defined for backward
+ compatibility.
+ use_cache (`bool`, *optional*, defaults to `False`):
+ Whether or not the model should return the last key/values attentions (not used by all models).
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+
+ Example:
+
+ ```python
+ >>> from transformers import MptConfig, MptModel
+
+ >>> # Initializing a Mpt configuration
+ >>> configuration = MptConfig()
+
+ >>> # Initializing a model (with random weights) from the configuration
+ >>> model = MptModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+ """
+
+ model_type = "mpt"
+ sub_configs = {"attn_config": MptAttentionConfig}
+ attribute_map = {
+ "num_attention_heads": "n_heads",
+ "hidden_size": "d_model",
+ "num_hidden_layers": "n_layers",
+ }
+
+ def __init__(
+ self,
+ d_model: int = 2048,
+ n_heads: int = 16,
+ n_layers: int = 24,
+ expansion_ratio: int = 4,
+ max_seq_len: int = 2048,
+ vocab_size: int = 50368,
+ resid_pdrop: float = 0.0,
+ layer_norm_epsilon: float = 1e-5,
+ emb_pdrop: float = 0.0,
+ learned_pos_emb: bool = True,
+ attn_config: MptAttentionConfig = None,
+ init_device: str = "cpu",
+ logit_scale: Optional[Union[float, str]] = None,
+ no_bias: bool = True,
+ verbose: int = 0,
+ embedding_fraction: float = 1.0,
+ norm_type: str = "low_precision_layernorm",
+ use_cache: bool = False,
+ initializer_range=0.02,
+ **kwargs,
+ ):
+ if attn_config is None:
+ self.attn_config = MptAttentionConfig()
+ elif isinstance(attn_config, dict):
+ self.attn_config = MptAttentionConfig(**attn_config)
+ else:
+ self.attn_config = attn_config
+ self.d_model = d_model
+ self.n_heads = n_heads
+ self.n_layers = n_layers
+ self.expansion_ratio = expansion_ratio
+ self.max_seq_len = max_seq_len
+ self.vocab_size = vocab_size
+ self.resid_pdrop = resid_pdrop
+ self.emb_pdrop = emb_pdrop
+ self.learned_pos_emb = learned_pos_emb
+ self.init_device = init_device
+ self.logit_scale = logit_scale
+ self.no_bias = no_bias
+ self.verbose = verbose
+ self.embedding_fraction = embedding_fraction
+ self.norm_type = norm_type
+ self.layer_norm_epsilon = layer_norm_epsilon
+ self.use_cache = use_cache
+ self.initializer_range = initializer_range
+ super().__init__(**kwargs)
+
+
+__all__ = ["MptConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpt/modeling_mpt.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpt/modeling_mpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..8dbd8235b86b37d9a3a2155eca8cd4bc855087fb
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mpt/modeling_mpt.py
@@ -0,0 +1,825 @@
+# coding=utf-8
+# Copyright 2023 HuggingFace Inc. team and MosaicML NLP team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MPT model."""
+
+import math
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
+from torch.nn import functional as F
+
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutputWithPastAndCrossAttentions,
+ CausalLMOutputWithCrossAttentions,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutputWithPast,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, logging
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_mpt import MptConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def build_mpt_alibi_tensor(num_heads, sequence_length, alibi_bias_max=8, device=None):
+ r"""
+ Link to paper: https://huggingface.co/papers/2108.12409 - Alibi tensor is not causal as the original paper mentions, it
+ relies on a translation invariance of softmax for quick implementation. This implementation has been copied from
+ the alibi implementation of MPT source code that led to slightly different results than the Bloom alibi:
+ https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L292
+ """
+ alibi = torch.arange(1 - sequence_length, 1, dtype=torch.int32, device=device).view(1, 1, 1, sequence_length)
+ num_heads_power_of_2 = 2 ** math.ceil(math.log2(num_heads))
+
+ base = torch.arange(1, num_heads_power_of_2 + 1, dtype=torch.int64, device=device).float()
+ base = base * (alibi_bias_max / num_heads_power_of_2)
+
+ slopes = 1.0 / torch.pow(2, base)
+ slopes = slopes.view(1, num_heads_power_of_2, 1, 1)
+
+ if num_heads_power_of_2 != num_heads:
+ slopes = torch.concat([slopes[:, 1::2, ...], slopes[:, ::2, ...]], dim=1)[:, :num_heads, ...]
+
+ alibi = alibi * slopes
+ return alibi.squeeze(0)
+
+
+class MptAttention(nn.Module):
+ """Multi-head self attention.
+ Using torch or triton attention implementation enables user to also use additive bias.
+ """
+
+ def __init__(self, config: MptConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.n_heads = config.n_heads
+ self.max_seq_length = config.max_seq_len
+ self.head_dim = self.hidden_size // self.n_heads
+ self.softmax_scale = config.attn_config.softmax_scale
+ if self.softmax_scale is None:
+ self.softmax_scale = 1 / math.sqrt(self.hidden_size / self.n_heads)
+
+ self.attn_dropout_p = config.attn_config.attn_pdrop
+ self.clip_qkv = config.attn_config.clip_qkv
+ self.Wqkv = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
+ self.out_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+ self.layer_idx = layer_idx
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_bias: torch.Tensor,
+ past_key_values: Optional[Cache] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ):
+ batch_size, seq_length = hidden_states.shape[:2]
+
+ mixed_qkv = self.Wqkv(hidden_states)
+ if self.clip_qkv:
+ mixed_qkv = mixed_qkv.clamp(min=-self.clip_qkv, max=self.clip_qkv)
+
+ query_states, key_states, value_states = mixed_qkv.chunk(3, dim=2)
+ query_states = query_states.reshape(batch_size, seq_length, self.n_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.reshape(batch_size, seq_length, self.n_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.reshape(batch_size, seq_length, self.n_heads, self.head_dim).transpose(1, 2)
+
+ if past_key_values is not None:
+ cache_kwargs = {"cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2)) * self.softmax_scale
+ query_length = seq_length if past_key_values is None else seq_length + past_key_values.get_seq_length()
+
+ if position_bias is not None:
+ if len(position_bias.shape) != 3:
+ raise ValueError(f"Expecting position_bias shape to be 3 dimensions, got {len(position_bias.shape)}")
+ key_length = key_states.shape[-2]
+
+ position_bias_query_index = max(0, position_bias.size(1) - query_length)
+ position_bias_key_index = max(0, position_bias.size(2) - key_length)
+
+ position_bias = position_bias[:, position_bias_query_index:, position_bias_key_index:]
+
+ attention_scores = attention_scores + position_bias
+
+ if attention_mask is not None:
+ attention_scores = attention_scores.masked_fill(attention_mask, torch.finfo(query_states.dtype).min)
+
+ # (batch_size, n_heads, seq_length, key_length)
+ attn_weights = nn.functional.softmax(attention_scores.float(), dim=-1).to(value_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attn_dropout_p, training=self.training)
+
+ context_states = torch.matmul(attn_weights, value_states)
+ context_states = context_states.permute(0, 2, 1, 3).contiguous().view(batch_size, seq_length, -1)
+ attn_output = self.out_proj(context_states)
+
+ return attn_output, attn_weights
+
+
+class MptMLP(nn.Module):
+ def __init__(self, config: MptConfig):
+ super().__init__()
+ hidden_size = config.hidden_size
+
+ self.up_proj = nn.Linear(hidden_size, 4 * hidden_size, bias=False)
+ self.act = nn.GELU(approximate="none")
+ self.down_proj = nn.Linear(4 * hidden_size, hidden_size, bias=False)
+ self.hidden_dropout = config.attn_config.attn_pdrop
+
+ def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.act(self.up_proj(hidden_states))
+
+ intermediate_output = self.down_proj(hidden_states)
+
+ output = F.dropout(intermediate_output, p=self.hidden_dropout, training=self.training)
+ output = output + residual
+
+ return output
+
+
+class MptBlock(GradientCheckpointingLayer):
+ def __init__(self, config: MptConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ hidden_size = config.hidden_size
+
+ self.norm_1 = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+ # backward compatibility with weights on the Hub
+ self.norm_1.bias = None
+
+ self.num_heads = config.n_heads
+ self.attn = MptAttention(config, layer_idx)
+
+ self.norm_2 = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+ # backward compatibility with weights on the Hub
+ self.norm_2.bias = None
+
+ self.ffn = MptMLP(config)
+
+ self.dropout_rate = config.attn_config.attn_pdrop
+ self.resid_attn_dropout = nn.Dropout(self.dropout_rate)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_bias: torch.Tensor,
+ attention_mask: torch.Tensor,
+ layer_past: Optional[Cache] = None,
+ use_cache: bool = False,
+ output_attentions: bool = False,
+ cache_position: Optional[torch.Tensor] = None,
+ ):
+ # hidden_states: [batch_size, seq_length, hidden_size]
+ # Layer norm at the beginning of the transformer layer.
+ layernorm_output = self.norm_1(hidden_states)
+
+ residual = hidden_states
+
+ # Self attention.
+ attn_outputs, attn_weights = self.attn(
+ layernorm_output,
+ position_bias=position_bias,
+ attention_mask=attention_mask,
+ past_key_values=layer_past,
+ cache_position=cache_position,
+ )
+
+ hidden_states = self.resid_attn_dropout(attn_outputs) + residual
+
+ layernorm_output = self.norm_2(hidden_states)
+
+ # Get residual
+ residual = hidden_states
+
+ # MLP.
+ output = self.ffn(layernorm_output, residual)
+ return output, attn_weights
+
+
+@auto_docstring
+class MptPreTrainedModel(PreTrainedModel):
+ config: MptConfig
+ base_model_prefix = "transformer"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MptBlock"]
+ _keys_to_ignore_on_load_missing = [r"lm_head.*."]
+
+ def __init__(self, *inputs, **kwargs):
+ super().__init__(*inputs, **kwargs)
+
+ def _init_weights(self, module: nn.Module):
+ """Initialize the weights."""
+ if isinstance(module, nn.Linear):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, LayerNorm):
+ if module.bias is not None:
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+ @staticmethod
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def _convert_to_mpt_cache(
+ past_key_values: tuple[tuple[torch.Tensor, torch.Tensor]],
+ ) -> tuple[tuple[torch.Tensor, torch.Tensor]]:
+ """
+ Converts the cache to the format expected by Mpt, i.e. to tuple(tuple([batch_size * num_heads, ...]))
+ """
+ batch_size, num_heads, head_dim, seq_length = past_key_values[0][0].shape
+ batch_size_times_num_heads = batch_size * num_heads
+ # key: [batch_size, num_heads, head_dim, seq_length] -> [batch_size * num_heads, head_dim, seq_length]
+ # value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size * num_heads, seq_length, head_dim]
+ return tuple(
+ (
+ layer_past[0].reshape(batch_size_times_num_heads, head_dim, seq_length),
+ layer_past[1].reshape(batch_size_times_num_heads, seq_length, head_dim),
+ )
+ for layer_past in past_key_values
+ )
+
+
+@auto_docstring
+class MptModel(MptPreTrainedModel):
+ def __init__(self, config: MptConfig):
+ super().__init__(config)
+
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.n_heads
+
+ # Embedding + LN Embedding
+ self.wte = nn.Embedding(config.vocab_size, self.hidden_size)
+
+ # Transformer blocks
+ self.blocks = nn.ModuleList([MptBlock(config, layer_idx=i) for i in range(config.n_layers)])
+
+ # Final Layer Norm
+ self.norm_f = LayerNorm(self.hidden_size, eps=config.layer_norm_epsilon)
+ # backward compatibility with weights on the Hub
+ self.norm_f.bias = None
+
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.wte
+
+ def build_mpt_alibi_tensor(self, num_heads, sequence_length, alibi_bias_max=8, device=None):
+ return build_mpt_alibi_tensor(num_heads, sequence_length, alibi_bias_max, device)
+
+ def set_input_embeddings(self, new_embeddings: torch.Tensor):
+ self.wte = new_embeddings
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[tuple[tuple[torch.Tensor, torch.Tensor], ...], Cache]] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs, # NOOP kwargs, for now
+ ) -> Union[tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
+ `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
+ (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
+
+ If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
+ `input_ids`.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ batch_size, seq_length = input_ids.shape
+ elif inputs_embeds is not None:
+ batch_size, seq_length, _ = inputs_embeds.shape
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ if inputs_embeds is None:
+ inputs_embeds = self.wte(input_ids)
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+ if use_cache and isinstance(past_key_values, tuple):
+ logger.warning_once(
+ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
+ "You should pass an instance of `DynamicCache` instead, e.g. "
+ "`past_key_values=DynamicCache.from_legacy_cache(past_key_values)`."
+ )
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+
+ hidden_states = inputs_embeds
+
+ all_self_attentions = () if output_attentions else None
+ all_hidden_states = () if output_hidden_states else None
+
+ # Compute alibi tensor: check build_alibi_tensor documentation
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+ seq_length_with_past = seq_length + past_key_values_length
+ if attention_mask is None:
+ attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
+ else:
+ attention_mask = attention_mask.to(hidden_states.device)
+
+ alibi = self.build_mpt_alibi_tensor(self.num_heads, self.config.max_seq_len, device=hidden_states.device)
+
+ causal_mask = _prepare_4d_causal_attention_mask(
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+ )
+ causal_mask = causal_mask.bool()
+
+ for block in self.blocks:
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ outputs = block(
+ hidden_states,
+ layer_past=past_key_values,
+ attention_mask=causal_mask,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ position_bias=alibi,
+ cache_position=cache_position,
+ )
+
+ hidden_states = outputs[0]
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (outputs[1],)
+
+ # Add last hidden state
+ hidden_states = self.norm_f(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attentions] if v is not None
+ )
+
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The MPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
+ embeddings).
+ """
+)
+class MptForCausalLM(MptPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: MptConfig):
+ super().__init__(config)
+ self.transformer = MptModel(config)
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_output_embeddings(self, new_embeddings: torch.Tensor):
+ self.lm_head = new_embeddings
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.Tensor, torch.Tensor], ...]] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
+ `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
+ (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
+
+ If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
+ `input_ids`.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.transformer(
+ input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+ hidden_states = transformer_outputs[0]
+
+ lm_logits = self.lm_head(hidden_states)
+
+ loss = None
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(lm_logits.device)
+ # Flatten the tokens
+ loss = self.loss_function(
+ lm_logits,
+ labels,
+ vocab_size=self.config.vocab_size,
+ **kwargs,
+ )
+
+ if not return_dict:
+ output = (lm_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return CausalLMOutputWithCrossAttentions(
+ loss=loss,
+ logits=lm_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The MPT Model transformer with a sequence classification head on top (linear layer).
+
+ [`MptForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-1) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """
+)
+class MptForSequenceClassification(MptPreTrainedModel):
+ def __init__(self, config: MptConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.transformer = MptModel(config)
+ self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.Tensor, torch.Tensor], ...]] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
+ `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
+ (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
+
+ If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
+ `input_ids`.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.transformer(
+ input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size = input_ids.shape[0]
+ else:
+ batch_size = inputs_embeds.shape[0]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ last_non_pad_token = -1
+ elif input_ids is not None:
+ # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+ non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+ token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
+ last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+ else:
+ last_non_pad_token = -1
+ logger.warning_once(
+ f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+ "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+ )
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+
+@auto_docstring
+class MptForTokenClassification(MptPreTrainedModel):
+ def __init__(self, config: MptConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.transformer = MptModel(config)
+ if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
+ classifier_dropout = config.classifier_dropout
+ elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
+ classifier_dropout = config.hidden_dropout
+ else:
+ classifier_dropout = 0.1
+ self.dropout = nn.Dropout(classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.Tensor, torch.Tensor], ...]] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **deprecated_arguments,
+ ) -> Union[tuple[torch.Tensor], TokenClassifierOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
+ `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
+ (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
+
+ If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
+ `input_ids`.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.transformer(
+ input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = transformer_outputs[0]
+ hidden_states = self.dropout(hidden_states)
+ logits = self.classifier(hidden_states)
+
+ loss = None
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ batch_size, seq_length = labels.shape
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(
+ logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
+ )
+
+ if not return_dict:
+ output = (logits,) + transformer_outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+
+@auto_docstring
+class MptForQuestionAnswering(MptPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.transformer = MptModel(config)
+ self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, QuestionAnsweringModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`):
+ `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values.get_seq_length()`
+ (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary.
+
+ If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as
+ `input_ids`.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.transformer(
+ input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "MptForCausalLM",
+ "MptModel",
+ "MptPreTrainedModel",
+ "MptForSequenceClassification",
+ "MptForTokenClassification",
+ "MptForQuestionAnswering",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2963ad0c97ba8c0c5bdc32ff9adca79ffc56cde3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mra import *
+ from .modeling_mra import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/configuration_mra.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/configuration_mra.py
new file mode 100644
index 0000000000000000000000000000000000000000..16b064c98f7e6a9ed4fe72d7149fb867380c904d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/configuration_mra.py
@@ -0,0 +1,137 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MRA model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MraConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MraModel`]. It is used to instantiate an MRA
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the Mra
+ [uw-madison/mra-base-512-4](https://huggingface.co/uw-madison/mra-base-512-4) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50265):
+ Vocabulary size of the Mra model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MraModel`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimension of the encoder layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 1):
+ The vocabulary size of the `token_type_ids` passed when calling [`MraModel`].
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-5):
+ The epsilon used by the layer normalization layers.
+ position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
+ Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`.
+ block_per_row (`int`, *optional*, defaults to 4):
+ Used to set the budget for the high resolution scale.
+ approx_mode (`str`, *optional*, defaults to `"full"`):
+ Controls whether both low and high resolution approximations are used. Set to `"full"` for both low and
+ high resolution and `"sparse"` for only low resolution.
+ initial_prior_first_n_blocks (`int`, *optional*, defaults to 0):
+ The initial number of blocks for which high resolution is used.
+ initial_prior_diagonal_n_blocks (`int`, *optional*, defaults to 0):
+ The number of diagonal blocks for which high resolution is used.
+
+ Example:
+
+ ```python
+ >>> from transformers import MraConfig, MraModel
+
+ >>> # Initializing a Mra uw-madison/mra-base-512-4 style configuration
+ >>> configuration = MraConfig()
+
+ >>> # Initializing a model (with random weights) from the uw-madison/mra-base-512-4 style configuration
+ >>> model = MraModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mra"
+
+ def __init__(
+ self,
+ vocab_size=50265,
+ hidden_size=768,
+ num_hidden_layers=12,
+ num_attention_heads=12,
+ intermediate_size=3072,
+ hidden_act="gelu",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=512,
+ type_vocab_size=1,
+ initializer_range=0.02,
+ layer_norm_eps=1e-5,
+ position_embedding_type="absolute",
+ block_per_row=4,
+ approx_mode="full",
+ initial_prior_first_n_blocks=0,
+ initial_prior_diagonal_n_blocks=0,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ **kwargs,
+ ):
+ super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.intermediate_size = intermediate_size
+ self.hidden_act = hidden_act
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.initializer_range = initializer_range
+ self.type_vocab_size = type_vocab_size
+ self.layer_norm_eps = layer_norm_eps
+ self.position_embedding_type = position_embedding_type
+ self.block_per_row = block_per_row
+ self.approx_mode = approx_mode
+ self.initial_prior_first_n_blocks = initial_prior_first_n_blocks
+ self.initial_prior_diagonal_n_blocks = initial_prior_diagonal_n_blocks
+
+
+__all__ = ["MraConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/convert_mra_pytorch_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/convert_mra_pytorch_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..6330e2fe9292bd18627e35b37b77d360b65f4af9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/convert_mra_pytorch_to_pytorch.py
@@ -0,0 +1,110 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert MRA checkpoints from the original repository. URL: https://github.com/mlpen/mra-attention"""
+
+import argparse
+
+import torch
+
+from transformers import MraConfig, MraForMaskedLM
+
+
+def rename_key(orig_key):
+ if "model" in orig_key:
+ orig_key = orig_key.replace("model.", "")
+ if "norm1" in orig_key:
+ orig_key = orig_key.replace("norm1", "attention.output.LayerNorm")
+ if "norm2" in orig_key:
+ orig_key = orig_key.replace("norm2", "output.LayerNorm")
+ if "norm" in orig_key:
+ orig_key = orig_key.replace("norm", "LayerNorm")
+ if "transformer" in orig_key:
+ layer_num = orig_key.split(".")[0].split("_")[-1]
+ orig_key = orig_key.replace(f"transformer_{layer_num}", f"encoder.layer.{layer_num}")
+ if "mha.attn" in orig_key:
+ orig_key = orig_key.replace("mha.attn", "attention.self")
+ if "mha" in orig_key:
+ orig_key = orig_key.replace("mha", "attention")
+ if "W_q" in orig_key:
+ orig_key = orig_key.replace("W_q", "self.query")
+ if "W_k" in orig_key:
+ orig_key = orig_key.replace("W_k", "self.key")
+ if "W_v" in orig_key:
+ orig_key = orig_key.replace("W_v", "self.value")
+ if "ff.0" in orig_key:
+ orig_key = orig_key.replace("ff.0", "intermediate.dense")
+ if "ff.2" in orig_key:
+ orig_key = orig_key.replace("ff.2", "output.dense")
+ if "ff" in orig_key:
+ orig_key = orig_key.replace("ff", "output.dense")
+ if "mlm_class" in orig_key:
+ orig_key = orig_key.replace("mlm.mlm_class", "cls.predictions.decoder")
+ if "mlm" in orig_key:
+ orig_key = orig_key.replace("mlm", "cls.predictions.transform")
+ if "backbone.backbone.encoders" in orig_key:
+ orig_key = orig_key.replace("backbone.backbone.encoders", "encoder.layer")
+ if "cls" not in orig_key:
+ orig_key = "mra." + orig_key
+
+ return orig_key
+
+
+def convert_checkpoint_helper(max_position_embeddings, orig_state_dict):
+ for key in orig_state_dict.copy():
+ val = orig_state_dict.pop(key)
+
+ if ("pooler" in key) or ("sen_class" in key):
+ continue
+ else:
+ orig_state_dict[rename_key(key)] = val
+
+ orig_state_dict["cls.predictions.bias"] = orig_state_dict["cls.predictions.decoder.bias"]
+ orig_state_dict["mra.embeddings.position_ids"] = torch.arange(max_position_embeddings).expand((1, -1)) + 2
+
+ return orig_state_dict
+
+
+def convert_mra_checkpoint(checkpoint_path, mra_config_file, pytorch_dump_path):
+ orig_state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=True)["model_state_dict"]
+ config = MraConfig.from_json_file(mra_config_file)
+ model = MraForMaskedLM(config)
+
+ new_state_dict = convert_checkpoint_helper(config.max_position_embeddings, orig_state_dict)
+
+ print(model.load_state_dict(new_state_dict))
+ model.eval()
+ model.save_pretrained(pytorch_dump_path)
+
+ print(f"Checkpoint successfully converted. Model saved at {pytorch_dump_path}")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
+ )
+ parser.add_argument(
+ "--config_file",
+ default=None,
+ type=str,
+ required=True,
+ help="The json file for Mra model config.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
+ )
+ args = parser.parse_args()
+ convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/modeling_mra.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/modeling_mra.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a37712e858073b10f47f1c5bc65f19aca5fef1e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mra/modeling_mra.py
@@ -0,0 +1,1392 @@
+# coding=utf-8
+# Copyright 2023 University of Wisconsin-Madison and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MRA model."""
+
+import math
+from pathlib import Path
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from torch.utils.cpp_extension import load
+
+from ...activations import ACT2FN
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutputWithCrossAttentions,
+ MaskedLMOutput,
+ MultipleChoiceModelOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import auto_docstring, is_cuda_platform, is_ninja_available, is_torch_cuda_available, logging
+from .configuration_mra import MraConfig
+
+
+logger = logging.get_logger(__name__)
+
+mra_cuda_kernel = None
+
+
+def load_cuda_kernels():
+ global mra_cuda_kernel
+ src_folder = Path(__file__).resolve().parent.parent.parent / "kernels" / "mra"
+
+ def append_root(files):
+ return [src_folder / file for file in files]
+
+ src_files = append_root(["cuda_kernel.cu", "cuda_launch.cu", "torch_extension.cpp"])
+
+ mra_cuda_kernel = load("cuda_kernel", src_files, verbose=True)
+
+
+def sparse_max(sparse_qk_prod, indices, query_num_block, key_num_block):
+ """
+ Computes maximum values for softmax stability.
+ """
+ if len(sparse_qk_prod.size()) != 4:
+ raise ValueError("sparse_qk_prod must be a 4-dimensional tensor.")
+
+ if len(indices.size()) != 2:
+ raise ValueError("indices must be a 2-dimensional tensor.")
+
+ if sparse_qk_prod.size(2) != 32:
+ raise ValueError("The size of the second dimension of sparse_qk_prod must be 32.")
+
+ if sparse_qk_prod.size(3) != 32:
+ raise ValueError("The size of the third dimension of sparse_qk_prod must be 32.")
+
+ index_vals = sparse_qk_prod.max(dim=-2).values.transpose(-1, -2)
+ index_vals = index_vals.contiguous()
+
+ indices = indices.int()
+ indices = indices.contiguous()
+
+ max_vals, max_vals_scatter = mra_cuda_kernel.index_max(index_vals, indices, query_num_block, key_num_block)
+ max_vals_scatter = max_vals_scatter.transpose(-1, -2)[:, :, None, :]
+
+ return max_vals, max_vals_scatter
+
+
+def sparse_mask(mask, indices, block_size=32):
+ """
+ Converts attention mask to a sparse mask for high resolution logits.
+ """
+ if len(mask.size()) != 2:
+ raise ValueError("mask must be a 2-dimensional tensor.")
+
+ if len(indices.size()) != 2:
+ raise ValueError("indices must be a 2-dimensional tensor.")
+
+ if mask.shape[0] != indices.shape[0]:
+ raise ValueError("mask and indices must have the same size in the zero-th dimension.")
+
+ batch_size, seq_len = mask.shape
+ num_block = seq_len // block_size
+
+ batch_idx = torch.arange(indices.size(0), dtype=torch.long, device=indices.device)
+ mask = mask.reshape(batch_size, num_block, block_size)
+ mask = mask[batch_idx[:, None], (indices % num_block).long(), :]
+
+ return mask
+
+
+def mm_to_sparse(dense_query, dense_key, indices, block_size=32):
+ """
+ Performs Sampled Dense Matrix Multiplication.
+ """
+ batch_size, query_size, dim = dense_query.size()
+ _, key_size, dim = dense_key.size()
+
+ if query_size % block_size != 0:
+ raise ValueError("query_size (size of first dimension of dense_query) must be divisible by block_size.")
+
+ if key_size % block_size != 0:
+ raise ValueError("key_size (size of first dimension of dense_key) must be divisible by block_size.")
+
+ dense_query = dense_query.reshape(batch_size, query_size // block_size, block_size, dim).transpose(-1, -2)
+ dense_key = dense_key.reshape(batch_size, key_size // block_size, block_size, dim).transpose(-1, -2)
+
+ if len(dense_query.size()) != 4:
+ raise ValueError("dense_query must be a 4-dimensional tensor.")
+
+ if len(dense_key.size()) != 4:
+ raise ValueError("dense_key must be a 4-dimensional tensor.")
+
+ if len(indices.size()) != 2:
+ raise ValueError("indices must be a 2-dimensional tensor.")
+
+ if dense_query.size(3) != 32:
+ raise ValueError("The third dimension of dense_query must be 32.")
+
+ if dense_key.size(3) != 32:
+ raise ValueError("The third dimension of dense_key must be 32.")
+
+ dense_query = dense_query.contiguous()
+ dense_key = dense_key.contiguous()
+
+ indices = indices.int()
+ indices = indices.contiguous()
+
+ return mra_cuda_kernel.mm_to_sparse(dense_query, dense_key, indices.int())
+
+
+def sparse_dense_mm(sparse_query, indices, dense_key, query_num_block, block_size=32):
+ """
+ Performs matrix multiplication of a sparse matrix with a dense matrix.
+ """
+ batch_size, key_size, dim = dense_key.size()
+
+ if key_size % block_size != 0:
+ raise ValueError("key_size (size of first dimension of dense_key) must be divisible by block_size.")
+
+ if sparse_query.size(2) != block_size:
+ raise ValueError("The size of the second dimension of sparse_query must be equal to the block_size.")
+
+ if sparse_query.size(3) != block_size:
+ raise ValueError("The size of the third dimension of sparse_query must be equal to the block_size.")
+
+ dense_key = dense_key.reshape(batch_size, key_size // block_size, block_size, dim).transpose(-1, -2)
+
+ if len(sparse_query.size()) != 4:
+ raise ValueError("sparse_query must be a 4-dimensional tensor.")
+
+ if len(dense_key.size()) != 4:
+ raise ValueError("dense_key must be a 4-dimensional tensor.")
+
+ if len(indices.size()) != 2:
+ raise ValueError("indices must be a 2-dimensional tensor.")
+
+ if dense_key.size(3) != 32:
+ raise ValueError("The size of the third dimension of dense_key must be 32.")
+
+ sparse_query = sparse_query.contiguous()
+
+ indices = indices.int()
+ indices = indices.contiguous()
+ dense_key = dense_key.contiguous()
+
+ dense_qk_prod = mra_cuda_kernel.sparse_dense_mm(sparse_query, indices, dense_key, query_num_block)
+ dense_qk_prod = dense_qk_prod.transpose(-1, -2).reshape(batch_size, query_num_block * block_size, dim)
+ return dense_qk_prod
+
+
+def transpose_indices(indices, dim_1_block, dim_2_block):
+ return ((indices % dim_2_block) * dim_1_block + torch.div(indices, dim_2_block, rounding_mode="floor")).long()
+
+
+class MraSampledDenseMatMul(torch.autograd.Function):
+ @staticmethod
+ def forward(ctx, dense_query, dense_key, indices, block_size):
+ sparse_qk_prod = mm_to_sparse(dense_query, dense_key, indices, block_size)
+ ctx.save_for_backward(dense_query, dense_key, indices)
+ ctx.block_size = block_size
+ return sparse_qk_prod
+
+ @staticmethod
+ def backward(ctx, grad):
+ dense_query, dense_key, indices = ctx.saved_tensors
+ block_size = ctx.block_size
+ query_num_block = dense_query.size(1) // block_size
+ key_num_block = dense_key.size(1) // block_size
+ indices_T = transpose_indices(indices, query_num_block, key_num_block)
+ grad_key = sparse_dense_mm(grad.transpose(-1, -2), indices_T, dense_query, key_num_block)
+ grad_query = sparse_dense_mm(grad, indices, dense_key, query_num_block)
+ return grad_query, grad_key, None, None
+
+ @staticmethod
+ def operator_call(dense_query, dense_key, indices, block_size=32):
+ return MraSampledDenseMatMul.apply(dense_query, dense_key, indices, block_size)
+
+
+class MraSparseDenseMatMul(torch.autograd.Function):
+ @staticmethod
+ def forward(ctx, sparse_query, indices, dense_key, query_num_block):
+ sparse_qk_prod = sparse_dense_mm(sparse_query, indices, dense_key, query_num_block)
+ ctx.save_for_backward(sparse_query, indices, dense_key)
+ ctx.query_num_block = query_num_block
+ return sparse_qk_prod
+
+ @staticmethod
+ def backward(ctx, grad):
+ sparse_query, indices, dense_key = ctx.saved_tensors
+ query_num_block = ctx.query_num_block
+ key_num_block = dense_key.size(1) // sparse_query.size(-1)
+ indices_T = transpose_indices(indices, query_num_block, key_num_block)
+ grad_key = sparse_dense_mm(sparse_query.transpose(-1, -2), indices_T, grad, key_num_block)
+ grad_query = mm_to_sparse(grad, dense_key, indices)
+ return grad_query, None, grad_key, None
+
+ @staticmethod
+ def operator_call(sparse_query, indices, dense_key, query_num_block):
+ return MraSparseDenseMatMul.apply(sparse_query, indices, dense_key, query_num_block)
+
+
+class MraReduceSum:
+ @staticmethod
+ def operator_call(sparse_query, indices, query_num_block, key_num_block):
+ batch_size, num_block, block_size, _ = sparse_query.size()
+
+ if len(sparse_query.size()) != 4:
+ raise ValueError("sparse_query must be a 4-dimensional tensor.")
+
+ if len(indices.size()) != 2:
+ raise ValueError("indices must be a 2-dimensional tensor.")
+
+ _, _, block_size, _ = sparse_query.size()
+ batch_size, num_block = indices.size()
+
+ sparse_query = sparse_query.sum(dim=2).reshape(batch_size * num_block, block_size)
+
+ batch_idx = torch.arange(indices.size(0), dtype=torch.long, device=indices.device)
+ global_idxes = (
+ torch.div(indices, key_num_block, rounding_mode="floor").long() + batch_idx[:, None] * query_num_block
+ ).reshape(batch_size * num_block)
+ temp = torch.zeros(
+ (batch_size * query_num_block, block_size), dtype=sparse_query.dtype, device=sparse_query.device
+ )
+ output = temp.index_add(0, global_idxes, sparse_query).reshape(batch_size, query_num_block, block_size)
+
+ output = output.reshape(batch_size, query_num_block * block_size)
+ return output
+
+
+def get_low_resolution_logit(query, key, block_size, mask=None, value=None):
+ """
+ Compute low resolution approximation.
+ """
+ batch_size, seq_len, head_dim = query.size()
+
+ num_block_per_row = seq_len // block_size
+
+ value_hat = None
+ if mask is not None:
+ token_count = mask.reshape(batch_size, num_block_per_row, block_size).sum(dim=-1)
+ query_hat = query.reshape(batch_size, num_block_per_row, block_size, head_dim).sum(dim=-2) / (
+ token_count[:, :, None] + 1e-6
+ )
+ key_hat = key.reshape(batch_size, num_block_per_row, block_size, head_dim).sum(dim=-2) / (
+ token_count[:, :, None] + 1e-6
+ )
+ if value is not None:
+ value_hat = value.reshape(batch_size, num_block_per_row, block_size, head_dim).sum(dim=-2) / (
+ token_count[:, :, None] + 1e-6
+ )
+ else:
+ token_count = block_size * torch.ones(batch_size, num_block_per_row, dtype=torch.float, device=query.device)
+ query_hat = query.reshape(batch_size, num_block_per_row, block_size, head_dim).mean(dim=-2)
+ key_hat = key.reshape(batch_size, num_block_per_row, block_size, head_dim).mean(dim=-2)
+ if value is not None:
+ value_hat = value.reshape(batch_size, num_block_per_row, block_size, head_dim).mean(dim=-2)
+
+ low_resolution_logit = torch.matmul(query_hat, key_hat.transpose(-1, -2)) / math.sqrt(head_dim)
+
+ low_resolution_logit_row_max = low_resolution_logit.max(dim=-1, keepdims=True).values
+
+ if mask is not None:
+ low_resolution_logit = (
+ low_resolution_logit - 1e4 * ((token_count[:, None, :] * token_count[:, :, None]) < 0.5).float()
+ )
+
+ return low_resolution_logit, token_count, low_resolution_logit_row_max, value_hat
+
+
+def get_block_idxes(
+ low_resolution_logit, num_blocks, approx_mode, initial_prior_first_n_blocks, initial_prior_diagonal_n_blocks
+):
+ """
+ Compute the indices of the subset of components to be used in the approximation.
+ """
+ batch_size, total_blocks_per_row, _ = low_resolution_logit.shape
+
+ if initial_prior_diagonal_n_blocks > 0:
+ offset = initial_prior_diagonal_n_blocks // 2
+ temp_mask = torch.ones(total_blocks_per_row, total_blocks_per_row, device=low_resolution_logit.device)
+ diagonal_mask = torch.tril(torch.triu(temp_mask, diagonal=-offset), diagonal=offset)
+ low_resolution_logit = low_resolution_logit + diagonal_mask[None, :, :] * 5e3
+
+ if initial_prior_first_n_blocks > 0:
+ low_resolution_logit[:, :initial_prior_first_n_blocks, :] = (
+ low_resolution_logit[:, :initial_prior_first_n_blocks, :] + 5e3
+ )
+ low_resolution_logit[:, :, :initial_prior_first_n_blocks] = (
+ low_resolution_logit[:, :, :initial_prior_first_n_blocks] + 5e3
+ )
+
+ top_k_vals = torch.topk(
+ low_resolution_logit.reshape(batch_size, -1), num_blocks, dim=-1, largest=True, sorted=False
+ )
+ indices = top_k_vals.indices
+
+ if approx_mode == "full":
+ threshold = top_k_vals.values.min(dim=-1).values
+ high_resolution_mask = (low_resolution_logit >= threshold[:, None, None]).float()
+ elif approx_mode == "sparse":
+ high_resolution_mask = None
+ else:
+ raise ValueError(f"{approx_mode} is not a valid approx_model value.")
+
+ return indices, high_resolution_mask
+
+
+def mra2_attention(
+ query,
+ key,
+ value,
+ mask,
+ num_blocks,
+ approx_mode,
+ block_size=32,
+ initial_prior_first_n_blocks=0,
+ initial_prior_diagonal_n_blocks=0,
+):
+ """
+ Use Mra to approximate self-attention.
+ """
+ if mra_cuda_kernel is None:
+ return torch.zeros_like(query).requires_grad_()
+
+ batch_size, num_head, seq_len, head_dim = query.size()
+ meta_batch = batch_size * num_head
+
+ if seq_len % block_size != 0:
+ raise ValueError("sequence length must be divisible by the block_size.")
+
+ num_block_per_row = seq_len // block_size
+
+ query = query.reshape(meta_batch, seq_len, head_dim)
+ key = key.reshape(meta_batch, seq_len, head_dim)
+ value = value.reshape(meta_batch, seq_len, head_dim)
+
+ if mask is not None:
+ query = query * mask[:, :, None]
+ key = key * mask[:, :, None]
+ value = value * mask[:, :, None]
+
+ if approx_mode == "full":
+ low_resolution_logit, token_count, low_resolution_logit_row_max, value_hat = get_low_resolution_logit(
+ query, key, block_size, mask, value
+ )
+ elif approx_mode == "sparse":
+ with torch.no_grad():
+ low_resolution_logit, token_count, low_resolution_logit_row_max, _ = get_low_resolution_logit(
+ query, key, block_size, mask
+ )
+ else:
+ raise Exception('approx_mode must be "full" or "sparse"')
+
+ with torch.no_grad():
+ low_resolution_logit_normalized = low_resolution_logit - low_resolution_logit_row_max
+ indices, high_resolution_mask = get_block_idxes(
+ low_resolution_logit_normalized,
+ num_blocks,
+ approx_mode,
+ initial_prior_first_n_blocks,
+ initial_prior_diagonal_n_blocks,
+ )
+
+ high_resolution_logit = MraSampledDenseMatMul.operator_call(
+ query, key, indices, block_size=block_size
+ ) / math.sqrt(head_dim)
+ max_vals, max_vals_scatter = sparse_max(high_resolution_logit, indices, num_block_per_row, num_block_per_row)
+ high_resolution_logit = high_resolution_logit - max_vals_scatter
+ if mask is not None:
+ high_resolution_logit = high_resolution_logit - 1e4 * (1 - sparse_mask(mask, indices)[:, :, :, None])
+ high_resolution_attn = torch.exp(high_resolution_logit)
+ high_resolution_attn_out = MraSparseDenseMatMul.operator_call(
+ high_resolution_attn, indices, value, num_block_per_row
+ )
+ high_resolution_normalizer = MraReduceSum.operator_call(
+ high_resolution_attn, indices, num_block_per_row, num_block_per_row
+ )
+
+ if approx_mode == "full":
+ low_resolution_attn = (
+ torch.exp(low_resolution_logit - low_resolution_logit_row_max - 1e4 * high_resolution_mask)
+ * token_count[:, None, :]
+ )
+
+ low_resolution_attn_out = (
+ torch.matmul(low_resolution_attn, value_hat)[:, :, None, :]
+ .repeat(1, 1, block_size, 1)
+ .reshape(meta_batch, seq_len, head_dim)
+ )
+ low_resolution_normalizer = (
+ low_resolution_attn.sum(dim=-1)[:, :, None].repeat(1, 1, block_size).reshape(meta_batch, seq_len)
+ )
+
+ log_correction = low_resolution_logit_row_max.repeat(1, 1, block_size).reshape(meta_batch, seq_len) - max_vals
+ if mask is not None:
+ log_correction = log_correction * mask
+
+ low_resolution_corr = torch.exp(log_correction * (log_correction <= 0).float())
+ low_resolution_attn_out = low_resolution_attn_out * low_resolution_corr[:, :, None]
+ low_resolution_normalizer = low_resolution_normalizer * low_resolution_corr
+
+ high_resolution_corr = torch.exp(-log_correction * (log_correction > 0).float())
+ high_resolution_attn_out = high_resolution_attn_out * high_resolution_corr[:, :, None]
+ high_resolution_normalizer = high_resolution_normalizer * high_resolution_corr
+
+ context_layer = (high_resolution_attn_out + low_resolution_attn_out) / (
+ high_resolution_normalizer[:, :, None] + low_resolution_normalizer[:, :, None] + 1e-6
+ )
+
+ elif approx_mode == "sparse":
+ context_layer = high_resolution_attn_out / (high_resolution_normalizer[:, :, None] + 1e-6)
+ else:
+ raise Exception('config.approx_mode must be "full" or "sparse"')
+
+ if mask is not None:
+ context_layer = context_layer * mask[:, :, None]
+
+ context_layer = context_layer.reshape(batch_size, num_head, seq_len, head_dim)
+
+ return context_layer
+
+
+class MraEmbeddings(nn.Module):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings + 2, config.hidden_size)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+ self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + 2)
+ self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+ self.register_buffer(
+ "token_type_ids",
+ torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
+ persistent=False,
+ )
+
+ def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+
+ if position_ids is None:
+ position_ids = self.position_ids[:, :seq_length]
+
+ # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
+ # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
+ # issue #5664
+ if token_type_ids is None:
+ if hasattr(self, "token_type_ids"):
+ buffered_token_type_ids = self.token_type_ids[:, :seq_length]
+ buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
+ token_type_ids = buffered_token_type_ids_expanded
+ else:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = inputs_embeds + token_type_embeddings
+ if self.position_embedding_type == "absolute":
+ position_embeddings = self.position_embeddings(position_ids)
+ embeddings += position_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+class MraSelfAttention(nn.Module):
+ def __init__(self, config, position_embedding_type=None):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+
+ kernel_loaded = mra_cuda_kernel is not None
+ if is_torch_cuda_available() and is_cuda_platform() and is_ninja_available() and not kernel_loaded:
+ try:
+ load_cuda_kernels()
+ except Exception as e:
+ logger.warning(f"Could not load the custom kernel for multi-scale deformable attention: {e}")
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+ self.position_embedding_type = (
+ position_embedding_type if position_embedding_type is not None else config.position_embedding_type
+ )
+
+ self.num_block = (config.max_position_embeddings // 32) * config.block_per_row
+ self.num_block = min(self.num_block, int((config.max_position_embeddings // 32) ** 2))
+
+ self.approx_mode = config.approx_mode
+ self.initial_prior_first_n_blocks = config.initial_prior_first_n_blocks
+ self.initial_prior_diagonal_n_blocks = config.initial_prior_diagonal_n_blocks
+
+ def forward(self, hidden_states, attention_mask=None):
+ batch_size, seq_len, _ = hidden_states.shape
+ query_layer = (
+ self.query(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ key_layer = (
+ self.key(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ value_layer = (
+ self.value(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+
+ # revert changes made by get_extended_attention_mask
+ attention_mask = 1.0 + attention_mask / 10000.0
+ attention_mask = (
+ attention_mask.squeeze()
+ .repeat(1, self.num_attention_heads, 1)
+ .reshape(batch_size * self.num_attention_heads, seq_len)
+ .int()
+ )
+
+ # The CUDA kernels are most efficient with inputs whose size is a multiple of a GPU's warp size (32). Inputs
+ # smaller than this are padded with zeros.
+ gpu_warp_size = 32
+
+ if self.attention_head_size < gpu_warp_size:
+ pad_size = batch_size, self.num_attention_heads, seq_len, gpu_warp_size - self.attention_head_size
+
+ query_layer = torch.cat([query_layer, torch.zeros(pad_size, device=query_layer.device)], dim=-1)
+ key_layer = torch.cat([key_layer, torch.zeros(pad_size, device=key_layer.device)], dim=-1)
+ value_layer = torch.cat([value_layer, torch.zeros(pad_size, device=value_layer.device)], dim=-1)
+
+ context_layer = mra2_attention(
+ query_layer.float(),
+ key_layer.float(),
+ value_layer.float(),
+ attention_mask.float(),
+ self.num_block,
+ approx_mode=self.approx_mode,
+ initial_prior_first_n_blocks=self.initial_prior_first_n_blocks,
+ initial_prior_diagonal_n_blocks=self.initial_prior_diagonal_n_blocks,
+ )
+
+ if self.attention_head_size < gpu_warp_size:
+ context_layer = context_layer[:, :, :, : self.attention_head_size]
+
+ context_layer = context_layer.reshape(batch_size, self.num_attention_heads, seq_len, self.attention_head_size)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+
+ outputs = (context_layer,)
+
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
+class MraSelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class MraAttention(nn.Module):
+ def __init__(self, config, position_embedding_type=None):
+ super().__init__()
+ self.self = MraSelfAttention(config, position_embedding_type=position_embedding_type)
+ self.output = MraSelfOutput(config)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+ )
+
+ # Prune linear layers
+ self.self.query = prune_linear_layer(self.self.query, index)
+ self.self.key = prune_linear_layer(self.self.key, index)
+ self.self.value = prune_linear_layer(self.self.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+ self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(self, hidden_states, attention_mask=None):
+ self_outputs = self.self(hidden_states, attention_mask)
+ attention_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate
+class MraIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOutput
+class MraOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class MraLayer(GradientCheckpointingLayer):
+ def __init__(self, config):
+ super().__init__()
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = MraAttention(config)
+ self.add_cross_attention = config.add_cross_attention
+ self.intermediate = MraIntermediate(config)
+ self.output = MraOutput(config)
+
+ def forward(self, hidden_states, attention_mask=None):
+ self_attention_outputs = self.attention(hidden_states, attention_mask)
+ attention_output = self_attention_outputs[0]
+
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ layer_output = apply_chunking_to_forward(
+ self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+ )
+ outputs = (layer_output,) + outputs
+
+ return outputs
+
+ def feed_forward_chunk(self, attention_output):
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+
+class MraEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList([MraLayer(config) for _ in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ head_mask=None,
+ output_hidden_states=False,
+ return_dict=True,
+ ):
+ all_hidden_states = () if output_hidden_states else None
+
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = layer_module(hidden_states, attention_mask)
+
+ hidden_states = layer_outputs[0]
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
+ return BaseModelOutputWithCrossAttentions(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform
+class MraPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ if isinstance(config.hidden_act, str):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Mra
+class MraLMPredictionHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.transform = MraPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+ # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+ self.decoder.bias = self.bias
+
+ def _tie_weights(self):
+ self.decoder.bias = self.bias
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Mra
+class MraOnlyMLMHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.predictions = MraLMPredictionHead(config)
+
+ def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
+ prediction_scores = self.predictions(sequence_output)
+ return prediction_scores
+
+
+@auto_docstring
+# Copied from transformers.models.yoso.modeling_yoso.YosoPreTrainedModel with Yoso->Mra,yoso->mra
+class MraPreTrainedModel(PreTrainedModel):
+ config: MraConfig
+ base_model_prefix = "mra"
+ supports_gradient_checkpointing = True
+
+ def _init_weights(self, module: nn.Module):
+ """Initialize the weights"""
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, MraLMPredictionHead):
+ module.bias.data.zero_()
+
+
+@auto_docstring
+class MraModel(MraPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.config = config
+
+ self.embeddings = MraEmbeddings(config)
+ self.encoder = MraEncoder(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutputWithCrossAttentions]:
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ batch_size, seq_length = input_shape
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones(((batch_size, seq_length)), device=device)
+
+ if token_type_ids is None:
+ if hasattr(self.embeddings, "token_type_ids"):
+ buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
+ buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
+ token_type_ids = buffered_token_type_ids_expanded
+ else:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ )
+ encoder_outputs = self.encoder(
+ embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = encoder_outputs[0]
+
+ if not return_dict:
+ return (sequence_output,) + encoder_outputs[1:]
+
+ return BaseModelOutputWithCrossAttentions(
+ last_hidden_state=sequence_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ cross_attentions=encoder_outputs.cross_attentions,
+ )
+
+
+@auto_docstring
+class MraForMaskedLM(MraPreTrainedModel):
+ _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.mra = MraModel(config)
+ self.cls = MraOnlyMLMHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.cls.predictions.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.cls.predictions.decoder = new_embeddings
+ self.cls.predictions.bias = new_embeddings.bias
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, MaskedLMOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mra(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+ prediction_scores = self.cls(sequence_output)
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss() # -100 index = padding token
+ masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[1:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return MaskedLMOutput(
+ loss=masked_lm_loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+# Copied from transformers.models.yoso.modeling_yoso.YosoClassificationHead with Yoso->Mra
+class MraClassificationHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+ self.config = config
+
+ def forward(self, features, **kwargs):
+ x = features[:, 0, :] # take token (equiv. to [CLS])
+ x = self.dropout(x)
+ x = self.dense(x)
+ x = ACT2FN[self.config.hidden_act](x)
+ x = self.dropout(x)
+ x = self.out_proj(x)
+ return x
+
+
+@auto_docstring(
+ custom_intro="""
+ MRA Model transformer with a sequence classification/regression head on top (a linear layer on top of
+ the pooled output) e.g. for GLUE tasks.
+ """
+)
+class MraForSequenceClassification(MraPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.mra = MraModel(config)
+ self.classifier = MraClassificationHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, SequenceClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mra(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class MraForMultipleChoice(MraPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.mra = MraModel(config)
+ self.pre_classifier = nn.Linear(config.hidden_size, config.hidden_size)
+ self.classifier = nn.Linear(config.hidden_size, 1)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, MultipleChoiceModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+ num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+ `input_ids` above)
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+ input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+ attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+ token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+ position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+ inputs_embeds = (
+ inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+ if inputs_embeds is not None
+ else None
+ )
+
+ outputs = self.mra(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_state = outputs[0] # (bs * num_choices, seq_len, dim)
+ pooled_output = hidden_state[:, 0] # (bs * num_choices, dim)
+ pooled_output = self.pre_classifier(pooled_output) # (bs * num_choices, dim)
+ pooled_output = nn.ReLU()(pooled_output) # (bs * num_choices, dim)
+ logits = self.classifier(pooled_output)
+
+ reshaped_logits = logits.view(-1, num_choices)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(reshaped_logits, labels)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return MultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class MraForTokenClassification(MraPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.mra = MraModel(config)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, TokenClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mra(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ # Only keep active parts of the loss
+ if attention_mask is not None:
+ active_loss = attention_mask.view(-1) == 1
+ active_logits = logits.view(-1, self.num_labels)
+ active_labels = torch.where(
+ active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
+ )
+ loss = loss_fct(active_logits, active_labels)
+ else:
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class MraForQuestionAnswering(MraPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ config.num_labels = 2
+ self.num_labels = config.num_labels
+
+ self.mra = MraModel(config)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ start_positions: Optional[torch.Tensor] = None,
+ end_positions: Optional[torch.Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, QuestionAnsweringModelOutput]:
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.mra(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1)
+ end_logits = end_logits.squeeze(-1)
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[1:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "MraForMaskedLM",
+ "MraForMultipleChoice",
+ "MraForQuestionAnswering",
+ "MraForSequenceClassification",
+ "MraForTokenClassification",
+ "MraLayer",
+ "MraModel",
+ "MraPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..444a8f8cc8e0203a4cd0151c6bff0644e4e3d6b6
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mt5 import *
+ from .modeling_flax_mt5 import *
+ from .modeling_mt5 import *
+ from .modeling_tf_mt5 import *
+ from .tokenization_mt5 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/configuration_mt5.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/configuration_mt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..bad66e63b1260587828ebca4822fc57aa635175a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/configuration_mt5.py
@@ -0,0 +1,182 @@
+# coding=utf-8
+# Copyright 2020, The T5 Authors and HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""mT5 model configuration"""
+
+from collections.abc import Mapping
+
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxSeq2SeqConfigWithPast
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MT5Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MT5Model`] or a [`TFMT5Model`]. It is used to
+ instantiate a mT5 model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the mT5
+ [google/mt5-small](https://huggingface.co/google/mt5-small) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Arguments:
+ vocab_size (`int`, *optional*, defaults to 250112):
+ Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`].
+ d_model (`int`, *optional*, defaults to 512):
+ Size of the encoder layers and the pooler layer.
+ d_kv (`int`, *optional*, defaults to 64):
+ Size of the key, query, value projections per attention head. In the conventional context, it is typically expected that `d_kv` has to be equal to `d_model // num_heads`.
+ But in the architecture of mt5-small, `d_kv` is not equal to `d_model //num_heads`. The `inner_dim` of the projection layer will be defined as `num_heads * d_kv`.
+ d_ff (`int`, *optional*, defaults to 1024):
+ Size of the intermediate feed forward layer in each `T5Block`.
+ num_layers (`int`, *optional*, defaults to 8):
+ Number of hidden layers in the Transformer encoder.
+ num_decoder_layers (`int`, *optional*):
+ Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
+ num_heads (`int`, *optional*, defaults to 6):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ relative_attention_num_buckets (`int`, *optional*, defaults to 32):
+ The number of buckets to use for each attention layer.
+ relative_attention_max_distance (`int`, *optional*, defaults to 128):
+ The maximum distance of the longer sequences for the bucket separation.
+ dropout_rate (`float`, *optional*, defaults to 0.1):
+ The ratio for all dropout layers.
+ classifier_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for classifier.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+ The epsilon used by the layer normalization layers.
+ initializer_factor (`float`, *optional*, defaults to 1):
+ A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+ testing).
+ feed_forward_proj (`string`, *optional*, defaults to `"gated-gelu"`):
+ Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models).
+ """
+
+ model_type = "mt5"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {
+ "hidden_size": "d_model",
+ "num_attention_heads": "num_heads",
+ "num_hidden_layers": "num_layers",
+ "head_dim": "d_kv",
+ }
+
+ def __init__(
+ self,
+ vocab_size=250112,
+ d_model=512,
+ d_kv=64,
+ d_ff=1024,
+ num_layers=8,
+ num_decoder_layers=None,
+ num_heads=6,
+ relative_attention_num_buckets=32,
+ relative_attention_max_distance=128,
+ dropout_rate=0.1,
+ layer_norm_epsilon=1e-6,
+ initializer_factor=1.0,
+ feed_forward_proj="gated-gelu",
+ is_encoder_decoder=True,
+ use_cache=True,
+ tokenizer_class="T5Tokenizer",
+ tie_word_embeddings=False,
+ pad_token_id=0,
+ eos_token_id=1,
+ decoder_start_token_id=0,
+ classifier_dropout=0.0,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.d_model = d_model
+ self.d_kv = d_kv
+ self.d_ff = d_ff
+ self.num_layers = num_layers
+ self.num_decoder_layers = (
+ num_decoder_layers if num_decoder_layers is not None else self.num_layers
+ ) # default = symmetry
+ self.num_heads = num_heads
+ self.relative_attention_num_buckets = relative_attention_num_buckets
+ self.relative_attention_max_distance = relative_attention_max_distance
+ self.dropout_rate = dropout_rate
+ self.classifier_dropout = classifier_dropout
+ self.layer_norm_epsilon = layer_norm_epsilon
+ self.initializer_factor = initializer_factor
+ self.feed_forward_proj = feed_forward_proj
+ self.use_cache = use_cache
+
+ act_info = self.feed_forward_proj.split("-")
+ self.dense_act_fn = act_info[-1]
+ self.is_gated_act = act_info[0] == "gated"
+
+ if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2:
+ raise ValueError(
+ f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer. "
+ "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
+ "'gated-gelu' or 'relu'"
+ )
+
+ # for backwards compatibility
+ if feed_forward_proj == "gated-gelu":
+ self.dense_act_fn = "gelu_new"
+
+ super().__init__(
+ is_encoder_decoder=is_encoder_decoder,
+ tokenizer_class=tokenizer_class,
+ tie_word_embeddings=tie_word_embeddings,
+ pad_token_id=pad_token_id,
+ eos_token_id=eos_token_id,
+ decoder_start_token_id=decoder_start_token_id,
+ **kwargs,
+ )
+
+
+class MT5OnnxConfig(OnnxSeq2SeqConfigWithPast):
+ @property
+ # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
+ common_inputs = {
+ "input_ids": {0: "batch", 1: "encoder_sequence"},
+ "attention_mask": {0: "batch", 1: "encoder_sequence"},
+ }
+ if self.use_past:
+ common_inputs["attention_mask"][1] = "past_encoder_sequence + sequence"
+ common_inputs["decoder_input_ids"] = {0: "batch"}
+ common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
+ else:
+ common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
+ common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
+
+ if self.use_past:
+ self.fill_with_past_key_values_(common_inputs, direction="inputs")
+
+ return common_inputs
+
+ @property
+ # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
+ def default_onnx_opset(self) -> int:
+ return 13
+
+ @property
+ def atol_for_validation(self) -> float:
+ return 5e-4
+
+
+__all__ = ["MT5Config", "MT5OnnxConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/modeling_flax_mt5.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/modeling_flax_mt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..13bd83b75034ba6de1260a0bd754f289979e4fa7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/modeling_flax_mt5.py
@@ -0,0 +1,123 @@
+# coding=utf-8
+# Copyright 2021 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Flax mT5 model."""
+
+import jax.numpy as jnp
+
+from ...utils import logging
+from ..t5.modeling_flax_t5 import FlaxT5EncoderModel, FlaxT5ForConditionalGeneration, FlaxT5Model
+from .configuration_mt5 import MT5Config
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "T5Config"
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right
+def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray:
+ """
+ Shift input ids one token to the right.
+ """
+ shifted_input_ids = jnp.zeros_like(input_ids)
+ shifted_input_ids = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1])
+ shifted_input_ids = shifted_input_ids.at[:, 0].set(decoder_start_token_id)
+
+ shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
+ return shifted_input_ids
+
+
+class FlaxMT5Model(FlaxT5Model):
+ r"""
+ This class overrides [`FlaxT5Model`]. Please check the superclass for the appropriate documentation alongside usage
+ examples.
+
+ Examples:
+
+ ```python
+ >>> from transformers import FlaxMT5Model, AutoTokenizer
+
+ >>> model = FlaxMT5Model.from_pretrained("google/mt5-small")
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+
+ >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+ >>> summary = "Weiter Verhandlung in Syrien."
+ >>> inputs = tokenizer(article, return_tensors="np")
+
+ >>> decoder_input_ids = tokenizer(text_target=summary, return_tensors="np").input_ids
+
+ >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=decoder_input_ids)
+ >>> hidden_states = outputs.last_hidden_state
+ ```"""
+
+ model_type = "mt5"
+ config_class = MT5Config
+
+
+class FlaxMT5EncoderModel(FlaxT5EncoderModel):
+ r"""
+ This class overrides [`FlaxT5EncoderModel`]. Please check the superclass for the appropriate documentation
+ alongside usage examples.
+
+ Examples:
+
+ ```python
+ >>> from transformers import FlaxT5EncoderModel, AutoTokenizer
+
+ >>> model = FlaxT5EncoderModel.from_pretrained("google/mt5-small")
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+
+ >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+ >>> summary = "Weiter Verhandlung in Syrien."
+ >>> inputs = tokenizer(article, return_tensors="np")
+
+ >>> decoder_input_ids = tokenizer(text_target=summary, return_tensors="np").input_ids
+
+ >>> outputs = model(input_ids=inputs["input_ids"])
+ >>> hidden_states = outputs.last_hidden_state
+ ```"""
+
+ model_type = "mt5"
+ config_class = MT5Config
+
+
+class FlaxMT5ForConditionalGeneration(FlaxT5ForConditionalGeneration):
+ r"""
+ This class overrides [`FlaxT5ForConditionalGeneration`]. Please check the superclass for the appropriate
+ documentation alongside usage examples.
+
+ Examples:
+
+ ```python
+ >>> from transformers import FlaxMT5ForConditionalGeneration, AutoTokenizer
+
+ >>> model = FlaxMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+
+ >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+ >>> summary = "Weiter Verhandlung in Syrien."
+ >>> inputs = tokenizer(article, return_tensors="np")
+
+ >>> decoder_input_ids = tokenizer(text_target=summary, return_tensors="np").input_ids
+
+ >>> outputs = model(**inputs, decoder_input_ids=decoder_input_ids)
+ >>> logits = outputs.logits
+ ```"""
+
+ model_type = "mt5"
+ config_class = MT5Config
+
+
+__all__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/modeling_mt5.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/modeling_mt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d8e052b324ac3e76a6f477214695d2bf36d6116
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/modeling_mt5.py
@@ -0,0 +1,2462 @@
+# coding=utf-8
+# Copyright 2020 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch mT5 model."""
+
+import copy
+import math
+import os
+import warnings
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import AttentionMaskConverter
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPastAndCrossAttentions,
+ Seq2SeqLMOutput,
+ Seq2SeqModelOutput,
+ Seq2SeqQuestionAnsweringModelOutput,
+ Seq2SeqSequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import (
+ DUMMY_INPUTS,
+ DUMMY_MASK,
+ add_start_docstrings,
+ auto_docstring,
+ is_torch_flex_attn_available,
+ is_torch_fx_proxy,
+ is_torchdynamo_compiling,
+ logging,
+)
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.model_parallel_utils import assert_device_map, get_device_map
+from .configuration_mt5 import MT5Config
+
+
+if is_torch_flex_attn_available():
+ from torch.nn.attention.flex_attention import BlockMask
+
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+logger = logging.get_logger(__name__)
+
+
+####################################################
+# This dict contains ids and associated url
+# for the pretrained weights provided with the models
+####################################################
+
+PARALLELIZE_DOCSTRING = r"""
+ This is an experimental feature and is a subject to change at a moment's notice.
+
+ Uses a device map to distribute attention modules of the model across several devices. If no device map is given,
+ it will evenly distribute blocks across all devices.
+
+ Args:
+ device_map (`dict[int, list]`, *optional*):
+ A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
+ automatically mapped to the first device (for esoteric reasons). That means that the first device should
+ have fewer attention modules mapped to it than other devices. For reference, the mt5 models have the
+ following number of attention modules:
+
+ - mt5-small: 6
+ - mt5-base: 12
+ - mt5-large: 24
+ - mt5-xl: 24
+ - mt5-xxl: 24
+
+ Example:
+
+ ```python
+ # Here is an example of a device map on a machine with 4 GPUs using mt5-xl, which has a total of 24 attention modules:
+ model = MT5ForConditionalGeneration.from_pretrained("mt5-xl")
+ device_map = {
+ 0: [0, 1, 2],
+ 1: [3, 4, 5, 6, 7, 8, 9],
+ 2: [10, 11, 12, 13, 14, 15, 16],
+ 3: [17, 18, 19, 20, 21, 22, 23],
+ }
+ model.parallelize(device_map)
+ ```
+"""
+DEPARALLELIZE_DOCSTRING = r"""
+ Moves the model to cpu from a model parallel state.
+
+ Example:
+
+ ```python
+ # On a 4 GPU machine with mt5-xl:
+ model = MT5ForConditionalGeneration.from_pretrained("Mt5-xl")
+ device_map = {
+ 0: [0, 1, 2],
+ 1: [3, 4, 5, 6, 7, 8, 9],
+ 2: [10, 11, 12, 13, 14, 15, 16],
+ 3: [17, 18, 19, 20, 21, 22, 23],
+ }
+ model.parallelize(device_map) # Splits the model across several devices
+ model.deparallelize() # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
+ ```
+"""
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->MT5
+class MT5LayerNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ Construct a layernorm module in the MT5 style. No bias and no subtraction of mean.
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ # MT5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
+ # Square Layer Normalization https://huggingface.co/papers/1910.07467 thus variance is calculated
+ # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
+ # half-precision inputs is done in fp32
+
+ variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+ # convert into half-precision if necessary
+ if self.weight.dtype in [torch.float16, torch.bfloat16]:
+ hidden_states = hidden_states.to(self.weight.dtype)
+
+ return self.weight * hidden_states
+
+
+# Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->MT5
+class MT5DenseActDense(nn.Module):
+ def __init__(self, config: MT5Config):
+ super().__init__()
+ self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
+ self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
+ self.dropout = nn.Dropout(config.dropout_rate)
+ self.act = ACT2FN[config.dense_act_fn]
+
+ def forward(self, hidden_states):
+ hidden_states = self.wi(hidden_states)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ if (
+ isinstance(self.wo.weight, torch.Tensor)
+ and hidden_states.dtype != self.wo.weight.dtype
+ and self.wo.weight.dtype != torch.int8
+ ):
+ hidden_states = hidden_states.to(self.wo.weight.dtype)
+ hidden_states = self.wo(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5->MT5
+class MT5DenseGatedActDense(nn.Module):
+ def __init__(self, config: MT5Config):
+ super().__init__()
+ self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
+ self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
+ self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
+ self.dropout = nn.Dropout(config.dropout_rate)
+ self.act = ACT2FN[config.dense_act_fn]
+
+ def forward(self, hidden_states):
+ hidden_gelu = self.act(self.wi_0(hidden_states))
+ hidden_linear = self.wi_1(hidden_states)
+ hidden_states = hidden_gelu * hidden_linear
+ hidden_states = self.dropout(hidden_states)
+
+ # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
+ # See https://github.com/huggingface/transformers/issues/20287
+ # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
+ if (
+ isinstance(self.wo.weight, torch.Tensor)
+ and hidden_states.dtype != self.wo.weight.dtype
+ and self.wo.weight.dtype != torch.int8
+ ):
+ hidden_states = hidden_states.to(self.wo.weight.dtype)
+
+ hidden_states = self.wo(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerFF with T5->MT5
+class MT5LayerFF(nn.Module):
+ def __init__(self, config: MT5Config):
+ super().__init__()
+ if config.is_gated_act:
+ self.DenseReluDense = MT5DenseGatedActDense(config)
+ else:
+ self.DenseReluDense = MT5DenseActDense(config)
+
+ self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+ self.dropout = nn.Dropout(config.dropout_rate)
+
+ def forward(self, hidden_states):
+ forwarded_states = self.layer_norm(hidden_states)
+ forwarded_states = self.DenseReluDense(forwarded_states)
+ hidden_states = hidden_states + self.dropout(forwarded_states)
+ return hidden_states
+
+
+# Copied from transformers.models.t5.modeling_t5.T5Attention with T5->MT5
+class MT5Attention(nn.Module):
+ def __init__(
+ self,
+ config: MT5Config,
+ has_relative_attention_bias=False,
+ layer_idx: Optional[int] = None,
+ ):
+ super().__init__()
+ self.is_decoder = config.is_decoder
+ self.has_relative_attention_bias = has_relative_attention_bias
+ self.relative_attention_num_buckets = config.relative_attention_num_buckets
+ self.relative_attention_max_distance = config.relative_attention_max_distance
+ self.d_model = config.d_model
+ self.key_value_proj_dim = config.d_kv
+ self.n_heads = config.num_heads
+ self.dropout = config.dropout_rate
+ self.inner_dim = self.n_heads * self.key_value_proj_dim
+ self.layer_idx = layer_idx
+ if layer_idx is None and self.is_decoder:
+ logger.warning_once(
+ f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
+ "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ # Mesh TensorFlow initialization to avoid scaling before softmax
+ self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
+ self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
+
+ if self.has_relative_attention_bias:
+ self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
+ self.pruned_heads = set()
+ self.gradient_checkpointing = False
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
+ )
+ # Prune linear layers
+ self.q = prune_linear_layer(self.q, index)
+ self.k = prune_linear_layer(self.k, index)
+ self.v = prune_linear_layer(self.v, index)
+ self.o = prune_linear_layer(self.o, index, dim=1)
+ # Update hyper params
+ self.n_heads = self.n_heads - len(heads)
+ self.inner_dim = self.key_value_proj_dim * self.n_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ @staticmethod
+ def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+ """
+ Adapted from Mesh Tensorflow:
+ https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+ Translate relative position to a bucket number for relative attention. The relative position is defined as
+ memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+ position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+ small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+ positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+ This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+ Args:
+ relative_position: an int32 Tensor
+ bidirectional: a boolean - whether the attention is bidirectional
+ num_buckets: an integer
+ max_distance: an integer
+
+ Returns:
+ a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+ """
+ relative_buckets = 0
+ if bidirectional:
+ num_buckets //= 2
+ relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
+ relative_position = torch.abs(relative_position)
+ else:
+ relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
+ # now relative_position is in the range [0, inf)
+
+ # half of the buckets are for exact increments in positions
+ max_exact = num_buckets // 2
+ is_small = relative_position < max_exact
+
+ # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ relative_position_if_large = max_exact + (
+ torch.log(relative_position.float() / max_exact)
+ / math.log(max_distance / max_exact)
+ * (num_buckets - max_exact)
+ ).to(torch.long)
+ relative_position_if_large = torch.min(
+ relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
+ )
+
+ relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
+ return relative_buckets
+
+ def compute_bias(self, query_length, key_length, device=None, cache_position=None):
+ """Compute binned relative position bias"""
+ if device is None:
+ device = self.relative_attention_bias.weight.device
+ if cache_position is None:
+ context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
+ else:
+ context_position = cache_position[:, None].to(device)
+ memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
+ relative_position = memory_position - context_position # shape (query_length, key_length)
+ relative_position_bucket = self._relative_position_bucket(
+ relative_position, # shape (query_length, key_length)
+ bidirectional=(not self.is_decoder),
+ num_buckets=self.relative_attention_num_buckets,
+ max_distance=self.relative_attention_max_distance,
+ )
+ values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
+ values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
+ return values
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states,
+ mask=None,
+ key_value_states=None,
+ position_bias=None,
+ past_key_values=None,
+ layer_head_mask=None,
+ query_length=None,
+ use_cache=False,
+ output_attentions=False,
+ cache_position=None,
+ ):
+ """
+ Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
+ """
+ # Input is (batch_size, seq_length, dim)
+ # Mask is (batch_size, 1, 1, key_length) (non-causal encoder) or (batch_size, 1, seq_length, key_length) (causal decoder)
+ batch_size, seq_length = hidden_states.shape[:2]
+
+ # if key_value_states are provided this layer is used as a cross-attention layer for the decoder
+ is_cross_attention = key_value_states is not None
+
+ query_states = self.q(hidden_states)
+ query_states = query_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
+
+ # Check is encoder-decoder model is being used. Otherwise we'll get `DynamicCache`
+ if past_key_values is not None and isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_states from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = key_value_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_states = curr_past_key_value.layers[self.layer_idx].keys
+ value_states = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_states = self.k(current_states)
+ value_states = self.v(current_states)
+ key_states = key_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
+ value_states = value_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_states, value_states = curr_past_key_value.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ # compute scores, equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
+ scores = torch.matmul(query_states, key_states.transpose(3, 2))
+
+ if position_bias is None:
+ key_length = key_states.shape[-2]
+ # cache position is 0-indexed so we add 1 to get the real length of queries (aka with past)
+ real_seq_length = query_length if query_length is not None else cache_position[-1] + 1
+ if not self.has_relative_attention_bias:
+ position_bias = torch.zeros(
+ (1, self.n_heads, seq_length, key_length), device=scores.device, dtype=scores.dtype
+ )
+ if self.gradient_checkpointing and self.training:
+ position_bias.requires_grad = True
+ else:
+ position_bias = self.compute_bias(
+ real_seq_length, key_length, device=scores.device, cache_position=cache_position
+ )
+ position_bias = position_bias[:, :, -seq_length:, :]
+
+ if mask is not None:
+ causal_mask = mask[:, :, :, : key_states.shape[-2]]
+ position_bias = position_bias + causal_mask
+
+ if self.pruned_heads:
+ mask = torch.ones(position_bias.shape[1])
+ mask[list(self.pruned_heads)] = 0
+ position_bias_masked = position_bias[:, mask.bool()]
+ else:
+ position_bias_masked = position_bias
+
+ scores += position_bias_masked
+
+ # (batch_size, n_heads, seq_length, key_length)
+ attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+ # Mask heads if we want to
+ if layer_head_mask is not None:
+ attn_weights = attn_weights * layer_head_mask
+
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.view(batch_size, -1, self.inner_dim)
+ attn_output = self.o(attn_output)
+
+ outputs = (attn_output, position_bias)
+
+ if output_attentions:
+ outputs = outputs + (attn_weights,)
+ return outputs
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->MT5
+class MT5LayerSelfAttention(nn.Module):
+ def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.SelfAttention = MT5Attention(
+ config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
+ )
+ self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+ self.dropout = nn.Dropout(config.dropout_rate)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_bias=None,
+ layer_head_mask=None,
+ past_key_values=None,
+ use_cache=False,
+ output_attentions=False,
+ cache_position=None,
+ ):
+ normed_hidden_states = self.layer_norm(hidden_states)
+ attention_output = self.SelfAttention(
+ normed_hidden_states,
+ mask=attention_mask,
+ position_bias=position_bias,
+ layer_head_mask=layer_head_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = hidden_states + self.dropout(attention_output[0])
+ outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5->MT5
+class MT5LayerCrossAttention(nn.Module):
+ def __init__(self, config, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.EncDecAttention = MT5Attention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
+ self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+ self.dropout = nn.Dropout(config.dropout_rate)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states,
+ key_value_states,
+ attention_mask=None,
+ position_bias=None,
+ layer_head_mask=None,
+ past_key_values=None,
+ use_cache=False,
+ query_length=None,
+ output_attentions=False,
+ cache_position=None,
+ ):
+ normed_hidden_states = self.layer_norm(hidden_states)
+ attention_output = self.EncDecAttention(
+ normed_hidden_states,
+ mask=attention_mask,
+ key_value_states=key_value_states,
+ position_bias=position_bias,
+ layer_head_mask=layer_head_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ query_length=query_length,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ layer_output = hidden_states + self.dropout(attention_output[0])
+ outputs = (layer_output,) + attention_output[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.t5.modeling_t5.T5Block with T5->MT5
+class MT5Block(GradientCheckpointingLayer):
+ def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.is_decoder = config.is_decoder
+ self.layer = nn.ModuleList()
+ self.layer.append(
+ MT5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx)
+ )
+ if self.is_decoder:
+ self.layer.append(MT5LayerCrossAttention(config, layer_idx=layer_idx))
+
+ self.layer.append(MT5LayerFF(config))
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states,
+ attention_mask=None,
+ position_bias=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ encoder_decoder_position_bias=None,
+ layer_head_mask=None,
+ cross_attn_layer_head_mask=None,
+ past_key_values=None,
+ use_cache=False,
+ output_attentions=False,
+ return_dict=True,
+ cache_position=None,
+ ):
+ self_attention_outputs = self.layer[0](
+ hidden_states,
+ attention_mask=attention_mask,
+ position_bias=position_bias,
+ layer_head_mask=layer_head_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = self_attention_outputs[0]
+ attention_outputs = self_attention_outputs[1:] # Keep self-attention outputs and relative position weights
+
+ # clamp inf values to enable fp16 training
+ if hidden_states.dtype == torch.float16:
+ clamp_value = torch.where(
+ torch.isinf(hidden_states).any(),
+ torch.finfo(hidden_states.dtype).max - 1000,
+ torch.finfo(hidden_states.dtype).max,
+ )
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ do_cross_attention = self.is_decoder and encoder_hidden_states is not None
+ if do_cross_attention:
+ cross_attention_outputs = self.layer[1](
+ hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ position_bias=encoder_decoder_position_bias,
+ layer_head_mask=cross_attn_layer_head_mask,
+ past_key_values=past_key_values,
+ query_length=cache_position[-1] + 1,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ )
+ hidden_states = cross_attention_outputs[0]
+
+ # clamp inf values to enable fp16 training
+ if hidden_states.dtype == torch.float16:
+ clamp_value = torch.where(
+ torch.isinf(hidden_states).any(),
+ torch.finfo(hidden_states.dtype).max - 1000,
+ torch.finfo(hidden_states.dtype).max,
+ )
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ # Keep cross-attention outputs and relative position weights
+ attention_outputs = attention_outputs + cross_attention_outputs[1:]
+
+ # Apply Feed Forward layer
+ hidden_states = self.layer[-1](hidden_states)
+
+ # clamp inf values to enable fp16 training
+ if hidden_states.dtype == torch.float16:
+ clamp_value = torch.where(
+ torch.isinf(hidden_states).any(),
+ torch.finfo(hidden_states.dtype).max - 1000,
+ torch.finfo(hidden_states.dtype).max,
+ )
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ outputs = (hidden_states,)
+
+ return (
+ outputs + attention_outputs
+ ) # hidden-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
+
+
+def load_tf_weights_in_mt5(model, config, tf_checkpoint_path):
+ """Load tf checkpoints in a pytorch model."""
+ try:
+ import re
+
+ import numpy as np
+ import tensorflow as tf
+ except ImportError:
+ logger.error(
+ "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
+ "https://www.tensorflow.org/install/ for installation instructions."
+ )
+ raise
+ tf_path = os.path.abspath(tf_checkpoint_path)
+ logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
+ # Load weights from TF model
+ init_vars = tf.train.list_variables(tf_path)
+ names = []
+ tf_weights = {}
+ for name, shape in init_vars:
+ logger.info(f"Loading TF weight {name} with shape {shape}")
+ array = tf.train.load_variable(tf_path, name)
+ names.append(name)
+ tf_weights[name] = array
+
+ for txt_name in names:
+ name = txt_name.split("/")
+ # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+ # which are not required for using pretrained model
+ if any(
+ n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
+ for n in name
+ ):
+ logger.info(f"Skipping {'/'.join(name)}")
+ tf_weights.pop(txt_name, None)
+ continue
+ if "_slot_" in name[-1]:
+ logger.info(f"Skipping {'/'.join(name)}")
+ tf_weights.pop(txt_name, None)
+ continue
+ pointer = model
+ array = tf_weights[txt_name]
+
+ for m_name in name:
+ if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
+ scope_names = re.split(r"_(\d+)", m_name)
+ else:
+ scope_names = [m_name]
+ if scope_names[0] in ["kernel", "scale", "embedding"]:
+ pointer = getattr(pointer, "weight")
+ elif scope_names[0] == "self_attention":
+ pointer = getattr(pointer, "layer")
+ pointer = pointer[0]
+ elif scope_names[0] == "enc_dec_attention":
+ pointer = getattr(pointer, "layer")
+ pointer = pointer[1]
+ elif scope_names[0] == "dense_relu_dense":
+ pointer = getattr(pointer, "layer")
+ pointer = pointer[2]
+ elif scope_names[0] == "rms_norm":
+ if hasattr(pointer, "layer_norm"):
+ pointer = getattr(pointer, "layer_norm")
+ elif hasattr(pointer, "final_layer_norm"):
+ pointer = getattr(pointer, "final_layer_norm")
+ elif scope_names[0] == "scale":
+ pointer = getattr(pointer, "weight")
+ elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
+ pointer = getattr(pointer, "bias")
+ elif scope_names[0] == "squad":
+ pointer = getattr(pointer, "classifier")
+ elif scope_names[0] == "decoder" and name[1] == "logits":
+ continue
+ elif scope_names[0] == "logits":
+ pointer = getattr(pointer, "lm_head")
+ elif scope_names[0] == "wi" and len(scope_names) > 1 and scope_names[1].isdigit():
+ pointer = getattr(pointer, f"wi_{scope_names[1]}")
+ continue
+ else:
+ try:
+ pointer = getattr(pointer, scope_names[0])
+ except AttributeError:
+ logger.info(f"Skipping {'/'.join(name)}")
+ continue
+ if len(scope_names) >= 2:
+ num = int(scope_names[1])
+ pointer = pointer[num]
+ if scope_names[0] not in ["kernel", "scale", "embedding"]:
+ pointer = getattr(pointer, "weight")
+ if scope_names[0] != "embedding":
+ logger.info(f"Transposing numpy weight of shape {array.shape} for {name}")
+ array = np.transpose(array)
+ try:
+ assert pointer.shape == array.shape, (
+ f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
+ )
+ except AssertionError as e:
+ e.args += (pointer.shape, array.shape)
+ raise
+ logger.info(f"Initialize PyTorch weight {name}")
+ pointer.data = torch.from_numpy(array.astype(np.float32))
+ tf_weights.pop(txt_name, None)
+
+ logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}.")
+ return model
+
+
+# Copied from transformers.models.t5.modeling_t5.T5ClassificationHead with T5->MT5
+class MT5ClassificationHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(self, config: MT5Config):
+ super().__init__()
+ self.dense = nn.Linear(config.d_model, config.d_model)
+ self.dropout = nn.Dropout(p=config.classifier_dropout)
+ self.out_proj = nn.Linear(config.d_model, config.num_labels)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.dense(hidden_states)
+ hidden_states = torch.tanh(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.out_proj(hidden_states)
+ return hidden_states
+
+
+@auto_docstring
+# Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel with T5->MT5, t5->mt5
+class MT5PreTrainedModel(PreTrainedModel):
+ config: MT5Config
+ load_tf_weights = load_tf_weights_in_mt5
+ base_model_prefix = "transformer"
+ is_parallelizable = True
+ supports_gradient_checkpointing = True
+ _can_compile_fullgraph = True
+
+ _no_split_modules = ["MT5Block"]
+ _keep_in_fp32_modules = ["wo"]
+
+ @property
+ def dummy_inputs(self):
+ input_ids = torch.tensor(DUMMY_INPUTS)
+ input_mask = torch.tensor(DUMMY_MASK)
+ dummy_inputs = {
+ "decoder_input_ids": input_ids,
+ "input_ids": input_ids,
+ "decoder_attention_mask": input_mask,
+ }
+ return dummy_inputs
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ factor = self.config.initializer_factor # Used for testing weights initialization
+ if isinstance(module, MT5LayerNorm):
+ module.weight.data.fill_(factor * 1.0)
+ elif isinstance(
+ module,
+ (MT5Model, MT5ForConditionalGeneration, MT5EncoderModel, MT5ForQuestionAnswering),
+ ):
+ # Mesh TensorFlow embeddings initialization
+ # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
+ module.shared.weight.data.normal_(mean=0.0, std=factor * 1.0)
+ if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
+ module.lm_head.weight.data.normal_(mean=0.0, std=factor * 1.0)
+ if hasattr(module, "qa_outputs"):
+ module.qa_outputs.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+ module.qa_outputs.bias.data.zero_()
+ elif isinstance(module, MT5ForTokenClassification):
+ if hasattr(module, "classifier"):
+ module.classifier.weight.data.normal_(mean=0.0, std=factor * 1.0)
+ module.classifier.bias.data.zero_()
+ elif isinstance(module, MT5ClassificationHead):
+ module.dense.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+ if hasattr(module.dense, "bias") and module.dense.bias is not None:
+ module.dense.bias.data.zero_()
+ module.out_proj.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+ if hasattr(module.out_proj, "bias") and module.out_proj.bias is not None:
+ module.out_proj.bias.data.zero_()
+ elif isinstance(module, MT5DenseActDense):
+ # Mesh TensorFlow FF initialization
+ # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
+ # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
+ module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+ if hasattr(module.wi, "bias") and module.wi.bias is not None:
+ module.wi.bias.data.zero_()
+ module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
+ if hasattr(module.wo, "bias") and module.wo.bias is not None:
+ module.wo.bias.data.zero_()
+ elif isinstance(module, MT5DenseGatedActDense):
+ module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+ if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None:
+ module.wi_0.bias.data.zero_()
+ module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
+ if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None:
+ module.wi_1.bias.data.zero_()
+ module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
+ if hasattr(module.wo, "bias") and module.wo.bias is not None:
+ module.wo.bias.data.zero_()
+ elif isinstance(module, MT5Attention):
+ # Mesh TensorFlow attention initialization to avoid scaling before softmax
+ # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
+ d_model = self.config.d_model
+ key_value_proj_dim = self.config.d_kv
+ n_heads = self.config.num_heads
+ module.q.weight.data.normal_(mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5))
+ module.k.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
+ module.v.weight.data.normal_(mean=0.0, std=factor * (d_model**-0.5))
+ module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
+ if module.has_relative_attention_bias:
+ module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
+
+ def _shift_right(self, input_ids):
+ decoder_start_token_id = self.config.decoder_start_token_id
+ pad_token_id = self.config.pad_token_id
+
+ if decoder_start_token_id is None:
+ raise ValueError(
+ "self.model.config.decoder_start_token_id has to be defined. In MT5 it is usually set to the pad_token_id. "
+ "See MT5 docs for more information."
+ )
+
+ # shift inputs to the right
+ if is_torch_fx_proxy(input_ids):
+ # Item assignment is not supported natively for proxies.
+ shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id)
+ shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
+ else:
+ shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+ shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
+ shifted_input_ids[..., 0] = decoder_start_token_id
+
+ if pad_token_id is None:
+ raise ValueError("self.model.config.pad_token_id has to be defined.")
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+ return shifted_input_ids
+
+
+# Copied from transformers.models.t5.modeling_t5.T5Stack with T5->MT5
+class MT5Stack(MT5PreTrainedModel):
+ def __init__(self, config, embed_tokens=None):
+ super().__init__(config)
+
+ self.embed_tokens = embed_tokens
+ self.is_decoder = config.is_decoder
+
+ self.block = nn.ModuleList(
+ [MT5Block(config, has_relative_attention_bias=bool(i == 0), layer_idx=i) for i in range(config.num_layers)]
+ )
+ self.final_layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+ self.dropout = nn.Dropout(config.dropout_rate)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+ # Model parallel
+ self.model_parallel = False
+ self.device_map = None
+ self.gradient_checkpointing = False
+
+ @add_start_docstrings(PARALLELIZE_DOCSTRING)
+ def parallelize(self, device_map=None):
+ warnings.warn(
+ "`MT5Stack.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model"
+ " with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
+ " `device_map` but it needs to be a dictionary module_name to device, so for instance {'block.0': 0,"
+ " 'block.1': 1, ...}",
+ FutureWarning,
+ )
+ # Check validity of device_map
+ self.device_map = (
+ get_device_map(len(self.block), range(torch.cuda.device_count())) if device_map is None else device_map
+ )
+ assert_device_map(self.device_map, len(self.block))
+ self.model_parallel = True
+ self.first_device = "cpu" if "cpu" in self.device_map else "cuda:" + str(min(self.device_map.keys()))
+ self.last_device = "cuda:" + str(max(self.device_map.keys()))
+ # Load onto devices
+ for k, v in self.device_map.items():
+ for layer in v:
+ cuda_device = "cuda:" + str(k)
+ self.block[layer] = self.block[layer].to(cuda_device)
+
+ # Set embed_tokens to first layer
+ self.embed_tokens = self.embed_tokens.to(self.first_device)
+ # Set final layer norm to last device
+ self.final_layer_norm = self.final_layer_norm.to(self.last_device)
+
+ @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
+ def deparallelize(self):
+ warnings.warn(
+ "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
+ FutureWarning,
+ )
+ self.model_parallel = False
+ self.device_map = None
+ self.first_device = "cpu"
+ self.last_device = "cpu"
+ for i in range(len(self.block)):
+ self.block[i] = self.block[i].to("cpu")
+ self.embed_tokens = self.embed_tokens.to("cpu")
+ self.final_layer_norm = self.final_layer_norm.to("cpu")
+ torch.cuda.empty_cache()
+
+ def set_input_embeddings(self, new_embeddings):
+ self.embed_tokens = new_embeddings
+
+ def forward(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ inputs_embeds=None,
+ head_mask=None,
+ cross_attn_head_mask=None,
+ past_key_values=None,
+ use_cache=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ cache_position=None,
+ ):
+ # Model parallel
+ if self.model_parallel:
+ torch.cuda.set_device(self.first_device)
+ self.embed_tokens = self.embed_tokens.to(self.first_device)
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ err_msg_prefix = "decoder_" if self.is_decoder else ""
+ raise ValueError(
+ f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
+ )
+ elif input_ids is not None:
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ err_msg_prefix = "decoder_" if self.is_decoder else ""
+ raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ if inputs_embeds is None:
+ if self.embed_tokens is None:
+ raise ValueError("You have to initialize the model with valid token embeddings")
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ batch_size, seq_length = input_shape
+
+ if use_cache is True:
+ if not self.is_decoder:
+ raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder")
+
+ if self.is_decoder:
+ if use_cache and past_key_values is None:
+ if self.config.is_encoder_decoder:
+ past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
+ else:
+ past_key_values = DynamicCache()
+ elif not self.is_decoder:
+ # do not pass cache object down the line for encoder stack
+ # it messes indexing later in decoder-stack because cache object is modified in-place
+ past_key_values = None
+
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+ if cache_position is None:
+ cache_position = torch.arange(
+ past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
+ )
+
+ if attention_mask is None and not is_torchdynamo_compiling():
+ # required mask seq length can be calculated via length of past cache
+ mask_seq_length = past_key_values_length + seq_length
+ attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
+
+ if self.config.is_decoder:
+ causal_mask = self._update_causal_mask(
+ attention_mask,
+ inputs_embeds,
+ cache_position,
+ past_key_values.self_attention_cache
+ if isinstance(past_key_values, EncoderDecoderCache)
+ else past_key_values,
+ output_attentions,
+ )
+ elif attention_mask is not None:
+ causal_mask = attention_mask[:, None, None, :]
+ causal_mask = causal_mask.to(dtype=inputs_embeds.dtype)
+ causal_mask = (1.0 - causal_mask) * torch.finfo(inputs_embeds.dtype).min
+ else:
+ causal_mask = None
+
+ # If a 2D or 3D attention mask is provided for the cross-attention
+ # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+ if self.is_decoder and encoder_hidden_states is not None:
+ encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+ encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+ if encoder_attention_mask is None:
+ encoder_attention_mask = torch.ones(
+ encoder_hidden_shape, device=inputs_embeds.device, dtype=torch.long
+ )
+ encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+ else:
+ encoder_extended_attention_mask = None
+
+ # Prepare head mask if needed
+ head_mask = self.get_head_mask(head_mask, self.config.num_layers)
+ cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+ all_cross_attentions = () if (output_attentions and self.is_decoder) else None
+ position_bias = None
+ encoder_decoder_position_bias = None
+
+ hidden_states = self.dropout(inputs_embeds)
+
+ for i, layer_module in enumerate(self.block):
+ layer_head_mask = head_mask[i]
+ cross_attn_layer_head_mask = cross_attn_head_mask[i]
+ # Model parallel
+ if self.model_parallel:
+ torch.cuda.set_device(hidden_states.device)
+ # Ensure that attention_mask is always on the same device as hidden_states
+ if causal_mask is not None:
+ causal_mask = causal_mask.to(hidden_states.device)
+ if position_bias is not None:
+ position_bias = position_bias.to(hidden_states.device)
+ if encoder_hidden_states is not None:
+ encoder_hidden_states = encoder_hidden_states.to(hidden_states.device)
+ if encoder_extended_attention_mask is not None:
+ encoder_extended_attention_mask = encoder_extended_attention_mask.to(hidden_states.device)
+ if encoder_decoder_position_bias is not None:
+ encoder_decoder_position_bias = encoder_decoder_position_bias.to(hidden_states.device)
+ if layer_head_mask is not None:
+ layer_head_mask = layer_head_mask.to(hidden_states.device)
+ if cross_attn_layer_head_mask is not None:
+ cross_attn_layer_head_mask = cross_attn_layer_head_mask.to(hidden_states.device)
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = layer_module(
+ hidden_states,
+ causal_mask,
+ position_bias,
+ encoder_hidden_states,
+ encoder_extended_attention_mask,
+ encoder_decoder_position_bias, # as a positional argument for gradient checkpointing
+ layer_head_mask=layer_head_mask,
+ cross_attn_layer_head_mask=cross_attn_layer_head_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ # We share the position biases between the layers - the first layer store them
+ # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
+ # (cross-attention position bias), (cross-attention weights)
+ position_bias = layer_outputs[1]
+ if self.is_decoder and encoder_hidden_states is not None:
+ encoder_decoder_position_bias = layer_outputs[3 if output_attentions else 2]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[2],)
+ if self.is_decoder:
+ all_cross_attentions = all_cross_attentions + (layer_outputs[4],)
+
+ # Model Parallel: If it's the last layer for that device, put things on the next device
+ if self.model_parallel:
+ for k, v in self.device_map.items():
+ if i == v[-1] and "cuda:" + str(k) != self.last_device:
+ hidden_states = hidden_states.to("cuda:" + str(k + 1))
+
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+
+ # Add last layer
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ hidden_states,
+ past_key_values,
+ all_hidden_states,
+ all_attentions,
+ all_cross_attentions,
+ ]
+ if v is not None
+ )
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_attentions,
+ cross_attentions=all_cross_attentions,
+ )
+
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, "BlockMask"],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ output_attentions: bool = False,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and (attention_mask == 0.0).any():
+ return attention_mask
+ return None
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ return attention_mask
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ sequence_length = input_tensor.shape[1]
+ if using_compilable_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ and not output_attentions
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+
+# Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+__HEAD_MASK_WARNING_MSG = """
+The input argument `head_mask` was split into two arguments `head_mask` and `decoder_head_mask`. Currently,
+`decoder_head_mask` is set to copy `head_mask`, but this feature is deprecated and will be removed in future versions.
+If you do not want to use any `decoder_head_mask` now, please set `decoder_head_mask = torch.ones(num_layers,
+num_heads)`.
+"""
+
+
+@auto_docstring
+class MT5Model(MT5PreTrainedModel):
+ r"""
+ Examples:
+
+ ```python
+ >>> from transformers import MT5Model, AutoTokenizer
+
+ >>> model = MT5Model.from_pretrained("google/mt5-small")
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+ >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+ >>> summary = "Weiter Verhandlung in Syrien."
+ >>> inputs = tokenizer(article, return_tensors="pt")
+ >>> labels = tokenizer(text_target=summary, return_tensors="pt")
+
+ >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"])
+ >>> hidden_states = outputs.last_hidden_state
+ ```"""
+
+ model_type = "mt5"
+ config: MT5Config
+ _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+ # Copied from transformers.models.t5.modeling_t5.T5Model.__init__ with T5->MT5
+ def __init__(self, config: MT5Config):
+ super().__init__(config)
+ self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+ encoder_config = copy.deepcopy(config)
+ encoder_config.is_decoder = False
+ encoder_config.use_cache = False
+ encoder_config.tie_encoder_decoder = False
+ self.encoder = MT5Stack(encoder_config, self.shared)
+
+ decoder_config = copy.deepcopy(config)
+ decoder_config.is_decoder = True
+ decoder_config.tie_encoder_decoder = False
+ decoder_config.num_layers = config.num_decoder_layers
+ self.decoder = MT5Stack(decoder_config, self.shared)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ # Model parallel
+ self.model_parallel = False
+ self.device_map = None
+
+ @add_start_docstrings(PARALLELIZE_DOCSTRING)
+ # Copied from transformers.models.t5.modeling_t5.T5Model.parallelize
+ def parallelize(self, device_map=None):
+ warnings.warn(
+ "`T5Model.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your model"
+ " with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
+ " `device_map` but it needs to be a dictionary module_name to device, so for instance {'encoder.block.0':"
+ " 0, 'encoder.block.1': 1, ...}",
+ FutureWarning,
+ )
+ self.device_map = (
+ get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
+ if device_map is None
+ else device_map
+ )
+ assert_device_map(self.device_map, len(self.encoder.block))
+ self.encoder.parallelize(self.device_map)
+ self.decoder.parallelize(self.device_map)
+ self.model_parallel = True
+
+ @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
+ # Copied from transformers.models.t5.modeling_t5.T5Model.deparallelize
+ def deparallelize(self):
+ warnings.warn(
+ "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
+ FutureWarning,
+ )
+ self.encoder.deparallelize()
+ self.decoder.deparallelize()
+ self.encoder = self.encoder.to("cpu")
+ self.decoder = self.decoder.to("cpu")
+ self.model_parallel = False
+ self.device_map = None
+ torch.cuda.empty_cache()
+
+ # Copied from transformers.models.t5.modeling_t5.T5Model.get_input_embeddings
+ def get_input_embeddings(self):
+ return self.shared
+
+ # Copied from transformers.models.t5.modeling_t5.T5Model.set_input_embeddings
+ def set_input_embeddings(self, new_embeddings):
+ self.shared = new_embeddings
+ self.encoder.set_input_embeddings(new_embeddings)
+ self.decoder.set_input_embeddings(new_embeddings)
+
+ # Copied from transformers.models.t5.modeling_t5.T5Model.get_encoder
+ def get_encoder(self):
+ return self.encoder
+
+ # Copied from transformers.models.t5.modeling_t5.T5Model.get_decoder
+ def get_decoder(self):
+ return self.decoder
+
+ # Copied from transformers.models.t5.modeling_t5.T5Model._prune_heads
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @auto_docstring
+ # Copied from transformers.models.t5.modeling_t5.T5Model.forward with google-t5/->google/, T5->MT5, t5->mt5
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.BoolTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ decoder_head_mask: Optional[torch.FloatTensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ decoder_inputs_embeds: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple[torch.FloatTensor], Seq2SeqModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
+ should be able to pad the inputs on both the right and the left.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for detail.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ MT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+ To know more on how to prepare `decoder_input_ids` for pretraining take a look at [MT5
+ Training](./mt5#training).
+ decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
+ `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MT5Model
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+ >>> model = MT5Model.from_pretrained("google/mt5-small")
+
+ >>> input_ids = tokenizer(
+ ... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
+ ... ).input_ids # Batch size 1
+ >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
+
+ >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for MT5Model.
+ >>> # This is not needed for torch's MT5ForConditionalGeneration as it does this internally using labels arg.
+ >>> decoder_input_ids = model._shift_right(decoder_input_ids)
+
+ >>> # forward pass
+ >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
+ >>> last_hidden_states = outputs.last_hidden_state
+ ```"""
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+ if head_mask is not None and decoder_head_mask is None:
+ if self.config.num_layers == self.config.num_decoder_layers:
+ warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
+ decoder_head_mask = head_mask
+
+ # Encode if needed (training, first prediction pass)
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+ encoder_outputs = BaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+
+ hidden_states = encoder_outputs[0]
+
+ # Set device for model parallelism
+ if self.model_parallel:
+ torch.cuda.set_device(self.decoder.first_device)
+ hidden_states = hidden_states.to(self.decoder.first_device)
+ if decoder_input_ids is not None:
+ decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
+ if attention_mask is not None:
+ attention_mask = attention_mask.to(self.decoder.first_device)
+ if decoder_attention_mask is not None:
+ decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
+
+ # Decode
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ inputs_embeds=decoder_inputs_embeds,
+ past_key_values=past_key_values,
+ encoder_hidden_states=hidden_states,
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return Seq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ MT5 Model with a `language modeling` head on top.
+ """
+)
+class MT5ForConditionalGeneration(MT5PreTrainedModel, GenerationMixin):
+ r"""
+ Examples:
+
+ ```python
+ >>> from transformers import MT5ForConditionalGeneration, AutoTokenizer
+
+ >>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+ >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+ >>> summary = "Weiter Verhandlung in Syrien."
+ >>> inputs = tokenizer(article, text_target=summary, return_tensors="pt")
+
+ >>> outputs = model(**inputs)
+ >>> loss = outputs.loss
+ ```"""
+
+ model_type = "mt5"
+ config: MT5Config
+ _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
+
+ # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.__init__ with T5->MT5
+ def __init__(self, config: MT5Config):
+ super().__init__(config)
+ self.model_dim = config.d_model
+
+ self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+ encoder_config = copy.deepcopy(config)
+ encoder_config.is_decoder = False
+ encoder_config.use_cache = False
+ encoder_config.tie_encoder_decoder = False
+ self.encoder = MT5Stack(encoder_config, self.shared)
+
+ decoder_config = copy.deepcopy(config)
+ decoder_config.is_decoder = True
+ decoder_config.tie_encoder_decoder = False
+ decoder_config.num_layers = config.num_decoder_layers
+ self.decoder = MT5Stack(decoder_config, self.shared)
+
+ self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ # Model parallel
+ self.model_parallel = False
+ self.device_map = None
+
+ @add_start_docstrings(PARALLELIZE_DOCSTRING)
+ # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.parallelize
+ def parallelize(self, device_map=None):
+ warnings.warn(
+ "`T5ForConditionalGeneration.parallelize` is deprecated and will be removed in v5 of Transformers, you"
+ " should load your model with `device_map='balanced'` in the call to `from_pretrained`. You can also"
+ " provide your own `device_map` but it needs to be a dictionary module_name to device, so for instance"
+ " {'encoder.block.0': 0, 'encoder.block.1': 1, ...}",
+ FutureWarning,
+ )
+ self.device_map = (
+ get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
+ if device_map is None
+ else device_map
+ )
+ assert_device_map(self.device_map, len(self.encoder.block))
+ self.encoder.parallelize(self.device_map)
+ self.decoder.parallelize(self.device_map)
+ self.lm_head = self.lm_head.to(self.decoder.first_device)
+ self.model_parallel = True
+
+ @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
+ # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.deparallelize
+ def deparallelize(self):
+ warnings.warn(
+ "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
+ FutureWarning,
+ )
+ self.encoder.deparallelize()
+ self.decoder.deparallelize()
+ self.encoder = self.encoder.to("cpu")
+ self.decoder = self.decoder.to("cpu")
+ self.lm_head = self.lm_head.to("cpu")
+ self.model_parallel = False
+ self.device_map = None
+ torch.cuda.empty_cache()
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, new_embeddings):
+ self.shared = new_embeddings
+ self.encoder.set_input_embeddings(new_embeddings)
+ self.decoder.set_input_embeddings(new_embeddings)
+
+ # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.get_encoder
+ def get_encoder(self):
+ return self.encoder
+
+ # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.get_decoder
+ def get_decoder(self):
+ return self.decoder
+
+ @auto_docstring
+ # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.forward with google-t5/->google/, T5->MT5, t5->mt5
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.BoolTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ decoder_head_mask: Optional[torch.FloatTensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.Tensor]]] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple[torch.FloatTensor], Seq2SeqLMOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
+ should be able to pad the inputs on both the right and the left.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for detail.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ MT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+ To know more on how to prepare `decoder_input_ids` for pretraining take a look at [MT5
+ Training](./mt5#training).
+ decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
+ `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
+ labels in `[0, ..., config.vocab_size]`
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MT5ForConditionalGeneration
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+ >>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
+
+ >>> # training
+ >>> input_ids = tokenizer("The walks in park", return_tensors="pt").input_ids
+ >>> labels = tokenizer(" cute dog the ", return_tensors="pt").input_ids
+ >>> outputs = model(input_ids=input_ids, labels=labels)
+ >>> loss = outputs.loss
+ >>> logits = outputs.logits
+
+ >>> # inference
+ >>> input_ids = tokenizer(
+ ... "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
+ ... ).input_ids # Batch size 1
+ >>> outputs = model.generate(input_ids)
+ >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+ >>> # studies have shown that owning a dog is good for you.
+ ```"""
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+ if head_mask is not None and decoder_head_mask is None:
+ if self.config.num_layers == self.config.num_decoder_layers:
+ warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
+ decoder_head_mask = head_mask
+
+ # Encode if needed (training, first prediction pass)
+ if encoder_outputs is None:
+ # Convert encoder inputs in embeddings if needed
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+ encoder_outputs = BaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+
+ hidden_states = encoder_outputs[0]
+
+ if self.model_parallel:
+ torch.cuda.set_device(self.decoder.first_device)
+
+ if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
+ # get decoder inputs from shifting lm labels to the right
+ decoder_input_ids = self._shift_right(labels)
+
+ # Set device for model parallelism
+ if self.model_parallel:
+ torch.cuda.set_device(self.decoder.first_device)
+ hidden_states = hidden_states.to(self.decoder.first_device)
+ if decoder_input_ids is not None:
+ decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
+ if attention_mask is not None:
+ attention_mask = attention_mask.to(self.decoder.first_device)
+ if decoder_attention_mask is not None:
+ decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
+
+ # Decode
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ inputs_embeds=decoder_inputs_embeds,
+ past_key_values=past_key_values,
+ encoder_hidden_states=hidden_states,
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ sequence_output = decoder_outputs[0]
+
+ # Set device for model parallelism
+ if self.model_parallel:
+ torch.cuda.set_device(self.encoder.first_device)
+ self.lm_head = self.lm_head.to(self.encoder.first_device)
+ sequence_output = sequence_output.to(self.lm_head.weight.device)
+
+ if self.config.tie_word_embeddings:
+ # Rescale output before projecting on vocab
+ # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
+ sequence_output = sequence_output * (self.model_dim**-0.5)
+
+ lm_logits = self.lm_head(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss(ignore_index=-100)
+ # move labels to correct device to enable PP
+ labels = labels.to(lm_logits.device)
+ loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
+ # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
+
+ if not return_dict:
+ output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
+ return ((loss,) + output) if loss is not None else output
+
+ return Seq2SeqLMOutput(
+ loss=loss,
+ logits=lm_logits,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+ # Copied from transformers.models.t5.modeling_t5.T5ForConditionalGeneration.prepare_decoder_input_ids_from_labels
+ def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+ return self._shift_right(labels)
+
+
+@auto_docstring
+class MT5EncoderModel(MT5PreTrainedModel):
+ r"""
+ Examples:
+
+ ```python
+ >>> from transformers import MT5EncoderModel, AutoTokenizer
+
+ >>> model = MT5EncoderModel.from_pretrained("google/mt5-small")
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+ >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+ >>> input_ids = tokenizer(article, return_tensors="pt").input_ids
+ >>> outputs = model(input_ids)
+ >>> hidden_state = outputs.last_hidden_state
+ ```"""
+
+ model_type = "mt5"
+ config: MT5Config
+ _tied_weights_keys = ["encoder.embed_tokens.weight"]
+
+ # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.__init__ with T5->MT5
+ def __init__(self, config: MT5Config):
+ super().__init__(config)
+ self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+ encoder_config = config
+ encoder_config.use_cache = False
+ encoder_config.is_encoder_decoder = False
+ self.encoder = MT5Stack(encoder_config, self.shared)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ # Model parallel
+ self.model_parallel = False
+ self.device_map = None
+
+ @add_start_docstrings(PARALLELIZE_DOCSTRING)
+ # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.parallelize
+ def parallelize(self, device_map=None):
+ warnings.warn(
+ "`T5EncoderModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load"
+ " your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
+ " `device_map` but it needs to be a dictionary module_name to device, so for instance {'block.0': 0,"
+ " 'block.1': 1, ...}",
+ FutureWarning,
+ )
+ self.device_map = (
+ get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
+ if device_map is None
+ else device_map
+ )
+ assert_device_map(self.device_map, len(self.encoder.block))
+ self.encoder.parallelize(self.device_map)
+ self.model_parallel = True
+
+ @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
+ # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.deparallelize
+ def deparallelize(self):
+ warnings.warn(
+ "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
+ FutureWarning,
+ )
+ self.encoder.deparallelize()
+ self.encoder = self.encoder.to("cpu")
+ self.model_parallel = False
+ self.device_map = None
+ torch.cuda.empty_cache()
+
+ # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.get_input_embeddings
+ def get_input_embeddings(self):
+ return self.shared
+
+ # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.set_input_embeddings
+ def set_input_embeddings(self, new_embeddings):
+ self.shared = new_embeddings
+ self.encoder.set_input_embeddings(new_embeddings)
+
+ # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.get_encoder
+ def get_encoder(self):
+ return self.encoder
+
+ # Copied from transformers.models.t5.modeling_t5.T5EncoderModel._prune_heads
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.block[layer].layer[0].SelfAttention.prune_heads(heads)
+
+ @auto_docstring
+ # Copied from transformers.models.t5.modeling_t5.T5EncoderModel.forward with google-t5/->google/, T5->MT5, t5->mt5
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.FloatTensor], BaseModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
+ should be able to pad the inputs on both the right and the left.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for detail.
+
+ To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MT5EncoderModel
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+ >>> model = MT5EncoderModel.from_pretrained("google/mt5-small")
+ >>> input_ids = tokenizer(
+ ... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
+ ... ).input_ids # Batch size 1
+ >>> outputs = model(input_ids=input_ids)
+ >>> last_hidden_states = outputs.last_hidden_state
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ return encoder_outputs
+
+
+@auto_docstring(
+ custom_intro="""
+ MT5 model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
+ tasks.
+ """
+)
+class MT5ForSequenceClassification(MT5PreTrainedModel):
+ _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+ # Copied from transformers.models.t5.modeling_t5.T5ForSequenceClassification.__init__ with T5->MT5
+ def __init__(self, config: MT5Config):
+ super().__init__(config)
+ self.transformer = MT5Model(config)
+ self.classification_head = MT5ClassificationHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ self.model_parallel = False
+
+ @auto_docstring
+ # Copied from transformers.models.t5.modeling_t5.T5ForSequenceClassification.forward with T5->MT5, t5->mt5
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, Seq2SeqSequenceClassifierOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
+ should be able to pad the inputs on both the right and the left.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for detail.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./mt5#training).
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ MT5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+ To know more on how to prepare `decoder_input_ids` for pretraining take a look at [MT5
+ Training](./mt5#training).
+ decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
+ `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ if labels is not None:
+ use_cache = False
+
+ if input_ids is None and inputs_embeds is not None:
+ raise NotImplementedError(
+ f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
+ )
+
+ # Copied from models.bart.modeling_bart.BartModel.forward different to other models, MT5 automatically creates
+ # decoder_input_ids from input_ids if no decoder_input_ids are provided
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ if input_ids is None:
+ raise ValueError(
+ "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
+ "passed, `input_ids` cannot be `None`. Please pass either "
+ "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
+ )
+ decoder_input_ids = self._shift_right(input_ids)
+
+ outputs = self.transformer(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ encoder_outputs=encoder_outputs,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = outputs[0]
+
+ eos_mask = input_ids.eq(self.config.eos_token_id).to(sequence_output.device)
+
+ if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
+ raise ValueError("All examples must have the same number of tokens.")
+ batch_size, _, hidden_size = sequence_output.shape
+ sentence_representation = sequence_output[eos_mask, :].view(batch_size, -1, hidden_size)[:, -1, :]
+ logits = self.classification_head(sentence_representation)
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.config.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.config.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return Seq2SeqSequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+@auto_docstring
+class MT5ForTokenClassification(MT5PreTrainedModel):
+ _tied_weights_keys = ["transformer.encoder.embed_tokens.weight"]
+
+ # Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification.__init__ with T5->MT5
+ def __init__(self, config: MT5Config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.transformer = MT5EncoderModel(config)
+ self.dropout = nn.Dropout(config.classifier_dropout)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ # Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification.forward with T5->MT5
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ labels: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], TokenClassifierOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. MT5 is a model with relative position embeddings so you
+ should be able to pad the inputs on both the right and the left.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for detail.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ To know more on how to prepare `input_ids` for pretraining take a look a [MT5 Training](./t5#training).
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.transformer(
+ input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ hidden_states = self.dropout(hidden_states)
+ logits = self.classifier(hidden_states)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits, outputs[2:-1])
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class MT5ForQuestionAnswering(MT5PreTrainedModel):
+ _keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+ # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.__init__ with T5->MT5
+ def __init__(self, config: MT5Config):
+ super().__init__(config)
+ self.model_dim = config.d_model
+
+ self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+ encoder_config = copy.deepcopy(config)
+ encoder_config.is_decoder = False
+ encoder_config.use_cache = False
+ encoder_config.tie_encoder_decoder = False
+ self.encoder = MT5Stack(encoder_config, self.shared)
+
+ decoder_config = copy.deepcopy(config)
+ decoder_config.is_decoder = True
+ decoder_config.tie_encoder_decoder = False
+ decoder_config.num_layers = config.num_decoder_layers
+ self.decoder = MT5Stack(decoder_config, self.shared)
+
+ self.num_labels = config.num_labels
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ self.model_parallel = False
+
+ # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.get_input_embeddings
+ def get_input_embeddings(self):
+ return self.shared
+
+ # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.set_input_embeddings
+ def set_input_embeddings(self, new_embeddings):
+ self.shared = new_embeddings
+ self.encoder.set_input_embeddings(new_embeddings)
+ self.decoder.set_input_embeddings(new_embeddings)
+
+ # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.get_encoder
+ def get_encoder(self):
+ return self.encoder
+
+ # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.get_decoder
+ def get_decoder(self):
+ return self.decoder
+
+ @auto_docstring
+ # Copied from transformers.models.t5.modeling_t5.T5ForQuestionAnswering.forward
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.BoolTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ decoder_head_mask: Optional[torch.FloatTensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.Tensor]]] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.FloatTensor], Seq2SeqQuestionAnsweringModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
+ should be able to pad the inputs on both the right and the left.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for detail.
+
+ [What are input IDs?](../glossary#input-ids)
+
+ To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+ To know more on how to prepare `decoder_input_ids` for pretraining take a look at [T5
+ Training](./t5#training).
+ decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
+ `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ if start_positions is not None and end_positions is not None:
+ use_cache = False
+
+ # Copied from models.bart.modeling_bart.BartModel.forward
+ # different to other models, T5 automatically creates decoder_input_ids from
+ # input_ids if no decoder_input_ids are provided
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ if input_ids is None:
+ raise ValueError(
+ "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
+ "passed, `input_ids` cannot be `None`. Please pass either "
+ "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
+ )
+ decoder_input_ids = self._shift_right(input_ids)
+
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+ if head_mask is not None and decoder_head_mask is None:
+ if self.config.num_layers == self.config.num_decoder_layers:
+ warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
+ decoder_head_mask = head_mask
+
+ # Encode if needed (training, first prediction pass)
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+ encoder_outputs = BaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+
+ hidden_states = encoder_outputs[0]
+
+ # Decode
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ inputs_embeds=decoder_inputs_embeds,
+ past_key_values=None,
+ encoder_hidden_states=hidden_states,
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = decoder_outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1).to(start_logits.device)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1).to(end_logits.device)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + decoder_outputs[1:] + encoder_outputs
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return Seq2SeqQuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+__all__ = [
+ "MT5EncoderModel",
+ "MT5ForConditionalGeneration",
+ "MT5ForQuestionAnswering",
+ "MT5ForSequenceClassification",
+ "MT5ForTokenClassification",
+ "MT5Model",
+ "MT5PreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/modeling_tf_mt5.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/modeling_tf_mt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..6152aea0a5acad5d1d6db9b7ee044cbde2bca976
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/modeling_tf_mt5.py
@@ -0,0 +1,98 @@
+# coding=utf-8
+# Copyright 2020 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tensorflow mT5 model."""
+
+from ...utils import logging
+from ..t5.modeling_tf_t5 import TFT5EncoderModel, TFT5ForConditionalGeneration, TFT5Model
+from .configuration_mt5 import MT5Config
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "T5Config"
+
+
+class TFMT5Model(TFT5Model):
+ r"""
+ This class overrides [`TFT5Model`]. Please check the superclass for the appropriate documentation alongside usage
+ examples.
+
+ Examples:
+
+ ```python
+ >>> from transformers import TFMT5Model, AutoTokenizer
+
+ >>> model = TFMT5Model.from_pretrained("google/mt5-small")
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+ >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+ >>> summary = "Weiter Verhandlung in Syrien."
+ >>> inputs = tokenizer(article, return_tensors="tf")
+ >>> labels = tokenizer(text_target=summary, return_tensors="tf")
+
+ >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"])
+ >>> hidden_states = outputs.last_hidden_state
+ ```"""
+
+ model_type = "mt5"
+ config_class = MT5Config
+
+
+class TFMT5ForConditionalGeneration(TFT5ForConditionalGeneration):
+ r"""
+ This class overrides [`TFT5ForConditionalGeneration`]. Please check the superclass for the appropriate
+ documentation alongside usage examples.
+
+ Examples:
+
+ ```python
+ >>> from transformers import TFMT5ForConditionalGeneration, AutoTokenizer
+
+ >>> model = TFMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+ >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+ >>> summary = "Weiter Verhandlung in Syrien."
+ >>> inputs = tokenizer(article, text_target=summary, return_tensors="tf")
+
+ >>> outputs = model(**inputs)
+ >>> loss = outputs.loss
+ ```"""
+
+ model_type = "mt5"
+ config_class = MT5Config
+
+
+class TFMT5EncoderModel(TFT5EncoderModel):
+ r"""
+ This class overrides [`TFT5EncoderModel`]. Please check the superclass for the appropriate documentation alongside
+ usage examples.
+
+ Examples:
+
+ ```python
+ >>> from transformers import TFMT5EncoderModel, AutoTokenizer
+
+ >>> model = TFMT5EncoderModel.from_pretrained("google/mt5-small")
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+ >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
+ >>> input_ids = tokenizer(article, return_tensors="tf").input_ids
+ >>> outputs = model(input_ids)
+ >>> hidden_state = outputs.last_hidden_state
+ ```"""
+
+ model_type = "mt5"
+ config_class = MT5Config
+
+
+__all__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/tokenization_mt5.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/tokenization_mt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3058816ff2032c41e2b0cdb6940705a7572faa0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/tokenization_mt5.py
@@ -0,0 +1,24 @@
+# coding=utf-8
+# Copyright 2020, The T5 Authors and HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""mT5 tokenization file"""
+
+from ..t5 import T5Tokenizer
+
+
+class MT5Tokenizer(T5Tokenizer):
+ pass
+
+
+__all__ = ["MT5Tokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/tokenization_mt5_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/tokenization_mt5_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..8737088cc44206bea1e2f9d1793ae49692ae35e8
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mt5/tokenization_mt5_fast.py
@@ -0,0 +1,24 @@
+# coding=utf-8
+# Copyright 2020, The T5 Authors and HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""mT5 tokenization file"""
+
+from ..t5 import T5TokenizerFast
+
+
+class MT5TokenizerFast(T5TokenizerFast):
+ pass
+
+
+__all__ = ["MT5TokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..880274309cbab486a90df05548a0e4d3f2ea0925
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_musicgen import *
+ from .modeling_musicgen import *
+ from .processing_musicgen import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/configuration_musicgen.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/configuration_musicgen.py
new file mode 100644
index 0000000000000000000000000000000000000000..878cc122f17d31d7d0bea3d9218663cfc585af9a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/configuration_musicgen.py
@@ -0,0 +1,248 @@
+# coding=utf-8
+# Copyright 2023 Meta AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MusicGen model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ..auto.configuration_auto import AutoConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class MusicgenDecoderConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of an [`MusicgenDecoder`]. It is used to instantiate a
+ MusicGen decoder according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the MusicGen
+ [facebook/musicgen-small](https://huggingface.co/facebook/musicgen-small) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 2048):
+ Vocabulary size of the MusicgenDecoder model. Defines the number of different tokens that can be
+ represented by the `inputs_ids` passed when calling [`MusicgenDecoder`].
+ hidden_size (`int`, *optional*, defaults to 1024):
+ Dimensionality of the layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 24):
+ Number of decoder layers.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer block.
+ ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer block.
+ activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the decoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, text_encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with. Typically, set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ initializer_factor (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ scale_embedding (`bool`, *optional*, defaults to `False`):
+ Scale embeddings by diving by sqrt(hidden_size).
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether the model should return the last key/values attentions (not used by all models)
+ num_codebooks (`int`, *optional*, defaults to 4):
+ The number of parallel codebooks forwarded to the model.
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+ Whether input and output word embeddings should be tied.
+ audio_channels (`int`, *optional*, defaults to 1
+ Number of channels in the audio data. Either 1 for mono or 2 for stereo. Stereo models generate a separate
+ audio stream for the left/right output channels. Mono models generate a single audio stream output.
+ """
+
+ model_type = "musicgen_decoder"
+ base_config_key = "decoder_config"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=2048,
+ max_position_embeddings=2048,
+ num_hidden_layers=24,
+ ffn_dim=4096,
+ num_attention_heads=16,
+ layerdrop=0.0,
+ use_cache=True,
+ activation_function="gelu",
+ hidden_size=1024,
+ dropout=0.1,
+ attention_dropout=0.0,
+ activation_dropout=0.0,
+ initializer_factor=0.02,
+ scale_embedding=False,
+ num_codebooks=4,
+ audio_channels=1,
+ pad_token_id=2048,
+ bos_token_id=2048,
+ eos_token_id=None,
+ tie_word_embeddings=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.ffn_dim = ffn_dim
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.activation_function = activation_function
+ self.initializer_factor = initializer_factor
+ self.layerdrop = layerdrop
+ self.use_cache = use_cache
+ self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
+ self.num_codebooks = num_codebooks
+
+ if audio_channels not in [1, 2]:
+ raise ValueError(f"Expected 1 (mono) or 2 (stereo) audio channels, got {audio_channels} channels.")
+ self.audio_channels = audio_channels
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+
+class MusicgenConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MusicgenModel`]. It is used to instantiate a
+ MusicGen model according to the specified arguments, defining the text encoder, audio encoder and MusicGen decoder
+ configs.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ kwargs (*optional*):
+ Dictionary of keyword arguments. Notably:
+
+ - **text_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
+ defines the text encoder config.
+ - **audio_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
+ defines the audio encoder config.
+ - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
+ the decoder config.
+
+ Example:
+
+ ```python
+ >>> from transformers import (
+ ... MusicgenConfig,
+ ... MusicgenDecoderConfig,
+ ... T5Config,
+ ... EncodecConfig,
+ ... MusicgenForConditionalGeneration,
+ ... )
+
+ >>> # Initializing text encoder, audio encoder, and decoder model configurations
+ >>> text_encoder_config = T5Config()
+ >>> audio_encoder_config = EncodecConfig()
+ >>> decoder_config = MusicgenDecoderConfig()
+
+ >>> configuration = MusicgenConfig.from_sub_models_config(
+ ... text_encoder_config, audio_encoder_config, decoder_config
+ ... )
+
+ >>> # Initializing a MusicgenForConditionalGeneration (with random weights) from the facebook/musicgen-small style configuration
+ >>> model = MusicgenForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ >>> config_text_encoder = model.config.text_encoder
+ >>> config_audio_encoder = model.config.audio_encoder
+ >>> config_decoder = model.config.decoder
+
+ >>> # Saving the model, including its configuration
+ >>> model.save_pretrained("musicgen-model")
+
+ >>> # loading model and config from pretrained folder
+ >>> musicgen_config = MusicgenConfig.from_pretrained("musicgen-model")
+ >>> model = MusicgenForConditionalGeneration.from_pretrained("musicgen-model", config=musicgen_config)
+ ```"""
+
+ model_type = "musicgen"
+ sub_configs = {
+ "text_encoder": AutoConfig,
+ "audio_encoder": AutoConfig,
+ "decoder": MusicgenDecoderConfig,
+ }
+ has_no_defaults_at_init = True
+
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+ if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
+ raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config")
+
+ text_encoder_config = kwargs.pop("text_encoder")
+ text_encoder_model_type = text_encoder_config.pop("model_type")
+
+ audio_encoder_config = kwargs.pop("audio_encoder")
+ audio_encoder_model_type = audio_encoder_config.pop("model_type")
+
+ decoder_config = kwargs.pop("decoder")
+
+ self.text_encoder = AutoConfig.for_model(text_encoder_model_type, **text_encoder_config)
+ self.audio_encoder = AutoConfig.for_model(audio_encoder_model_type, **audio_encoder_config)
+ self.decoder = MusicgenDecoderConfig(**decoder_config)
+ self.is_encoder_decoder = True
+ self.initializer_factor = self.decoder.initializer_factor
+
+ @classmethod
+ def from_sub_models_config(
+ cls,
+ text_encoder_config: PretrainedConfig,
+ audio_encoder_config: PretrainedConfig,
+ decoder_config: MusicgenDecoderConfig,
+ **kwargs,
+ ):
+ r"""
+ Instantiate a [`MusicgenConfig`] (or a derived class) from text encoder, audio encoder and decoder
+ configurations.
+
+ Returns:
+ [`MusicgenConfig`]: An instance of a configuration object
+ """
+
+ return cls(
+ text_encoder=text_encoder_config.to_dict(),
+ audio_encoder=audio_encoder_config.to_dict(),
+ decoder=decoder_config.to_dict(),
+ **kwargs,
+ )
+
+ @property
+ # This is a property because you might want to change the codec model on the fly
+ def sampling_rate(self):
+ return self.audio_encoder.sampling_rate
+
+
+__all__ = ["MusicgenConfig", "MusicgenDecoderConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/convert_musicgen_transformers.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/convert_musicgen_transformers.py
new file mode 100644
index 0000000000000000000000000000000000000000..b561a673c2430f0362b8261377cb041548c6f3df
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/convert_musicgen_transformers.py
@@ -0,0 +1,236 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert MusicGen checkpoints from the original repository."""
+
+import argparse
+from collections import OrderedDict
+from pathlib import Path
+
+import torch
+from audiocraft.models import MusicGen
+
+from transformers import (
+ AutoFeatureExtractor,
+ AutoTokenizer,
+ EncodecModel,
+ MusicgenDecoderConfig,
+ MusicgenForConditionalGeneration,
+ MusicgenProcessor,
+ T5EncoderModel,
+)
+from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+EXPECTED_MISSING_KEYS = ["model.decoder.embed_positions.weights"]
+
+
+def rename_keys(name):
+ if "emb" in name:
+ name = name.replace("emb", "model.decoder.embed_tokens")
+ if "transformer" in name:
+ name = name.replace("transformer", "model.decoder")
+ if "cross_attention" in name:
+ name = name.replace("cross_attention", "encoder_attn")
+ if "linear1" in name:
+ name = name.replace("linear1", "fc1")
+ if "linear2" in name:
+ name = name.replace("linear2", "fc2")
+ if "norm1" in name:
+ name = name.replace("norm1", "self_attn_layer_norm")
+ if "norm_cross" in name:
+ name = name.replace("norm_cross", "encoder_attn_layer_norm")
+ if "norm2" in name:
+ name = name.replace("norm2", "final_layer_norm")
+ if "out_norm" in name:
+ name = name.replace("out_norm", "model.decoder.layer_norm")
+ if "linears" in name:
+ name = name.replace("linears", "lm_heads")
+ if "condition_provider.conditioners.description.output_proj" in name:
+ name = name.replace("condition_provider.conditioners.description.output_proj", "enc_to_dec_proj")
+ return name
+
+
+def rename_state_dict(state_dict: OrderedDict, hidden_size: int) -> tuple[dict, dict]:
+ """Function that takes the fairseq Musicgen state dict and renames it according to the HF
+ module names. It further partitions the state dict into the decoder (LM) state dict, and that for the
+ encoder-decoder projection."""
+ keys = list(state_dict.keys())
+ enc_dec_proj_state_dict = {}
+ for key in keys:
+ val = state_dict.pop(key)
+ key = rename_keys(key)
+ if "in_proj_weight" in key:
+ # split fused qkv proj
+ state_dict[key.replace("in_proj_weight", "q_proj.weight")] = val[:hidden_size, :]
+ state_dict[key.replace("in_proj_weight", "k_proj.weight")] = val[hidden_size : 2 * hidden_size, :]
+ state_dict[key.replace("in_proj_weight", "v_proj.weight")] = val[-hidden_size:, :]
+ elif "enc_to_dec_proj" in key:
+ enc_dec_proj_state_dict[key[len("enc_to_dec_proj.") :]] = val
+ else:
+ state_dict[key] = val
+ return state_dict, enc_dec_proj_state_dict
+
+
+def decoder_config_from_checkpoint(checkpoint: str) -> MusicgenDecoderConfig:
+ if checkpoint.endswith("small"):
+ # default config values
+ hidden_size = 1024
+ num_hidden_layers = 24
+ num_attention_heads = 16
+ elif checkpoint.endswith("medium"):
+ hidden_size = 1536
+ num_hidden_layers = 48
+ num_attention_heads = 24
+ elif checkpoint.endswith("large"):
+ hidden_size = 2048
+ num_hidden_layers = 48
+ num_attention_heads = 32
+ else:
+ raise ValueError(
+ "Checkpoint should be one of `['small', 'medium', 'large']` for the mono checkpoints, "
+ "`['facebook/musicgen-stereo-small', 'facebook/musicgen-stereo-medium', 'facebook/musicgen-stereo-large']` "
+ f"for the stereo checkpoints, or a custom checkpoint with the checkpoint size as a suffix, got {checkpoint}."
+ )
+
+ if "stereo" in checkpoint:
+ audio_channels = 2
+ num_codebooks = 8
+ else:
+ audio_channels = 1
+ num_codebooks = 4
+
+ config = MusicgenDecoderConfig(
+ hidden_size=hidden_size,
+ ffn_dim=hidden_size * 4,
+ num_hidden_layers=num_hidden_layers,
+ num_attention_heads=num_attention_heads,
+ num_codebooks=num_codebooks,
+ audio_channels=audio_channels,
+ )
+ return config
+
+
+@torch.no_grad()
+def convert_musicgen_checkpoint(
+ checkpoint, pytorch_dump_folder=None, repo_id=None, device="cpu", safe_serialization=False
+):
+ fairseq_model = MusicGen.get_pretrained(checkpoint, device=device)
+ decoder_config = decoder_config_from_checkpoint(checkpoint)
+
+ decoder_state_dict = fairseq_model.lm.state_dict()
+ decoder_state_dict, enc_dec_proj_state_dict = rename_state_dict(
+ decoder_state_dict, hidden_size=decoder_config.hidden_size
+ )
+
+ text_encoder = T5EncoderModel.from_pretrained("google-t5/t5-base")
+ audio_encoder = EncodecModel.from_pretrained("facebook/encodec_32khz")
+ decoder = MusicgenForCausalLM(decoder_config).eval()
+
+ # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
+ missing_keys, unexpected_keys = decoder.load_state_dict(decoder_state_dict, strict=False)
+
+ for key in missing_keys.copy():
+ if key.startswith(("text_encoder", "audio_encoder")) or key in EXPECTED_MISSING_KEYS:
+ missing_keys.remove(key)
+
+ if len(missing_keys) > 0:
+ raise ValueError(f"Missing key(s) in state_dict: {missing_keys}")
+
+ if len(unexpected_keys) > 0:
+ raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}")
+
+ # init the composite model
+ model = MusicgenForConditionalGeneration(text_encoder=text_encoder, audio_encoder=audio_encoder, decoder=decoder)
+
+ # load the pre-trained enc-dec projection (from the decoder state dict)
+ model.enc_to_dec_proj.load_state_dict(enc_dec_proj_state_dict)
+
+ # check we can do a forward pass
+ input_ids = torch.arange(0, 2 * decoder_config.num_codebooks, dtype=torch.long).reshape(2, -1)
+ decoder_input_ids = input_ids.reshape(2 * decoder_config.num_codebooks, -1)
+
+ with torch.no_grad():
+ logits = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids).logits
+
+ if logits.shape != (2 * decoder_config.num_codebooks, 1, 2048):
+ raise ValueError("Incorrect shape for logits")
+
+ # now construct the processor
+ tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
+ feature_extractor = AutoFeatureExtractor.from_pretrained(
+ "facebook/encodec_32khz", padding_side="left", feature_size=decoder_config.audio_channels
+ )
+
+ processor = MusicgenProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
+
+ # set the appropriate bos/pad token ids
+ model.generation_config.decoder_start_token_id = 2048
+ model.generation_config.pad_token_id = 2048
+
+ # set other default generation config params
+ model.generation_config.max_length = int(30 * audio_encoder.config.frame_rate)
+ model.generation_config.do_sample = True
+ model.generation_config.guidance_scale = 3.0
+
+ if pytorch_dump_folder is not None:
+ Path(pytorch_dump_folder).mkdir(exist_ok=True)
+ logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}")
+ model.save_pretrained(pytorch_dump_folder, safe_serialization=safe_serialization)
+ processor.save_pretrained(pytorch_dump_folder)
+
+ if repo_id:
+ logger.info(f"Pushing model {checkpoint} to {repo_id}")
+ model.push_to_hub(repo_id, safe_serialization=safe_serialization)
+ processor.push_to_hub(repo_id)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--checkpoint",
+ default="small",
+ type=str,
+ help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: "
+ "`['small', 'medium', 'large']` for the mono checkpoints, "
+ "`['facebook/musicgen-stereo-small', 'facebook/musicgen-stereo-medium', 'facebook/musicgen-stereo-large']` "
+ "for the stereo checkpoints, or a custom checkpoint with the checkpoint size as a suffix.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder",
+ required=True,
+ default=None,
+ type=str,
+ help="Path to the output PyTorch model directory.",
+ )
+ parser.add_argument(
+ "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
+ )
+ parser.add_argument(
+ "--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
+ )
+ parser.add_argument(
+ "--safe_serialization",
+ action="store_true",
+ help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).",
+ )
+
+ args = parser.parse_args()
+ convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/modeling_musicgen.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/modeling_musicgen.py
new file mode 100644
index 0000000000000000000000000000000000000000..0df49dae8a35a149c70772ddaa553d2666e0619f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/modeling_musicgen.py
@@ -0,0 +1,2458 @@
+# coding=utf-8
+# Copyright 2023 Meta AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Musicgen model."""
+
+import copy
+import inspect
+import math
+import random
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Callable, Optional, Union
+
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import (
+ ClassifierFreeGuidanceLogitsProcessor,
+ GenerationConfig,
+ GenerationMixin,
+ GenerationMode,
+ LogitsProcessorList,
+ StoppingCriteriaList,
+)
+from ...modeling_attn_mask_utils import (
+ _prepare_4d_attention_mask,
+ _prepare_4d_attention_mask_for_sdpa,
+ _prepare_4d_causal_attention_mask,
+ _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from ...modeling_flash_attention_utils import (
+ FlashAttentionKwargs,
+)
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPastAndCrossAttentions,
+ CausalLMOutputWithCrossAttentions,
+ ModelOutput,
+ Seq2SeqLMOutput,
+)
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import auto_docstring, is_torch_flex_attn_available, logging
+from ...utils.deprecation import deprecate_kwarg
+from ..auto.configuration_auto import AutoConfig
+from ..auto.modeling_auto import AutoModel
+from .configuration_musicgen import MusicgenConfig, MusicgenDecoderConfig
+
+
+if is_torch_flex_attn_available():
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+
+if TYPE_CHECKING:
+ from ...generation.streamers import BaseStreamer
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring
+class MusicgenUnconditionalInput(ModelOutput):
+ r"""
+ encoder_outputs (`tuple[torch.FloatTensor]` of length 1, with tensor shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the text encoder model.
+ attention_mask (`torch.LongTensor`) of shape `(batch_size, sequence_length)`, *optional*):
+ Encoder attention mask to avoid performing attention on padding token indices. Mask values selected in `[0,
+ 1]`: 1 for tokens that are **not masked**, 0 for tokens that are **masked**.
+ guidance_scale (`float`, *optional*):
+ Guidance scale for classifier free guidance, setting the balance between the conditional logits (predicted
+ from the prompts) and the unconditional logits (predicted without prompts).
+ """
+
+ encoder_outputs: tuple[torch.FloatTensor] = None
+ attention_mask: Optional[torch.LongTensor] = None
+ guidance_scale: Optional[float] = None
+
+
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+ """
+ Shift input ids one token to the right.
+ """
+ # transpose to get (bsz, num_codebooks, seq_len)
+ input_ids = input_ids.transpose(1, 2)
+ shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+ shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
+ if decoder_start_token_id is None:
+ raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
+ shifted_input_ids[..., 0] = decoder_start_token_id
+
+ if pad_token_id is None:
+ raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+ return shifted_input_ids
+
+
+class MusicgenSinusoidalPositionalEmbedding(nn.Module):
+ """This module produces sinusoidal positional embeddings of any length."""
+
+ def __init__(self, num_positions: int, embedding_dim: int):
+ super().__init__()
+ self.embedding_dim = embedding_dim
+ self.make_weights(num_positions, embedding_dim)
+
+ def make_weights(self, num_embeddings: int, embedding_dim: int):
+ emb_weights = self.get_embedding(num_embeddings, embedding_dim)
+ if hasattr(self, "weights"):
+ # in forward put the weights on the correct dtype and device of the param
+ emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
+
+ self.register_buffer("weights", emb_weights, persistent=False)
+
+ @staticmethod
+ def get_embedding(num_embeddings: int, embedding_dim: int):
+ """
+ Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the
+ description in Section 3.5 of "Attention Is All You Need".
+ """
+ half_dim = embedding_dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
+ emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
+ emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=1).view(num_embeddings, -1)
+ if embedding_dim % 2 == 1:
+ # zero pad
+ emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
+ return emb.to(torch.get_default_dtype())
+
+ @torch.no_grad()
+ def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
+ bsz, codebooks, seq_len = input_ids.size()
+ # Create the position ids from the input token ids.
+ position_ids = (torch.arange(seq_len) + past_key_values_length).to(input_ids.device)
+ # expand embeddings if needed
+ if seq_len > self.weights.size(0):
+ self.make_weights(seq_len, self.embedding_dim)
+ return self.weights.index_select(0, position_ids.view(-1)).detach()
+
+
+# Copied from transformers.models.bart.modeling_bart.eager_attention_forward
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: Optional[float] = None,
+ dropout: float = 0.0,
+ head_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+):
+ if scaling is None:
+ scaling = query.size(-1) ** -0.5
+
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ if head_mask is not None:
+ attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
+
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class MusicgenAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: Optional[float] = 0.0,
+ is_decoder: Optional[bool] = False,
+ bias: Optional[bool] = True,
+ is_causal: Optional[bool] = False,
+ config: Optional[MusicgenConfig] = None,
+ layer_idx: Optional[int] = None,
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+ self.config = config
+
+ if (self.head_dim * num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+ self.is_causal = is_causal
+ self.layer_idx = layer_idx
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ key_value_states: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ cache_position: Optional[torch.Tensor] = None,
+ # TODO: we need a refactor so that the different attention modules can get their specific kwargs
+ # ATM, we have mixed things encoder, decoder, and encoder-decoder attn
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+
+ # determine input shapes
+ bsz, tgt_len = hidden_states.shape[:-1]
+ src_len = key_value_states.shape[1] if is_cross_attention else tgt_len
+
+ q_input_shape = (bsz, tgt_len, -1, self.head_dim)
+ kv_input_shape = (bsz, src_len, -1, self.head_dim)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states).view(*q_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ if isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_layer from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = key_value_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_states = curr_past_key_value.layers[self.layer_idx].keys
+ value_states = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_states = self.k_proj(current_states).view(*kv_input_shape).transpose(1, 2)
+ value_states = self.v_proj(current_states).view(*kv_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_states, value_states = curr_past_key_value.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=self.scaling,
+ output_attentions=output_attentions,
+ head_mask=layer_head_mask,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+class MusicgenDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MusicgenDecoderConfig, layer_idx=None):
+ super().__init__()
+ self.embed_dim = config.hidden_size
+
+ self.self_attn = MusicgenAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.num_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ bias=False,
+ is_causal=True,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.encoder_attn = MusicgenAttention(
+ self.embed_dim,
+ config.num_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ bias=False,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=False)
+ self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=False)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = True,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ encoder_hidden_states (`torch.FloatTensor`):
+ cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
+ encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`.
+ cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
+ size `(decoder_attention_heads,)`.
+ past_key_values (`Tuple(torch.FloatTensor)`): cached past key and value projection states
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # Cross-Attention Block
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ hidden_states, cross_attn_weights = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ layer_head_mask=cross_attn_layer_head_mask,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+ return outputs
+
+
+@auto_docstring
+class MusicgenPreTrainedModel(PreTrainedModel):
+ config: MusicgenDecoderConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MusicgenDecoderLayer", "MusicgenAttention"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ def _init_weights(self, module):
+ std = self.config.initializer_factor
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+
+class MusicgenDecoder(MusicgenPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MusicgenDecoderLayer`]
+ """
+
+ def __init__(self, config: MusicgenDecoderConfig):
+ super().__init__(config)
+ self.dropout = config.dropout
+ self.layerdrop = config.layerdrop
+ self.max_target_positions = config.max_position_embeddings
+ self.d_model = config.hidden_size
+ self.num_codebooks = config.num_codebooks
+ self.embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0
+
+ embed_dim = config.vocab_size + 1
+ self.embed_tokens = nn.ModuleList(
+ [nn.Embedding(embed_dim, config.hidden_size) for _ in range(config.num_codebooks)]
+ )
+
+ self.embed_positions = MusicgenSinusoidalPositionalEmbedding(
+ config.max_position_embeddings,
+ config.hidden_size,
+ )
+
+ self.layers = nn.ModuleList(
+ [MusicgenDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]
+ )
+ self.layer_norm = nn.LayerNorm(config.hidden_size)
+ self.attn_implementation = config._attn_implementation
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPastAndCrossAttentions]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.
+
+ Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
+ such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+
+
+ The `input_ids` will automatically be converted from shape `(batch_size * num_codebooks,
+ target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If
+ you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of
+ frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks,
+ target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as
+ `input_ids`.
+
+
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
+ the decoder.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
+ cross-attention on hidden heads. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ # (bsz * codebooks, seq_len) -> (bsz, codebooks, seq_len)
+ input = input_ids.reshape(-1, self.num_codebooks, input_ids.shape[-1])
+ bsz, num_codebooks, seq_len = input.shape
+ input_shape = (bsz, seq_len)
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ input = inputs_embeds[:, :, -1:]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing`. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ if use_cache and past_key_values is None:
+ past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
+ if use_cache and isinstance(past_key_values, tuple):
+ logger.warning_once(
+ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
+ "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
+ "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
+ )
+ past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
+
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+
+ if inputs_embeds is None:
+ inputs_embeds = sum([self.embed_tokens[codebook](input[:, codebook]) for codebook in range(num_codebooks)])
+
+ attention_mask = self._update_causal_mask(
+ attention_mask,
+ input_shape,
+ inputs_embeds,
+ past_key_values_length,
+ )
+ encoder_attention_mask = self._update_cross_attn_mask(
+ encoder_hidden_states,
+ encoder_attention_mask,
+ input_shape,
+ inputs_embeds,
+ )
+
+ # embed positions
+ positions = self.embed_positions(input, past_key_values_length)
+ hidden_states = inputs_embeds + positions.to(inputs_embeds.device)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
+
+ # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
+ for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
+ if attn_mask is not None:
+ if attn_mask.size()[0] != len(self.layers):
+ raise ValueError(
+ f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+ f" {attn_mask.size()[0]}."
+ )
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ dropout_probability = random.uniform(0, 1)
+ if self.training and (dropout_probability < self.layerdrop):
+ continue
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask,
+ encoder_hidden_states, # as a positional argument for gradient checkpointing
+ encoder_attention_mask=encoder_attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if encoder_hidden_states is not None:
+ all_cross_attentions += (layer_outputs[2],)
+
+ hidden_states = self.layer_norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions]
+ if v is not None
+ )
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ )
+
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, None],
+ input_shape: torch.Size,
+ inputs_embeds: torch.Tensor,
+ past_key_values_length: int,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ # 2d mask is passed through the layers
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+ attention_mask,
+ input_shape,
+ inputs_embeds,
+ past_key_values_length,
+ )
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ # Other attention flavors support in-built causal (when `mask is None`)
+ # while we need to create our specific block mask regardless
+ elif attention_mask is None:
+ attention_mask = make_flex_block_causal_mask(
+ torch.ones(
+ size=(input_shape),
+ device=inputs_embeds.device,
+ )
+ )
+ else:
+ # 4d mask is passed through the layers
+ attention_mask = _prepare_4d_causal_attention_mask(
+ attention_mask, input_shape, inputs_embeds, past_key_values_length
+ )
+
+ return attention_mask
+
+ def _update_cross_attn_mask(
+ self,
+ encoder_hidden_states: Union[torch.Tensor, None],
+ encoder_attention_mask: Union[torch.Tensor, None],
+ input_shape: torch.Size,
+ inputs_embeds: torch.Tensor,
+ ):
+ # expand encoder attention mask
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ if self.config._attn_implementation == "flash_attention_2":
+ encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
+ encoder_attention_mask,
+ inputs_embeds.dtype,
+ tgt_len=input_shape[-1],
+ )
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(encoder_attention_mask, torch.Tensor):
+ encoder_attention_mask = make_flex_block_causal_mask(
+ encoder_attention_mask,
+ query_length=input_shape[-1],
+ is_causal=False,
+ )
+ else:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask(
+ encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+ )
+
+ return encoder_attention_mask
+
+
+@auto_docstring
+class MusicgenModel(MusicgenPreTrainedModel):
+ def __init__(self, config: MusicgenDecoderConfig):
+ super().__init__(config)
+ self.decoder = MusicgenDecoder(config)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.decoder.embed_tokens = value
+
+ def get_decoder(self):
+ return self.decoder
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPastAndCrossAttentions]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.
+
+ Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
+ such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+
+
+ The `input_ids` will automatically be converted from shape `(batch_size * num_codebooks,
+ target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If
+ you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of
+ frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks,
+ target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as
+ `input_ids`.
+
+
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
+ the decoder.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
+ cross-attention on hidden heads. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
+ decoder_outputs = self.decoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ head_mask=head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ if not return_dict:
+ return decoder_outputs
+
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ hidden_states=decoder_outputs.hidden_states,
+ attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The MusicGen decoder model with a language modelling head on top.
+ """
+)
+class MusicgenForCausalLM(MusicgenPreTrainedModel, GenerationMixin):
+ def __init__(self, config: MusicgenDecoderConfig):
+ super().__init__(config)
+
+ self.model = MusicgenModel(config)
+
+ self.num_codebooks = config.num_codebooks
+ self.lm_heads = nn.ModuleList(
+ [nn.Linear(config.hidden_size, config.vocab_size, bias=False) for _ in range(config.num_codebooks)]
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.decoder.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_heads
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_heads = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model.decoder = decoder
+
+ def get_decoder(self):
+ return self.model.decoder
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.
+
+ Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
+ such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+
+
+ The `input_ids` will automatically be converted from shape `(batch_size * num_codebooks,
+ target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If
+ you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of
+ frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks,
+ target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as
+ `input_ids`.
+
+
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
+ the decoder.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
+ cross-attention on hidden heads. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks)`, *optional*):
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if (labels is not None) and (input_ids is None and inputs_embeds is None):
+ input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.bos_token_id)
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ head_mask=head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ hidden_states = outputs[0]
+
+ lm_logits = torch.stack([head(hidden_states) for head in self.lm_heads], dim=1)
+
+ loss = None
+ if labels is not None:
+ # since encoder hidden states have been concatenated to the decoder hidden states,
+ # we take the last timestamps corresponding to labels
+ logits = lm_logits[:, :, -labels.shape[1] :]
+
+ loss_fct = CrossEntropyLoss()
+ loss = torch.zeros([], device=self.device)
+
+ # per codebook cross-entropy
+ # -100 labels are ignored
+ labels = labels.masked_fill(labels == self.config.pad_token_id, -100)
+
+ # per codebook cross-entropy
+ # ref: https://github.com/facebookresearch/audiocraft/blob/69fea8b290ad1b4b40d28f92d1dfc0ab01dbab85/audiocraft/solvers/musicgen.py#L242-L243
+ for codebook in range(self.config.num_codebooks):
+ codebook_logits = logits[:, codebook].contiguous().view(-1, logits.shape[-1])
+ codebook_labels = labels[..., codebook].contiguous().view(-1)
+ loss += loss_fct(codebook_logits, codebook_labels)
+
+ loss = loss / self.config.num_codebooks
+
+ # (bsz, num_codebooks, seq_len, vocab_size) -> (bsz * num_codebooks, seq_len, vocab_size)
+ lm_logits = lm_logits.reshape(-1, *lm_logits.shape[2:])
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return CausalLMOutputWithCrossAttentions(
+ loss=loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ cross_attentions=outputs.cross_attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ attention_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ head_mask=None,
+ cross_attn_head_mask=None,
+ past_key_values=None,
+ use_cache=True,
+ delay_pattern_mask=None,
+ guidance_scale=None,
+ **kwargs,
+ ):
+ # Overwritten -- MusicGen has custom processing
+ if delay_pattern_mask is None:
+ input_ids, delay_pattern_mask = self.build_delay_pattern_mask(
+ input_ids,
+ pad_token_id=self.generation_config.pad_token_id,
+ max_length=self.generation_config.max_length,
+ )
+
+ # apply the delay pattern mask
+ input_ids = self.apply_delay_pattern_mask(input_ids, delay_pattern_mask)
+
+ if guidance_scale is not None and guidance_scale > 1:
+ # for classifier free guidance we need to replicate the decoder args across the batch dim (we'll split these
+ # before sampling)
+ input_ids = input_ids.repeat((2, 1))
+ if attention_mask is not None:
+ attention_mask = attention_mask.repeat((2, 1))
+
+ if past_key_values is not None:
+ input_ids = input_ids[:, -1:]
+
+ return {
+ "input_ids": input_ids,
+ "attention_mask": attention_mask,
+ "encoder_hidden_states": encoder_hidden_states,
+ "encoder_attention_mask": encoder_attention_mask,
+ "head_mask": head_mask,
+ "cross_attn_head_mask": cross_attn_head_mask,
+ "past_key_values": past_key_values,
+ "use_cache": use_cache,
+ }
+
+ def build_delay_pattern_mask(
+ self, input_ids: torch.LongTensor, pad_token_id: int, max_length: Optional[int] = None
+ ):
+ """Build a delayed pattern mask to the input_ids. Each codebook is offset by the previous codebook by
+ one, giving a delayed pattern mask at the start of sequence and end of sequence. Take the example where there
+ are 4 codebooks and a max sequence length of 8, we have the delayed pattern mask of shape `(codebooks,
+ seq_len)`:
+ - [P, -1, -1, -1, -1, P, P, P]
+ - [P, P, -1, -1, -1, -1, P, P]
+ - [P, P, P, -1, -1, -1, -1, P]
+ - [P, P, P, P, -1, -1, -1, -1]
+ where P is the special padding token id and -1 indicates that the token is valid for prediction. If we include
+ a prompt (decoder input ids), the -1 positions indicate where new tokens should be predicted. Otherwise, the
+ mask is set to the value in the prompt:
+ - [P, a, b, -1, -1, P, P, P]
+ - [P, P, c, d, -1, -1, P, P]
+ - [P, P, P, e, f, -1, -1, P]
+ - [P, P, P, P, g, h, -1, -1]
+ where a-h indicate the input prompt (decoder input ids) that are offset by 1. Now, we only override the -1
+ tokens in our prediction.
+ """
+ # (bsz * num_codebooks, seq_len) -> (bsz, num_codebooks, seq_len)
+ input_ids = input_ids.reshape(-1, self.num_codebooks, input_ids.shape[-1])
+ bsz, num_codebooks, seq_len = input_ids.shape
+
+ max_length = max_length if max_length is not None else self.generation_config.max_length
+ input_ids_shifted = (
+ torch.ones((bsz, num_codebooks, max_length), dtype=torch.long, device=input_ids.device) * -1
+ )
+
+ channel_codebooks = num_codebooks // 2 if self.config.audio_channels == 2 else num_codebooks
+ # we only apply the mask if we have a large enough seq len - otherwise we return as is
+ if max_length < 2 * channel_codebooks - 1:
+ return input_ids.reshape(bsz * num_codebooks, -1), input_ids_shifted.reshape(bsz * num_codebooks, -1)
+
+ # fill the shifted ids with the prompt entries, offset by the codebook idx
+ for codebook in range(channel_codebooks):
+ if self.config.audio_channels == 1:
+ # mono channel - loop over the codebooks one-by-one
+ input_ids_shifted[:, codebook, codebook : seq_len + codebook] = input_ids[:, codebook]
+ else:
+ # left/right channels are interleaved in the generated codebooks, so handle one then the other
+ input_ids_shifted[:, 2 * codebook, codebook : seq_len + codebook] = input_ids[:, 2 * codebook]
+ input_ids_shifted[:, 2 * codebook + 1, codebook : seq_len + codebook] = input_ids[:, 2 * codebook + 1]
+
+ # construct a pattern mask that indicates the positions of padding tokens for each codebook
+ # first fill the upper triangular part (the EOS padding)
+ delay_pattern = torch.triu(
+ torch.ones((channel_codebooks, max_length), dtype=torch.bool), diagonal=max_length - channel_codebooks + 1
+ )
+ # then fill the lower triangular part (the BOS padding)
+ delay_pattern = delay_pattern + torch.tril(torch.ones((channel_codebooks, max_length), dtype=torch.bool))
+
+ if self.config.audio_channels == 2:
+ # for left/right channel we need to duplicate every row of the pattern mask in an interleaved fashion
+ delay_pattern = delay_pattern.repeat_interleave(2, dim=0)
+
+ mask = ~delay_pattern.to(input_ids.device)
+ input_ids = mask * input_ids_shifted + ~mask * pad_token_id
+
+ # find the first position to start generating - this is the first place we have the -1 token
+ # and will always be in the first codebook (since it has no codebook offset)
+ first_codebook_ids = input_ids[:, 0, :]
+ start_ids = (first_codebook_ids == -1).nonzero()[:, 1]
+ if len(start_ids) > 0:
+ first_start_id = min(start_ids)
+ else:
+ # we have no tokens that need to be filled - return entire matrix of input ids
+ first_start_id = seq_len
+
+ # (bsz * num_codebooks, seq_len) -> (bsz, num_codebooks, seq_len)
+ pattern_mask = input_ids.reshape(bsz * num_codebooks, -1)
+ input_ids = input_ids[..., :first_start_id].reshape(bsz * num_codebooks, -1)
+ return input_ids, pattern_mask
+
+ @staticmethod
+ def apply_delay_pattern_mask(input_ids, decoder_pad_token_mask):
+ """Apply a delay pattern mask to the decoder input ids, only preserving predictions where
+ the mask is set to -1, and otherwise setting to the value detailed in the mask."""
+ seq_len = input_ids.shape[-1]
+ decoder_pad_token_mask = decoder_pad_token_mask[..., :seq_len]
+ input_ids = torch.where(decoder_pad_token_mask == -1, input_ids, decoder_pad_token_mask)
+ return input_ids
+
+ @torch.no_grad()
+ def generate(
+ self,
+ inputs: Optional[torch.Tensor] = None,
+ generation_config: Optional[GenerationConfig] = None,
+ logits_processor: Optional[LogitsProcessorList] = None,
+ stopping_criteria: Optional[StoppingCriteriaList] = None,
+ synced_gpus: Optional[bool] = None,
+ streamer: Optional["BaseStreamer"] = None,
+ **kwargs,
+ ):
+ """
+
+ Generates sequences of token ids for models with a language modeling head.
+
+
+
+ Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
+ model's default generation configuration. You can override any `generation_config` by passing the corresponding
+ parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
+
+ For an overview of generation strategies and code examples, check out the [following
+ guide](./generation_strategies).
+
+
+
+ Parameters:
+ inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
+ The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
+ method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
+ should be in the format `input_ids`. For encoder-decoder models *inputs* can represent any of
+ `input_ids`, `input_values`, `input_features`, or `pixel_values`.
+ generation_config (`~generation.GenerationConfig`, *optional*):
+ The generation configuration to be used as base parametrization for the generation call. `**kwargs`
+ passed to generate matching the attributes of `generation_config` will override them. If
+ `generation_config` is not provided, the default will be used, which had the following loading
+ priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
+ configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
+ default values, whose documentation should be checked to parameterize generation.
+ logits_processor (`LogitsProcessorList`, *optional*):
+ Custom logits processors that complement the default logits processors built from arguments and
+ generation config. If a logit processor is passed that is already created with the arguments or a
+ generation config an error is thrown. This feature is intended for advanced users.
+ stopping_criteria (`StoppingCriteriaList`, *optional*):
+ Custom stopping criteria that complement the default stopping criteria built from arguments and a
+ generation config. If a stopping criteria is passed that is already created with the arguments or a
+ generation config an error is thrown. This feature is intended for advanced users.
+ synced_gpus (`bool`, *optional*, defaults to `False`):
+ Whether to continue running the while loop until max_length (needed to avoid deadlocking with
+ `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
+ streamer (`BaseStreamer`, *optional*):
+ Streamer object that will be used to stream the generated sequences. Generated tokens are passed
+ through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
+ kwargs (`dict[str, Any]`, *optional*):
+ Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
+ forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
+ specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
+
+ Return:
+ [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
+ or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.
+
+ If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
+ [`~utils.ModelOutput`] types are:
+
+ - [`~generation.GenerateDecoderOnlyOutput`],
+ - [`~generation.GenerateBeamDecoderOnlyOutput`]
+
+ If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
+ [`~utils.ModelOutput`] types are:
+
+ - [`~generation.GenerateEncoderDecoderOutput`],
+ - [`~generation.GenerateBeamEncoderDecoderOutput`]
+ """
+ # 1. Handle `generation_config` and kwargs that might update it, and validate the resulting objects
+ if generation_config is None:
+ generation_config = self.generation_config
+
+ generation_config = copy.deepcopy(generation_config)
+ model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
+ generation_config.validate()
+ self._validate_model_kwargs(model_kwargs.copy())
+
+ # 2. Set generation parameters if not already defined
+ logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+ stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+
+ requires_attention_mask = "encoder_outputs" not in model_kwargs
+ kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
+
+ # 3. Define model inputs`
+ input_ids, model_input_name, model_kwargs = self._prepare_model_inputs(
+ inputs, generation_config.bos_token_id, model_kwargs
+ )
+ batch_size = input_ids.shape[0] // self.num_codebooks
+ self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=input_ids.device)
+
+ # 4. Define other model kwargs
+ model_kwargs["use_cache"] = generation_config.use_cache
+ model_kwargs["guidance_scale"] = generation_config.guidance_scale
+
+ if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
+ model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
+ input_ids, generation_config, model_kwargs
+ )
+
+ # 5. Prepare `max_length` depending on other stopping criteria.
+ input_ids_length = input_ids.shape[-1]
+ has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+ has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
+ generation_config = self._prepare_generated_length(
+ generation_config=generation_config,
+ has_default_max_length=has_default_max_length,
+ has_default_min_length=has_default_min_length,
+ model_input_name=model_input_name,
+ inputs_tensor=input_ids,
+ input_ids_length=input_ids_length,
+ )
+
+ self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
+
+ # 6. Prepare the cache.
+ # - `model_kwargs` may be updated in place with a cache as defined by the parameters in `generation_config`.
+ # - different models have a different cache name expected by the model (default = "past_key_values")
+ # - `max_length`, prepared above, is used to determine the maximum cache length
+ max_cache_length = generation_config.max_length - 1
+ if (
+ input_ids_length.shape[1] != input_ids_length
+ and model_input_name == "inputs_embeds"
+ and not self.config.is_encoder_decoder
+ ):
+ max_cache_length += input_ids_length.shape[1]
+ self._prepare_cache_for_generation(
+ generation_config,
+ model_kwargs,
+ assistant_model=None,
+ batch_size=batch_size,
+ max_cache_length=max_cache_length,
+ )
+
+ # 7. Prepare `input_ids` which will be used for auto-regressive generation
+ # Build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen)
+ input_ids, delay_pattern_mask = self.build_delay_pattern_mask(
+ input_ids,
+ pad_token_id=generation_config._decoder_start_token_tensor,
+ max_length=generation_config.max_length,
+ )
+
+ if streamer is not None:
+ streamer.put(input_ids.cpu())
+
+ # stash the delay mask so that we don't have to recompute it in each forward pass
+ model_kwargs["delay_pattern_mask"] = delay_pattern_mask
+
+ # 8. determine generation mode
+ generation_mode = generation_config.get_generation_mode()
+
+ # 9. prepare batched CFG externally (to enable coexistence with the unbatched CFG)
+ if generation_config.guidance_scale is not None and generation_config.guidance_scale > 1:
+ logits_processor.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale))
+ generation_config.guidance_scale = None
+
+ # 10. prepare distribution pre_processing samplers
+ logits_processor = self._get_logits_processor(
+ generation_config=generation_config,
+ input_ids_seq_length=input_ids_length,
+ encoder_input_ids=input_ids,
+ prefix_allowed_tokens_fn=None,
+ logits_processor=logits_processor,
+ device=input_ids.device,
+ )
+
+ # 10. prepare stopping criteria
+ stopping_criteria = self._get_stopping_criteria(
+ generation_config=generation_config, stopping_criteria=stopping_criteria
+ )
+
+ if generation_mode in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH):
+ # expand input_ids with `num_return_sequences` additional sequences per batch
+ input_ids, model_kwargs = self._expand_inputs_for_generation(
+ input_ids=input_ids,
+ expand_size=generation_config.num_return_sequences,
+ **model_kwargs,
+ )
+
+ # 11. run sample
+ outputs = self._sample(
+ input_ids,
+ logits_processor=logits_processor,
+ stopping_criteria=stopping_criteria,
+ generation_config=generation_config,
+ synced_gpus=synced_gpus,
+ streamer=streamer,
+ **model_kwargs,
+ )
+
+ else:
+ raise ValueError(
+ "Got incompatible mode for generation, should be one of greedy or sampling. "
+ "Ensure that beam search is de-activated by setting `num_beams=1` and `num_beam_groups=1`."
+ )
+
+ if generation_config.return_dict_in_generate:
+ output_ids = outputs.sequences
+ else:
+ output_ids = outputs
+
+ # apply the pattern mask to the final ids
+ output_ids = self.apply_delay_pattern_mask(output_ids, model_kwargs["delay_pattern_mask"])
+
+ # revert the pattern delay mask by filtering the pad token id
+ output_ids = output_ids[output_ids != generation_config._pad_token_tensor].reshape(
+ batch_size, self.num_codebooks, -1
+ )
+
+ if generation_config.return_dict_in_generate:
+ outputs.sequences = output_ids
+ return outputs
+ else:
+ return output_ids
+
+
+@auto_docstring(
+ custom_intro="""
+ The composite MusicGen model with a text encoder, audio encoder and Musicgen decoder,
+ """
+)
+class MusicgenForConditionalGeneration(MusicgenPreTrainedModel, GenerationMixin):
+ config: MusicgenConfig
+ base_model_prefix = "encoder_decoder"
+ main_input_name = "input_ids"
+ supports_gradient_checkpointing = True
+
+ def __init__(
+ self,
+ config: Optional[MusicgenConfig] = None,
+ text_encoder: Optional[PreTrainedModel] = None,
+ audio_encoder: Optional[PreTrainedModel] = None,
+ decoder: Optional[MusicgenForCausalLM] = None,
+ ):
+ r"""
+ text_encoder (`PreTrainedModel`, *optional*):
+ The text encoder model that encodes text into hidden states for conditioning.
+ audio_encoder (`PreTrainedModel`, *optional*):
+ The audio encoder model that encodes audio into hidden states for conditioning.
+ decoder (`MusicgenForCausalLM`, *optional*):
+ The decoder model that generates audio tokens based on conditioning signals.
+ """
+ if config is None and (text_encoder is None or audio_encoder is None or decoder is None):
+ raise ValueError(
+ "Either a configuration has to be provided, or all three of text encoder, audio encoder and MusicGen decoder."
+ )
+ if config is None:
+ config = MusicgenConfig.from_sub_models_config(text_encoder.config, audio_encoder.config, decoder.config)
+ else:
+ if not isinstance(config, self.config_class):
+ raise ValueError(f"Config: {config} has to be of type {self.config_class}")
+
+ if config.decoder.cross_attention_hidden_size is not None:
+ if config.decoder.cross_attention_hidden_size != config.text_encoder.hidden_size:
+ raise ValueError(
+ "If `cross_attention_hidden_size` is specified in the MusicGen decoder's configuration, it has to be equal"
+ f" to the text encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for"
+ f" `config.decoder.cross_attention_hidden_size` and {config.text_encoder.hidden_size} for"
+ " `config.text_encoder.hidden_size`."
+ )
+
+ # initialize with config
+ super().__init__(config)
+
+ if text_encoder is None:
+ from ..auto.modeling_auto import AutoModelForTextEncoding
+
+ text_encoder = AutoModelForTextEncoding.from_config(config.text_encoder)
+
+ if audio_encoder is None:
+ from ..auto.modeling_auto import AutoModel
+
+ audio_encoder = AutoModel.from_config(config.audio_encoder)
+
+ if decoder is None:
+ decoder = MusicgenForCausalLM._from_config(config.decoder)
+
+ self.text_encoder = text_encoder
+ self.audio_encoder = audio_encoder
+ self.decoder = decoder
+
+ if self.text_encoder.config.to_dict() != self.config.text_encoder.to_dict():
+ logger.warning(
+ f"Config of the text_encoder: {self.text_encoder.__class__} is overwritten by shared text_encoder config:"
+ f" {self.config.text_encoder}"
+ )
+ if self.audio_encoder.config.to_dict() != self.config.audio_encoder.to_dict():
+ logger.warning(
+ f"Config of the audio_encoder: {self.audio_encoder.__class__} is overwritten by shared audio_encoder config:"
+ f" {self.config.audio_encoder}"
+ )
+ if self.decoder.config.to_dict() != self.config.decoder.to_dict():
+ logger.warning(
+ f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config:"
+ f" {self.config.decoder}"
+ )
+
+ # make sure that the individual model's config refers to the shared config
+ # so that the updates to the config will be synced
+ self.config.text_encoder._attn_implementation = self.text_encoder.config._attn_implementation
+ self.config.audio_encoder._attn_implementation = self.audio_encoder.config._attn_implementation
+ self.config.decoder._attn_implementation = self.decoder.config._attn_implementation
+ self.text_encoder.config = self.config.text_encoder
+ self.audio_encoder.config = self.config.audio_encoder
+ self.decoder.config = self.config.decoder
+
+ # text encoder outputs might need to be projected to different dimension for decoder
+ if (
+ self.text_encoder.config.hidden_size != self.decoder.config.hidden_size
+ and self.decoder.config.cross_attention_hidden_size is None
+ ):
+ self.enc_to_dec_proj = nn.Linear(self.text_encoder.config.hidden_size, self.decoder.config.hidden_size)
+
+ if self.text_encoder.get_output_embeddings() is not None:
+ raise ValueError(
+ f"The encoder {self.text_encoder} should not have a LM Head. Please use a model without and LM Head"
+ )
+
+ decoder_signature = set(inspect.signature(self.decoder.forward).parameters.keys())
+ if "encoder_hidden_states" not in decoder_signature:
+ raise ValueError(
+ "The selected decoder is not prepared for the encoder hidden states to be passed. Please see the "
+ "following discussion on GitHub: https://github.com/huggingface/transformers/issues/23350"
+ )
+
+ # tie text encoder, decoder weights if config set accordingly
+ self.tie_weights()
+
+ def tie_weights(self):
+ # tie text encoder & decoder if needed
+ if self.config.tie_encoder_decoder:
+ # tie text encoder and decoder base model
+ decoder_base_model_prefix = self.decoder.base_model_prefix
+ tied_weights = self._tie_encoder_decoder_weights(
+ self.text_encoder,
+ self.decoder._modules[decoder_base_model_prefix],
+ self.decoder.base_model_prefix,
+ "text_encoder",
+ )
+ # Setting a dynamic variable instead of `_tied_weights_keys` because it's a class
+ # attributed not an instance member, therefore modifying it will modify the entire class
+ # Leading to issues on subsequent calls by different tests or subsequent calls.
+ self._dynamic_tied_weights_keys = tied_weights
+
+ def get_audio_encoder(self):
+ return self.audio_encoder
+
+ def get_text_encoder(self):
+ return self.text_encoder
+
+ def get_encoder(self):
+ # get the text encoder to compute the encoder hidden-states for generation
+ return self.get_text_encoder()
+
+ def get_decoder(self):
+ return self.decoder
+
+ def get_input_embeddings(self):
+ return self.text_encoder.get_input_embeddings()
+
+ def get_output_embeddings(self):
+ return self.decoder.get_output_embeddings()
+
+ def set_output_embeddings(self, new_embeddings):
+ return self.decoder.set_output_embeddings(new_embeddings)
+
+ @classmethod
+ def from_sub_models_pretrained(
+ cls,
+ text_encoder_pretrained_model_name_or_path: Optional[str] = None,
+ audio_encoder_pretrained_model_name_or_path: Optional[str] = None,
+ decoder_pretrained_model_name_or_path: Optional[str] = None,
+ *model_args,
+ **kwargs,
+ ) -> PreTrainedModel:
+ r"""
+ Instantiate a text encoder, an audio encoder, and a MusicGen decoder from one, two or three base classes of the
+ library from pretrained model checkpoints.
+
+
+ The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
+ the model, you need to first set it back in training mode with `model.train()`.
+
+ Params:
+ text_encoder_pretrained_model_name_or_path (`str`, *optional*):
+ Information necessary to initiate the text encoder. Can be either:
+
+ - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
+ - A path to a *directory* containing model weights saved using
+ [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
+
+ audio_encoder_pretrained_model_name_or_path (`str`, *optional*):
+ Information necessary to initiate the audio encoder. Can be either:
+
+ - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
+ - A path to a *directory* containing model weights saved using
+ [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
+
+ decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`):
+ Information necessary to initiate the decoder. Can be either:
+
+ - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
+ - A path to a *directory* containing model weights saved using
+ [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
+
+ model_args (remaining positional arguments, *optional*):
+ All remaining positional arguments will be passed to the underlying model's `__init__` method.
+
+ kwargs (remaining dictionary of keyword arguments, *optional*):
+ Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
+ `output_attentions=True`).
+
+ - To update the text encoder configuration, use the prefix *text_encoder_* for each configuration
+ parameter.
+ - To update the audio encoder configuration, use the prefix *audio_encoder_* for each configuration
+ parameter.
+ - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
+ - To update the parent model configuration, do not use a prefix for each configuration parameter.
+
+ Behaves differently depending on whether a `config` is provided or automatically loaded.
+
+ Example:
+
+ ```python
+ >>> from transformers import MusicgenForConditionalGeneration
+
+ >>> # initialize a musicgen model from a t5 text encoder, encodec audio encoder, and musicgen decoder
+ >>> model = MusicgenForConditionalGeneration.from_sub_models_pretrained(
+ ... text_encoder_pretrained_model_name_or_path="google-t5/t5-base",
+ ... audio_encoder_pretrained_model_name_or_path="facebook/encodec_24khz",
+ ... decoder_pretrained_model_name_or_path="facebook/musicgen-small",
+ ... )
+ >>> # saving model after fine-tuning
+ >>> model.save_pretrained("./musicgen-ft")
+ >>> # load fine-tuned model
+ >>> model = MusicgenForConditionalGeneration.from_pretrained("./musicgen-ft")
+ ```"""
+
+ kwargs_text_encoder = {
+ argument[len("text_encoder_") :]: value
+ for argument, value in kwargs.items()
+ if argument.startswith("text_encoder_")
+ }
+
+ kwargs_audio_encoder = {
+ argument[len("audio_encoder_") :]: value
+ for argument, value in kwargs.items()
+ if argument.startswith("audio_encoder_")
+ }
+
+ kwargs_decoder = {
+ argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
+ }
+
+ # remove text encoder, audio encoder and decoder kwargs from kwargs
+ for key in kwargs_text_encoder:
+ del kwargs["text_encoder_" + key]
+ for key in kwargs_audio_encoder:
+ del kwargs["audio_encoder_" + key]
+ for key in kwargs_decoder:
+ del kwargs["decoder_" + key]
+
+ # Load and initialize the encoder and decoder
+ # The distinction between encoder and decoder at the model level is made
+ # by the value of the flag `is_decoder` that we need to set correctly.
+ text_encoder = kwargs_text_encoder.pop("model", None)
+ if text_encoder is None:
+ if text_encoder_pretrained_model_name_or_path is None:
+ raise ValueError(
+ "If `text_encoder_model` is not defined as an argument, a `text_encoder_pretrained_model_name_or_path` has "
+ "to be defined."
+ )
+
+ if "config" not in kwargs_text_encoder:
+ encoder_config, kwargs_text_encoder = AutoConfig.from_pretrained(
+ text_encoder_pretrained_model_name_or_path, **kwargs_text_encoder, return_unused_kwargs=True
+ )
+
+ if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
+ logger.info(
+ f"Initializing {text_encoder_pretrained_model_name_or_path} as a text_encoder model "
+ "from a decoder model. Cross-attention and casual mask are disabled."
+ )
+ encoder_config.is_decoder = False
+ encoder_config.add_cross_attention = False
+
+ kwargs_text_encoder["config"] = encoder_config
+
+ text_encoder = AutoModel.from_pretrained(
+ text_encoder_pretrained_model_name_or_path, *model_args, **kwargs_text_encoder
+ )
+
+ audio_encoder = kwargs_audio_encoder.pop("model", None)
+ if audio_encoder is None:
+ if audio_encoder_pretrained_model_name_or_path is None:
+ raise ValueError(
+ "If `audio_encoder_model` is not defined as an argument, an `audio_encoder_pretrained_model_name_or_path` has "
+ "to be defined."
+ )
+
+ if "config" not in kwargs_audio_encoder:
+ encoder_config, kwargs_audio_encoder = AutoConfig.from_pretrained(
+ audio_encoder_pretrained_model_name_or_path, **kwargs_audio_encoder, return_unused_kwargs=True
+ )
+
+ if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
+ logger.info(
+ f"Initializing {audio_encoder_pretrained_model_name_or_path} as an audio_encoder model "
+ "from a decoder model. Cross-attention and casual mask are disabled."
+ )
+ encoder_config.is_decoder = False
+ encoder_config.add_cross_attention = False
+
+ kwargs_audio_encoder["config"] = encoder_config
+
+ audio_encoder = AutoModel.from_pretrained(
+ audio_encoder_pretrained_model_name_or_path, *model_args, **kwargs_audio_encoder
+ )
+
+ decoder = kwargs_decoder.pop("model", None)
+ if decoder is None:
+ if decoder_pretrained_model_name_or_path is None:
+ raise ValueError(
+ "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
+ "to be defined."
+ )
+
+ if "config" not in kwargs_decoder:
+ decoder_config, kwargs_decoder = AutoConfig.from_pretrained(
+ decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
+ )
+
+ if isinstance(decoder_config, MusicgenConfig):
+ decoder_config = decoder_config.decoder
+
+ if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
+ logger.info(
+ f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
+ f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
+ f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
+ )
+ decoder_config.is_decoder = True
+ decoder_config.add_cross_attention = True
+
+ kwargs_decoder["config"] = decoder_config
+
+ if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
+ logger.warning(
+ f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
+ f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
+ "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
+ "passed to `.from_sub_models_pretrained(...)` are set to `True` or do not pass a "
+ "`decoder_config` to `.from_sub_models_pretrained(...)`"
+ )
+
+ decoder = MusicgenForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
+
+ # instantiate config with corresponding kwargs
+ config = MusicgenConfig.from_sub_models_config(
+ text_encoder.config, audio_encoder.config, decoder.config, **kwargs
+ )
+ return cls(text_encoder=text_encoder, audio_encoder=audio_encoder, decoder=decoder, config=config)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.BoolTensor] = None,
+ input_values: Optional[torch.FloatTensor] = None,
+ padding_mask: Optional[torch.BoolTensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.BoolTensor] = None,
+ encoder_outputs: Optional[tuple[torch.FloatTensor]] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple, Seq2SeqLMOutput]:
+ r"""
+ padding_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.
+
+ Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
+ such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+
+
+ The `decoder_input_ids` will automatically be converted from shape `(batch_size * num_codebooks,
+ target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If
+ you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of
+ frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks,
+ target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as
+ `decoder_input_ids`.
+
+
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks)`, *optional*):
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+
+ Examples:
+ ```python
+ >>> from transformers import AutoProcessor, MusicgenForConditionalGeneration
+ >>> import torch
+
+ >>> processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
+ >>> model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
+
+ >>> inputs = processor(
+ ... text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
+ ... padding=True,
+ ... return_tensors="pt",
+ ... )
+
+ >>> pad_token_id = model.generation_config.pad_token_id
+ >>> decoder_input_ids = (
+ ... torch.ones((inputs.input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long)
+ ... * pad_token_id
+ ... )
+
+ >>> logits = model(**inputs, decoder_input_ids=decoder_input_ids).logits
+ >>> logits.shape # (bsz * num_codebooks, tgt_len, vocab_size)
+ torch.Size([8, 1, 2048])
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ kwargs_text_encoder = {
+ argument[len("text_encoder_")]: value
+ for argument, value in kwargs.items()
+ if argument.startswith("text_encoder_")
+ }
+
+ kwargs_audio_encoder = {
+ argument[len("audio_encoder_")]: value
+ for argument, value in kwargs.items()
+ if argument.startswith("audio_encoder_")
+ }
+
+ kwargs_decoder = {
+ argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
+ }
+
+ if encoder_outputs is None:
+ encoder_outputs = self.text_encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ **kwargs_text_encoder,
+ )
+ elif isinstance(encoder_outputs, tuple):
+ encoder_outputs = BaseModelOutput(*encoder_outputs)
+
+ encoder_hidden_states = encoder_outputs[0]
+
+ # optionally project encoder_hidden_states
+ if (
+ self.text_encoder.config.hidden_size != self.decoder.config.hidden_size
+ and self.decoder.config.cross_attention_hidden_size is None
+ ):
+ encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
+
+ if attention_mask is not None:
+ encoder_hidden_states = encoder_hidden_states * attention_mask[..., None]
+
+ if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
+ decoder_input_ids = shift_tokens_right(
+ labels, self.config.decoder.pad_token_id, self.config.decoder.decoder_start_token_id
+ )
+
+ elif decoder_input_ids is None and decoder_inputs_embeds is None:
+ audio_encoder_outputs = self.audio_encoder(
+ input_values=input_values,
+ padding_mask=padding_mask,
+ **kwargs_audio_encoder,
+ )
+ audio_codes = audio_encoder_outputs.audio_codes
+ frames, bsz, codebooks, seq_len = audio_codes.shape
+ if frames != 1:
+ raise ValueError(
+ f"Expected 1 frame in the audio code outputs, got {frames} frames. Ensure chunking is "
+ "disabled by setting `chunk_length=None` in the audio encoder."
+ )
+
+ if self.config.decoder.audio_channels == 2 and audio_codes.shape[2] == self.decoder.num_codebooks // 2:
+ # mono input through encodec that we convert to stereo
+ audio_codes = audio_codes.repeat_interleave(2, dim=2)
+
+ decoder_input_ids = audio_codes[0, ...].reshape(bsz * self.decoder.num_codebooks, seq_len)
+
+ # Decode
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=attention_mask,
+ inputs_embeds=decoder_inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ use_cache=use_cache,
+ past_key_values=past_key_values,
+ return_dict=return_dict,
+ labels=labels,
+ **kwargs_decoder,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return Seq2SeqLMOutput(
+ loss=decoder_outputs.loss,
+ logits=decoder_outputs.logits,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ decoder_input_ids,
+ past_key_values=None,
+ attention_mask=None,
+ head_mask=None,
+ decoder_attention_mask=None,
+ decoder_head_mask=None,
+ cross_attn_head_mask=None,
+ use_cache=None,
+ encoder_outputs=None,
+ decoder_delay_pattern_mask=None,
+ guidance_scale=None,
+ cache_position=None,
+ **kwargs,
+ ):
+ # Overwritten -- MusicGen has custom processing
+ if decoder_delay_pattern_mask is None:
+ decoder_input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
+ decoder_input_ids,
+ self.generation_config.pad_token_id,
+ max_length=self.generation_config.max_length,
+ )
+
+ # apply the delay pattern mask
+ decoder_input_ids = self.decoder.apply_delay_pattern_mask(decoder_input_ids, decoder_delay_pattern_mask)
+
+ if guidance_scale is not None and guidance_scale > 1:
+ # for classifier free guidance we need to replicate the decoder args across the batch dim (we'll split these
+ # before sampling)
+ decoder_input_ids = decoder_input_ids.repeat((2, 1))
+ if decoder_attention_mask is not None:
+ decoder_attention_mask = decoder_attention_mask.repeat((2, 1))
+
+ if past_key_values is not None:
+ if cache_position[-1] >= decoder_input_ids.shape[1]:
+ decoder_input_ids = decoder_input_ids[:, -cache_position.shape[0] :]
+ elif (
+ decoder_input_ids.shape[1] != cache_position.shape[0]
+ ): # Default case (the "else", a no op, is Exception 2)
+ decoder_input_ids = decoder_input_ids[:, cache_position]
+ else:
+ # Default to old behavior: keep only final ID
+ decoder_input_ids = decoder_input_ids[:, -1:]
+
+ return {
+ "input_ids": None, # encoder_outputs is defined. input_ids not needed
+ "encoder_outputs": encoder_outputs,
+ "past_key_values": past_key_values,
+ "decoder_input_ids": decoder_input_ids,
+ "attention_mask": attention_mask,
+ "decoder_attention_mask": decoder_attention_mask,
+ "head_mask": head_mask,
+ "decoder_head_mask": decoder_head_mask,
+ "cross_attn_head_mask": cross_attn_head_mask,
+ "use_cache": use_cache,
+ }
+
+ def _prepare_decoder_input_ids_for_generation(
+ self,
+ batch_size: int,
+ model_input_name: str,
+ model_kwargs: dict[str, torch.Tensor],
+ decoder_start_token_id: Optional[int] = None,
+ bos_token_id: Optional[int] = None,
+ device: torch.device = None,
+ ) -> tuple[torch.LongTensor, dict[str, torch.Tensor]]:
+ """Prepares `decoder_input_ids` for generation with encoder-decoder models"""
+
+ # 1. Check whether the user has defined `decoder_input_ids` manually. To facilitate in terms of input naming,
+ # we also allow the user to pass it under `input_ids`, if the encoder does not use it as the main input.
+ if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
+ decoder_input_ids = model_kwargs.pop("decoder_input_ids")
+ elif "input_ids" in model_kwargs and model_input_name != "input_ids":
+ decoder_input_ids = model_kwargs.pop("input_ids")
+ else:
+ decoder_input_ids = None
+
+ # 2. Encoder-decoder models expect the `decoder_input_ids` to start with a special token. Let's ensure that.
+ decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
+ if device is None:
+ device = self.device
+ decoder_input_ids_start = (
+ torch.ones((batch_size * self.decoder.num_codebooks, 1), dtype=torch.long, device=device)
+ * decoder_start_token_id
+ )
+
+ # no user input -> use decoder_start_token_id as decoder_input_ids
+ if decoder_input_ids is None:
+ decoder_input_ids = decoder_input_ids_start
+
+ # user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
+ # decoder_attention_mask if provided)
+ elif (decoder_input_ids[..., 0] != decoder_start_token_id).all().item():
+ decoder_input_ids = torch.cat([decoder_input_ids_start, decoder_input_ids], dim=-1)
+ if "decoder_attention_mask" in model_kwargs:
+ decoder_attention_mask = model_kwargs["decoder_attention_mask"]
+ decoder_attention_mask = torch.cat(
+ (torch.ones_like(decoder_attention_mask)[:, :1], decoder_attention_mask),
+ dim=-1,
+ )
+ model_kwargs["decoder_attention_mask"] = decoder_attention_mask
+
+ return decoder_input_ids, model_kwargs
+
+ def _prepare_text_encoder_kwargs_for_generation(
+ self,
+ inputs_tensor: torch.Tensor,
+ model_kwargs,
+ model_input_name: Optional[str],
+ generation_config: GenerationConfig,
+ ) -> dict[str, Any]:
+ # 1. get text encoder
+ encoder = self.get_text_encoder()
+ # Compatibility with Accelerate big model inference: we need the encoder to outputs stuff on the same device
+ # as the inputs.
+ if hasattr(encoder, "_hf_hook"):
+ encoder._hf_hook.io_same_device = True
+
+ # 2. Prepare encoder args and encoder kwargs from model kwargs.
+ irrelevant_prefix = ["decoder_", "cross_attn", "use_cache"]
+ encoder_kwargs = {
+ argument: value
+ for argument, value in model_kwargs.items()
+ if not any(argument.startswith(p) for p in irrelevant_prefix)
+ }
+ encoder_signature = set(inspect.signature(encoder.forward).parameters)
+ encoder_accepts_wildcard = "kwargs" in encoder_signature or "model_kwargs" in encoder_signature
+ if not encoder_accepts_wildcard:
+ encoder_kwargs = {
+ argument: value for argument, value in encoder_kwargs.items() if argument in encoder_signature
+ }
+ encoder_kwargs["output_attentions"] = generation_config.output_attentions
+ encoder_kwargs["output_hidden_states"] = generation_config.output_hidden_states
+ guidance_scale = generation_config.guidance_scale
+
+ # 3. make sure that encoder returns `ModelOutput`
+ model_input_name = model_input_name if model_input_name is not None else self.text_encoder.main_input_name
+ encoder_kwargs["return_dict"] = True
+ encoder_kwargs[model_input_name] = inputs_tensor
+ last_hidden_state = encoder(**encoder_kwargs).last_hidden_state
+
+ # for classifier free guidance we need to add a 'null' input to our encoder hidden states
+ if guidance_scale is not None and guidance_scale > 1:
+ last_hidden_state = torch.concatenate([last_hidden_state, torch.zeros_like(last_hidden_state)], dim=0)
+ if "attention_mask" in model_kwargs:
+ model_kwargs["attention_mask"] = torch.concatenate(
+ [model_kwargs["attention_mask"], torch.zeros_like(model_kwargs["attention_mask"])], dim=0
+ )
+
+ model_kwargs["encoder_outputs"] = BaseModelOutput(last_hidden_state=last_hidden_state)
+
+ return model_kwargs
+
+ def _prepare_audio_encoder_kwargs_for_generation(
+ self, input_values, model_kwargs, model_input_name: Optional[str] = None
+ ):
+ # 1. get audio encoder
+ encoder = self.get_audio_encoder()
+ # Compatibility with Accelerate big model inference: we need the encoder to outputs stuff on the same device
+ # as the inputs.
+ if hasattr(encoder, "_hf_hook"):
+ encoder._hf_hook.io_same_device = True
+
+ # 2. Prepare encoder args and encoder kwargs from model kwargs.
+ irrelevant_prefix = ["decoder_", "cross_attn", "use_cache"]
+ encoder_kwargs = {
+ argument: value
+ for argument, value in model_kwargs.items()
+ if not any(argument.startswith(p) for p in irrelevant_prefix)
+ }
+ encoder_signature = set(inspect.signature(encoder.forward).parameters)
+ encoder_accepts_wildcard = "kwargs" in encoder_signature or "model_kwargs" in encoder_signature
+ if not encoder_accepts_wildcard:
+ encoder_kwargs = {
+ argument: value for argument, value in encoder_kwargs.items() if argument in encoder_signature
+ }
+
+ # 3. make sure that encoder returns `ModelOutput`
+ model_input_name = model_input_name if model_input_name is not None else self.audio_encoder.main_input_name
+ encoder_kwargs["return_dict"] = True
+
+ if self.decoder.config.audio_channels == 1:
+ encoder_kwargs[model_input_name] = input_values
+ audio_encoder_outputs = encoder.encode(**encoder_kwargs)
+ audio_codes = audio_encoder_outputs.audio_codes
+ audio_scales = audio_encoder_outputs.audio_scales
+
+ frames, bsz, codebooks, seq_len = audio_codes.shape
+
+ else:
+ if input_values.shape[1] != 2:
+ raise ValueError(
+ f"Expected stereo audio (2-channels) but example has {input_values.shape[1]} channel."
+ )
+
+ encoder_kwargs[model_input_name] = input_values[:, :1, :]
+ audio_encoder_outputs_left = encoder.encode(**encoder_kwargs)
+ audio_codes_left = audio_encoder_outputs_left.audio_codes
+ audio_scales_left = audio_encoder_outputs_left.audio_scales
+
+ encoder_kwargs[model_input_name] = input_values[:, 1:, :]
+ audio_encoder_outputs_right = encoder.encode(**encoder_kwargs)
+ audio_codes_right = audio_encoder_outputs_right.audio_codes
+ audio_scales_right = audio_encoder_outputs_right.audio_scales
+
+ frames, bsz, codebooks, seq_len = audio_codes_left.shape
+ # copy alternating left/right channel codes into stereo codebook
+ audio_codes = audio_codes_left.new_ones((frames, bsz, 2 * codebooks, seq_len))
+
+ audio_codes[:, :, ::2, :] = audio_codes_left
+ audio_codes[:, :, 1::2, :] = audio_codes_right
+
+ if audio_scales_left != [None] or audio_scales_right != [None]:
+ audio_scales = torch.stack([audio_scales_left, audio_scales_right], dim=1)
+ else:
+ audio_scales = [None] * bsz
+
+ if frames != 1:
+ raise ValueError(
+ f"Expected 1 frame in the audio code outputs, got {frames} frames. Ensure chunking is "
+ "disabled by setting `chunk_length=None` in the audio encoder."
+ )
+
+ decoder_input_ids = audio_codes[0, ...].reshape(bsz * self.decoder.num_codebooks, seq_len)
+
+ model_kwargs["decoder_input_ids"] = decoder_input_ids
+ model_kwargs["audio_scales"] = audio_scales
+ return model_kwargs
+
+ def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+ return shift_tokens_right(labels, self.config.decoder.pad_token_id, self.config.decoder.bos_token_id)
+
+ def resize_token_embeddings(self, *args, **kwargs):
+ raise NotImplementedError(
+ "Resizing the embedding layers via the EncoderDecoderModel directly is not supported. Please use the"
+ " respective methods of the wrapped objects (model.encoder.resize_token_embeddings(...) or"
+ " model.decoder.resize_token_embeddings(...))"
+ )
+
+ def freeze_audio_encoder(self):
+ """
+ Freeze the audio encoder weights.
+ """
+ for param in self.audio_encoder.parameters():
+ param.requires_grad = False
+ self.audio_encoder._requires_grad = False
+
+ def freeze_text_encoder(self):
+ """
+ Freeze the text encoder weights.
+ """
+ for param in self.text_encoder.parameters():
+ param.requires_grad = False
+ self.text_encoder._requires_grad = False
+
+ def _maybe_initialize_input_ids_for_generation(
+ self,
+ inputs: Optional[torch.Tensor] = None,
+ bos_token_id: Optional[int] = None,
+ model_kwargs: Optional[dict[str, torch.Tensor]] = None,
+ ) -> torch.LongTensor:
+ """Initializes input ids for generation, if necessary."""
+ if inputs is not None:
+ return inputs
+
+ encoder_outputs = model_kwargs.get("encoder_outputs")
+ if encoder_outputs is not None:
+ # make dummy input_ids with value -100, as a sanity check ensuring that they won't be used for encoding
+ shape = encoder_outputs[0].size()[:-1]
+ return torch.ones(shape, dtype=torch.long, device=self.device) * -100
+
+ if bos_token_id is None:
+ raise ValueError("`bos_token_id` has to be defined when no `input_ids` are provided.")
+
+ # If there is some tensor in `model_kwargs`, we can infer the batch size from it. This is helpful with
+ # soft-prompting or in multimodal implementations built on top of decoder-only language models.
+ batch_size = 1
+ for value in model_kwargs.values():
+ if isinstance(value, torch.Tensor):
+ batch_size = value.shape[0]
+ break
+ return torch.ones((batch_size, 1), dtype=torch.long, device=self.device) * bos_token_id
+
+ def _get_decoder_start_token_id(
+ self, decoder_start_token_id: Optional[Union[int, list[int]]] = None, bos_token_id: Optional[int] = None
+ ) -> int:
+ decoder_start_token_id = (
+ decoder_start_token_id
+ if decoder_start_token_id is not None
+ else self.generation_config.decoder_start_token_id
+ )
+ bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id
+
+ if decoder_start_token_id is not None:
+ return decoder_start_token_id
+ elif bos_token_id is not None:
+ return bos_token_id
+ raise ValueError(
+ "`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
+ )
+
+ @torch.no_grad()
+ def generate(
+ self,
+ inputs: Optional[torch.Tensor] = None,
+ generation_config: Optional[GenerationConfig] = None,
+ logits_processor: Optional[LogitsProcessorList] = None,
+ stopping_criteria: Optional[StoppingCriteriaList] = None,
+ synced_gpus: Optional[bool] = None,
+ streamer: Optional["BaseStreamer"] = None,
+ **kwargs,
+ ):
+ """
+
+ Generates sequences of token ids for models with a language modeling head.
+
+
+
+ Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
+ model's default generation configuration. You can override any `generation_config` by passing the corresponding
+ parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
+
+ For an overview of generation strategies and code examples, check out the [following
+ guide](./generation_strategies).
+
+
+
+ Parameters:
+ inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
+ The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
+ method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
+ should be in the format `input_ids`. For encoder-decoder models *inputs* can represent any of
+ `input_ids`, `input_values`, `input_features`, or `pixel_values`.
+ generation_config (`~generation.GenerationConfig`, *optional*):
+ The generation configuration to be used as base parametrization for the generation call. `**kwargs`
+ passed to generate matching the attributes of `generation_config` will override them. If
+ `generation_config` is not provided, the default will be used, which had the following loading
+ priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
+ configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
+ default values, whose documentation should be checked to parameterize generation.
+ logits_processor (`LogitsProcessorList`, *optional*):
+ Custom logits processors that complement the default logits processors built from arguments and
+ generation config. If a logit processor is passed that is already created with the arguments or a
+ generation config an error is thrown. This feature is intended for advanced users.
+ stopping_criteria (`StoppingCriteriaList`, *optional*):
+ Custom stopping criteria that complement the default stopping criteria built from arguments and a
+ generation config. If a stopping criteria is passed that is already created with the arguments or a
+ generation config an error is thrown. This feature is intended for advanced users.
+ synced_gpus (`bool`, *optional*, defaults to `False`):
+ Whether to continue running the while loop until max_length (needed to avoid deadlocking with
+ `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
+ streamer (`BaseStreamer`, *optional*):
+ Streamer object that will be used to stream the generated sequences. Generated tokens are passed
+ through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
+ kwargs (`dict[str, Any]`, *optional*):
+ Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
+ forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
+ specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
+
+ Return:
+ [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
+ or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.
+
+ If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
+ [`~utils.ModelOutput`] types are:
+
+ - [`~generation.GenerateDecoderOnlyOutput`],
+ - [`~generation.GenerateBeamDecoderOnlyOutput`]
+
+ If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
+ [`~utils.ModelOutput`] types are:
+
+ - [`~generation.GenerateEncoderDecoderOutput`],
+ - [`~generation.GenerateBeamEncoderDecoderOutput`]
+ """
+ # 1. Handle `generation_config` and kwargs that might update it, and validate the resulting objects
+ if generation_config is None:
+ generation_config = self.generation_config
+
+ generation_config = copy.deepcopy(generation_config)
+ model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
+ generation_config.validate()
+ self._validate_model_kwargs(model_kwargs.copy())
+
+ if model_kwargs.get("encoder_outputs") is not None and type(model_kwargs["encoder_outputs"]) is tuple:
+ # wrap the unconditional outputs as a BaseModelOutput for compatibility with the rest of generate
+ model_kwargs["encoder_outputs"] = BaseModelOutput(last_hidden_state=model_kwargs["encoder_outputs"][0])
+
+ # 2. Set generation parameters if not already defined
+ logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+ stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+
+ requires_attention_mask = "encoder_outputs" not in model_kwargs
+ kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
+
+ # 3. Define model inputs
+ inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
+ inputs, generation_config.bos_token_id, model_kwargs
+ )
+ batch_size = inputs_tensor.shape[0]
+ self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=inputs_tensor.device)
+
+ # 4. Define other model kwargs
+ model_kwargs["use_cache"] = generation_config.use_cache
+ model_kwargs["guidance_scale"] = generation_config.guidance_scale
+
+ if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
+ model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
+ inputs_tensor, generation_config, model_kwargs
+ )
+
+ if "encoder_outputs" not in model_kwargs:
+ # encoder_outputs are created and added to `model_kwargs`
+ model_kwargs = self._prepare_text_encoder_kwargs_for_generation(
+ inputs_tensor, model_kwargs, model_input_name, generation_config
+ )
+
+ if "decoder_input_ids" not in model_kwargs and "input_values" in model_kwargs:
+ model_kwargs = self._prepare_audio_encoder_kwargs_for_generation(
+ model_kwargs["input_values"],
+ model_kwargs,
+ )
+
+ # 5. Prepare `input_ids` which will be used for auto-regressive generation
+ input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
+ batch_size=batch_size,
+ model_input_name=model_input_name,
+ model_kwargs=model_kwargs,
+ decoder_start_token_id=generation_config._decoder_start_token_tensor,
+ bos_token_id=generation_config._bos_token_tensor,
+ device=inputs_tensor.device,
+ )
+
+ # 6. Prepare `max_length` depending on other stopping criteria.
+ input_ids_length = input_ids.shape[-1]
+ has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+ has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
+ generation_config = self._prepare_generated_length(
+ generation_config=generation_config,
+ has_default_max_length=has_default_max_length,
+ has_default_min_length=has_default_min_length,
+ model_input_name=model_input_name,
+ inputs_tensor=inputs_tensor,
+ input_ids_length=input_ids_length,
+ )
+
+ # build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen)
+ input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
+ input_ids,
+ pad_token_id=generation_config._decoder_start_token_tensor,
+ max_length=generation_config.max_length,
+ )
+ # stash the delay mask so that we don't have to recompute in each forward pass
+ model_kwargs["decoder_delay_pattern_mask"] = decoder_delay_pattern_mask
+
+ # input_ids are ready to be placed on the streamer (if used)
+ if streamer is not None:
+ streamer.put(input_ids.cpu())
+
+ # 7. determine generation mode
+ generation_mode = generation_config.get_generation_mode()
+
+ # 8. prepare batched CFG externally (to enable coexistence with the unbatched CFG)
+ if generation_config.guidance_scale is not None and generation_config.guidance_scale > 1:
+ logits_processor.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale))
+ generation_config.guidance_scale = None
+
+ # 9. prepare distribution pre_processing samplers
+ logits_processor = self._get_logits_processor(
+ generation_config=generation_config,
+ input_ids_seq_length=input_ids_length,
+ encoder_input_ids=inputs_tensor,
+ prefix_allowed_tokens_fn=None,
+ logits_processor=logits_processor,
+ device=input_ids.device,
+ )
+
+ # 10. prepare stopping criteria
+ stopping_criteria = self._get_stopping_criteria(
+ generation_config=generation_config, stopping_criteria=stopping_criteria
+ )
+
+ if generation_mode in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH):
+ # expand input_ids with `num_return_sequences` additional sequences per batch
+ input_ids, model_kwargs = self._expand_inputs_for_generation(
+ input_ids=input_ids,
+ expand_size=generation_config.num_return_sequences,
+ is_encoder_decoder=self.config.is_encoder_decoder,
+ **model_kwargs,
+ )
+
+ # 11. run sample
+ outputs = self._sample(
+ input_ids,
+ logits_processor=logits_processor,
+ stopping_criteria=stopping_criteria,
+ generation_config=generation_config,
+ synced_gpus=synced_gpus,
+ streamer=streamer,
+ **model_kwargs,
+ )
+
+ else:
+ raise ValueError(
+ "Got incompatible mode for generation, should be one of greedy or sampling. "
+ "Ensure that beam search is de-activated by setting `num_beams=1` and `num_beam_groups=1`."
+ )
+
+ if generation_config.return_dict_in_generate:
+ output_ids = outputs.sequences
+ else:
+ output_ids = outputs
+
+ # apply the pattern mask to the final ids
+ output_ids = self.decoder.apply_delay_pattern_mask(output_ids, model_kwargs["decoder_delay_pattern_mask"])
+
+ # revert the pattern delay mask by filtering the pad token id
+ output_ids = output_ids[output_ids != generation_config._pad_token_tensor].reshape(
+ batch_size, self.decoder.num_codebooks, -1
+ )
+
+ # append the frame dimension back to the audio codes
+ output_ids = output_ids[None, ...]
+
+ audio_scales = model_kwargs.get("audio_scales")
+ if audio_scales is None:
+ audio_scales = [None] * batch_size
+
+ if self.decoder.config.audio_channels == 1:
+ output_values = self.audio_encoder.decode(
+ output_ids,
+ audio_scales=audio_scales,
+ ).audio_values
+ else:
+ codec_outputs_left = self.audio_encoder.decode(output_ids[:, :, ::2, :], audio_scales=audio_scales)
+ output_values_left = codec_outputs_left.audio_values
+
+ codec_outputs_right = self.audio_encoder.decode(output_ids[:, :, 1::2, :], audio_scales=audio_scales)
+ output_values_right = codec_outputs_right.audio_values
+
+ output_values = torch.cat([output_values_left, output_values_right], dim=1)
+
+ if generation_config.return_dict_in_generate:
+ outputs.sequences = output_values
+ return outputs
+ else:
+ return output_values
+
+ def get_unconditional_inputs(self, num_samples=1):
+ """
+ Helper function to get null inputs for unconditional generation, enabling the model to be used without the
+ feature extractor or tokenizer.
+
+ Args:
+ num_samples (int, *optional*):
+ Number of audio samples to unconditionally generate.
+ max_new_tokens (int, *optional*):
+ Number of tokens to generate for each sample. More tokens means longer audio samples, at the expense of
+ longer inference (since more audio tokens need to be generated per sample).
+
+ Example:
+ ```python
+ >>> from transformers import MusicgenForConditionalGeneration
+
+ >>> model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
+
+ >>> # get the unconditional (or 'null') inputs for the model
+ >>> unconditional_inputs = model.get_unconditional_inputs(num_samples=1)
+ >>> audio_samples = model.generate(**unconditional_inputs, max_new_tokens=256)
+ ```"""
+ last_hidden_state = torch.zeros(
+ (num_samples, 1, self.config.text_encoder.hidden_size), device=self.device, dtype=self.dtype
+ )
+
+ attention_mask = torch.zeros((num_samples, 1), device=self.device, dtype=torch.long)
+
+ return MusicgenUnconditionalInput(
+ encoder_outputs=(last_hidden_state,),
+ attention_mask=attention_mask,
+ guidance_scale=1.0,
+ )
+
+
+__all__ = ["MusicgenForConditionalGeneration", "MusicgenForCausalLM", "MusicgenModel", "MusicgenPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/processing_musicgen.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/processing_musicgen.py
new file mode 100644
index 0000000000000000000000000000000000000000..167c27f47bda16af0416e301c8cdf43c419ebfba
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen/processing_musicgen.py
@@ -0,0 +1,144 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Text/audio processor class for MusicGen
+"""
+
+from typing import Optional
+
+import numpy as np
+
+from ...processing_utils import ProcessorMixin
+from ...utils import to_numpy
+
+
+class MusicgenProcessor(ProcessorMixin):
+ r"""
+ Constructs a MusicGen processor which wraps an EnCodec feature extractor and a T5 tokenizer into a single processor
+ class.
+
+ [`MusicgenProcessor`] offers all the functionalities of [`EncodecFeatureExtractor`] and [`TTokenizer`]. See
+ [`~MusicgenProcessor.__call__`] and [`~MusicgenProcessor.decode`] for more information.
+
+ Args:
+ feature_extractor (`EncodecFeatureExtractor`):
+ An instance of [`EncodecFeatureExtractor`]. The feature extractor is a required input.
+ tokenizer (`T5Tokenizer`):
+ An instance of [`T5Tokenizer`]. The tokenizer is a required input.
+ """
+
+ feature_extractor_class = "EncodecFeatureExtractor"
+ tokenizer_class = ("T5Tokenizer", "T5TokenizerFast")
+
+ def __init__(self, feature_extractor, tokenizer):
+ super().__init__(feature_extractor, tokenizer)
+ self.current_processor = self.feature_extractor
+ self._in_target_context_manager = False
+
+ def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True):
+ return self.tokenizer.get_decoder_prompt_ids(task=task, language=language, no_timestamps=no_timestamps)
+
+ def __call__(self, *args, **kwargs):
+ """
+ Forwards the `audio` argument to EncodecFeatureExtractor's [`~EncodecFeatureExtractor.__call__`] and the `text`
+ argument to [`~T5Tokenizer.__call__`]. Please refer to the docstring of the above two methods for more
+ information.
+ """
+ # For backward compatibility
+ if self._in_target_context_manager:
+ return self.current_processor(*args, **kwargs)
+
+ audio = kwargs.pop("audio", None)
+ sampling_rate = kwargs.pop("sampling_rate", None)
+ text = kwargs.pop("text", None)
+ if len(args) > 0:
+ audio = args[0]
+ args = args[1:]
+
+ if audio is None and text is None:
+ raise ValueError("You need to specify either an `audio` or `text` input to process.")
+
+ if text is not None:
+ inputs = self.tokenizer(text, **kwargs)
+
+ if audio is not None:
+ audio_inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs)
+
+ if audio is None:
+ return inputs
+
+ elif text is None:
+ return audio_inputs
+
+ else:
+ inputs["input_values"] = audio_inputs["input_values"]
+ if "padding_mask" in audio_inputs:
+ inputs["padding_mask"] = audio_inputs["padding_mask"]
+ return inputs
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method is used to decode either batches of audio outputs from the MusicGen model, or batches of token ids
+ from the tokenizer. In the case of decoding token ids, this method forwards all its arguments to T5Tokenizer's
+ [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information.
+ """
+ audio_values = kwargs.pop("audio", None)
+ padding_mask = kwargs.pop("padding_mask", None)
+
+ if len(args) > 0:
+ audio_values = args[0]
+ args = args[1:]
+
+ if audio_values is not None:
+ return self._decode_audio(audio_values, padding_mask=padding_mask)
+ else:
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to T5Tokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the
+ docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ def _decode_audio(self, audio_values, padding_mask: Optional = None) -> list[np.ndarray]:
+ """
+ This method strips any padding from the audio values to return a list of numpy audio arrays.
+ """
+ audio_values = to_numpy(audio_values)
+ bsz, channels, seq_len = audio_values.shape
+
+ if padding_mask is None:
+ return list(audio_values)
+
+ padding_mask = to_numpy(padding_mask)
+
+ # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
+ # token (so that the generated audio values are **not** treated as padded tokens)
+ difference = seq_len - padding_mask.shape[-1]
+ padding_value = 1 - self.feature_extractor.padding_value
+ padding_mask = np.pad(padding_mask, ((0, 0), (0, difference)), "constant", constant_values=padding_value)
+
+ audio_values = audio_values.tolist()
+ for i in range(bsz):
+ sliced_audio = np.asarray(audio_values[i])[
+ padding_mask[i][None, :] != self.feature_extractor.padding_value
+ ]
+ audio_values[i] = sliced_audio.reshape(channels, -1)
+
+ return audio_values
+
+
+__all__ = ["MusicgenProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..51456aac76b0e0c5666acadf0d63008a7c3b50d9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_musicgen_melody import *
+ from .modeling_musicgen_melody import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/configuration_musicgen_melody.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/configuration_musicgen_melody.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4285151c4d0189938b3dfa59e0102770610becd
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/configuration_musicgen_melody.py
@@ -0,0 +1,261 @@
+# coding=utf-8
+# Copyright 2024 Meta AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Musicgen Melody model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ..auto.configuration_auto import AutoConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class MusicgenMelodyDecoderConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of an [`MusicgenMelodyDecoder`]. It is used to instantiate a
+ Musicgen Melody decoder according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the Musicgen Melody
+ [facebook/musicgen-melody](https://huggingface.co/facebook/musicgen-melody) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 2048):
+ Vocabulary size of the MusicgenMelodyDecoder model. Defines the number of different tokens that can be
+ represented by the `inputs_ids` passed when calling [`MusicgenMelodyDecoder`].
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with. Typically, set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ num_hidden_layers (`int`, *optional*, defaults to 24):
+ Number of decoder layers.
+ ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer block.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer block.
+ layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether the model should return the last key/values attentions (not used by all models)
+ activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the decoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ hidden_size (`int`, *optional*, defaults to 1024):
+ Dimensionality of the layers and the pooler layer.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, text_encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ initializer_factor (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ scale_embedding (`bool`, *optional*, defaults to `False`):
+ Scale embeddings by diving by sqrt(hidden_size).
+ num_codebooks (`int`, *optional*, defaults to 4):
+ The number of parallel codebooks forwarded to the model.
+ audio_channels (`int`, *optional*, defaults to 1):
+ Number of audio channels used by the model (either mono or stereo). Stereo models generate a separate
+ audio stream for the left/right output channels. Mono models generate a single audio stream output.
+ pad_token_id (`int`, *optional*, defaults to 2048): The id of the *padding* token.
+ bos_token_id (`int`, *optional*, defaults to 2048): The id of the *beginning-of-sequence* token.
+ eos_token_id (`int`, *optional*): The id of the *end-of-sequence* token.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie word embeddings with the text encoder.
+ """
+
+ model_type = "musicgen_melody_decoder"
+ base_config_key = "decoder_config"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=2048,
+ max_position_embeddings=2048,
+ num_hidden_layers=24,
+ ffn_dim=4096,
+ num_attention_heads=16,
+ layerdrop=0.0,
+ use_cache=True,
+ activation_function="gelu",
+ hidden_size=1024,
+ dropout=0.1,
+ attention_dropout=0.0,
+ activation_dropout=0.0,
+ initializer_factor=0.02,
+ scale_embedding=False,
+ num_codebooks=4,
+ audio_channels=1,
+ pad_token_id=2048,
+ bos_token_id=2048,
+ eos_token_id=None,
+ tie_word_embeddings=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.ffn_dim = ffn_dim
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.activation_function = activation_function
+ self.initializer_factor = initializer_factor
+ self.layerdrop = layerdrop
+ self.use_cache = use_cache
+ self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
+ self.num_codebooks = num_codebooks
+
+ if audio_channels not in [1, 2]:
+ raise ValueError(f"Expected 1 (mono) or 2 (stereo) audio channels, got {audio_channels} channels.")
+ self.audio_channels = audio_channels
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+
+class MusicgenMelodyConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MusicgenMelodyModel`]. It is used to instantiate a
+ Musicgen Melody model according to the specified arguments, defining the text encoder, audio encoder and Musicgen Melody decoder
+ configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the Musicgen Melody
+ [facebook/musicgen-melody](https://huggingface.co/facebook/musicgen-melody) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ num_chroma (`int`, *optional*, defaults to 12): Number of chroma bins to use.
+ chroma_length (`int`, *optional*, defaults to 235):
+ Maximum chroma duration if audio is used to condition the model. Corresponds to the maximum duration used during training.
+ kwargs (*optional*):
+ Dictionary of keyword arguments. Notably:
+
+ - **text_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
+ defines the text encoder config.
+ - **audio_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
+ defines the audio encoder config.
+ - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
+ the decoder config.
+
+ Example:
+
+ ```python
+ >>> from transformers import (
+ ... MusicgenMelodyConfig,
+ ... MusicgenMelodyDecoderConfig,
+ ... T5Config,
+ ... EncodecConfig,
+ ... MusicgenMelodyForConditionalGeneration,
+ ... )
+
+ >>> # Initializing text encoder, audio encoder, and decoder model configurations
+ >>> text_encoder_config = T5Config()
+ >>> audio_encoder_config = EncodecConfig()
+ >>> decoder_config = MusicgenMelodyDecoderConfig()
+
+ >>> configuration = MusicgenMelodyConfig.from_sub_models_config(
+ ... text_encoder_config, audio_encoder_config, decoder_config
+ ... )
+
+ >>> # Initializing a MusicgenMelodyForConditionalGeneration (with random weights) from the facebook/musicgen-melody style configuration
+ >>> model = MusicgenMelodyForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ >>> config_text_encoder = model.config.text_encoder
+ >>> config_audio_encoder = model.config.audio_encoder
+ >>> config_decoder = model.config.decoder
+
+ >>> # Saving the model, including its configuration
+ >>> model.save_pretrained("musicgen_melody-model")
+
+ >>> # loading model and config from pretrained folder
+ >>> musicgen_melody_config = MusicgenMelodyConfig.from_pretrained("musicgen_melody-model")
+ >>> model = MusicgenMelodyForConditionalGeneration.from_pretrained("musicgen_melody-model", config=musicgen_melody_config)
+ ```"""
+
+ model_type = "musicgen_melody"
+ sub_configs = {
+ "text_encoder": AutoConfig,
+ "audio_encoder": AutoConfig,
+ "decoder": MusicgenMelodyDecoderConfig,
+ }
+ has_no_defaults_at_init = True
+
+ def __init__(
+ self,
+ num_chroma=12,
+ chroma_length=235,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
+ raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config")
+
+ text_encoder_config = kwargs.pop("text_encoder")
+ text_encoder_model_type = text_encoder_config.pop("model_type")
+
+ audio_encoder_config = kwargs.pop("audio_encoder")
+ audio_encoder_model_type = audio_encoder_config.pop("model_type")
+
+ decoder_config = kwargs.pop("decoder")
+
+ self.text_encoder = AutoConfig.for_model(text_encoder_model_type, **text_encoder_config)
+ self.audio_encoder = AutoConfig.for_model(audio_encoder_model_type, **audio_encoder_config)
+ self.decoder = MusicgenMelodyDecoderConfig(**decoder_config)
+ self.is_encoder_decoder = False
+
+ self.num_chroma = num_chroma
+ self.chroma_length = chroma_length
+
+ @classmethod
+ def from_sub_models_config(
+ cls,
+ text_encoder_config: PretrainedConfig,
+ audio_encoder_config: PretrainedConfig,
+ decoder_config: MusicgenMelodyDecoderConfig,
+ **kwargs,
+ ):
+ r"""
+ Instantiate a [`MusicgenMelodyConfig`] (or a derived class) from text encoder, audio encoder and decoder
+ configurations.
+
+ Returns:
+ [`MusicgenMelodyConfig`]: An instance of a configuration object
+ """
+
+ return cls(
+ text_encoder=text_encoder_config.to_dict(),
+ audio_encoder=audio_encoder_config.to_dict(),
+ decoder=decoder_config.to_dict(),
+ **kwargs,
+ )
+
+ @property
+ # This is a property because you might want to change the codec model on the fly
+ def sampling_rate(self):
+ return self.audio_encoder.sampling_rate
+
+
+__all__ = ["MusicgenMelodyConfig", "MusicgenMelodyDecoderConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/convert_musicgen_melody_transformers.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/convert_musicgen_melody_transformers.py
new file mode 100644
index 0000000000000000000000000000000000000000..3db5c2c8e33a55b518ac622b2d737d9f2dafa9b9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/convert_musicgen_melody_transformers.py
@@ -0,0 +1,267 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert Musicgen Melody checkpoints from the original repository."""
+
+import argparse
+from collections import OrderedDict
+from pathlib import Path
+
+import torch
+from audiocraft.models import MusicGen
+
+from transformers import (
+ AutoTokenizer,
+ EncodecModel,
+ T5EncoderModel,
+)
+from transformers.models.musicgen_melody.configuration_musicgen_melody import MusicgenMelodyDecoderConfig
+from transformers.models.musicgen_melody.feature_extraction_musicgen_melody import MusicgenMelodyFeatureExtractor
+from transformers.models.musicgen_melody.modeling_musicgen_melody import (
+ MusicgenMelodyForCausalLM,
+ MusicgenMelodyForConditionalGeneration,
+)
+from transformers.models.musicgen_melody.processing_musicgen_melody import MusicgenMelodyProcessor
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+EXPECTED_MISSING_KEYS = ["model.decoder.embed_positions.weights"]
+EXPECTED_ADDITIONAL_KEYS = ["condition_provider.conditioners.self_wav.chroma.spec.window"]
+
+
+def rename_keys(name):
+ if "emb" in name:
+ name = name.replace("emb", "model.decoder.embed_tokens")
+ if "transformer" in name:
+ name = name.replace("transformer", "model.decoder")
+ if "cross_attention" in name:
+ name = name.replace("cross_attention", "encoder_attn")
+ if "linear1" in name:
+ name = name.replace("linear1", "fc1")
+ if "linear2" in name:
+ name = name.replace("linear2", "fc2")
+ if "norm1" in name:
+ name = name.replace("norm1", "self_attn_layer_norm")
+ if "norm_cross" in name:
+ name = name.replace("norm_cross", "encoder_attn_layer_norm")
+ if "norm2" in name:
+ name = name.replace("norm2", "final_layer_norm")
+ if "out_norm" in name:
+ name = name.replace("out_norm", "model.decoder.layer_norm")
+ if "linears" in name:
+ name = name.replace("linears", "lm_heads")
+ if "condition_provider.conditioners.description.output_proj" in name:
+ name = name.replace("condition_provider.conditioners.description.output_proj", "enc_to_dec_proj")
+ if "condition_provider.conditioners.self_wav.output_proj" in name:
+ name = name.replace("condition_provider.conditioners.self_wav.output_proj", "audio_enc_to_dec_proj")
+ return name
+
+
+def rename_state_dict(state_dict: OrderedDict, hidden_size: int) -> tuple[dict, dict]:
+ """Function that takes the fairseq MusicgenMelody state dict and renames it according to the HF
+ module names. It further partitions the state dict into the decoder (LM) state dict, and that for the
+ text encoder projection and for the audio encoder projection."""
+ keys = list(state_dict.keys())
+ enc_dec_proj_state_dict = {}
+ audio_enc_to_dec_proj_state_dict = {}
+ for key in keys:
+ val = state_dict.pop(key)
+ key = rename_keys(key)
+ if "in_proj_weight" in key:
+ # split fused qkv proj
+ state_dict[key.replace("in_proj_weight", "q_proj.weight")] = val[:hidden_size, :]
+ state_dict[key.replace("in_proj_weight", "k_proj.weight")] = val[hidden_size : 2 * hidden_size, :]
+ state_dict[key.replace("in_proj_weight", "v_proj.weight")] = val[-hidden_size:, :]
+ elif "audio_enc_to_dec_proj" in key:
+ audio_enc_to_dec_proj_state_dict[key[len("audio_enc_to_dec_proj.") :]] = val
+ elif "enc_to_dec_proj" in key:
+ enc_dec_proj_state_dict[key[len("enc_to_dec_proj.") :]] = val
+ else:
+ state_dict[key] = val
+ return state_dict, enc_dec_proj_state_dict, audio_enc_to_dec_proj_state_dict
+
+
+def decoder_config_from_checkpoint(checkpoint: str) -> MusicgenMelodyDecoderConfig:
+ if checkpoint == "facebook/musicgen-melody" or checkpoint == "facebook/musicgen-stereo-melody":
+ hidden_size = 1536
+ num_hidden_layers = 48
+ num_attention_heads = 24
+ elif checkpoint == "facebook/musicgen-melody-large" or checkpoint == "facebook/musicgen-stereo-melody-large":
+ hidden_size = 2048
+ num_hidden_layers = 48
+ num_attention_heads = 32
+ else:
+ raise ValueError(
+ "Checkpoint should be one of `['facebook/musicgen-melody', 'facebook/musicgen-melody-large']` for the mono checkpoints, "
+ "or `['facebook/musicgen-stereo-melody', 'facebook/musicgen-stereo-melody-large']` "
+ f"for the stereo checkpoints, got {checkpoint}."
+ )
+
+ if "stereo" in checkpoint:
+ audio_channels = 2
+ num_codebooks = 8
+ else:
+ audio_channels = 1
+ num_codebooks = 4
+
+ config = MusicgenMelodyDecoderConfig(
+ hidden_size=hidden_size,
+ ffn_dim=hidden_size * 4,
+ num_hidden_layers=num_hidden_layers,
+ num_attention_heads=num_attention_heads,
+ num_codebooks=num_codebooks,
+ audio_channels=audio_channels,
+ )
+ return config
+
+
+@torch.no_grad()
+def convert_musicgen_melody_checkpoint(
+ checkpoint, pytorch_dump_folder=None, repo_id=None, device="cpu", test_same_output=False
+):
+ fairseq_model = MusicGen.get_pretrained(checkpoint, device=args.device)
+ decoder_config = decoder_config_from_checkpoint(checkpoint)
+
+ decoder_state_dict = fairseq_model.lm.state_dict()
+ decoder_state_dict, enc_dec_proj_state_dict, audio_enc_to_dec_proj_state_dict = rename_state_dict(
+ decoder_state_dict, hidden_size=decoder_config.hidden_size
+ )
+
+ text_encoder = T5EncoderModel.from_pretrained("t5-base")
+ audio_encoder = EncodecModel.from_pretrained("facebook/encodec_32khz")
+ decoder = MusicgenMelodyForCausalLM(decoder_config).eval()
+
+ # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
+ missing_keys, unexpected_keys = decoder.load_state_dict(decoder_state_dict, strict=False)
+
+ for key in missing_keys.copy():
+ if key.startswith(("text_encoder", "audio_encoder")) or key in EXPECTED_MISSING_KEYS:
+ missing_keys.remove(key)
+
+ for key in unexpected_keys.copy():
+ if key in EXPECTED_ADDITIONAL_KEYS:
+ unexpected_keys.remove(key)
+
+ if len(missing_keys) > 0:
+ raise ValueError(f"Missing key(s) in state_dict: {missing_keys}")
+
+ if len(unexpected_keys) > 0:
+ raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}")
+
+ # init the composite model
+ model = MusicgenMelodyForConditionalGeneration(
+ text_encoder=text_encoder, audio_encoder=audio_encoder, decoder=decoder
+ ).to(args.device)
+
+ # load the pre-trained enc-dec projection (from the decoder state dict)
+ model.enc_to_dec_proj.load_state_dict(enc_dec_proj_state_dict)
+
+ # load the pre-trained audio encoder projection (from the decoder state dict)
+ model.audio_enc_to_dec_proj.load_state_dict(audio_enc_to_dec_proj_state_dict)
+
+ # check we can do a forward pass
+ input_ids = torch.arange(0, 2 * decoder_config.num_codebooks, dtype=torch.long).reshape(2, -1).to(device)
+ decoder_input_ids = input_ids.reshape(2 * decoder_config.num_codebooks, -1).to(device)
+
+ with torch.no_grad():
+ logits = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids).logits
+
+ output_length = 1 + input_ids.shape[1] + model.config.chroma_length
+ if logits.shape != (2 * decoder_config.num_codebooks, output_length, 2048):
+ raise ValueError("Incorrect shape for logits")
+
+ # now construct the processor
+ tokenizer = AutoTokenizer.from_pretrained("t5-base")
+ feature_extractor = MusicgenMelodyFeatureExtractor()
+
+ processor = MusicgenMelodyProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
+
+ # set the appropriate bos/pad token ids
+ model.generation_config.decoder_start_token_id = 2048
+ model.generation_config.pad_token_id = 2048
+
+ # set other default generation config params
+ model.generation_config.max_length = int(30 * audio_encoder.config.frame_rate)
+ model.generation_config.do_sample = True
+ model.generation_config.guidance_scale = 3.0
+
+ if test_same_output:
+ # check same output than original model
+ decoder_input_ids = torch.ones_like(decoder_input_ids).to(device) * model.generation_config.pad_token_id
+ with torch.no_grad():
+ decoder_input_ids = decoder_input_ids[: decoder_config.num_codebooks]
+ inputs = processor(text=["gen"], return_tensors="pt", padding=True).to(device)
+ logits = model(**inputs, decoder_input_ids=decoder_input_ids).logits
+
+ attributes, prompt_tokens = fairseq_model._prepare_tokens_and_attributes(["gen"], None)
+ original_logits = fairseq_model.lm.forward(
+ decoder_input_ids.reshape(1, decoder_config.num_codebooks, -1), attributes
+ )
+
+ torch.testing.assert_close(
+ original_logits.squeeze(2).reshape(decoder_config.num_codebooks, -1),
+ logits[:, -1],
+ rtol=1e-5,
+ atol=5e-5,
+ )
+
+ if pytorch_dump_folder is not None:
+ Path(pytorch_dump_folder).mkdir(exist_ok=True)
+ logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}")
+ model.save_pretrained(pytorch_dump_folder)
+ processor.save_pretrained(pytorch_dump_folder)
+
+ if repo_id:
+ logger.info(f"Pushing model {checkpoint} to {repo_id}")
+ model.push_to_hub(repo_id, create_pr=True)
+ processor.push_to_hub(repo_id, create_pr=True)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--checkpoint",
+ default="facebook/musicgen-melody",
+ type=str,
+ help="Checkpoint size of the Musicgen Melody model you'd like to convert. Can be one of: "
+ "`['facebook/musicgen-melody', 'facebook/musicgen-melody-large']` for the mono checkpoints, or "
+ "`['facebook/musicgen-stereo-melody', 'facebook/musicgen-stereo-melody-large']` "
+ "for the stereo checkpoints.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder",
+ default=None,
+ type=str,
+ help="Path to the output PyTorch model directory.",
+ )
+ parser.add_argument(
+ "--push_to_hub",
+ default="musicgen-melody",
+ type=str,
+ help="Where to upload the converted model on the 🤗 hub.",
+ )
+ parser.add_argument(
+ "--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
+ )
+ parser.add_argument("--test_same_output", default=False, type=bool, help="If `True`, test if same output logits.")
+
+ args = parser.parse_args()
+ convert_musicgen_melody_checkpoint(
+ args.checkpoint, args.pytorch_dump_folder, args.push_to_hub, args.device, args.test_same_output
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/feature_extraction_musicgen_melody.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/feature_extraction_musicgen_melody.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec23899e91e90925e00f79e160a9e8d64de11d99
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/feature_extraction_musicgen_melody.py
@@ -0,0 +1,336 @@
+# coding=utf-8
+# Copyright 2024 Meta AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Feature extractor class for Musicgen Melody
+"""
+
+import copy
+from typing import Any, Optional, Union
+
+import numpy as np
+
+from ...audio_utils import chroma_filter_bank
+from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
+from ...feature_extraction_utils import BatchFeature
+from ...utils import TensorType, is_torch_available, is_torchaudio_available, logging
+from ...utils.import_utils import requires
+
+
+if is_torch_available():
+ import torch
+
+if is_torchaudio_available():
+ import torchaudio
+
+logger = logging.get_logger(__name__)
+
+
+@requires(backends=("torchaudio",))
+class MusicgenMelodyFeatureExtractor(SequenceFeatureExtractor):
+ r"""
+ Constructs a MusicgenMelody feature extractor.
+
+ This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
+ most of the main methods. Users should refer to this superclass for more information regarding those methods.
+
+ This class extracts chroma features from audio processed by [Demucs](https://github.com/adefossez/demucs/tree/main) or
+ directly from raw audio waveform.
+
+ Args:
+ feature_size (`int`, *optional*, defaults to 12):
+ The feature dimension of the extracted features.
+ sampling_rate (`int`, *optional*, defaults to 32000):
+ The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
+ hop_length (`int`, *optional*, defaults to 4096):
+ Length of the overlapping windows for the STFT used to obtain the Mel Frequency coefficients.
+ chunk_length (`int`, *optional*, defaults to 30):
+ The maximum number of chunks of `sampling_rate` samples used to trim and pad longer or shorter audio
+ sequences.
+ n_fft (`int`, *optional*, defaults to 16384):
+ Size of the Fourier transform.
+ num_chroma (`int`, *optional*, defaults to 12):
+ Number of chroma bins to use.
+ padding_value (`float`, *optional*, defaults to 0.0):
+ Padding value used to pad the audio.
+ return_attention_mask (`bool`, *optional*, defaults to `False`):
+ Whether to return the attention mask. Can be overwritten when calling the feature extractor.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+
+
+ For Whisper models, `attention_mask` should always be passed for batched inference, to avoid subtle
+ bugs.
+
+
+ stem_indices (`list[int]`, *optional*, defaults to `[3, 2]`):
+ Stem channels to extract if demucs outputs are passed.
+ """
+
+ model_input_names = ["input_features"]
+
+ def __init__(
+ self,
+ feature_size=12,
+ sampling_rate=32000,
+ hop_length=4096,
+ chunk_length=30,
+ n_fft=16384,
+ num_chroma=12,
+ padding_value=0.0,
+ return_attention_mask=False, # pad inputs to max length with silence token (zero) and no attention mask
+ stem_indices=[3, 2],
+ **kwargs,
+ ):
+ super().__init__(
+ feature_size=feature_size,
+ sampling_rate=sampling_rate,
+ padding_value=padding_value,
+ return_attention_mask=return_attention_mask,
+ **kwargs,
+ )
+ self.n_fft = n_fft
+ self.hop_length = hop_length
+ self.chunk_length = chunk_length
+ self.n_samples = chunk_length * sampling_rate
+ self.sampling_rate = sampling_rate
+ self.chroma_filters = torch.from_numpy(
+ chroma_filter_bank(sampling_rate=sampling_rate, num_frequency_bins=n_fft, tuning=0, num_chroma=num_chroma)
+ ).float()
+ self.spectrogram = torchaudio.transforms.Spectrogram(
+ n_fft=n_fft, win_length=n_fft, hop_length=hop_length, power=2, center=True, pad=0, normalized=True
+ )
+ self.stem_indices = stem_indices
+
+ def _torch_extract_fbank_features(self, waveform: torch.Tensor) -> torch.Tensor:
+ """
+ Compute the chroma spectrogram of the provided audio using the torchaudio spectrogram implementation and the librosa chroma features.
+ """
+
+ # if wav length is not long enough, pad it
+ wav_length = waveform.shape[-1]
+ if wav_length < self.n_fft:
+ pad = self.n_fft - wav_length
+ rest = 0 if pad % 2 == 0 else 1
+ waveform = torch.nn.functional.pad(waveform, (pad // 2, pad // 2 + rest), "constant", 0)
+
+ # squeeze alongside channel dimension
+ spec = self.spectrogram(waveform).squeeze(1)
+
+ # sum along the frequency dimension
+ raw_chroma = torch.einsum("cf, ...ft->...ct", self.chroma_filters, spec)
+
+ # normalise with max value
+ norm_chroma = torch.nn.functional.normalize(raw_chroma, p=float("inf"), dim=-2, eps=1e-6)
+
+ # transpose time and chroma dimension -> (batch, time, chroma)
+ norm_chroma = norm_chroma.transpose(1, 2)
+
+ # replace max value alongside chroma dimension with 1 and replace the rest with 0
+ idx = norm_chroma.argmax(-1, keepdim=True)
+ norm_chroma[:] = 0
+ norm_chroma.scatter_(dim=-1, index=idx, value=1)
+
+ return norm_chroma
+
+ def _extract_stem_indices(self, audio, sampling_rate=None):
+ """
+ Extracts stems from the output of the [Demucs](https://github.com/adefossez/demucs/tree/main) audio separation model,
+ then converts to mono-channel and resample to the feature extractor sampling rate.
+
+ Args:
+ audio (`torch.Tensor` of shape `(batch_size, num_stems, channel_size, audio_length)`):
+ The output of the Demucs model to be processed.
+ sampling_rate (`int`, *optional*):
+ Demucs sampling rate. If not specified, defaults to `44000`.
+ """
+ sampling_rate = 44000 if sampling_rate is None else sampling_rate
+
+ # extract "vocals" and "others" sources from audio encoder (demucs) output
+ # [batch_size, num_stems, channel_size, audio_length]
+ wav = audio[:, torch.tensor(self.stem_indices)]
+
+ # merge extracted stems to single waveform
+ wav = wav.sum(1)
+
+ # convert to mono-channel waveform
+ wav = wav.mean(dim=1, keepdim=True)
+
+ # resample to model sampling rate
+ # not equivalent to julius.resample
+ if sampling_rate != self.sampling_rate:
+ wav = torchaudio.functional.resample(
+ wav, sampling_rate, self.sampling_rate, rolloff=0.945, lowpass_filter_width=24
+ )
+
+ # [batch_size, 1, audio_length] -> [batch_size, audio_length]
+ wav = wav.squeeze(1)
+
+ return wav
+
+ def __call__(
+ self,
+ audio: Union[np.ndarray, list[float], list[np.ndarray], list[list[float]]],
+ truncation: bool = True,
+ pad_to_multiple_of: Optional[int] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_attention_mask: Optional[bool] = None,
+ padding: Optional[str] = True,
+ max_length: Optional[int] = None,
+ sampling_rate: Optional[int] = None,
+ **kwargs,
+ ) -> BatchFeature:
+ """
+ Main method to featurize and prepare for the model one or several sequence(s).
+
+ Args:
+ audio (`torch.Tensor`, `np.ndarray`, `list[float]`, `list[np.ndarray]`, `list[torch.Tensor]`, `list[list[float]]`):
+ The sequence or batch of sequences to be padded. Each sequence can be a torch tensor, a numpy array, a list of float
+ values, a list of numpy arrays, a list of torch tensors, or a list of list of float values.
+ If `audio` is the output of Demucs, it has to be a torch tensor of shape `(batch_size, num_stems, channel_size, audio_length)`.
+ Otherwise, it must be mono or stereo channel audio.
+ truncation (`bool`, *optional*, default to `True`):
+ Activates truncation to cut input sequences longer than *max_length* to *max_length*.
+ pad_to_multiple_of (`int`, *optional*, defaults to None):
+ If set will pad the sequence to a multiple of the provided value.
+
+ This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
+ `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
+ If set, will return tensors instead of list of python integers. Acceptable values are:
+
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
+ - `'np'`: Return Numpy `np.ndarray` objects.
+ return_attention_mask (`bool`, *optional*):
+ Whether to return the attention mask. If left to the default, will return the attention mask according
+ to the specific feature_extractor's default.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+
+ For Musicgen Melody models, audio `attention_mask` is not necessary.
+
+
+ padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
+ Select a strategy to pad the returned sequences (according to the model's padding side and padding
+ index) among:
+
+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+ sequence if provided).
+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+ acceptable input length for the model if that argument is not provided.
+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+ lengths).
+ max_length (`int`, *optional*):
+ Maximum length of the returned list and optionally padding length (see above).
+ sampling_rate (`int`, *optional*):
+ The sampling rate at which the `audio` input was sampled. It is strongly recommended to pass
+ `sampling_rate` at the forward call to prevent silent errors.
+ Note that if `audio` is the output of Demucs, `sampling_rate` must be the sampling rate at which Demucs operates.
+ """
+
+ if sampling_rate is None:
+ logger.warning_once(
+ f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. "
+ "Failing to do so can result in silent errors that might be hard to debug."
+ )
+
+ if isinstance(audio, torch.Tensor) and len(audio.shape) == 4:
+ logger.warning_once(
+ "`audio` is a 4-dimensional torch tensor and has thus been recognized as the output of `Demucs`. "
+ "If this is not the case, make sure to read Musicgen Melody docstrings and "
+ "to correct `audio` to get the right behaviour."
+ "Link to the docstrings: https://huggingface.co/docs/transformers/main/en/model_doc/musicgen_melody"
+ )
+ audio = self._extract_stem_indices(audio, sampling_rate=sampling_rate)
+ elif sampling_rate is not None and sampling_rate != self.sampling_rate:
+ audio = torchaudio.functional.resample(
+ audio, sampling_rate, self.sampling_rate, rolloff=0.945, lowpass_filter_width=24
+ )
+
+ is_batched = isinstance(audio, (np.ndarray, torch.Tensor)) and len(audio.shape) > 1
+ is_batched = is_batched or (
+ isinstance(audio, (list, tuple)) and (isinstance(audio[0], (torch.Tensor, np.ndarray, tuple, list)))
+ )
+
+ if is_batched and not isinstance(audio[0], torch.Tensor):
+ audio = [torch.tensor(speech, dtype=torch.float32).unsqueeze(-1) for speech in audio]
+ elif is_batched:
+ audio = [speech.unsqueeze(-1) for speech in audio]
+ elif not is_batched and not isinstance(audio, torch.Tensor):
+ audio = torch.tensor(audio, dtype=torch.float32).unsqueeze(-1)
+
+ if isinstance(audio[0], torch.Tensor) and audio[0].dtype is torch.float64:
+ audio = [speech.to(torch.float32) for speech in audio]
+
+ # always return batch
+ if not is_batched:
+ audio = [audio]
+
+ if len(audio[0].shape) == 3:
+ logger.warning_once(
+ "`audio` has been detected as a batch of stereo signals. Will be convert to mono signals. "
+ "If this is an undesired behaviour, make sure to read Musicgen Melody docstrings and "
+ "to correct `audio` to get the right behaviour."
+ "Link to the docstrings: https://huggingface.co/docs/transformers/main/en/model_doc/musicgen_melody"
+ )
+ # convert to mono-channel waveform
+ audio = [stereo.mean(dim=0) for stereo in audio]
+
+ batched_speech = BatchFeature({"input_features": audio})
+
+ padded_inputs = self.pad(
+ batched_speech,
+ padding=padding,
+ max_length=max_length if max_length else self.n_samples,
+ truncation=truncation,
+ pad_to_multiple_of=pad_to_multiple_of,
+ return_attention_mask=return_attention_mask,
+ return_tensors="pt",
+ )
+
+ input_features = self._torch_extract_fbank_features(padded_inputs["input_features"].squeeze(-1))
+
+ padded_inputs["input_features"] = input_features
+
+ if return_attention_mask:
+ # rescale from raw audio length to spectrogram length
+ padded_inputs["attention_mask"] = padded_inputs["attention_mask"][:, :: self.hop_length]
+
+ if return_tensors is not None:
+ padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
+
+ return padded_inputs
+
+ def to_dict(self) -> dict[str, Any]:
+ """
+ Serializes this instance to a Python dictionary. Returns:
+ `dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
+ """
+ output = copy.deepcopy(self.__dict__)
+ output["feature_extractor_type"] = self.__class__.__name__
+ if "mel_filters" in output:
+ del output["mel_filters"]
+ if "window" in output:
+ del output["window"]
+ if "chroma_filters" in output:
+ del output["chroma_filters"]
+ if "spectrogram" in output:
+ del output["spectrogram"]
+ return output
+
+
+__all__ = ["MusicgenMelodyFeatureExtractor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py
new file mode 100644
index 0000000000000000000000000000000000000000..559f7375977b7a80e1c017acae95d41b90042096
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py
@@ -0,0 +1,2294 @@
+# coding=utf-8
+# Copyright 2024 Meta AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Musicgen Melody model."""
+
+import copy
+import inspect
+import math
+import random
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Callable, Optional, Union
+
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import (
+ ClassifierFreeGuidanceLogitsProcessor,
+ GenerationConfig,
+ GenerationMixin,
+ GenerationMode,
+ LogitsProcessorList,
+ StoppingCriteriaList,
+)
+from ...modeling_attn_mask_utils import (
+ _prepare_4d_causal_attention_mask,
+ _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from ...modeling_flash_attention_utils import (
+ FlashAttentionKwargs,
+)
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, ModelOutput
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import auto_docstring, is_torch_flex_attn_available, logging
+from ...utils.deprecation import deprecate_kwarg
+from ..auto.configuration_auto import AutoConfig
+from ..auto.modeling_auto import AutoModel, AutoModelForTextEncoding
+from .configuration_musicgen_melody import MusicgenMelodyConfig, MusicgenMelodyDecoderConfig
+
+
+if is_torch_flex_attn_available():
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+
+if TYPE_CHECKING:
+ from ...generation.streamers import BaseStreamer
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Musicgen Melody autoregressive outputs.
+ """
+)
+class MusicgenMelodyOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of conditional hidden-states representing the concatenation of the projected text encoder output and the projected audio encoder output.
+ Used as a conditional signal.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ encoder_hidden_states: Optional[torch.FloatTensor] = None
+
+
+# Copied from transformers.models.musicgen.modeling_musicgen.shift_tokens_right
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+ """
+ Shift input ids one token to the right.
+ """
+ # transpose to get (bsz, num_codebooks, seq_len)
+ input_ids = input_ids.transpose(1, 2)
+ shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+ shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
+ if decoder_start_token_id is None:
+ raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
+ shifted_input_ids[..., 0] = decoder_start_token_id
+
+ if pad_token_id is None:
+ raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+ return shifted_input_ids
+
+
+# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenSinusoidalPositionalEmbedding with Musicgen->MusicgenMelody
+class MusicgenMelodySinusoidalPositionalEmbedding(nn.Module):
+ """This module produces sinusoidal positional embeddings of any length."""
+
+ def __init__(self, num_positions: int, embedding_dim: int):
+ super().__init__()
+ self.embedding_dim = embedding_dim
+ self.make_weights(num_positions, embedding_dim)
+
+ def make_weights(self, num_embeddings: int, embedding_dim: int):
+ emb_weights = self.get_embedding(num_embeddings, embedding_dim)
+ if hasattr(self, "weights"):
+ # in forward put the weights on the correct dtype and device of the param
+ emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
+
+ self.register_buffer("weights", emb_weights, persistent=False)
+
+ @staticmethod
+ def get_embedding(num_embeddings: int, embedding_dim: int):
+ """
+ Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the
+ description in Section 3.5 of "Attention Is All You Need".
+ """
+ half_dim = embedding_dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
+ emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
+ emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=1).view(num_embeddings, -1)
+ if embedding_dim % 2 == 1:
+ # zero pad
+ emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
+ return emb.to(torch.get_default_dtype())
+
+ @torch.no_grad()
+ # Ignore copy
+ def forward(self, inputs_embeds: torch.Tensor, past_key_values_length: int = 0):
+ bsz, seq_len, _ = inputs_embeds.size()
+ # Create the position ids from the input token ids.
+ position_ids = (torch.arange(seq_len) + past_key_values_length).to(inputs_embeds.device)
+ # expand embeddings if needed
+ if seq_len > self.weights.size(0):
+ self.make_weights(seq_len, self.embedding_dim)
+ return self.weights.index_select(0, position_ids.view(-1)).detach()
+
+
+# Copied from transformers.models.bart.modeling_bart.eager_attention_forward
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: Optional[float] = None,
+ dropout: float = 0.0,
+ head_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+):
+ if scaling is None:
+ scaling = query.size(-1) ** -0.5
+
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ if head_mask is not None:
+ attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
+
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenAttention with Musicgen->MusicgenMelody
+class MusicgenMelodyAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: Optional[float] = 0.0,
+ is_decoder: Optional[bool] = False,
+ bias: Optional[bool] = True,
+ is_causal: Optional[bool] = False,
+ config: Optional[MusicgenMelodyConfig] = None,
+ layer_idx: Optional[int] = None,
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+ self.config = config
+
+ if (self.head_dim * num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+ self.is_causal = is_causal
+ self.layer_idx = layer_idx
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ key_value_states: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ cache_position: Optional[torch.Tensor] = None,
+ # TODO: we need a refactor so that the different attention modules can get their specific kwargs
+ # ATM, we have mixed things encoder, decoder, and encoder-decoder attn
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+
+ # determine input shapes
+ bsz, tgt_len = hidden_states.shape[:-1]
+ src_len = key_value_states.shape[1] if is_cross_attention else tgt_len
+
+ q_input_shape = (bsz, tgt_len, -1, self.head_dim)
+ kv_input_shape = (bsz, src_len, -1, self.head_dim)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states).view(*q_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ if isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_layer from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = key_value_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_states = curr_past_key_value.layers[self.layer_idx].keys
+ value_states = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_states = self.k_proj(current_states).view(*kv_input_shape).transpose(1, 2)
+ value_states = self.v_proj(current_states).view(*kv_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_states, value_states = curr_past_key_value.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=self.scaling,
+ output_attentions=output_attentions,
+ head_mask=layer_head_mask,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+class MusicgenMelodyDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MusicgenMelodyDecoderConfig, layer_idx=None):
+ super().__init__()
+ self.embed_dim = config.hidden_size
+
+ self.self_attn = MusicgenMelodyAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.num_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ bias=False,
+ is_causal=True,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=False)
+ self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=False)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = True,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(attention_heads,)`.
+ past_key_values (`Tuple(torch.FloatTensor)`): cached past key and value projection states
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ return hidden_states, self_attn_weights
+
+
+@auto_docstring
+# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenPreTrainedModel with Musicgen->MusicgenMelody
+class MusicgenMelodyPreTrainedModel(PreTrainedModel):
+ config: MusicgenMelodyDecoderConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["MusicgenMelodyDecoderLayer", "MusicgenMelodyAttention"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ def _init_weights(self, module):
+ std = self.config.initializer_factor
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+
+# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenDecoder with MUSICGEN->MUSICGEN_MELODY,Musicgen->MusicgenMelody
+class MusicgenMelodyDecoder(MusicgenMelodyPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MusicgenMelodyDecoderLayer`]
+ """
+
+ def __init__(self, config: MusicgenMelodyDecoderConfig):
+ super().__init__(config)
+ self.dropout = config.dropout
+ self.layerdrop = config.layerdrop
+ self.max_target_positions = config.max_position_embeddings
+ self.d_model = config.hidden_size
+ self.num_codebooks = config.num_codebooks
+ self.embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0
+
+ embed_dim = config.vocab_size + 1
+ self.embed_tokens = nn.ModuleList(
+ [nn.Embedding(embed_dim, config.hidden_size) for _ in range(config.num_codebooks)]
+ )
+
+ self.embed_positions = MusicgenMelodySinusoidalPositionalEmbedding(
+ config.max_position_embeddings,
+ config.hidden_size,
+ )
+
+ self.layers = nn.ModuleList(
+ [MusicgenMelodyDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]
+ )
+ self.layer_norm = nn.LayerNorm(config.hidden_size)
+ self.attn_implementation = config._attn_implementation
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ # Ignore copy
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.
+
+ Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
+ such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+
+
+ The `input_ids` will automatically be converted from shape `(batch_size * num_codebooks,
+ target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If
+ you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of
+ frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks,
+ target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as
+ `input_ids`.
+
+
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states representing the concatenation of the text encoder output and the processed audio encoder output.
+ Used as a conditional signal and will thus be concatenated to the projected `decoder_input_ids`.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing attention on conditional hidden states. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ # (bsz * codebooks, seq_len) -> (bsz, codebooks, seq_len)
+ input = input_ids.reshape(-1, self.num_codebooks, input_ids.shape[-1])
+ bsz, num_codebooks, seq_len = input.shape
+ input_shape = (bsz, seq_len)
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ input = inputs_embeds[:, :, -1:]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing`. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ if use_cache and past_key_values is None:
+ past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
+ if use_cache and isinstance(past_key_values, tuple):
+ logger.warning_once(
+ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
+ "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
+ "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
+ )
+ past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
+
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+ if inputs_embeds is None:
+ inputs_embeds = sum([self.embed_tokens[codebook](input[:, codebook]) for codebook in range(num_codebooks)])
+
+ if encoder_hidden_states is not None:
+ # take care of attention masks
+ if encoder_attention_mask is not None and attention_mask is None:
+ attention_mask = torch.ones(inputs_embeds.shape[:2], device=inputs_embeds.device)
+
+ if attention_mask is not None:
+ if encoder_attention_mask is None:
+ encoder_attention_mask = torch.ones(encoder_hidden_states.shape[:2], device=attention_mask.device)
+ attention_mask = torch.cat([encoder_attention_mask, attention_mask], dim=1)
+
+ # fuse encoder_hidden_states and inputs_embeds
+ inputs_embeds = torch.cat([encoder_hidden_states, inputs_embeds], dim=1)
+
+ input_shape = inputs_embeds.size()[:-1]
+
+ attention_mask = self._update_causal_mask(
+ attention_mask,
+ input_shape,
+ inputs_embeds,
+ past_key_values_length,
+ )
+
+ # embed positions
+ positions = self.embed_positions(inputs_embeds, past_key_values_length)
+ hidden_states = inputs_embeds + positions.to(inputs_embeds.device)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ if head_mask.size()[0] != len(self.layers):
+ raise ValueError(
+ f"The `head_mask` should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ dropout_probability = random.uniform(0, 1)
+ if self.training and (dropout_probability < self.layerdrop):
+ continue
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_attentions += (layer_outputs[1],)
+
+ hidden_states = self.layer_norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, past_key_values, all_hidden_states, all_attentions] if v is not None
+ )
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_attentions,
+ )
+
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, None],
+ input_shape: torch.Size,
+ inputs_embeds: torch.Tensor,
+ past_key_values_length: int,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ # 2d mask is passed through the layers
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+ attention_mask,
+ input_shape,
+ inputs_embeds,
+ past_key_values_length,
+ )
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ # Other attention flavors support in-built causal (when `mask is None`)
+ # while we need to create our specific block mask regardless
+ elif attention_mask is None:
+ attention_mask = make_flex_block_causal_mask(
+ torch.ones(
+ size=(input_shape),
+ device=inputs_embeds.device,
+ )
+ )
+ else:
+ # 4d mask is passed through the layers
+ attention_mask = _prepare_4d_causal_attention_mask(
+ attention_mask, input_shape, inputs_embeds, past_key_values_length
+ )
+
+ return attention_mask
+
+ # Ignore copy
+ def _update_cross_attn_mask(
+ self,
+ encoder_hidden_states: Union[torch.Tensor, None],
+ encoder_attention_mask: Union[torch.Tensor, None],
+ input_shape: torch.Size,
+ inputs_embeds: torch.Tensor,
+ ):
+ # MusicgenMelody doesn't apply cross attention, hence it's ignored here
+ # and only exists to not confuse any copy checks
+ pass
+
+
+@auto_docstring
+# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenModel with MUSICGEN->MUSICGEN_MELODY,Musicgen->MusicgenMelody
+class MusicgenMelodyModel(MusicgenMelodyPreTrainedModel):
+ def __init__(self, config: MusicgenMelodyDecoderConfig):
+ super().__init__(config)
+ self.decoder = MusicgenMelodyDecoder(config)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.decoder.embed_tokens = value
+
+ def get_decoder(self):
+ return self.decoder
+
+ @auto_docstring
+ # Ignore copy
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.
+
+ Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
+ such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+
+
+ The `input_ids` will automatically be converted from shape `(batch_size * num_codebooks,
+ target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If
+ you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of
+ frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks,
+ target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as
+ `input_ids`.
+
+
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states representing the concatenation of the text encoder output and the processed audio encoder output.
+ Used as a conditional signal and will thus be concatenated to the projected `decoder_input_ids`.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing attention on conditional hidden states. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
+ decoder_outputs = self.decoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ head_mask=head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ if not return_dict:
+ return decoder_outputs
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ hidden_states=decoder_outputs.hidden_states,
+ attentions=decoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The Musicgen Melody decoder model with a language modelling head on top.
+ """
+)
+# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenForCausalLM with MUSICGEN->MUSICGEN_MELODY,Musicgen->MusicgenMelody,MusicGen->Musicgen Melody
+class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel, GenerationMixin):
+ def __init__(self, config: MusicgenMelodyDecoderConfig):
+ super().__init__(config)
+
+ self.model = MusicgenMelodyModel(config)
+
+ self.num_codebooks = config.num_codebooks
+ self.lm_heads = nn.ModuleList(
+ [nn.Linear(config.hidden_size, config.vocab_size, bias=False) for _ in range(config.num_codebooks)]
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.decoder.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_heads
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_heads = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model.decoder = decoder
+
+ def get_decoder(self):
+ return self.model.decoder
+
+ @auto_docstring
+ # Ignore copy
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ labels: Optional[torch.LongTensor] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, MusicgenMelodyOutputWithPast]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.
+
+ Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
+ such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+
+
+
+ The `input_ids` will automatically be converted from shape `(batch_size * num_codebooks,
+ target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If
+ you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of
+ frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks,
+ target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as
+ `input_ids`.
+
+
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states representing the concatenation of the text encoder output and the processed audio encoder output.
+ Used as a conditional signal and will thus be concatenated to the projected `decoder_input_ids`.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing attention on conditional hidden states. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks)`, *optional*):
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if (labels is not None) and (input_ids is None and inputs_embeds is None):
+ input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.bos_token_id)
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ head_mask=head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ hidden_states = outputs[0]
+
+ lm_logits = torch.stack([head(hidden_states) for head in self.lm_heads], dim=1)
+
+ loss = None
+ if labels is not None:
+ # since encoder hidden states have been concatenated to the decoder hidden states,
+ # we take the last timestamps corresponding to labels
+ logits = lm_logits[:, :, -labels.shape[1] :]
+
+ loss_fct = CrossEntropyLoss()
+ loss = torch.zeros([], device=self.device)
+
+ # per codebook cross-entropy
+ # ref: https://github.com/facebookresearch/audiocraft/blob/69fea8b290ad1b4b40d28f92d1dfc0ab01dbab85/audiocraft/solvers/musicgen.py#L242-L243
+ # -100 labels are ignored
+ labels = labels.masked_fill(labels == self.config.pad_token_id, -100)
+
+ # per codebook cross-entropy
+ for codebook in range(self.config.num_codebooks):
+ codebook_logits = logits[:, codebook].contiguous().view(-1, logits.shape[-1])
+ codebook_labels = labels[..., codebook].contiguous().view(-1)
+ loss += loss_fct(codebook_logits, codebook_labels)
+
+ loss = loss / self.config.num_codebooks
+
+ # (bsz, num_codebooks, seq_len, vocab_size) -> (bsz * num_codebooks, seq_len, vocab_size)
+ lm_logits = lm_logits.reshape(-1, *lm_logits.shape[2:])
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return MusicgenMelodyOutputWithPast(
+ loss=loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ # Ignore copy
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ attention_mask=None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ head_mask=None,
+ past_key_values=None,
+ use_cache=True,
+ delay_pattern_mask=None,
+ guidance_scale=None,
+ **kwargs,
+ ):
+ # Overwritten -- MusicGen has custom processing
+ if delay_pattern_mask is None:
+ input_ids, delay_pattern_mask = self.build_delay_pattern_mask(
+ input_ids,
+ pad_token_id=self.generation_config.pad_token_id,
+ max_length=self.generation_config.max_length,
+ )
+
+ # apply the delay pattern mask
+ input_ids = self.apply_delay_pattern_mask(input_ids, delay_pattern_mask)
+
+ if guidance_scale is not None and guidance_scale > 1:
+ # for classifier free guidance we need to replicate the decoder args across the batch dim (we'll split these
+ # before sampling)
+ input_ids = input_ids.repeat((2, 1))
+ if attention_mask is not None:
+ attention_mask = attention_mask.repeat((2, 1))
+
+ if encoder_hidden_states is not None:
+ encoder_hidden_states = torch.concatenate(
+ [encoder_hidden_states, torch.zeros_like(encoder_hidden_states)], dim=0
+ )
+
+ if encoder_attention_mask is not None:
+ encoder_attention_mask = torch.concatenate(
+ encoder_attention_mask, torch.zeros_like(encoder_attention_mask), dim=0
+ )
+
+ if past_key_values is not None:
+ input_ids = input_ids[:, -1:]
+
+ # we only want to use conditional signal in the 1st generation step but keeping the attention mask
+ encoder_hidden_states = None
+
+ return {
+ "input_ids": input_ids,
+ "attention_mask": attention_mask,
+ "encoder_hidden_states": encoder_hidden_states,
+ "encoder_attention_mask": encoder_attention_mask,
+ "head_mask": head_mask,
+ "past_key_values": past_key_values,
+ "use_cache": use_cache,
+ }
+
+ def build_delay_pattern_mask(
+ self, input_ids: torch.LongTensor, pad_token_id: int, max_length: Optional[int] = None
+ ):
+ """Build a delayed pattern mask to the input_ids. Each codebook is offset by the previous codebook by
+ one, giving a delayed pattern mask at the start of sequence and end of sequence. Take the example where there
+ are 4 codebooks and a max sequence length of 8, we have the delayed pattern mask of shape `(codebooks,
+ seq_len)`:
+ - [P, -1, -1, -1, -1, P, P, P]
+ - [P, P, -1, -1, -1, -1, P, P]
+ - [P, P, P, -1, -1, -1, -1, P]
+ - [P, P, P, P, -1, -1, -1, -1]
+ where P is the special padding token id and -1 indicates that the token is valid for prediction. If we include
+ a prompt (decoder input ids), the -1 positions indicate where new tokens should be predicted. Otherwise, the
+ mask is set to the value in the prompt:
+ - [P, a, b, -1, -1, P, P, P]
+ - [P, P, c, d, -1, -1, P, P]
+ - [P, P, P, e, f, -1, -1, P]
+ - [P, P, P, P, g, h, -1, -1]
+ where a-h indicate the input prompt (decoder input ids) that are offset by 1. Now, we only override the -1
+ tokens in our prediction.
+ """
+ # (bsz * num_codebooks, seq_len) -> (bsz, num_codebooks, seq_len)
+ input_ids = input_ids.reshape(-1, self.num_codebooks, input_ids.shape[-1])
+ bsz, num_codebooks, seq_len = input_ids.shape
+
+ max_length = max_length if max_length is not None else self.generation_config.max_length
+ input_ids_shifted = (
+ torch.ones((bsz, num_codebooks, max_length), dtype=torch.long, device=input_ids.device) * -1
+ )
+
+ channel_codebooks = num_codebooks // 2 if self.config.audio_channels == 2 else num_codebooks
+ # we only apply the mask if we have a large enough seq len - otherwise we return as is
+ if max_length < 2 * channel_codebooks - 1:
+ return input_ids.reshape(bsz * num_codebooks, -1), input_ids_shifted.reshape(bsz * num_codebooks, -1)
+
+ # fill the shifted ids with the prompt entries, offset by the codebook idx
+ for codebook in range(channel_codebooks):
+ if self.config.audio_channels == 1:
+ # mono channel - loop over the codebooks one-by-one
+ input_ids_shifted[:, codebook, codebook : seq_len + codebook] = input_ids[:, codebook]
+ else:
+ # left/right channels are interleaved in the generated codebooks, so handle one then the other
+ input_ids_shifted[:, 2 * codebook, codebook : seq_len + codebook] = input_ids[:, 2 * codebook]
+ input_ids_shifted[:, 2 * codebook + 1, codebook : seq_len + codebook] = input_ids[:, 2 * codebook + 1]
+
+ # construct a pattern mask that indicates the positions of padding tokens for each codebook
+ # first fill the upper triangular part (the EOS padding)
+ delay_pattern = torch.triu(
+ torch.ones((channel_codebooks, max_length), dtype=torch.bool), diagonal=max_length - channel_codebooks + 1
+ )
+ # then fill the lower triangular part (the BOS padding)
+ delay_pattern = delay_pattern + torch.tril(torch.ones((channel_codebooks, max_length), dtype=torch.bool))
+
+ if self.config.audio_channels == 2:
+ # for left/right channel we need to duplicate every row of the pattern mask in an interleaved fashion
+ delay_pattern = delay_pattern.repeat_interleave(2, dim=0)
+
+ mask = ~delay_pattern.to(input_ids.device)
+ input_ids = mask * input_ids_shifted + ~mask * pad_token_id
+
+ # find the first position to start generating - this is the first place we have the -1 token
+ # and will always be in the first codebook (since it has no codebook offset)
+ first_codebook_ids = input_ids[:, 0, :]
+ start_ids = (first_codebook_ids == -1).nonzero()[:, 1]
+ if len(start_ids) > 0:
+ first_start_id = min(start_ids)
+ else:
+ # we have no tokens that need to be filled - return entire matrix of input ids
+ first_start_id = seq_len
+
+ # (bsz * num_codebooks, seq_len) -> (bsz, num_codebooks, seq_len)
+ pattern_mask = input_ids.reshape(bsz * num_codebooks, -1)
+ input_ids = input_ids[..., :first_start_id].reshape(bsz * num_codebooks, -1)
+ return input_ids, pattern_mask
+
+ @staticmethod
+ def apply_delay_pattern_mask(input_ids, decoder_pad_token_mask):
+ """Apply a delay pattern mask to the decoder input ids, only preserving predictions where
+ the mask is set to -1, and otherwise setting to the value detailed in the mask."""
+ seq_len = input_ids.shape[-1]
+ decoder_pad_token_mask = decoder_pad_token_mask[..., :seq_len]
+ input_ids = torch.where(decoder_pad_token_mask == -1, input_ids, decoder_pad_token_mask)
+ return input_ids
+
+ @torch.no_grad()
+ # Ignore copy
+ def generate(
+ self,
+ inputs: Optional[torch.Tensor] = None,
+ generation_config: Optional[GenerationConfig] = None,
+ logits_processor: Optional[LogitsProcessorList] = None,
+ stopping_criteria: Optional[StoppingCriteriaList] = None,
+ synced_gpus: Optional[bool] = None,
+ streamer: Optional["BaseStreamer"] = None,
+ **kwargs,
+ ):
+ """
+
+ Generates sequences of token ids for models with a language modeling head.
+
+
+
+ Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
+ model's default generation configuration. You can override any `generation_config` by passing the corresponding
+ parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
+
+ For an overview of generation strategies and code examples, check out the [following
+ guide](./generation_strategies).
+
+
+
+ Parameters:
+ inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
+ The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
+ method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
+ should be in the format `input_ids`. For encoder-decoder models *inputs* can represent any of
+ `input_ids`, `input_values`, `input_features`, or `pixel_values`.
+ generation_config (`~generation.GenerationConfig`, *optional*):
+ The generation configuration to be used as base parametrization for the generation call. `**kwargs`
+ passed to generate matching the attributes of `generation_config` will override them. If
+ `generation_config` is not provided, the default will be used, which had the following loading
+ priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
+ configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
+ default values, whose documentation should be checked to parameterize generation.
+ logits_processor (`LogitsProcessorList`, *optional*):
+ Custom logits processors that complement the default logits processors built from arguments and
+ generation config. If a logit processor is passed that is already created with the arguments or a
+ generation config an error is thrown. This feature is intended for advanced users.
+ stopping_criteria (`StoppingCriteriaList`, *optional*):
+ Custom stopping criteria that complement the default stopping criteria built from arguments and a
+ generation config. If a stopping criteria is passed that is already created with the arguments or a
+ generation config an error is thrown. This feature is intended for advanced users.
+ synced_gpus (`bool`, *optional*, defaults to `False`):
+ Whether to continue running the while loop until max_length (needed to avoid deadlocking with
+ `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
+ streamer (`BaseStreamer`, *optional*):
+ Streamer object that will be used to stream the generated sequences. Generated tokens are passed
+ through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
+ kwargs (`dict[str, Any]`, *optional*):
+ Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
+ forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
+ specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
+
+ Return:
+ [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
+ or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.
+
+ If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
+ [`~utils.ModelOutput`] types are:
+
+ - [`~generation.GenerateDecoderOnlyOutput`],
+ - [`~generation.GenerateBeamDecoderOnlyOutput`]
+
+ If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
+ [`~utils.ModelOutput`] types are:
+
+ - [`~generation.GenerateEncoderDecoderOutput`],
+ - [`~generation.GenerateBeamEncoderDecoderOutput`]
+ """
+ # 1. Handle `generation_config` and kwargs that might update it, and validate the resulting objects
+ if generation_config is None:
+ generation_config = self.generation_config
+
+ generation_config = copy.deepcopy(generation_config)
+ model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
+ generation_config.validate()
+ self._validate_model_kwargs(model_kwargs.copy())
+
+ # 2. Set generation parameters if not already defined
+ logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+ stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+
+ requires_attention_mask = "encoder_outputs" not in model_kwargs
+ kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
+
+ # 3. Define model inputs`
+ input_ids, model_input_name, model_kwargs = self._prepare_model_inputs(
+ inputs, generation_config.bos_token_id, model_kwargs
+ )
+ batch_size = input_ids.shape[0] // self.num_codebooks
+ self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=input_ids.device)
+
+ # 4. Define other model kwargs
+ model_kwargs["use_cache"] = generation_config.use_cache
+ model_kwargs["guidance_scale"] = generation_config.guidance_scale
+
+ if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
+ model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
+ input_ids, generation_config, model_kwargs
+ )
+
+ # 5. Prepare `max_length` depending on other stopping criteria.
+ input_ids_length = input_ids.shape[-1]
+ has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+ has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
+ generation_config = self._prepare_generated_length(
+ generation_config=generation_config,
+ has_default_max_length=has_default_max_length,
+ has_default_min_length=has_default_min_length,
+ model_input_name=model_input_name,
+ inputs_tensor=input_ids,
+ input_ids_length=input_ids_length,
+ )
+
+ # 6. Prepare `input_ids` which will be used for auto-regressive generation
+ # Build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to Musicgen)
+ input_ids, delay_pattern_mask = self.build_delay_pattern_mask(
+ input_ids,
+ pad_token_id=generation_config._decoder_start_token_tensor,
+ max_length=generation_config.max_length,
+ )
+
+ if streamer is not None:
+ streamer.put(input_ids.cpu())
+
+ # stash the delay mask so that we don't have to recompute it in each forward pass
+ model_kwargs["delay_pattern_mask"] = delay_pattern_mask
+
+ # 7. determine generation mode
+ generation_mode = generation_config.get_generation_mode()
+
+ # 8. prepare batched CFG externally (to enable coexistence with the unbatched CFG)
+ if generation_config.guidance_scale is not None and generation_config.guidance_scale > 1:
+ logits_processor.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale))
+ generation_config.guidance_scale = None
+
+ # 9. prepare distribution pre_processing samplers
+ logits_processor = self._get_logits_processor(
+ generation_config=generation_config,
+ input_ids_seq_length=input_ids_length,
+ encoder_input_ids=input_ids,
+ prefix_allowed_tokens_fn=None,
+ logits_processor=logits_processor,
+ device=input_ids.device,
+ )
+
+ # 10. prepare stopping criteria
+ stopping_criteria = self._get_stopping_criteria(
+ generation_config=generation_config, stopping_criteria=stopping_criteria
+ )
+
+ if generation_mode in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH):
+ # expand input_ids with `num_return_sequences` additional sequences per batch
+ input_ids, model_kwargs = self._expand_inputs_for_generation(
+ input_ids=input_ids,
+ expand_size=generation_config.num_return_sequences,
+ **model_kwargs,
+ )
+
+ # 11. run sample
+ outputs = self._sample(
+ input_ids,
+ logits_processor=logits_processor,
+ stopping_criteria=stopping_criteria,
+ generation_config=generation_config,
+ synced_gpus=synced_gpus,
+ streamer=streamer,
+ **model_kwargs,
+ )
+
+ else:
+ raise ValueError(
+ "Got incompatible mode for generation, should be one of greedy or sampling. "
+ "Ensure that beam search is de-activated by setting `num_beams=1` and `num_beam_groups=1`."
+ )
+
+ if generation_config.return_dict_in_generate:
+ output_ids = outputs.sequences
+ else:
+ output_ids = outputs
+
+ # apply the pattern mask to the final ids
+ output_ids = self.apply_delay_pattern_mask(output_ids, model_kwargs["delay_pattern_mask"])
+
+ # revert the pattern delay mask by filtering the pad token id
+ output_ids = output_ids[output_ids != generation_config._pad_token_tensor].reshape(
+ batch_size, self.num_codebooks, -1
+ )
+
+ if generation_config.return_dict_in_generate:
+ outputs.sequences = output_ids
+ return outputs
+ else:
+ return output_ids
+
+
+@auto_docstring
+class MusicgenMelodyForConditionalGeneration(PreTrainedModel, GenerationMixin):
+ config: MusicgenMelodyConfig
+ main_input_name = "input_ids"
+ supports_gradient_checkpointing = True
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ def __init__(
+ self,
+ config: MusicgenMelodyConfig = None,
+ text_encoder: Optional[PreTrainedModel] = None,
+ audio_encoder: Optional[PreTrainedModel] = None,
+ decoder: Optional[MusicgenMelodyForCausalLM] = None,
+ ):
+ r"""
+ text_encoder (`PreTrainedModel`, *optional*):
+ The text encoder model that encodes text into hidden states for conditioning.
+ audio_encoder (`PreTrainedModel`, *optional*):
+ The audio encoder model that encodes audio into hidden states for conditioning.
+ decoder (`MusicgenMelodyForCausalLM`, *optional*):
+ The decoder model that generates audio tokens based on conditioning signals.
+ """
+ if config is None and None in (text_encoder, audio_encoder, decoder):
+ raise ValueError(
+ "Either a configuration has to be provided, or all three of text encoder, audio encoder and Musicgen Melody decoder."
+ )
+ if config is None:
+ config = MusicgenMelodyConfig.from_sub_models_config(
+ text_encoder.config, audio_encoder.config, decoder.config
+ )
+ else:
+ if not isinstance(config, self.config_class):
+ raise ValueError(f"Config: {config} has to be of type {self.config_class}")
+
+ # initialize with config
+ super().__init__(config)
+
+ if text_encoder is None:
+ text_encoder = AutoModelForTextEncoding.from_config(config.text_encoder)
+
+ if audio_encoder is None:
+ audio_encoder = AutoModel.from_config(config.audio_encoder)
+
+ if decoder is None:
+ decoder = MusicgenMelodyForCausalLM._from_config(config.decoder)
+
+ self.text_encoder = text_encoder
+ self.audio_encoder = audio_encoder
+ self.decoder = decoder
+
+ # make sure that the individual model's config refers to the shared config
+ # so that the updates to the config will be synced
+ self.config.text_encoder._attn_implementation = self.text_encoder.config._attn_implementation
+ self.config.audio_encoder._attn_implementation = self.audio_encoder.config._attn_implementation
+ self.config.decoder._attn_implementation = self.decoder.config._attn_implementation
+ self.text_encoder.config = self.config.text_encoder
+ self.audio_encoder.config = self.config.audio_encoder
+ self.decoder.config = self.config.decoder
+
+ # text encoder outputs might need to be projected to different dimension for decoder
+ if self.text_encoder.config.hidden_size != self.decoder.config.hidden_size:
+ self.enc_to_dec_proj = nn.Linear(self.text_encoder.config.hidden_size, self.decoder.config.hidden_size)
+
+ # audio encoder outputs after chroma extraction might need to be projected to different dimension for decoder
+ if self.config.num_chroma != self.decoder.config.hidden_size:
+ self.audio_enc_to_dec_proj = nn.Linear(self.config.num_chroma, self.decoder.config.hidden_size)
+
+ if self.text_encoder.get_output_embeddings() is not None:
+ raise ValueError(
+ f"The encoder {self.text_encoder} should not have a LM Head. Please use a model without and LM Head"
+ )
+
+ # Initialize projection layers weights and tie text encoder and decoder weights if set accordingly
+ self.post_init()
+
+ def _init_weights(self, module):
+ # MusicgenMelodyForConditionalGeneration is made of PreTrainedModels that have already been initialized
+ # Projection layers still need to be initialized.
+ std = self.decoder.config.initializer_factor
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+
+ def tie_weights(self):
+ # tie text encoder & decoder if needed
+ if self.config.tie_encoder_decoder:
+ # tie text encoder and decoder base model
+ decoder_base_model_prefix = self.decoder.base_model_prefix
+ tied_weights = self._tie_encoder_decoder_weights(
+ self.text_encoder,
+ self.decoder._modules[decoder_base_model_prefix],
+ self.decoder.base_model_prefix,
+ "text_encoder",
+ )
+ # Setting a dynamic variable instead of `_tied_weights_keys` because it's a class
+ # attributed not an instance member, therefore modifying it will modify the entire class
+ # Leading to issues on subsequent calls by different tests or subsequent calls.
+ self._dynamic_tied_weights_keys = tied_weights
+
+ def get_text_encoder(self):
+ return self.text_encoder
+
+ def get_encoder(self):
+ # get the text encoder to compute the conditioning hidden-states for generation
+ return self.get_text_encoder()
+
+ def get_decoder(self):
+ return self.decoder
+
+ def get_input_embeddings(self):
+ return self.text_encoder.get_input_embeddings()
+
+ def get_output_embeddings(self):
+ return self.decoder.get_output_embeddings()
+
+ def set_output_embeddings(self, new_embeddings):
+ return self.decoder.set_output_embeddings(new_embeddings)
+
+ @classmethod
+ # Copied from transformers.models.musicgen.modeling_musicgen.MusicgenForConditionalGeneration.from_sub_models_pretrained with Musicgen->MusicgenMelody, musicgen-small->musicgen-melody
+ def from_sub_models_pretrained(
+ cls,
+ text_encoder_pretrained_model_name_or_path: Optional[str] = None,
+ audio_encoder_pretrained_model_name_or_path: Optional[str] = None,
+ decoder_pretrained_model_name_or_path: Optional[str] = None,
+ *model_args,
+ **kwargs,
+ ) -> PreTrainedModel:
+ r"""
+ Instantiate a text encoder, an audio encoder, and a MusicGen decoder from one, two or three base classes of the
+ library from pretrained model checkpoints.
+
+
+ The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
+ the model, you need to first set it back in training mode with `model.train()`.
+
+ Params:
+ text_encoder_pretrained_model_name_or_path (`str`, *optional*):
+ Information necessary to initiate the text encoder. Can be either:
+
+ - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
+ - A path to a *directory* containing model weights saved using
+ [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
+
+ audio_encoder_pretrained_model_name_or_path (`str`, *optional*):
+ Information necessary to initiate the audio encoder. Can be either:
+
+ - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
+ - A path to a *directory* containing model weights saved using
+ [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
+
+ decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`):
+ Information necessary to initiate the decoder. Can be either:
+
+ - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
+ - A path to a *directory* containing model weights saved using
+ [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
+
+ model_args (remaining positional arguments, *optional*):
+ All remaining positional arguments will be passed to the underlying model's `__init__` method.
+
+ kwargs (remaining dictionary of keyword arguments, *optional*):
+ Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
+ `output_attentions=True`).
+
+ - To update the text encoder configuration, use the prefix *text_encoder_* for each configuration
+ parameter.
+ - To update the audio encoder configuration, use the prefix *audio_encoder_* for each configuration
+ parameter.
+ - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
+ - To update the parent model configuration, do not use a prefix for each configuration parameter.
+
+ Behaves differently depending on whether a `config` is provided or automatically loaded.
+
+ Example:
+
+ ```python
+ >>> from transformers import MusicgenMelodyForConditionalGeneration
+
+ >>> # initialize a musicgen model from a t5 text encoder, encodec audio encoder, and musicgen decoder
+ >>> model = MusicgenMelodyForConditionalGeneration.from_sub_models_pretrained(
+ ... text_encoder_pretrained_model_name_or_path="google-t5/t5-base",
+ ... audio_encoder_pretrained_model_name_or_path="facebook/encodec_24khz",
+ ... decoder_pretrained_model_name_or_path="facebook/musicgen-melody",
+ ... )
+ >>> # saving model after fine-tuning
+ >>> model.save_pretrained("./musicgen-ft")
+ >>> # load fine-tuned model
+ >>> model = MusicgenMelodyForConditionalGeneration.from_pretrained("./musicgen-ft")
+ ```"""
+
+ kwargs_text_encoder = {
+ argument[len("text_encoder_") :]: value
+ for argument, value in kwargs.items()
+ if argument.startswith("text_encoder_")
+ }
+
+ kwargs_audio_encoder = {
+ argument[len("audio_encoder_") :]: value
+ for argument, value in kwargs.items()
+ if argument.startswith("audio_encoder_")
+ }
+
+ kwargs_decoder = {
+ argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
+ }
+
+ # remove text encoder, audio encoder and decoder kwargs from kwargs
+ for key in kwargs_text_encoder:
+ del kwargs["text_encoder_" + key]
+ for key in kwargs_audio_encoder:
+ del kwargs["audio_encoder_" + key]
+ for key in kwargs_decoder:
+ del kwargs["decoder_" + key]
+
+ # Load and initialize the encoder and decoder
+ # The distinction between encoder and decoder at the model level is made
+ # by the value of the flag `is_decoder` that we need to set correctly.
+ text_encoder = kwargs_text_encoder.pop("model", None)
+ if text_encoder is None:
+ if text_encoder_pretrained_model_name_or_path is None:
+ raise ValueError(
+ "If `text_encoder_model` is not defined as an argument, a `text_encoder_pretrained_model_name_or_path` has "
+ "to be defined."
+ )
+
+ if "config" not in kwargs_text_encoder:
+ encoder_config, kwargs_text_encoder = AutoConfig.from_pretrained(
+ text_encoder_pretrained_model_name_or_path, **kwargs_text_encoder, return_unused_kwargs=True
+ )
+
+ if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
+ logger.info(
+ f"Initializing {text_encoder_pretrained_model_name_or_path} as a text_encoder model "
+ "from a decoder model. Cross-attention and casual mask are disabled."
+ )
+ encoder_config.is_decoder = False
+ encoder_config.add_cross_attention = False
+
+ kwargs_text_encoder["config"] = encoder_config
+
+ text_encoder = AutoModel.from_pretrained(
+ text_encoder_pretrained_model_name_or_path, *model_args, **kwargs_text_encoder
+ )
+
+ audio_encoder = kwargs_audio_encoder.pop("model", None)
+ if audio_encoder is None:
+ if audio_encoder_pretrained_model_name_or_path is None:
+ raise ValueError(
+ "If `audio_encoder_model` is not defined as an argument, an `audio_encoder_pretrained_model_name_or_path` has "
+ "to be defined."
+ )
+
+ if "config" not in kwargs_audio_encoder:
+ encoder_config, kwargs_audio_encoder = AutoConfig.from_pretrained(
+ audio_encoder_pretrained_model_name_or_path, **kwargs_audio_encoder, return_unused_kwargs=True
+ )
+
+ if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
+ logger.info(
+ f"Initializing {audio_encoder_pretrained_model_name_or_path} as an audio_encoder model "
+ "from a decoder model. Cross-attention and casual mask are disabled."
+ )
+ encoder_config.is_decoder = False
+ encoder_config.add_cross_attention = False
+
+ kwargs_audio_encoder["config"] = encoder_config
+
+ audio_encoder = AutoModel.from_pretrained(
+ audio_encoder_pretrained_model_name_or_path, *model_args, **kwargs_audio_encoder
+ )
+
+ decoder = kwargs_decoder.pop("model", None)
+ if decoder is None:
+ if decoder_pretrained_model_name_or_path is None:
+ raise ValueError(
+ "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has "
+ "to be defined."
+ )
+
+ if "config" not in kwargs_decoder:
+ decoder_config, kwargs_decoder = AutoConfig.from_pretrained(
+ decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True
+ )
+
+ if isinstance(decoder_config, MusicgenMelodyConfig):
+ decoder_config = decoder_config.decoder
+
+ if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
+ logger.info(
+ f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention"
+ f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if"
+ f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers."
+ )
+ decoder_config.is_decoder = True
+ decoder_config.add_cross_attention = True
+
+ kwargs_decoder["config"] = decoder_config
+
+ if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False:
+ logger.warning(
+ f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. "
+ f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, "
+ "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` "
+ "passed to `.from_sub_models_pretrained(...)` are set to `True` or do not pass a "
+ "`decoder_config` to `.from_sub_models_pretrained(...)`"
+ )
+
+ decoder = MusicgenMelodyForCausalLM.from_pretrained(
+ decoder_pretrained_model_name_or_path, **kwargs_decoder
+ )
+
+ # instantiate config with corresponding kwargs
+ config = MusicgenMelodyConfig.from_sub_models_config(
+ text_encoder.config, audio_encoder.config, decoder.config, **kwargs
+ )
+ return cls(text_encoder=text_encoder, audio_encoder=audio_encoder, decoder=decoder, config=config)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.BoolTensor] = None,
+ input_features: Optional[torch.FloatTensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.BoolTensor] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple, MusicgenMelodyOutputWithPast]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary, corresponding to the sequence of audio codes.
+
+ Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes,
+ such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+
+
+ The `decoder_input_ids` will automatically be converted from shape `(batch_size * num_codebooks,
+ target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If
+ you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of
+ frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks,
+ target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as
+ `decoder_input_ids`.
+
+
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of conditional hidden-states representing the concatenation of the projected text encoder output and the projected audio encoder output.
+ Used as a conditional signal and will thus be concatenated to the projected `decoder_input_ids`.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks)`, *optional*):
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+
+ Examples:
+ ```python
+ >>> from transformers import AutoProcessor, MusicgenMelodyForConditionalGeneration
+ >>> import torch
+
+ >>> processor = AutoProcessor.from_pretrained("facebook/musicgen-melody")
+ >>> model = MusicgenMelodyForConditionalGeneration.from_pretrained("facebook/musicgen-melody")
+
+ >>> inputs = processor(
+ ... text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
+ ... padding=True,
+ ... return_tensors="pt",
+ ... )
+
+ >>> pad_token_id = model.generation_config.pad_token_id
+ >>> decoder_input_ids = (
+ ... torch.ones((inputs.input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long)
+ ... * pad_token_id
+ ... )
+
+ >>> logits = model(**inputs, decoder_input_ids=decoder_input_ids).logits
+ >>> logits.shape # (bsz * num_codebooks, encoder_len + tgt_len, vocab_size)
+ torch.Size([8, 249, 2048])
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ kwargs_text_encoder = {
+ argument[len("text_encoder_")]: value
+ for argument, value in kwargs.items()
+ if argument.startswith("text_encoder_")
+ }
+
+ kwargs_decoder = {
+ argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
+ }
+
+ if encoder_hidden_states is None:
+ if inputs_embeds is not None or input_ids is not None:
+ encoder_outputs = self.text_encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ **kwargs_text_encoder,
+ )
+
+ encoder_hidden_states = encoder_outputs[0]
+
+ # optionally project encoder_hidden_states
+ if self.text_encoder.config.hidden_size != self.decoder.config.hidden_size:
+ encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
+
+ if attention_mask is not None and encoder_hidden_states is not None:
+ encoder_hidden_states = encoder_hidden_states * attention_mask[..., None]
+
+ # set a default audio conditional hidden states if text is not None
+ if encoder_hidden_states is not None and input_features is None:
+ input_features = torch.zeros(
+ (encoder_hidden_states.shape[0], 1, self.config.num_chroma),
+ device=self.device,
+ dtype=self.dtype,
+ )
+ input_features[:, :, 0] = 1
+
+ if input_features is not None:
+ audio_hidden_states = input_features
+
+ # optionally project audio_hidden_states ->
+ # (batch_size, seq_len, num_chroma) -> (batch_size, seq_len, hidden_size)
+ if self.config.num_chroma != self.decoder.config.hidden_size:
+ audio_hidden_states = self.audio_enc_to_dec_proj(audio_hidden_states)
+
+ # pad or truncate to config.chroma_length
+ if audio_hidden_states.shape[1] < self.config.chroma_length:
+ n_repeat = int(math.ceil(self.config.chroma_length / audio_hidden_states.shape[1]))
+ audio_hidden_states = audio_hidden_states.repeat(1, n_repeat, 1)
+ else:
+ logger.warning(
+ f"The conditional audio signal is of length {audio_hidden_states.shape[1]}, which exceeds"
+ f"the maximum chroma duration of {self.config.chroma_length}."
+ f"The audio will be truncated to {self.config.chroma_length} frames."
+ )
+ audio_hidden_states = audio_hidden_states[:, : self.config.chroma_length]
+
+ if encoder_hidden_states is not None:
+ encoder_hidden_states = torch.cat([audio_hidden_states, encoder_hidden_states], dim=1)
+ else:
+ encoder_hidden_states = audio_hidden_states
+
+ if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
+ decoder_input_ids = shift_tokens_right(
+ labels, self.config.decoder.pad_token_id, self.config.decoder.bos_token_id
+ )
+
+ # Decode
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ inputs_embeds=decoder_inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ use_cache=use_cache,
+ past_key_values=past_key_values,
+ return_dict=return_dict,
+ labels=labels,
+ **kwargs_decoder,
+ )
+
+ if not return_dict:
+ return decoder_outputs + (encoder_hidden_states,)
+
+ return MusicgenMelodyOutputWithPast(
+ loss=decoder_outputs.loss,
+ logits=decoder_outputs.logits,
+ past_key_values=decoder_outputs.past_key_values,
+ hidden_states=decoder_outputs.hidden_states,
+ attentions=decoder_outputs.attentions,
+ encoder_hidden_states=encoder_hidden_states,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ decoder_input_ids,
+ encoder_hidden_states=None,
+ past_key_values=None,
+ attention_mask=None,
+ decoder_attention_mask=None,
+ decoder_head_mask=None,
+ use_cache=None,
+ decoder_delay_pattern_mask=None,
+ guidance_scale=None,
+ **kwargs,
+ ):
+ # Overwritten -- MusicGen has custom processing
+ if decoder_delay_pattern_mask is None:
+ decoder_input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
+ decoder_input_ids,
+ self.generation_config.pad_token_id,
+ max_length=self.generation_config.max_length,
+ )
+
+ # apply the delay pattern mask
+ decoder_input_ids = self.decoder.apply_delay_pattern_mask(decoder_input_ids, decoder_delay_pattern_mask)
+
+ if guidance_scale is not None and guidance_scale > 1:
+ # for classifier free guidance we need to replicate the decoder args across the batch dim (we'll split these
+ # before sampling)
+ decoder_input_ids = decoder_input_ids.repeat((2, 1))
+ if decoder_attention_mask is not None:
+ decoder_attention_mask = decoder_attention_mask.repeat((2, 1))
+
+ if past_key_values is not None:
+ past_length = past_key_values.get_seq_length()
+
+ # Some generation methods already pass only the last input ID
+ if decoder_input_ids.shape[1] > past_length:
+ remove_prefix_length = past_length
+ else:
+ # Default to old behavior: keep only final ID
+ remove_prefix_length = decoder_input_ids.shape[1] - 1
+
+ decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]
+
+ # we only want to use conditional signal in the 1st generation step but keeping the attention mask
+ encoder_hidden_states = None
+ # we also have to update the attention mask
+
+ return {
+ "input_ids": None, # encoder_hidden_states is defined. input_ids not needed
+ "encoder_hidden_states": encoder_hidden_states,
+ "past_key_values": past_key_values,
+ "decoder_input_ids": decoder_input_ids,
+ "attention_mask": attention_mask,
+ "decoder_attention_mask": decoder_attention_mask,
+ "decoder_head_mask": decoder_head_mask,
+ "use_cache": use_cache,
+ }
+
+ # Copied from transformers.models.musicgen.modeling_musicgen.MusicgenForConditionalGeneration._prepare_decoder_input_ids_for_generation
+ def _prepare_decoder_input_ids_for_generation(
+ self,
+ batch_size: int,
+ model_input_name: str,
+ model_kwargs: dict[str, torch.Tensor],
+ decoder_start_token_id: Optional[int] = None,
+ bos_token_id: Optional[int] = None,
+ device: torch.device = None,
+ ) -> tuple[torch.LongTensor, dict[str, torch.Tensor]]:
+ """Prepares `decoder_input_ids` for generation with encoder-decoder models"""
+
+ # 1. Check whether the user has defined `decoder_input_ids` manually. To facilitate in terms of input naming,
+ # we also allow the user to pass it under `input_ids`, if the encoder does not use it as the main input.
+ if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
+ decoder_input_ids = model_kwargs.pop("decoder_input_ids")
+ elif "input_ids" in model_kwargs and model_input_name != "input_ids":
+ decoder_input_ids = model_kwargs.pop("input_ids")
+ else:
+ decoder_input_ids = None
+
+ # 2. Encoder-decoder models expect the `decoder_input_ids` to start with a special token. Let's ensure that.
+ decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
+ if device is None:
+ device = self.device
+ decoder_input_ids_start = (
+ torch.ones((batch_size * self.decoder.num_codebooks, 1), dtype=torch.long, device=device)
+ * decoder_start_token_id
+ )
+
+ # no user input -> use decoder_start_token_id as decoder_input_ids
+ if decoder_input_ids is None:
+ decoder_input_ids = decoder_input_ids_start
+
+ # user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
+ # decoder_attention_mask if provided)
+ elif (decoder_input_ids[..., 0] != decoder_start_token_id).all().item():
+ decoder_input_ids = torch.cat([decoder_input_ids_start, decoder_input_ids], dim=-1)
+ if "decoder_attention_mask" in model_kwargs:
+ decoder_attention_mask = model_kwargs["decoder_attention_mask"]
+ decoder_attention_mask = torch.cat(
+ (torch.ones_like(decoder_attention_mask)[:, :1], decoder_attention_mask),
+ dim=-1,
+ )
+ model_kwargs["decoder_attention_mask"] = decoder_attention_mask
+
+ return decoder_input_ids, model_kwargs
+
+ def _prepare_encoder_hidden_states_kwargs_for_generation(
+ self,
+ inputs_tensor: torch.Tensor,
+ model_kwargs,
+ model_input_name: Optional[str],
+ generation_config: GenerationConfig,
+ ) -> dict[str, Any]:
+ encoder_hidden_states = None
+ # attention mask is consumed once to produce text conditional hidden states through the text encoder
+ encoder_attention_mask = model_kwargs.pop("attention_mask")
+ guidance_scale = generation_config.guidance_scale
+
+ # 1. condition on text
+ if inputs_tensor is not None:
+ encoder = self.get_text_encoder()
+ # Compatibility with Accelerate big model inference: we need the encoder to outputs stuff on the same device
+ # as the inputs.
+ if hasattr(encoder, "_hf_hook"):
+ encoder._hf_hook.io_same_device = True
+
+ # Prepare args and kwargs from model kwargs.
+ irrelevant_prefix = ["decoder_", "use_cache"]
+ encoder_kwargs = {
+ argument: value
+ for argument, value in model_kwargs.items()
+ if not any(argument.startswith(p) for p in irrelevant_prefix)
+ }
+ encoder_signature = set(inspect.signature(encoder.forward).parameters)
+ encoder_accepts_wildcard = "kwargs" in encoder_signature or "model_kwargs" in encoder_signature
+ if not encoder_accepts_wildcard:
+ encoder_kwargs = {
+ argument: value for argument, value in encoder_kwargs.items() if argument in encoder_signature
+ }
+ encoder_kwargs["output_attentions"] = generation_config.output_attentions
+ encoder_kwargs["output_hidden_states"] = generation_config.output_hidden_states
+
+ # make sure that encoder returns `ModelOutput`
+ model_input_name = model_input_name if model_input_name is not None else self.text_encoder.main_input_name
+ encoder_kwargs["return_dict"] = True
+ encoder_kwargs[model_input_name] = inputs_tensor
+ if encoder_attention_mask is not None:
+ encoder_kwargs["attention_mask"] = encoder_attention_mask
+ encoder_hidden_states = encoder(**encoder_kwargs).last_hidden_state
+
+ # optionally project encoder_hidden_states
+ if self.text_encoder.config.hidden_size != self.decoder.config.hidden_size:
+ encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
+
+ # for classifier free guidance we need to add a 'null' input to our encoder hidden states
+ if guidance_scale is not None and guidance_scale > 1:
+ encoder_hidden_states = torch.concatenate(
+ [encoder_hidden_states, torch.zeros_like(encoder_hidden_states)], dim=0
+ )
+ if encoder_attention_mask is not None:
+ encoder_attention_mask = torch.concatenate(
+ [encoder_attention_mask, torch.zeros_like(encoder_attention_mask)], dim=0
+ )
+ if encoder_attention_mask is not None:
+ encoder_hidden_states = encoder_hidden_states * encoder_attention_mask[..., None]
+
+ # 2. condition on audio
+ audio_hidden_states = model_kwargs.get("input_features", None)
+
+ if inputs_tensor is not None:
+ if audio_hidden_states is not None:
+ null_audio_hidden_states = torch.zeros_like(audio_hidden_states)
+ else:
+ null_audio_hidden_states = torch.zeros(
+ (inputs_tensor.shape[0], 1, self.config.num_chroma), device=self.device, dtype=self.dtype
+ )
+ null_audio_hidden_states[:, :, 0] = 1
+
+ if audio_hidden_states is None:
+ audio_hidden_states = null_audio_hidden_states
+
+ if audio_hidden_states is not None:
+ # for classifier free guidance we need to add a 'null' input to our audio hidden states
+ if guidance_scale is not None and guidance_scale > 1:
+ audio_hidden_states = torch.concatenate([audio_hidden_states, null_audio_hidden_states], dim=0)
+
+ # optionally project audio_hidden_states ->
+ # (batch_size, seq_len, num_chroma) -> (batch_size, seq_len, hidden_size)
+ if self.config.num_chroma != self.decoder.config.hidden_size:
+ audio_hidden_states = self.audio_enc_to_dec_proj(audio_hidden_states)
+
+ # pad or truncate to config.chroma_length
+ if audio_hidden_states.shape[1] < self.config.chroma_length:
+ n_repeat = int(math.ceil(self.config.chroma_length / audio_hidden_states.shape[1]))
+ audio_hidden_states = audio_hidden_states.repeat(1, n_repeat, 1)
+ audio_hidden_states = audio_hidden_states[:, : self.config.chroma_length]
+
+ if encoder_hidden_states is not None:
+ encoder_hidden_states = torch.cat([audio_hidden_states, encoder_hidden_states], dim=1)
+ else:
+ encoder_hidden_states = audio_hidden_states
+
+ model_kwargs["encoder_hidden_states"] = encoder_hidden_states
+
+ return model_kwargs
+
+ def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+ return shift_tokens_right(labels, self.config.decoder.pad_token_id, self.config.decoder.bos_token_id)
+
+ def resize_token_embeddings(self, *args, **kwargs):
+ raise NotImplementedError(
+ "Resizing the embedding layers via the EncoderDecoderModel directly is not supported. Please use the"
+ " respective methods of the wrapped objects (model.encoder.resize_token_embeddings(...) or"
+ " model.decoder.resize_token_embeddings(...))"
+ )
+
+ def _maybe_initialize_input_ids_for_generation(
+ self,
+ inputs: Optional[torch.Tensor] = None,
+ bos_token_id: Optional[int] = None,
+ model_kwargs: Optional[dict[str, torch.Tensor]] = None,
+ ) -> torch.LongTensor:
+ """Initializes input ids for generation, if necessary."""
+ if inputs is not None:
+ return inputs
+
+ if bos_token_id is None:
+ raise ValueError("`bos_token_id` has to be defined when no `input_ids` are provided.")
+
+ # If there is some tensor in `model_kwargs`, we can infer the batch size from it. This is helpful with
+ # soft-prompting or in multimodal implementations built on top of decoder-only language models.
+ batch_size = 1
+ for value in model_kwargs.values():
+ if isinstance(value, torch.Tensor):
+ batch_size = value.shape[0]
+ break
+ return torch.ones((batch_size, 1), dtype=torch.long, device=self.device) * bos_token_id
+
+ def freeze_audio_encoder(self):
+ """
+ Freeze the audio encoder weights.
+ """
+ for param in self.audio_encoder.parameters():
+ param.requires_grad = False
+ self.audio_encoder._requires_grad = False
+
+ def freeze_text_encoder(self):
+ """
+ Freeze the text encoder weights.
+ """
+ for param in self.text_encoder.parameters():
+ param.requires_grad = False
+ self.text_encoder._requires_grad = False
+
+ # Copied from transformers.models.musicgen.modeling_musicgen.MusicgenForConditionalGeneration._get_decoder_start_token_id
+ def _get_decoder_start_token_id(
+ self, decoder_start_token_id: Optional[Union[int, list[int]]] = None, bos_token_id: Optional[int] = None
+ ) -> int:
+ decoder_start_token_id = (
+ decoder_start_token_id
+ if decoder_start_token_id is not None
+ else self.generation_config.decoder_start_token_id
+ )
+ bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id
+
+ if decoder_start_token_id is not None:
+ return decoder_start_token_id
+ elif bos_token_id is not None:
+ return bos_token_id
+ raise ValueError(
+ "`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
+ )
+
+ @torch.no_grad()
+ def generate(
+ self,
+ inputs: Optional[torch.Tensor] = None,
+ generation_config: Optional[GenerationConfig] = None,
+ logits_processor: Optional[LogitsProcessorList] = None,
+ stopping_criteria: Optional[StoppingCriteriaList] = None,
+ synced_gpus: Optional[bool] = None,
+ streamer: Optional["BaseStreamer"] = None,
+ **kwargs,
+ ):
+ """
+
+ Generates sequences of token ids for models with a language modeling head.
+
+
+
+ Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
+ model's default generation configuration. You can override any `generation_config` by passing the corresponding
+ parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
+
+ For an overview of generation strategies and code examples, check out the [following
+ guide](./generation_strategies).
+
+
+
+ Parameters:
+ inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
+ The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
+ method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
+ should be in the format `input_ids`. For encoder-decoder models *inputs* can represent any of
+ `input_ids`, `input_values`, `input_features`, or `pixel_values`.
+ generation_config (`~generation.GenerationConfig`, *optional*):
+ The generation configuration to be used as base parametrization for the generation call. `**kwargs`
+ passed to generate matching the attributes of `generation_config` will override them. If
+ `generation_config` is not provided, the default will be used, which had the following loading
+ priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
+ configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
+ default values, whose documentation should be checked to parameterize generation.
+ logits_processor (`LogitsProcessorList`, *optional*):
+ Custom logits processors that complement the default logits processors built from arguments and
+ generation config. If a logit processor is passed that is already created with the arguments or a
+ generation config an error is thrown. This feature is intended for advanced users.
+ stopping_criteria (`StoppingCriteriaList`, *optional*):
+ Custom stopping criteria that complement the default stopping criteria built from arguments and a
+ generation config. If a stopping criteria is passed that is already created with the arguments or a
+ generation config an error is thrown. This feature is intended for advanced users.
+ synced_gpus (`bool`, *optional*, defaults to `False`):
+ Whether to continue running the while loop until max_length (needed to avoid deadlocking with
+ `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
+ streamer (`BaseStreamer`, *optional*):
+ Streamer object that will be used to stream the generated sequences. Generated tokens are passed
+ through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
+ kwargs (`dict[str, Any]`, *optional*):
+ Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
+ forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
+ specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
+
+ Return:
+ [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
+ or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.
+
+ If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
+ [`~utils.ModelOutput`] types are:
+
+ - [`~generation.GenerateDecoderOnlyOutput`],
+ - [`~generation.GenerateBeamDecoderOnlyOutput`]
+
+ If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
+ [`~utils.ModelOutput`] types are:
+
+ - [`~generation.GenerateEncoderDecoderOutput`],
+ - [`~generation.GenerateBeamEncoderDecoderOutput`]
+ """
+ # 1. Handle `generation_config` and kwargs that might update it, and validate the resulting objects
+ if generation_config is None:
+ generation_config = self.generation_config
+
+ generation_config = copy.deepcopy(generation_config)
+ model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
+ generation_config.validate()
+ self._validate_model_kwargs(model_kwargs.copy())
+
+ # 2. Set generation parameters if not already defined
+ logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+ stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+
+ requires_attention_mask = "encoder_outputs" not in model_kwargs
+ kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None
+
+ # 3. Define model inputs
+ inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
+ inputs, generation_config.bos_token_id, model_kwargs
+ )
+ batch_size = inputs_tensor.shape[0]
+ self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=inputs_tensor.device)
+
+ # 4. Define other model kwargs
+ model_kwargs["use_cache"] = generation_config.use_cache
+ model_kwargs["guidance_scale"] = generation_config.guidance_scale
+
+ if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
+ model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
+ inputs_tensor, generation_config, model_kwargs
+ )
+
+ if "encoder_hidden_states" not in model_kwargs:
+ # encoder_hidden_states are created and added to `model_kwargs`
+ model_kwargs = self._prepare_encoder_hidden_states_kwargs_for_generation(
+ inputs_tensor, model_kwargs, model_input_name, generation_config
+ )
+
+ # 5. Prepare `input_ids` which will be used for auto-regressive generation
+ input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
+ batch_size=batch_size,
+ model_input_name=model_input_name,
+ model_kwargs=model_kwargs,
+ decoder_start_token_id=generation_config._decoder_start_token_tensor,
+ bos_token_id=generation_config._bos_token_tensor,
+ device=inputs_tensor.device,
+ )
+
+ # 6. Prepare `max_length` depending on other stopping criteria.
+ input_ids_length = input_ids.shape[-1]
+ has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+ has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None
+ generation_config = self._prepare_generated_length(
+ generation_config=generation_config,
+ has_default_max_length=has_default_max_length,
+ has_default_min_length=has_default_min_length,
+ model_input_name=model_input_name,
+ inputs_tensor=inputs_tensor,
+ input_ids_length=input_ids_length,
+ )
+
+ self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
+
+ # 7. Prepare the cache.
+ # - `model_kwargs` may be updated in place with a cache as defined by the parameters in `generation_config`.
+ # - different models have a different cache name expected by the model (default = "past_key_values")
+ # - `max_length`, prepared above, is used to determine the maximum cache length
+ max_cache_length = generation_config.max_length - 1
+ if (
+ inputs_tensor.shape[1] != input_ids_length
+ and model_input_name == "inputs_embeds"
+ and not self.config.is_encoder_decoder
+ ):
+ max_cache_length += inputs_tensor.shape[1]
+ self._prepare_cache_for_generation(
+ generation_config,
+ model_kwargs,
+ assistant_model=None,
+ batch_size=batch_size,
+ max_cache_length=max_cache_length,
+ )
+
+ # build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen)
+ input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
+ input_ids,
+ pad_token_id=generation_config._decoder_start_token_tensor,
+ max_length=generation_config.max_length,
+ )
+ # stash the delay mask so that we don't have to recompute in each forward pass
+ model_kwargs["decoder_delay_pattern_mask"] = decoder_delay_pattern_mask
+
+ # input_ids are ready to be placed on the streamer (if used)
+ if streamer is not None:
+ streamer.put(input_ids.cpu())
+
+ # 8. determine generation mode
+ generation_mode = generation_config.get_generation_mode()
+
+ # 9. prepare batched CFG externally (to enable coexistence with the unbatched CFG)
+ if generation_config.guidance_scale is not None and generation_config.guidance_scale > 1:
+ logits_processor.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale))
+ generation_config.guidance_scale = None
+
+ # 10. prepare distribution pre_processing samplers
+ logits_processor = self._get_logits_processor(
+ generation_config=generation_config,
+ input_ids_seq_length=input_ids_length,
+ encoder_input_ids=inputs_tensor,
+ prefix_allowed_tokens_fn=None,
+ logits_processor=logits_processor,
+ device=input_ids.device,
+ )
+
+ # 10. prepare stopping criteria
+ stopping_criteria = self._get_stopping_criteria(
+ generation_config=generation_config, stopping_criteria=stopping_criteria
+ )
+
+ if generation_mode in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH):
+ # expand input_ids with `num_return_sequences` additional sequences per batch
+ input_ids, model_kwargs = self._expand_inputs_for_generation(
+ input_ids=input_ids,
+ expand_size=generation_config.num_return_sequences,
+ is_encoder_decoder=self.config.is_encoder_decoder,
+ **model_kwargs,
+ )
+
+ # 11. run sample
+ outputs = self._sample(
+ input_ids,
+ logits_processor=logits_processor,
+ stopping_criteria=stopping_criteria,
+ generation_config=generation_config,
+ synced_gpus=synced_gpus,
+ streamer=streamer,
+ **model_kwargs,
+ )
+
+ else:
+ raise ValueError(
+ "Got incompatible mode for generation, should be one of greedy or sampling. "
+ "Ensure that beam search is de-activated by setting `num_beams=1` and `num_beam_groups=1`."
+ )
+
+ if generation_config.return_dict_in_generate:
+ output_ids = outputs.sequences
+ else:
+ output_ids = outputs
+
+ # apply the pattern mask to the final ids
+ output_ids = self.decoder.apply_delay_pattern_mask(output_ids, model_kwargs["decoder_delay_pattern_mask"])
+
+ # revert the pattern delay mask by filtering the pad token id
+ output_ids = output_ids[output_ids != generation_config._pad_token_tensor].reshape(
+ batch_size, self.decoder.num_codebooks, -1
+ )
+
+ # append the frame dimension back to the audio codes
+ output_ids = output_ids[None, ...]
+
+ audio_scales = model_kwargs.get("audio_scales")
+ if audio_scales is None:
+ audio_scales = [None] * batch_size
+
+ if self.decoder.config.audio_channels == 1:
+ output_values = self.audio_encoder.decode(
+ output_ids,
+ audio_scales=audio_scales,
+ ).audio_values
+ else:
+ codec_outputs_left = self.audio_encoder.decode(output_ids[:, :, ::2, :], audio_scales=audio_scales)
+ output_values_left = codec_outputs_left.audio_values
+
+ codec_outputs_right = self.audio_encoder.decode(output_ids[:, :, 1::2, :], audio_scales=audio_scales)
+ output_values_right = codec_outputs_right.audio_values
+
+ output_values = torch.cat([output_values_left, output_values_right], dim=1)
+
+ if generation_config.return_dict_in_generate:
+ outputs.sequences = output_values
+ return outputs
+ else:
+ return output_values
+
+
+__all__ = [
+ "MusicgenMelodyForConditionalGeneration",
+ "MusicgenMelodyForCausalLM",
+ "MusicgenMelodyModel",
+ "MusicgenMelodyPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/processing_musicgen_melody.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/processing_musicgen_melody.py
new file mode 100644
index 0000000000000000000000000000000000000000..19e99947f073876ee5fd30aa6c195f5f0c65b14b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/musicgen_melody/processing_musicgen_melody.py
@@ -0,0 +1,180 @@
+# coding=utf-8
+# Copyright 2024 Meta AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Text/audio processor class for MusicGen Melody
+"""
+
+from typing import Optional
+
+import numpy as np
+
+from ...processing_utils import ProcessorMixin
+from ...utils import to_numpy
+from ...utils.import_utils import requires
+
+
+@requires(backends=("torchaudio",))
+class MusicgenMelodyProcessor(ProcessorMixin):
+ r"""
+ Constructs a MusicGen Melody processor which wraps a Wav2Vec2 feature extractor - for raw audio waveform processing - and a T5 tokenizer into a single processor
+ class.
+
+ [`MusicgenProcessor`] offers all the functionalities of [`MusicgenMelodyFeatureExtractor`] and [`T5Tokenizer`]. See
+ [`~MusicgenProcessor.__call__`] and [`~MusicgenProcessor.decode`] for more information.
+
+ Args:
+ feature_extractor (`MusicgenMelodyFeatureExtractor`):
+ An instance of [`MusicgenMelodyFeatureExtractor`]. The feature extractor is a required input.
+ tokenizer (`T5Tokenizer`):
+ An instance of [`T5Tokenizer`]. The tokenizer is a required input.
+ """
+
+ feature_extractor_class = "MusicgenMelodyFeatureExtractor"
+ tokenizer_class = ("T5Tokenizer", "T5TokenizerFast")
+
+ def __init__(self, feature_extractor, tokenizer):
+ super().__init__(feature_extractor, tokenizer)
+
+ # Copied from transformers.models.musicgen.processing_musicgen.MusicgenProcessor.get_decoder_prompt_ids
+ def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True):
+ return self.tokenizer.get_decoder_prompt_ids(task=task, language=language, no_timestamps=no_timestamps)
+
+ def __call__(self, audio=None, text=None, **kwargs):
+ """
+ Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `audio`
+ and `kwargs` arguments to MusicgenMelodyFeatureExtractor's [`~MusicgenMelodyFeatureExtractor.__call__`] if `audio` is not
+ `None` to pre-process the audio. It also forwards the `text` and `kwargs` arguments to
+ PreTrainedTokenizer's [`~PreTrainedTokenizer.__call__`] if `text` is not `None`. Please refer to the docstring of the above two methods for more information.
+
+ Args:
+ audio (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
+ The audio or batch of audios to be prepared. Each audio can be NumPy array or PyTorch tensor. In case
+ of a NumPy array/PyTorch tensor, each audio should be a mono-stereo signal of shape (T), where T is the sample length of the audio.
+ text (`str`, `list[str]`, `list[list[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+ kwargs (*optional*):
+ Remaining dictionary of keyword arguments that will be passed to the feature extractor and/or the
+ tokenizer.
+ Returns:
+ [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **input_features** -- Audio input features to be fed to a model. Returned when `audio` is not `None`.
+ - **attention_mask** -- List of token indices specifying which tokens should be attended to by the model when `text` is not `None`.
+ When only `audio` is specified, returns the timestamps attention mask.
+ """
+
+ sampling_rate = kwargs.pop("sampling_rate", None)
+
+ if audio is None and text is None:
+ raise ValueError("You need to specify either an `audio` or `text` input to process.")
+
+ if text is not None:
+ inputs = self.tokenizer(text, **kwargs)
+ if audio is not None:
+ audio_inputs = self.feature_extractor(audio, sampling_rate=sampling_rate, **kwargs)
+
+ if text is None:
+ return audio_inputs
+ elif audio is None:
+ return inputs
+ else:
+ inputs["input_features"] = audio_inputs["input_features"]
+ return inputs
+
+ # Copied from transformers.models.musicgen.processing_musicgen.MusicgenProcessor.batch_decode with padding_mask->attention_mask
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method is used to decode either batches of audio outputs from the MusicGen model, or batches of token ids
+ from the tokenizer. In the case of decoding token ids, this method forwards all its arguments to T5Tokenizer's
+ [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information.
+ """
+ audio_values = kwargs.pop("audio", None)
+ attention_mask = kwargs.pop("attention_mask", None)
+
+ if len(args) > 0:
+ audio_values = args[0]
+ args = args[1:]
+
+ if audio_values is not None:
+ return self._decode_audio(audio_values, attention_mask=attention_mask)
+ else:
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ # Copied from transformers.models.musicgen.processing_musicgen.MusicgenProcessor.decode
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to T5Tokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the
+ docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ # Copied from transformers.models.musicgen.processing_musicgen.MusicgenProcessor._decode_audio with padding_mask->attention_mask
+ def _decode_audio(self, audio_values, attention_mask: Optional = None) -> list[np.ndarray]:
+ """
+ This method strips any padding from the audio values to return a list of numpy audio arrays.
+ """
+ audio_values = to_numpy(audio_values)
+ bsz, channels, seq_len = audio_values.shape
+
+ if attention_mask is None:
+ return list(audio_values)
+
+ attention_mask = to_numpy(attention_mask)
+
+ # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
+ # token (so that the generated audio values are **not** treated as padded tokens)
+ difference = seq_len - attention_mask.shape[-1]
+ padding_value = 1 - self.feature_extractor.padding_value
+ attention_mask = np.pad(attention_mask, ((0, 0), (0, difference)), "constant", constant_values=padding_value)
+
+ audio_values = audio_values.tolist()
+ for i in range(bsz):
+ sliced_audio = np.asarray(audio_values[i])[
+ attention_mask[i][None, :] != self.feature_extractor.padding_value
+ ]
+ audio_values[i] = sliced_audio.reshape(channels, -1)
+
+ return audio_values
+
+ def get_unconditional_inputs(self, num_samples=1, return_tensors="pt"):
+ """
+ Helper function to get null inputs for unconditional generation, enabling the model to be used without the
+ feature extractor or tokenizer.
+
+ Args:
+ num_samples (int, *optional*):
+ Number of audio samples to unconditionally generate.
+
+ Example:
+ ```python
+ >>> from transformers import MusicgenMelodyForConditionalGeneration, MusicgenMelodyProcessor
+
+ >>> model = MusicgenMelodyForConditionalGeneration.from_pretrained("facebook/musicgen-melody")
+
+ >>> # get the unconditional (or 'null') inputs for the model
+ >>> processor = MusicgenMelodyProcessor.from_pretrained("facebook/musicgen-melody")
+ >>> unconditional_inputs = processor.get_unconditional_inputs(num_samples=1)
+
+ >>> audio_samples = model.generate(**unconditional_inputs, max_new_tokens=256)
+ ```"""
+ inputs = self.tokenizer([""] * num_samples, return_tensors=return_tensors, return_attention_mask=True)
+ inputs["attention_mask"][:] = 0
+
+ return inputs
+
+
+__all__ = ["MusicgenMelodyProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..beab37f65c1a8daa20e27a816db5e832c49f6fa0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_mvp import *
+ from .modeling_mvp import *
+ from .tokenization_mvp import *
+ from .tokenization_mvp_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/configuration_mvp.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/configuration_mvp.py
new file mode 100644
index 0000000000000000000000000000000000000000..c216e53ed81ab350e7648859ced14d2dbcda8dca
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/configuration_mvp.py
@@ -0,0 +1,183 @@
+# coding=utf-8
+# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MVP model configuration"""
+
+import warnings
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MvpConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`MvpModel`]. It is used to instantiate a MVP model
+ according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the MVP [RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp)
+ architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50267):
+ Vocabulary size of the MVP model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`MvpModel`].
+ d_model (`int`, *optional*, defaults to 1024):
+ Dimensionality of the layers and the pooler layer.
+ encoder_layers (`int`, *optional*, defaults to 12):
+ Number of encoder layers.
+ decoder_layers (`int`, *optional*, defaults to 12):
+ Number of decoder layers.
+ encoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ decoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ decoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ encoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ classifier_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for classifier.
+ max_position_embeddings (`int`, *optional*, defaults to 1024):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ encoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ decoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ scale_embedding (`bool`, *optional*, defaults to `False`):
+ Scale embeddings by diving by sqrt(d_model).
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models).
+ forced_eos_token_id (`int`, *optional*, defaults to 2):
+ The id of the token to force as the last generated token when `max_length` is reached. Usually set to
+ `eos_token_id`.
+ use_prompt (`bool`, *optional*, defaults to `False`):
+ Whether or not to use prompt.
+ prompt_length (`int`, *optional*, defaults to 100):
+ The length of prompt.
+ prompt_mid_dim (`int`, *optional*, defaults to 800):
+ Dimensionality of the "intermediate" layer in prompt.
+ Example:
+
+ ```python
+ >>> from transformers import MvpConfig, MvpModel
+
+ >>> # Initializing a MVP RUCAIBox/mvp style configuration
+ >>> configuration = MvpConfig()
+
+ >>> # Initializing a model (with random weights) from the RUCAIBox/mvp style configuration
+ >>> model = MvpModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "mvp"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
+
+ def __init__(
+ self,
+ vocab_size=50267,
+ max_position_embeddings=1024,
+ encoder_layers=12,
+ encoder_ffn_dim=4096,
+ encoder_attention_heads=16,
+ decoder_layers=12,
+ decoder_ffn_dim=4096,
+ decoder_attention_heads=16,
+ encoder_layerdrop=0.0,
+ decoder_layerdrop=0.0,
+ activation_function="gelu",
+ d_model=1024,
+ dropout=0.1,
+ attention_dropout=0.0,
+ activation_dropout=0.0,
+ init_std=0.02,
+ classifier_dropout=0.0,
+ scale_embedding=False,
+ use_cache=True,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ is_encoder_decoder=True,
+ decoder_start_token_id=2,
+ forced_eos_token_id=2,
+ use_prompt=False,
+ prompt_length=100,
+ prompt_mid_dim=800,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.d_model = d_model
+ self.encoder_ffn_dim = encoder_ffn_dim
+ self.encoder_layers = encoder_layers
+ self.encoder_attention_heads = encoder_attention_heads
+ self.decoder_ffn_dim = decoder_ffn_dim
+ self.decoder_layers = decoder_layers
+ self.decoder_attention_heads = decoder_attention_heads
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.activation_function = activation_function
+ self.init_std = init_std
+ self.encoder_layerdrop = encoder_layerdrop
+ self.decoder_layerdrop = decoder_layerdrop
+ self.classifier_dropout = classifier_dropout
+ self.use_cache = use_cache
+ self.num_hidden_layers = encoder_layers
+ self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
+ self.use_prompt = use_prompt
+ self.prompt_length = prompt_length
+ self.prompt_mid_dim = prompt_mid_dim
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ is_encoder_decoder=is_encoder_decoder,
+ decoder_start_token_id=decoder_start_token_id,
+ forced_eos_token_id=forced_eos_token_id,
+ **kwargs,
+ )
+
+ if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
+ self.forced_bos_token_id = self.bos_token_id
+ warnings.warn(
+ f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
+ "The config can simply be saved and uploaded again to be fixed."
+ )
+
+
+__all__ = ["MvpConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/modeling_mvp.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/modeling_mvp.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2d1072f6a71a35387a73fdf2ffb01e86b10055a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/modeling_mvp.py
@@ -0,0 +1,1789 @@
+# coding=utf-8
+# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MVP model."""
+
+import math
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import (
+ _prepare_4d_attention_mask,
+ _prepare_4d_causal_attention_mask,
+)
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutput,
+ BaseModelOutputWithPastAndCrossAttentions,
+ CausalLMOutputWithCrossAttentions,
+ Seq2SeqLMOutput,
+ Seq2SeqModelOutput,
+ Seq2SeqQuestionAnsweringModelOutput,
+ Seq2SeqSequenceClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, logging
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_mvp import MvpConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+ """
+ Shift input ids one token to the right.
+ """
+ shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+ shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
+ shifted_input_ids[:, 0] = decoder_start_token_id
+
+ if pad_token_id is None:
+ raise ValueError("self.model.config.pad_token_id has to be defined.")
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+ return shifted_input_ids
+
+
+# Copied from transformers.models.bart.modeling_bart.BartLearnedPositionalEmbedding with Bart->Mvp
+class MvpLearnedPositionalEmbedding(nn.Embedding):
+ """
+ This module learns positional embeddings up to a fixed maximum size.
+ """
+
+ def __init__(self, num_embeddings: int, embedding_dim: int):
+ # Mvp is set up so that if padding_idx is specified then offset the embedding ids by 2
+ # and adjust num_embeddings appropriately. Other models don't have this hack
+ self.offset = 2
+ super().__init__(num_embeddings + self.offset, embedding_dim)
+
+ def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0, position_ids: torch.Tensor = None):
+ """`input_ids' shape is expected to be [bsz x seqlen]."""
+
+ if position_ids is None:
+ bsz, seq_len = input_ids.shape[:2]
+ position_ids = torch.arange(
+ past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
+ ).expand(bsz, -1)
+ else:
+ position_ids = position_ids.unsqueeze(0)
+
+ return super().forward(position_ids + self.offset)
+
+
+class MvpAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: Optional[float] = 0.0,
+ is_decoder: Optional[bool] = False,
+ bias: Optional[bool] = True,
+ layer_idx: Optional[bool] = None,
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+
+ if (self.head_dim * num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+ self.layer_idx = layer_idx
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ key_value_states: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ attn_prompt: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+
+ bsz, tgt_len, _ = hidden_states.size()
+
+ # get query proj
+ query_states = self.q_proj(hidden_states) * self.scaling
+
+ if past_key_values is not None:
+ if isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_states from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = key_value_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_states = curr_past_key_value.layers[self.layer_idx].keys
+ value_states = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_states = self.k_proj(current_states)
+ value_states = self.v_proj(current_states)
+ key_states = key_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_states, value_states = curr_past_key_value.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ if attn_prompt is not None:
+ key_states = torch.cat([attn_prompt[0].expand(bsz, -1, -1, -1), key_states], dim=2)
+ value_states = torch.cat([attn_prompt[1].expand(bsz, -1, -1, -1), value_states], dim=2)
+ if attention_mask is not None:
+ prompt_mask = torch.zeros(bsz, 1, tgt_len, attn_prompt[0].size(1)).to(attention_mask.device)
+ attention_mask = torch.cat([prompt_mask, attention_mask], dim=(-1))
+
+ proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+ query_states = query_states.view(bsz, tgt_len, self.num_heads, self.head_dim).transpose(1, 2)
+ query_states = query_states.reshape(*proj_shape)
+ key_states = key_states.reshape(*proj_shape)
+ value_states = value_states.reshape(*proj_shape)
+
+ src_len = key_states.size(1)
+ attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
+
+ if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, tgt_len, src_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
+ attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ if layer_head_mask is not None:
+ if layer_head_mask.size() != (self.num_heads,):
+ raise ValueError(
+ f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
+ f" {layer_head_mask.size()}"
+ )
+ attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+ attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+ if output_attentions:
+ # this operation is a bit awkward, but it's required to
+ # make sure that attn_weights keeps its gradient.
+ # In order to do so, attn_weights have to be reshaped
+ # twice and have to be reused in the following
+ attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+ attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
+ else:
+ attn_weights_reshaped = None
+
+ attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+ attn_output = torch.bmm(attn_probs, value_states)
+
+ if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
+ attn_output = attn_output.transpose(1, 2)
+
+ # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
+ # partitioned aross GPUs when using tensor-parallelism.
+ attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
+
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights_reshaped
+
+
+class MvpEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MvpConfig):
+ super().__init__()
+ self.embed_dim = config.d_model
+ self.self_attn = MvpAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.encoder_attention_heads,
+ dropout=config.attention_dropout,
+ )
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+ self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
+ self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ attention_mask: torch.FloatTensor,
+ layer_head_mask: torch.FloatTensor,
+ self_attn_prompt: torch.FloatTensor,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`.
+ self_attn_prompt (`torch.FloatTensor`): prompt of self attention of shape
+ `(2, encoder_attention_heads, pro_len, head_dim)`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ attn_prompt=self_attn_prompt,
+ output_attentions=output_attentions,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ if hidden_states.dtype == torch.float16 and (
+ torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
+ ):
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ return hidden_states, attn_weights
+
+
+class MvpDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: MvpConfig, layer_idx=None):
+ super().__init__()
+ self.embed_dim = config.d_model
+
+ self.self_attn = MvpAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ layer_idx=layer_idx,
+ )
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.activation_dropout = config.activation_dropout
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.encoder_attn = MvpAttention(
+ self.embed_dim,
+ config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ layer_idx=layer_idx,
+ )
+ self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
+ self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
+ self_attn_prompt: Optional[torch.Tensor] = None,
+ cross_attn_prompt: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = True,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ encoder_hidden_states (`torch.FloatTensor`):
+ cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
+ encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`.
+ cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
+ size `(decoder_attention_heads,)`.
+ self_attn_prompt (`torch.FloatTensor`): prompt of self attention of shape
+ `(2, decoder_attention_heads, pro_len, head_dim)`.
+ cross_attn_prompt (`torch.FloatTensor`): prompt of cross attention of shape
+ `(2, decoder_attention_heads, pro_len, head_dim)`.
+ past_key_values (`Tuple(torch.FloatTensor)`): cached past key and value projection states
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ attn_prompt=self_attn_prompt,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Cross-Attention Block
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+
+ hidden_states, cross_attn_weights = self.encoder_attn(
+ hidden_states=hidden_states,
+ key_value_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ layer_head_mask=cross_attn_layer_head_mask,
+ attn_prompt=cross_attn_prompt,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ return outputs
+
+
+# Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->MVP
+class MvpClassificationHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(
+ self,
+ input_dim: int,
+ inner_dim: int,
+ num_classes: int,
+ pooler_dropout: float,
+ ):
+ super().__init__()
+ self.dense = nn.Linear(input_dim, inner_dim)
+ self.dropout = nn.Dropout(p=pooler_dropout)
+ self.out_proj = nn.Linear(inner_dim, num_classes)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.dense(hidden_states)
+ hidden_states = torch.tanh(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.out_proj(hidden_states)
+ return hidden_states
+
+
+class MvpPrompt(nn.Module):
+ """Layer-wise prompt for encoder or decoder."""
+
+ def __init__(self, config, num_layers, num_heads):
+ super().__init__()
+ self.prompt_length = config.prompt_length
+ self.num_layers = num_layers
+ self.num_heads = num_heads
+ self.head_dim = config.d_model // num_heads
+ self.dropout = nn.Dropout(p=config.dropout)
+ self.prompt_embedding = nn.Embedding(config.prompt_length, config.d_model)
+ self.prompt_trans = nn.Sequential(
+ nn.Linear(config.d_model, config.prompt_mid_dim),
+ nn.GELU(),
+ nn.Linear(config.prompt_mid_dim, num_layers * 2 * config.d_model),
+ )
+
+ def forward(self, prompt_ids: torch.Tensor) -> tuple[torch.Tensor]:
+ prompt = self.prompt_trans(self.prompt_embedding(prompt_ids))
+ prompt = prompt.view(self.prompt_length, self.num_layers * 2, self.num_heads, self.head_dim)
+ prompt = self.dropout(prompt)
+ prompt = prompt.permute([1, 2, 0, 3]).split(2)
+ return prompt
+
+
+@auto_docstring
+class MvpPreTrainedModel(PreTrainedModel):
+ config: MvpConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+
+ def _init_weights(self, module):
+ std = self.config.init_std
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+ @property
+ def dummy_inputs(self):
+ pad_token = self.config.pad_token_id
+ input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
+ dummy_inputs = {
+ "attention_mask": input_ids.ne(pad_token),
+ "input_ids": input_ids,
+ }
+ return dummy_inputs
+
+
+class MvpEncoder(MvpPreTrainedModel):
+ """
+ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
+ [`MvpEncoderLayer`].
+
+ Args:
+ config: MvpConfig
+ embed_tokens (nn.Embedding): output embedding
+ use_prompt (bool): whether to use prompt
+ """
+
+ def __init__(
+ self, config: MvpConfig, embed_tokens: Optional[nn.Embedding] = None, use_prompt: Optional[bool] = False
+ ):
+ super().__init__(config)
+
+ self.dropout = config.dropout
+ self.layerdrop = config.encoder_layerdrop
+
+ embed_dim = config.d_model
+ self.padding_idx = config.pad_token_id
+ self.max_source_positions = config.max_position_embeddings
+ self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
+
+ if embed_tokens is not None:
+ self.embed_tokens = embed_tokens
+ else:
+ self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
+
+ self.embed_positions = MvpLearnedPositionalEmbedding(
+ config.max_position_embeddings,
+ embed_dim,
+ )
+ self.layers = nn.ModuleList([MvpEncoderLayer(config) for _ in range(config.encoder_layers)])
+ self.layernorm_embedding = nn.LayerNorm(embed_dim)
+
+ self.use_prompt = use_prompt
+ if use_prompt:
+ self.prompt_length = config.prompt_length
+ self.self_attn_prompt = MvpPrompt(
+ config,
+ config.encoder_layers,
+ config.encoder_attention_heads,
+ )
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, BaseModelOutput]:
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input = input_ids
+ input_shape = input.shape
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ input = inputs_embeds[:, :, -1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+ embed_pos = self.embed_positions(input)
+
+ hidden_states = inputs_embeds + embed_pos
+ hidden_states = self.layernorm_embedding(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ # layer-wise prompt
+ if self.use_prompt:
+ prompt_ids = torch.arange(self.prompt_length).to(self.device)
+ self_attn_prompt = self.self_attn_prompt(prompt_ids)
+
+ # expand attention_mask
+ if attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ if head_mask.size()[0] != (len(self.layers)):
+ raise ValueError(
+ f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ to_drop = False
+ if self.training:
+ dropout_probability = torch.rand([])
+ if dropout_probability < self.layerdrop: # skip the layer
+ to_drop = True
+
+ if to_drop:
+ layer_outputs = (None, None)
+ else:
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ self_attn_prompt=(self_attn_prompt[idx] if self.use_prompt else None),
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+ return BaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+ )
+
+
+class MvpDecoder(MvpPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MvpDecoderLayer`]
+
+ Args:
+ config: MvpConfig
+ embed_tokens (nn.Embedding): output embedding
+ use_prompt (bool): whether to use prompt
+ """
+
+ def __init__(
+ self, config: MvpConfig, embed_tokens: Optional[nn.Embedding] = None, use_prompt: Optional[bool] = False
+ ):
+ super().__init__(config)
+ self.dropout = config.dropout
+ self.layerdrop = config.decoder_layerdrop
+ self.padding_idx = config.pad_token_id
+ self.max_target_positions = config.max_position_embeddings
+ self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+
+ if embed_tokens is not None:
+ self.embed_tokens = embed_tokens
+ else:
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)
+
+ self.embed_positions = MvpLearnedPositionalEmbedding(
+ config.max_position_embeddings,
+ config.d_model,
+ )
+ self.layers = nn.ModuleList([MvpDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)])
+ self.layernorm_embedding = nn.LayerNorm(config.d_model)
+
+ self.use_prompt = use_prompt
+ if use_prompt:
+ self.prompt_length = config.prompt_length
+ self.self_attn_prompt = MvpPrompt(
+ config,
+ config.decoder_layers,
+ config.decoder_attention_heads,
+ )
+ self.cross_attn_prompt = MvpPrompt(
+ config,
+ config.decoder_layers,
+ config.decoder_attention_heads,
+ )
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, BaseModelOutputWithPastAndCrossAttentions]:
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
+ cross-attention on hidden heads. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+ shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+ cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+ that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+ all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ input = input_ids
+ input_shape = input_ids.shape
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ input = inputs_embeds[:, :, -1]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ if use_cache and past_key_values is None:
+ past_key_values = (
+ EncoderDecoderCache(DynamicCache(), DynamicCache())
+ if encoder_hidden_states is not None
+ else DynamicCache()
+ )
+ if use_cache and isinstance(past_key_values, tuple):
+ logger.warning_once(
+ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
+ "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
+ "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
+ )
+ past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
+
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+ attention_mask = _prepare_4d_causal_attention_mask(
+ attention_mask, input_shape, inputs_embeds, past_key_values_length
+ )
+
+ # expand encoder attention mask
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask(
+ encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+ )
+
+ # embed positions
+ positions = self.embed_positions(input, past_key_values_length)
+
+ hidden_states = inputs_embeds + positions
+ hidden_states = self.layernorm_embedding(hidden_states)
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ # layer-wise prompt
+ if self.use_prompt:
+ prompt_ids = torch.arange(self.prompt_length).to(self.device)
+ self_attn_prompt = self.self_attn_prompt(prompt_ids)
+ cross_attn_prompt = self.cross_attn_prompt(prompt_ids)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
+
+ # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
+ for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
+ if attn_mask is not None:
+ if attn_mask.size()[0] != (len(self.layers)):
+ raise ValueError(
+ f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+ if self.training:
+ dropout_probability = torch.rand([])
+ if dropout_probability < self.layerdrop:
+ continue
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask,
+ encoder_hidden_states, # as positional argument for gradient checkpointing
+ encoder_attention_mask=encoder_attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
+ self_attn_prompt=(self_attn_prompt[idx] if self.use_prompt else None),
+ cross_attn_prompt=(cross_attn_prompt[idx] if self.use_prompt else None),
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ )
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if encoder_hidden_states is not None:
+ all_cross_attentions += (layer_outputs[2],)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions]
+ if v is not None
+ )
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ )
+
+
+@auto_docstring
+class MvpModel(MvpPreTrainedModel):
+ _keys_to_ignore_on_load_unexpected = ["final_logits_bias"]
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+ def __init__(self, config: MvpConfig):
+ super().__init__(config)
+
+ padding_idx, vocab_size = config.pad_token_id, config.vocab_size
+ self.use_prompt = config.use_prompt
+ self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
+
+ self.encoder = MvpEncoder(config, self.shared, config.use_prompt)
+ self.decoder = MvpDecoder(config, self.shared, config.use_prompt)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, value):
+ self.shared = value
+ self.encoder.embed_tokens = self.shared
+ self.decoder.embed_tokens = self.shared
+
+ def get_encoder(self):
+ return self.encoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ def set_lightweight_tuning(self):
+ assert self.use_prompt, "If you want to use lightweight tuning, make sure that `use_prompt=True`."
+
+ self.requires_grad_(False)
+ self.encoder.self_attn_prompt.requires_grad_(True)
+ self.decoder.self_attn_prompt.requires_grad_(True)
+ self.decoder.cross_attn_prompt.requires_grad_(True)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[list[torch.FloatTensor]] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, Seq2SeqModelOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ Mvp uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should read [`modeling_mvp._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more
+ information on the default strategy.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ """
+ # different to other models, Mvp automatically creates decoder_input_ids from
+ # input_ids if no decoder_input_ids are provided
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ if input_ids is None:
+ raise ValueError(
+ "If no `decoder_input_ids` or `decoder_inputs_embeds` are "
+ "passed, `input_ids` cannot be `None`. Please pass either "
+ "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
+ )
+
+ decoder_input_ids = shift_tokens_right(
+ input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
+ )
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
+ elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+ encoder_outputs = BaseModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ )
+
+ # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return Seq2SeqModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ cross_attentions=decoder_outputs.cross_attentions,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The MVP Model with a language modeling head. Can be used for various text generation tasks.
+ """
+)
+class MvpForConditionalGeneration(MvpPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
+
+ def __init__(self, config: MvpConfig):
+ super().__init__(config)
+ self.model = MvpModel(config)
+ self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
+ self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_encoder(self):
+ return self.model.get_encoder()
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def resize_token_embeddings(
+ self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
+ ) -> nn.Embedding:
+ new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
+ self._resize_final_logits_bias(new_num_tokens)
+ return new_embeddings
+
+ def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
+ old_num_tokens = self.final_logits_bias.shape[-1]
+ if new_num_tokens <= old_num_tokens:
+ new_bias = self.final_logits_bias[:, :new_num_tokens]
+ else:
+ extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
+ new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
+ self.register_buffer("final_logits_bias", new_bias)
+
+ def set_lightweight_tuning(self):
+ self.model.set_lightweight_tuning()
+ self.lm_head.requires_grad_(False)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[list[torch.FloatTensor]] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, Seq2SeqLMOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ Mvp uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should read [`modeling_mvp._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more
+ information on the default strategy.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example of summarization:
+
+ Fine-tuning a model
+ ```python
+ >>> import torch
+ >>> from transformers import AutoTokenizer, MvpForConditionalGeneration
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("RUCAIBox/mvp")
+ >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp")
+
+ >>> inputs = tokenizer(
+ ... "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.",
+ ... return_tensors="pt",
+ ... )
+ >>> labels = tokenizer("Bad Reasons To Quit Your Job", return_tensors="pt")["input_ids"]
+
+ >>> loss = model(**inputs, labels=labels).loss
+ >>> loss.backward()
+ ```
+
+ Inference after the model fine-tuned
+ ```python
+ >>> with torch.no_grad():
+ ... generated_ids = model.generate(**inputs)
+
+ >>> generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+ ```
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if labels is not None:
+ if use_cache:
+ logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
+ use_cache = False
+ if decoder_input_ids is None and decoder_inputs_embeds is None:
+ decoder_input_ids = shift_tokens_right(
+ labels, self.config.pad_token_id, self.config.decoder_start_token_id
+ )
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ encoder_outputs=encoder_outputs,
+ decoder_attention_mask=decoder_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+ lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (lm_logits,) + outputs[1:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return Seq2SeqLMOutput(
+ loss=masked_lm_loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+ def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
+ return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
+
+@auto_docstring(
+ custom_intro="""
+ Mvp model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
+ tasks.
+ """
+)
+class MvpForSequenceClassification(MvpPreTrainedModel):
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+ def __init__(self, config: MvpConfig, **kwargs):
+ super().__init__(config, **kwargs)
+ self.model = MvpModel(config)
+ self.classification_head = MvpClassificationHead(
+ config.d_model,
+ config.d_model,
+ config.num_labels,
+ config.classifier_dropout,
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_lightweight_tuning(self):
+ self.model.set_lightweight_tuning()
+ self.classification_head.requires_grad_(False)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, Seq2SeqSequenceClassifierOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ Mvp uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should read [`modeling_mvp._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more
+ information on the default strategy.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+ Example of single-label classification:
+
+ Fine-tuning a model on `num_labels` classes
+ ```python
+ >>> import torch
+ >>> from transformers import AutoTokenizer, MvpForSequenceClassification
+
+ >>> num_labels = 2 # for example, this is a binary classification task
+ >>> tokenizer = AutoTokenizer.from_pretrained("RUCAIBox/mvp")
+ >>> model = MvpForSequenceClassification.from_pretrained("RUCAIBox/mvp", num_labels=num_labels)
+
+ >>> inputs = tokenizer("Classify: Hello, my dog is cute", return_tensors="pt")
+ >>> labels = torch.tensor(1) # the real label for inputs
+
+ >>> loss = model(**inputs, labels=labels).loss
+ >>> loss.backward()
+ ```
+
+ Inference after the model fine-tuned
+ ```python
+ >>> with torch.no_grad():
+ ... logits = model(**inputs).logits
+
+ >>> predicted_class_id = logits.argmax()
+ ```
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ if labels is not None:
+ use_cache = False
+
+ if input_ids is None and inputs_embeds is not None:
+ raise NotImplementedError(
+ f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
+ )
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ encoder_outputs=encoder_outputs,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = outputs[0] # last hidden state
+
+ eos_mask = input_ids.eq(self.config.eos_token_id).to(hidden_states.device)
+
+ if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
+ raise ValueError("All examples must have the same number of tokens.")
+ sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
+ :, -1, :
+ ]
+ logits = self.classification_head(sentence_representation)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.config.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.config.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return Seq2SeqSequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+@auto_docstring
+class MvpForQuestionAnswering(MvpPreTrainedModel):
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ config.num_labels = 2
+ self.num_labels = config.num_labels
+
+ self.model = MvpModel(config)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_lightweight_tuning(self):
+ self.model.set_lightweight_tuning()
+ self.qa_outputs.requires_grad_(False)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[list[torch.FloatTensor]] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple, Seq2SeqQuestionAnsweringModelOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ Mvp uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values`
+ is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).
+
+ For translation and summarization training, `decoder_input_ids` should be provided. If no
+ `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
+ for denoising pre-training following the paper.
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+
+ If you want to change padding behavior, you should read [`modeling_mvp._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more
+ information on the default strategy.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ Example:
+
+ Fine-tuning a model for extrative question answering, and our model also supports generative question answering
+ using `BartForConditionalGeneration`
+ ```python
+ >>> import torch
+ >>> from transformers import AutoTokenizer, MvpForQuestionAnswering
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("RUCAIBox/mvp")
+ >>> model = MvpForQuestionAnswering.from_pretrained("RUCAIBox/mvp")
+
+ >>> inputs = tokenizer(
+ ... "Answer the following question: Who was Jim Henson? [SEP] Jim Henson was a nice puppet",
+ ... return_tensors="pt",
+ ... )
+ >>> target_start_index = torch.tensor([18])
+ >>> target_end_index = torch.tensor([19])
+
+ >>> loss = model(**inputs, start_positions=target_start_index, end_positions=target_end_index).loss
+ >>> loss.backward()
+ ```
+
+ Inference after the model fine-tuned
+ ```python
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+
+ >>> answer_start_index = outputs.start_logits.argmax()
+ >>> answer_end_index = outputs.end_logits.argmax()
+
+ >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
+ >>> predict_answer = tokenizer.decode(predict_answer_tokens)
+ ```
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ if start_positions is not None and end_positions is not None:
+ use_cache = False
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ decoder_attention_mask=decoder_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ encoder_outputs=encoder_outputs,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (
+ start_logits,
+ end_logits,
+ ) + outputs[1:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return Seq2SeqQuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ past_key_values=outputs.past_key_values,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ decoder_attentions=outputs.decoder_attentions,
+ cross_attentions=outputs.cross_attentions,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ )
+
+
+# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->Mvp
+class MvpDecoderWrapper(MvpPreTrainedModel):
+ """
+ This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
+ used in combination with the [`EncoderDecoderModel`] framework.
+ """
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.decoder = MvpDecoder(config)
+
+ def forward(self, *args, **kwargs):
+ return self.decoder(*args, **kwargs)
+
+
+class MvpForCausalLM(MvpPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ config.is_decoder = True
+ config.is_encoder_decoder = False
+ super().__init__(config)
+ self.model = MvpDecoderWrapper(config)
+
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.decoder.embed_tokens = value
+
+ def set_decoder(self, decoder):
+ self.model.decoder = decoder
+
+ def get_decoder(self):
+ return self.model.decoder
+
+ def set_lightweight_tuning(self):
+ self.model.set_lightweight_tuning()
+ self.lm_head.requires_grad_(False)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
+ r"""
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, MvpForCausalLM
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("RUCAIBox/mvp")
+ >>> model = MvpForCausalLM.from_pretrained("RUCAIBox/mvp", add_cross_attention=False)
+
+ >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+ >>> outputs = model(**inputs)
+
+ >>> logits = outputs.logits
+ >>> list(logits.shape)
+ [1, 8, 50267]
+ ```"""
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model.decoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ head_mask=head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ logits = self.lm_head(outputs[0])
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithCrossAttentions(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ cross_attentions=outputs.cross_attentions,
+ )
+
+
+__all__ = [
+ "MvpForCausalLM",
+ "MvpForConditionalGeneration",
+ "MvpForQuestionAnswering",
+ "MvpForSequenceClassification",
+ "MvpModel",
+ "MvpPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/tokenization_mvp.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/tokenization_mvp.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6039df2dc02564378f6f83ef1770ded9000d31f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/tokenization_mvp.py
@@ -0,0 +1,394 @@
+# coding=utf-8
+# Copyright 2022 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from functools import lru_cache
+from typing import Optional
+
+import regex as re
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
+
+# See all MVP models at https://huggingface.co/models?filter=mvp
+
+
+@lru_cache
+def bytes_to_unicode():
+ """
+ Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
+ characters the bpe code barfs on.
+
+ The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
+ if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
+ decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
+ tables between utf-8 bytes and unicode strings.
+ """
+ bs = (
+ list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
+ )
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8 + n)
+ n += 1
+ cs = [chr(n) for n in cs]
+ return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+ """
+ Return set of symbol pairs in a word.
+
+ Word is represented as tuple of symbols (symbols being variable-length strings).
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+class MvpTokenizer(PreTrainedTokenizer):
+ """
+ Constructs a MVP tokenizer, which is smilar to the RoBERTa tokenizer, using byte-level Byte-Pair-Encoding.
+
+ This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
+ be encoded differently whether it is at the beginning of the sentence (without space) or not:
+
+ ```python
+ >>> from transformers import MvpTokenizer
+
+ >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp")
+ >>> tokenizer("Hello world")["input_ids"]
+ [0, 31414, 232, 2]
+
+ >>> tokenizer(" Hello world")["input_ids"]
+ [0, 20920, 232, 2]
+ ```
+
+ You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
+ call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
+
+
+
+ When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
+
+
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ errors (`str`, *optional*, defaults to `"replace"`):
+ Paradigm to follow when decoding bytes to UTF-8. See
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ add_prefix_space (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. (MVP tokenizer detect beginning of words by the preceding space).
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ def __init__(
+ self,
+ vocab_file,
+ merges_file,
+ errors="replace",
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ add_prefix_space=False,
+ **kwargs,
+ ):
+ bos_token = AddedToken(bos_token, special=True) if isinstance(bos_token, str) else bos_token
+ eos_token = AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token
+ sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token
+ cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token
+ unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token
+
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
+ with open(vocab_file, encoding="utf-8") as vocab_handle:
+ self.encoder = json.load(vocab_handle)
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ self.errors = errors # how to handle errors in decoding
+ self.byte_encoder = bytes_to_unicode()
+ self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+ with open(merges_file, encoding="utf-8") as merges_handle:
+ bpe_merges = merges_handle.read().split("\n")[1:-1]
+ bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
+ self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+ self.cache = {}
+ self.add_prefix_space = add_prefix_space
+
+ # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
+ self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
+
+ super().__init__(
+ errors=errors,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ add_prefix_space=add_prefix_space,
+ **kwargs,
+ )
+
+ @property
+ def vocab_size(self):
+ return len(self.encoder)
+
+ def get_vocab(self):
+ vocab = self.encoder.copy()
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ def bpe(self, token):
+ if token in self.cache:
+ return self.cache[token]
+ word = tuple(token)
+ pairs = get_pairs(word)
+
+ if not pairs:
+ return token
+
+ while True:
+ bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+ if bigram not in self.bpe_ranks:
+ break
+ first, second = bigram
+ new_word = []
+ i = 0
+ while i < len(word):
+ try:
+ j = word.index(first, i)
+ except ValueError:
+ new_word.extend(word[i:])
+ break
+ else:
+ new_word.extend(word[i:j])
+ i = j
+
+ if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+ new_word.append(first + second)
+ i += 2
+ else:
+ new_word.append(word[i])
+ i += 1
+ new_word = tuple(new_word)
+ word = new_word
+ if len(word) == 1:
+ break
+ else:
+ pairs = get_pairs(word)
+ word = " ".join(word)
+ self.cache[token] = word
+ return word
+
+ def _tokenize(self, text):
+ """Tokenize a string."""
+ bpe_tokens = []
+ for token in re.findall(self.pat, text):
+ token = "".join(
+ self.byte_encoder[b] for b in token.encode("utf-8")
+ ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
+ bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
+ return bpe_tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.decoder.get(index)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ text = "".join(tokens)
+ text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
+ return text
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ merge_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
+ )
+
+ with open(vocab_file, "w", encoding="utf-8") as f:
+ f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+ index = 0
+ with open(merge_file, "w", encoding="utf-8") as writer:
+ writer.write("#version: 0.2\n")
+ for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
+ " Please check that the tokenizer is not corrupted!"
+ )
+ index = token_index
+ writer.write(" ".join(bpe_tokens) + "\n")
+ index += 1
+
+ return vocab_file, merge_file
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A MVP sequence has the following format:
+
+ - single sequence: ` X `
+ - pair of sequences: ` A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+ cls = [self.cls_token_id]
+ sep = [self.sep_token_id]
+ return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is None:
+ return [1] + ([0] * len(token_ids_0)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. MVP does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
+ add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
+ if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
+ text = " " + text
+ return (text, kwargs)
+
+
+__all__ = ["MvpTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/tokenization_mvp_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/tokenization_mvp_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca0bc6b165f7687496999943d8e4293bd6458396
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/mvp/tokenization_mvp_fast.py
@@ -0,0 +1,274 @@
+# coding=utf-8
+# Copyright 2022 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+from typing import Optional
+
+from tokenizers import processors
+
+from ...tokenization_utils_base import AddedToken, BatchEncoding
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import logging
+from .tokenization_mvp import MvpTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
+
+# See all MVP models at https://huggingface.co/models?filter=mvp
+
+
+class MvpTokenizerFast(PreTrainedTokenizerFast):
+ r"""
+ Construct a "fast" MVP tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer,
+ using byte-level Byte-Pair-Encoding.
+
+ This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
+ be encoded differently whether it is at the beginning of the sentence (without space) or not:
+
+ ```python
+ >>> from transformers import MvpTokenizerFast
+
+ >>> tokenizer = MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")
+ >>> tokenizer("Hello world")["input_ids"]
+ [0, 31414, 232, 2]
+
+ >>> tokenizer(" Hello world")["input_ids"]
+ [0, 20920, 232, 2]
+ ```
+
+ You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
+ call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
+
+
+
+ When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
+
+
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ errors (`str`, *optional*, defaults to `"replace"`):
+ Paradigm to follow when decoding bytes to UTF-8. See
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ add_prefix_space (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+ other word. (MVP tokenizer detect beginning of words by the preceding space).
+ trim_offsets (`bool`, *optional*, defaults to `True`):
+ Whether the post processing step should trim offsets to avoid including whitespaces.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+ slow_tokenizer_class = MvpTokenizer
+
+ def __init__(
+ self,
+ vocab_file=None,
+ merges_file=None,
+ tokenizer_file=None,
+ errors="replace",
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ add_prefix_space=False,
+ trim_offsets=True,
+ **kwargs,
+ ):
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+ sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
+ cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+ super().__init__(
+ vocab_file,
+ merges_file,
+ tokenizer_file=tokenizer_file,
+ errors=errors,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ add_prefix_space=add_prefix_space,
+ trim_offsets=trim_offsets,
+ **kwargs,
+ )
+
+ # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
+ tokenizer_component = "post_processor"
+ tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None)
+ if tokenizer_component_instance:
+ state = json.loads(tokenizer_component_instance.__getstate__())
+
+ # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
+ if "sep" in state:
+ state["sep"] = tuple(state["sep"])
+ if "cls" in state:
+ state["cls"] = tuple(state["cls"])
+
+ changes_to_apply = False
+
+ if state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
+ state["add_prefix_space"] = add_prefix_space
+ changes_to_apply = True
+
+ if state.get("trim_offsets", trim_offsets) != trim_offsets:
+ state["trim_offsets"] = trim_offsets
+ changes_to_apply = True
+
+ if changes_to_apply:
+ component_class = getattr(processors, state.pop("type"))
+ new_value = component_class(**state)
+ setattr(self.backend_tokenizer, tokenizer_component, new_value)
+
+ @property
+ def mask_token(self) -> str:
+ """
+ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
+ having been set.
+
+ MVP tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
+ comprise the space before the **.
+ """
+ if self._mask_token is None:
+ if self.verbose:
+ logger.error("Using mask_token, but it is not set yet.")
+ return None
+ return str(self._mask_token)
+
+ @mask_token.setter
+ def mask_token(self, value):
+ """
+ Overriding the default behavior of the mask token to have it eat the space before it.
+
+ This is needed to preserve backward compatibility with all the previously used models based on Mvp.
+ """
+ # Mask token behave like a normal word, i.e. include the space before it
+ # So we set lstrip to True
+ value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value
+ self._mask_token = value
+
+ def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
+ is_split_into_words = kwargs.get("is_split_into_words", False)
+
+ if is_split_into_words and not self.add_prefix_space:
+ raise ValueError(
+ f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
+ "to use it with pretokenized inputs."
+ )
+
+ return super()._batch_encode_plus(*args, **kwargs)
+
+ def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
+ is_split_into_words = kwargs.get("is_split_into_words", False)
+
+ if is_split_into_words and not self.add_prefix_space:
+ raise ValueError(
+ f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
+ "to use it with pretokenized inputs."
+ )
+
+ return super()._encode_plus(*args, **kwargs)
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+ return tuple(files)
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
+ if token_ids_1 is None:
+ return output
+
+ return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. MVP does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+
+__all__ = ["MvpTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/myt5/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/myt5/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..65c8190ee6d94d7f63727202392a625d04aecdb4
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/myt5/__init__.py
@@ -0,0 +1,26 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .tokenization_myt5 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/myt5/convert_myt5_original_tf_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/myt5/convert_myt5_original_tf_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..39653e4b1c77d0383e11645c4763b3ef53e9d851
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/myt5/convert_myt5_original_tf_checkpoint_to_pytorch.py
@@ -0,0 +1,60 @@
+# coding=utf-8
+# Copyright 2024 The MyT5 authors and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert MyT5 checkpoint."""
+
+import argparse
+
+from transformers import T5Config, T5ForConditionalGeneration, load_tf_weights_in_t5
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+
+
+# Copied from transformers.models.t5.convert_t5_original_tf_checkpoint_to_pytorch.convert_tf_checkpoint_to_pytorch
+def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
+ # Initialise PyTorch model
+ config = T5Config.from_json_file(config_file)
+ print(f"Building PyTorch model from configuration: {config}")
+ model = T5ForConditionalGeneration(config)
+
+ # Load weights from tf checkpoint
+ load_tf_weights_in_t5(model, config, tf_checkpoint_path)
+
+ # Save pytorch-model
+ print(f"Save PyTorch model to {pytorch_dump_path}")
+ model.save_pretrained(pytorch_dump_path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
+ )
+ parser.add_argument(
+ "--config_file",
+ default=None,
+ type=str,
+ required=True,
+ help=(
+ "The config json file corresponding to the pre-trained MyT5 model. \nThis specifies the model architecture."
+ ),
+ )
+ parser.add_argument(
+ "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
+ )
+ args = parser.parse_args()
+ convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/myt5/tokenization_myt5.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/myt5/tokenization_myt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..251e3d602b993d8335927cdbeed5518800f2bfb1
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/myt5/tokenization_myt5.py
@@ -0,0 +1,380 @@
+# coding=utf-8
+# Copyright 2024
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization class for model MyT5."""
+
+import json
+import os
+import warnings
+from collections import defaultdict
+from typing import Optional, Union
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "byte_maps.json"}
+
+
+class ByteRewriter:
+ """
+ Byte rewriter class for MyT5 tokenizer.
+ This class is used to rewrite bytes using a hash tree. The hash tree is constructed from a set of rewriting rules.
+
+ Args:
+ rewriting_rules (`str` or `dict[str, str]`):
+ A path to a json file containing the rewriting rules or a dictionary containing the rewriting rules.
+
+ """
+
+ LEAF = "[LEAF]"
+
+ def __init__(self, rewriting_rules: Union[str, dict[str, str]]):
+ if isinstance(rewriting_rules, str):
+ with open(rewriting_rules, "r") as f:
+ rewriting_rules = json.load(f)
+ elif not isinstance(rewriting_rules, dict):
+ raise TypeError(
+ f"rewriting_rules should be either a path to json file or a dict, got {type(rewriting_rules)}"
+ )
+
+ self.hash_tree = self.construct_hash_tree(rewriting_rules)
+ reverse_rewriting_rules = {v: k for k, v in rewriting_rules.items()}
+ self.reverse_hash_tree = self.construct_hash_tree(reverse_rewriting_rules)
+
+ def add_leaf(self, hash_tree: dict[str, Union[dict, list[str]]], byte_in_sequence: str, byte_out_sequence: str):
+ """
+ Add a leaf with the output byte sequence to the hash tree.
+ """
+ byte_in_list = byte_in_sequence.split(" ")
+ byte_out_list = byte_out_sequence.split(" ")
+
+ tree_pointer = hash_tree
+ for b in byte_in_list:
+ if b not in tree_pointer:
+ tree_pointer[b] = {}
+ tree_pointer = tree_pointer[b]
+
+ tree_pointer[self.LEAF] = byte_out_list
+
+ def construct_hash_tree(self, rewriting_rules: dict[str, str]) -> dict[str, Union[dict, list[str]]]:
+ """
+ Construct a hash tree for rewritten byte sequences.
+ """
+ hash_tree = defaultdict(dict)
+ for b in (f"{x:02x}" for x in range(256)):
+ hash_tree[b][self.LEAF] = [b]
+
+ for in_sequence, out_sequence in rewriting_rules.items():
+ self.add_leaf(hash_tree, in_sequence, out_sequence)
+
+ return hash_tree
+
+ def search_hash_tree(self, byte_sequence: list[str]) -> Union[None, list[str]]:
+ """
+ Search the hash tree and return the rewritten byte sequence if found.
+ """
+ tree_pointer = self.hash_tree
+ for b in byte_sequence:
+ if b in tree_pointer:
+ tree_pointer = tree_pointer[b]
+ else:
+ return None
+
+ return tree_pointer[self.LEAF]
+
+ def rewrite_bytes(self, in_bytes: list[str], reverse=False) -> list[str]:
+ """
+ Rewrite a sequence of bytes using the hash tree.
+
+ Args:
+ in_bytes (`list[str]`): A list of bytes to be rewritten.
+ reverse (`bool`): If True, decoding is performed with the reverse hash tree.
+ Returns:
+ `list[str]`: The rewritten byte sequence.
+ """
+ out_bytes = []
+ b_start = 0
+ b_end = 0
+
+ while b_start < len(in_bytes):
+ tree_pointer = self.hash_tree if not reverse else self.reverse_hash_tree
+ for j in range(b_start, len(in_bytes)):
+ b = in_bytes[j]
+ if b in tree_pointer:
+ tree_pointer = tree_pointer[b]
+ elif j == b_start:
+ cur_leaf = [b]
+ b_end = j
+ break
+ else:
+ break
+ if self.LEAF in tree_pointer:
+ cur_leaf = tree_pointer[self.LEAF]
+ b_end = j
+ out_bytes.extend(cur_leaf)
+ b_start = b_end + 1
+
+ return out_bytes
+
+
+class MyT5Tokenizer(PreTrainedTokenizer):
+ """
+ Construct a MyT5 tokenizer.
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`): The file containing the byte rewriting rules.
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ extra_ids (`int`, *optional*, defaults to 125):
+ Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
+ accessible as "" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
+ indexed from the end of the vocabulary up to beginning ("" is the last token in the vocabulary
+ like in ByT5 preprocessing see
+ [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
+ additional_special_tokens (`list[str]`, *optional*):
+ Additional special tokens used by the tokenizer.
+ """
+
+ model_input_names = ["input_ids", "attention_mask"]
+ vocab_files_names = VOCAB_FILES_NAMES
+
+ def __init__(
+ self,
+ vocab_file,
+ eos_token=" ",
+ unk_token="",
+ pad_token="",
+ extra_ids=125,
+ additional_special_tokens=None,
+ **kwargs,
+ ) -> None:
+ # Add extra_ids to the special token list
+ if extra_ids > 0 and additional_special_tokens is None:
+ additional_special_tokens = [f"" for i in range(extra_ids)]
+ elif extra_ids > 0 and additional_special_tokens is not None and len(additional_special_tokens) > 0:
+ # Check that we have the right number of extra_id special tokens
+ extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
+ if extra_tokens != extra_ids:
+ raise ValueError(
+ f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
+ " provided to MyT5Tokenizer. In this case the additional_special_tokens must include the"
+ " extra_ids tokens"
+ )
+
+ pad_token = AddedToken(pad_token, lstrip=True, rstrip=True) if isinstance(pad_token, str) else pad_token
+ eos_token = AddedToken(eos_token, lstrip=True, rstrip=True) if isinstance(eos_token, str) else eos_token
+ unk_token = AddedToken(unk_token, lstrip=True, rstrip=True) if isinstance(unk_token, str) else unk_token
+ # unk token needs to be in the vocab with correct index
+ self._added_tokens_decoder = {0: pad_token, 1: eos_token, 2: unk_token}
+ self.offset = len(self._added_tokens_decoder)
+ self._utf_vocab_size = 2**8 # utf is 8 bits
+
+ # Load byte maps
+ self.byte_maps = json.load(open(vocab_file, "r"))
+
+ self.decompose_rewriter = ByteRewriter(self.byte_maps["decompose_map"])
+ self.merge_rewriter = ByteRewriter(self.byte_maps["merge_map"])
+
+ super().__init__(
+ eos_token=eos_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ extra_ids=0,
+ additional_special_tokens=additional_special_tokens,
+ **kwargs,
+ )
+
+ @property
+ def vocab_size(self):
+ return self._utf_vocab_size
+
+ # Copied from transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.get_vocab
+ def get_vocab(self):
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.offset)}
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ # Copied from transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.get_special_tokens_mask
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ # normal case: some special tokens
+ if token_ids_1 is None:
+ return ([0] * len(token_ids_0)) + [1]
+ return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+
+ def _add_eos_if_not_present(self, token_ids: list[int]) -> list[int]:
+ """Do not add eos again if user already added it."""
+ if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
+ warnings.warn(
+ f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
+ " eos tokens being added."
+ )
+ return token_ids
+ else:
+ return token_ids + [self.eos_token_id]
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. MyT5 does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+ """
+ eos = [self.eos_token_id]
+
+ if token_ids_1 is None:
+ return len(token_ids_0 + eos) * [0]
+ return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
+
+ # Copied from transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.build_inputs_with_special_tokens
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. A sequence has the following format:
+
+ - single sequence: `X `
+ - pair of sequences: `A B `
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ token_ids_0 = self._add_eos_if_not_present(token_ids_0)
+ if token_ids_1 is None:
+ return token_ids_0
+ else:
+ token_ids_1 = self._add_eos_if_not_present(token_ids_1)
+ return token_ids_0 + token_ids_1
+
+ def _tokenize(self, text: str, **kwargs) -> list[str]:
+ """Take as input a string and return a list of strings (tokens) for words/sub-words.
+ Represents tokens in two character hex format"""
+
+ tokens = [f"{i:02x}" for i in text.encode("utf-8")]
+ tokens = self.morphological_encode(tokens)
+ return tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+
+ if len(token) != 2:
+ token_id = None
+ else:
+ token_id = int(token, 16) + self.offset
+
+ return token_id
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ token = f"{index - self.offset:02x}"
+ return token
+
+ def morphological_encode(self, indices: list[str]) -> list[str]:
+ # Decompose and merge morphological sequences
+ indices = self.decompose_rewriter.rewrite_bytes(indices, reverse=False)
+ indices = self.merge_rewriter.rewrite_bytes(indices, reverse=False)
+ return indices
+
+ def morphological_decode(self, indices: list[str]) -> list[str]:
+ # Demerge and compose morphological sequences
+ indices = self.merge_rewriter.rewrite_bytes(indices, reverse=True)
+ indices = self.decompose_rewriter.rewrite_bytes(indices, reverse=True)
+ return indices
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ bstring = b""
+
+ out_tokens = []
+ for token in tokens:
+ if token in self.added_tokens_decoder:
+ out_tokens.append(self.added_tokens_decoder[token])
+ elif token in self.added_tokens_encoder:
+ out_tokens.append(token)
+ else:
+ out_tokens.append(token)
+
+ out_tokens = self.morphological_decode(out_tokens)
+ _added_tokens = set(self.added_tokens_decoder.values()) | set(self.added_tokens_encoder)
+ for token in out_tokens:
+ if token in _added_tokens:
+ bstring += bytes(token, "utf-8")
+ else:
+ bstring += bytes.fromhex(token)
+ string = bstring.decode("utf-8", errors="ignore")
+ return string
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if os.path.isdir(save_directory):
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ else:
+ vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
+ with open(vocab_file, "w", encoding="utf-8") as writer:
+ writer.write(json.dumps(self.byte_maps, indent=2, ensure_ascii=False))
+ return (vocab_file,)
+
+
+__all__ = ["MyT5Tokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bd66ddd622e067fc6b2863ffdc745a9e09f2307
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_nemotron import *
+ from .modeling_nemotron import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/configuration_nemotron.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/configuration_nemotron.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a15bb96b0684417d11b669c2b2d97fe72194f9b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/configuration_nemotron.py
@@ -0,0 +1,156 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc. team. All rights reserved.
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Nemotron model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...modeling_rope_utils import rope_config_validation
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class NemotronConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`NemotronModel`]. It is used to instantiate an Nemotron
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the Nemotron-8B.
+ e.g. [nvidia/nemotron-3-8b-base-4k-hf](https://huggingface.co/nvidia/nemotron-3-8b-base-4k-hf).
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 256000):
+ Vocabulary size of the Nemotron model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`NemotronModel`]
+ hidden_size (`int`, *optional*, defaults to 6144):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 24576):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 48):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ head_dim (`int`, *optional*):
+ Projection weights dimension in multi-head attention. Set to hidden_size // num_attention_heads if None
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+ `num_attention_heads`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 4096):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.0134):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 2):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 3):
+ End of stream token id.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ partial_rotary_factor (`float`, *optional*, defaults to 0.5): Percentage of the query and keys which will have rotary embedding.
+ attention_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ mlp_bias (`bool`, *optional*, defaults to `False`):
+ Whether to use a bias in up_proj and down_proj layers in the MLP layers.
+
+ ```python
+ >>> from transformers import NemotronModel, NemotronConfig
+
+ >>> # Initializing a Nemotron nemotron-15b style configuration
+ >>> configuration = NemotronConfig()
+
+ >>> # Initializing a model from the nemotron-15b style configuration
+ >>> model = NemotronModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "nemotron"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=256000,
+ hidden_size=6144,
+ intermediate_size=24576,
+ num_hidden_layers=32,
+ num_attention_heads=48,
+ head_dim=None,
+ num_key_value_heads=None,
+ hidden_act="relu2",
+ max_position_embeddings=4096,
+ initializer_range=0.0134,
+ norm_eps=1e-5,
+ use_cache=True,
+ pad_token_id=None,
+ bos_token_id=2,
+ eos_token_id=3,
+ tie_word_embeddings=False,
+ rope_theta=10000.0,
+ partial_rotary_factor=0.5,
+ attention_bias=False,
+ attention_dropout=0.0,
+ mlp_bias=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.norm_eps = norm_eps
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.partial_rotary_factor = partial_rotary_factor
+ rope_config_validation(self)
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.mlp_bias = mlp_bias
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+
+__all__ = ["NemotronConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/convert_nemotron_nemo_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/convert_nemotron_nemo_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..07a5c51a3c4bcd1062e39c51223f2a46ff8544f0
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/convert_nemotron_nemo_to_hf.py
@@ -0,0 +1,346 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+import shutil
+from argparse import ArgumentParser
+from collections import OrderedDict
+
+import torch
+from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer
+from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
+from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
+from nemo.utils import logging
+from pytorch_lightning import Trainer
+
+from transformers import LlamaTokenizer, PreTrainedTokenizerFast
+from transformers.convert_slow_tokenizer import LlamaConverter
+
+
+"""
+Script to convert a nemotron checkpoint in nemo (mcore path) into a HuggingFace checkpoint.
+This script can be used to 1) generate only the HF weights, or 2) generate an entire HF model folder.
+
+1) Generate only HF weights from a nemo file:
+
+ python convert_nemotron_nemo_to_hf.py \
+ --input_name_or_path /path/to/file.nemo or /path/to/extracted_folder \
+ --output_path /path/to/pytorch_model.bin
+
+2) Generate the full HF model folder
+
+ python convert_nemotron_nemo_to_hf.py \
+ --input_name_or_path /path/to/file.nemo or /path/to/extracted_folder \
+ --hf_input_path /path/to/input_hf_folder \
+ --hf_output_path /path/to/output_hf_folder \
+
+ Use the --cpu-only flag if the model cannot fit in the GPU (e.g. Nemotron4 340b).
+ However this option makes the conversion script significantly slower.
+"""
+
+
+def get_args():
+ parser = ArgumentParser()
+ parser.add_argument(
+ "--input_name_or_path",
+ type=str,
+ default=None,
+ required=True,
+ help="Path to .nemo file or extracted folder",
+ )
+ parser.add_argument("--output_path", type=str, default=None, required=False, help="Path to HF .bin file")
+ parser.add_argument(
+ "--hf_input_path",
+ type=str,
+ default=None,
+ help="A HF model path, e.g. a folder containing https://huggingface.co/nvidia/Minitron-8B-Base",
+ )
+ parser.add_argument(
+ "--hf_output_path",
+ type=str,
+ default=None,
+ help="Output HF model path, with the same format as above but user's own weights",
+ )
+ parser.add_argument(
+ "--precision",
+ type=str,
+ default=None,
+ help="Precision of output weights."
+ "Defaults to precision of the input nemo weights (model.cfg.trainer.precision)",
+ )
+ parser.add_argument(
+ "--cpu-only",
+ action="store_true",
+ help="Load model in cpu only. Useful if the model cannot fit in GPU memory, "
+ "but this option makes the conversion script significantly slower.",
+ )
+ args = parser.parse_args()
+ return args
+
+
+def convert_hf_config(nemo_config, tokenizer, vocab_size, dtype, hf_output_path, hf_url="nvidia/Minitron-8B-Base"):
+ """
+ Convert NeMo config to HF config
+ """
+ NEMO_ACT2HF = {
+ "squared-relu": "relu2",
+ "fast-swiglu": "silu",
+ }
+ DTYPE2HF = {
+ torch.bfloat16: "bfloat16",
+ torch.float16: "float16",
+ torch.float32: "float32",
+ }
+ hf_config = {
+ "_name_or_path": hf_url,
+ "architectures": ["NemotronForCausalLM"],
+ "bos_token_id": tokenizer.bos_id,
+ "eos_token_id": tokenizer.eos_id,
+ "hidden_act": NEMO_ACT2HF[nemo_config.activation],
+ "hidden_size": nemo_config.hidden_size,
+ "initializer_range": nemo_config.init_method_std,
+ "intermediate_size": nemo_config.ffn_hidden_size,
+ "max_position_embeddings": nemo_config.max_position_embeddings,
+ "model_type": "nemotron",
+ "num_attention_heads": nemo_config.num_attention_heads,
+ "num_hidden_layers": nemo_config.num_layers,
+ "num_key_value_heads": nemo_config.get("num_query_groups", nemo_config.num_attention_heads),
+ "norm_eps": nemo_config.layernorm_epsilon,
+ "rope_theta": nemo_config.get("rotary_base", 10000),
+ "partial_rotary_factor": nemo_config.get("rotary_percentage", 1.0),
+ "tie_word_embeddings": False,
+ "torch_dtype": DTYPE2HF[dtype],
+ "transformers_version": "4.32.0.dev0", # TODO
+ "use_cache": True,
+ "vocab_size": vocab_size,
+ }
+ if nemo_config.kv_channels is not None:
+ hf_config["kv_channels"] = nemo_config.kv_channels
+ json.dump(hf_config, open(f"{hf_output_path}/config.json", "w"), indent=2)
+
+
+def convert(input_nemo_file, output_hf_file, precision=None, cpu_only=False) -> None:
+ """
+ Convert NeMo weights to HF weights
+ """
+ dummy_trainer = Trainer(devices=1, accelerator="cpu", strategy=NLPDDPStrategy())
+ model_config = MegatronGPTModel.restore_from(input_nemo_file, trainer=dummy_trainer, return_config=True)
+ model_config.tensor_model_parallel_size = 1
+ model_config.pipeline_model_parallel_size = 1
+ model_config.sequence_parallel = False
+ model_config.transformer_engine = True
+ if cpu_only:
+ map_location = torch.device("cpu")
+ model_config.use_cpu_initialization = True
+ model_config.dist_ckpt_load_on_device = False
+ else:
+ map_location = None
+
+ if cpu_only:
+ logging.info("******** Loading model on CPU. This will take a significant amount of time.")
+
+ model = MegatronGPTModel.restore_from(
+ input_nemo_file, trainer=dummy_trainer, override_config_path=model_config, map_location=map_location
+ )
+
+ vocab_size = model.padded_vocab_size
+
+ if precision is None:
+ precision = model.cfg.precision
+ if precision in [32, "32"]:
+ dtype = torch.float32
+ elif precision in [16, "16", "16-mixed"]:
+ dtype = torch.float16
+ elif precision in ["bf16", "bf16-mixed"]:
+ dtype = torch.bfloat16
+ else:
+ logging.warning(f"Precision string {precision} is not recognized, falling back to fp32")
+ dtype = torch.float32 # fallback
+ logging.info(f"Using precision {dtype}")
+
+ def param_to_weights(param):
+ return param.to(dtype)
+
+ checkpoint = OrderedDict()
+
+ hidden_size = model.cfg.hidden_size
+ head_num = model.cfg.num_attention_heads
+ num_layers = model.cfg.num_layers
+ ffn_hidden_size = model.cfg.ffn_hidden_size
+ num_query_groups = model.cfg.get("num_query_groups", head_num) # different num_query_groups for 70B
+ if num_query_groups is None:
+ num_query_groups = head_num
+ heads_per_group = head_num // num_query_groups
+ qkv_total_dim = head_num + 2 * num_query_groups
+
+ # Embedding
+ embed_weight = model.state_dict()["model.embedding.word_embeddings.weight"]
+ embed_weights_base_name = "model.embed_tokens.weight"
+ checkpoint[embed_weights_base_name] = param_to_weights(embed_weight)
+
+ for l in range(int(num_layers)):
+ print(f"converting layer {l}")
+
+ qkv_weights = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_qkv.weight"]
+ qkv_weights = qkv_weights.reshape([qkv_total_dim, -1, hidden_size])
+
+ q_slice = torch.cat(
+ [
+ torch.arange((heads_per_group + 2) * i, (heads_per_group + 2) * i + heads_per_group)
+ for i in range(num_query_groups)
+ ]
+ )
+ k_slice = torch.arange(heads_per_group, qkv_total_dim, (heads_per_group + 2))
+ v_slice = torch.arange(heads_per_group + 1, qkv_total_dim, (heads_per_group + 2))
+ ## Example of slices
+ ## (without GQA): num_query_groups = head_num = 32,
+ ## q_slice = [0, 3, 6, 9 , ... 90, 93]
+ ## k_slice = [1, 4, 7, 10, ... 91, 94]
+ ## v_slice = [2, 5, 8, 11, ... 92, 95]
+ ## (with GQA): num_query_groups = 8, head_num = 64
+ ## q_slice = [0, 1, .. 6, 7, 10, 11, .. 16, 17, 20, 21, .. 67, 70, ... 76, 77]
+ ## k_slice = [8, 18, 28, ... 68, 78]
+ ## v_slice = [9, 19, 29, ... 69, 79]
+
+ q_weights_base_name = f"model.layers.{l}.self_attn.q_proj.weight"
+ k_weights_base_name = f"model.layers.{l}.self_attn.k_proj.weight"
+ v_weights_base_name = f"model.layers.{l}.self_attn.v_proj.weight"
+
+ checkpoint[q_weights_base_name] = param_to_weights(qkv_weights[q_slice].reshape(-1, hidden_size))
+ checkpoint[k_weights_base_name] = param_to_weights(qkv_weights[k_slice].reshape(-1, hidden_size))
+ checkpoint[v_weights_base_name] = param_to_weights(qkv_weights[v_slice].reshape(-1, hidden_size))
+
+ # attention dense
+ o_weight = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_proj.weight"]
+ o_weight_base_name = f"model.layers.{l}.self_attn.o_proj.weight"
+ checkpoint[o_weight_base_name] = param_to_weights(o_weight)
+
+ # mlp
+ mlp_weights = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc1.weight"]
+ mlp_up_proj_weight = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc2.weight"]
+
+ if mlp_weights.shape[0] != mlp_up_proj_weight.shape[1]:
+ # Has projection (used for swi-glu)
+ logging.warning(
+ "Gated projection layers detected in NeMo checkpoint. Currently Nemotron HF does not support gated MLP."
+ )
+ assert mlp_weights.shape[0] == 2 * mlp_up_proj_weight.shape[1]
+
+ mlp_down_proj_weight = mlp_weights[:ffn_hidden_size, :]
+ mlp_gate_proj_weight = mlp_weights[ffn_hidden_size:, :]
+
+ mlp_down_proj_base_name = f"model.layers.{l}.mlp.gate_proj.weight"
+ mlp_gate_proj_base_name = f"model.layers.{l}.mlp.up_proj.weight"
+
+ checkpoint[mlp_down_proj_base_name] = param_to_weights(mlp_down_proj_weight)
+ checkpoint[mlp_gate_proj_base_name] = param_to_weights(mlp_gate_proj_weight)
+ else:
+ mlp_down_proj_weight = mlp_weights
+ mlp_down_proj_base_name = f"model.layers.{l}.mlp.up_proj.weight"
+ checkpoint[mlp_down_proj_base_name] = param_to_weights(mlp_down_proj_weight)
+
+ mlp_up_proj_base_name = f"model.layers.{l}.mlp.down_proj.weight"
+ checkpoint[mlp_up_proj_base_name] = param_to_weights(mlp_up_proj_weight)
+
+ # layernorm
+ input_ln_weight = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_qkv.layer_norm_weight"]
+ input_ln_base_name = f"model.layers.{l}.input_layernorm.weight"
+ checkpoint[input_ln_base_name] = param_to_weights(input_ln_weight)
+ if (
+ model.state_dict().get(f"model.decoder.layers.{l}.self_attention.linear_qkv.layer_norm_bias", None)
+ is not None
+ ):
+ input_ln_bias = model.state_dict()[f"model.decoder.layers.{l}.self_attention.linear_qkv.layer_norm_bias"]
+ input_ln_bias_name = f"model.layers.{l}.input_layernorm.bias"
+ checkpoint[input_ln_bias_name] = param_to_weights(input_ln_bias)
+
+ post_attn_ln_weight = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc1.layer_norm_weight"]
+ post_attn_ln_base_name = f"model.layers.{l}.post_attention_layernorm.weight"
+ checkpoint[post_attn_ln_base_name] = param_to_weights(post_attn_ln_weight)
+ if model.state_dict().get(f"model.decoder.layers.{l}.mlp.linear_fc1.layer_norm_bias", None) is not None:
+ post_attn_ln_bias = model.state_dict()[f"model.decoder.layers.{l}.mlp.linear_fc1.layer_norm_bias"]
+ post_attn_ln_bias_name = f"model.layers.{l}.post_attention_layernorm.bias"
+ checkpoint[post_attn_ln_bias_name] = param_to_weights(post_attn_ln_bias)
+
+ print(f"done layer {l}")
+
+ final_ln_weight = model.state_dict()["model.decoder.final_layernorm.weight"]
+ final_ln_base_name = "model.norm.weight"
+ checkpoint[final_ln_base_name] = param_to_weights(final_ln_weight)
+ if model.state_dict().get("model.decoder.final_layernorm.bias", None) is not None:
+ final_ln_bias = model.state_dict()["model.decoder.final_layernorm.bias"]
+ final_ln_bias_name = "model.norm.bias"
+ checkpoint[final_ln_bias_name] = param_to_weights(final_ln_bias)
+
+ output_layer_weight = model.state_dict()["model.output_layer.weight"]
+ output_layer_base_name = "lm_head.weight"
+ checkpoint[output_layer_base_name] = param_to_weights(output_layer_weight)
+
+ os.makedirs(os.path.dirname(output_hf_file), exist_ok=True)
+ torch.save(checkpoint, output_hf_file)
+ logging.info(f"Weights saved to {output_hf_file}")
+
+ return model_config, model.tokenizer, dtype, vocab_size
+
+
+def extract_nemotron_tokenizer(nemo_file, model_config, output_hf_path, nemo_tokenizer):
+ tokenizer_cfg = model_config.tokenizer
+ if tokenizer_cfg.library == "sentencepiece":
+ # For sentencepiece tokenizer, we are wrapping with HF's LlamaTokenizer
+ # and convert it to a PreTrainedTokenizerFast
+ tokenizer_fn = tokenizer_cfg.model[5:]
+ output_tokenizer = f"{output_hf_path}/tokenizer.model"
+ if nemo_file.endswith(".nemo"):
+ import tarfile
+
+ archive = tarfile.open(nemo_file, "r")
+ tokenizer_filename = "./" + tokenizer_fn # exclude 'nemo:' prefix
+ archive.extract(tokenizer_filename, output_hf_path)
+ archive.close()
+ os.rename(f"{output_hf_path}/{tokenizer_fn}", output_tokenizer)
+ elif os.path.isdir(nemo_file):
+ shutil.copy(f"{nemo_file}/{tokenizer_fn}", output_tokenizer)
+ # We use LlamaTokenizer for sentencepiece based tokenizer
+ tokenizer = LlamaTokenizer.from_pretrained(output_hf_path, legacy=False)
+ # Convert the LlamaTokenizer to a PreTrainedTokenizerFast instance
+ tokenizer = PreTrainedTokenizerFast(
+ tokenizer_object=LlamaConverter(tokenizer).converted(), model_input_names=["input_ids", "token_type_ids"]
+ )
+ tokenizer.save_pretrained(output_hf_path)
+ logging.info(f"Setencepiece tokenizer has been saved to {output_tokenizer}")
+ elif isinstance(nemo_tokenizer, AutoTokenizer):
+ nemo_tokenizer.tokenizer.save_pretrained(output_hf_path)
+ logging.info(f"HF AutoTokenizer has been saved to {output_hf_path}")
+ else:
+ raise ValueError(f"Unsupported tokenizer type: library: {tokenizer_cfg.library}, type: {tokenizer_cfg.type}")
+
+
+if __name__ == "__main__":
+ args = get_args()
+ if not args.hf_output_path:
+ assert args.output_path is not None, "Need to provide either output_path or hf_output_path"
+ else:
+ args.output_path = f"{args.hf_output_path}/pytorch_model.bin"
+ logging.info(f"weight will be saved to {args.output_path}")
+
+ nemo_config, nemo_tokenizer, dtype, vocab_size = convert(
+ args.input_name_or_path, args.output_path, precision=args.precision, cpu_only=args.cpu_only
+ )
+ if args.hf_input_path and args.hf_output_path:
+ convert_hf_config(nemo_config, nemo_tokenizer, vocab_size, dtype, args.hf_output_path, args.hf_input_path)
+ extract_nemotron_tokenizer(args.input_name_or_path, nemo_config, args.hf_output_path, nemo_tokenizer)
+ else:
+ logging.info("`hf_input_path` and/or `hf_output_path` not provided, not generating full HF model.")
+ logging.info(f".bin file is saved to {args.output_path}")
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/modeling_nemotron.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/modeling_nemotron.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8e2afb8897086f9521d0bac056714ab2bf83b9c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nemotron/modeling_nemotron.py
@@ -0,0 +1,969 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc. team. All rights reserved.
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Nemotron model."""
+
+import math
+from typing import Optional, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import Size, Tensor, nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import AttentionMaskConverter
+from ...modeling_flash_attention_utils import _flash_attention_forward, flash_attn_supports_top_left_mask
+from ...modeling_layers import (
+ GenericForQuestionAnswering,
+ GenericForSequenceClassification,
+ GenericForTokenClassification,
+ GradientCheckpointingLayer,
+)
+from ...modeling_outputs import (
+ BaseModelOutputWithPast,
+ CausalLMOutputWithPast,
+)
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_nemotron import NemotronConfig
+
+
+if is_torch_flex_attn_available():
+ from torch.nn.attention.flex_attention import BlockMask
+
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+
+logger = logging.get_logger(__name__)
+
+
+def _cast_if_autocast_enabled(device_type, *args):
+ if not torch.is_autocast_enabled():
+ return args
+ else:
+ # NOTE: `torch.get_autocast_dtype` is there starting from PyTorch 2.4
+ target_dtype = (
+ torch.get_autocast_dtype(device_type)
+ if hasattr(torch, "get_autocast_dtype")
+ else torch.get_autocast_gpu_dtype()
+ )
+ return torch.amp.autocast_mode._cast(args, device_type, target_dtype)
+
+
+class NemotronLayerNorm1P(nn.LayerNorm):
+ def __init__(
+ self,
+ normalized_shape: Union[int, list[int], Size],
+ eps: float = 1e-5,
+ elementwise_affine: bool = True,
+ bias: bool = True,
+ device=None,
+ dtype=None,
+ ):
+ super().__init__(normalized_shape, eps, elementwise_affine, bias, device, dtype)
+
+ def forward(self, input: Tensor) -> Tensor:
+ device_type = input.device.type if input.device.type != "mps" else "cpu"
+ args = _cast_if_autocast_enabled(
+ device_type, input, self.normalized_shape, self.weight + 1, self.bias, self.eps
+ )
+ with torch.autocast(device_type=input.device.type, enabled=False):
+ return F.layer_norm(*args)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron
+class NemotronRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ # Ignore copy
+ def __init__(
+ self,
+ config: NemotronConfig,
+ device=None,
+ ):
+ super().__init__()
+
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+
+ rot_dim = cos.shape[-1]
+ # If q_pass/k_pass is empty, rotary pos embedding is applied to all tensor q/k
+ q, q_pass = q[..., :rot_dim], q[..., rot_dim:]
+ k, k_pass = k[..., :rot_dim], k[..., rot_dim:]
+
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return torch.cat((q_embed, q_pass), dim=-1), torch.cat((k_embed, k_pass), dim=-1)
+
+
+class NemotronMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ return self.down_proj(self.act_fn(self.up_proj(x)))
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class NemotronAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: NemotronConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = config.head_dim
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.rope_theta = config.rope_theta
+ self.partial_rotary_factor = config.partial_rotary_factor
+ self.is_causal = True
+
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+ self.o_proj = nn.Linear(self.head_dim * self.num_heads, self.hidden_size, bias=config.attention_bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ if position_embeddings is not None:
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+ if attention_mask is not None: # no matter the length, we just slice it
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.reshape(bsz, q_len, -1)
+
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights
+
+
+# NO LONGER EXIST Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron
+# TODO cyril: modular
+class NemotronFlashAttention2(NemotronAttention):
+ """
+ Nemotron flash attention module. This module inherits from `NemotronAttention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+ self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ if isinstance(past_key_values, StaticCache):
+ raise ValueError(
+ "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
+ "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
+ )
+
+ output_attentions = False
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ # Flash attention requires the input to have the shape
+ # batch_size x seq_length x head_dim x hidden_dim
+ # therefore we just need to keep the original shape
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ if position_embeddings is not None:
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+ # to be able to avoid many of these transpose/reshape/view.
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ dropout_rate = self.attention_dropout if self.training else 0.0
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in the correct dtype just to be sure everything works as expected.
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+ # in fp32. (NemotronRMSNorm handles it correctly)
+
+ input_dtype = query_states.dtype
+ device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
+ if input_dtype == torch.float32:
+ if torch.is_autocast_enabled():
+ # NOTE: `torch.get_autocast_dtype` is there starting from PyTorch 2.4
+ target_dtype = (
+ torch.get_autocast_dtype(device_type)
+ if hasattr(torch, "get_autocast_dtype")
+ else torch.get_autocast_gpu_dtype()
+ )
+ # Handle the case where the model is quantized
+ elif hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ else:
+ target_dtype = self.q_proj.weight.dtype
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ attn_output = _flash_attention_forward(
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ q_len,
+ position_ids=position_ids,
+ dropout=dropout_rate,
+ sliding_window=getattr(self, "sliding_window", None),
+ use_top_left_mask=self._flash_attn_uses_top_left_mask,
+ is_causal=self.is_causal,
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights
+
+
+# NO LONGER EXIST Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron
+# TODO cyril: modular
+class NemotronSdpaAttention(NemotronAttention):
+ """
+ Nemotron attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `NemotronAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+ SDPA API.
+ """
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ if output_attentions:
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+ logger.warning_once(
+ "NemotronModel is using NemotronSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ return super().forward(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ if position_embeddings is not None:
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ causal_mask = attention_mask
+ if attention_mask is not None:
+ causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ if query_states.device.type == "cuda" and causal_mask is not None:
+ query_states = query_states.contiguous()
+ key_states = key_states.contiguous()
+ value_states = value_states.contiguous()
+
+ # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+ # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+ is_causal = causal_mask is None and q_len > 1
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=causal_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ is_causal=is_causal,
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.view(bsz, q_len, -1)
+
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, None
+
+
+NEMOTRON_ATTENTION_CLASSES = {
+ "eager": NemotronAttention,
+ "flash_attention_2": NemotronFlashAttention2,
+ "sdpa": NemotronSdpaAttention,
+}
+
+
+# copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron
+# no longer copied after attention refactors
+class NemotronDecoderLayer(GradientCheckpointingLayer):
+ # Ignore copy
+ def __init__(self, config: NemotronConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = NEMOTRON_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+ self.mlp = NemotronMLP(config)
+ self.input_layernorm = NemotronLayerNorm1P(config.hidden_size, eps=config.norm_eps)
+ self.post_attention_layernorm = NemotronLayerNorm1P(config.hidden_size, eps=config.norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
+ **kwargs,
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence
+ position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
+ Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+ with `head_dim` being the embedding dimension of each attention head.
+ kwargs (`dict`, *optional*):
+ Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+ into the model
+ """
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ return outputs
+
+
+@auto_docstring
+class NemotronPreTrainedModel(PreTrainedModel):
+ config: NemotronConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["NemotronDecoderLayer"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, NemotronLayerNorm1P):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+
+
+@auto_docstring
+class NemotronModel(NemotronPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`NemotronDecoderLayer`]
+
+ Args:
+ config: NemotronConfig
+ """
+
+ def __init__(self, config: NemotronConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [NemotronDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.norm = NemotronLayerNorm1P(config.hidden_size, eps=config.norm_eps)
+ self.rotary_emb = NemotronRotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> BaseModelOutputWithPast:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+ )
+ use_cache = False
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = self._update_causal_mask(
+ attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+ )
+
+ # embed positions
+ hidden_states = inputs_embeds
+
+ # create position embeddings to be shared across the decoder layers
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, "BlockMask"],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ output_attentions: bool = False,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and (attention_mask == 0.0).any():
+ return attention_mask
+ return None
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ return attention_mask
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ sequence_length = input_tensor.shape[1]
+ if using_compilable_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ and not output_attentions
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+
+# TODO: re-enable check: Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron
+class NemotronForCausalLM(NemotronPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = NemotronModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs,
+ ) -> CausalLMOutputWithPast:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, NemotronForCausalLM
+
+ >>> model = NemotronForCausalLM.from_pretrained("nvidia/nemotron-3-8b-base-4k-hf")
+ >>> tokenizer = AutoTokenizer.from_pretrained("nvidia/nemotron-3-8b-base-4k-hf")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs: BaseModelOutputWithPast = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ cache_position=cache_position,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+class NemotronForSequenceClassification(GenericForSequenceClassification, NemotronPreTrainedModel): ...
+
+
+class NemotronForQuestionAnswering(GenericForQuestionAnswering, NemotronPreTrainedModel):
+ base_model_prefix = "transformer"
+
+
+class NemotronForTokenClassification(GenericForTokenClassification, NemotronPreTrainedModel): ...
+
+
+__all__ = [
+ "NemotronForQuestionAnswering",
+ "NemotronForCausalLM",
+ "NemotronModel",
+ "NemotronPreTrainedModel",
+ "NemotronForSequenceClassification",
+ "NemotronForTokenClassification",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cdb326098a30020f0d9d2cc4c083090bce70d5a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .tokenization_nllb import *
+ from .tokenization_nllb_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb/tokenization_nllb.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb/tokenization_nllb.py
new file mode 100644
index 0000000000000000000000000000000000000000..4962a642bb3181d0d0e918530681ba7ff40ef70e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb/tokenization_nllb.py
@@ -0,0 +1,394 @@
+# coding=utf-8
+# Copyright 2022 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from shutil import copyfile
+from typing import Any, Optional
+
+import sentencepiece as spm
+
+from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
+from ...utils import logging
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+SPIECE_UNDERLINE = "▁"
+
+VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
+
+
+FAIRSEQ_LANGUAGE_CODES = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn'] # fmt: skip
+
+
+@requires(backends=("sentencepiece",))
+class NllbTokenizer(PreTrainedTokenizer):
+ """
+ Construct an NLLB tokenizer.
+
+ Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
+ [SentencePiece](https://github.com/google/sentencepiece).
+
+ The tokenization method is ` ` for source language documents, and `
+ ` for target language documents.
+
+ Examples:
+
+ ```python
+ >>> from transformers import NllbTokenizer
+
+ >>> tokenizer = NllbTokenizer.from_pretrained(
+ ... "facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="fra_Latn"
+ ... )
+ >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
+ >>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie."
+ >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt")
+ ```
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `""`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ tokenizer_file (`str`, *optional*):
+ The path to a tokenizer file to use instead of the vocab file.
+ src_lang (`str`, *optional*):
+ The language to use as source language for translation.
+ tgt_lang (`str`, *optional*):
+ The language to use as target language for translation.
+ sp_model_kwargs (`dict[str, str]`):
+ Additional keyword arguments to pass to the model initialization.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ prefix_tokens: list[int] = []
+ suffix_tokens: list[int] = []
+
+ def __init__(
+ self,
+ vocab_file,
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ tokenizer_file=None,
+ src_lang=None,
+ tgt_lang=None,
+ sp_model_kwargs: Optional[dict[str, Any]] = None,
+ additional_special_tokens=None,
+ legacy_behaviour=False,
+ **kwargs,
+ ):
+ if additional_special_tokens is None:
+ additional_special_tokens = FAIRSEQ_LANGUAGE_CODES
+ bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
+ pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
+ eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
+ unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = (
+ AddedToken(mask_token, normalized=True, lstrip=True, special=True)
+ if isinstance(mask_token, str)
+ else mask_token
+ )
+
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+ self.legacy_behaviour = legacy_behaviour
+
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.Load(str(vocab_file))
+ self.vocab_file = vocab_file
+ # Original fairseq vocab and spm vocab must be "aligned":
+ # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
+ # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
+ # fairseq | '' | '' | ' ' | '' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
+ # spm | '' | '' | ' ' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
+
+ # unk token needs to be in the vocab with correct index
+ self._added_tokens_decoder = {0: bos_token, 1: pad_token, 2: eos_token, 3: unk_token}
+ # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
+ self.fairseq_offset = 1
+ self.sp_model_size = len(self.sp_model)
+
+ super().__init__(
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ pad_token=pad_token,
+ mask_token=mask_token,
+ tokenizer_file=tokenizer_file,
+ src_lang=src_lang,
+ tgt_lang=tgt_lang,
+ additional_special_tokens=additional_special_tokens,
+ sp_model_kwargs=self.sp_model_kwargs,
+ legacy_behaviour=legacy_behaviour,
+ **kwargs,
+ )
+
+ self._src_lang = src_lang if src_lang is not None else "eng_Latn"
+ self.cur_lang_code_id = self.convert_tokens_to_ids(self._src_lang)
+ self.tgt_lang = tgt_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+ state["sp_model"] = None
+ state["sp_model_proto"] = self.sp_model.serialized_model_proto()
+ return state
+
+ def __setstate__(self, d):
+ self.__dict__ = d
+
+ # for backward compatibility
+ if not hasattr(self, "sp_model_kwargs"):
+ self.sp_model_kwargs = {}
+
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+ @property
+ def vocab_size(self):
+ return len(self.sp_model) + self.fairseq_offset
+
+ @property
+ def src_lang(self) -> str:
+ return self._src_lang
+
+ @src_lang.setter
+ def src_lang(self, new_src_lang: str) -> None:
+ self._src_lang = new_src_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ def get_special_tokens_mask(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
+ ) -> list[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+
+ Returns:
+ `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ prefix_ones = [1] * len(self.prefix_tokens)
+ suffix_ones = [1] * len(self.suffix_tokens)
+ if token_ids_1 is None:
+ return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
+ return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. An NLLB sequence has the following format, where `X` represents the sequence:
+
+ - `input_ids` (for encoder) `X [eos, src_lang_code]`
+ - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
+
+ BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
+ separator.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return self.prefix_tokens + token_ids_0 + self.suffix_tokens
+ # We don't expect to process pairs, but leave the pair logic for API consistency
+ return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. nllb does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+
+ """
+
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ def _build_translation_inputs(
+ self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
+ ):
+ """Used by translation pipeline, to prepare inputs for the generate function"""
+ if src_lang is None or tgt_lang is None:
+ raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+ self.src_lang = src_lang
+ inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
+ tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
+ inputs["forced_bos_token_id"] = tgt_lang_id
+ return inputs
+
+ def get_vocab(self):
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ def _tokenize(self, text: str) -> list[str]:
+ return self.sp_model.encode(text, out_type=str)
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ spm_id = self.sp_model.PieceToId(token)
+ # Need to return unknown token if the SP model returned 0
+ return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ return self.sp_model.IdToPiece(index - self.fairseq_offset)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (strings for sub-words) in a single string."""
+ out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
+ return out_string
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+ elif not os.path.isfile(self.vocab_file):
+ with open(out_vocab_file, "wb") as fi:
+ content_spiece_model = self.sp_model.serialized_model_proto()
+ fi.write(content_spiece_model)
+
+ return (out_vocab_file,)
+
+ def prepare_seq2seq_batch(
+ self,
+ src_texts: list[str],
+ src_lang: str = "eng_Latn",
+ tgt_texts: Optional[list[str]] = None,
+ tgt_lang: str = "fra_Latn",
+ **kwargs,
+ ) -> BatchEncoding:
+ self.src_lang = src_lang
+ self.tgt_lang = tgt_lang
+ return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
+
+ def _switch_to_input_mode(self):
+ return self.set_src_lang_special_tokens(self.src_lang)
+
+ def _switch_to_target_mode(self):
+ return self.set_tgt_lang_special_tokens(self.tgt_lang)
+
+ def set_src_lang_special_tokens(self, src_lang) -> None:
+ """Reset the special tokens to the source lang setting.
+ - In legacy mode: No prefix and suffix=[eos, src_lang_code].
+ - In default mode: Prefix=[src_lang_code], suffix = [eos]
+ """
+ self.cur_lang_code = self.convert_tokens_to_ids(src_lang)
+ if self.legacy_behaviour:
+ self.prefix_tokens = []
+ self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
+ else:
+ self.prefix_tokens = [self.cur_lang_code]
+ self.suffix_tokens = [self.eos_token_id]
+
+ def set_tgt_lang_special_tokens(self, lang: str) -> None:
+ """Reset the special tokens to the target lang setting.
+ - In legacy mode: No prefix and suffix=[eos, tgt_lang_code].
+ - In default mode: Prefix=[tgt_lang_code], suffix = [eos]
+ """
+ self.cur_lang_code = self.convert_tokens_to_ids(lang)
+ if self.legacy_behaviour:
+ self.prefix_tokens = []
+ self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
+ else:
+ self.prefix_tokens = [self.cur_lang_code]
+ self.suffix_tokens = [self.eos_token_id]
+
+
+__all__ = ["NllbTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb/tokenization_nllb_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb/tokenization_nllb_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..5300b3942b5d261e5e83be8c55f1a41da3427b09
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb/tokenization_nllb_fast.py
@@ -0,0 +1,327 @@
+# coding=utf-8
+# Copyright 2022 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from shutil import copyfile
+from typing import Optional
+
+from tokenizers import processors
+
+from ...tokenization_utils import AddedToken, BatchEncoding
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import is_sentencepiece_available, logging
+
+
+if is_sentencepiece_available():
+ from .tokenization_nllb import NllbTokenizer
+else:
+ NllbTokenizer = None
+
+
+logger = logging.get_logger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
+
+
+FAIRSEQ_LANGUAGE_CODES = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn'] # fmt: skip
+
+
+class NllbTokenizerFast(PreTrainedTokenizerFast):
+ """
+ Construct a "fast" NLLB tokenizer (backed by HuggingFace's *tokenizers* library). Based on
+ [BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ The tokenization method is ` ` for source language documents, and `
+ ` for target language documents.
+
+ Examples:
+
+ ```python
+ >>> from transformers import NllbTokenizerFast
+
+ >>> tokenizer = NllbTokenizerFast.from_pretrained(
+ ... "facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="fra_Latn"
+ ... )
+ >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
+ >>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie."
+ >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt")
+ ```
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the beginning of
+ sequence. The token used is the `cls_token`.
+
+
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+
+
+ When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+ The token used is the `sep_token`.
+
+
+
+ sep_token (`str`, *optional*, defaults to `" "`):
+ The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+ sequence classification or for a text and a question for question answering. It is also used as the last
+ token of a sequence built with special tokens.
+ cls_token (`str`, *optional*, defaults to `""`):
+ The classifier token which is used when doing sequence classification (classification of the whole sequence
+ instead of per-token classification). It is the first token of the sequence when built with special tokens.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ mask_token (`str`, *optional*, defaults to `""`):
+ The token used for masking values. This is the token used when training this model with masked language
+ modeling. This is the token which the model will try to predict.
+ tokenizer_file (`str`, *optional*):
+ The path to a tokenizer file to use instead of the vocab file.
+ src_lang (`str`, *optional*):
+ The language to use as source language for translation.
+ tgt_lang (`str`, *optional*):
+ The language to use as target language for translation.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+ slow_tokenizer_class = NllbTokenizer
+
+ prefix_tokens: list[int] = []
+ suffix_tokens: list[int] = []
+
+ def __init__(
+ self,
+ vocab_file=None,
+ tokenizer_file=None,
+ bos_token="",
+ eos_token=" ",
+ sep_token=" ",
+ cls_token="",
+ unk_token="",
+ pad_token="",
+ mask_token="",
+ src_lang=None,
+ tgt_lang=None,
+ additional_special_tokens=None,
+ legacy_behaviour=False,
+ **kwargs,
+ ):
+ if additional_special_tokens is None:
+ additional_special_tokens = FAIRSEQ_LANGUAGE_CODES
+
+ self.vocab_file = vocab_file
+ # Mask token behave like a normal word, i.e. include the space before it
+ mask_token = (
+ AddedToken(mask_token, normalized=True, lstrip=True, special=True)
+ if isinstance(mask_token, str)
+ else mask_token
+ )
+ self.legacy_behaviour = legacy_behaviour
+ super().__init__(
+ vocab_file=vocab_file,
+ tokenizer_file=tokenizer_file,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ sep_token=sep_token,
+ cls_token=cls_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ src_lang=src_lang,
+ tgt_lang=tgt_lang,
+ mask_token=mask_token,
+ additional_special_tokens=additional_special_tokens,
+ legacy_behaviour=legacy_behaviour,
+ **kwargs,
+ )
+
+ self._src_lang = src_lang if src_lang is not None else "eng_Latn"
+ self.cur_lang_code = self.convert_tokens_to_ids(self._src_lang)
+ self.tgt_lang = tgt_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ @property
+ def src_lang(self) -> str:
+ return self._src_lang
+
+ @src_lang.setter
+ def src_lang(self, new_src_lang: str) -> None:
+ self._src_lang = new_src_lang
+ self.set_src_lang_special_tokens(self._src_lang)
+
+ def build_inputs_with_special_tokens(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+ adding special tokens. The special tokens depend on calling set_lang.
+
+ An NLLB sequence has the following format, where `X` represents the sequence:
+
+ - `input_ids` (for encoder) `X [eos, src_lang_code]`
+ - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
+
+ BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
+ separator.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs to which the special tokens will be added.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+ """
+ if token_ids_1 is None:
+ return self.prefix_tokens + token_ids_0 + self.suffix_tokens
+ # We don't expect to process pairs, but leave the pair logic for API consistency
+ return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
+ ) -> list[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. nllb does not
+ make use of token type ids, therefore a list of zeros is returned.
+
+ Args:
+ token_ids_0 (`list[int]`):
+ List of IDs.
+ token_ids_1 (`list[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+
+ Returns:
+ `list[int]`: List of zeros.
+
+ """
+
+ sep = [self.sep_token_id]
+ cls = [self.cls_token_id]
+
+ if token_ids_1 is None:
+ return len(cls + token_ids_0 + sep) * [0]
+ return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+ def _build_translation_inputs(
+ self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
+ ):
+ """Used by translation pipeline, to prepare inputs for the generate function"""
+ if src_lang is None or tgt_lang is None:
+ raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
+ self.src_lang = src_lang
+ inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
+ tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
+ inputs["forced_bos_token_id"] = tgt_lang_id
+ return inputs
+
+ def prepare_seq2seq_batch(
+ self,
+ src_texts: list[str],
+ src_lang: str = "eng_Latn",
+ tgt_texts: Optional[list[str]] = None,
+ tgt_lang: str = "fra_Latn",
+ **kwargs,
+ ) -> BatchEncoding:
+ self.src_lang = src_lang
+ self.tgt_lang = tgt_lang
+ return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
+
+ def _switch_to_input_mode(self):
+ return self.set_src_lang_special_tokens(self.src_lang)
+
+ def _switch_to_target_mode(self):
+ return self.set_tgt_lang_special_tokens(self.tgt_lang)
+
+ def set_src_lang_special_tokens(self, src_lang) -> None:
+ """Reset the special tokens to the source lang setting.
+ - In legacy mode: No prefix and suffix=[eos, src_lang_code].
+ - In default mode: Prefix=[src_lang_code], suffix = [eos]
+ """
+ self.cur_lang_code = self.convert_tokens_to_ids(src_lang)
+
+ if self.legacy_behaviour:
+ self.prefix_tokens = []
+ self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
+ else:
+ self.prefix_tokens = [self.cur_lang_code]
+ self.suffix_tokens = [self.eos_token_id]
+
+ prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
+ suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
+
+ self._tokenizer.post_processor = processors.TemplateProcessing(
+ single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
+ pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
+ special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
+ )
+
+ def set_tgt_lang_special_tokens(self, lang: str) -> None:
+ """Reset the special tokens to the target lang setting.
+ - In legacy mode: No prefix and suffix=[eos, tgt_lang_code].
+ - In default mode: Prefix=[tgt_lang_code], suffix = [eos]
+ """
+ self.cur_lang_code = self.convert_tokens_to_ids(lang)
+ if self.legacy_behaviour:
+ self.prefix_tokens = []
+ self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
+ else:
+ self.prefix_tokens = [self.cur_lang_code]
+ self.suffix_tokens = [self.eos_token_id]
+
+ prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
+ suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)
+
+ self._tokenizer.post_processor = processors.TemplateProcessing(
+ single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
+ pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
+ special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
+ )
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not self.can_save_slow_tokenizer:
+ raise ValueError(
+ "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
+ "tokenizer."
+ )
+
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory.")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+
+ return (out_vocab_file,)
+
+
+__all__ = ["NllbTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e21bde18010d3c81455b0a66eed8263c47a0e5de
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_nllb_moe import *
+ from .modeling_nllb_moe import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/configuration_nllb_moe.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/configuration_nllb_moe.py
new file mode 100644
index 0000000000000000000000000000000000000000..61cb2197c84d906e41833cf87eb2bebfb4fbd984
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/configuration_nllb_moe.py
@@ -0,0 +1,219 @@
+# coding=utf-8
+# Copyright 2023, HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""NLLB-MoE model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class NllbMoeConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`NllbMoeModel`]. It is used to instantiate an
+ NLLB-MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the NLLB-MoE
+ [facebook/nllb-moe-54b](https://huggingface.co/facebook/nllb-moe-54b) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50265):
+ Vocabulary size of the NllbMoe model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`NllbMoeModel`] or
+ d_model (`int`, *optional*, defaults to 1024):
+ Dimensionality of the layers and the pooler layer.
+ encoder_layers (`int`, *optional*, defaults to 12):
+ Number of encoder layers.
+ decoder_layers (`int`, *optional*, defaults to 12):
+ Number of decoder layers.
+ encoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ decoder_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ decoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ encoder_ffn_dim (`int`, *optional*, defaults to 4096):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
+ activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ activation_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for activations inside the fully connected layer.
+ classifier_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for classifier.
+ max_position_embeddings (`int`, *optional*, defaults to 1024):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ encoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ decoder_layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
+ for more details.
+ second_expert_policy ( `str`, *optional*, default to `"all"`):
+ The policy used for the sampling the probability of being sampled to a second expert for each token.
+ normalize_router_prob_before_dropping (`bool`, *optional*, defaults to `True`):
+ Whether or not to normalize the router probabilities before applying a mask based on the experts capacity
+ (capacity dropping).
+ batch_prioritized_routing (`bool`, *optional*, defaults to `True`):
+ Whether or not to orders the tokens by their router probabilities before capacity dropping. This means that
+ the tokens that have the highest probabilities will be routed before other tokens that might be further in
+ the sequence.
+ moe_eval_capacity_token_fraction (`float`, *optional*, defaults to 1.0):
+ Fraction of tokens as capacity during validation, if set to negative, uses the same as training. Should be
+ in range: (0.0, 1.0].
+ num_experts (`int`, *optional*, defaults to 128):
+ Number of experts for each NllbMoeSparseMlp layer.
+ expert_capacity (`int`, *optional*, defaults to 64):
+ Number of tokens that can be stored in each expert.
+ encoder_sparse_step (`int`, *optional*, defaults to 4):
+ Frequency of the sparse layers in the encoder. 4 means that one out of 4 layers will be sparse.
+ decoder_sparse_step (`int`, *optional*, defaults to 4):
+ Frequency of the sparse layers in the decoder. 4 means that one out of 4 layers will be sparse.
+ router_dtype (`str`, *optional*, default to `"float32"`):
+ The `dtype` used for the routers. It is preferable to keep the `dtype` to `"float32"` as specified in the
+ *selective precision* discussion in [the paper](https://huggingface.co/papers/2101.03961).
+ router_ignore_padding_tokens (`bool`, *optional*, defaults to `False`):
+ Whether to ignore padding tokens when routing. if `False`, the padding tokens are not routed to any
+ experts.
+ router_bias (`bool`, *optional*, defaults to `False`):
+ Whether or not the classifier of the router should have a bias.
+ moe_token_dropout (`float`, *optional*, default to 0.2):
+ Masking rate for MoE expert output masking (EOM), which is implemented via a Dropout2d on the expert
+ outputs.
+ output_router_logits (`bool`, *optional*, defaults to `False`):
+ Whether or not to return the router logits. Only set to `True` to get the auxiliary loss when training.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models).
+
+ Example:
+
+ ```python
+ >>> from transformers import NllbMoeModel, NllbMoeConfig
+
+ >>> # Initializing a NllbMoe facebook/nllb-moe-54b style configuration
+ >>> configuration = NllbMoeConfig()
+
+ >>> # Initializing a model from the facebook/nllb-moe-54b style configuration
+ >>> model = NllbMoeModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "nllb-moe"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
+
+ def __init__(
+ self,
+ vocab_size=128112,
+ max_position_embeddings=1024,
+ encoder_layers=12,
+ encoder_ffn_dim=4096,
+ encoder_attention_heads=16,
+ decoder_layers=12,
+ decoder_ffn_dim=4096,
+ decoder_attention_heads=16,
+ encoder_layerdrop=0.05,
+ decoder_layerdrop=0.05,
+ use_cache=True,
+ is_encoder_decoder=True,
+ activation_function="relu",
+ d_model=1024,
+ dropout=0.1,
+ attention_dropout=0.1,
+ activation_dropout=0.0,
+ init_std=0.02,
+ decoder_start_token_id=2,
+ scale_embedding=True,
+ router_bias=False,
+ router_dtype="float32",
+ router_ignore_padding_tokens=False,
+ num_experts=128,
+ expert_capacity=64,
+ encoder_sparse_step=4,
+ decoder_sparse_step=4,
+ router_z_loss_coef=0.001,
+ router_aux_loss_coef=0.001,
+ second_expert_policy="all",
+ normalize_router_prob_before_dropping=False,
+ batch_prioritized_routing=False,
+ moe_eval_capacity_token_fraction=1.0,
+ moe_token_dropout=0.2,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ output_router_logits=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.d_model = d_model
+ self.encoder_ffn_dim = encoder_ffn_dim
+ self.encoder_layers = encoder_layers
+ self.encoder_attention_heads = encoder_attention_heads
+ self.decoder_ffn_dim = decoder_ffn_dim
+ self.decoder_layers = decoder_layers
+ self.decoder_attention_heads = decoder_attention_heads
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.activation_function = activation_function
+ self.init_std = init_std
+ self.encoder_layerdrop = encoder_layerdrop
+ self.decoder_layerdrop = decoder_layerdrop
+ self.use_cache = use_cache
+ self.num_hidden_layers = encoder_layers
+ self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
+ self.router_z_loss_coef = router_z_loss_coef
+ self.router_aux_loss_coef = router_aux_loss_coef
+ self.decoder_sparse_step = decoder_sparse_step
+ self.encoder_sparse_step = encoder_sparse_step
+ self.num_experts = num_experts
+ self.expert_capacity = expert_capacity
+ self.router_bias = router_bias
+ if router_dtype not in ["float32", "float16", "bfloat16"]:
+ raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}")
+ self.router_dtype = router_dtype
+
+ self.router_ignore_padding_tokens = router_ignore_padding_tokens
+ self.batch_prioritized_routing = batch_prioritized_routing
+ self.second_expert_policy = second_expert_policy
+ self.normalize_router_prob_before_dropping = normalize_router_prob_before_dropping
+ self.moe_eval_capacity_token_fraction = moe_eval_capacity_token_fraction
+ self.moe_token_dropout = moe_token_dropout
+ self.output_router_logits = output_router_logits
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ is_encoder_decoder=is_encoder_decoder,
+ decoder_start_token_id=decoder_start_token_id,
+ **kwargs,
+ )
+
+
+__all__ = ["NllbMoeConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/convert_nllb_moe_sharded_original_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/convert_nllb_moe_sharded_original_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef2e3d0d90dd1e78afd9122646a08d1b86f831eb
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/convert_nllb_moe_sharded_original_checkpoint_to_pytorch.py
@@ -0,0 +1,161 @@
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import json
+import os
+
+import torch
+from torch import nn
+
+from transformers import NllbMoeConfig, NllbMoeModel
+from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
+
+
+def remove_ignore_keys_(state_dict):
+ ignore_keys = [
+ "encoder.version",
+ "decoder.version",
+ "model.encoder.version",
+ "model.decoder.version",
+ "decoder.output_projection.weight",
+ "_float_tensor",
+ "encoder.embed_positions._float_tensor",
+ "decoder.embed_positions._float_tensor",
+ ]
+ for k in ignore_keys:
+ state_dict.pop(k, None)
+
+
+def make_linear_from_emb(emb):
+ vocab_size, emb_size = emb.weight.shape
+ lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
+ lin_layer.weight.data = emb.weight.data
+ return lin_layer
+
+
+def rename_fairseq_keys(state_dict, expert_idx=None):
+ new_dict = {}
+ for old_key in state_dict:
+ key = old_key
+ if "moe_layer.experts." in key:
+ if expert_idx is not None:
+ key = key.replace("moe_layer.experts.0", f"ffn.experts.expert_{expert_idx}")
+ else:
+ key = key.replace("moe_layer.experts.", "ffn.experts.expert_")
+ if "gate" in key:
+ key = key.replace(".moe_layer.gate.wg", ".ffn.router.classifier")
+ if "fc2" and "experts" not in key:
+ key = key.replace(".fc2.", ".ffn.fc2.")
+ if "fc1" and "experts" not in key:
+ key = key.replace(".fc1.", ".ffn.fc1.")
+ if ".encoder_attn." in key:
+ key = key.replace(".encoder_attn.", ".cross_attention.")
+ if "encoder_attn_layer_norm" in key:
+ key = key.replace("encoder_attn_layer_norm", "cross_attention_layer_norm")
+ if "final_layer_norm" in key:
+ key = key.replace("final_layer_norm", "ff_layer_norm")
+ new_dict[key] = state_dict[old_key]
+ return new_dict
+
+
+def shard_on_the_fly(switch_checkpoint_path, dump_path, num_experts, dtype, weights_name: str = WEIGHTS_NAME):
+ sharded_state_dicts = []
+ total_size = 0
+ os.makedirs(dump_path, exist_ok=True)
+
+ for expert in range(num_experts):
+ expert_path = switch_checkpoint_path + f"-rank-{expert}.pt"
+ if os.path.isfile(expert_path):
+ expert_state = torch.load(expert_path, weights_only=True)["model"]
+ remove_ignore_keys_(expert_state)
+ expert_state = rename_fairseq_keys(expert_state, expert)
+ save_path = os.path.join(
+ dump_path, weights_name.replace(".bin", f"-{len(sharded_state_dicts) + 1:05d}-of-???.bin")
+ )
+ torch.save(expert_state, save_path)
+ sharded_state_dicts.append(expert_state.keys())
+ total_size += sum([value.numel() for key, value in expert_state.items()]) * (
+ expert_state[list(expert_state)[0]].element_size()
+ )
+
+ # Add the last block
+ save_path = os.path.join(
+ dump_path, weights_name.replace(".bin", f"-{len(sharded_state_dicts) + 1:05d}-of-???.bin")
+ )
+ shared_weights = torch.load(switch_checkpoint_path + "-shared.pt", weights_only=True)["model"]
+ remove_ignore_keys_(shared_weights)
+ shared_weights = rename_fairseq_keys(shared_weights, None)
+ shared_weights["shared.weight"] = shared_weights["decoder.embed_tokens.weight"]
+ sharded_state_dicts.append(shared_weights.keys())
+
+ # If we only have the shared weights (dummy model/experts saved on the same file)
+ if len(sharded_state_dicts) == 1:
+ save_path = os.path.join(dump_path, weights_name)
+ torch.save(shared_weights, save_path)
+ return {weights_name: sharded_state_dicts[0]}, None
+ else:
+ torch.save(shared_weights, save_path)
+ # Otherwise, let's build the index
+ weight_map = {}
+ for idx, shard in enumerate(sharded_state_dicts):
+ shard_file = weights_name.replace(".bin", f"-{idx + 1:05d}-of-{len(sharded_state_dicts):05d}.bin")
+ temp_filename = os.path.join(dump_path, weights_name.replace(".bin", f"-{idx + 1:05d}-of-???.bin"))
+ os.rename(temp_filename, os.path.join(dump_path, shard_file))
+ for key in shard:
+ weight_map[key] = shard_file
+
+ # Add the metadata
+ metadata = {"total_size": total_size}
+ index = {"metadata": metadata, "weight_map": weight_map}
+
+ with open(os.path.join(dump_path, WEIGHTS_INDEX_NAME), "w", encoding="utf-8") as f:
+ content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+ f.write(content)
+
+ return metadata, index
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--nllb_moe_checkpoint_path",
+ default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
+ type=str,
+ required=False,
+ help="Path to a directory containing a folder per layer. Follows the original Google format.",
+ )
+ parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
+ parser.add_argument(
+ "--pytorch_dump_folder_path",
+ default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
+ type=str,
+ required=False,
+ help="Path to the output pytorch model.",
+ )
+ args = parser.parse_args()
+ metadata, index = shard_on_the_fly(
+ args.nllb_moe_checkpoint_path,
+ args.pytorch_dump_folder_path,
+ 128,
+ args.dtype,
+ )
+
+ config = NllbMoeConfig.from_pretrained(
+ "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
+ )
+ config.save_pretrained(args.pytorch_dump_folder_path)
+ model = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
+ print("Done")
+ model.save_pretrained(args.pytorch_dump_folder_path)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/modeling_nllb_moe.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/modeling_nllb_moe.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5b786b9767cf685f598a0398edfa4c90d6ea1a1
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nllb_moe/modeling_nllb_moe.py
@@ -0,0 +1,1779 @@
+# coding=utf-8
+# Copyright 2023 NllbMoe Authors and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch NLLB-MoE model."""
+
+import math
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.fsdp import is_fsdp_managed_module
+from ...modeling_attn_mask_utils import (
+ _prepare_4d_attention_mask,
+ _prepare_4d_attention_mask_for_sdpa,
+ _prepare_4d_causal_attention_mask,
+ _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ MoEModelOutput,
+ MoEModelOutputWithPastAndCrossAttentions,
+ Seq2SeqMoEModelOutput,
+ Seq2SeqMoEOutput,
+)
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import auto_docstring, is_torch_flex_attn_available, logging
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_nllb_moe import NllbMoeConfig
+
+
+if is_torch_flex_attn_available():
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+
+logger = logging.get_logger(__name__)
+
+
+####################################################
+# This dict contains ids and associated url
+# for the pretrained weights provided with the models
+####################################################
+
+
+# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+ """
+ Shift input ids one token to the right.
+ """
+ shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+ shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
+ shifted_input_ids[:, 0] = decoder_start_token_id
+
+ if pad_token_id is None:
+ raise ValueError("self.model.config.pad_token_id has to be defined.")
+ # replace possible -100 values in labels by `pad_token_id`
+ shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+ return shifted_input_ids
+
+
+# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
+def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
+ """
+ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
+ are ignored. This is modified from fairseq's `utils.make_positions`.
+
+ Args:
+ x: torch.Tensor x:
+
+ Returns: torch.Tensor
+ """
+ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
+ mask = input_ids.ne(padding_idx).int()
+ incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
+ return incremental_indices.long() + padding_idx
+
+
+def load_balancing_loss_func(router_probs: torch.Tensor, expert_indices: torch.Tensor) -> float:
+ r"""
+ Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+ See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
+ function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+ experts is too unbalanced.
+
+ Args:
+ router_probs (`torch.Tensor`):
+ Probability assigned to each expert per token. Shape: [batch_size, seqeunce_length, num_experts].
+ expert_indices (`torch.Tensor`):
+ Indices tensor of shape [batch_size, seqeunce_length] identifying the selected expert for a given token.
+
+ Returns:
+ The auxiliary loss.
+ """
+ if router_probs is None:
+ return 0
+
+ num_experts = router_probs.shape[-1]
+
+ # cast the expert indices to int64, otherwise one-hot encoding will fail
+ if expert_indices.dtype != torch.int64:
+ expert_indices = expert_indices.to(torch.int64)
+
+ if len(expert_indices.shape) == 2:
+ expert_indices = expert_indices.unsqueeze(2)
+
+ expert_mask = torch.nn.functional.one_hot(expert_indices, num_experts)
+
+ # For a given token, determine if it was routed to a given expert.
+ expert_mask = torch.max(expert_mask, axis=-2).values
+
+ # cast to float32 otherwise mean will fail
+ expert_mask = expert_mask.to(torch.float32)
+ tokens_per_group_and_expert = torch.mean(expert_mask, axis=-2)
+
+ router_prob_per_group_and_expert = torch.mean(router_probs, axis=-2)
+ return torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert) * (num_experts**2)
+
+
+# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100ScaledWordEmbedding with M2M100->NllbMoe
+class NllbMoeScaledWordEmbedding(nn.Embedding):
+ """
+ This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
+ """
+
+ def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
+ super().__init__(num_embeddings, embedding_dim, padding_idx)
+ self.embed_scale = embed_scale
+
+ def forward(self, input_ids: torch.Tensor):
+ return super().forward(input_ids) * self.embed_scale
+
+
+# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding
+class NllbMoeSinusoidalPositionalEmbedding(nn.Module):
+ """This module produces sinusoidal positional embeddings of any length."""
+
+ def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
+ super().__init__()
+ self.offset = 2
+ self.embedding_dim = embedding_dim
+ self.padding_idx = padding_idx
+ self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
+
+ def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
+ emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
+ if hasattr(self, "weights"):
+ # in forward put the weights on the correct dtype and device of the param
+ emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
+
+ self.register_buffer("weights", emb_weights, persistent=False)
+
+ @staticmethod
+ def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
+ """
+ Build sinusoidal embeddings.
+
+ This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
+ "Attention Is All You Need".
+ """
+ half_dim = embedding_dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
+ emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
+ if embedding_dim % 2 == 1:
+ # zero pad
+ emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
+ if padding_idx is not None:
+ emb[padding_idx, :] = 0
+
+ return emb.to(torch.get_default_dtype())
+
+ @torch.no_grad()
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ past_key_values_length: int = 0,
+ ):
+ if input_ids is not None:
+ bsz, seq_len = input_ids.size()
+ # Create the position ids from the input token ids. Any padded tokens remain padded.
+ position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to(
+ input_ids.device
+ )
+ else:
+ bsz, seq_len = inputs_embeds.size()[:-1]
+ position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length)
+
+ # expand embeddings if needed
+ max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
+ if max_pos > self.weights.size(0):
+ self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
+
+ return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()
+
+ def create_position_ids_from_inputs_embeds(self, inputs_embeds, past_key_values_length):
+ """
+ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
+
+ Args:
+ inputs_embeds: torch.Tensor
+
+ Returns: torch.Tensor
+ """
+ input_shape = inputs_embeds.size()[:-1]
+ sequence_length = input_shape[1]
+
+ position_ids = torch.arange(
+ self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+ )
+ return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length
+
+
+class NllbMoeTop2Router(nn.Module):
+ """
+ Router using tokens choose top-2 experts assignment.
+
+ This router uses the same mechanism as in NLLB-MoE from the fairseq repository. Items are sorted by router_probs
+ and then routed to their choice of expert until the expert's expert_capacity is reached. **There is no guarantee
+ that each token is processed by an expert**, or that each expert receives at least one token.
+
+ The router combining weights are also returned to make sure that the states that are not updated will be masked.
+
+ """
+
+ def __init__(self, config: NllbMoeConfig):
+ super().__init__()
+ self.num_experts = config.num_experts
+ self.expert_capacity = config.expert_capacity
+ self.classifier = nn.Linear(config.hidden_size, self.num_experts, bias=config.router_bias)
+ self.router_ignore_padding_tokens = config.router_ignore_padding_tokens
+ self.dtype = getattr(torch, config.router_dtype)
+
+ self.second_expert_policy = config.second_expert_policy
+ self.normalize_router_prob_before_dropping = config.normalize_router_prob_before_dropping
+ self.batch_prioritized_routing = config.batch_prioritized_routing
+ self.moe_eval_capacity_token_fraction = config.moe_eval_capacity_token_fraction
+
+ def _cast_classifier(self):
+ r"""
+ `bitsandbytes` `Linear8bitLt` layers does not support manual casting Therefore we need to check if they are an
+ instance of the `Linear8bitLt` class by checking special attributes.
+ """
+ if not (hasattr(self.classifier, "SCB") or hasattr(self.classifier, "CB")):
+ self.classifier = self.classifier.to(self.dtype)
+
+ def normalize_router_probabilities(self, router_probs, top_1_mask, top_2_mask):
+ top_1_max_probs = (router_probs * top_1_mask).sum(dim=1)
+ top_2_max_probs = (router_probs * top_2_mask).sum(dim=1)
+ denom_s = torch.clamp(top_1_max_probs + top_2_max_probs, min=torch.finfo(router_probs.dtype).eps)
+ top_1_max_probs = top_1_max_probs / denom_s
+ top_2_max_probs = top_2_max_probs / denom_s
+ return top_1_max_probs, top_2_max_probs
+
+ def route_tokens(
+ self,
+ router_logits: torch.Tensor,
+ input_dtype: torch.dtype = torch.float32,
+ padding_mask: Optional[torch.LongTensor] = None,
+ ) -> tuple:
+ """
+ Computes the `dispatch_mask` and the `dispatch_weights` for each experts. The masks are adapted to the expert
+ capacity.
+ """
+ nb_tokens = router_logits.shape[0]
+ # Apply Softmax and cast back to the original `dtype`
+ router_probs = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(input_dtype)
+ top_1_expert_index = torch.argmax(router_probs, dim=-1)
+ top_1_mask = torch.nn.functional.one_hot(top_1_expert_index, num_classes=self.num_experts)
+
+ if self.second_expert_policy == "sampling":
+ gumbel = torch.distributions.gumbel.Gumbel(0, 1).rsample
+ router_logits += gumbel(router_logits.shape).to(router_logits.device)
+
+ # replace top_1_expert_index with min values
+ logits_except_top_1 = router_logits.masked_fill(top_1_mask.bool(), float("-inf"))
+ top_2_expert_index = torch.argmax(logits_except_top_1, dim=-1)
+ top_2_mask = torch.nn.functional.one_hot(top_2_expert_index, num_classes=self.num_experts)
+
+ if self.normalize_router_prob_before_dropping:
+ top_1_max_probs, top_2_max_probs = self.normalize_router_probabilities(
+ router_probs, top_1_mask, top_2_mask
+ )
+
+ if self.second_expert_policy == "random":
+ top_2_max_probs = (router_probs * top_2_mask).sum(dim=1)
+ sampled = (2 * top_2_max_probs) > torch.rand_like(top_2_max_probs.float())
+ top_2_mask = top_2_mask * sampled.repeat(self.num_experts, 1).transpose(1, 0)
+
+ if padding_mask is not None and not self.router_ignore_padding_tokens:
+ if len(padding_mask.shape) == 4:
+ # only get the last causal mask
+ padding_mask = padding_mask[:, :, -1, :].reshape(-1)[-nb_tokens:]
+ non_padding = ~padding_mask.bool()
+ top_1_mask = top_1_mask * non_padding.unsqueeze(-1).to(top_1_mask.dtype)
+ top_2_mask = top_2_mask * non_padding.unsqueeze(-1).to(top_1_mask.dtype)
+
+ if self.batch_prioritized_routing:
+ # sort tokens based on their routing probability
+ # to make sure important tokens are routed, first
+ importance_scores = -1 * router_probs.max(dim=1)[0]
+ sorted_top_1_mask = top_1_mask[importance_scores.argsort(dim=0)]
+ sorted_cumsum1 = (torch.cumsum(sorted_top_1_mask, dim=0) - 1) * sorted_top_1_mask
+ locations1 = sorted_cumsum1[importance_scores.argsort(dim=0).argsort(dim=0)]
+
+ sorted_top_2_mask = top_2_mask[importance_scores.argsort(dim=0)]
+ sorted_cumsum2 = (torch.cumsum(sorted_top_2_mask, dim=0) - 1) * sorted_top_2_mask
+ locations2 = sorted_cumsum2[importance_scores.argsort(dim=0).argsort(dim=0)]
+ # Update 2nd's location by accounting for locations of 1st
+ locations2 += torch.sum(top_1_mask, dim=0, keepdim=True)
+
+ else:
+ locations1 = torch.cumsum(top_1_mask, dim=0) - 1
+ locations2 = torch.cumsum(top_2_mask, dim=0) - 1
+ # Update 2nd's location by accounting for locations of 1st
+ locations2 += torch.sum(top_1_mask, dim=0, keepdim=True)
+
+ if not self.training and self.moe_eval_capacity_token_fraction > 0:
+ self.expert_capacity = math.ceil(self.moe_eval_capacity_token_fraction * nb_tokens)
+ else:
+ capacity = 2 * math.ceil(nb_tokens / self.num_experts)
+ self.expert_capacity = capacity if self.expert_capacity is None else self.expert_capacity
+
+ # Remove locations outside capacity from ( cumsum < capacity = False will not be routed)
+ top_1_mask = top_1_mask * torch.lt(locations1, self.expert_capacity)
+ top_2_mask = top_2_mask * torch.lt(locations2, self.expert_capacity)
+
+ if not self.normalize_router_prob_before_dropping:
+ top_1_max_probs, top_2_max_probs = self.normalize_router_probabilities(
+ router_probs, top_1_mask, top_2_mask
+ )
+
+ # Calculate combine_weights and dispatch_mask
+ gates1 = top_1_max_probs[:, None] * top_1_mask
+ gates2 = top_2_max_probs[:, None] * top_2_mask
+ router_probs = gates1 + gates2
+
+ return top_1_mask, router_probs
+
+ def forward(self, hidden_states: torch.Tensor, padding_mask: Optional[torch.LongTensor] = None) -> tuple:
+ r"""
+ The hidden states are reshaped to simplify the computation of the router probabilities (combining weights for
+ each experts.)
+
+ Args:
+ hidden_states (`torch.Tensor`):
+ (batch_size, sequence_length, hidden_dim) from which router probabilities are computed.
+ Returns:
+ top_1_mask (`torch.Tensor` of shape (batch_size, sequence_length)):
+ Index tensor of shape [batch_size, sequence_length] corresponding to the expert selected for each token
+ using the top1 probabilities of the router.
+ router_probabilities (`torch.Tensor` of shape (batch_size, sequence_length, nump_experts)):
+ Tensor of shape (batch_size, sequence_length, num_experts) corresponding to the probabilities for each
+ token and expert. Used for routing tokens to experts.
+ router_logits (`torch.Tensor` of shape (batch_size, sequence_length))):
+ Logits tensor of shape (batch_size, sequence_length, num_experts) corresponding to raw router logits.
+ This is used later for computing router z-loss.
+ """
+ self.input_dtype = hidden_states.dtype
+ batch_size, sequence_length, hidden_dim = hidden_states.shape
+ hidden_states = hidden_states.reshape((batch_size * sequence_length), hidden_dim)
+ hidden_states = hidden_states.to(self.dtype)
+ self._cast_classifier()
+ router_logits = self.classifier(hidden_states)
+ top_1_mask, router_probs = self.route_tokens(router_logits, self.input_dtype, padding_mask)
+ return top_1_mask, router_probs
+
+
+class NllbMoeDenseActDense(nn.Module):
+ def __init__(self, config: NllbMoeConfig, ffn_dim: int):
+ super().__init__()
+ self.fc1 = nn.Linear(config.d_model, ffn_dim)
+ self.fc2 = nn.Linear(ffn_dim, config.d_model)
+ self.dropout = nn.Dropout(config.activation_dropout)
+ self.act = ACT2FN[config.activation_function]
+
+ def forward(self, hidden_states):
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.act(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ if (
+ isinstance(self.fc2.weight, torch.Tensor)
+ and hidden_states.dtype != self.fc2.weight.dtype
+ and (self.fc2.weight.dtype != torch.int8 and self.fc2.weight.dtype != torch.uint8)
+ ):
+ hidden_states = hidden_states.to(self.fc2.weight.dtype)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+class NllbMoeSparseMLP(nn.Module):
+ r"""
+ Implementation of the NLLB-MoE sparse MLP module.
+ """
+
+ def __init__(self, config: NllbMoeConfig, ffn_dim: int, expert_class: nn.Module = NllbMoeDenseActDense):
+ super().__init__()
+ self.router = NllbMoeTop2Router(config)
+ self.moe_token_dropout = config.moe_token_dropout
+ self.token_dropout = nn.Dropout(self.moe_token_dropout)
+ self.num_experts = config.num_experts
+
+ self.experts = nn.ModuleDict()
+ for idx in range(self.num_experts):
+ self.experts[f"expert_{idx}"] = expert_class(config, ffn_dim)
+
+ def forward(self, hidden_states: torch.Tensor, padding_mask: Optional[torch.Tensor] = False):
+ r"""
+ The goal of this forward pass is to have the same number of operation as the equivalent `NllbMoeDenseActDense`
+ (mlp) layer. This means that all of the hidden states should be processed at most twice ( since we are using a
+ top_2 gating mechanism). This means that we keep the complexity to O(batch_size x sequence_length x hidden_dim)
+ instead of O(num_experts x batch_size x sequence_length x hidden_dim).
+
+ 1- Get the `router_probs` from the `router`. The shape of the `router_mask` is `(batch_size X sequence_length,
+ num_expert)` and corresponds to the boolean version of the `router_probs`. The inputs are masked using the
+ `router_mask`.
+
+ 2- Dispatch the hidden_states to its associated experts. The router probabilities are used to weight the
+ contribution of each experts when updating the masked hidden states.
+
+ Args:
+ hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_dim)`):
+ The hidden states
+ padding_mask (`torch.Tensor`, *optional*, defaults to `False`):
+ Attention mask. Can be in the causal form or not.
+
+ Returns:
+ hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_dim)`):
+ Updated hidden states
+ router_logits (`torch.Tensor` of shape `(batch_size, sequence_length, num_experts)`):
+ Needed for computing the loss
+
+ """
+ batch_size, sequence_length, hidden_dim = hidden_states.shape
+
+ top_1_mask, router_probs = self.router(hidden_states, padding_mask)
+ router_mask = router_probs.bool()
+ hidden_states = hidden_states.reshape((batch_size * sequence_length), hidden_dim)
+ masked_hidden_states = torch.einsum("bm,be->ebm", hidden_states, router_mask)
+ for idx, expert in enumerate(self.experts.values()):
+ token_indices = router_mask[:, idx]
+ combining_weights = router_probs[token_indices, idx]
+ expert_output = expert(masked_hidden_states[idx, token_indices])
+ if self.moe_token_dropout > 0:
+ if self.training:
+ expert_output = self.token_dropout(expert_output)
+ else:
+ expert_output *= 1 - self.moe_token_dropout
+ masked_hidden_states[idx, token_indices] = torch.einsum("b,be->be", combining_weights, expert_output)
+ hidden_states = masked_hidden_states.sum(dim=0).reshape(batch_size, sequence_length, hidden_dim)
+
+ top_1_expert_index = torch.argmax(top_1_mask, dim=-1)
+ return hidden_states, (router_probs, top_1_expert_index)
+
+
+# Copied from transformers.models.bart.modeling_bart.eager_attention_forward
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: Optional[float] = None,
+ dropout: float = 0.0,
+ head_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+):
+ if scaling is None:
+ scaling = query.size(-1) ** -0.5
+
+ attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ if head_mask is not None:
+ attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
+
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenAttention with Musicgen->NllbMoe,key_value_states->encoder_hidden_states
+class NllbMoeAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: Optional[float] = 0.0,
+ is_decoder: Optional[bool] = False,
+ bias: Optional[bool] = True,
+ is_causal: Optional[bool] = False,
+ config: Optional[NllbMoeConfig] = None,
+ layer_idx: Optional[int] = None,
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+ self.config = config
+
+ if (self.head_dim * num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+ self.is_causal = is_causal
+ self.layer_idx = layer_idx
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ cache_position: Optional[torch.Tensor] = None,
+ # TODO: we need a refactor so that the different attention modules can get their specific kwargs
+ # ATM, we have mixed things encoder, decoder, and encoder-decoder attn
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if encoder_hidden_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = encoder_hidden_states is not None
+
+ # determine input shapes
+ bsz, tgt_len = hidden_states.shape[:-1]
+ src_len = encoder_hidden_states.shape[1] if is_cross_attention else tgt_len
+
+ q_input_shape = (bsz, tgt_len, -1, self.head_dim)
+ kv_input_shape = (bsz, src_len, -1, self.head_dim)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states).view(*q_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ if isinstance(past_key_values, EncoderDecoderCache):
+ is_updated = past_key_values.is_updated.get(self.layer_idx)
+ if is_cross_attention:
+ # after the first generated id, we can subsequently re-use all key/value_layer from cache
+ curr_past_key_value = past_key_values.cross_attention_cache
+ else:
+ curr_past_key_value = past_key_values.self_attention_cache
+ else:
+ curr_past_key_value = past_key_values
+
+ current_states = encoder_hidden_states if is_cross_attention else hidden_states
+ if is_cross_attention and past_key_values is not None and is_updated:
+ # reuse k,v, cross_attentions
+ key_states = curr_past_key_value.layers[self.layer_idx].keys
+ value_states = curr_past_key_value.layers[self.layer_idx].values
+ else:
+ key_states = self.k_proj(current_states).view(*kv_input_shape).transpose(1, 2)
+ value_states = self.v_proj(current_states).view(*kv_input_shape).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ cache_position = cache_position if not is_cross_attention else None
+ key_states, value_states = curr_past_key_value.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+ # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+ if is_cross_attention:
+ past_key_values.is_updated[self.layer_idx] = True
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=self.scaling,
+ output_attentions=output_attentions,
+ head_mask=layer_head_mask,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+class NllbMoeEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: NllbMoeConfig, is_sparse: bool = False):
+ super().__init__()
+ self.embed_dim = config.d_model
+ self.is_sparse = is_sparse
+ self.self_attn = NllbMoeAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.encoder_attention_heads,
+ dropout=config.attention_dropout,
+ config=config,
+ )
+ self.attn_dropout = nn.Dropout(config.dropout)
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ if not self.is_sparse:
+ self.ffn = NllbMoeDenseActDense(config, ffn_dim=config.encoder_ffn_dim)
+ else:
+ self.ffn = NllbMoeSparseMLP(config, ffn_dim=config.encoder_ffn_dim)
+ self.ff_layer_norm = nn.LayerNorm(config.d_model)
+ self.ff_dropout = nn.Dropout(config.activation_dropout)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ layer_head_mask: torch.Tensor,
+ output_attentions: bool = False,
+ output_router_logits: bool = False,
+ ) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`):
+ input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`):
+ attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very
+ large negative values.
+ layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states = self.attn_dropout(hidden_states)
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+
+ hidden_states = self.ff_layer_norm(hidden_states)
+ if self.is_sparse:
+ hidden_states, router_states = self.ffn(hidden_states, attention_mask)
+ else:
+ # router_states set to None to track which layers have None gradients.
+ hidden_states, router_states = self.ffn(hidden_states), None
+
+ hidden_states = self.ff_dropout(hidden_states)
+
+ hidden_states = residual + hidden_states
+
+ if hidden_states.dtype == torch.float16 and (
+ torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
+ ):
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ if output_router_logits:
+ outputs += (router_states,)
+
+ return outputs
+
+
+class NllbMoeDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: NllbMoeConfig, is_sparse: bool = False, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.embed_dim = config.d_model
+ self.is_sparse = is_sparse
+ self.self_attn = NllbMoeAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.decoder_attention_heads,
+ dropout=config.attention_dropout,
+ is_decoder=True,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+ self.attn_dropout = nn.Dropout(config.dropout)
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+ self.cross_attention = NllbMoeAttention(
+ self.embed_dim,
+ config.decoder_attention_heads,
+ config.attention_dropout,
+ is_decoder=True,
+ config=config,
+ layer_idx=layer_idx,
+ )
+ self.cross_attention_layer_norm = nn.LayerNorm(self.embed_dim)
+ if not self.is_sparse:
+ self.ffn = NllbMoeDenseActDense(config, ffn_dim=config.decoder_ffn_dim)
+ else:
+ self.ffn = NllbMoeSparseMLP(config, ffn_dim=config.decoder_ffn_dim)
+ self.ff_layer_norm = nn.LayerNorm(config.d_model)
+ self.ff_dropout = nn.Dropout(config.activation_dropout)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ output_router_logits: Optional[bool] = False,
+ use_cache: Optional[bool] = True,
+ cache_position: Optional[torch.Tensor] = True,
+ ) -> torch.Tensor:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`):
+ input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`):
+ attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very
+ large negative values.
+ encoder_hidden_states (`torch.FloatTensor`):
+ cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
+ encoder_attention_mask (`torch.FloatTensor`):
+ encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by
+ very large negative values.
+ layer_head_mask (`torch.FloatTensor`):
+ mask for attention heads in a given layer of size `(encoder_attention_heads,)`.
+ cross_attn_layer_head_mask (`torch.FloatTensor`):
+ mask for cross-attention heads in a given layer of size `(decoder_attention_heads,)`.
+ past_key_values (`Tuple(torch.FloatTensor)`):
+ cached past key and value projection states
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = self.attn_dropout(hidden_states)
+ hidden_states = residual + hidden_states
+
+ # Cross-Attention Block
+ cross_attn_weights = None
+ if encoder_hidden_states is not None:
+ residual = hidden_states
+ hidden_states = self.cross_attention_layer_norm(hidden_states)
+
+ hidden_states, cross_attn_weights = self.cross_attention(
+ hidden_states=hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ past_key_values=past_key_values,
+ attention_mask=encoder_attention_mask,
+ layer_head_mask=cross_attn_layer_head_mask,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ )
+ hidden_states = self.attn_dropout(hidden_states)
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+
+ hidden_states = self.ff_layer_norm(hidden_states)
+ if self.is_sparse:
+ hidden_states, router_states = self.ffn(hidden_states, attention_mask)
+ else:
+ hidden_states, router_states = self.ffn(hidden_states), None
+
+ hidden_states = self.ff_dropout(hidden_states)
+
+ hidden_states = residual + hidden_states
+
+ # clamp inf values to enable fp16 training
+ if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ if output_router_logits:
+ outputs += (router_states,)
+
+ return outputs
+
+
+@auto_docstring
+class NllbMoePreTrainedModel(PreTrainedModel):
+ config: NllbMoeConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["NllbMoeEncoderLayer", "NllbMoeDecoderLayer"]
+ # TODO: If anyone is up to it to make sure tests pass etc
+ # Flash attention has problems due to not preparing masks the same way as eager/sdpa
+ # SDPA has more flaky logits which requires more time to look into tests
+ _supports_flash_attn = False
+ _supports_sdpa = False
+ _supports_flex_attn = False
+
+ def _init_weights(self, module: nn.Module):
+ """Initialize the weights"""
+ std = self.config.init_std
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+
+
+class NllbMoeEncoder(NllbMoePreTrainedModel):
+ """
+ Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
+ [`NllbMoeEncoderLayer`].
+
+ Args:
+ config:
+ NllbMoeConfig
+ embed_tokens (nn.Embedding):
+ output embedding
+ """
+
+ def __init__(self, config: NllbMoeConfig, embed_tokens: Optional[nn.Embedding] = None):
+ super().__init__(config)
+
+ self.dropout = config.dropout
+ self.layerdrop = config.encoder_layerdrop
+
+ embed_dim = config.d_model
+ self.padding_idx = config.pad_token_id
+ self.max_source_positions = config.max_position_embeddings
+ embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
+
+ self.embed_tokens = NllbMoeScaledWordEmbedding(
+ config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale
+ )
+
+ if embed_tokens is not None:
+ self.embed_tokens.weight = embed_tokens.weight
+
+ self.embed_positions = NllbMoeSinusoidalPositionalEmbedding(
+ config.max_position_embeddings,
+ embed_dim,
+ self.padding_idx,
+ )
+ sparse_step = config.encoder_sparse_step
+ self.layers = nn.ModuleList()
+ for i in range(config.encoder_layers):
+ is_sparse = (i + 1) % sparse_step == 0 if sparse_step > 0 else False
+ self.layers.append(NllbMoeEncoderLayer(config, is_sparse))
+
+ self.layer_norm = nn.LayerNorm(config.d_model)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ):
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ output_router_logits (`bool`, *optional*):
+ Whether or not to return the logits of all the routers. They are useful for computing the router loss,
+ and should not be returned during inference.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ embed_pos = self.embed_positions(input_ids, inputs_embeds)
+ embed_pos = embed_pos.to(inputs_embeds.device)
+
+ hidden_states = inputs_embeds + embed_pos
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ attention_mask = self._update_full_mask(
+ attention_mask,
+ inputs_embeds,
+ )
+
+ encoder_states = () if output_hidden_states else None
+ all_router_probs = () if output_router_logits else None
+ all_attentions = () if output_attentions else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ if head_mask is not None:
+ if head_mask.size()[0] != len(self.layers):
+ raise ValueError(
+ f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+
+ for idx, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = torch.rand([])
+ if self.training and (dropout_probability < self.layerdrop): # skip the layer
+ layer_outputs = (None, None, None)
+ else:
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ output_attentions=output_attentions,
+ output_router_logits=output_router_logits,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions += (layer_outputs[1],)
+
+ if output_router_logits:
+ all_router_probs += (layer_outputs[-1],)
+
+ last_hidden_state = self.layer_norm(hidden_states)
+
+ if output_hidden_states:
+ encoder_states += (last_hidden_state,)
+
+ if not return_dict:
+ return tuple(
+ v for v in [last_hidden_state, encoder_states, all_attentions, all_router_probs] if v is not None
+ )
+
+ return MoEModelOutput(
+ last_hidden_state=last_hidden_state,
+ hidden_states=encoder_states,
+ attentions=all_attentions,
+ router_probs=all_router_probs,
+ )
+
+ # Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_full_mask
+ def _update_full_mask(
+ self,
+ attention_mask: Union[torch.Tensor, None],
+ inputs_embeds: torch.Tensor,
+ ):
+ if attention_mask is not None:
+ if self.config._attn_implementation == "flash_attention_2":
+ attention_mask = attention_mask if 0 in attention_mask else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & head_mask can not be supported when using SDPA, fall back to
+ # the manual implementation that requires a 4D causal mask in all cases.
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, inputs_embeds.dtype)
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask, is_causal=False)
+ else:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
+
+ return attention_mask
+
+
+class NllbMoeDecoder(NllbMoePreTrainedModel):
+ """
+ Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`NllbMoeDecoderLayer`]
+
+ Args:
+ config:
+ NllbMoeConfig
+ embed_tokens (nn.Embedding):
+ output embedding
+ """
+
+ def __init__(self, config: NllbMoeConfig, embed_tokens: Optional[nn.Embedding] = None):
+ super().__init__(config)
+ self.dropout = config.dropout
+ self.layerdrop = config.decoder_layerdrop
+ self.padding_idx = config.pad_token_id
+ self.max_target_positions = config.max_position_embeddings
+ embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+
+ self.embed_tokens = NllbMoeScaledWordEmbedding(
+ config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale
+ )
+
+ if embed_tokens is not None:
+ self.embed_tokens.weight = embed_tokens.weight
+
+ self.embed_positions = NllbMoeSinusoidalPositionalEmbedding(
+ config.max_position_embeddings,
+ config.d_model,
+ self.padding_idx,
+ )
+
+ sparse_step = config.decoder_sparse_step
+ self.layers = nn.ModuleList()
+ for i in range(config.decoder_layers):
+ is_sparse = (i + 1) % sparse_step == 0 if sparse_step > 0 else False
+ self.layers.append(NllbMoeDecoderLayer(config, is_sparse, layer_idx=i))
+
+ self.layer_norm = nn.LayerNorm(config.d_model)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = True,
+ ):
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+ of the decoder.
+ encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+ Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+ selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
+ cross-attention on hidden heads. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+ shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+ cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+ that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+ all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ output_router_logits (`bool`, *optional*):
+ Whether or not to return the logits of all the routers. They are useful for computing the router loss,
+ and should not be returned during inference.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ # initialize `past_key_values`
+ if use_cache and past_key_values is None:
+ past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
+ if use_cache and isinstance(past_key_values, tuple):
+ logger.warning_once(
+ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
+ "You should pass an instance of `EncoderDecoderCache` instead, e.g. "
+ "`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
+ )
+ past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
+
+ past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+ attention_mask = self._update_causal_mask(
+ attention_mask,
+ input_shape,
+ inputs_embeds,
+ past_key_values_length,
+ )
+ encoder_attention_mask = self._update_cross_attn_mask(
+ encoder_hidden_states,
+ encoder_attention_mask,
+ input_shape,
+ inputs_embeds,
+ )
+
+ # embed positions
+ positions = self.embed_positions(input_ids, inputs_embeds, past_key_values_length)
+ positions = positions.to(inputs_embeds.device)
+
+ hidden_states = inputs_embeds + positions
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_router_probs = () if output_router_logits else None
+ all_cross_attentions = () if output_attentions else None
+
+ # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
+ for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
+ if attn_mask is not None:
+ if attn_mask.size()[0] != len(self.layers):
+ raise ValueError(
+ f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+ synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
+
+ for idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ dropout_probability = torch.rand([])
+
+ skip_the_layer = self.training and dropout_probability < self.layerdrop
+ if not skip_the_layer or synced_gpus:
+ layer_head_mask = head_mask[idx] if head_mask is not None else None
+ cross_attn_layer_head_mask = cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
+
+ # under fsdp or deepspeed zero3 all gpus must run in sync
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask,
+ encoder_hidden_states, # as a positional argument for gradient checkpointing
+ encoder_attention_mask=encoder_attention_mask,
+ layer_head_mask=layer_head_mask,
+ cross_attn_layer_head_mask=cross_attn_layer_head_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_router_logits=output_router_logits,
+ cache_position=cache_position,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if skip_the_layer:
+ continue
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+ all_cross_attentions += (layer_outputs[2],)
+
+ if output_router_logits:
+ all_router_probs += (layer_outputs[-1],)
+
+ hidden_states = self.layer_norm(hidden_states)
+
+ # Add last layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [
+ hidden_states,
+ past_key_values,
+ all_hidden_states,
+ all_self_attns,
+ all_cross_attentions,
+ all_router_probs,
+ ]
+ if v is not None
+ )
+ return MoEModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ cross_attentions=all_cross_attentions,
+ router_probs=all_router_probs,
+ )
+
+ # Copied from transformers.models.musicgen.modeling_musicgen.MusicgenDecoder._update_causal_mask
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, None],
+ input_shape: torch.Size,
+ inputs_embeds: torch.Tensor,
+ past_key_values_length: int,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ # 2d mask is passed through the layers
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+ attention_mask,
+ input_shape,
+ inputs_embeds,
+ past_key_values_length,
+ )
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ # Other attention flavors support in-built causal (when `mask is None`)
+ # while we need to create our specific block mask regardless
+ elif attention_mask is None:
+ attention_mask = make_flex_block_causal_mask(
+ torch.ones(
+ size=(input_shape),
+ device=inputs_embeds.device,
+ )
+ )
+ else:
+ # 4d mask is passed through the layers
+ attention_mask = _prepare_4d_causal_attention_mask(
+ attention_mask, input_shape, inputs_embeds, past_key_values_length
+ )
+
+ return attention_mask
+
+ # Copied from transformers.models.musicgen.modeling_musicgen.MusicgenDecoder._update_cross_attn_mask
+ def _update_cross_attn_mask(
+ self,
+ encoder_hidden_states: Union[torch.Tensor, None],
+ encoder_attention_mask: Union[torch.Tensor, None],
+ input_shape: torch.Size,
+ inputs_embeds: torch.Tensor,
+ ):
+ # expand encoder attention mask
+ if encoder_hidden_states is not None and encoder_attention_mask is not None:
+ if self.config._attn_implementation == "flash_attention_2":
+ encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
+ elif self.config._attn_implementation == "sdpa":
+ # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
+ encoder_attention_mask,
+ inputs_embeds.dtype,
+ tgt_len=input_shape[-1],
+ )
+ elif self.config._attn_implementation == "flex_attention":
+ if isinstance(encoder_attention_mask, torch.Tensor):
+ encoder_attention_mask = make_flex_block_causal_mask(
+ encoder_attention_mask,
+ query_length=input_shape[-1],
+ is_causal=False,
+ )
+ else:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ encoder_attention_mask = _prepare_4d_attention_mask(
+ encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+ )
+
+ return encoder_attention_mask
+
+
+@auto_docstring
+class NllbMoeModel(NllbMoePreTrainedModel):
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+
+ def __init__(self, config: NllbMoeConfig):
+ super().__init__(config)
+
+ padding_idx, vocab_size = config.pad_token_id, config.vocab_size
+ embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+ self.shared = NllbMoeScaledWordEmbedding(vocab_size, config.d_model, padding_idx, embed_scale=embed_scale)
+
+ self.encoder = NllbMoeEncoder(config, self.shared)
+ self.decoder = NllbMoeDecoder(config, self.shared)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, value):
+ self.shared = value
+ self.encoder.embed_tokens = self.shared
+ self.decoder.embed_tokens = self.shared
+
+ def _tie_weights(self):
+ if self.config.tie_word_embeddings:
+ self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
+ self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
+
+ def get_encoder(self):
+ return self.encoder
+
+ def get_decoder(self):
+ return self.decoder
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = True,
+ ) -> Union[tuple[torch.Tensor], Seq2SeqMoEModelOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ NllbMoe uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, NllbMoeModel
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts")
+ >>> model = SwitchTransformersModel.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts")
+
+ >>> input_ids = tokenizer(
+ ... "Studies have been shown that owning a dog is good for you", return_tensors="pt"
+ ... ).input_ids # Batch size 1
+ >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1
+
+ >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for NllbMoeModel
+ >>> decoder_input_ids = model._shift_right(decoder_input_ids)
+
+ >>> # forward pass
+ >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
+ >>> last_hidden_states = outputs.last_hidden_state
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ output_router_logits=output_router_logits,
+ return_dict=return_dict,
+ )
+ # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
+ elif return_dict and not isinstance(encoder_outputs, MoEModelOutput):
+ encoder_outputs = MoEModelOutput(
+ last_hidden_state=encoder_outputs[0],
+ hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+ attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+ router_probs=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
+ )
+
+ # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
+ decoder_outputs = self.decoder(
+ input_ids=decoder_input_ids,
+ attention_mask=decoder_attention_mask,
+ encoder_hidden_states=encoder_outputs[0],
+ encoder_attention_mask=attention_mask,
+ head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ output_router_logits=output_router_logits,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ if not return_dict:
+ return decoder_outputs + encoder_outputs
+
+ return Seq2SeqMoEModelOutput(
+ past_key_values=decoder_outputs.past_key_values,
+ cross_attentions=decoder_outputs.cross_attentions,
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ decoder_attentions=decoder_outputs.attentions,
+ encoder_router_logits=encoder_outputs.router_probs,
+ decoder_router_logits=decoder_outputs.router_probs,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The NllbMoe Model with a language modeling head. Can be used for summarization.
+ """
+)
+class NllbMoeForConditionalGeneration(NllbMoePreTrainedModel, GenerationMixin):
+ base_model_prefix = "model"
+ _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
+
+ def __init__(self, config: NllbMoeConfig):
+ super().__init__(config)
+ self.model = NllbMoeModel(config)
+ self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+ self.router_z_loss_coef = config.router_z_loss_coef
+ self.router_aux_loss_coef = config.router_aux_loss_coef
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_encoder(self):
+ return self.model.get_encoder()
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ decoder_input_ids: Optional[torch.LongTensor] = None,
+ decoder_attention_mask: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ decoder_head_mask: Optional[torch.Tensor] = None,
+ cross_attn_head_mask: Optional[torch.Tensor] = None,
+ encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ ) -> Union[tuple[torch.Tensor], Seq2SeqMoEOutput]:
+ r"""
+ decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Indices of decoder input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are decoder input IDs?](../glossary#decoder-input-ids)
+
+ NllbMoe uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
+ `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+ `past_key_values`).
+ decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
+ Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+ be used by default.
+ cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
+ 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example Translation:
+
+ ```python
+ >>> from transformers import AutoTokenizer, NllbMoeForConditionalGeneration
+
+ >>> model = NllbMoeForConditionalGeneration.from_pretrained("facebook/nllb-moe-54b")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-moe-54b")
+
+ >>> text_to_translate = "Life is like a box of chocolates"
+ >>> model_inputs = tokenizer(text_to_translate, return_tensors="pt")
+
+ >>> # translate to French
+ >>> gen_tokens = model.generate(**model_inputs, forced_bos_token_id=tokenizer.get_lang_id("eng_Latn"))
+ >>> print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True))
+ ```
+ """
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_router_logits = (
+ output_router_logits if output_router_logits is not None else self.config.output_router_logits
+ )
+ if labels is not None:
+ if decoder_input_ids is None:
+ decoder_input_ids = shift_tokens_right(
+ labels, self.config.pad_token_id, self.config.decoder_start_token_id
+ )
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ decoder_input_ids=decoder_input_ids,
+ encoder_outputs=encoder_outputs,
+ decoder_attention_mask=decoder_attention_mask,
+ head_mask=head_mask,
+ decoder_head_mask=decoder_head_mask,
+ cross_attn_head_mask=cross_attn_head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ decoder_inputs_embeds=decoder_inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ output_router_logits=output_router_logits,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+ lm_logits = self.lm_head(outputs[0])
+
+ loss = None
+ encoder_aux_loss = None
+ decoder_aux_loss = None
+
+ if labels is not None:
+ loss_fct = CrossEntropyLoss(ignore_index=-100)
+ # todo check in the config if router loss enables
+
+ if output_router_logits:
+ encoder_router_logits = outputs[-1]
+ decoder_router_logits = outputs[3 if output_attentions else 4]
+
+ # Compute the router loss (z_loss + auxiliary loss) for each router in the encoder and decoder
+ encoder_router_logits, encoder_expert_indexes = self._unpack_router_logits(encoder_router_logits)
+ encoder_aux_loss = load_balancing_loss_func(encoder_router_logits, encoder_expert_indexes)
+
+ decoder_router_logits, decoder_expert_indexes = self._unpack_router_logits(decoder_router_logits)
+ decoder_aux_loss = load_balancing_loss_func(decoder_router_logits, decoder_expert_indexes)
+
+ loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
+
+ if output_router_logits and labels is not None:
+ aux_loss = self.router_aux_loss_coef * (encoder_aux_loss + decoder_aux_loss)
+ loss = loss + aux_loss
+
+ output = (loss,) if loss is not None else ()
+ if not return_dict:
+ output += (lm_logits,)
+ if output_router_logits: # only return the loss if they are not None
+ output += (
+ encoder_aux_loss,
+ decoder_aux_loss,
+ *outputs[1:],
+ )
+ else:
+ output += outputs[1:]
+
+ return output
+
+ return Seq2SeqMoEOutput(
+ loss=loss,
+ logits=lm_logits,
+ past_key_values=outputs.past_key_values,
+ cross_attentions=outputs.cross_attentions,
+ encoder_aux_loss=encoder_aux_loss,
+ decoder_aux_loss=decoder_aux_loss,
+ encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+ encoder_hidden_states=outputs.encoder_hidden_states,
+ decoder_hidden_states=outputs.decoder_hidden_states,
+ encoder_attentions=outputs.encoder_attentions,
+ decoder_attentions=outputs.decoder_attentions,
+ encoder_router_logits=outputs.encoder_router_logits,
+ decoder_router_logits=outputs.decoder_router_logits,
+ )
+
+ def _unpack_router_logits(self, router_outputs):
+ total_router_logits = []
+ total_expert_indexes = []
+ for router_output in router_outputs:
+ if router_output is not None:
+ router_logits, expert_indexes = router_output
+ total_router_logits.append(router_logits)
+ total_expert_indexes.append(expert_indexes)
+
+ total_router_logits = torch.cat(total_router_logits, dim=1) if len(total_router_logits) > 0 else None
+ total_expert_indexes = torch.stack(total_expert_indexes, dim=1) if len(total_expert_indexes) > 0 else None
+ return total_router_logits, total_expert_indexes
+
+
+__all__ = [
+ "NllbMoeForConditionalGeneration",
+ "NllbMoeModel",
+ "NllbMoePreTrainedModel",
+ "NllbMoeTop2Router",
+ "NllbMoeSparseMLP",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cd3208bfa203196d0c31d6b3a1037e4f5ba22f4
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .image_processing_nougat import *
+ from .image_processing_nougat_fast import *
+ from .processing_nougat import *
+ from .tokenization_nougat_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/convert_nougat_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/convert_nougat_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8096ad864a8ea56c246b47506b78cdc7eb28b1e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/convert_nougat_to_hf.py
@@ -0,0 +1,282 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert Nougat checkpoints using the original `nougat` library. URL:
+https://github.com/facebookresearch/nougat/tree/main"""
+
+import argparse
+
+import torch
+from huggingface_hub import hf_hub_download
+from nougat import NougatModel
+from nougat.dataset.rasterize import rasterize_paper
+from nougat.utils.checkpoint import get_checkpoint
+from PIL import Image
+
+from transformers import (
+ DonutSwinConfig,
+ DonutSwinModel,
+ MBartConfig,
+ MBartForCausalLM,
+ NougatImageProcessor,
+ NougatProcessor,
+ NougatTokenizerFast,
+ VisionEncoderDecoderModel,
+)
+
+
+def get_configs(model):
+ original_config = model.config
+
+ encoder_config = DonutSwinConfig(
+ image_size=original_config.input_size,
+ patch_size=4,
+ depths=original_config.encoder_layer,
+ num_heads=[4, 8, 16, 32],
+ window_size=original_config.window_size,
+ embed_dim=128,
+ )
+ decoder_config = MBartConfig(
+ is_decoder=True,
+ is_encoder_decoder=False,
+ add_cross_attention=True,
+ decoder_layers=original_config.decoder_layer,
+ max_position_embeddings=original_config.max_position_embeddings,
+ vocab_size=len(
+ model.decoder.tokenizer
+ ), # several special tokens are added to the vocab of XLMRobertaTokenizer, see repo on the hub (added_tokens.json)
+ scale_embedding=True,
+ add_final_layer_norm=True,
+ tie_word_embeddings=False,
+ )
+
+ return encoder_config, decoder_config
+
+
+# Copied from transformers.models.donut.convert_donut_to_pytorch.rename_key
+def rename_key(name):
+ if "encoder.model" in name:
+ name = name.replace("encoder.model", "encoder")
+ if "decoder.model" in name:
+ name = name.replace("decoder.model", "decoder")
+ if "patch_embed.proj" in name:
+ name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection")
+ if "patch_embed.norm" in name:
+ name = name.replace("patch_embed.norm", "embeddings.norm")
+ if name.startswith("encoder"):
+ if "layers" in name:
+ name = "encoder." + name
+ if "attn.proj" in name:
+ name = name.replace("attn.proj", "attention.output.dense")
+ if "attn" in name and "mask" not in name:
+ name = name.replace("attn", "attention.self")
+ if "norm1" in name:
+ name = name.replace("norm1", "layernorm_before")
+ if "norm2" in name:
+ name = name.replace("norm2", "layernorm_after")
+ if "mlp.fc1" in name:
+ name = name.replace("mlp.fc1", "intermediate.dense")
+ if "mlp.fc2" in name:
+ name = name.replace("mlp.fc2", "output.dense")
+
+ if name == "encoder.norm.weight":
+ name = "encoder.layernorm.weight"
+ if name == "encoder.norm.bias":
+ name = "encoder.layernorm.bias"
+
+ return name
+
+
+# Copied from transformers.models.donut.convert_donut_to_pytorch.convert_state_dict
+def convert_state_dict(orig_state_dict, model):
+ for key in orig_state_dict.copy():
+ val = orig_state_dict.pop(key)
+
+ if "qkv" in key:
+ key_split = key.split(".")
+ layer_num = int(key_split[3])
+ block_num = int(key_split[5])
+ dim = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
+
+ if "weight" in key:
+ orig_state_dict[
+ f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight"
+ ] = val[:dim, :]
+ orig_state_dict[f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight"] = (
+ val[dim : dim * 2, :]
+ )
+ orig_state_dict[
+ f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight"
+ ] = val[-dim:, :]
+ else:
+ orig_state_dict[f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias"] = (
+ val[:dim]
+ )
+ orig_state_dict[f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = (
+ val[dim : dim * 2]
+ )
+ orig_state_dict[f"encoder.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias"] = (
+ val[-dim:]
+ )
+ elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
+ # HuggingFace implementation doesn't use attn_mask buffer
+ # and model doesn't use final LayerNorms for the encoder
+ pass
+ else:
+ orig_state_dict[rename_key(key)] = val
+
+ return orig_state_dict
+
+
+def convert_nougat_checkpoint(model_tag, pytorch_dump_folder_path=None, push_to_hub=False):
+ # load original model
+ checkpoint_path = get_checkpoint(None, model_tag)
+ original_model = NougatModel.from_pretrained(checkpoint_path)
+ original_model.eval()
+
+ # load HuggingFace model
+ encoder_config, decoder_config = get_configs(original_model)
+ encoder = DonutSwinModel(encoder_config)
+ decoder = MBartForCausalLM(decoder_config)
+ model = VisionEncoderDecoderModel(encoder=encoder, decoder=decoder)
+ model.eval()
+
+ state_dict = original_model.state_dict()
+ new_state_dict = convert_state_dict(state_dict, model)
+ model.load_state_dict(new_state_dict)
+
+ # verify results on PDF
+ filepath = hf_hub_download(repo_id="ysharma/nougat", filename="input/nougat.pdf", repo_type="space")
+ images = rasterize_paper(pdf=filepath, return_pil=True)
+ image = Image.open(images[0])
+
+ tokenizer_file = checkpoint_path / "tokenizer.json"
+ tokenizer = NougatTokenizerFast(tokenizer_file=str(tokenizer_file))
+ tokenizer.pad_token = ""
+ tokenizer.bos_token = ""
+ tokenizer.eos_token = " "
+ tokenizer.unk_token = ""
+ tokenizer.model_max_length = original_model.config.max_length
+
+ size = {"height": original_model.config.input_size[0], "width": original_model.config.input_size[1]}
+ image_processor = NougatImageProcessor(
+ do_align_long_axis=original_model.config.align_long_axis,
+ size=size,
+ )
+ processor = NougatProcessor(image_processor=image_processor, tokenizer=tokenizer)
+
+ # verify pixel_values
+ pixel_values = processor(image, return_tensors="pt").pixel_values
+ original_pixel_values = original_model.encoder.prepare_input(image).unsqueeze(0)
+
+ assert torch.allclose(original_pixel_values, pixel_values)
+
+ # verify patch embeddings
+ original_patch_embed = original_model.encoder.model.patch_embed(pixel_values)
+ patch_embeddings, _ = model.encoder.embeddings(pixel_values)
+ assert torch.allclose(original_patch_embed, patch_embeddings)
+
+ # verify encoder hidden states
+ original_last_hidden_state = original_model.encoder(pixel_values)
+ last_hidden_state = model.encoder(pixel_values).last_hidden_state
+ assert torch.allclose(original_last_hidden_state, last_hidden_state, atol=1e-2)
+
+ # NOTE original model does not use tied weights for embeddings of decoder
+ original_embeddings = original_model.decoder.model.model.decoder.embed_tokens
+ embeddings = model.decoder.model.decoder.embed_tokens
+ assert torch.allclose(original_embeddings.weight, embeddings.weight, atol=1e-3)
+
+ # verify decoder hidden states
+ prompt = "hello world"
+ decoder_input_ids = original_model.decoder.tokenizer(
+ prompt, add_special_tokens=False, return_tensors="pt"
+ ).input_ids
+ decoder_attention_mask = torch.ones_like(decoder_input_ids)
+ original_logits = original_model(
+ image_tensors=pixel_values, decoder_input_ids=decoder_input_ids, attention_mask=decoder_attention_mask
+ ).logits
+ logits = model(
+ pixel_values,
+ decoder_input_ids=decoder_input_ids[:, :-1],
+ decoder_attention_mask=decoder_attention_mask[:, :-1],
+ ).logits
+ assert torch.allclose(original_logits, logits, atol=1e-3)
+
+ # verify generation
+ outputs = model.generate(
+ pixel_values,
+ min_length=1,
+ max_length=30,
+ pad_token_id=tokenizer.pad_token_id,
+ eos_token_id=tokenizer.eos_token_id,
+ use_cache=True,
+ bad_words_ids=[
+ [tokenizer.unk_token_id],
+ ],
+ return_dict_in_generate=True,
+ do_sample=False,
+ )
+ generated = tokenizer.batch_decode(outputs.sequences, skip_special_tokens=True)[0]
+
+ if model_tag == "0.1.0-base":
+ expected_generation = "# Nougat: Neural Optical Understanding for Academic Documents\n\nLukas Blecher\n\nCorrespondence to: lblec"
+ elif model_tag == "0.1.0-small":
+ expected_generation = (
+ "# Nougat: Neural Optical Understanding for Academic Documents\n\nLukas Blecher\n\nCorrespondence to: lble"
+ )
+ else:
+ raise ValueError(f"Unexpected model tag: {model_tag}")
+
+ assert generated == expected_generation
+ print("Looks ok!")
+
+ if pytorch_dump_folder_path is not None:
+ print(f"Saving model and processor to {pytorch_dump_folder_path}")
+ model.save_pretrained(pytorch_dump_folder_path)
+ processor.save_pretrained(pytorch_dump_folder_path)
+
+ if push_to_hub:
+ tag_to_name = {"0.1.0-base": "nougat-base", "0.1.0-small": "nougat-small"}
+ model_name = tag_to_name[model_tag]
+
+ model.push_to_hub(f"facebook/{model_name}")
+ processor.push_to_hub(f"facebook/{model_name}")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--model_tag",
+ default="0.1.0-base",
+ required=False,
+ type=str,
+ choices=["0.1.0-base", "0.1.0-small"],
+ help="Tag of the original model you'd like to convert.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path",
+ default=None,
+ required=False,
+ type=str,
+ help="Path to the output PyTorch model directory.",
+ )
+ parser.add_argument(
+ "--push_to_hub",
+ action="store_true",
+ help="Whether or not to push the converted model and processor to the 🤗 hub.",
+ )
+
+ args = parser.parse_args()
+ convert_nougat_checkpoint(args.model_tag, args.pytorch_dump_folder_path, args.push_to_hub)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/image_processing_nougat.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/image_processing_nougat.py
new file mode 100644
index 0000000000000000000000000000000000000000..827686a6066d38a792667d6a67c8d91028d3f938
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/image_processing_nougat.py
@@ -0,0 +1,522 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for Nougat."""
+
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import (
+ get_resize_output_image_size,
+ pad,
+ resize,
+ to_channel_dimension_format,
+ to_pil_image,
+)
+from ...image_utils import (
+ IMAGENET_DEFAULT_MEAN,
+ IMAGENET_DEFAULT_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ get_image_size,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import TensorType, filter_out_non_signature_kwargs, logging
+from ...utils.import_utils import is_vision_available
+
+
+logger = logging.get_logger(__name__)
+
+
+if is_vision_available():
+ import PIL
+
+
+class NougatImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a Nougat image processor.
+
+ Args:
+ do_crop_margin (`bool`, *optional*, defaults to `True`):
+ Whether to crop the image margins.
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
+ `do_resize` in the `preprocess` method.
+ size (`dict[str, int]` *optional*, defaults to `{"height": 896, "width": 672}`):
+ Size of the image after resizing. Can be overridden by `size` in the `preprocess` method.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
+ Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
+ do_thumbnail (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image using thumbnail method.
+ do_align_long_axis (`bool`, *optional*, defaults to `False`):
+ Whether to align the long axis of the image with the long axis of `size` by rotating by 90 degrees.
+ do_pad (`bool`, *optional*, defaults to `True`):
+ Whether to pad the images to the largest image size in the batch.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
+ parameter in the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
+ `preprocess` method.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`float` or `list[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
+ Image standard deviation.
+ """
+
+ model_input_names = ["pixel_values"]
+
+ def __init__(
+ self,
+ do_crop_margin: bool = True,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ do_thumbnail: bool = True,
+ do_align_long_axis: bool = False,
+ do_pad: bool = True,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+
+ size = size if size is not None else {"height": 896, "width": 672}
+ size = get_size_dict(size)
+
+ self.do_crop_margin = do_crop_margin
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.do_thumbnail = do_thumbnail
+ self.do_align_long_axis = do_align_long_axis
+ self.do_pad = do_pad
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
+ self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
+
+ def python_find_non_zero(self, image: np.array):
+ """This is a reimplementation of a findNonZero function equivalent to cv2."""
+ non_zero_indices = np.column_stack(np.nonzero(image))
+ idxvec = non_zero_indices[:, [1, 0]]
+ idxvec = idxvec.reshape(-1, 1, 2)
+ return idxvec
+
+ def python_bounding_rect(self, coordinates):
+ """This is a reimplementation of a BoundingRect function equivalent to cv2."""
+ min_values = np.min(coordinates, axis=(0, 1)).astype(int)
+ max_values = np.max(coordinates, axis=(0, 1)).astype(int)
+ x_min, y_min = min_values[0], min_values[1]
+ width = max_values[0] - x_min + 1
+ height = max_values[1] - y_min + 1
+ return x_min, y_min, width, height
+
+ def crop_margin(
+ self,
+ image: np.array,
+ gray_threshold: int = 200,
+ data_format: Optional[ChannelDimension] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.array:
+ """
+ Crops the margin of the image. Gray pixels are considered margin (i.e., pixels with a value below the
+ threshold).
+
+ Args:
+ image (`np.array`):
+ The image to be cropped.
+ gray_threshold (`int`, *optional*, defaults to `200`)
+ Value below which pixels are considered to be gray.
+ data_format (`ChannelDimension`, *optional*):
+ The channel dimension format of the output image. If unset, will use the inferred format from the
+ input.
+ input_data_format (`ChannelDimension`, *optional*):
+ The channel dimension format of the input image. If unset, will use the inferred format from the input.
+ """
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(image)
+
+ image = to_pil_image(image, input_data_format=input_data_format)
+ data = np.array(image.convert("L")).astype(np.uint8)
+ max_val = data.max()
+ min_val = data.min()
+ if max_val == min_val:
+ image = np.array(image)
+ image = to_channel_dimension_format(image, input_data_format, ChannelDimension.LAST)
+ image = (
+ to_channel_dimension_format(image, data_format, input_data_format)
+ if data_format is not None
+ else image
+ )
+ return image
+ data = (data - min_val) / (max_val - min_val) * 255
+ gray = data < gray_threshold
+ coords = self.python_find_non_zero(gray)
+ x_min, y_min, width, height = self.python_bounding_rect(coords)
+ image = image.crop((x_min, y_min, x_min + width, y_min + height))
+ image = np.array(image).astype(np.uint8)
+ image = to_channel_dimension_format(image, input_data_format, ChannelDimension.LAST)
+
+ image = (
+ to_channel_dimension_format(image, data_format, input_data_format) if data_format is not None else image
+ )
+
+ return image
+
+ # Copied from transformers.models.donut.image_processing_donut.DonutImageProcessor.align_long_axis
+ def align_long_axis(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Align the long axis of the image to the longest axis of the specified size.
+
+ Args:
+ image (`np.ndarray`):
+ The image to be aligned.
+ size (`dict[str, int]`):
+ The size `{"height": h, "width": w}` to align the long axis to.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The data format of the output image. If unset, the same format as the input image is used.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+
+ Returns:
+ `np.ndarray`: The aligned image.
+ """
+ input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+ output_height, output_width = size["height"], size["width"]
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(image)
+
+ if input_data_format == ChannelDimension.LAST:
+ rot_axes = (0, 1)
+ elif input_data_format == ChannelDimension.FIRST:
+ rot_axes = (1, 2)
+ else:
+ raise ValueError(f"Unsupported data format: {input_data_format}")
+
+ if (output_width < output_height and input_width > input_height) or (
+ output_width > output_height and input_width < input_height
+ ):
+ image = np.rot90(image, 3, axes=rot_axes)
+
+ if data_format is not None:
+ image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+
+ return image
+
+ def pad_image(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Pad the image to the specified size at the top, bottom, left and right.
+
+ Args:
+ image (`np.ndarray`):
+ The image to be padded.
+ size (`dict[str, int]`):
+ The size `{"height": h, "width": w}` to pad the image to.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The data format of the output image. If unset, the same format as the input image is used.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ output_height, output_width = size["height"], size["width"]
+ input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+
+ delta_width = output_width - input_width
+ delta_height = output_height - input_height
+
+ pad_top = delta_height // 2
+ pad_left = delta_width // 2
+
+ pad_bottom = delta_height - pad_top
+ pad_right = delta_width - pad_left
+
+ padding = ((pad_top, pad_bottom), (pad_left, pad_right))
+ return pad(image, padding, data_format=data_format, input_data_format=input_data_format)
+
+ # Copied from transformers.models.donut.image_processing_donut.DonutImageProcessor.thumbnail
+ def thumbnail(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize the image to make a thumbnail. The image is resized so that no dimension is larger than any
+ corresponding dimension of the specified size.
+
+ Args:
+ image (`np.ndarray`):
+ The image to be resized.
+ size (`dict[str, int]`):
+ The size `{"height": h, "width": w}` to resize the image to.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ The resampling filter to use.
+ data_format (`Optional[Union[str, ChannelDimension]]`, *optional*):
+ The data format of the output image. If unset, the same format as the input image is used.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+ output_height, output_width = size["height"], size["width"]
+
+ # We always resize to the smallest of either the input or output size.
+ height = min(input_height, output_height)
+ width = min(input_width, output_width)
+
+ if height == input_height and width == input_width:
+ return image
+
+ if input_height > input_width:
+ width = int(input_width * height / input_height)
+ elif input_width > input_height:
+ height = int(input_height * width / input_width)
+
+ return resize(
+ image,
+ size=(height, width),
+ resample=resample,
+ reducing_gap=2.0,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ # Copied from transformers.models.donut.image_processing_donut.DonutImageProcessor.resize
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resizes `image` to `(height, width)` specified by `size` using the PIL library.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`dict[str, int]`):
+ Size of the output image.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ Resampling filter to use when resiizing the image.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ size = get_size_dict(size)
+ shortest_edge = min(size["height"], size["width"])
+ output_size = get_resize_output_image_size(
+ image, size=shortest_edge, default_to_square=False, input_data_format=input_data_format
+ )
+ resized_image = resize(
+ image,
+ size=output_size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+ return resized_image
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ do_crop_margin: Optional[bool] = None,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_thumbnail: Optional[bool] = None,
+ do_align_long_axis: Optional[bool] = None,
+ do_pad: Optional[bool] = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[Union[int, float]] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> PIL.Image.Image:
+ """
+ Preprocess an image or batch of images.
+
+ Args:
+ images (`ImageInput`):
+ Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255.
+ do_crop_margin (`bool`, *optional*, defaults to `self.do_crop_margin`):
+ Whether to crop the image margins.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`dict[str, int]`, *optional*, defaults to `self.size`):
+ Size of the image after resizing. Shortest edge of the image is resized to min(size["height"],
+ size["width"]) with the longest edge resized to keep the input aspect ratio.
+ resample (`int`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+ has an effect if `do_resize` is set to `True`.
+ do_thumbnail (`bool`, *optional*, defaults to `self.do_thumbnail`):
+ Whether to resize the image using thumbnail method.
+ do_align_long_axis (`bool`, *optional*, defaults to `self.do_align_long_axis`):
+ Whether to align the long axis of the image with the long axis of `size` by rotating by 90 degrees.
+ do_pad (`bool`, *optional*, defaults to `self.do_pad`):
+ Whether to pad the images to the largest image size in the batch.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image by the specified scale `rescale_factor`.
+ rescale_factor (`int` or `float`, *optional*, defaults to `self.rescale_factor`):
+ Scale factor to use if rescaling the image.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to use for normalization.
+ image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to use for normalization.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: defaults to the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ """
+ do_crop_margin = do_crop_margin if do_crop_margin is not None else self.do_crop_margin
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ resample = resample if resample is not None else self.resample
+ do_thumbnail = do_thumbnail if do_thumbnail is not None else self.do_thumbnail
+ do_align_long_axis = do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
+ do_pad = do_pad if do_pad is not None else self.do_pad
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+
+ images = make_list_of_images(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_pad=do_pad,
+ size_divisibility=size, # There is no pad divisibility in this processor, but pad requires the size arg.
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if do_rescale and is_scaled_image(images[0]):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ if do_crop_margin:
+ images = [self.crop_margin(image, input_data_format=input_data_format) for image in images]
+
+ if do_align_long_axis:
+ images = [self.align_long_axis(image, size=size, input_data_format=input_data_format) for image in images]
+
+ if do_resize:
+ images = [
+ self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ if do_thumbnail:
+ images = [self.thumbnail(image=image, size=size, input_data_format=input_data_format) for image in images]
+
+ if do_pad:
+ images = [self.pad_image(image=image, size=size, input_data_format=input_data_format) for image in images]
+
+ if do_rescale:
+ images = [
+ self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ if do_normalize:
+ images = [
+ self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+ for image in images
+ ]
+
+ images = [
+ to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
+ ]
+
+ data = {"pixel_values": images}
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+
+__all__ = ["NougatImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/image_processing_nougat_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/image_processing_nougat_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..29e1d6e21758d0dce0cee1f5b14eefaec8d43390
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/image_processing_nougat_fast.py
@@ -0,0 +1,327 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for Nougat."""
+
+from typing import Optional, Union
+
+from ...image_processing_utils import BatchFeature
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_transforms import (
+ get_resize_output_image_size,
+)
+from ...image_utils import (
+ IMAGENET_DEFAULT_MEAN,
+ IMAGENET_DEFAULT_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ SizeDict,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+)
+
+
+if is_torch_available():
+ import torch
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+class NougatFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ """
+ Args:
+ do_crop_margin (`bool`, *optional*, defaults to `True`):
+ Whether to crop the image margins.
+ do_thumbnail (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image using thumbnail method.
+ do_align_long_axis (`bool`, *optional*, defaults to `False`):
+ Whether to align the long axis of the image with the long axis of `size` by rotating by 90 degrees.
+ do_pad (`bool`, *optional*, defaults to `True`):
+ Whether to pad the images to the largest image size in the batch.
+ """
+
+ do_crop_margin: Optional[bool]
+ do_thumbnail: Optional[bool]
+ do_align_long_axis: Optional[bool]
+ do_pad: Optional[bool]
+
+
+@auto_docstring
+class NougatImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BILINEAR
+ image_mean = IMAGENET_DEFAULT_MEAN
+ image_std = IMAGENET_DEFAULT_STD
+ size = {"height": 896, "width": 672}
+ do_resize: bool = (True,)
+ do_normalize: bool = True
+ do_thumbnail: bool = True
+ do_align_long_axis: bool = False
+ do_pad: bool = True
+ do_rescale = True
+ do_crop_margin: bool = True
+ valid_kwargs = NougatFastImageProcessorKwargs
+
+ def __init__(self, **kwargs: Unpack[NougatFastImageProcessorKwargs]):
+ super().__init__(**kwargs)
+
+ @auto_docstring
+ def preprocess(self, images: ImageInput, **kwargs: Unpack[NougatFastImageProcessorKwargs]) -> BatchFeature:
+ return super().preprocess(images, **kwargs)
+
+ def python_find_non_zero(
+ self,
+ image: "torch.Tensor",
+ ):
+ """This is a reimplementation of a findNonZero function equivalent to cv2."""
+
+ non_zero_indices = torch.nonzero(image, as_tuple=False)
+ idxvec = non_zero_indices[:, [2, 1]]
+ idxvec = idxvec.reshape(-1, 1, 2)
+ return idxvec
+
+ def python_bounding_rect(self, coordinates):
+ """This is a reimplementation of a BoundingRect function equivalent to cv2."""
+
+ min_values = torch.amin(coordinates, axis=(0, 1)).to(torch.int)
+ max_values = torch.amax(coordinates, axis=(0, 1)).to(torch.int)
+
+ x_min, y_min = min_values[0], min_values[1]
+ width = max_values[0] - x_min + 1
+ height = max_values[1] - y_min + 1
+ return x_min, y_min, width, height
+
+ def crop_margin(
+ self,
+ image: "torch.Tensor",
+ gray_threshold: int = 200,
+ ) -> "torch.Tensor":
+ """
+ Crops the margin of the image. Gray pixels are considered margin (i.e., pixels with a value below the
+ threshold).
+
+ Args:
+ image (`torch.Tensor`):
+ The image to be cropped.
+ gray_threshold (`int`, *optional*, defaults to `200`)
+ Value below which pixels are considered to be gray.
+ """
+ data = F.rgb_to_grayscale(image, num_output_channels=1)
+
+ max_val = torch.max(data)
+ min_val = torch.min(data)
+
+ if max_val == min_val:
+ return image
+ data = (data - min_val) / (max_val - min_val) * 255
+ gray = data < gray_threshold
+ coords = self.python_find_non_zero(gray)
+ x_min, y_min, width, height = self.python_bounding_rect(coords)
+ image = image[:, y_min : y_min + height, x_min : x_min + width]
+
+ return image
+
+ def align_long_axis(
+ self,
+ image: "torch.Tensor",
+ size: SizeDict,
+ ) -> "torch.Tensor":
+ """
+ Align the long axis of the image to the longest axis of the specified size.
+
+ Args:
+ image (`torch.Tensor`):
+ The image to be aligned.
+ size (`Dict[str, int]`):
+ The size `{"height": h, "width": w}` to align the long axis to.
+ Returns:
+ `torch.Tensor`: The aligned image.
+ """
+ input_height, input_width = image.shape[-2:]
+ output_height, output_width = size.height, size.width
+
+ if (output_width < output_height and input_width > input_height) or (
+ output_width > output_height and input_width < input_height
+ ):
+ image = torch.rot90(image, 3, dims=[1, 2])
+
+ return image
+
+ def thumbnail(
+ self,
+ image: "torch.Tensor",
+ size: SizeDict,
+ ) -> "torch.Tensor":
+ """
+ Resize the image to make a thumbnail. The image is resized so that no dimension is larger than any
+ corresponding dimension of the specified size.
+
+ Args:
+ image (`torch.tensor`):
+ The image to be resized.
+ size (`Dict[str, int]`):
+ The size `{"height": h, "width": w}` to resize the image to.
+ """
+
+ input_height, input_width = image.shape[-2:]
+ output_height, output_width = size.height, size.width
+
+ # We always resize to the smallest of either the input or output size.
+ height = min(input_height, output_height)
+ width = min(input_width, output_width)
+
+ if height == input_height and width == input_width:
+ return image
+
+ if input_height > input_width:
+ width = int(input_width * height / input_height)
+ elif input_width > input_height:
+ height = int(input_height * width / input_width)
+
+ new_size = (height, width)
+
+ return F.resize(image, new_size, interpolation=F.InterpolationMode.BICUBIC)
+
+ def pad_images(
+ self,
+ image: "torch.Tensor",
+ size: SizeDict,
+ ) -> "torch.Tensor":
+ """
+ Pads a batch of images to the specified size at the top, bottom, left and right.
+
+ Args:
+ image (`torch.tensor`):
+ The image to be padded.
+ size (`Dict[str, int]`):
+ The size `{"height": h, "width": w}` to pad the image to.
+ """
+ input_height, input_width = image.shape[-2:]
+ output_height, output_width = size.height, size.width
+
+ delta_width = output_width - input_width
+ delta_height = output_height - input_height
+
+ pad_top = delta_height // 2
+ pad_left = delta_width // 2
+
+ pad_bottom = delta_height - pad_top
+ pad_right = delta_width - pad_left
+
+ padding = (pad_left, pad_top, pad_right, pad_bottom)
+ return F.pad(image, padding)
+
+ def resize(
+ self,
+ image: "torch.Tensor",
+ size: SizeDict,
+ interpolation: "F.InterpolationMode" = None,
+ antialias: bool = True,
+ **kwargs,
+ ) -> "torch.Tensor":
+ """
+ Resize an image to `(size["height"], size["width"])`.
+
+ Args:
+ image (`torch.Tensor`):
+ Image to resize.
+ size (`SizeDict`):
+ Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
+ interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BICUBIC`):
+ `InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`.
+
+ Returns:
+ `torch.Tensor`: The resized image.
+ """
+ interpolation = interpolation if interpolation is not None else F.InterpolationMode.BICUBIC
+
+ shortest_edge = min(size["height"], size["width"])
+
+ new_size = get_resize_output_image_size(
+ image, size=shortest_edge, default_to_square=False, input_data_format=ChannelDimension.FIRST
+ )
+ return F.resize(image, new_size, interpolation=interpolation, antialias=antialias)
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ do_resize: bool,
+ size: SizeDict,
+ do_align_long_axis: bool,
+ do_thumbnail: bool,
+ do_pad: bool,
+ interpolation: Optional["F.InterpolationMode"],
+ do_center_crop: bool,
+ crop_size: SizeDict,
+ do_rescale: bool,
+ rescale_factor: float,
+ do_normalize: bool,
+ do_crop_margin: bool,
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ disable_grouping: bool,
+ return_tensors: Optional[Union[str, TensorType]],
+ **kwargs,
+ ) -> BatchFeature:
+ # Crop images
+ images = [self.crop_margin(image) for image in images]
+
+ # Group images by size for batched resizing
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ resized_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_align_long_axis:
+ stacked_images = self.align_long_axis(image=stacked_images, size=size)
+ if do_resize:
+ stacked_images = self.resize(image=stacked_images, size=size)
+ if do_thumbnail:
+ stacked_images = self.thumbnail(image=stacked_images, size=size)
+ if do_pad:
+ stacked_images = self.pad_images(image=stacked_images, size=size)
+ resized_images_grouped[shape] = stacked_images
+ resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+ # Group images by size for further processing
+ # Needed in case do_resize is False, or resize returns images with different sizes
+ grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+ processed_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ # Fused rescale and normalize
+ stacked_images = self.rescale_and_normalize(
+ stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ processed_images_grouped[shape] = stacked_images
+
+ processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+ processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+
+ return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
+
+
+__all__ = ["NougatImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/processing_nougat.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/processing_nougat.py
new file mode 100644
index 0000000000000000000000000000000000000000..2213c9cdbcda5e43ec61debf1bb36b45618f3fff
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/processing_nougat.py
@@ -0,0 +1,163 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for Nougat.
+"""
+
+from typing import Optional, Union
+
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput, TruncationStrategy
+
+from ...processing_utils import ProcessorMixin
+from ...utils import PaddingStrategy, TensorType
+
+
+class NougatProcessor(ProcessorMixin):
+ r"""
+ Constructs a Nougat processor which wraps a Nougat image processor and a Nougat tokenizer into a single processor.
+
+ [`NougatProcessor`] offers all the functionalities of [`NougatImageProcessor`] and [`NougatTokenizerFast`]. See the
+ [`~NougatProcessor.__call__`] and [`~NougatProcessor.decode`] for more information.
+
+ Args:
+ image_processor ([`NougatImageProcessor`]):
+ An instance of [`NougatImageProcessor`]. The image processor is a required input.
+ tokenizer ([`NougatTokenizerFast`]):
+ An instance of [`NougatTokenizerFast`]. The tokenizer is a required input.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "AutoImageProcessor"
+ tokenizer_class = "AutoTokenizer"
+
+ def __init__(self, image_processor, tokenizer):
+ super().__init__(image_processor, tokenizer)
+ self.current_processor = self.image_processor
+
+ def __call__(
+ self,
+ images=None,
+ text=None,
+ do_crop_margin: Optional[bool] = None,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: "PILImageResampling" = None, # noqa: F821
+ do_thumbnail: Optional[bool] = None,
+ do_align_long_axis: Optional[bool] = None,
+ do_pad: Optional[bool] = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[Union[int, float]] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ data_format: Optional["ChannelDimension"] = "channels_first", # noqa: F821
+ input_data_format: Optional[Union[str, "ChannelDimension"]] = None, # noqa: F821
+ text_pair: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]] = None,
+ text_target: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+ text_pair_target: Optional[
+ Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]
+ ] = None,
+ add_special_tokens: bool = True,
+ padding: Union[bool, str, PaddingStrategy] = False,
+ truncation: Union[bool, str, TruncationStrategy] = None,
+ max_length: Optional[int] = None,
+ stride: int = 0,
+ is_split_into_words: bool = False,
+ pad_to_multiple_of: Optional[int] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ return_token_type_ids: Optional[bool] = None,
+ return_attention_mask: Optional[bool] = None,
+ return_overflowing_tokens: bool = False,
+ return_special_tokens_mask: bool = False,
+ return_offsets_mapping: bool = False,
+ return_length: bool = False,
+ verbose: bool = True,
+ ):
+ if images is None and text is None:
+ raise ValueError("You need to specify either an `images` or `text` input to process.")
+
+ if images is not None:
+ inputs = self.image_processor(
+ images,
+ do_crop_margin=do_crop_margin,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ do_thumbnail=do_thumbnail,
+ do_align_long_axis=do_align_long_axis,
+ do_pad=do_pad,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ return_tensors=return_tensors,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ if text is not None:
+ encodings = self.tokenizer(
+ text,
+ text_pair=text_pair,
+ text_target=text_target,
+ text_pair_target=text_pair_target,
+ add_special_tokens=add_special_tokens,
+ padding=padding,
+ truncation=truncation,
+ max_length=max_length,
+ stride=stride,
+ is_split_into_words=is_split_into_words,
+ pad_to_multiple_of=pad_to_multiple_of,
+ return_tensors=return_tensors,
+ return_token_type_ids=return_token_type_ids,
+ return_attention_mask=return_attention_mask,
+ return_overflowing_tokens=return_overflowing_tokens,
+ return_special_tokens_mask=return_special_tokens_mask,
+ return_offsets_mapping=return_offsets_mapping,
+ return_length=return_length,
+ verbose=verbose,
+ )
+
+ if text is None:
+ return inputs
+ elif images is None:
+ return encodings
+ else:
+ inputs["labels"] = encodings["input_ids"]
+ return inputs
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to NougatTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer
+ to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to NougatTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ def post_process_generation(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to NougatTokenizer's [`~PreTrainedTokenizer.post_process_generation`].
+ Please refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.post_process_generation(*args, **kwargs)
+
+
+__all__ = ["NougatProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/tokenization_nougat_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/tokenization_nougat_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..482918158976a3b46d888a1361eaf7b3c24f87b7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nougat/tokenization_nougat_fast.py
@@ -0,0 +1,620 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fast tokenizer class for Nougat.
+"""
+
+import re
+from functools import partial
+from multiprocessing import Pool
+from typing import Optional, Union
+
+import numpy as np
+
+from transformers.tokenization_utils_base import INIT_TOKENIZER_DOCSTRING
+from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
+from transformers.utils import add_end_docstrings
+
+from ...utils import is_levenshtein_available, is_nltk_available, logging, requires_backends
+
+
+if is_levenshtein_available():
+ from Levenshtein import ratio
+
+if is_nltk_available():
+ import nltk
+
+
+logger = logging.get_logger(__name__)
+
+
+INIT_TOKENIZER_DOCSTRING += """
+ tokenizer_object ([`tokenizers.Tokenizer`]):
+ A [`tokenizers.Tokenizer`] object from 🤗 tokenizers to instantiate from. See [Using tokenizers from 🤗
+ tokenizers](../fast_tokenizers) for more information.
+ tokenizer_file ([`str`]):
+ A path to a local JSON file representing a previously serialized [`tokenizers.Tokenizer`] object from 🤗
+ tokenizers.
+"""
+
+
+VOCAB_FILES_NAMES = {"tokenizer_file": "tokenizer.json"}
+
+
+def markdown_compatible(text: str) -> str:
+ """
+ Make text compatible with Markdown formatting.
+
+ This function makes various text formatting adjustments to make it compatible with Markdown.
+
+ Args:
+ text (`str`):
+ The input text to be made Markdown-compatible.
+
+ Returns:
+ `str`: The Markdown-compatible text.
+ """
+ # equation tag
+ # Replace lines that start with a pattern like (decimal) \[some text\] with \[[some text] \tag{decimal}\].
+ text = re.sub(r"^\(([\d.]+[a-zA-Z]?)\) \\\[(.+?)\\\]$", r"\[\2 \\tag{\1}\]", text, flags=re.M)
+ # Replace lines that start with a pattern like \[some text\] (decimal) with \[[some text] \tag{decimal}\].
+ text = re.sub(r"^\\\[(.+?)\\\] \(([\d.]+[a-zA-Z]?)\)$", r"\[\1 \\tag{\2}\]", text, flags=re.M)
+ # Replace lines that start with a pattern like \[some text\] (digits) \[another text\] with \[[some text] \tag{digits}\] [another text].
+ text = re.sub(
+ r"^\\\[(.+?)\\\] \(([\d.]+[a-zA-Z]?)\) (\\\[.+?\\\])$",
+ r"\[\1 \\tag{\2}\] \3",
+ text,
+ flags=re.M,
+ )
+ # multi line
+ text = text.replace(r"\. ", ". ")
+ # bold formatting
+ text = text.replace(r"\bm{", r"\mathbf{").replace(r"{\\bm ", r"\mathbf{")
+ text = re.sub(r"\\mbox{ ?\\boldmath\$(.*?)\$}", r"\\mathbf{\1}", text)
+ # Reformat urls (http, ftp and https only) to markdown [url](url) clickable format
+ text = re.sub(
+ r"((?:http|ftp|https):\/\/(?:[\w_-]+(?:(?:\.[\w_-]+)+))(?:[\w.,@?^=%&:\/~+#-]*[\w@?^=%&\/~+#-]))",
+ r"[\1](\1)",
+ text,
+ )
+ # algorithms
+ text = re.sub(r"```\s*(.+?)\s*```", r"```\n\1\n```", text, flags=re.S)
+
+ return text
+
+
+def normalize_list_like_lines(generation):
+ """
+ Normalize lines in the given text that resemble list items. The function looks for lines that start optionally with
+ '-' or '*', possibly followed by Roman numerals or digits indicating nesting levels. The function reformats such
+ lines to make them more structured.
+
+ Args:
+ generation (str): The input text containing lines that need to be normalized.
+
+ Returns:
+ str: The input text with the list-like lines normalized.
+
+ Note:
+ The function uses regular expressions to identify and reformat the list-like lines. The patterns capture
+ optional bullet points, nesting levels indicated by numerals, and the actual list item content. The
+ normalization adjusts the bullet point style and nesting levels based on the captured patterns.
+ """
+
+ lines = generation.split("\n")
+ output_lines = []
+ for line_no, line in enumerate(lines):
+ match = re.search(r". ([-*]) ", line)
+ if not match or line[0] not in ("-", "*"):
+ output_lines.append(line)
+ continue # Doesn't fit the pattern we want, no changes
+ delim = match.group(1) + " "
+ splits = line.split(delim)[1:]
+ replacement = ""
+ delim1 = line[0] + " "
+
+ for i, item in enumerate(splits):
+ level = 0
+ potential_numeral, _, rest = item.strip().partition(" ")
+ if not rest:
+ continue
+ # Infer current nesting level based on detected numbering
+ if re.match(r"^[\dixv]+((?:\.[\dixv])?)+$", potential_numeral, flags=re.I | re.M):
+ level = potential_numeral.count(".")
+
+ replacement += (
+ ("\n" if i > 0 else "") + ("\t" * level) + (delim if i > 0 or line_no == 0 else delim1) + item.strip()
+ )
+
+ if line_no == len(lines) - 1: # If this is the last line in the generation
+ replacement += "\n" # Add an empty line to the end of the generation
+
+ output_lines.append(replacement)
+
+ return "\n".join(output_lines)
+
+
+def find_next_punctuation(text: str, start_idx=0):
+ """
+ Find the index of the next punctuation mark.
+
+ Args:
+ text (`str`):
+ String to examine
+ start_idx (`int`, *optional*)
+ Index where to start
+ """
+
+ for i in range(start_idx, len(text)):
+ if text[i] in [".", "?", "!", "\n"]:
+ return i
+
+ return None
+
+
+def truncate_repetitions(text: str, min_len: int = 30) -> str:
+ """
+ Attempt to truncate repeating segments in the input string.
+
+ This function looks for the longest repeating substring at the end of the input string and truncates it to appear
+ only once. To be considered for removal, repetitions need to be continuous.
+
+ Args:
+ text (`str`):
+ The input raw prediction to be truncated.
+ min_len (int):
+ The minimum length of the repeating segment.
+
+ Returns:
+ `str`: The input string with repeated segments truncated.
+ """
+ text_lower = text.lower()
+ text_length = len(text_lower)
+
+ if text_length < 2 * min_len:
+ return text
+
+ # try to find a length at which the tail is repeating
+ max_repetition_length = None
+ for repetition_length in range(min_len, int(text_length / 2)):
+ # check if there is a repetition at the end
+ same = True
+ for i in range(0, repetition_length):
+ if text_lower[text_length - repetition_length - i - 1] != text_lower[text_length - i - 1]:
+ same = False
+ break
+
+ if same:
+ max_repetition_length = repetition_length
+
+ if max_repetition_length is None:
+ return text
+
+ lcs = text_lower[-max_repetition_length:]
+
+ # remove all but the last repetition
+ substituted_text = text
+ substituted_text_lower = text_lower
+ while substituted_text_lower.endswith(lcs):
+ substituted_text = substituted_text[:-max_repetition_length]
+ substituted_text_lower = substituted_text_lower[:-max_repetition_length]
+
+ # this is the tail with the repetitions
+ repeating_tail = text_lower[len(substituted_text_lower) :]
+
+ # add until next punctuation and make sure last sentence is not repeating
+ substituted_text_lower_out = substituted_text_lower
+ while True:
+ sentence_end = find_next_punctuation(text_lower, len(substituted_text_lower_out))
+ sentence_start = find_next_punctuation(text_lower[::-1], len(substituted_text_lower_out))
+ if sentence_end and sentence_start:
+ sentence = text_lower[sentence_start:sentence_end]
+ substituted_text_lower_out = text_lower[: sentence_end + 1]
+ if sentence in repeating_tail:
+ break
+ else:
+ break
+
+ text_out = text[: len(substituted_text_lower_out)]
+
+ return text_out
+
+
+def remove_numbers(lines):
+ def _clean(s):
+ return re.sub(r"(?:[\d_]|\*\*)", "", s).strip()
+
+ if isinstance(lines, str):
+ return _clean(lines)
+ out = []
+ for l in lines:
+ out.append(_clean(l))
+ return out
+
+
+def get_slices(lines, clean_lines):
+ """
+ Get slices of text based on specific criteria within the lines.
+
+ This function identifies and returns slices of text from the input lines based on certain conditions.
+
+ These conditions were chosen by the Nougat authors:
+ - The slice is less than 200 characters long.
+ - The slice is more than 3 characters long.
+ - The slice does not start with "[MISSING_PAGE".
+ - The slice is either the same as the next slice or the ratio of the two in terms of Levensthein distance is
+ greater than 0.9.
+
+ Args:
+ lines (`list[str]`):
+ The list of lines containing the text.
+ clean_lines (`list[str]`):
+ A cleaned version of the text (without numbers).
+
+ Returns:
+ `list[tuple]`: A list of tuples representing the start and end indices of text slices.
+ """
+ indices = np.zeros(len(lines))
+ for i in range(len(lines) - 1):
+ j = i + 1
+ while not clean_lines[j] and j < len(lines) - 1:
+ j += 1
+ if (
+ len(clean_lines[i]) < 200
+ and len(clean_lines[i]) > 3
+ and len(clean_lines[j]) < 200
+ and len(clean_lines[j]) > 3
+ and not clean_lines[i].startswith("[MISSING_PAGE")
+ and (clean_lines[i] == clean_lines[j] or ratio(clean_lines[i], clean_lines[j]) > 0.9)
+ ):
+ indices[i:j] = 1
+ ids = np.where(indices)[0]
+ slices = []
+ if len(ids) == 0:
+ return slices
+ j0 = 0
+ for j, x in enumerate(np.diff(ids) > 3):
+ if x:
+ slices.append((ids[j0], ids[j] + 2))
+ j0 = j + 1
+ slices.append((ids[j0], ids[-1] + 2))
+ return [sli for sli in slices if sli[1] - sli[0] > 15]
+
+
+def remove_slice_from_lines(lines, clean_text, slice) -> str:
+ """
+ Remove a slice of text from the lines based on specific criteria.
+
+ This function identifies a slice of text within the lines and removes it based on certain conditions.
+
+ Args:
+ lines (list of str): The list of lines containing the text.
+ clean_text (list of str): A cleaned version of the text (without numbers).
+ slice (tuple): A tuple representing the start and end indices of the slice to be removed.
+
+ Returns:
+ str: The removed slice of text as a single string.
+ """
+ base = clean_text[slice[0]]
+ section = list(slice)
+ check_start_flag = False
+ # backwards pass, at most 5 lines
+ for line_idx in range(max(0, slice[0] - 1), max(0, slice[0] - 5), -1):
+ if not lines[line_idx]:
+ continue
+ if lines[line_idx] == "## References":
+ section[0] = line_idx
+ break
+ elif ratio(base, remove_numbers(lines[line_idx])) < 0.9:
+ section[0] = line_idx + 1
+ potential_ref = remove_numbers(lines[max(0, line_idx - 1)].partition("* [")[-1])
+ if len(potential_ref) >= 0.75 * len(base) and ratio(base, potential_ref) < 0.9:
+ section[0] = line_idx
+ check_start_flag = True
+ break
+ # forward pass, at most 5 lines
+ for line_idx in range(min(len(lines), slice[1]), min(len(lines), slice[1] + 5)):
+ if ratio(base, remove_numbers(lines[line_idx])) < 0.9:
+ section[1] = line_idx
+ break
+ if len(lines) <= section[1]:
+ section[1] = len(lines) - 1
+ to_delete = "\n".join(lines[section[0] : section[1] + 1])
+ # cut off next page content
+ itera, iterb = enumerate(lines[section[1] - 1]), enumerate(lines[section[1]])
+ while True:
+ try:
+ (ia, a) = next(itera)
+ while a.isnumeric():
+ (ia, a) = next(itera)
+ (ib, b) = next(iterb)
+ while b.isnumeric():
+ (ib, b) = next(iterb)
+ if a != b:
+ break
+ except StopIteration:
+ break
+ if check_start_flag and "* [" in to_delete:
+ to_delete = "* [" + to_delete.partition("* [")[-1]
+ try:
+ delta = len(lines[section[1]]) - ib - 1
+ if delta > 0:
+ to_delete = to_delete[:-delta]
+ except UnboundLocalError:
+ pass
+
+ return to_delete.strip()
+
+
+@add_end_docstrings(INIT_TOKENIZER_DOCSTRING)
+class NougatTokenizerFast(PreTrainedTokenizerFast):
+ """
+ Fast tokenizer for Nougat (backed by HuggingFace tokenizers library).
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods. This class mainly adds Nougat-specific
+ methods for postprocessing the generated text.
+
+ Args:
+ vocab_file (`str`, *optional*):
+ [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
+ contains the vocabulary necessary to instantiate a tokenizer.
+ tokenizer_file (`str`, *optional*):
+ [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
+ contains everything needed to load the tokenizer.
+
+ clean_up_tokenization_spaces (`str`, *optional*, defaults to `False`):
+ Whether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra
+ spaces.
+
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+
+ bos_token (`str`, *optional*, defaults to `""`):
+ The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+ eos_token (`str`, *optional*, defaults to `" "`):
+ The end of sequence token.
+
+ pad_token (`str`, *optional*, defaults to `""`):
+ The token used for padding, for example when batching sequences of different lengths.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+ slow_tokenizer_class = None
+
+ def __init__(
+ self,
+ vocab_file=None,
+ tokenizer_file=None,
+ clean_up_tokenization_spaces=False,
+ unk_token="",
+ bos_token="",
+ eos_token=" ",
+ pad_token="",
+ **kwargs,
+ ):
+ super().__init__(
+ vocab_file=vocab_file,
+ tokenizer_file=tokenizer_file,
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+ unk_token=unk_token,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ pad_token=pad_token,
+ **kwargs,
+ )
+ self.vocab_file = vocab_file
+
+ def remove_hallucinated_references(self, text: str) -> str:
+ """
+ Remove hallucinated or missing references from the text.
+
+ This function identifies and removes references that are marked as missing or hallucinated from the input text.
+
+ Args:
+ text (`str`):
+ The input text containing references.
+
+ Returns:
+ `str`: The text with hallucinated references removed.
+ """
+ lines = text.split("\n")
+ if len(lines) == 0:
+ return ""
+ clean_lines = remove_numbers(lines)
+ slices = get_slices(lines, clean_lines)
+ to_delete = []
+ for slice in slices:
+ to_delete.append(remove_slice_from_lines(lines, clean_lines, slice))
+ for to_delete in reversed(to_delete):
+ text = text.replace(to_delete, "\n\n[MISSING_PAGE_POST]\n\n")
+ text = re.sub(
+ r"## References\n+\[MISSING_PAGE_POST(:\d+)?\]",
+ "\n\n[MISSING_PAGE_POST\\1]",
+ text,
+ )
+ return text
+
+ def correct_tables(self, generation: str) -> str:
+ """
+ Takes a generated string and fixes tables/tabulars to make them match the markdown format needed.
+
+ Args:
+ generation (str): The generated text to be postprocessed.
+
+ Returns:
+ str: The postprocessed text.
+
+ Example:
+
+ ```python
+ correct_tables("\\begin{table} \\begin{tabular}{l l} & \\ \\end{tabular} \\end{table}")
+ "\\begin{table}\n\\begin{tabular}{l l} & \\ \\end{tabular}\n\\end{table}"
+ ```
+ """
+ # remove obvious wrong tables
+ for l in generation.split("\n"):
+ if l.count("\\begin{tabular}") > 15 or l.count("\\multicolumn") > 60 or l.count("&") > 400:
+ generation = generation.replace(l, "")
+ # whitespace corrections
+
+ generation = generation.replace("\\begin{table} \\begin{tabular}", "\\begin{table}\n\\begin{tabular}")
+ generation = generation.replace("\\end{tabular} \\end{table}", "\\end{tabular}\n\\end{table}")
+ generation = generation.replace("\\end{table} Tab", "\\end{table}\nTab")
+
+ generation = re.sub(r"(^.+)\\begin{tab", r"\1\n\\begin{tab", generation, flags=re.M)
+
+ # Remove left-aligned empty LaTeX tabular blocks.
+ generation = generation.replace(r"\begin{tabular}{l l} & \\ \end{tabular}", "")
+ # Remove tabulars with just 2 newline characters.
+ generation = generation.replace("\\begin{tabular}{}\n\n\\end{tabular}", "")
+ return generation
+
+ def post_process_single(self, generation: str, fix_markdown: bool = True) -> str:
+ """
+ Postprocess a single generated text. Regular expressions used here are taken directly from the Nougat article
+ authors. These expressions are commented for clarity and tested end-to-end in most cases.
+
+ Args:
+ generation (str): The generated text to be postprocessed.
+ fix_markdown (bool, optional): Whether to perform Markdown formatting fixes. Default is True.
+
+ Returns:
+ str: The postprocessed text.
+ """
+ generation = re.sub(
+ r"(?:\n|^)#+ \d*\W? ?(.{100,})", r"\n\1", generation
+ ) # too long section titles probably are none
+ generation = generation.strip()
+ # Remove LaTeX left margin tag
+ generation = generation.replace("\n* [leftmargin=*]\n", "\n")
+ # Remove lines with markdown headings starting with #, with numerals,
+ # and possibly roman numerals with trailing spaces and newlines
+ generation = re.sub(r"^#+ (?:[\d+\.]+|[ixv\.]+)?\s*(?:$|\n\s*)", "", generation, flags=re.M)
+ # most likely hallucinated titles
+ lines = generation.split("\n")
+ if lines[-1].startswith("#") and lines[-1].lstrip("#").startswith(" ") and len(lines) > 1:
+ logger.info("Likely hallucinated title at the end of the page: " + lines[-1])
+ generation = "\n".join(lines[:-1])
+ # obvious repetition detection
+ generation = truncate_repetitions(generation)
+ # Reference corrections
+ generation = self.remove_hallucinated_references(generation)
+ # Remove lines starting with asterisks and numbers like "*[1]" and followed by capital letters and periods (ie too long references)
+ generation = re.sub(r"^\* \[\d+\](\s?[A-W]\.+\s?){10,}.*$", "", generation, flags=re.M)
+ # Remove empty brackets after a reference number in brackets. *[12][]ABC will become *[12]ABC
+ generation = re.sub(r"^(\* \[\d+\])\[\](.*)$", r"\1\2", generation, flags=re.M)
+ # Remove single characters before or after 2 new lines
+ generation = re.sub(r"(^\w\n\n|\n\n\w$)", "", generation)
+ # pmc math artifact correction
+ generation = re.sub(
+ r"([\s.,()])_([a-zA-Z0-9])__([a-zA-Z0-9]){1,3}_([\s.,:()])",
+ r"\1\(\2_{\3}\)\4",
+ generation,
+ )
+ generation = re.sub(r"([\s.,\d])_([a-zA-Z0-9])_([\s.,\d;])", r"\1\(\2\)\3", generation)
+ # footnote mistakes
+ generation = re.sub(
+ r"(\nFootnote .*?:) (?:footnotetext|thanks):\W*(.*(?:\n\n|$))",
+ r"\1 \2",
+ generation,
+ )
+ # TODO Come up with footnote formatting inside a table
+ generation = re.sub(r"\[FOOTNOTE:.+?\](.*?)\[ENDFOOTNOTE\]", "", generation)
+ # itemize post processing
+ generation = normalize_list_like_lines(generation)
+
+ if generation.endswith((".", "}")):
+ generation += "\n\n"
+ if re.match(r"[A-Z0-9,;:]$", generation):
+ # add space in case it there is a comma or word ending
+ generation += " "
+ elif generation.startswith(("#", "**", "\\begin")):
+ generation = "\n\n" + generation
+ elif generation.split("\n")[-1].startswith(("#", "Figure", "Table")):
+ generation = generation + "\n\n"
+ else:
+ try:
+ last_word = generation.split(" ")[-1]
+ if last_word in nltk.corpus.words.words():
+ generation += " "
+ except LookupError:
+ # add space just in case. Will split words but better than concatenating them
+ generation += " "
+
+ # table corrections
+ generation = self.correct_tables(generation)
+ # Remove optional, empty square brackets after begin{array}
+ generation = generation.replace("\\begin{array}[]{", "\\begin{array}{")
+ # Remove empty or malformed LaTeX tabular blocks with 2 or more columns specified, with spaces and ampersands.
+ generation = re.sub(
+ r"\\begin{tabular}{([clr ]){2,}}\s*[& ]*\s*(\\\\)? \\end{tabular}",
+ "",
+ generation,
+ )
+ # Remove lines containing "S.A.B." one or more times. Was included in Nougat's code.
+ generation = re.sub(r"(\*\*S\. A\. B\.\*\*\n+){2,}", "", generation)
+ # Remove markdown-style headers that are incomplete or empty on multiple lines.
+ generation = re.sub(r"^#+( [\[\d\w])?$", "", generation, flags=re.M)
+ # Remove lines with just one period.
+ generation = re.sub(r"^\.\s*$", "", generation, flags=re.M)
+ # Replace instances of three or more newlines with just two newlines.
+ generation = re.sub(r"\n{3,}", "\n\n", generation)
+ if fix_markdown:
+ return markdown_compatible(generation)
+ else:
+ return generation
+
+ def post_process_generation(
+ self,
+ generation: Union[str, list[str]],
+ fix_markdown: bool = True,
+ num_workers: Optional[int] = None,
+ ) -> Union[str, list[str]]:
+ """
+ Postprocess a generated text or a list of generated texts.
+
+ This function can be used to perform postprocessing on generated text, such as fixing Markdown formatting.
+
+ Postprocessing is quite slow so it is recommended to use multiprocessing to speed up the process.
+
+ Args:
+ generation (Union[str, list[str]]):
+ The generated text or a list of generated texts.
+ fix_markdown (`bool`, *optional*, defaults to `True`):
+ Whether to perform Markdown formatting fixes.
+ num_workers (`int`, *optional*):
+ Optional number of workers to pass to leverage multiprocessing (postprocessing several texts in
+ parallel).
+
+ Returns:
+ Union[str, list[str]]: The postprocessed text or list of postprocessed texts.
+ """
+ requires_backends(self, ["nltk", "levenshtein"])
+
+ if isinstance(generation, list):
+ if num_workers is not None and isinstance(num_workers, int):
+ with Pool(num_workers) as p:
+ return p.map(partial(self.post_process_single, fix_markdown=fix_markdown), generation)
+ else:
+ return [self.post_process_single(s, fix_markdown=fix_markdown) for s in generation]
+ else:
+ return self.post_process_single(generation, fix_markdown=fix_markdown)
+
+
+__all__ = ["NougatTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c32df6d8db909207e4867e119d2a7fca4a0bd907
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_nystromformer import *
+ from .modeling_nystromformer import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/configuration_nystromformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/configuration_nystromformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..96a48b99fda4cd7ff056ceeb441889653682bec1
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/configuration_nystromformer.py
@@ -0,0 +1,132 @@
+# coding=utf-8
+# Copyright 2022 UW-Madison and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Nystromformer model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class NystromformerConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`NystromformerModel`]. It is used to instantiate
+ an Nystromformer model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the Nystromformer
+ [uw-madison/nystromformer-512](https://huggingface.co/uw-madison/nystromformer-512) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 30000):
+ Vocabulary size of the Nystromformer model. Defines the number of different tokens that can be represented
+ by the `inputs_ids` passed when calling [`NystromformerModel`].
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimension of the encoder layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ intermediate_size (`int`, *optional*, defaults to 3072):
+ Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` are supported.
+ hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention probabilities.
+ max_position_embeddings (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ type_vocab_size (`int`, *optional*, defaults to 2):
+ The vocabulary size of the `token_type_ids` passed when calling [`NystromformerModel`].
+ segment_means_seq_len (`int`, *optional*, defaults to 64):
+ Sequence length used in segment-means.
+ num_landmarks (`int`, *optional*, defaults to 64):
+ The number of landmark (or Nystrom) points to use in Nystrom approximation of the softmax self-attention
+ matrix.
+ conv_kernel_size (`int`, *optional*, defaults to 65):
+ The kernel size of depthwise convolution used in Nystrom approximation.
+ inv_coeff_init_option (`bool`, *optional*, defaults to `False`):
+ Whether or not to use exact coefficient computation for the initial values for the iterative method of
+ calculating the Moore-Penrose inverse of a matrix.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+ The epsilon used by the layer normalization layers.
+
+ Example:
+
+ ```python
+ >>> from transformers import NystromformerModel, NystromformerConfig
+
+ >>> # Initializing a Nystromformer uw-madison/nystromformer-512 style configuration
+ >>> configuration = NystromformerConfig()
+
+ >>> # Initializing a model from the uw-madison/nystromformer-512 style configuration
+ >>> model = NystromformerModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "nystromformer"
+
+ def __init__(
+ self,
+ vocab_size=30000,
+ hidden_size=768,
+ num_hidden_layers=12,
+ num_attention_heads=12,
+ intermediate_size=3072,
+ hidden_act="gelu_new",
+ hidden_dropout_prob=0.1,
+ attention_probs_dropout_prob=0.1,
+ max_position_embeddings=510,
+ type_vocab_size=2,
+ segment_means_seq_len=64,
+ num_landmarks=64,
+ conv_kernel_size=65,
+ inv_coeff_init_option=False,
+ initializer_range=0.02,
+ layer_norm_eps=1e-5,
+ pad_token_id=1,
+ bos_token_id=0,
+ eos_token_id=2,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.intermediate_size = intermediate_size
+ self.hidden_act = hidden_act
+ self.hidden_dropout_prob = hidden_dropout_prob
+ self.attention_probs_dropout_prob = attention_probs_dropout_prob
+ self.initializer_range = initializer_range
+ self.type_vocab_size = type_vocab_size
+ self.segment_means_seq_len = segment_means_seq_len
+ self.num_landmarks = num_landmarks
+ self.conv_kernel_size = conv_kernel_size
+ self.inv_coeff_init_option = inv_coeff_init_option
+ self.layer_norm_eps = layer_norm_eps
+ super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+
+__all__ = ["NystromformerConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/convert_nystromformer_original_pytorch_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/convert_nystromformer_original_pytorch_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..934a23e0103ba250af6e3da18ad6c7dc763b335a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/convert_nystromformer_original_pytorch_checkpoint_to_pytorch.py
@@ -0,0 +1,111 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Convert Nystromformer checkpoints from the original repository."""
+
+import argparse
+
+import torch
+
+from transformers import NystromformerConfig, NystromformerForMaskedLM
+
+
+def rename_key(orig_key):
+ if "model" in orig_key:
+ orig_key = orig_key.replace("model.", "")
+ if "norm1" in orig_key:
+ orig_key = orig_key.replace("norm1", "attention.output.LayerNorm")
+ if "norm2" in orig_key:
+ orig_key = orig_key.replace("norm2", "output.LayerNorm")
+ if "norm" in orig_key:
+ orig_key = orig_key.replace("norm", "LayerNorm")
+ if "transformer" in orig_key:
+ layer_num = orig_key.split(".")[0].split("_")[-1]
+ orig_key = orig_key.replace(f"transformer_{layer_num}", f"encoder.layer.{layer_num}")
+ if "mha.attn" in orig_key:
+ orig_key = orig_key.replace("mha.attn", "attention.self")
+ if "mha" in orig_key:
+ orig_key = orig_key.replace("mha", "attention")
+ if "W_q" in orig_key:
+ orig_key = orig_key.replace("W_q", "self.query")
+ if "W_k" in orig_key:
+ orig_key = orig_key.replace("W_k", "self.key")
+ if "W_v" in orig_key:
+ orig_key = orig_key.replace("W_v", "self.value")
+ if "ff1" in orig_key:
+ orig_key = orig_key.replace("ff1", "intermediate.dense")
+ if "ff2" in orig_key:
+ orig_key = orig_key.replace("ff2", "output.dense")
+ if "ff" in orig_key:
+ orig_key = orig_key.replace("ff", "output.dense")
+ if "mlm_class" in orig_key:
+ orig_key = orig_key.replace("mlm.mlm_class", "cls.predictions.decoder")
+ if "mlm" in orig_key:
+ orig_key = orig_key.replace("mlm", "cls.predictions.transform")
+ if "cls" not in orig_key:
+ orig_key = "nystromformer." + orig_key
+
+ return orig_key
+
+
+def convert_checkpoint_helper(config, orig_state_dict):
+ for key in orig_state_dict.copy():
+ val = orig_state_dict.pop(key)
+
+ if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
+ continue
+ else:
+ orig_state_dict[rename_key(key)] = val
+
+ orig_state_dict["cls.predictions.bias"] = orig_state_dict["cls.predictions.decoder.bias"]
+ orig_state_dict["nystromformer.embeddings.position_ids"] = (
+ torch.arange(config.max_position_embeddings).expand((1, -1)) + 2
+ )
+
+ return orig_state_dict
+
+
+def convert_nystromformer_checkpoint(checkpoint_path, nystromformer_config_file, pytorch_dump_path):
+ orig_state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=True)["model_state_dict"]
+ config = NystromformerConfig.from_json_file(nystromformer_config_file)
+ model = NystromformerForMaskedLM(config)
+
+ new_state_dict = convert_checkpoint_helper(config, orig_state_dict)
+
+ model.load_state_dict(new_state_dict)
+ model.eval()
+ model.save_pretrained(pytorch_dump_path)
+
+ print(f"Checkpoint successfully converted. Model saved at {pytorch_dump_path}")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--pytorch_model_path", default=None, type=str, required=True, help="Path to Nystromformer pytorch checkpoint."
+ )
+ parser.add_argument(
+ "--config_file",
+ default=None,
+ type=str,
+ required=True,
+ help="The json file for Nystromformer model config.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
+ )
+ args = parser.parse_args()
+ convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/modeling_nystromformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/modeling_nystromformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..45e69b6b469389d0c57390c2fb0582cad4a07f53
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/nystromformer/modeling_nystromformer.py
@@ -0,0 +1,1018 @@
+# coding=utf-8
+# Copyright 2022 UW-Madison The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Nystromformer model."""
+
+import math
+from typing import Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutputWithPastAndCrossAttentions,
+ MaskedLMOutput,
+ MultipleChoiceModelOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
+from ...utils import (
+ auto_docstring,
+ logging,
+)
+from .configuration_nystromformer import NystromformerConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class NystromformerEmbeddings(nn.Module):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+ self.position_embeddings = nn.Embedding(config.max_position_embeddings + 2, config.hidden_size)
+ self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+ # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+ # any TensorFlow checkpoint file
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+ self.register_buffer(
+ "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + 2, persistent=False
+ )
+ self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+ self.register_buffer(
+ "token_type_ids",
+ torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
+ persistent=False,
+ )
+
+ def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
+ if input_ids is not None:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+
+ if position_ids is None:
+ position_ids = self.position_ids[:, :seq_length]
+
+ # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
+ # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
+ # issue #5664
+ if token_type_ids is None:
+ if hasattr(self, "token_type_ids"):
+ buffered_token_type_ids = self.token_type_ids[:, :seq_length]
+ buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
+ token_type_ids = buffered_token_type_ids_expanded
+ else:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.word_embeddings(input_ids)
+ token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+ embeddings = inputs_embeds + token_type_embeddings
+ if self.position_embedding_type == "absolute":
+ position_embeddings = self.position_embeddings(position_ids)
+ embeddings += position_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+class NystromformerSelfAttention(nn.Module):
+ def __init__(self, config, position_embedding_type=None):
+ super().__init__()
+ if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+ raise ValueError(
+ f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+ f"heads ({config.num_attention_heads})"
+ )
+
+ self.num_attention_heads = config.num_attention_heads
+ self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+ self.num_landmarks = config.num_landmarks
+ self.seq_len = config.segment_means_seq_len
+ self.conv_kernel_size = config.conv_kernel_size
+
+ if config.inv_coeff_init_option:
+ self.init_option = config["inv_init_coeff_option"]
+ else:
+ self.init_option = "original"
+
+ self.query = nn.Linear(config.hidden_size, self.all_head_size)
+ self.key = nn.Linear(config.hidden_size, self.all_head_size)
+ self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+ self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+ self.position_embedding_type = position_embedding_type or getattr(
+ config, "position_embedding_type", "absolute"
+ )
+
+ if self.conv_kernel_size is not None:
+ self.conv = nn.Conv2d(
+ in_channels=self.num_attention_heads,
+ out_channels=self.num_attention_heads,
+ kernel_size=(self.conv_kernel_size, 1),
+ padding=(self.conv_kernel_size // 2, 0),
+ bias=False,
+ groups=self.num_attention_heads,
+ )
+
+ # Function to approximate Moore-Penrose inverse via the iterative method
+ def iterative_inv(self, mat, n_iter=6):
+ identity = torch.eye(mat.size(-1), device=mat.device)
+ key = mat
+
+ # The entries of key are positive and ||key||_{\infty} = 1 due to softmax
+ if self.init_option == "original":
+ # This original implementation is more conservative to compute coefficient of Z_0.
+ value = 1 / torch.max(torch.sum(key, dim=-2)) * key.transpose(-1, -2)
+ else:
+ # This is the exact coefficient computation, 1 / ||key||_1, of initialization of Z_0, leading to faster convergence.
+ value = 1 / torch.max(torch.sum(key, dim=-2), dim=-1).values[:, :, None, None] * key.transpose(-1, -2)
+
+ for _ in range(n_iter):
+ key_value = torch.matmul(key, value)
+ value = torch.matmul(
+ 0.25 * value,
+ 13 * identity
+ - torch.matmul(key_value, 15 * identity - torch.matmul(key_value, 7 * identity - key_value)),
+ )
+ return value
+
+ def forward(self, hidden_states, attention_mask=None, output_attentions=False):
+ batch_size, seq_length, _ = hidden_states.shape
+ query_layer = (
+ self.query(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ key_layer = (
+ self.key(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ value_layer = (
+ self.value(hidden_states)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+
+ query_layer = query_layer / math.sqrt(math.sqrt(self.attention_head_size))
+ key_layer = key_layer / math.sqrt(math.sqrt(self.attention_head_size))
+
+ if self.num_landmarks == self.seq_len:
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in NystromformerModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+ context_layer = torch.matmul(attention_probs, value_layer)
+
+ else:
+ q_landmarks = query_layer.reshape(
+ -1,
+ self.num_attention_heads,
+ self.num_landmarks,
+ self.seq_len // self.num_landmarks,
+ self.attention_head_size,
+ ).mean(dim=-2)
+ k_landmarks = key_layer.reshape(
+ -1,
+ self.num_attention_heads,
+ self.num_landmarks,
+ self.seq_len // self.num_landmarks,
+ self.attention_head_size,
+ ).mean(dim=-2)
+
+ kernel_1 = torch.nn.functional.softmax(torch.matmul(query_layer, k_landmarks.transpose(-1, -2)), dim=-1)
+ kernel_2 = torch.nn.functional.softmax(torch.matmul(q_landmarks, k_landmarks.transpose(-1, -2)), dim=-1)
+
+ attention_scores = torch.matmul(q_landmarks, key_layer.transpose(-1, -2))
+
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in NystromformerModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ kernel_3 = nn.functional.softmax(attention_scores, dim=-1)
+ attention_probs = torch.matmul(kernel_1, self.iterative_inv(kernel_2))
+ new_value_layer = torch.matmul(kernel_3, value_layer)
+ context_layer = torch.matmul(attention_probs, new_value_layer)
+
+ if self.conv_kernel_size is not None:
+ context_layer += self.conv(value_layer)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(*new_context_layer_shape)
+
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
+class NystromformerSelfOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class NystromformerAttention(nn.Module):
+ def __init__(self, config, position_embedding_type=None):
+ super().__init__()
+ self.self = NystromformerSelfAttention(config, position_embedding_type=position_embedding_type)
+ self.output = NystromformerSelfOutput(config)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+ )
+
+ # Prune linear layers
+ self.self.query = prune_linear_layer(self.self.query, index)
+ self.self.key = prune_linear_layer(self.self.key, index)
+ self.self.value = prune_linear_layer(self.self.value, index)
+ self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+ # Update hyper params and store pruned heads
+ self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+ self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def forward(self, hidden_states, attention_mask=None, output_attentions=False):
+ self_outputs = self.self(hidden_states, attention_mask, output_attentions)
+ attention_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
+ return outputs
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Nystromformer
+class NystromformerIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Nystromformer
+class NystromformerOutput(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+ def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states + input_tensor)
+ return hidden_states
+
+
+class NystromformerLayer(GradientCheckpointingLayer):
+ def __init__(self, config):
+ super().__init__()
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = NystromformerAttention(config)
+ self.add_cross_attention = config.add_cross_attention
+ self.intermediate = NystromformerIntermediate(config)
+ self.output = NystromformerOutput(config)
+
+ def forward(self, hidden_states, attention_mask=None, output_attentions=False):
+ self_attention_outputs = self.attention(hidden_states, attention_mask, output_attentions=output_attentions)
+ attention_output = self_attention_outputs[0]
+
+ outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
+
+ layer_output = apply_chunking_to_forward(
+ self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+ )
+ outputs = (layer_output,) + outputs
+
+ return outputs
+
+ def feed_forward_chunk(self, attention_output):
+ intermediate_output = self.intermediate(attention_output)
+ layer_output = self.output(intermediate_output, attention_output)
+ return layer_output
+
+
+class NystromformerEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList([NystromformerLayer(config) for _ in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ):
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attentions = () if output_attentions else None
+
+ for i, layer_module in enumerate(self.layer):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ layer_outputs = layer_module(hidden_states, attention_mask, output_attentions)
+
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attentions,
+ )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->Nystromformer
+class NystromformerPredictionHeadTransform(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ if isinstance(config.hidden_act, str):
+ self.transform_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.transform_act_fn = config.hidden_act
+ self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.transform_act_fn(hidden_states)
+ hidden_states = self.LayerNorm(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Nystromformer
+class NystromformerLMPredictionHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.transform = NystromformerPredictionHeadTransform(config)
+
+ # The output weights are the same as the input embeddings, but there is
+ # an output-only bias for each token.
+ self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+ # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+ self.decoder.bias = self.bias
+
+ def _tie_weights(self):
+ self.decoder.bias = self.bias
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states)
+ return hidden_states
+
+
+# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Nystromformer
+class NystromformerOnlyMLMHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.predictions = NystromformerLMPredictionHead(config)
+
+ def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
+ prediction_scores = self.predictions(sequence_output)
+ return prediction_scores
+
+
+@auto_docstring
+class NystromformerPreTrainedModel(PreTrainedModel):
+ config: NystromformerConfig
+ base_model_prefix = "nystromformer"
+ supports_gradient_checkpointing = True
+
+ def _init_weights(self, module):
+ """Initialize the weights"""
+ if isinstance(module, (nn.Linear, nn.Conv2d)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+@auto_docstring
+class NystromformerModel(NystromformerPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.config = config
+
+ self.embeddings = NystromformerEmbeddings(config)
+ self.encoder = NystromformerEncoder(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embeddings.word_embeddings
+
+ def set_input_embeddings(self, value):
+ self.embeddings.word_embeddings = value
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+ class PreTrainedModel
+ """
+ for layer, heads in heads_to_prune.items():
+ self.encoder.layer[layer].attention.prune_heads(heads)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ batch_size, seq_length = input_shape
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ if attention_mask is None:
+ attention_mask = torch.ones(((batch_size, seq_length)), device=device)
+
+ if token_type_ids is None:
+ if hasattr(self.embeddings, "token_type_ids"):
+ buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
+ buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
+ token_type_ids = buffered_token_type_ids_expanded
+ else:
+ token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ )
+ encoder_outputs = self.encoder(
+ embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ sequence_output = encoder_outputs[0]
+
+ if not return_dict:
+ return (sequence_output,) + encoder_outputs[1:]
+
+ return BaseModelOutputWithPastAndCrossAttentions(
+ last_hidden_state=sequence_output,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ cross_attentions=encoder_outputs.cross_attentions,
+ )
+
+
+@auto_docstring
+class NystromformerForMaskedLM(NystromformerPreTrainedModel):
+ _tied_weights_keys = ["cls.predictions.decoder"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.nystromformer = NystromformerModel(config)
+ self.cls = NystromformerOnlyMLMHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_output_embeddings(self):
+ return self.cls.predictions.decoder
+
+ def set_output_embeddings(self, new_embeddings):
+ self.cls.predictions.decoder = new_embeddings
+ self.cls.predictions.bias = new_embeddings.bias
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], MaskedLMOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+ config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+ loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.nystromformer(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+ prediction_scores = self.cls(sequence_output)
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss() # -100 index = padding token
+ masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[1:]
+ return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+ return MaskedLMOutput(
+ loss=masked_lm_loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+class NystromformerClassificationHead(nn.Module):
+ """Head for sentence-level classification tasks."""
+
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+ self.config = config
+
+ def forward(self, features, **kwargs):
+ x = features[:, 0, :] # take token (equiv. to [CLS])
+ x = self.dropout(x)
+ x = self.dense(x)
+ x = ACT2FN[self.config.hidden_act](x)
+ x = self.dropout(x)
+ x = self.out_proj(x)
+ return x
+
+
+@auto_docstring(
+ custom_intro="""
+ Nyströmformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+ pooled output) e.g. for GLUE tasks.
+ """
+)
+class NystromformerForSequenceClassification(NystromformerPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.nystromformer = NystromformerModel(config)
+ self.classifier = NystromformerClassificationHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.nystromformer(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class NystromformerForMultipleChoice(NystromformerPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.nystromformer = NystromformerModel(config)
+ self.pre_classifier = nn.Linear(config.hidden_size, config.hidden_size)
+ self.classifier = nn.Linear(config.hidden_size, 1)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], MultipleChoiceModelOutput]:
+ r"""
+ input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+ num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+ `input_ids` above)
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+ num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+ input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+ attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+ token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+ position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+ inputs_embeds = (
+ inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+ if inputs_embeds is not None
+ else None
+ )
+
+ outputs = self.nystromformer(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_state = outputs[0] # (bs * num_choices, seq_len, dim)
+ pooled_output = hidden_state[:, 0] # (bs * num_choices, dim)
+ pooled_output = self.pre_classifier(pooled_output) # (bs * num_choices, dim)
+ pooled_output = nn.ReLU()(pooled_output) # (bs * num_choices, dim)
+ logits = self.classifier(pooled_output)
+
+ reshaped_logits = logits.view(-1, num_choices)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(reshaped_logits, labels)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return MultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class NystromformerForTokenClassification(NystromformerPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.nystromformer = NystromformerModel(config)
+ self.dropout = nn.Dropout(config.hidden_dropout_prob)
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], TokenClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.nystromformer(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TokenClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring
+class NystromformerForQuestionAnswering(NystromformerPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ config.num_labels = 2
+ self.num_labels = config.num_labels
+
+ self.nystromformer = NystromformerModel(config)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.nystromformer(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1)
+ end_logits = end_logits.squeeze(-1)
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[1:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = [
+ "NystromformerForMaskedLM",
+ "NystromformerForMultipleChoice",
+ "NystromformerForQuestionAnswering",
+ "NystromformerForSequenceClassification",
+ "NystromformerForTokenClassification",
+ "NystromformerLayer",
+ "NystromformerModel",
+ "NystromformerPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..139af5473e41fabbe978ee4b9c956dfd9b6e4453
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 EleutherAI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_olmo import *
+ from .modeling_olmo import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/configuration_olmo.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/configuration_olmo.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1bf0971b55dd1caad8f44eff84c7f9dbb063b30
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/configuration_olmo.py
@@ -0,0 +1,198 @@
+# coding=utf-8
+# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""OLMo model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class OlmoConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`OlmoModel`]. It is used to instantiate an OLMo
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the [allenai/OLMo-7B-hf](https://huggingface.co/allenai/OLMo-7B-hf).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50304):
+ Vocabulary size of the OLMo model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`OlmoModel`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 11008):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+ `num_attention_heads`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*, defaults to 1):
+ Padding token id.
+ bos_token_id (`int`, *optional*):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 50279):
+ End of stream token id.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+ these scaling strategies behave:
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+ experimental feature, subject to breaking API changes in future versions.
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ clip_qkv (`float`, *optional*):
+ If not `None`, elements of query, key and value attention states are clipped so that their
+ absolute value does not exceed this value.
+
+ ```python
+ >>> from transformers import OlmoModel, OlmoConfig
+
+ >>> # Initializing a OLMo 7B style configuration
+ >>> configuration = OlmoConfig()
+
+ >>> # Initializing a model from the OLMo 7B style configuration
+ >>> model = OlmoModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "olmo"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ base_model_tp_plan = {
+ "layers.*.self_attn.q_proj": "colwise",
+ "layers.*.self_attn.k_proj": "colwise",
+ "layers.*.self_attn.v_proj": "colwise",
+ "layers.*.self_attn.o_proj": "rowwise",
+ "layers.*.mlp.gate_proj": "colwise",
+ "layers.*.mlp.up_proj": "colwise",
+ "layers.*.mlp.down_proj": "rowwise",
+ }
+ base_model_pp_plan = {
+ "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+ "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+ "norm": (["hidden_states"], ["hidden_states"]),
+ }
+
+ def __init__(
+ self,
+ vocab_size=50304,
+ hidden_size=4096,
+ intermediate_size=11008,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=None,
+ hidden_act="silu",
+ max_position_embeddings=2048,
+ initializer_range=0.02,
+ use_cache=True,
+ pad_token_id=1,
+ bos_token_id=None,
+ eos_token_id=50279,
+ tie_word_embeddings=False,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ attention_bias=False,
+ attention_dropout=0.0,
+ clip_qkv=None,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ self._rope_scaling_validation()
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.clip_qkv = clip_qkv
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+ def _rope_scaling_validation(self):
+ """
+ Validate the `rope_scaling` configuration.
+ """
+ if self.rope_scaling is None:
+ return
+
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+ raise ValueError(
+ f"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, got {self.rope_scaling}"
+ )
+ rope_scaling_type = self.rope_scaling.get("type", None)
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+ raise ValueError(
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+ )
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
+
+
+__all__ = ["OlmoConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/convert_olmo_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/convert_olmo_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0b590a03058f2540a83afb540ad2e59fe789ef3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/convert_olmo_weights_to_hf.py
@@ -0,0 +1,248 @@
+# Copyright 2024 EleutherAI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import gc
+import json
+import os
+import shutil
+from pathlib import Path
+
+import torch
+import yaml
+from tokenizers import Tokenizer
+
+from transformers import OlmoConfig, OlmoForCausalLM
+from transformers.models.gpt_neox.tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
+
+
+"""
+Sample usage:
+
+```
+python src/transformers/models/olmo/convert_olmo_weights_to_hf.py \
+ --input_dir /path/to/downloaded/olmo/weights --model_size 7B --output_dir /output/path
+```
+
+Thereafter, models can be loaded via:
+
+```py
+from transformers import OlmoForCausalLM, AutoTokenizer
+
+model = OlmoForCausalLM.from_pretrained("/output/path")
+tokenizer = AutoTokenizer.from_pretrained("/output/path")
+```
+
+Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
+come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
+"""
+
+
+def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
+ return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
+
+
+def read_json(path):
+ with open(path, "r") as f:
+ return json.load(f)
+
+
+def write_json(text, path):
+ with open(path, "w") as f:
+ json.dump(text, f)
+
+
+def write_model(model_path, input_base_path, tokenizer_path=None, safe_serialization=True, fix_eos_token_id=True):
+ os.makedirs(model_path, exist_ok=True)
+ tmp_model_path = os.path.join(model_path, "tmp")
+ os.makedirs(tmp_model_path, exist_ok=True)
+
+ config_path = Path(input_base_path) / "config.yaml"
+ olmo_config = yaml.safe_load(config_path.read_text())["model"]
+
+ n_layers = olmo_config["n_layers"]
+ n_heads = olmo_config["n_heads"]
+ dim = olmo_config["d_model"]
+ dims_per_head = dim // n_heads
+ base = 10000.0
+ inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
+ max_position_embeddings = olmo_config["max_sequence_length"]
+
+ vocab_size = olmo_config.get("embedding_size", olmo_config["vocab_size"])
+
+ if olmo_config.get("n_kv_heads", None) is not None:
+ num_key_value_heads = olmo_config["n_kv_heads"] # for GQA / MQA
+ elif olmo_config["multi_query_attention"]: # compatibility with other checkpoints
+ num_key_value_heads = 1
+ else:
+ num_key_value_heads = n_heads
+
+ print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
+
+ # Not sharded
+ # (The sharded implementation would also work, but this is simpler.)
+ loaded = torch.load(os.path.join(input_base_path, "model.pt"), map_location="cpu", weights_only=True)
+
+ param_count = 0
+ index_dict = {"weight_map": {}}
+ for layer_i in range(n_layers):
+ filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
+ # Unsharded
+ # TODO: Layernorm stuff
+ # TODO: multi query attention
+ fused_dims = [dim, dims_per_head * num_key_value_heads, dims_per_head * num_key_value_heads]
+ q_proj_weight, k_proj_weight, v_proj_weight = torch.split(
+ loaded[f"transformer.blocks.{layer_i}.att_proj.weight"], fused_dims, dim=0
+ )
+ up_proj_weight, gate_proj_weight = torch.chunk(
+ loaded[f"transformer.blocks.{layer_i}.ff_proj.weight"], 2, dim=0
+ )
+ state_dict = {
+ f"model.layers.{layer_i}.self_attn.q_proj.weight": q_proj_weight,
+ f"model.layers.{layer_i}.self_attn.k_proj.weight": k_proj_weight,
+ f"model.layers.{layer_i}.self_attn.v_proj.weight": v_proj_weight,
+ f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"transformer.blocks.{layer_i}.attn_out.weight"],
+ f"model.layers.{layer_i}.mlp.gate_proj.weight": gate_proj_weight,
+ f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"transformer.blocks.{layer_i}.ff_out.weight"],
+ f"model.layers.{layer_i}.mlp.up_proj.weight": up_proj_weight,
+ }
+
+ state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
+
+ for k, v in state_dict.items():
+ index_dict["weight_map"][k] = filename
+ param_count += v.numel()
+ torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+ filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
+
+ # Unsharded
+ # TODO: Deal with weight-tying
+ state_dict = {
+ "model.embed_tokens.weight": loaded["transformer.wte.weight"],
+ "lm_head.weight": loaded["transformer.ff_out.weight"]
+ if "transformer.ff_out.weight" in loaded
+ else loaded["transformer.wte.weight"],
+ }
+
+ for k, v in state_dict.items():
+ index_dict["weight_map"][k] = filename
+ param_count += v.numel()
+ torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+ # Write configs
+ index_dict["metadata"] = {"total_size": param_count * 2}
+ write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
+
+ if olmo_config.get("mlp_hidden_size", None) is not None:
+ intermediate_size = olmo_config["mlp_hidden_size"] // 2
+ else:
+ intermediate_size = (dim * olmo_config["mlp_ratio"]) // 2
+
+ config = OlmoConfig(
+ vocab_size=vocab_size,
+ hidden_size=dim,
+ intermediate_size=intermediate_size,
+ num_hidden_layers=n_layers,
+ num_attention_heads=n_heads,
+ num_key_value_heads=num_key_value_heads,
+ max_position_embeddings=max_position_embeddings,
+ pad_token_id=olmo_config["pad_token_id"],
+ bos_token_id=None,
+ eos_token_id=olmo_config["eos_token_id"],
+ tie_word_embeddings=olmo_config["weight_tying"],
+ rope_theta=base,
+ clip_qkv=olmo_config.get("clip_qkv"),
+ )
+ config.save_pretrained(tmp_model_path)
+
+ # Make space so we can load the model properly now.
+ del state_dict
+ del loaded
+ gc.collect()
+
+ if tokenizer_path is not None:
+ _write_tokenizer(model_path, config, tokenizer_path, fix_eos_token_id)
+
+ print("Loading the checkpoint in a OLMo model.")
+ model = OlmoForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.float32)
+ # Avoid saving this as part of the config.
+ del model.config._name_or_path
+ print("Saving in the Transformers format.")
+ model.save_pretrained(model_path, safe_serialization=safe_serialization)
+ shutil.rmtree(tmp_model_path)
+
+
+def _write_tokenizer(
+ output_path: Path, config: OlmoConfig, input_tokenizer_path: Path, fix_eos_token_id: bool = True
+) -> None:
+ print(f"Saving a {GPTNeoXTokenizerFast.__name__} to {output_path}.")
+
+ base_tokenizer = Tokenizer.from_file(str(input_tokenizer_path))
+
+ eos_token_id = config.eos_token_id if config.eos_token_id is not None else base_tokenizer.get_vocab_size() - 1
+ pad_token_id = config.pad_token_id if config.pad_token_id is not None else eos_token_id
+
+ if fix_eos_token_id and eos_token_id == 0:
+ # Fixing a bug in OLMo where eos token id was incorrectly set
+ print("Changing eos_token_id from 0 to 50279.")
+ eos_token_id = 50279
+
+ tokenizer = GPTNeoXTokenizerFast(
+ tokenizer_object=base_tokenizer,
+ eos_token=base_tokenizer.decode([eos_token_id], skip_special_tokens=False),
+ pad_token=base_tokenizer.decode([pad_token_id], skip_special_tokens=False),
+ unk_token=None,
+ bos_token=None,
+ )
+
+ tokenizer.save_pretrained(output_path)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--input_dir",
+ required=True,
+ help="Location of OLMo weights, which contains config.yaml and model.pt.",
+ )
+ parser.add_argument(
+ "--tokenizer_json_path",
+ default=None,
+ help="Location of OLMo tokenizer json file.",
+ )
+ parser.add_argument(
+ "--output_dir",
+ required=True,
+ help="Location to write HF model and tokenizer",
+ )
+ parser.add_argument(
+ "--no_fix_eos_token_id",
+ action="store_false",
+ dest="fix_eos_token_id",
+ help="If set, does not change eos token id from 0 to 50279 if it is 0. Changing 0 to 50279 is a bug fix, so use this option with care.",
+ )
+ parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
+ # Different OLMo versions used different default values for max_position_embeddings, hence the need to be able to specify which version is being used.
+ args = parser.parse_args()
+ write_model(
+ model_path=args.output_dir,
+ input_base_path=args.input_dir,
+ safe_serialization=args.safe_serialization,
+ tokenizer_path=args.tokenizer_json_path,
+ fix_eos_token_id=args.fix_eos_token_id,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/modeling_olmo.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/modeling_olmo.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba1774d54328afdbdf076724ac4605636b369c89
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/modeling_olmo.py
@@ -0,0 +1,471 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/olmo/modular_olmo.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_olmo.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...masking_utils import create_causal_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import check_model_inputs
+from .configuration_olmo import OlmoConfig
+
+
+class OlmoLayerNorm(nn.Module):
+ """LayerNorm but with no learnable weight or bias."""
+
+ def __init__(self, hidden_size: int) -> None:
+ super().__init__()
+ self.normalized_shape = (hidden_size,)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ orig_dtype = hidden_states.dtype
+ return F.layer_norm(hidden_states.to(dtype=torch.float32), self.normalized_shape, None, None, eps=1e-5).to(
+ orig_dtype
+ )
+
+
+class OlmoMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+ return down_proj
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ q_type, k_type = q.dtype, k.dtype
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed.to(q_type), k_embed.to(k_type)
+
+
+class OlmoAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: OlmoConfig, layer_idx: int):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = config.attention_dropout
+ self.is_causal = True
+
+ self.q_proj = nn.Linear(
+ config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.k_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.v_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.o_proj = nn.Linear(
+ config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+ )
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ if self.config.clip_qkv is not None:
+ query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+ key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+ value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+
+ query_states = query_states.view(hidden_shape).transpose(1, 2)
+ key_states = key_states.view(hidden_shape).transpose(1, 2)
+ value_states = value_states.view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class OlmoDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: OlmoConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.self_attn = OlmoAttention(config=config, layer_idx=layer_idx)
+
+ self.mlp = OlmoMLP(config)
+ self.input_layernorm = OlmoLayerNorm(config.hidden_size)
+ self.post_attention_layernorm = OlmoLayerNorm(config.hidden_size)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> torch.Tensor:
+ residual = hidden_states
+ hidden_states = self.input_layernorm(hidden_states)
+ # Self Attention
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+ return hidden_states
+
+
+class OlmoRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: OlmoConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+ return cos, sin
+
+
+@auto_docstring
+class OlmoPreTrainedModel(PreTrainedModel):
+ config: OlmoConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["OlmoDecoderLayer"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ _can_compile_fullgraph = True
+ _supports_attention_backend = True
+ _can_record_outputs = {
+ "hidden_states": OlmoDecoderLayer,
+ "attentions": OlmoAttention,
+ }
+
+
+@auto_docstring
+class OlmoModel(OlmoPreTrainedModel):
+ def __init__(self, config: OlmoConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [OlmoDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.norm = OlmoLayerNorm(config.hidden_size)
+ self.rotary_emb = OlmoRotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> BaseModelOutputWithPast:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache(config=self.config)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position: torch.Tensor = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = create_causal_mask(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ hidden_states = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+
+ hidden_states = self.norm(hidden_states)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+@auto_docstring
+class OlmoForCausalLM(OlmoPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+ _tp_plan = {"lm_head": "colwise_rep"}
+ _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = OlmoModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> CausalLMOutputWithPast:
+ r"""
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, OlmoForCausalLM
+
+ >>> model = OlmoForCausalLM.from_pretrained("meta-olmo/Olmo-2-7b-hf")
+ >>> tokenizer = AutoTokenizer.from_pretrained("meta-olmo/Olmo-2-7b-hf")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ outputs: BaseModelOutputWithPast = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = ["OlmoForCausalLM", "OlmoModel", "OlmoPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/modular_olmo.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/modular_olmo.py
new file mode 100644
index 0000000000000000000000000000000000000000..f54b9106345eccb216c79b89931455fc47f02e5b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo/modular_olmo.py
@@ -0,0 +1,174 @@
+from typing import Callable, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+
+from ...cache_utils import Cache
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
+from ...utils import logging
+from ...utils.deprecation import deprecate_kwarg
+from ..llama.modeling_llama import (
+ LlamaAttention,
+ LlamaDecoderLayer,
+ LlamaForCausalLM,
+ LlamaMLP,
+ LlamaModel,
+ LlamaRotaryEmbedding,
+ eager_attention_forward,
+ rotate_half,
+)
+from .configuration_olmo import OlmoConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class OlmoLayerNorm(nn.Module):
+ """LayerNorm but with no learnable weight or bias."""
+
+ def __init__(self, hidden_size: int) -> None:
+ super().__init__()
+ self.normalized_shape = (hidden_size,)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ orig_dtype = hidden_states.dtype
+ return F.layer_norm(hidden_states.to(dtype=torch.float32), self.normalized_shape, None, None, eps=1e-5).to(
+ orig_dtype
+ )
+
+
+class OlmoMLP(LlamaMLP):
+ def __init__(self, config):
+ super().__init__(config)
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ q_type, k_type = q.dtype, k.dtype
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed.to(q_type), k_embed.to(k_type)
+
+
+class OlmoAttention(LlamaAttention):
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ if self.config.clip_qkv is not None:
+ query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+ key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+ value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+
+ query_states = query_states.view(hidden_shape).transpose(1, 2)
+ key_states = key_states.view(hidden_shape).transpose(1, 2)
+ value_states = value_states.view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class OlmoDecoderLayer(LlamaDecoderLayer):
+ def __init__(self, config: OlmoConfig, layer_idx: int):
+ super().__init__(config, layer_idx)
+ self.input_layernorm = OlmoLayerNorm(config.hidden_size)
+ self.post_attention_layernorm = OlmoLayerNorm(config.hidden_size)
+ self.self_attn = OlmoAttention(config=config, layer_idx=layer_idx)
+
+
+# This is identical to LlamaRotaryEmbedding except the output cos and sin are returned
+# as float32 rather than the input type.
+class OlmoRotaryEmbedding(LlamaRotaryEmbedding):
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+ return cos, sin
+
+
+class OlmoModel(LlamaModel):
+ def __init__(self, config: OlmoConfig):
+ super().__init__(config)
+ self.layers = nn.ModuleList(
+ [OlmoDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.norm = OlmoLayerNorm(config.hidden_size)
+
+
+class OlmoForCausalLM(LlamaForCausalLM):
+ pass
+
+
+__all__ = [
+ "OlmoForCausalLM",
+ "OlmoModel",
+ "OlmoPreTrainedModel", # noqa: F822
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2161a4948b5e32f600af135c33330c2e2c353c7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 EleutherAI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_olmo2 import *
+ from .modeling_olmo2 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/configuration_olmo2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/configuration_olmo2.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7a0dabaf4e6d054a5dbb739ed7cab6619e73d8a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/configuration_olmo2.py
@@ -0,0 +1,180 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/olmo2/modular_olmo2.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_olmo2.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+
+from ...configuration_utils import PretrainedConfig
+
+
+class Olmo2Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the [allenai/Olmo2-7B-1124-hf](https://huggingface.co/allenai/Olmo2-7B-1124-hf).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50304):
+ Vocabulary size of the Olmo2 model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`Olmo2Model`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 11008):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+ `num_attention_heads`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*, defaults to 1):
+ Padding token id.
+ bos_token_id (`int`, *optional*):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 50279):
+ End of stream token id.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+ these scaling strategies behave:
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+ experimental feature, subject to breaking API changes in future versions.
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+
+ ```python
+ >>> from transformers import Olmo2Model, Olmo2Config
+
+ >>> # Initializing a Olmo2 7B style configuration
+ >>> configuration = Olmo2Config()
+
+ >>> # Initializing a model from the Olmo2 7B style configuration
+ >>> model = Olmo2Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+ """
+
+ model_type = "olmo2"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ base_model_tp_plan = {
+ "layers.*.self_attn.q_proj": "colwise_rep", # we need to replicate here due to the added norm on q and k
+ "layers.*.self_attn.k_proj": "colwise_rep", # we need to replicate here due to the added norm on q and k
+ "layers.*.self_attn.v_proj": "colwise_rep", # we need to replicate here due to the added norm on q and k
+ "layers.*.self_attn.o_proj": "rowwise_rep", # we need to replicate here due to the added norm on q and k
+ "layers.*.mlp.gate_proj": "colwise",
+ "layers.*.mlp.up_proj": "colwise",
+ "layers.*.mlp.down_proj": "rowwise",
+ }
+ base_model_pp_plan = {
+ "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+ "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+ "norm": (["hidden_states"], ["hidden_states"]),
+ }
+
+ def __init__(
+ self,
+ vocab_size=50304,
+ hidden_size=4096,
+ intermediate_size=11008,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=None,
+ hidden_act="silu",
+ max_position_embeddings=2048,
+ initializer_range=0.02,
+ use_cache=True,
+ pad_token_id=1,
+ bos_token_id=None,
+ eos_token_id=50279,
+ tie_word_embeddings=False,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ attention_bias=False,
+ attention_dropout=0.0,
+ rms_norm_eps=1e-5,
+ **kwargs,
+ ):
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ self._rope_scaling_validation()
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+
+ self.rms_norm_eps = rms_norm_eps
+
+ def _rope_scaling_validation(self):
+ """
+ Validate the `rope_scaling` configuration.
+ """
+ if self.rope_scaling is None:
+ return
+
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+ raise ValueError(
+ f"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, got {self.rope_scaling}"
+ )
+ rope_scaling_type = self.rope_scaling.get("type", None)
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+ raise ValueError(
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+ )
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
+
+
+__all__ = ["Olmo2Config"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/convert_olmo2_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/convert_olmo2_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b515aae69bd6eab0c895ec685e143554ea8efb9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/convert_olmo2_weights_to_hf.py
@@ -0,0 +1,306 @@
+# Copyright 2024 EleutherAI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+import argparse
+import gc
+import json
+import os
+import shutil
+from pathlib import Path
+from typing import Any
+
+import torch
+import yaml
+from tokenizers import Tokenizer
+
+from transformers import Olmo2Config, Olmo2ForCausalLM
+from transformers.models.gpt2.tokenization_gpt2_fast import GPT2TokenizerFast
+
+
+"""
+Sample usage:
+
+```
+python src/transformers/models/olmo2/convert_olmo2_weights_to_hf.py \
+ --input_dir /path/to/downloaded/olmo2/weights --model_size 7B --output_dir /output/path
+```
+
+Thereafter, models can be loaded via:
+
+```py
+from transformers import Olmo2ForCausalLM, AutoTokenizer
+
+model = Olmo2ForCausalLM.from_pretrained("/output/path")
+tokenizer = AutoTokenizer.from_pretrained("/output/path")
+```
+
+Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
+come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
+"""
+
+
+def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
+ return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
+
+
+def read_json(path):
+ with open(path, "r") as f:
+ return json.load(f)
+
+
+def write_json(text, path):
+ with open(path, "w") as f:
+ json.dump(text, f)
+
+
+def write_model(
+ model_path,
+ input_base_path,
+ include_tokenizer=True,
+ tokenizer_path=None,
+ safe_serialization=True,
+ fix_eos_token_id=True,
+ tmp_cleanup=True,
+):
+ os.makedirs(model_path, exist_ok=True)
+ tmp_model_path = os.path.join(model_path, "tmp")
+ os.makedirs(tmp_model_path, exist_ok=True)
+
+ config_path = Path(input_base_path) / "config.yaml"
+ olmo2_config = yaml.safe_load(config_path.read_text())["model"]
+
+ if not olmo2_config.get("attention_layer_norm", False):
+ raise RuntimeError("OLMo2 checkpoints must have attention layer norm")
+ if not olmo2_config.get("norm_after", False):
+ raise RuntimeError("OLMo2 checkpoints must set norm_after to True")
+
+ n_layers = olmo2_config["n_layers"]
+ n_heads = olmo2_config["n_heads"]
+ dim = olmo2_config["d_model"]
+ dims_per_head = dim // n_heads
+ base = olmo2_config["rope_theta"]
+ inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
+ max_position_embeddings = olmo2_config["max_sequence_length"]
+
+ vocab_size = olmo2_config.get("embedding_size", olmo2_config["vocab_size"])
+
+ if olmo2_config.get("n_kv_heads", None) is not None:
+ num_key_value_heads = olmo2_config["n_kv_heads"] # for GQA / MQA
+ elif olmo2_config["multi_query_attention"]: # compatibility with other checkpoints
+ num_key_value_heads = 1
+ else:
+ num_key_value_heads = n_heads
+
+ print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
+
+ # Not sharded
+ # (The sharded implementation would also work, but this is simpler.)
+ loaded = torch.load(os.path.join(input_base_path, "model.pt"), map_location="cpu", weights_only=True)
+
+ param_count = 0
+ index_dict: dict[str, Any] = {"weight_map": {}}
+ for layer_i in range(n_layers):
+ filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
+ # Unsharded
+ # TODO: Layernorm stuff
+ # TODO: multi query attention
+ fused_dims = [dim, dims_per_head * num_key_value_heads, dims_per_head * num_key_value_heads]
+ q_proj_weight, k_proj_weight, v_proj_weight = torch.split(
+ loaded[f"transformer.blocks.{layer_i}.att_proj.weight"], fused_dims, dim=0
+ )
+ up_proj_weight, gate_proj_weight = torch.chunk(
+ loaded[f"transformer.blocks.{layer_i}.ff_proj.weight"], 2, dim=0
+ )
+ state_dict = {
+ f"model.layers.{layer_i}.self_attn.q_proj.weight": q_proj_weight,
+ f"model.layers.{layer_i}.self_attn.k_proj.weight": k_proj_weight,
+ f"model.layers.{layer_i}.self_attn.v_proj.weight": v_proj_weight,
+ f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"transformer.blocks.{layer_i}.attn_out.weight"],
+ f"model.layers.{layer_i}.self_attn.q_norm.weight": loaded[f"transformer.blocks.{layer_i}.q_norm.weight"],
+ f"model.layers.{layer_i}.self_attn.k_norm.weight": loaded[f"transformer.blocks.{layer_i}.k_norm.weight"],
+ f"model.layers.{layer_i}.mlp.gate_proj.weight": gate_proj_weight,
+ f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"transformer.blocks.{layer_i}.ff_out.weight"],
+ f"model.layers.{layer_i}.mlp.up_proj.weight": up_proj_weight,
+ f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[
+ f"transformer.blocks.{layer_i}.attn_norm.weight"
+ ],
+ f"model.layers.{layer_i}.post_feedforward_layernorm.weight": loaded[
+ f"transformer.blocks.{layer_i}.ff_norm.weight"
+ ],
+ }
+
+ state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
+
+ for k, v in state_dict.items():
+ index_dict["weight_map"][k] = filename
+ param_count += v.numel()
+ torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+ filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
+
+ # Unsharded
+ # TODO: Deal with weight-tying
+ state_dict = {
+ "model.embed_tokens.weight": loaded["transformer.wte.weight"],
+ "model.norm.weight": loaded["transformer.ln_f.weight"],
+ "lm_head.weight": loaded["transformer.ff_out.weight"]
+ if "transformer.ff_out.weight" in loaded
+ else loaded["transformer.wte.weight"],
+ }
+
+ for k, v in state_dict.items():
+ index_dict["weight_map"][k] = filename
+ param_count += v.numel()
+ torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+ # Write configs
+ index_dict["metadata"] = {"total_size": param_count * 2}
+ write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
+
+ if olmo2_config.get("mlp_hidden_size", None) is not None:
+ intermediate_size = olmo2_config["mlp_hidden_size"] // 2
+ else:
+ intermediate_size = (dim * olmo2_config["mlp_ratio"]) // 2
+
+ if fix_eos_token_id and olmo2_config["eos_token_id"] == 0:
+ # Fixing a bug in OLMo where eos token id was incorrectly set
+ print("Changing eos_token_id from 0 to 50279.")
+ olmo2_config["eos_token_id"] = 50279
+
+ config = Olmo2Config(
+ vocab_size=vocab_size,
+ hidden_size=dim,
+ intermediate_size=intermediate_size,
+ num_hidden_layers=n_layers,
+ num_attention_heads=n_heads,
+ num_key_value_heads=num_key_value_heads,
+ max_position_embeddings=max_position_embeddings,
+ pad_token_id=olmo2_config["pad_token_id"],
+ bos_token_id=None,
+ eos_token_id=olmo2_config["eos_token_id"],
+ tie_word_embeddings=olmo2_config["weight_tying"],
+ rms_norm_eps=olmo2_config["layer_norm_eps"],
+ rope_theta=base,
+ )
+ config.save_pretrained(tmp_model_path)
+
+ # Make space so we can load the model properly now.
+ del state_dict
+ del loaded
+ gc.collect()
+
+ if include_tokenizer:
+ _write_tokenizer(model_path, config, input_base_path, tokenizer_path)
+
+ print("Loading the checkpoint in a OLMo2 model.")
+ model = Olmo2ForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.float32)
+ # Avoid saving this as part of the config.
+ del model.config._name_or_path
+ print("Saving in the Transformers format.")
+ model.save_pretrained(model_path, safe_serialization=safe_serialization)
+ if tmp_cleanup:
+ # Make cleanup optional; attempting to `rmtree` the `tmp_model_path` causes
+ # errors if using NFS.
+ shutil.rmtree(tmp_model_path)
+
+
+def _write_tokenizer(
+ output_path: Path,
+ config: Olmo2Config,
+ checkpoint_dir: str,
+ input_tokenizer_path: Path | None,
+) -> None:
+ print(f"Saving a {GPT2TokenizerFast.__name__} to {output_path}.")
+
+ if input_tokenizer_path is not None:
+ base_tokenizer = Tokenizer.from_file(str(input_tokenizer_path))
+ else:
+ config_path = Path(checkpoint_dir) / "config.yaml"
+ tokenizer_config = yaml.safe_load(config_path.read_text())["tokenizer"]
+
+ # Initialize tokenizer and validate vocab size.
+ if Path(tokenizer_config["identifier"]).is_file():
+ base_tokenizer = Tokenizer.from_file(tokenizer_config["identifier"])
+ else:
+ base_tokenizer = Tokenizer.from_pretrained(tokenizer_config["identifier"])
+
+ eos_token_id = config.eos_token_id if config.eos_token_id is not None else base_tokenizer.get_vocab_size() - 1
+ pad_token_id = config.pad_token_id if config.pad_token_id is not None else eos_token_id
+
+ tokenizer = GPT2TokenizerFast(
+ tokenizer_object=base_tokenizer,
+ eos_token=base_tokenizer.decode([eos_token_id], skip_special_tokens=False),
+ pad_token=base_tokenizer.decode([pad_token_id], skip_special_tokens=False),
+ )
+
+ tokenizer.save_pretrained(output_path)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--input_dir",
+ required=True,
+ help="Location of OLMo2 weights, which contains config.yaml and model.pt.",
+ )
+ parser.add_argument(
+ "--no_tokenizer",
+ action="store_false",
+ dest="include_tokenizer",
+ help="If set, do not convert OLMo tokenizer to HF tokenizer.",
+ )
+ parser.add_argument(
+ "--tokenizer_json_path",
+ type=Path,
+ default=None,
+ help="Location of OLMo2 tokenizer json file. Defaults to what is set in the config file.",
+ )
+ parser.add_argument(
+ "--output_dir",
+ required=True,
+ help="Location to write HF model and tokenizer",
+ )
+ parser.add_argument(
+ "--no_fix_eos_token_id",
+ action="store_false",
+ dest="fix_eos_token_id",
+ help="If set, does not change eos token id from 0 to 50279 if it is 0. Changing 0 to 50279 is a bug fix, so use this option with care.",
+ )
+ parser.add_argument(
+ "--no_tmp_cleanup",
+ action="store_false",
+ dest="tmp_cleanup",
+ help="If passed, don't remove temp dir at end of HF conversion.",
+ )
+ parser.add_argument(
+ "--no_safe_serialization",
+ action="store_false",
+ dest="safe_serialization",
+ help="Whether or not to save using `safetensors`.",
+ )
+ args = parser.parse_args()
+ write_model(
+ model_path=args.output_dir,
+ input_base_path=args.input_dir,
+ safe_serialization=args.safe_serialization,
+ include_tokenizer=args.include_tokenizer,
+ tokenizer_path=args.tokenizer_json_path,
+ fix_eos_token_id=args.fix_eos_token_id,
+ tmp_cleanup=args.tmp_cleanup,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/modeling_olmo2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/modeling_olmo2.py
new file mode 100644
index 0000000000000000000000000000000000000000..07df928b049764301e0a2f256f2da512d110188b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/modeling_olmo2.py
@@ -0,0 +1,476 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/olmo2/modular_olmo2.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_olmo2.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+from typing import Callable, Optional, Union
+
+import torch
+import torch.nn as nn
+
+from transformers.utils.generic import TransformersKwargs
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...masking_utils import create_causal_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import auto_docstring, can_return_tuple
+from ...utils.deprecation import deprecate_kwarg
+from ...utils.generic import check_model_inputs
+from .configuration_olmo2 import Olmo2Config
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class Olmo2RMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ Olmo2RMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return (self.weight * hidden_states).to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs: Unpack[TransformersKwargs],
+):
+ key_states = repeat_kv(key, module.num_key_value_groups)
+ value_states = repeat_kv(value, module.num_key_value_groups)
+
+ attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+ if attention_mask is not None:
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ q_type, k_type = q.dtype, k.dtype
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed.to(q_type), k_embed.to(k_type)
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+class Olmo2Attention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: Olmo2Config, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+ self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+ self.scaling = self.head_dim**-0.5
+ self.attention_dropout = config.attention_dropout
+ self.is_causal = True
+
+ self.q_proj = nn.Linear(
+ config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.k_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.v_proj = nn.Linear(
+ config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.o_proj = nn.Linear(
+ config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+ )
+ self.q_norm = Olmo2RMSNorm(config.num_attention_heads * self.head_dim, config.rms_norm_eps)
+ self.k_norm = Olmo2RMSNorm(config.num_key_value_heads * self.head_dim, config.rms_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_norm(self.q_proj(hidden_states))
+ key_states = self.k_norm(self.k_proj(hidden_states))
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(hidden_shape).transpose(1, 2)
+ key_states = key_states.view(hidden_shape).transpose(1, 2)
+ value_states = value_states.view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+class Olmo2MLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+ return down_proj
+
+
+class Olmo2DecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: Olmo2Config, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.self_attn = Olmo2Attention(config=config, layer_idx=layer_idx)
+
+ self.mlp = Olmo2MLP(config)
+ self.post_attention_layernorm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_feedforward_layernorm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> torch.Tensor:
+ residual = hidden_states
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = self.post_feedforward_layernorm(hidden_states)
+ hidden_states = residual + hidden_states
+ return hidden_states
+
+
+class Olmo2RotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: Olmo2Config, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+ return cos, sin
+
+
+@auto_docstring
+class Olmo2PreTrainedModel(PreTrainedModel):
+ config: Olmo2Config
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["Olmo2DecoderLayer"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ _can_compile_fullgraph = True
+ _supports_attention_backend = True
+ _can_record_outputs = {
+ "hidden_states": Olmo2DecoderLayer,
+ "attentions": Olmo2Attention,
+ }
+
+
+@auto_docstring
+class Olmo2Model(Olmo2PreTrainedModel):
+ def __init__(self, config: Olmo2Config):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [Olmo2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.norm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.rotary_emb = Olmo2RotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @check_model_inputs
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> BaseModelOutputWithPast:
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache(config=self.config)
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position: torch.Tensor = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = create_causal_mask(
+ config=self.config,
+ input_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ )
+
+ hidden_states = inputs_embeds
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ hidden_states = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+
+ hidden_states = self.norm(hidden_states)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ )
+
+
+@auto_docstring
+class Olmo2ForCausalLM(Olmo2PreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+ _tp_plan = {"lm_head": "colwise_rep"}
+ _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = Olmo2Model(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> CausalLMOutputWithPast:
+ r"""
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, Olmo2ForCausalLM
+
+ >>> model = Olmo2ForCausalLM.from_pretrained("meta-olmo2/Olmo2-2-7b-hf")
+ >>> tokenizer = AutoTokenizer.from_pretrained("meta-olmo2/Olmo2-2-7b-hf")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ outputs: BaseModelOutputWithPast = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs.last_hidden_state
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+__all__ = ["Olmo2ForCausalLM", "Olmo2Model", "Olmo2PreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/modular_olmo2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/modular_olmo2.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7e4706976cca705e81bd64ec937440d44143463
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmo2/modular_olmo2.py
@@ -0,0 +1,321 @@
+from typing import Callable, Optional
+
+import torch
+import torch.nn as nn
+
+from transformers.utils.generic import TransformersKwargs
+
+from ...cache_utils import Cache
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
+from ...processing_utils import Unpack
+from ...utils import logging
+from ...utils.deprecation import deprecate_kwarg
+from ..llama.modeling_llama import LlamaPreTrainedModel, LlamaRMSNorm, eager_attention_forward
+from ..olmo.configuration_olmo import OlmoConfig
+from ..olmo.modeling_olmo import (
+ OlmoAttention,
+ OlmoDecoderLayer,
+ OlmoForCausalLM,
+ OlmoModel,
+ OlmoRotaryEmbedding,
+ apply_rotary_pos_emb,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+class Olmo2Config(OlmoConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the [allenai/Olmo2-7B-1124-hf](https://huggingface.co/allenai/Olmo2-7B-1124-hf).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50304):
+ Vocabulary size of the Olmo2 model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`Olmo2Model`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 11008):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+ `num_attention_heads`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*, defaults to 1):
+ Padding token id.
+ bos_token_id (`int`, *optional*):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 50279):
+ End of stream token id.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+ these scaling strategies behave:
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+ experimental feature, subject to breaking API changes in future versions.
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+
+ ```python
+ >>> from transformers import Olmo2Model, Olmo2Config
+
+ >>> # Initializing a Olmo2 7B style configuration
+ >>> configuration = Olmo2Config()
+
+ >>> # Initializing a model from the Olmo2 7B style configuration
+ >>> model = Olmo2Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+ """
+
+ model_type = "olmo2"
+ base_model_tp_plan = {
+ "layers.*.self_attn.q_proj": "colwise_rep", # we need to replicate here due to the added norm on q and k
+ "layers.*.self_attn.k_proj": "colwise_rep", # we need to replicate here due to the added norm on q and k
+ "layers.*.self_attn.v_proj": "colwise_rep", # we need to replicate here due to the added norm on q and k
+ "layers.*.self_attn.o_proj": "rowwise_rep", # we need to replicate here due to the added norm on q and k
+ "layers.*.mlp.gate_proj": "colwise",
+ "layers.*.mlp.up_proj": "colwise",
+ "layers.*.mlp.down_proj": "rowwise",
+ }
+ base_model_pp_plan = {
+ "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+ "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+ "norm": (["hidden_states"], ["hidden_states"]),
+ }
+
+ def __init__(
+ self,
+ vocab_size=50304,
+ hidden_size=4096,
+ intermediate_size=11008,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=None,
+ hidden_act="silu",
+ max_position_embeddings=2048,
+ initializer_range=0.02,
+ use_cache=True,
+ pad_token_id=1,
+ bos_token_id=None,
+ eos_token_id=50279,
+ tie_word_embeddings=False,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ attention_bias=False,
+ attention_dropout=0.0,
+ rms_norm_eps=1e-5,
+ **kwargs,
+ ):
+ super().__init__(
+ vocab_size=vocab_size,
+ hidden_size=hidden_size,
+ intermediate_size=intermediate_size,
+ num_hidden_layers=num_hidden_layers,
+ num_attention_heads=num_attention_heads,
+ num_key_value_heads=num_key_value_heads,
+ hidden_act=hidden_act,
+ max_position_embeddings=max_position_embeddings,
+ initializer_range=initializer_range,
+ use_cache=use_cache,
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ rope_theta=rope_theta,
+ rope_scaling=rope_scaling,
+ attention_bias=attention_bias,
+ attention_dropout=attention_dropout,
+ **kwargs,
+ )
+
+ self.rms_norm_eps = rms_norm_eps
+ del self.clip_qkv
+
+
+# OLMo2 RMS norm is identical to Llama RMS norm except:
+# - Weight and hidden states are multiplied before converting back to the input dtype, rather than after.
+class Olmo2RMSNorm(LlamaRMSNorm):
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return (self.weight * hidden_states).to(input_dtype)
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+# Olmo2 attention is identical to OLMo attention except:
+# - Norm is applied to attention queries and keys.
+# - No qkv clipping.
+class Olmo2Attention(OlmoAttention):
+ def __init__(self, config: Olmo2Config, layer_idx: Optional[int] = None):
+ super().__init__(config, layer_idx=layer_idx)
+ self.q_norm = Olmo2RMSNorm(config.num_attention_heads * self.head_dim, config.rms_norm_eps)
+ self.k_norm = Olmo2RMSNorm(config.num_key_value_heads * self.head_dim, config.rms_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ position_embeddings: tuple[torch.Tensor, torch.Tensor],
+ attention_mask: Optional[torch.Tensor],
+ past_key_values: Optional[Cache] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ input_shape = hidden_states.shape[:-1]
+ hidden_shape = (*input_shape, -1, self.head_dim)
+
+ query_states = self.q_norm(self.q_proj(hidden_states))
+ key_states = self.k_norm(self.k_proj(hidden_states))
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(hidden_shape).transpose(1, 2)
+ key_states = key_states.view(hidden_shape).transpose(1, 2)
+ value_states = value_states.view(hidden_shape).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.attention_dropout,
+ scaling=self.scaling,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+ attn_output = self.o_proj(attn_output)
+ return attn_output, attn_weights
+
+
+# The OLMo2 layers are identical to those of the OLMo model except:
+# - RMSNorm is used instead of standard layer norm.
+# - Norm is applied after attention/feedforward rather than before.
+class Olmo2DecoderLayer(OlmoDecoderLayer):
+ def __init__(self, config: Olmo2Config, layer_idx: int):
+ super().__init__(config, layer_idx=layer_idx)
+ self.post_attention_layernorm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_feedforward_layernorm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.self_attn = Olmo2Attention(config=config, layer_idx=layer_idx)
+ del self.input_layernorm
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> torch.Tensor:
+ residual = hidden_states
+ hidden_states, _ = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = self.post_feedforward_layernorm(hidden_states)
+ hidden_states = residual + hidden_states
+ return hidden_states
+
+
+class Olmo2RotaryEmbedding(OlmoRotaryEmbedding):
+ pass
+
+
+class Olmo2PreTrainedModel(LlamaPreTrainedModel):
+ pass
+
+
+# The OLMo2 model is identical to the OLMo model, except RMSNorm is used instead of
+# standard layer norm for the output norm.
+class Olmo2Model(OlmoModel):
+ def __init__(self, config: Olmo2Config):
+ super().__init__(config)
+ self.norm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.layers = nn.ModuleList(
+ [Olmo2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+
+
+# The heads now only need to redefine the model inside to the correct `RobertaModel`
+class Olmo2ForCausalLM(OlmoForCausalLM):
+ pass
+
+
+__all__ = [
+ "Olmo2Config",
+ "Olmo2ForCausalLM",
+ "Olmo2Model",
+ "Olmo2PreTrainedModel", # noqa: F822
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0e18d77cbad11bf218b6cd304f703b4d7935eef
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_olmoe import *
+ from .modeling_olmoe import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/configuration_olmoe.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/configuration_olmoe.py
new file mode 100644
index 0000000000000000000000000000000000000000..864d06b64d7ad48d904ab549b4d107a7832666d3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/configuration_olmoe.py
@@ -0,0 +1,182 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""OLMoE model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...modeling_rope_utils import rope_config_validation
+
+
+class OlmoeConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`OlmoeModel`]. It is used to instantiate an OLMoE
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the [allenai/OLMoE-1B-7B-0924](https://huggingface.co/allenai/OLMoE-1B-7B-0924).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50304):
+ Vocabulary size of the OLMoE model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`OlmoeModel`]
+ hidden_size (`int`, *optional*, defaults to 2048):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 2048):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 16):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details, check out [this
+ paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+ `num_attention_heads`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 4096):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*, defaults to 1):
+ Padding token id.
+ bos_token_id (`int`, *optional*):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 50279):
+ End of stream token id.
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+ these scaling strategies behave:
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+ experimental feature, subject to breaking API changes in future versions.
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ clip_qkv (`float`, *optional*):
+ If not `None`, elements of query, key and value attention states are clipped so that their
+ absolute value does not exceed this value.
+ num_experts_per_tok (`int`, *optional*, defaults to 8):
+ Number of selected experts.
+ num_experts (`int`, *optional*, defaults to 64):
+ Number of routed experts.
+ output_router_logits (`bool`, *optional*, defaults to `False`):
+ Whether or not the router logits should be returned by the model. Enabling this will also
+ allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
+ router_aux_loss_coef (`float`, *optional*, defaults to 0.01):
+ The aux loss factor for the total loss.
+ norm_topk_prob (`bool`, *optional*, defaults to `False`):
+ Whether to normalize the topk probabilities.
+
+ ```python
+ >>> from transformers import OlmoeModel, OlmoeConfig
+
+ >>> # Initializing a OLMoE 7B A1B style configuration
+ >>> configuration = OlmoeConfig()
+
+ >>> # Initializing a model from the OLMoE 7B A1B style configuration
+ >>> model = OlmoeModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "olmoe"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=50304,
+ hidden_size=2048,
+ intermediate_size=2048,
+ num_hidden_layers=16,
+ num_attention_heads=16,
+ num_key_value_heads=None,
+ hidden_act="silu",
+ max_position_embeddings=4096,
+ initializer_range=0.02,
+ rms_norm_eps=1e-05,
+ use_cache=True,
+ pad_token_id=1,
+ bos_token_id=None,
+ eos_token_id=50279,
+ tie_word_embeddings=False,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ attention_bias=False,
+ attention_dropout=0.0,
+ clip_qkv=None,
+ num_experts_per_tok=8,
+ num_experts=64,
+ output_router_logits=False,
+ router_aux_loss_coef=0.01,
+ norm_topk_prob=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.clip_qkv = clip_qkv
+ self.num_experts_per_tok = num_experts_per_tok
+ self.num_experts = num_experts
+ self.output_router_logits = output_router_logits
+ self.router_aux_loss_coef = router_aux_loss_coef
+ self.norm_topk_prob = norm_topk_prob
+ # Validate the correctness of rotary position embeddings parameters
+ # BC: if there is a 'type' field, move it to 'rope_type'.
+ if self.rope_scaling is not None and "type" in self.rope_scaling:
+ self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+ rope_config_validation(self)
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+
+__all__ = ["OlmoeConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/convert_olmoe_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/convert_olmoe_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fc5a49c7e5e47c0a4f14b2fe1fc316a6426679d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/convert_olmoe_weights_to_hf.py
@@ -0,0 +1,281 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Example for running:
+0. Cp ckpts to local
+aws s3 cp --recursive s3://ai2-llm/checkpoints/OLMoE/olmoe-8x1b-newhp-newds-final-annealFrom1200000/step23842 /data/niklas/llm/checkpoints/olmoe-8x1b-newhp-newds-final-annealFrom1200000_step23842
+1. Unshard your OLMoE checkpoint using https://github.com/allenai/OLMo/blob/7d63fe09d23cf23714da5aa633a44a90180195da/scripts/unshard.py
+python OLMo/scripts/unshard.py /data/niklas/llm/checkpoints/23485/step954000 /data/niklas/llm/checkpoints/1b-954000-unsharded --model-only
+python OLMo/scripts/unshard.py /data/niklas/llm/checkpoints/23485/step954000 /data/niklas/llm/checkpoints/1b-954000-unsharded --model-only
+python OLMo/scripts/unshard.py /data/niklas/llm/checkpoints/olmoe-8x1b-newhp-newds-final-annealFrom1200000_step23842 /data/niklas/llm/checkpoints/olmoe-8x1b-newhp-newds-final-annealFrom1200000_step23842-unsharded --model-only
+2. Convert to transformers
+rm -rf olmoe; mkdir olmoe; python /data/niklas/transformers/src/transformers/models/olmoe/convert_olmoe_weights_to_hf.py --input_dir /data/niklas/llm/checkpoints/olmoe-8x1b-newhp-newds-final-annealFrom1200000_step23842-unsharded --tokenizer_json_path /data/niklas/llm/checkpoints/olmoe-step1200000-unsharded/tokenizer.json --output_dir olmoe
+3. Load model via:
+```
+from transformers import OlmoeForCausalLM, AutoTokenizer
+import torch
+model = OlmoeForCausalLM.from_pretrained("../transformers/olmoe", torch_dtype=torch.bfloat16).cuda()
+model = OlmoeForCausalLM.from_pretrained("../transformers/olmoe").cuda()
+tokenizer = AutoTokenizer.from_pretrained("../transformers/olmoe")
+inputs = tokenizer("Bitcoin is", return_tensors="pt")
+inputs = {k: v.cuda() for k, v in inputs.items()}
+out = model.generate(**inputs, max_length=64)
+print(tokenizer.decode(out[0]))
+# > # Bitcoin is a digital currency that is created and held electronically. No one controls it. Bitcoins aren’t printed, like dollars or euros – they’re produced by people and businesses running computers all around the world, using software that solves mathematical
+# Or quick sanity check:
+o = model(torch.tensor([[0, 1]]).cuda())
+# If the checkpoint is not converted to BF16 but kept in FP32:
+# > # Bitcoin is a digital currency that is not controlled by any central authority. It is a peer-to-peer payment system that allows users to send and receive payments from anywhere in the world. Bitcoin is also known as a cryptocurrency because it uses cryptography to secure transactions and prevent fraud.
+```
+
+Note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
+come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
+
+Compare with OLMo codebase:
+```
+from olmo.model import OLMo
+import torch
+model = OLMo.from_checkpoint("/data/niklas/llm/checkpoints/olmoe-step1200000-unsharded-pt")
+model = model.cuda()
+model = model.to(torch.bfloat16)
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("../transformers/olmoe")
+inputs = tokenizer("Bitcoin is", return_tensors="pt")
+inputs = {k: v.cuda() for k, v in inputs.items()}
+out = model.generate(**inputs)
+print(tokenizer.decode(out[0][0][0]))
+# Bitcoin is a digital currency that is created and held electronically. No one controls it. Bitcoins aren’t printed, like dollars or euros – they’re produced by people and businesses running computers all around the world, using software that solves mathematical problems. It’s the first example of a growing category of money
+# Or quick sanity check:
+o = model(torch.tensor([[0, 1]]).cuda())
+```
+"""
+
+import argparse
+import gc
+import json
+import os
+import shutil
+from pathlib import Path
+
+import torch
+import yaml
+from tokenizers import Tokenizer
+
+from transformers import OlmoeConfig, OlmoeForCausalLM
+from transformers.models.gpt_neox.tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
+
+
+def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
+ return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
+
+
+def read_json(path):
+ with open(path, "r") as f:
+ return json.load(f)
+
+
+def write_json(text, path):
+ with open(path, "w") as f:
+ json.dump(text, f)
+
+
+def write_model(model_path, input_base_path, tokenizer_path=None, safe_serialization=True, fix_eos_token_id=True):
+ os.makedirs(model_path, exist_ok=True)
+ tmp_model_path = os.path.join(model_path, "tmp")
+ os.makedirs(tmp_model_path, exist_ok=True)
+
+ config_path = Path(input_base_path) / "config.yaml"
+ olmoe_config = yaml.safe_load(config_path.read_text())["model"]
+
+ if fix_eos_token_id:
+ olmoe_config["eos_token_id"] = 50279
+
+ n_layers = olmoe_config["n_layers"]
+ n_heads = olmoe_config["n_heads"]
+ dim = olmoe_config["d_model"]
+ dims_per_head = dim // n_heads
+ base = 10000.0
+ inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
+ max_position_embeddings = olmoe_config["max_sequence_length"]
+
+ vocab_size = olmoe_config.get("embedding_size", olmoe_config["vocab_size"])
+
+ if olmoe_config.get("n_kv_heads", None) is not None:
+ num_key_value_heads = olmoe_config["n_kv_heads"] # for GQA / MQA
+ elif olmoe_config["multi_query_attention"]: # compatibility with other checkpoints
+ num_key_value_heads = 1
+ else:
+ num_key_value_heads = n_heads
+
+ print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
+
+ # Not sharded
+ loaded = torch.load(os.path.join(input_base_path, "model.pt"), map_location="cpu", weights_only=True)
+
+ param_count = 0
+ index_dict = {"weight_map": {}}
+ for layer_i in range(n_layers):
+ filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
+ fused_dims = [dim, dims_per_head * num_key_value_heads, dims_per_head * num_key_value_heads]
+ q_proj_weight, k_proj_weight, v_proj_weight = torch.split(
+ loaded[f"transformer.blocks.{layer_i}.att_proj.weight"], fused_dims, dim=0
+ )
+ state_dict = {
+ f"model.layers.{layer_i}.self_attn.q_proj.weight": q_proj_weight,
+ f"model.layers.{layer_i}.self_attn.k_proj.weight": k_proj_weight,
+ f"model.layers.{layer_i}.self_attn.v_proj.weight": v_proj_weight,
+ f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"transformer.blocks.{layer_i}.attn_out.weight"],
+ f"model.layers.{layer_i}.self_attn.q_norm.weight": loaded[f"transformer.blocks.{layer_i}.q_norm.weight"],
+ f"model.layers.{layer_i}.self_attn.k_norm.weight": loaded[f"transformer.blocks.{layer_i}.k_norm.weight"],
+ f"model.layers.{layer_i}.mlp.gate.weight": loaded[f"transformer.blocks.{layer_i}.ffn.router.layer.weight"],
+ f"model.layers.{layer_i}.input_layernorm.weight": loaded[f"transformer.blocks.{layer_i}.attn_norm.weight"],
+ f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[
+ f"transformer.blocks.{layer_i}.ff_norm.weight"
+ ],
+ }
+
+ num_experts = loaded[f"transformer.blocks.{layer_i}.ffn.router.layer.weight"].shape[0]
+ dim_per_expert = loaded[f"transformer.blocks.{layer_i}.ffn.experts.mlp.w1"].shape[0] // num_experts
+ for expert_i in range(num_experts):
+ state_dict[f"model.layers.{layer_i}.mlp.experts.{expert_i}.gate_proj.weight"] = loaded[
+ f"transformer.blocks.{layer_i}.ffn.experts.mlp.w1"
+ ][dim_per_expert * expert_i : dim_per_expert * (expert_i + 1), :]
+ state_dict[f"model.layers.{layer_i}.mlp.experts.{expert_i}.up_proj.weight"] = loaded[
+ f"transformer.blocks.{layer_i}.ffn.experts.mlp.v1"
+ ][dim_per_expert * expert_i : dim_per_expert * (expert_i + 1), :]
+ state_dict[f"model.layers.{layer_i}.mlp.experts.{expert_i}.down_proj.weight"] = loaded[
+ f"transformer.blocks.{layer_i}.ffn.experts.mlp.w2"
+ ][dim_per_expert * expert_i : dim_per_expert * (expert_i + 1), :].T.contiguous()
+
+ state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
+
+ for k, v in state_dict.items():
+ index_dict["weight_map"][k] = filename
+ param_count += v.numel()
+ torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+ filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
+
+ # Unsharded
+ state_dict = {
+ "model.embed_tokens.weight": loaded["transformer.wte.weight"],
+ "lm_head.weight": loaded["transformer.ff_out.weight"],
+ "model.norm.weight": loaded["transformer.ln_f.weight"],
+ }
+
+ for k, v in state_dict.items():
+ index_dict["weight_map"][k] = filename
+ param_count += v.numel()
+ torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+ # Write configs
+ index_dict["metadata"] = {"total_size": param_count * 2}
+ write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
+
+ config = OlmoeConfig(
+ vocab_size=vocab_size,
+ hidden_size=dim,
+ intermediate_size=dim_per_expert,
+ num_hidden_layers=n_layers,
+ num_attention_heads=n_heads,
+ num_key_value_heads=num_key_value_heads,
+ max_position_embeddings=max_position_embeddings,
+ pad_token_id=olmoe_config["pad_token_id"],
+ bos_token_id=None,
+ eos_token_id=olmoe_config["eos_token_id"],
+ tie_word_embeddings=olmoe_config["weight_tying"],
+ rope_theta=base,
+ clip_qkv=olmoe_config.get("clip_qkv"),
+ )
+ config.save_pretrained(tmp_model_path)
+
+ # Make space so we can load the model properly now.
+ del state_dict
+ del loaded
+ gc.collect()
+
+ if tokenizer_path is not None:
+ _write_tokenizer(model_path, config, tokenizer_path, fix_eos_token_id)
+
+ print("Loading the checkpoint in a OLMoE model.")
+ model = OlmoeForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.bfloat16)
+ # Avoid saving this as part of the config.
+ del model.config._name_or_path
+ print("Saving in the Transformers format.")
+ model.save_pretrained(model_path, safe_serialization=safe_serialization)
+ shutil.rmtree(tmp_model_path)
+
+
+def _write_tokenizer(
+ output_path: Path, config: OlmoeConfig, input_tokenizer_path: Path, fix_eos_token_id: bool = True
+) -> None:
+ print(f"Saving a {GPTNeoXTokenizerFast.__name__} to {output_path}.")
+
+ base_tokenizer = Tokenizer.from_file(str(input_tokenizer_path))
+
+ eos_token_id = config.eos_token_id if config.eos_token_id is not None else base_tokenizer.get_vocab_size() - 1
+ pad_token_id = config.pad_token_id if config.pad_token_id is not None else eos_token_id
+
+ if fix_eos_token_id and eos_token_id == 0:
+ # Fixing a bug in OLMo where eos token id was incorrectly set
+ print("Changing eos_token_id from 0 to 50279.")
+ eos_token_id = 50279
+
+ tokenizer = GPTNeoXTokenizerFast(
+ tokenizer_object=base_tokenizer,
+ eos_token=base_tokenizer.decode([eos_token_id], skip_special_tokens=False),
+ pad_token=base_tokenizer.decode([pad_token_id], skip_special_tokens=False),
+ unk_token=None,
+ bos_token=None,
+ )
+
+ tokenizer.save_pretrained(output_path)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--input_dir",
+ required=True,
+ help="Location of OLMoE weights, which contains config.yaml and model.pt.",
+ )
+ parser.add_argument(
+ "--tokenizer_json_path",
+ default=None,
+ help="Location of OLMoE tokenizer json file.",
+ )
+ parser.add_argument(
+ "--output_dir",
+ required=True,
+ help="Location to write HF model and tokenizer",
+ )
+ parser.add_argument(
+ "--no_fix_eos_token_id",
+ action="store_false",
+ dest="fix_eos_token_id",
+ help="If set, does not change eos token id from 0 to 50279 if it is 0. Changing 0 to 50279 is a bug fix, so use this option with care.",
+ )
+ parser.add_argument(
+ "--safe_serialization", type=bool, default=True, help="Whether or not to save using `safetensors`."
+ )
+ args = parser.parse_args()
+ write_model(
+ model_path=args.output_dir,
+ input_base_path=args.input_dir,
+ safe_serialization=args.safe_serialization,
+ tokenizer_path=args.tokenizer_json_path,
+ fix_eos_token_id=args.fix_eos_token_id,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/modeling_olmoe.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/modeling_olmoe.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a4b970815f3f0b9e48f1247f6d0384e78bda402
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/olmoe/modeling_olmoe.py
@@ -0,0 +1,1108 @@
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch OLMoE model."""
+
+import math
+from typing import Optional, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import AttentionMaskConverter
+from ...modeling_flash_attention_utils import flash_attn_supports_top_left_mask, is_flash_attn_available
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, logging
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_olmoe import OlmoeConfig
+
+
+if is_flash_attn_available():
+ from ...modeling_flash_attention_utils import _flash_attention_forward
+
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.qwen2_moe.modeling_qwen2_moe.load_balancing_loss_func
+def load_balancing_loss_func(
+ gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
+ num_experts: Optional[int] = None,
+ top_k=2,
+ attention_mask: Optional[torch.Tensor] = None,
+) -> Union[torch.Tensor, int]:
+ r"""
+ Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+ See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
+ function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+ experts is too unbalanced.
+
+ Args:
+ gate_logits:
+ Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+ shape [batch_size X sequence_length, num_experts].
+ num_experts:
+ Number of experts
+ top_k:
+ The number of experts to route per-token, can be also interpreted as the `top-k` routing
+ parameter.
+ attention_mask (`torch.Tensor`, *optional*):
+ The attention_mask used in forward function
+ shape [batch_size X sequence_length] if not None.
+
+ Returns:
+ The auxiliary loss.
+ """
+ if gate_logits is None or not isinstance(gate_logits, tuple):
+ return 0
+
+ if isinstance(gate_logits, tuple):
+ compute_device = gate_logits[0].device
+ concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+ routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+ _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+ if attention_mask is None:
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.mean(routing_weights, dim=0)
+ else:
+ batch_size, sequence_length = attention_mask.shape
+ num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+ expert_attention_mask = (
+ attention_mask[None, :, :, None, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+ .reshape(-1, top_k, num_experts)
+ .to(compute_device)
+ )
+
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+ expert_attention_mask, dim=0
+ )
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+ router_per_expert_attention_mask = (
+ attention_mask[None, :, :, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, routing_weights.shape[1]))
+ .reshape(-1, routing_weights.shape[1])
+ .to(compute_device)
+ )
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+ router_per_expert_attention_mask, dim=0
+ )
+
+ device_index = routing_weights.device.index if routing_weights.device.index is not None else 0
+ rank = routing_weights.shape[1] * int(device_index)
+ overall_loss = torch.sum(
+ tokens_per_expert[:, rank : rank + routing_weights.shape[1]] * router_prob_per_expert.unsqueeze(0)
+ )
+ return overall_loss * num_experts
+
+
+class OlmoeRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-5):
+ """
+ OlmoeRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Olmoe
+class OlmoeRotaryEmbedding(nn.Module):
+ inv_freq: torch.Tensor # fix linting for `register_buffer`
+
+ def __init__(self, config: OlmoeConfig, device=None):
+ super().__init__()
+ # BC: "rope_type" was originally "type"
+ if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+ else:
+ self.rope_type = "default"
+ self.max_seq_len_cached = config.max_position_embeddings
+ self.original_max_seq_len = config.max_position_embeddings
+
+ self.config = config
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self.original_inv_freq = self.inv_freq
+
+ @torch.no_grad()
+ @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
+ def forward(self, x, position_ids):
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+ position_ids_expanded = position_ids[:, None, :].float()
+
+ device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+ with torch.autocast(device_type=device_type, enabled=False): # Force float32
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos() * self.attention_scaling
+ sin = emb.sin() * self.attention_scaling
+
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`, *optional*):
+ Deprecated and unused.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos.unsqueeze(unsqueeze_dim)
+ sin = sin.unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+# Copied from transformers.models.olmo.modeling_olmo.OlmoMLP with Olmo->Olmoe
+class OlmoeMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+ return down_proj
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class OlmoeAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: OlmoeConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.rope_theta = config.rope_theta
+ self.is_causal = True
+
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+ self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
+ self.q_norm = OlmoeRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
+ self.k_norm = OlmoeRMSNorm(
+ (self.hidden_size // self.num_heads) * self.num_key_value_heads, eps=config.rms_norm_eps
+ )
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_norm(self.q_proj(hidden_states))
+ key_states = self.k_norm(self.k_proj(hidden_states))
+ value_states = self.v_proj(hidden_states)
+
+ if self.config.clip_qkv is not None:
+ query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+ key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+ value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+ if attention_mask is not None: # no matter the length, we just slice it
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+ attn_weights = attn_weights + causal_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights
+
+
+class OlmoeFlashAttention2(OlmoeAttention):
+ """
+ OLMoE flash attention module. This module inherits from `OlmoeAttention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+ self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ output_attentions = False
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_norm(self.q_proj(hidden_states))
+ key_states = self.k_norm(self.k_proj(hidden_states))
+ value_states = self.v_proj(hidden_states)
+ if self.config.clip_qkv is not None:
+ query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+ key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+ value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+
+ # Flash attention requires the input to have the shape
+ # batch_size x seq_length x head_dim x hidden_dim
+ # therefore we just need to keep the original shape
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+ # to be able to avoid many of these transpose/reshape/view.
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ dropout_rate = self.attention_dropout if self.training else 0.0
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in the correct dtype just to be sure everything works as expected.
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+ # in fp32. (OlmoeRMSNorm handles it correctly)
+
+ input_dtype = query_states.dtype
+ device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
+ if input_dtype == torch.float32:
+ if torch.is_autocast_enabled():
+ target_dtype = (
+ torch.get_autocast_dtype(device_type)
+ if hasattr(torch, "get_autocast_dtype")
+ else torch.get_autocast_gpu_dtype()
+ )
+ # Handle the case where the model is quantized
+ elif hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ else:
+ target_dtype = self.q_proj.weight.dtype
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ attn_output = _flash_attention_forward(
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ q_len,
+ dropout=dropout_rate,
+ use_top_left_mask=self._flash_attn_uses_top_left_mask,
+ is_causal=self.is_causal,
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights
+
+
+class OlmoeSdpaAttention(OlmoeAttention):
+ """
+ OLMoE attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `OlmoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+ SDPA API.
+ """
+
+ # Adapted from OlmoeAttention.forward
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ if output_attentions:
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+ logger.warning_once(
+ "OlmoeModel is using OlmoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ return super().forward(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_norm(self.q_proj(hidden_states))
+ key_states = self.k_norm(self.k_proj(hidden_states))
+ value_states = self.v_proj(hidden_states)
+
+ if self.config.clip_qkv is not None:
+ query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+ key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+ value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ cos, sin = position_embeddings
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+ if past_key_values is not None:
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+ key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ causal_mask = attention_mask
+ # if attention_mask is not None and cache_position is not None:
+ if attention_mask is not None:
+ causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ if query_states.device.type == "cuda" and causal_mask is not None:
+ query_states = query_states.contiguous()
+ key_states = key_states.contiguous()
+ value_states = value_states.contiguous()
+
+ # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+ # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+ is_causal = causal_mask is None and q_len > 1
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=causal_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ is_causal=is_causal,
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, None
+
+
+OLMOE_ATTENTION_CLASSES = {
+ "eager": OlmoeAttention,
+ "flash_attention_2": OlmoeFlashAttention2,
+ "sdpa": OlmoeSdpaAttention,
+}
+
+
+class OlmoeSparseMoeBlock(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.num_experts = config.num_experts
+ self.top_k = config.num_experts_per_tok
+ self.norm_topk_prob = config.norm_topk_prob
+ self.gate = nn.Linear(config.hidden_size, self.num_experts, bias=False)
+ self.experts = nn.ModuleList([OlmoeMLP(config) for _ in range(self.num_experts)])
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ batch_size, sequence_length, hidden_dim = hidden_states.shape
+ hidden_states = hidden_states.view(-1, hidden_dim)
+ # router_logits: (batch * sequence_length, n_experts)
+ router_logits = self.gate(hidden_states)
+
+ routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+ routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+ if self.norm_topk_prob:
+ routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+ # we cast back to the input dtype
+ routing_weights = routing_weights.to(hidden_states.dtype)
+
+ final_hidden_states = torch.zeros(
+ (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+ )
+
+ # One hot encode the selected experts to create an expert mask
+ # this will be used to easily index which expert is going to be selected
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+ # Loop over all available experts in the model and perform the computation on each expert
+ for expert_idx in range(self.num_experts):
+ expert_layer = self.experts[expert_idx]
+ idx, top_x = torch.where(expert_mask[expert_idx])
+
+ # Index the correct hidden states and compute the expert hidden state for
+ # the current expert. We need to make sure to multiply the output hidden
+ # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+ current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+ current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
+
+ # However `index_add_` only support torch tensors for indexing so we'll use
+ # the `top_x` tensor here.
+ final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+ final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+ return final_hidden_states, router_logits
+
+
+class OlmoeDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: OlmoeConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+
+ self.self_attn = OLMOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+ self.mlp = OlmoeSparseMoeBlock(config)
+ self.input_layernorm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ output_attentions: Optional[bool] = False,
+ output_router_logits: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ cache_position: Optional[torch.LongTensor] = None,
+ position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+ **kwargs,
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_router_logits (`bool`, *optional*):
+ Whether or not to return the logits of all the routers. They are useful for computing the router loss,
+ and should not be returned during inference.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence
+ position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
+ Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+ with `head_dim` being the embedding dimension of each attention head.
+ kwargs (`dict`, *optional*):
+ Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+ into the model
+ """
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states, router_logits = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if output_router_logits:
+ outputs += (router_logits,)
+
+ return outputs
+
+
+@auto_docstring
+class OlmoePreTrainedModel(PreTrainedModel):
+ config: OlmoeConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["OlmoeDecoderLayer"]
+ _skip_keys_device_placement = ["past_key_values"]
+ _supports_flash_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = False # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, OlmoeRMSNorm):
+ module.weight.data.fill_(1.0)
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+
+@auto_docstring
+class OlmoeModel(OlmoePreTrainedModel):
+ def __init__(self, config: OlmoeConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [OlmoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self.norm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.rotary_emb = OlmoeRotaryEmbedding(config=config)
+ self.gradient_checkpointing = False
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[Cache, list[torch.FloatTensor]]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, MoeModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_router_logits = (
+ output_router_logits if output_router_logits is not None else self.config.output_router_logits
+ )
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+ )
+ use_cache = False
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
+ if not isinstance(past_key_values, (type(None), Cache)):
+ raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+
+ if cache_position is None:
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+ if position_ids is None:
+ position_ids = cache_position.unsqueeze(0)
+
+ causal_mask = self._update_causal_mask(
+ attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+ )
+
+ # embed positions
+ hidden_states = inputs_embeds
+
+ # create position embeddings to be shared across the decoder layers
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_router_logits = () if output_router_logits else None
+
+ for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ output_router_logits=output_router_logits,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ position_embeddings=position_embeddings,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if output_router_logits and layer_outputs[-1] is not None:
+ all_router_logits += (layer_outputs[-1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns] if v is not None
+ )
+ return MoeModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ router_logits=all_router_logits,
+ )
+
+ def _update_causal_mask(
+ self,
+ attention_mask: torch.Tensor,
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ output_attentions: bool,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and 0.0 in attention_mask:
+ return attention_mask
+ return None
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_static_cache = isinstance(past_key_values, StaticCache)
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype, device = input_tensor.dtype, input_tensor.device
+ sequence_length = input_tensor.shape[1]
+ if using_static_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ device=device,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ and not output_attentions
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ device: torch.device,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ device (`torch.device`):
+ The device to place the 4D attention mask on.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+
+class OlmoeForCausalLM(OlmoePreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = OlmoeModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ self.router_aux_loss_coef = config.router_aux_loss_coef
+ self.num_experts = config.num_experts
+ self.num_experts_per_tok = config.num_experts_per_tok
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs,
+ ) -> Union[tuple, MoeCausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, OlmoeForCausalLM
+
+ >>> model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924")
+ >>> tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ 'Hey, are you conscious? Can you talk to me?\nI’m not sure if you’re conscious of this, but I’m'
+ ```
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_router_logits = (
+ output_router_logits if output_router_logits is not None else self.config.output_router_logits
+ )
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ output_router_logits=output_router_logits,
+ return_dict=return_dict,
+ cache_position=cache_position,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+ aux_loss = None
+ if output_router_logits:
+ aux_loss = load_balancing_loss_func(
+ outputs.router_logits if return_dict else outputs[-1],
+ self.num_experts,
+ self.num_experts_per_tok,
+ attention_mask,
+ )
+ if labels is not None:
+ loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ if output_router_logits:
+ output = (aux_loss,) + output
+ return (loss,) + output if loss is not None else output
+
+ return MoeCausalLMOutputWithPast(
+ loss=loss,
+ aux_loss=aux_loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ router_logits=outputs.router_logits,
+ )
+
+
+__all__ = ["OlmoeForCausalLM", "OlmoeModel", "OlmoePreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8b49479b2599b9cc689f9ef281b61fd32ae01c9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_omdet_turbo import *
+ from .modeling_omdet_turbo import *
+ from .processing_omdet_turbo import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py
new file mode 100644
index 0000000000000000000000000000000000000000..e11cc563db13ec69ed9e7c175541c91f41f4dbf2
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py
@@ -0,0 +1,304 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""OmDet-Turbo model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
+from ..auto import CONFIG_MAPPING
+
+
+logger = logging.get_logger(__name__)
+
+
+class OmDetTurboConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`OmDetTurboForObjectDetection`].
+ It is used to instantiate a OmDet-Turbo model according to the specified arguments, defining the model architecture
+ Instantiating a configuration with the defaults will yield a similar configuration to that of the OmDet-Turbo
+ [omlab/omdet-turbo-swin-tiny-hf](https://huggingface.co/omlab/omdet-turbo-swin-tiny-hf) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ text_config (`PretrainedConfig`, *optional*):
+ The configuration of the text backbone.
+ backbone_config (`PretrainedConfig`, *optional*):
+ The configuration of the vision backbone.
+ use_timm_backbone (`bool`, *optional*, defaults to `True`):
+ Whether to use the timm for the vision backbone.
+ backbone (`str`, *optional*, defaults to `"swin_tiny_patch4_window7_224"`):
+ The name of the pretrained vision backbone to use. If `use_pretrained_backbone=False` a randomly initialized
+ backbone with the same architecture `backbone` is used.
+ backbone_kwargs (`dict`, *optional*):
+ Additional kwargs for the vision backbone.
+ use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
+ Whether to use a pretrained vision backbone.
+ apply_layernorm_after_vision_backbone (`bool`, *optional*, defaults to `True`):
+ Whether to apply layer normalization on the feature maps of the vision backbone output.
+ image_size (`int`, *optional*, defaults to 640):
+ The size (resolution) of each image.
+ disable_custom_kernels (`bool`, *optional*, defaults to `False`):
+ Whether to disable custom kernels.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon value for layer normalization.
+ batch_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon value for batch normalization.
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ text_projection_in_dim (`int`, *optional*, defaults to 512):
+ The input dimension for the text projection.
+ text_projection_out_dim (`int`, *optional*, defaults to 512):
+ The output dimension for the text projection.
+ task_encoder_hidden_dim (`int`, *optional*, defaults to 1024):
+ The feedforward dimension for the task encoder.
+ class_embed_dim (`int`, *optional*, defaults to 512):
+ The dimension of the classes embeddings.
+ class_distance_type (`str`, *optional*, defaults to `"cosine"`):
+ The type of of distance to compare predicted classes to projected classes embeddings.
+ Can be `"cosine"` or `"dot"`.
+ num_queries (`int`, *optional*, defaults to 900):
+ The number of queries.
+ csp_activation (`str`, *optional*, defaults to `"silu"`):
+ The activation function of the Cross Stage Partial (CSP) networks of the encoder.
+ conv_norm_activation (`str`, *optional*, defaults to `"gelu"`):
+ The activation function of the ConvNormLayer layers of the encoder.
+ encoder_feedforward_activation (`str`, *optional*, defaults to `"relu"`):
+ The activation function for the feedforward network of the encoder.
+ encoder_feedforward_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout rate following the activation of the encoder feedforward network.
+ encoder_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout rate of the encoder multi-head attention module.
+ hidden_expansion (`int`, *optional*, defaults to 1):
+ The hidden expansion of the CSP networks in the encoder.
+ vision_features_channels (`tuple(int)`, *optional*, defaults to `[256, 256, 256]`):
+ The projected vision features channels used as inputs for the decoder.
+ encoder_hidden_dim (`int`, *optional*, defaults to 256):
+ The hidden dimension of the encoder.
+ encoder_in_channels (`List(int)`, *optional*, defaults to `[192, 384, 768]`):
+ The input channels for the encoder.
+ encoder_projection_indices (`List(int)`, *optional*, defaults to `[2]`):
+ The indices of the input features projected by each layers.
+ encoder_attention_heads (`int`, *optional*, defaults to 8):
+ The number of attention heads for the encoder.
+ encoder_dim_feedforward (`int`, *optional*, defaults to 2048):
+ The feedforward dimension for the encoder.
+ encoder_layers (`int`, *optional*, defaults to 1):
+ The number of layers in the encoder.
+ positional_encoding_temperature (`int`, *optional*, defaults to 10000):
+ The positional encoding temperature in the encoder.
+ num_feature_levels (`int`, *optional*, defaults to 3):
+ The number of feature levels for the multi-scale deformable attention module of the decoder.
+ decoder_hidden_dim (`int`, *optional*, defaults to 256):
+ The hidden dimension of the decoder.
+ decoder_num_heads (`int`, *optional*, defaults to 8):
+ The number of heads for the decoder.
+ decoder_num_layers (`int`, *optional*, defaults to 6):
+ The number of layers for the decoder.
+ decoder_activation (`str`, *optional*, defaults to `"relu"`):
+ The activation function for the decoder.
+ decoder_dim_feedforward (`int`, *optional*, defaults to 2048):
+ The feedforward dimension for the decoder.
+ decoder_num_points (`int`, *optional*, defaults to 4):
+ The number of points sampled in the decoder multi-scale deformable attention module.
+ decoder_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout rate for the decoder.
+ eval_size (`tuple[int, int]`, *optional*):
+ Height and width used to computes the effective height and width of the position embeddings after taking
+ into account the stride (see RTDetr).
+ learn_initial_query (`bool`, *optional*, defaults to `False`):
+ Whether to learn the initial query.
+ cache_size (`int`, *optional*, defaults to 100):
+ The cache size for the classes and prompts caches.
+ is_encoder_decoder (`bool`, *optional*, defaults to `True`):
+ Whether the model is used as an encoder-decoder model or not.
+ kwargs (`dict[str, Any]`, *optional*):
+ Additional parameters from the architecture. The values in kwargs will be saved as part of the configuration
+ and can be used to control the model outputs.
+
+ Examples:
+
+ ```python
+ >>> from transformers import OmDetTurboConfig, OmDetTurboForObjectDetection
+
+ >>> # Initializing a OmDet-Turbo omlab/omdet-turbo-swin-tiny-hf style configuration
+ >>> configuration = OmDetTurboConfig()
+
+ >>> # Initializing a model (with random weights) from the omlab/omdet-turbo-swin-tiny-hf style configuration
+ >>> model = OmDetTurboForObjectDetection(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "omdet-turbo"
+ attribute_map = {
+ "encoder_hidden_dim": "d_model",
+ "num_attention_heads": "encoder_attention_heads",
+ }
+
+ def __init__(
+ self,
+ text_config=None,
+ backbone_config=None,
+ use_timm_backbone=True,
+ backbone="swin_tiny_patch4_window7_224",
+ backbone_kwargs=None,
+ use_pretrained_backbone=False,
+ apply_layernorm_after_vision_backbone=True,
+ image_size=640,
+ disable_custom_kernels=False,
+ layer_norm_eps=1e-5,
+ batch_norm_eps=1e-5,
+ init_std=0.02,
+ text_projection_in_dim=512,
+ text_projection_out_dim=512,
+ task_encoder_hidden_dim=1024,
+ class_embed_dim=512,
+ class_distance_type="cosine",
+ num_queries=900,
+ csp_activation="silu",
+ conv_norm_activation="gelu",
+ encoder_feedforward_activation="relu",
+ encoder_feedforward_dropout=0.0,
+ encoder_dropout=0.0,
+ hidden_expansion=1,
+ vision_features_channels=[256, 256, 256],
+ encoder_hidden_dim=256,
+ encoder_in_channels=[192, 384, 768],
+ encoder_projection_indices=[2],
+ encoder_attention_heads=8,
+ encoder_dim_feedforward=2048,
+ encoder_layers=1,
+ positional_encoding_temperature=10000,
+ num_feature_levels=3,
+ decoder_hidden_dim=256,
+ decoder_num_heads=8,
+ decoder_num_layers=6,
+ decoder_activation="relu",
+ decoder_dim_feedforward=2048,
+ decoder_num_points=4,
+ decoder_dropout=0.0,
+ eval_size=None,
+ learn_initial_query=False,
+ cache_size=100,
+ is_encoder_decoder=True,
+ **kwargs,
+ ):
+ if use_timm_backbone:
+ if backbone_config is None:
+ backbone_kwargs = {
+ "out_indices": [1, 2, 3],
+ "img_size": image_size,
+ "always_partition": True,
+ }
+ elif backbone_config is None:
+ logger.info("`backbone_config` is `None`. Initializing the config with the default `swin` vision config.")
+ backbone_config = CONFIG_MAPPING["swin"](
+ window_size=7,
+ image_size=image_size,
+ embed_dim=96,
+ depths=[2, 2, 6, 2],
+ num_heads=[3, 6, 12, 24],
+ out_indices=[2, 3, 4],
+ )
+ elif isinstance(backbone_config, dict):
+ backbone_model_type = backbone_config.get("model_type")
+ config_class = CONFIG_MAPPING[backbone_model_type]
+ backbone_config = config_class.from_dict(backbone_config)
+
+ verify_backbone_config_arguments(
+ use_timm_backbone=use_timm_backbone,
+ use_pretrained_backbone=use_pretrained_backbone,
+ backbone=backbone,
+ backbone_config=backbone_config,
+ backbone_kwargs=backbone_kwargs,
+ )
+
+ if text_config is None:
+ logger.info(
+ "`text_config` is `None`. Initializing the config with the default `clip_text_model` text config."
+ )
+ text_config = CONFIG_MAPPING["clip_text_model"]()
+ elif isinstance(text_config, dict):
+ text_model_type = text_config.get("model_type")
+ text_config = CONFIG_MAPPING[text_model_type](**text_config)
+
+ if class_distance_type not in ["cosine", "dot"]:
+ raise ValueError(
+ f"Invalid `class_distance_type`. It should be either `cosine` or `dot`, but got {class_distance_type}."
+ )
+
+ self.text_config = text_config
+ self.backbone_config = backbone_config
+ self.use_timm_backbone = use_timm_backbone
+ self.backbone = backbone
+ self.backbone_kwargs = backbone_kwargs
+ self.use_pretrained_backbone = use_pretrained_backbone
+ self.apply_layernorm_after_vision_backbone = apply_layernorm_after_vision_backbone
+ self.image_size = image_size
+ self.disable_custom_kernels = disable_custom_kernels
+ self.layer_norm_eps = layer_norm_eps
+ self.batch_norm_eps = batch_norm_eps
+ self.init_std = init_std
+ self.text_projection_in_dim = text_projection_in_dim
+ self.text_projection_out_dim = text_projection_out_dim
+ self.task_encoder_hidden_dim = task_encoder_hidden_dim
+ self.class_embed_dim = class_embed_dim
+ self.class_distance_type = class_distance_type
+ self.num_queries = num_queries
+ self.csp_activation = csp_activation
+ self.conv_norm_activation = conv_norm_activation
+ self.encoder_feedforward_activation = encoder_feedforward_activation
+ self.encoder_feedforward_dropout = encoder_feedforward_dropout
+ self.encoder_dropout = encoder_dropout
+ self.hidden_expansion = hidden_expansion
+ self.vision_features_channels = vision_features_channels
+ self.encoder_hidden_dim = encoder_hidden_dim
+ self.encoder_in_channels = encoder_in_channels
+ self.encoder_projection_indices = encoder_projection_indices
+ self.encoder_attention_heads = encoder_attention_heads
+ self.encoder_dim_feedforward = encoder_dim_feedforward
+ self.encoder_layers = encoder_layers
+ self.positional_encoding_temperature = positional_encoding_temperature
+ self.num_feature_levels = num_feature_levels
+ self.decoder_hidden_dim = decoder_hidden_dim
+ self.decoder_num_heads = decoder_num_heads
+ self.decoder_num_layers = decoder_num_layers
+ self.decoder_activation = decoder_activation
+ self.decoder_dim_feedforward = decoder_dim_feedforward
+ self.decoder_num_points = decoder_num_points
+ self.decoder_dropout = decoder_dropout
+ self.eval_size = eval_size
+ self.learn_initial_query = learn_initial_query
+ self.cache_size = cache_size
+ self.is_encoder_decoder = is_encoder_decoder
+
+ super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
+
+ @property
+ def sub_configs(self):
+ sub_configs = {}
+ backbone_config = getattr(self, "backbone_config", None)
+ text_config = getattr(self, "text_config", None)
+ if isinstance(backbone_config, PretrainedConfig):
+ sub_configs["backbone_config"] = type(backbone_config)
+ if isinstance(text_config, PretrainedConfig):
+ sub_configs["text_config"] = type(text_config)
+ return sub_configs
+
+
+__all__ = ["OmDetTurboConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/convert_omdet_turbo_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/convert_omdet_turbo_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab6f0573023cef8ef2949ee9487ba6b408e2135f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/convert_omdet_turbo_to_hf.py
@@ -0,0 +1,349 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert OmDet-Turbo checkpoints from the original repository.
+
+URL: https://github.com/om-ai-lab/OmDet"""
+
+import argparse
+
+import requests
+import torch
+from PIL import Image
+
+from transformers import (
+ CLIPTokenizer,
+ DetrImageProcessor,
+ OmDetTurboConfig,
+ OmDetTurboForObjectDetection,
+ OmDetTurboProcessor,
+)
+
+
+IMAGE_MEAN = [123.675, 116.28, 103.53]
+IMAGE_STD = [58.395, 57.12, 57.375]
+
+
+def get_omdet_turbo_config(model_name, use_timm_backbone):
+ if "tiny" in model_name:
+ window_size = 7
+ embed_dim = 96
+ depths = (2, 2, 6, 2)
+ num_heads = (3, 6, 12, 24)
+ image_size = 640
+ else:
+ raise ValueError("Model not supported, only supports tiny variant.")
+
+ config = OmDetTurboConfig(
+ backbone_window_size=window_size,
+ backbone_image_size=image_size,
+ backbone_embed_dim=embed_dim,
+ backbone_depths=depths,
+ backbone_num_heads=num_heads,
+ backbone_out_indices=(1, 2, 3),
+ text_config={"model_type": "clip_text_model"},
+ use_timm_backbone=use_timm_backbone,
+ backbone="swin_tiny_patch4_window7_224" if use_timm_backbone else None,
+ apply_layernorm_after_vision_backbone=bool(use_timm_backbone),
+ use_pretrained_backbone=False,
+ )
+
+ return config
+
+
+def create_rename_keys_vision(state_dict, config):
+ rename_keys = []
+ # fmt: off
+ ########################################## VISION BACKBONE - START
+ for layer_name in state_dict:
+ if layer_name.startswith("backbone") and not layer_name.startswith("backbone.norm"):
+ if config.use_timm_backbone:
+ layer_name_replace = layer_name.replace("backbone", "vision_backbone.vision_backbone._backbone")
+ layer_name_replace = layer_name_replace.replace(".layers.", ".layers_")
+ if "downsample" in layer_name:
+ # get layer number
+ layer_num = int(layer_name.split(".")[2])
+ layer_name_replace = layer_name_replace.replace(f"{layer_num}.downsample", f"{layer_num+1}.downsample")
+ else:
+ layer_name_replace = layer_name.replace("backbone", "vision_backbone.vision_backbone")
+ layer_name_replace = layer_name_replace.replace("patch_embed.proj", "embeddings.patch_embeddings.projection")
+ layer_name_replace = layer_name_replace.replace("patch_embed.norm", "embeddings.norm")
+ if layer_name.startswith("backbone.layers"):
+ layer_name_replace = layer_name_replace.replace("norm1", "layernorm_before")
+ layer_name_replace = layer_name_replace.replace("norm2", "layernorm_after")
+ layer_name_replace = layer_name_replace.replace("attn.proj", "attention.output.dense")
+ layer_name_replace = layer_name_replace.replace("mlp.fc1", "intermediate.dense")
+ layer_name_replace = layer_name_replace.replace("mlp.fc2", "output.dense")
+ layer_name_replace = layer_name_replace.replace(".layers.", ".encoder.layers.")
+ layer_name_replace = layer_name_replace.replace(".attn.", ".attention.self.")
+ elif layer_name.startswith("backbone.norm"):
+ layer_num = int(layer_name.split("norm")[1].split(".")[0])
+ if config.use_timm_backbone:
+ layer_name_replace = layer_name.replace("backbone", "vision_backbone")
+ layer_name_replace = layer_name_replace.replace(f"norm{layer_num}", f"layer_norms.{layer_num-1}")
+ else:
+ layer_name_replace = layer_name.replace(f"backbone.norm{layer_num}", f"vision_backbone.vision_backbone.hidden_states_norms.stage{layer_num+1}")
+ else:
+ continue
+ rename_keys.append((layer_name, layer_name_replace))
+ ########################################## VISION BACKBONE - END
+
+ ########################################## ENCODER - START
+ for layer_name in state_dict:
+ if "neck" in layer_name:
+ layer_name_replace = layer_name.replace("neck", "encoder")
+ layer_name_replace = layer_name_replace.replace("input_proj", "channel_projection_layers")
+ if "fpn_blocks" in layer_name or "pan_blocks" in layer_name or "lateral_convs" in layer_name or "downsample_convs" in layer_name:
+ layer_name_replace = layer_name_replace.replace(".m.", ".bottlenecks.")
+ layer_name_replace = layer_name_replace.replace(".cv", ".conv")
+ layer_name_replace = layer_name_replace.replace(".bn", ".norm")
+ if "encoder_layer" in layer_name:
+ layer_name_replace = layer_name_replace.replace("encoder_layer", "encoder.0.layers.0")
+ layer_name_replace = layer_name_replace.replace(".linear", ".fc")
+ layer_name_replace = layer_name_replace.replace("norm1", "self_attn_layer_norm")
+ layer_name_replace = layer_name_replace.replace("norm2", "final_layer_norm")
+ rename_keys.append((layer_name, layer_name_replace))
+ ########################################## ENCODER - END
+
+ ########################################## DECODER - START
+ for layer_name in state_dict:
+ if layer_name.startswith("decoder"):
+ layer_name_replace = layer_name.replace("decoder.decoder.layers", "decoder.layers")
+ layer_name_replace = layer_name_replace.replace("input_proj", "channel_projection_layers")
+ layer_name_replace = layer_name_replace.replace("query_pos_head", "query_position_head")
+ layer_name_replace = layer_name_replace.replace("enc_bbox_head", "encoder_bbox_head")
+ layer_name_replace = layer_name_replace.replace("enc_output", "encoder_vision_features")
+ layer_name_replace = layer_name_replace.replace("dec_score_head", "decoder_class_head")
+ layer_name_replace = layer_name_replace.replace("dec_bbox_head", "decoder_bbox_head")
+ layer_name_replace = layer_name_replace.replace("enc_score_head", "encoder_class_head")
+ rename_keys.append((layer_name, layer_name_replace))
+ ########################################## DECODER - END
+ # fmt: on
+ return rename_keys
+
+
+def create_rename_keys_language(state_dict):
+ rename_keys = []
+ # fmt: off
+ for layer_name in state_dict:
+ if layer_name.startswith("language_backbone") and not layer_name.startswith("language_backbone.text_projection"):
+ layer_name_replace = layer_name.replace("language_backbone", "language_backbone.model.text_model")
+ layer_name_replace = layer_name_replace.replace("transformer.resblocks", "encoder.layers")
+ layer_name_replace = layer_name_replace.replace("token_embedding", "embeddings.token_embedding")
+ layer_name_replace = layer_name_replace.replace("positional_embedding", "embeddings.position_embedding.weight")
+ layer_name_replace = layer_name_replace.replace(".attn", ".self_attn")
+ layer_name_replace = layer_name_replace.replace(".mlp.c_fc", ".mlp.fc1")
+ layer_name_replace = layer_name_replace.replace(".mlp.c_proj", ".mlp.fc2")
+ layer_name_replace = layer_name_replace.replace("ln_final", "final_layer_norm")
+ layer_name_replace = layer_name_replace.replace(".ln_", ".layer_norm")
+ rename_keys.append((layer_name, layer_name_replace))
+ # fmt: on
+ return rename_keys
+
+
+def rename_key(dct, old, new):
+ val = dct.pop(old)
+ dct[new] = val
+
+
+# we split up the matrix of each encoder layer into queries, keys and values
+def read_in_q_k_v_vision(state_dict, config):
+ state_dict_keys = list(state_dict.keys())
+ for layer_name_vision in state_dict_keys:
+ if layer_name_vision.startswith("vision_backbone") and "qkv" in layer_name_vision:
+ layer_num = int(layer_name_vision.split(".")[4])
+ hidden_size = config.backbone_config.embed_dim * 2**layer_num
+ if "weight" in layer_name_vision:
+ in_proj_weight = state_dict.pop(layer_name_vision)
+ state_dict[layer_name_vision.replace("qkv.weight", "key.weight")] = in_proj_weight[:hidden_size, :]
+ state_dict[layer_name_vision.replace("qkv.weight", "query.weight")] = in_proj_weight[
+ hidden_size : hidden_size * 2, :
+ ]
+ state_dict[layer_name_vision.replace("qkv.weight", "value.weight")] = in_proj_weight[-hidden_size:, :]
+ elif "bias" in layer_name_vision:
+ in_proj_bias = state_dict.pop(layer_name_vision)
+ state_dict[layer_name_vision.replace("qkv.bias", "key.bias")] = in_proj_bias[:hidden_size]
+ state_dict[layer_name_vision.replace("qkv.bias", "query.bias")] = in_proj_bias[
+ hidden_size : hidden_size * 2
+ ]
+ state_dict[layer_name_vision.replace("qkv.bias", "value.bias")] = in_proj_bias[-hidden_size:]
+
+
+def read_in_q_k_v_text(state_dict, config):
+ state_dict_keys = list(state_dict.keys())
+ hidden_size = config.text_config.projection_dim
+ for layer_name_text in state_dict_keys:
+ if layer_name_text.startswith("language_backbone") and "in_proj" in layer_name_text:
+ if "weight" in layer_name_text:
+ in_proj_weight = state_dict.pop(layer_name_text)
+ state_dict[layer_name_text.replace("in_proj_weight", "q_proj.weight")] = in_proj_weight[
+ :hidden_size, :
+ ]
+ state_dict[layer_name_text.replace("in_proj_weight", "k_proj.weight")] = in_proj_weight[
+ hidden_size : hidden_size * 2, :
+ ]
+ state_dict[layer_name_text.replace("in_proj_weight", "v_proj.weight")] = in_proj_weight[
+ -hidden_size:, :
+ ]
+ elif "bias" in layer_name_text:
+ in_proj_bias = state_dict.pop(layer_name_text)
+ state_dict[layer_name_text.replace("in_proj_bias", "q_proj.bias")] = in_proj_bias[:hidden_size]
+ state_dict[layer_name_text.replace("in_proj_bias", "k_proj.bias")] = in_proj_bias[
+ hidden_size : hidden_size * 2
+ ]
+ state_dict[layer_name_text.replace("in_proj_bias", "v_proj.bias")] = in_proj_bias[-hidden_size:]
+
+
+def read_in_q_k_v_encoder(state_dict, config):
+ embed_dim = config.encoder_hidden_dim
+ # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
+ in_proj_weight = state_dict.pop("encoder.encoder.0.layers.0.self_attn.in_proj_weight")
+ in_proj_bias = state_dict.pop("encoder.encoder.0.layers.0.self_attn.in_proj_bias")
+ # next, add query, keys and values (in that order) to the state dict
+ state_dict["encoder.encoder.0.layers.0.self_attn.query.weight"] = in_proj_weight[:embed_dim, :]
+ state_dict["encoder.encoder.0.layers.0.self_attn.query.bias"] = in_proj_bias[:embed_dim]
+ state_dict["encoder.encoder.0.layers.0.self_attn.key.weight"] = in_proj_weight[embed_dim : embed_dim * 2, :]
+ state_dict["encoder.encoder.0.layers.0.self_attn.key.bias"] = in_proj_bias[embed_dim : embed_dim * 2]
+ state_dict["encoder.encoder.0.layers.0.self_attn.value.weight"] = in_proj_weight[-embed_dim:, :]
+ state_dict["encoder.encoder.0.layers.0.self_attn.value.bias"] = in_proj_bias[-embed_dim:]
+
+
+def read_in_q_k_v_decoder(state_dict, config):
+ for layer_num in range(config.decoder_num_layers):
+ embed_dim = config.decoder_hidden_dim
+ # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
+ in_proj_weight = state_dict.pop(f"decoder.layers.{layer_num}.self_attn.in_proj_weight")
+ in_proj_bias = state_dict.pop(f"decoder.layers.{layer_num}.self_attn.in_proj_bias")
+ # next, add query, keys and values (in that order) to the state dict
+ state_dict[f"decoder.layers.{layer_num}.self_attn.query.weight"] = in_proj_weight[:embed_dim, :]
+ state_dict[f"decoder.layers.{layer_num}.self_attn.query.bias"] = in_proj_bias[:embed_dim]
+ state_dict[f"decoder.layers.{layer_num}.self_attn.key.weight"] = in_proj_weight[embed_dim : embed_dim * 2, :]
+ state_dict[f"decoder.layers.{layer_num}.self_attn.key.bias"] = in_proj_bias[embed_dim : embed_dim * 2]
+ state_dict[f"decoder.layers.{layer_num}.self_attn.value.weight"] = in_proj_weight[-embed_dim:, :]
+ state_dict[f"decoder.layers.{layer_num}.self_attn.value.bias"] = in_proj_bias[-embed_dim:]
+
+
+def run_test(model, processor):
+ # We will verify our results on an image of cute cats
+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+
+ classes = ["cat", "remote"]
+ task = "Detect {}.".format(", ".join(classes))
+ inputs = processor(image, text=classes, task=task, return_tensors="pt")
+
+ # Running forward
+ with torch.no_grad():
+ outputs = model(**inputs)
+
+ predicted_slice = outputs[1][0, :3, :3]
+ print(predicted_slice)
+ expected_slice = torch.tensor([[0.9427, -2.5958], [0.2105, -3.4569], [-2.6364, -4.1610]])
+
+ assert torch.allclose(predicted_slice, expected_slice, atol=1e-4)
+ print("Looks ok!")
+
+
+@torch.no_grad()
+def convert_omdet_turbo_checkpoint(args):
+ model_name = args.model_name
+ pytorch_dump_folder_path = args.pytorch_dump_folder_path
+ push_to_hub = args.push_to_hub
+ use_timm_backbone = args.use_timm_backbone
+
+ checkpoint_mapping = {
+ "omdet-turbo-tiny": [
+ "https://huggingface.co/omlab/OmDet-Turbo_tiny_SWIN_T/resolve/main/OmDet-Turbo_tiny_SWIN_T.pth",
+ "https://huggingface.co/omlab/OmDet-Turbo_tiny_SWIN_T/resolve/main/ViT-B-16.pt",
+ ],
+ }
+ # Define default OmDetTurbo configuration
+ config = get_omdet_turbo_config(model_name, use_timm_backbone)
+
+ # Load original checkpoint
+ checkpoint_url = checkpoint_mapping[model_name]
+ original_state_dict_vision = torch.hub.load_state_dict_from_url(checkpoint_url[0], map_location="cpu")["model"]
+ original_state_dict_vision = {k.replace("module.", ""): v for k, v in original_state_dict_vision.items()}
+
+ # Rename keys
+ new_state_dict = original_state_dict_vision.copy()
+ rename_keys_vision = create_rename_keys_vision(new_state_dict, config)
+
+ rename_keys_language = create_rename_keys_language(new_state_dict)
+
+ for src, dest in rename_keys_vision:
+ rename_key(new_state_dict, src, dest)
+
+ for src, dest in rename_keys_language:
+ rename_key(new_state_dict, src, dest)
+
+ if not use_timm_backbone:
+ read_in_q_k_v_vision(new_state_dict, config)
+ read_in_q_k_v_text(new_state_dict, config)
+ read_in_q_k_v_encoder(new_state_dict, config)
+ read_in_q_k_v_decoder(new_state_dict, config)
+ # add "model" prefix to all keys
+ new_state_dict = {f"model.{k}": v for k, v in new_state_dict.items()}
+
+ # Load HF model
+ model = OmDetTurboForObjectDetection(config)
+ model.eval()
+ missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False)
+ print("Missing keys:", missing_keys)
+ print("Unexpected keys:", unexpected_keys)
+
+ image_processor = DetrImageProcessor(
+ size={"height": config.backbone_image_size, "width": config.backbone_image_size},
+ do_rescale=False,
+ image_mean=IMAGE_MEAN,
+ image_std=IMAGE_STD,
+ do_pad=False,
+ )
+ tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+ processor = OmDetTurboProcessor(image_processor=image_processor, tokenizer=tokenizer)
+
+ # end-to-end consistency test
+ run_test(model, processor)
+
+ if pytorch_dump_folder_path is not None:
+ model.save_pretrained(pytorch_dump_folder_path)
+ processor.save_pretrained(pytorch_dump_folder_path)
+
+ if push_to_hub:
+ model.push_to_hub(f"omlab/{model_name}")
+ processor.push_to_hub(f"omlab/{model_name}")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--model_name",
+ default="omdet-turbo-tiny",
+ type=str,
+ choices=["omdet-turbo-tiny"],
+ help="Name of the OmDetTurbo model you'd like to convert.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
+ )
+ parser.add_argument(
+ "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
+ )
+ parser.add_argument(
+ "--use_timm_backbone", action="store_true", help="Whether or not to use timm backbone for vision backbone."
+ )
+
+ args = parser.parse_args()
+ convert_omdet_turbo_checkpoint(args)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py
new file mode 100644
index 0000000000000000000000000000000000000000..966be71d700b1e0d5f17279b54d0aee71f6ac16e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py
@@ -0,0 +1,1643 @@
+# coding=utf-8
+# Copyright 2024 Om Research Lab and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch OmDet-Turbo model."""
+
+import math
+import warnings
+from collections import OrderedDict
+from dataclasses import dataclass
+from functools import lru_cache
+from typing import Optional, Union
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+from ...activations import ACT2CLS, ACT2FN
+from ...file_utils import (
+ ModelOutput,
+)
+from ...integrations import use_kernel_forward_from_hub
+from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, logging
+from ...utils.backbone_utils import load_backbone
+from ..auto import AutoModel
+from .configuration_omdet_turbo import OmDetTurboConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of the OmDetTurboHybridEncoder.
+ """
+)
+class OmDetTurboEncoderOutput(ModelOutput):
+ r"""
+ last_hidden_state (`torch.FloatTensor`):
+ Last hidden states of the encoder.
+ extracted_states (`tuple[torch.FloatTensor]`):
+ The extracted states from the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) of the encoder.
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ extracted_states: tuple[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of the OmDetTurboDecoder.
+ """
+)
+class OmDetTurboDecoderOutput(ModelOutput):
+ r"""
+ last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Sequence of hidden-states at the output of the last layer of the decoder.
+ decoder_coords (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
+ The predicted coordinates of the objects.
+ decoder_classes (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes)`):
+ The predicted classes of the objects.
+ encoder_coord_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
+ The predicted coordinates of the objects from the encoder.
+ encoder_class_logits (`tuple[torch.FloatTensor]` of shape `(batch_size, num_queries, num_classes)`):
+ The predicted class of the objects from the encoder.
+ init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
+ The initial reference points.
+ intermediate_reference_points (`tuple[tuple[torch.FloatTensor]]`):
+ The intermediate reference points.
+ """
+
+ last_hidden_state: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ decoder_coords: Optional[torch.FloatTensor] = None
+ decoder_classes: Optional[torch.FloatTensor] = None
+ encoder_coord_logits: Optional[torch.FloatTensor] = None
+ encoder_class_logits: tuple[torch.FloatTensor] = None
+ init_reference_points: Optional[torch.FloatTensor] = None
+ intermediate_reference_points: tuple[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Output type of [`OmDetTurboObjectDetectionOutput`].
+ """
+)
+class OmDetTurboObjectDetectionOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor`):
+ The loss value.
+ decoder_coord_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
+ The predicted coordinates logits of the objects.
+ decoder_class_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes)`):
+ The predicted class of the objects.
+ init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
+ The initial reference points.
+ intermediate_reference_points (`tuple[tuple[torch.FloatTensor]]`):
+ The intermediate reference points.
+ encoder_coord_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
+ The predicted coordinates of the objects from the encoder.
+ encoder_class_logits (`tuple[torch.FloatTensor]`):
+ The predicted class of the objects from the encoder.
+ encoder_extracted_states (`torch.FloatTensor`):
+ The extracted states from the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) of the encoder.
+ decoder_hidden_states (`tuple[torch.FloatTensor]`, *optional*):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
+ plus the initial embedding outputs.
+ decoder_attentions (`tuple[tuple[torch.FloatTensor]]`, *optional*):
+ Tuple of tuples of `torch.FloatTensor` (one for attention for each layer) of shape `(batch_size, num_heads,
+ sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
+ weighted average in the self-attention, cross-attention and multi-scale deformable attention heads.
+ encoder_hidden_states (`tuple[torch.FloatTensor]`, *optional*):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
+ plus the initial embedding outputs.
+ encoder_attentions (`tuple[tuple[torch.FloatTensor]]`, *optional*):
+ Tuple of tuples of `torch.FloatTensor` (one for attention for each layer) of shape `(batch_size, num_heads,
+ sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
+ weighted average in the self-attention, cross-attention and multi-scale deformable attention heads.
+ classes_structure (`torch.LongTensor`, *optional*):
+ The number of queried classes for each image.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ decoder_coord_logits: Optional[torch.FloatTensor] = None
+ decoder_class_logits: Optional[torch.FloatTensor] = None
+ init_reference_points: Optional[torch.FloatTensor] = None
+ intermediate_reference_points: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ encoder_coord_logits: Optional[torch.FloatTensor] = None
+ encoder_class_logits: tuple[torch.FloatTensor] = None
+ encoder_extracted_states: Optional[torch.FloatTensor] = None
+ decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ decoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ encoder_attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
+ classes_structure: Optional[torch.LongTensor] = None
+
+
+@use_kernel_forward_from_hub("MultiScaleDeformableAttention")
+# Copied from transformers.models.deformable_detr.modeling_deformable_detr.MultiScaleDeformableAttention
+class MultiScaleDeformableAttention(nn.Module):
+ def forward(
+ self,
+ value: Tensor,
+ value_spatial_shapes: Tensor,
+ value_spatial_shapes_list: list[tuple],
+ level_start_index: Tensor,
+ sampling_locations: Tensor,
+ attention_weights: Tensor,
+ im2col_step: int,
+ ):
+ batch_size, _, num_heads, hidden_dim = value.shape
+ _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
+ value_list = value.split([height * width for height, width in value_spatial_shapes_list], dim=1)
+ sampling_grids = 2 * sampling_locations - 1
+ sampling_value_list = []
+ for level_id, (height, width) in enumerate(value_spatial_shapes_list):
+ # batch_size, height*width, num_heads, hidden_dim
+ # -> batch_size, height*width, num_heads*hidden_dim
+ # -> batch_size, num_heads*hidden_dim, height*width
+ # -> batch_size*num_heads, hidden_dim, height, width
+ value_l_ = (
+ value_list[level_id]
+ .flatten(2)
+ .transpose(1, 2)
+ .reshape(batch_size * num_heads, hidden_dim, height, width)
+ )
+ # batch_size, num_queries, num_heads, num_points, 2
+ # -> batch_size, num_heads, num_queries, num_points, 2
+ # -> batch_size*num_heads, num_queries, num_points, 2
+ sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1)
+ # batch_size*num_heads, hidden_dim, num_queries, num_points
+ sampling_value_l_ = nn.functional.grid_sample(
+ value_l_,
+ sampling_grid_l_,
+ mode="bilinear",
+ padding_mode="zeros",
+ align_corners=False,
+ )
+ sampling_value_list.append(sampling_value_l_)
+ # (batch_size, num_queries, num_heads, num_levels, num_points)
+ # -> (batch_size, num_heads, num_queries, num_levels, num_points)
+ # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points)
+ attention_weights = attention_weights.transpose(1, 2).reshape(
+ batch_size * num_heads, 1, num_queries, num_levels * num_points
+ )
+ output = (
+ (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
+ .sum(-1)
+ .view(batch_size, num_heads * hidden_dim, num_queries)
+ )
+ return output.transpose(1, 2).contiguous()
+
+
+class OmDetTurboLRUCache:
+ def __init__(self, capacity: int):
+ self.cache = OrderedDict()
+ self.capacity = capacity
+ self.current_load = 0
+
+ def has(self, key) -> bool:
+ return key in self.cache
+
+ def get(self, key):
+ """
+ Get the value of the key if the key exists in the cache, otherwise return None.
+ Move the key to the end of the cache to show that it was recently used.
+ """
+ if key not in self.cache:
+ return None
+ self.cache.move_to_end(key)
+ return self.cache[key]
+
+ def put(self, key, value) -> None:
+ """
+ Add the key-value pair to the cache.
+ Move the key to the end of the cache to show that it was recently used.
+ If the cache is full, remove the first key (least recently used).
+ """
+ if key not in self.cache:
+ self.current_load += 1
+ if self.current_load > self.capacity:
+ self.cache.popitem(last=False)
+ self.current_load -= 1
+
+ self.cache[key] = value
+ self.cache.move_to_end(key)
+
+
+class OmDetTurboLanguageBackbone(nn.Module):
+ def __init__(self, config: OmDetTurboConfig):
+ super().__init__()
+ self.model = AutoModel.from_config(config.text_config)
+ self.text_projection = nn.Parameter(torch.zeros(config.text_projection_in_dim, config.text_projection_out_dim))
+
+ def forward(self, hidden_states, mask=None, encode_type="task"):
+ text_outputs = self.model(hidden_states)
+ pooled_output = text_outputs[0]
+ if encode_type == "task":
+ if mask is None:
+ raise ValueError("mask is required for task encoding")
+ max_len = (mask != 0).sum(1).max().item()
+ truncated_mask = mask[:, :max_len]
+ truncated_output = pooled_output[:, :max_len, :]
+ return truncated_output.transpose(0, 1), truncated_mask
+ elif encode_type == "class":
+ max_pooled_output = pooled_output[torch.arange(pooled_output.shape[0]), hidden_states.argmax(dim=-1)]
+ projected_output = max_pooled_output @ self.text_projection
+ return projected_output
+ else:
+ raise ValueError(f"encode_type {encode_type} is not supported")
+
+
+class OmDetTurboVisionBackbone(nn.Module):
+ def __init__(self, config: OmDetTurboConfig):
+ super().__init__()
+ self.apply_layernorm_after_vision_backbone = config.apply_layernorm_after_vision_backbone
+ self.vision_backbone = load_backbone(config)
+ self.layer_norms = nn.ModuleList(
+ [nn.LayerNorm(in_channel_dim, eps=config.layer_norm_eps) for in_channel_dim in config.encoder_in_channels]
+ )
+
+ def forward(self, pixel_values):
+ outputs = self.vision_backbone(pixel_values).feature_maps
+ if self.apply_layernorm_after_vision_backbone:
+ outputs = [
+ layer_norm(output).permute(0, 3, 1, 2).contiguous()
+ for layer_norm, output in zip(self.layer_norms, outputs)
+ ]
+
+ return outputs
+
+
+# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrMultiscaleDeformableAttention with DeformableDetr->OmDetTurbo, Deformable DETR->OmDet-Turbo
+class OmDetTurboMultiscaleDeformableAttention(nn.Module):
+ """
+ Multiscale deformable attention as proposed in Deformable DETR.
+ """
+
+ def __init__(self, config: OmDetTurboConfig, num_heads: int, n_points: int):
+ super().__init__()
+
+ self.attn = MultiScaleDeformableAttention()
+
+ if config.d_model % num_heads != 0:
+ raise ValueError(
+ f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}"
+ )
+ dim_per_head = config.d_model // num_heads
+ # check if dim_per_head is power of 2
+ if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0):
+ warnings.warn(
+ "You'd better set embed_dim (d_model) in OmDetTurboMultiscaleDeformableAttention to make the"
+ " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA"
+ " implementation."
+ )
+
+ self.im2col_step = 64
+
+ self.d_model = config.d_model
+ self.n_levels = config.num_feature_levels
+ self.n_heads = num_heads
+ self.n_points = n_points
+
+ self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2)
+ self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points)
+ self.value_proj = nn.Linear(config.d_model, config.d_model)
+ self.output_proj = nn.Linear(config.d_model, config.d_model)
+
+ self.disable_custom_kernels = config.disable_custom_kernels
+
+ def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
+ return tensor if position_embeddings is None else tensor + position_embeddings
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ position_embeddings: Optional[torch.Tensor] = None,
+ reference_points=None,
+ spatial_shapes=None,
+ spatial_shapes_list=None,
+ level_start_index=None,
+ output_attentions: bool = False,
+ ):
+ # add position embeddings to the hidden states before projecting to queries and keys
+ if position_embeddings is not None:
+ hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
+
+ batch_size, num_queries, _ = hidden_states.shape
+ batch_size, sequence_length, _ = encoder_hidden_states.shape
+ # Ignore copy
+ total_elements = sum([shape[0] * shape[1] for shape in spatial_shapes_list])
+ if total_elements != sequence_length:
+ raise ValueError(
+ "Make sure to align the spatial shapes with the sequence length of the encoder hidden states"
+ )
+
+ value = self.value_proj(encoder_hidden_states)
+ if attention_mask is not None:
+ # we invert the attention_mask
+ value = value.masked_fill(~attention_mask[..., None], float(0))
+ value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads)
+ sampling_offsets = self.sampling_offsets(hidden_states).view(
+ batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2
+ )
+ attention_weights = self.attention_weights(hidden_states).view(
+ batch_size, num_queries, self.n_heads, self.n_levels * self.n_points
+ )
+ attention_weights = F.softmax(attention_weights, -1).view(
+ batch_size, num_queries, self.n_heads, self.n_levels, self.n_points
+ )
+ # batch_size, num_queries, n_heads, n_levels, n_points, 2
+ num_coordinates = reference_points.shape[-1]
+ if num_coordinates == 2:
+ offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
+ sampling_locations = (
+ reference_points[:, :, None, :, None, :]
+ + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+ )
+ elif num_coordinates == 4:
+ sampling_locations = (
+ reference_points[:, :, None, :, None, :2]
+ + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
+ )
+ else:
+ raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")
+
+ output = self.attn(
+ value,
+ spatial_shapes,
+ spatial_shapes_list,
+ level_start_index,
+ sampling_locations,
+ attention_weights,
+ self.im2col_step,
+ )
+
+ output = self.output_proj(output)
+
+ return output, attention_weights
+
+
+# Copied from transformers.models.rt_detr.modeling_rt_detr.RTDetrConvNormLayer with RTDetr->OmDetTurbo
+class OmDetTurboConvNormLayer(nn.Module):
+ def __init__(self, config, in_channels, out_channels, kernel_size, stride, padding=None, activation=None):
+ super().__init__()
+ self.conv = nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride,
+ padding=(kernel_size - 1) // 2 if padding is None else padding,
+ bias=False,
+ )
+ self.norm = nn.BatchNorm2d(out_channels, config.batch_norm_eps)
+ self.activation = nn.Identity() if activation is None else ACT2CLS[activation]()
+
+ def forward(self, hidden_state):
+ hidden_state = self.conv(hidden_state)
+ hidden_state = self.norm(hidden_state)
+ hidden_state = self.activation(hidden_state)
+ return hidden_state
+
+
+# Copied from transformers.models.rt_detr.modeling_rt_detr.RTDetrRepVggBlock with RTDetr->OmDetTurbo, activation_function->csp_activation
+class OmDetTurboRepVggBlock(nn.Module):
+ """
+ RepVGG architecture block introduced by the work "RepVGG: Making VGG-style ConvNets Great Again".
+ """
+
+ def __init__(self, config: OmDetTurboConfig):
+ super().__init__()
+
+ activation = config.csp_activation
+ hidden_channels = int(config.encoder_hidden_dim * config.hidden_expansion)
+ self.conv1 = OmDetTurboConvNormLayer(config, hidden_channels, hidden_channels, 3, 1, padding=1)
+ self.conv2 = OmDetTurboConvNormLayer(config, hidden_channels, hidden_channels, 1, 1, padding=0)
+ self.activation = nn.Identity() if activation is None else ACT2CLS[activation]()
+
+ def forward(self, x):
+ y = self.conv1(x) + self.conv2(x)
+ return self.activation(y)
+
+
+# Copied from transformers.models.rt_detr.modeling_rt_detr.RTDetrCSPRepLayer with RTDetr->OmDetTurbo, activation_function->csp_activation
+class OmDetTurboCSPRepLayer(nn.Module):
+ """
+ Cross Stage Partial (CSP) network layer with RepVGG blocks.
+ """
+
+ def __init__(self, config: OmDetTurboConfig):
+ super().__init__()
+
+ in_channels = config.encoder_hidden_dim * 2
+ out_channels = config.encoder_hidden_dim
+ num_blocks = 3
+ activation = config.csp_activation
+
+ hidden_channels = int(out_channels * config.hidden_expansion)
+ self.conv1 = OmDetTurboConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation)
+ self.conv2 = OmDetTurboConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation)
+ self.bottlenecks = nn.Sequential(*[OmDetTurboRepVggBlock(config) for _ in range(num_blocks)])
+ if hidden_channels != out_channels:
+ self.conv3 = OmDetTurboConvNormLayer(config, hidden_channels, out_channels, 1, 1, activation=activation)
+ else:
+ self.conv3 = nn.Identity()
+
+ def forward(self, hidden_state):
+ hidden_state_1 = self.conv1(hidden_state)
+ hidden_state_1 = self.bottlenecks(hidden_state_1)
+ hidden_state_2 = self.conv2(hidden_state)
+ return self.conv3(hidden_state_1 + hidden_state_2)
+
+
+class OmDetTurboMultiheadAttention(nn.Module):
+ """Equivalent implementation of nn.MultiheadAttention with `batch_first=True`."""
+
+ def __init__(self, config, hidden_size, num_attention_heads, dropout):
+ super().__init__()
+ if hidden_size % num_attention_heads != 0:
+ raise ValueError(
+ f"The hidden size ({hidden_size}) is not a multiple of the number of attention "
+ f"heads ({num_attention_heads})"
+ )
+ self.num_attention_heads = num_attention_heads
+ self.attention_head_size = int(hidden_size / num_attention_heads)
+ self.all_head_size = self.num_attention_heads * self.attention_head_size
+ self.query = nn.Linear(hidden_size, self.all_head_size)
+ self.key = nn.Linear(hidden_size, self.all_head_size)
+ self.value = nn.Linear(hidden_size, self.all_head_size)
+ self.out_proj = nn.Linear(hidden_size, hidden_size)
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(
+ self,
+ queries: torch.Tensor,
+ keys: torch.Tensor,
+ values: torch.Tensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor]:
+ batch_size, seq_length, _ = queries.shape
+ query_layer = (
+ self.query(queries)
+ .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+ .transpose(1, 2)
+ )
+ key_layer = (
+ self.key(keys).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
+ )
+ value_layer = (
+ self.value(values).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
+ )
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+
+ if attention_mask is not None:
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+ # This is actually dropping out entire tokens to attend to, which might
+ # seem a bit unusual, but is taken from the original Transformer paper.
+ attention_probs = self.dropout(attention_probs)
+
+ context_layer = torch.matmul(attention_probs, value_layer)
+
+ context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+ new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+ context_layer = context_layer.view(new_context_layer_shape)
+
+ context_layer = self.out_proj(context_layer)
+
+ outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+
+ return outputs
+
+
+class OmDetTurboEncoderLayer(nn.Module):
+ def __init__(self, config: OmDetTurboConfig):
+ super().__init__()
+ self.self_attn = OmDetTurboMultiheadAttention(
+ config,
+ hidden_size=config.encoder_hidden_dim,
+ num_attention_heads=config.num_attention_heads,
+ dropout=config.encoder_dropout,
+ )
+ self.self_attn_layer_norm = nn.LayerNorm(config.encoder_hidden_dim, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.encoder_dropout)
+ self.activation_fn = ACT2FN[config.encoder_feedforward_activation]
+ self.encoder_feedforward_dropout = nn.Dropout(config.encoder_feedforward_dropout)
+ self.fc1 = nn.Linear(config.encoder_hidden_dim, config.encoder_dim_feedforward)
+ self.fc2 = nn.Linear(config.encoder_dim_feedforward, config.encoder_hidden_dim)
+ self.final_layer_norm = nn.LayerNorm(config.encoder_hidden_dim, eps=config.layer_norm_eps)
+
+ @staticmethod
+ def with_pos_embed(tensor, pos_embed):
+ return tensor if pos_embed is None else tensor + pos_embed
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ position_embeddings: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ ):
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`): attention mask of size
+ `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
+ values.
+ position_embeddings (`torch.FloatTensor`, *optional*):
+ Object queries (also called content embeddings), to be added to the hidden states.
+ output_attentions (`bool`, *optional*, defaults to `False`):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+ query = key = self.with_pos_embed(hidden_states, position_embeddings)
+
+ hidden_states = self.self_attn(
+ queries=query,
+ keys=key,
+ values=hidden_states,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+ hidden_states, attentions = hidden_states if output_attentions else (hidden_states[0], None)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = self.encoder_feedforward_dropout(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout(hidden_states)
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+ if self.training:
+ if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ if output_attentions:
+ return hidden_states, attentions
+
+ return (hidden_states,)
+
+
+class OmDetTurboEncoder(nn.Module):
+ def __init__(self, config: OmDetTurboConfig):
+ super().__init__()
+
+ self.layers = nn.ModuleList([OmDetTurboEncoderLayer(config) for _ in range(config.encoder_layers)])
+
+ def forward(
+ self, src, src_mask=None, pos_embed=None, output_attentions: bool = False
+ ) -> tuple[Union[torch.Tensor, tuple[torch.Tensor]]]:
+ hidden_states = src
+ attention = () if output_attentions else None
+ for layer in self.layers:
+ hidden_states = layer(
+ hidden_states,
+ attention_mask=src_mask,
+ position_embeddings=pos_embed,
+ output_attentions=output_attentions,
+ )
+ if output_attentions:
+ attention = attention + (hidden_states[1],)
+ hidden_states = hidden_states[0]
+
+ return hidden_states, attention
+
+
+class OmDetTurboHybridEncoder(nn.Module):
+ """
+ Encoder consisting of channel projection layers, a set of `OmDetTurboEncoder`, a top-down Feature Pyramid Network
+ (FPN) and a bottom-up Path Aggregation Network (PAN). More details on the paper: https://huggingface.co/papers/2304.08069
+
+ Args:
+ config: OmDetTurboConfig
+ """
+
+ def __init__(self, config: OmDetTurboConfig):
+ super().__init__()
+ self.config = config
+ self.in_channels = config.encoder_in_channels
+ self.encoder_hidden_dim = config.encoder_hidden_dim
+ self.encoder_projection_indices = config.encoder_projection_indices
+ self.positional_encoding_temperature = config.positional_encoding_temperature
+ self.eval_size = config.eval_size
+ self.out_channels = [self.encoder_hidden_dim for _ in self.in_channels]
+
+ self.channel_projection_layers = nn.ModuleList()
+ for in_channel in self.in_channels:
+ self.channel_projection_layers.append(
+ nn.Sequential(
+ nn.Conv2d(in_channel, self.encoder_hidden_dim, kernel_size=(1, 1), bias=False),
+ nn.BatchNorm2d(self.encoder_hidden_dim),
+ )
+ )
+
+ # encoder transformer
+ self.encoder = nn.ModuleList([OmDetTurboEncoder(config) for _ in range(len(self.encoder_projection_indices))])
+ # top-down fpn
+ self.lateral_convs = nn.ModuleList()
+ self.fpn_blocks = nn.ModuleList()
+ for _ in range(len(self.in_channels) - 1, 0, -1):
+ self.lateral_convs.append(
+ OmDetTurboConvNormLayer(
+ config,
+ in_channels=self.encoder_hidden_dim,
+ out_channels=self.encoder_hidden_dim,
+ kernel_size=1,
+ stride=1,
+ activation=config.conv_norm_activation,
+ )
+ )
+ self.fpn_blocks.append(OmDetTurboCSPRepLayer(config))
+
+ # bottom-up pan
+ self.downsample_convs = nn.ModuleList()
+ self.pan_blocks = nn.ModuleList()
+ for _ in range(len(self.in_channels) - 1):
+ self.downsample_convs.append(
+ OmDetTurboConvNormLayer(
+ config,
+ in_channels=self.encoder_hidden_dim,
+ out_channels=self.encoder_hidden_dim,
+ kernel_size=3,
+ stride=2,
+ activation=config.conv_norm_activation,
+ )
+ )
+ self.pan_blocks.append(OmDetTurboCSPRepLayer(config))
+
+ @staticmethod
+ def build_2d_sincos_position_embedding(
+ width, height, embed_dim=256, temperature=10000.0, device="cpu", dtype=torch.float32
+ ):
+ grid_w = torch.arange(int(width), dtype=dtype, device=device)
+ grid_h = torch.arange(int(height), dtype=dtype, device=device)
+ grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij")
+ if embed_dim % 4 != 0:
+ raise ValueError("Embed dimension must be divisible by 4 for 2D sin-cos position embedding")
+ pos_dim = embed_dim // 4
+ omega = torch.arange(pos_dim, dtype=dtype, device=device) / pos_dim
+ omega = 1.0 / (temperature**omega)
+
+ out_w = grid_w.flatten()[..., None] @ omega[None]
+ out_h = grid_h.flatten()[..., None] @ omega[None]
+
+ return torch.concat([out_w.sin(), out_w.cos(), out_h.sin(), out_h.cos()], dim=1)[None, :, :]
+
+ def forward(
+ self,
+ inputs_embeddings=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ r"""
+ Args:
+ inputs_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Flattened feature map (output of the backbone + projection layers) that is passed to the encoder.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ hidden_states = inputs_embeddings
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+ # get projection features
+ projected_features = [self.channel_projection_layers[i](feature) for i, feature in enumerate(hidden_states)]
+ # encoder
+ for encoder_layer_index, feature_to_project_index in enumerate(self.encoder_projection_indices):
+ if output_hidden_states:
+ encoder_states = encoder_states + (projected_features[feature_to_project_index],)
+ height, width = projected_features[feature_to_project_index].shape[2:]
+ # flatten [batch, channel, height, width] to [batch, height*width, channel]
+ src_flatten = projected_features[feature_to_project_index].flatten(2).permute(0, 2, 1)
+ if self.training or self.eval_size is None:
+ pos_embed = self.build_2d_sincos_position_embedding(
+ width,
+ height,
+ self.encoder_hidden_dim,
+ self.positional_encoding_temperature,
+ device=src_flatten.device,
+ dtype=src_flatten.dtype,
+ ).to(src_flatten.device, src_flatten.dtype)
+ else:
+ pos_embed = None
+ layer_outputs = self.encoder[encoder_layer_index](
+ src_flatten,
+ pos_embed=pos_embed,
+ output_attentions=output_attentions,
+ )
+ projected_features[feature_to_project_index] = (
+ layer_outputs[0].permute(0, 2, 1).reshape(-1, self.encoder_hidden_dim, height, width).contiguous()
+ )
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (projected_features[feature_to_project_index],)
+
+ # Feature Pyramid Network (FPN)
+ fpn_feature_maps = [projected_features[-1]]
+ for idx in range(len(self.in_channels) - 1, 0, -1):
+ feat_high = fpn_feature_maps[0]
+ feat_low = projected_features[idx - 1]
+ feat_high = self.lateral_convs[len(self.in_channels) - 1 - idx](feat_high)
+ fpn_feature_maps[0] = feat_high
+ upsample_feat = F.interpolate(feat_high, scale_factor=2.0, mode="nearest")
+ fps_map = self.fpn_blocks[len(self.in_channels) - 1 - idx](torch.concat([upsample_feat, feat_low], dim=1))
+ fpn_feature_maps.insert(0, fps_map)
+
+ # Path Aggregation Network (PAN)
+ fpn_states = [fpn_feature_maps[0]]
+ for idx in range(len(self.in_channels) - 1):
+ feat_low = fpn_states[-1]
+ feat_high = fpn_feature_maps[idx + 1]
+ downsample_feat = self.downsample_convs[idx](feat_low)
+ hidden_states = self.pan_blocks[idx](
+ torch.concat([downsample_feat, feat_high.to(downsample_feat.device)], dim=1)
+ )
+ fpn_states.append(hidden_states)
+ if not return_dict:
+ return (fpn_states[-1], encoder_states, all_attentions, fpn_states)
+ return OmDetTurboEncoderOutput(
+ last_hidden_state=fpn_states[-1],
+ hidden_states=encoder_states,
+ attentions=all_attentions,
+ extracted_states=fpn_states,
+ )
+
+
+class OmDetTurboMLPWithDropout(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.linear1 = nn.Linear(config.class_embed_dim, config.task_encoder_hidden_dim)
+ self.activation = ACT2FN[config.decoder_activation]
+ self.dropout = nn.Dropout(config.decoder_dropout)
+ self.linear2 = nn.Linear(config.task_encoder_hidden_dim, config.class_embed_dim)
+
+ def forward(self, x):
+ return self.linear2(self.dropout(self.activation(self.linear1(x))))
+
+
+class OmDetTurboMLP(nn.Module):
+ """Very simple multi-layer perceptron (also called FFN)"""
+
+ def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+ super().__init__()
+ self.num_layers = num_layers
+ hidden_layers_dims = [hidden_dim] * (num_layers - 1)
+ layers_dims = [input_dim] + hidden_layers_dims + [output_dim]
+ self.layers = nn.ModuleList(
+ [nn.Linear(in_dim, out_dim) for in_dim, out_dim in zip(layers_dims[:-1], layers_dims[1:])]
+ )
+
+ def forward(self, x):
+ for i, layer in enumerate(self.layers):
+ x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+ return x
+
+
+class OmDetTurboResidualLayer(nn.Module):
+ """
+ A residual connection followed by a layer norm.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ self.norm1 = nn.LayerNorm(config.class_embed_dim, eps=config.layer_norm_eps)
+ self.dropout = nn.Dropout(config.decoder_dropout)
+
+ def forward(self, x, y):
+ return self.norm1(x + self.dropout(y))
+
+
+class OmDetTurboTaskEncoder(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.mlp = OmDetTurboMLPWithDropout(config)
+ self.res1 = OmDetTurboResidualLayer(config)
+
+ def forward(self, x):
+ mlp_out = self.mlp(x)
+ x = self.res1(x, mlp_out)
+ return x
+
+
+class OmDetTurboDeformableTransformerDecoderLayer(GradientCheckpointingLayer):
+ """
+ A single layer of the Deformable Transformer Decoder.
+ """
+
+ def __init__(self, config):
+ super().__init__()
+ # self attention
+ self.self_attn = OmDetTurboMultiheadAttention(
+ config,
+ hidden_size=config.decoder_hidden_dim,
+ num_attention_heads=config.decoder_num_heads,
+ dropout=config.decoder_dropout,
+ )
+ self.dropout1 = nn.Dropout(config.decoder_dropout)
+ self.norm1 = nn.LayerNorm(config.decoder_hidden_dim, eps=config.layer_norm_eps)
+
+ # cross attention
+ self.cross_attn = OmDetTurboMultiscaleDeformableAttention(
+ config, num_heads=config.decoder_num_heads, n_points=config.decoder_num_points
+ )
+ self.dropout2 = nn.Dropout(config.decoder_dropout)
+ self.norm2 = nn.LayerNorm(config.decoder_hidden_dim, eps=config.layer_norm_eps)
+
+ # feed forward network
+ self.linear1 = nn.Linear(config.decoder_hidden_dim, config.decoder_dim_feedforward)
+ self.act = ACT2FN[config.decoder_activation]
+ self.dropout3 = nn.Dropout(config.decoder_dropout)
+ self.linear2 = nn.Linear(config.decoder_dim_feedforward, config.decoder_hidden_dim)
+ self.dropout4 = nn.Dropout(config.decoder_dropout)
+ self.norm3 = nn.LayerNorm(config.decoder_hidden_dim, eps=config.layer_norm_eps)
+
+ self.output_attentions = config.output_attentions
+ self.output_hidden_states = config.output_hidden_states
+
+ @staticmethod
+ def with_pos_embed(tensor, pos):
+ return tensor if pos is None else tensor + pos
+
+ def forward(
+ self,
+ decoder_embeddings,
+ task_features,
+ reference_points,
+ vision_features,
+ vision_shapes,
+ vision_shapes_list,
+ level_start_index=None,
+ attention_mask=None,
+ padding_mask=None,
+ query_position=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.output_attentions
+ output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
+
+ origin_embedding_len = decoder_embeddings.shape[1]
+
+ # self attention
+ query = key = self.with_pos_embed(decoder_embeddings, query_position)
+ # combine task_features with query, key, value
+ task_features = task_features.transpose(0, 1)
+ query = torch.cat((query, task_features), dim=1)
+ key = torch.cat((key, task_features), dim=1)
+ decoder_embeddings = torch.cat((decoder_embeddings, task_features), dim=1)
+
+ outputs = self.self_attn(
+ query,
+ key,
+ decoder_embeddings,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ )
+ context, self_attention = outputs if output_attentions else (outputs[0], None)
+ decoder_embeddings = decoder_embeddings + self.dropout1(context)
+ decoder_embeddings = self.norm1(decoder_embeddings)
+
+ task_features = decoder_embeddings[:, origin_embedding_len:, :].transpose(0, 1)
+ decoder_embeddings = decoder_embeddings[:, :origin_embedding_len, :]
+
+ # cross attention
+ hidden_states = self.with_pos_embed(decoder_embeddings, query_position)
+ reference_points = reference_points.unsqueeze(2)
+ outputs, cross_attention = self.cross_attn(
+ hidden_states=hidden_states,
+ attention_mask=padding_mask,
+ encoder_hidden_states=vision_features,
+ reference_points=reference_points,
+ spatial_shapes=vision_shapes,
+ spatial_shapes_list=vision_shapes_list,
+ level_start_index=level_start_index,
+ )
+ decoder_embeddings = decoder_embeddings + self.dropout2(outputs)
+ residual = self.norm2(decoder_embeddings)
+
+ # feed forward network
+ decoder_embeddings = self.linear2(self.dropout3(self.act(self.linear1(residual))))
+ decoder_embeddings = residual + self.dropout4(decoder_embeddings)
+ decoder_embeddings = self.norm3(decoder_embeddings)
+
+ return (
+ decoder_embeddings,
+ task_features,
+ self_attention if output_attentions else None,
+ cross_attention if output_attentions else None,
+ )
+
+
+@auto_docstring
+class OmDetTurboPreTrainedModel(PreTrainedModel):
+ config: OmDetTurboConfig
+ base_model_prefix = "model"
+ main_input_name = "pixel_values"
+
+ def _init_weights(self, module):
+ def linear_init_(module_to_init):
+ bound = 1 / math.sqrt(module_to_init.weight.shape[0])
+ nn.init.uniform_(module_to_init.weight, -bound, bound)
+ if hasattr(module_to_init, "bias") and module_to_init.bias is not None:
+ nn.init.uniform_(module_to_init.bias, -bound, bound)
+
+ if isinstance(module, OmDetTurboEncoderLayer):
+ linear_init_(module.fc1)
+ linear_init_(module.fc2)
+ elif isinstance(module, OmDetTurboDecoder):
+ nn.init.constant_(module.encoder_bbox_head.layers[-1].weight, 0.0)
+ nn.init.constant_(module.encoder_bbox_head.layers[-1].bias, 0.0)
+ for mlp in module.decoder_bbox_head:
+ nn.init.constant_(mlp.layers[-1].weight, 0.0)
+ nn.init.constant_(mlp.layers[-1].bias, 0.0)
+ linear_init_(module.encoder_vision_features[0])
+ nn.init.xavier_uniform_(module.encoder_vision_features[0].weight)
+ if module.learn_initial_query:
+ nn.init.xavier_uniform_(module.tgt_embed.weight)
+ nn.init.xavier_uniform_(module.query_position_head.layers[0].weight)
+ nn.init.xavier_uniform_(module.query_position_head.layers[1].weight)
+ for layer in module.channel_projection_layers:
+ nn.init.xavier_uniform_(layer[0].weight)
+ elif isinstance(module, OmDetTurboLanguageBackbone):
+ nn.init.normal_(module.text_projection, std=self.config.text_projection_in_dim**-0.5)
+ elif isinstance(module, (nn.Linear, nn.Conv2d)):
+ module.weight.data.normal_(mean=0.0, std=self.config.init_std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d)):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+
+ def _set_gradient_checkpointing(self, module, value=False):
+ if isinstance(module, OmDetTurboDecoder):
+ module.gradient_checkpointing = value
+
+ @staticmethod
+ def _get_cache_key_at_index(input_ids, attention_mask, index):
+ input_ids = input_ids[index]
+ input_mask = attention_mask[index]
+ cache_key = tuple(input_ids[input_mask != 0].tolist())
+ return cache_key
+
+ def get_cached_class_embeddings(self, classes_input_ids, classes_attention_mask):
+ not_cached_index = []
+ not_cached_classes = []
+ total_embeddings = []
+ for idx, _ in enumerate(classes_input_ids):
+ cache_key = self._get_cache_key_at_index(classes_input_ids, classes_attention_mask, idx)
+ if self.language_cache_class.has(cache_key):
+ total_embeddings.append(self.language_cache_class.get(cache_key))
+ else:
+ total_embeddings.append(None)
+ not_cached_index.append(idx)
+ not_cached_classes.append(cache_key)
+
+ if not_cached_classes:
+ not_cached_classes_ids = torch.stack([classes_input_ids[idx] for idx in not_cached_index])
+ embeddings = self.language_backbone(not_cached_classes_ids, encode_type="class")
+ for idx, emb in enumerate(embeddings):
+ idx_to_put = not_cached_index[idx]
+ total_embeddings[idx_to_put] = emb
+ self.language_cache_class.put(not_cached_classes[idx], emb)
+
+ total_class_embs = torch.stack(total_embeddings).to(self.device)
+ return total_class_embs
+
+ def get_cached_task_embeddings(self, tasks_input_ids, tasks_attention_mask):
+ not_cached_index = []
+ not_cached_tasks = []
+ total_task_features = []
+ total_task_masks = []
+ for idx, _ in enumerate(tasks_input_ids):
+ cache_key = self._get_cache_key_at_index(tasks_input_ids, tasks_attention_mask, idx)
+ if self.language_cache_prompt.has(cache_key):
+ task_feature, task_mask = self.language_cache_prompt.get(cache_key)
+ total_task_features.append(task_feature)
+ total_task_masks.append(task_mask)
+ else:
+ total_task_features.append(None)
+ total_task_masks.append(None)
+ not_cached_index.append(idx)
+ not_cached_tasks.append(cache_key)
+
+ if not_cached_tasks:
+ not_cached_index_ids = torch.stack([tasks_input_ids[idx] for idx in not_cached_index])
+ not_cached_mask = torch.stack([tasks_attention_mask[idx] for idx in not_cached_index])
+ embeddings, masks = self.language_backbone(not_cached_index_ids, mask=not_cached_mask, encode_type="task")
+
+ for idx in range(embeddings.shape[1]):
+ emb = embeddings[:, [idx], :]
+ idx_to_put = not_cached_index[idx]
+ cur_mask = torch.unsqueeze(masks[idx], dim=0).to(self.device)
+ total_task_features[idx_to_put] = emb
+ total_task_masks[idx_to_put] = cur_mask
+ self.language_cache_prompt.put(not_cached_tasks[idx], (emb, cur_mask))
+
+ # pad before concat if needed
+ max_len = max([task.shape[0] for task in total_task_features])
+ for idx, task in enumerate(total_task_features):
+ if task.shape[0] < max_len:
+ pad_size = max_len - task.shape[0]
+ total_task_features[idx] = F.pad(task, (0, 0, 0, 0, 0, pad_size))
+ total_task_masks[idx] = F.pad(total_task_masks[idx], (0, pad_size))
+
+ total_task_features = torch.cat(total_task_features, dim=1).to(self.device)
+ total_task_masks = torch.cat(total_task_masks, dim=0).to(self.device)
+
+ return total_task_features, total_task_masks
+
+ def get_language_embedding(
+ self,
+ classes_input_ids,
+ classes_attention_mask,
+ tasks_input_ids,
+ tasks_attention_mask,
+ classes_structure,
+ ):
+ batched_classes_embeddings = self.get_cached_class_embeddings(classes_input_ids, classes_attention_mask)
+ # regroup class embeddings using saved structure
+ max_class_size = torch.max(classes_structure)
+ class_embeddings_regrouped = []
+ start = 0
+ for size in classes_structure:
+ pad_size = max_class_size - size
+ class_embeddings_regrouped.append(
+ F.pad(batched_classes_embeddings[start : start + size], (0, 0, 0, pad_size)).unsqueeze(1)
+ )
+ start += size
+ class_embeddings = torch.cat(class_embeddings_regrouped, dim=1)
+
+ task_embeddings, task_mask = self.get_cached_task_embeddings(tasks_input_ids, tasks_attention_mask)
+
+ return class_embeddings, task_embeddings, task_mask
+
+
+def _cosine_similarity_scaled(a, b, logit_scale):
+ a = a / a.norm(dim=2, keepdim=True).clamp_min(1e-12)
+ b = b / b.norm(dim=1, keepdim=True).clamp_min(1e-12)
+ logit_scale = logit_scale.exp()
+ logits_per_image = logit_scale * torch.bmm(a, b)
+ return logits_per_image
+
+
+def get_class_similarity(class_distance_type, cls_feature, class_proj):
+ logit_scale = torch.tensor(1 / 0.07).log()
+ if class_distance_type == "cosine":
+ class_logits = _cosine_similarity_scaled(cls_feature, class_proj, logit_scale)
+ elif class_distance_type == "dot":
+ class_logits = torch.bmm(cls_feature, class_proj)
+ else:
+ raise Exception(f"Unknown class_distance_type {class_distance_type}")
+ return class_logits
+
+
+def _inverse_sigmoid(x, eps=1e-5):
+ x = x.clamp(min=0, max=1)
+ x1 = x.clamp(min=eps)
+ x2 = (1 - x).clamp(min=eps)
+ return torch.log(x1 / x2)
+
+
+class OmDetTurboDecoder(OmDetTurboPreTrainedModel):
+ def __init__(self, config: OmDetTurboConfig):
+ self.config = config
+ super().__init__(config)
+ self.gradient_checkpointing = False
+
+ hidden_dim = config.decoder_hidden_dim
+ self.num_queries = config.num_queries
+ self.class_distance_type = config.class_distance_type
+ self.learn_initial_query = config.learn_initial_query
+
+ # backbone feature projection
+ self.channel_projection_layers = nn.ModuleList(
+ nn.Sequential(nn.Conv2d(x, hidden_dim, 1, bias=False), nn.BatchNorm2d(hidden_dim))
+ for x in config.vision_features_channels
+ )
+ self.task_encoder = OmDetTurboTaskEncoder(config)
+ if config.class_embed_dim != hidden_dim:
+ self.task_project = nn.Linear(config.class_embed_dim, hidden_dim)
+
+ # Transformer module
+ self.layers = nn.ModuleList(
+ [OmDetTurboDeformableTransformerDecoderLayer(config) for _ in range(config.decoder_num_layers)]
+ )
+ self.decoder_num_layers = config.decoder_num_layers
+ # decoder embedding
+ if self.learn_initial_query:
+ self.tgt_embed = nn.Embedding(self.num_queries, hidden_dim)
+ self.query_position_head = OmDetTurboMLP(
+ input_dim=4, hidden_dim=2 * hidden_dim, output_dim=hidden_dim, num_layers=2
+ )
+
+ # encoder head
+ self.encoder_vision_features = nn.Sequential(
+ nn.Linear(hidden_dim, hidden_dim), nn.LayerNorm(hidden_dim, eps=config.layer_norm_eps)
+ )
+ self.encoder_class_head = nn.Linear(config.class_embed_dim, hidden_dim)
+ self.encoder_bbox_head = OmDetTurboMLP(input_dim=hidden_dim, hidden_dim=hidden_dim, output_dim=4, num_layers=3)
+
+ # decoder head
+ self.decoder_class_head = nn.ModuleList(
+ [nn.Linear(config.class_embed_dim, hidden_dim) for _ in range(config.decoder_num_layers)]
+ )
+ self.decoder_bbox_head = nn.ModuleList(
+ [OmDetTurboMLP(hidden_dim, hidden_dim, 4, num_layers=3) for _ in range(config.decoder_num_layers)]
+ )
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @lru_cache(maxsize=32)
+ def generate_anchors(self, spatial_shapes=None, grid_size=0.05, device="cpu", dtype=torch.float32):
+ # We always generate anchors in float32 to preserve equivalence between
+ # dynamic and static anchor inference
+ # Ignore copy
+ if spatial_shapes is None:
+ raise ValueError("spatial_shapes must be provided")
+
+ anchors = []
+ for level, (height, width) in enumerate(spatial_shapes):
+ grid_y, grid_x = torch.meshgrid(
+ torch.arange(end=height, dtype=dtype, device=device),
+ torch.arange(end=width, dtype=dtype, device=device),
+ indexing="ij",
+ )
+ grid_xy = torch.stack([grid_x, grid_y], -1)
+ valid_wh = torch.tensor([width, height], dtype=dtype, device=device)
+ grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_wh
+ wh = torch.ones_like(grid_xy, dtype=dtype, device=device) * grid_size * (2.0**level)
+ anchors.append(torch.concat([grid_xy, wh], -1).reshape(-1, height * width, 4))
+ # define the valid range for anchor coordinates
+ eps = 1e-2
+ anchors = torch.concat(anchors, 1)
+ valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True)
+ anchors = torch.log(anchors / (1 - anchors))
+ anchors = torch.where(valid_mask, anchors, torch.inf)
+
+ return anchors, valid_mask
+
+ def _get_encoder_input(self, vision_features):
+ # get projection features
+ vision_features = [self.channel_projection_layers[i](feat) for i, feat in enumerate(vision_features)]
+ # get encoder inputs
+ new_vision_features = []
+ new_vision_shapes_list = []
+ for feat in vision_features:
+ height, width = feat.shape[2:]
+ # [batch_size, channels, height, width] -> [batch_size, height*width, channels]
+ new_vision_features.append(feat.flatten(2).permute(0, 2, 1))
+ # [num_feature_levels, 2]
+ new_vision_shapes_list.append((height, width))
+
+ # [batch_size, height*width, channels]
+ new_vision_features = torch.cat(new_vision_features, 1)
+ new_vision_shapes = torch.tensor(new_vision_shapes_list, dtype=torch.int64, device=vision_features[0].device)
+ level_start_index = torch.cat((new_vision_shapes.new_zeros((1,)), new_vision_shapes.prod(1).cumsum(0)[:-1]))
+
+ return new_vision_features, new_vision_shapes, new_vision_shapes_list, level_start_index
+
+ def _get_decoder_input(
+ self, vision_features, vision_shapes, class_features, denoise_embeddings=None, denoise_bboxes=None
+ ):
+ batch_size = len(vision_features)
+ # prepare input for decoder
+ anchors, valid_mask = self.generate_anchors(
+ vision_shapes, device=vision_features.device, dtype=vision_features.dtype
+ )
+ predicted_class_features = self.encoder_vision_features(
+ torch.where(
+ valid_mask,
+ vision_features,
+ torch.tensor(0.0, dtype=vision_features.dtype, device=vision_features.device),
+ )
+ )
+
+ original_class_projected = self.encoder_class_head(class_features).permute(1, 2, 0)
+ encoder_class_similarity = get_class_similarity(
+ self.class_distance_type, predicted_class_features, original_class_projected
+ )
+
+ # dynamic anchors + static content
+ # (batch_size, height*width, 4)
+ encoder_outputs_bboxes = self.encoder_bbox_head(predicted_class_features) + anchors
+
+ # query selection
+ # (batch_size, num_queries)
+ topk_ind = torch.topk(encoder_class_similarity.max(-1).values, self.num_queries, dim=1).indices.view(-1)
+ # (batch_size, num_queries)
+ batch_ind = (
+ torch.arange(end=batch_size, dtype=topk_ind.dtype, device=topk_ind.device)
+ .unsqueeze(-1)
+ .repeat(1, self.num_queries)
+ .view(-1)
+ )
+
+ reference_points = encoder_outputs_bboxes[batch_ind, topk_ind].view(batch_size, self.num_queries, -1)
+ encoder_bboxes = reference_points.sigmoid()
+ if denoise_bboxes is not None:
+ reference_points = torch.cat([denoise_bboxes, reference_points], 1)
+ if self.training:
+ reference_points = reference_points.detach()
+ encoder_class_similarity = encoder_class_similarity[batch_ind, topk_ind].view(batch_size, self.num_queries, -1)
+
+ if self.learn_initial_query:
+ embeddings = self.tgt_embed.weight.unsqueeze(0).repeat(batch_size, 1, 1)
+ else:
+ embeddings = predicted_class_features[batch_ind, topk_ind].view(batch_size, self.num_queries, -1)
+ if self.training:
+ embeddings = embeddings.detach()
+ if denoise_embeddings is not None:
+ embeddings = torch.cat([denoise_embeddings, embeddings], 1)
+
+ return embeddings, reference_points, encoder_bboxes, encoder_class_similarity, anchors
+
+ def forward(
+ self,
+ vision_features,
+ class_features,
+ task_features,
+ task_mask,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ """
+ Args:
+ vision_features (`torch.FloatTensor`): The sequence of vision features. shape depends on the vision
+ backbone.
+ class_features (`torch.FloatTensor`): The sequence of class features of shape
+ `(class_sequence_length, batch_size, class_embed_dim)`.
+ task_features (`torch.FloatTensor`): The sequence of task features of shape
+ `(task_sequence_length, batch_size, decoder_hidden_dim)`.
+ task_mask (`torch.LongTensor`): The mask for the task features of shape `(batch_size, task_sequence_length)`.
+ output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention
+ layers. See `attentions` under returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See
+ `hidden_states` under returned tensors for more detail.
+ return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain
+ tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ vision_features, vision_shapes, vision_shapes_list, level_start_index = self._get_encoder_input(
+ vision_features
+ )
+
+ # todo add denoising for training
+ denoise_embeddings, denoise_bboxes, key_padding_mask = None, None, None
+ batch_size = task_mask.shape[0]
+
+ # compose attn_mask for vision_emb and task_emb fusion
+ task_features = self.task_encoder(task_features)
+ if self.task_project is not None:
+ task_features = self.task_project(task_features)
+ src_key_mask = (task_mask == 0).detach()
+ attn_mask_len = self.num_queries
+ fusion_size = attn_mask_len + task_features.shape[0]
+ key_padding_mask = torch.zeros([batch_size, fusion_size], dtype=torch.bool).to(task_features.device)
+ key_padding_mask[:, attn_mask_len:] = src_key_mask
+ attention_mask = _prepare_4d_attention_mask(~key_padding_mask, dtype=vision_features.dtype)
+ decoder_embeddings, reference_points, encoder_bboxes, encoder_class_similarity, init_reference_points = (
+ self._get_decoder_input(
+ vision_features, tuple(vision_shapes_list), class_features, denoise_embeddings, denoise_bboxes
+ )
+ )
+
+ all_hidden_states = () if output_hidden_states else None
+ all_attns = () if output_attentions else None
+ all_self_attns = () if output_attentions else None
+ all_cross_attns = () if output_attentions else None
+ predicted_class_features = decoder_embeddings
+
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (predicted_class_features,)
+ decoder_bboxes = []
+ decoder_classes = []
+ last_refined_bbox = None
+ reference_points = reference_points.sigmoid()
+ for i, layer in enumerate(self.layers):
+ predicted_class_features, task_features, self_attention, cross_attention = layer(
+ predicted_class_features,
+ task_features,
+ reference_points,
+ vision_features,
+ vision_shapes,
+ vision_shapes_list,
+ level_start_index=level_start_index,
+ attention_mask=attention_mask,
+ query_position=self.query_position_head(reference_points),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ )
+ if output_attentions:
+ all_self_attns = all_self_attns + (self_attention,)
+ all_cross_attns = all_cross_attns + (cross_attention,)
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (predicted_class_features,)
+
+ refined_bbox = torch.sigmoid(
+ self.decoder_bbox_head[i](predicted_class_features) + _inverse_sigmoid(reference_points)
+ )
+ original_class_projected = self.decoder_class_head[i](class_features).permute(1, 2, 0)
+ if self.training:
+ decoder_classes.append(
+ get_class_similarity(
+ class_distance_type=self.class_distance_type,
+ cls_feature=predicted_class_features,
+ class_proj=original_class_projected,
+ )
+ )
+ if i == 0:
+ decoder_bboxes.append(refined_bbox)
+ else:
+ decoder_bboxes.append(
+ torch.sigmoid(
+ self.decoder_bbox_head[i](predicted_class_features) + _inverse_sigmoid(last_refined_bbox)
+ )
+ )
+ elif i == self.decoder_num_layers - 1:
+ decoder_classes.append(
+ get_class_similarity(self.class_distance_type, predicted_class_features, original_class_projected)
+ )
+ decoder_bboxes.append(refined_bbox)
+ break
+ last_refined_bbox = refined_bbox
+ reference_points = refined_bbox.detach() if self.training else refined_bbox
+ if output_attentions:
+ all_attns += (all_self_attns, all_cross_attns)
+
+ last_hidden_state = predicted_class_features
+ decoder_bboxes = torch.stack(decoder_bboxes)
+ decoder_classes = torch.stack(decoder_classes)
+
+ if not return_dict:
+ return (
+ last_hidden_state,
+ all_hidden_states,
+ all_attns,
+ decoder_bboxes,
+ decoder_classes,
+ encoder_bboxes,
+ encoder_class_similarity,
+ init_reference_points,
+ reference_points,
+ )
+
+ return OmDetTurboDecoderOutput(
+ last_hidden_state=last_hidden_state,
+ hidden_states=all_hidden_states,
+ attentions=all_attns,
+ decoder_coords=decoder_bboxes,
+ decoder_classes=decoder_classes,
+ encoder_coord_logits=encoder_bboxes,
+ encoder_class_logits=encoder_class_similarity,
+ init_reference_points=init_reference_points,
+ intermediate_reference_points=reference_points,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ OmDetTurbo Model (consisting of a vision and a text backbone, and encoder-decoder architecture) outputting
+ bounding boxes and classes scores for tasks such as COCO detection.
+ """
+)
+class OmDetTurboForObjectDetection(OmDetTurboPreTrainedModel):
+ def __init__(self, config: OmDetTurboConfig):
+ super().__init__(config)
+ self.vision_backbone = OmDetTurboVisionBackbone(config)
+ self.language_backbone = OmDetTurboLanguageBackbone(config)
+ self.encoder = OmDetTurboHybridEncoder(config)
+ self.decoder = OmDetTurboDecoder(config)
+ self.num_queries = config.num_queries
+
+ self.language_cache_class = OmDetTurboLRUCache(config.cache_size)
+ self.language_cache_prompt = OmDetTurboLRUCache(config.cache_size)
+ self.vocab_size = config.text_config.vocab_size
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_backbone.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_backbone.model.set_input_embeddings(value)
+
+ def resize_token_embeddings(
+ self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None, mean_resizing: bool = True
+ ) -> nn.Embedding:
+ model_embeds = self.language_backbone.model.resize_token_embeddings(
+ new_num_tokens=new_num_tokens, pad_to_multiple_of=pad_to_multiple_of, mean_resizing=mean_resizing
+ )
+ self.config.text_config.vocab_size = model_embeds.num_embeddings
+ self.vocab_size = model_embeds.num_embeddings
+ return model_embeds
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: torch.FloatTensor,
+ classes_input_ids: torch.LongTensor,
+ classes_attention_mask: torch.LongTensor,
+ tasks_input_ids: torch.LongTensor,
+ tasks_attention_mask: torch.LongTensor,
+ classes_structure: torch.LongTensor,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.FloatTensor], OmDetTurboObjectDetectionOutput]:
+ r"""
+ classes_input_ids (`torch.LongTensor` of shape `(total_classes (>= batch_size), sequence_length)`):
+ Indices of input classes sequence tokens in the vocabulary of the language model.
+ Several classes can be provided for each tasks, thus the tokenized classes are flattened
+ and the structure of the classes is provided in the `classes_structure` argument.
+
+ Indices can be obtained using [`OmDetTurboProcessor`]. See [`OmDetTurboProcessor.__call__`] for
+ details.
+
+ [What are input IDs?](../glossary#input-ids)
+ classes_attention_mask (`torch.BoolTensor` of shape `(total_classes (>= batch_size), num_classes, sequence_length)`):
+ Attention mask for the classes. This is a binary mask that indicates which tokens should be attended to,
+ and which should not.
+ tasks_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input tasks sequence tokens in the vocabulary of the language model.
+
+ Indices can be obtained using [`OmDetTurboProcessor`]. See [`OmDetTurboProcessor.__call__`] for
+ details.
+
+ [What are input IDs?](../glossary#input-ids)
+ tasks_attention_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`):
+ Attention mask for the tasks. This is a binary mask that indicates which tokens should be attended to,
+ and which should not.
+ classes_structure (torch.LongTensor of shape `(batch_size)`):
+ Structure of the classes. This tensor indicates the number of classes for each task.
+
+ Examples:
+
+ ```python
+ >>> import requests
+ >>> from PIL import Image
+
+ >>> from transformers import AutoProcessor, OmDetTurboForObjectDetection
+
+ >>> processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
+ >>> model = OmDetTurboForObjectDetection.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
+
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+ >>> classes = ["cat", "remote"]
+ >>> task = "Detect {}.".format(", ".join(classes))
+ >>> inputs = processor(image, text=classes, task=task, return_tensors="pt")
+
+ >>> outputs = model(**inputs)
+
+ >>> # convert outputs (bounding boxes and class logits)
+ >>> results = processor.post_process_grounded_object_detection(
+ ... outputs,
+ ... classes=classes,
+ ... target_sizes=[image.size[::-1]],
+ ... score_threshold=0.3,
+ ... nms_threshold=0.3,
+ >>> )[0]
+ >>> for score, class_name, box in zip(results["scores"], results["classes"], results["boxes"]):
+ ... box = [round(i, 1) for i in box.tolist()]
+ ... print(
+ ... f"Detected {class_name} with confidence "
+ ... f"{round(score.item(), 2)} at location {box}"
+ ... )
+ Detected remote with confidence 0.76 at location [39.9, 71.3, 176.5, 117.9]
+ Detected cat with confidence 0.72 at location [345.1, 22.5, 639.7, 371.9]
+ Detected cat with confidence 0.65 at location [12.7, 53.8, 315.5, 475.3]
+ Detected remote with confidence 0.57 at location [333.4, 75.6, 370.7, 187.0]
+ ```"""
+ if labels is not None:
+ raise NotImplementedError("Training is not implemented yet")
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ loss = None
+ image_features = self.vision_backbone(pixel_values)
+ encoder_outputs = self.encoder(
+ image_features,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ class_features, task_features, task_mask = self.get_language_embedding(
+ classes_input_ids,
+ classes_attention_mask,
+ tasks_input_ids,
+ tasks_attention_mask,
+ classes_structure,
+ )
+ encoder_extracted_states = encoder_outputs.extracted_states if return_dict else encoder_outputs[-1]
+ decoder_outputs = self.decoder(
+ encoder_extracted_states,
+ class_features,
+ task_features,
+ task_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ if not return_dict:
+ return tuple(
+ output
+ for output in [
+ loss,
+ decoder_outputs[3][-1],
+ decoder_outputs[4][-1],
+ decoder_outputs[7],
+ decoder_outputs[8],
+ decoder_outputs[5],
+ decoder_outputs[6],
+ encoder_outputs[-1],
+ decoder_outputs[1],
+ decoder_outputs[2],
+ encoder_outputs[1],
+ encoder_outputs[2],
+ classes_structure,
+ ]
+ if output is not None
+ )
+
+ return OmDetTurboObjectDetectionOutput(
+ loss=loss,
+ decoder_coord_logits=decoder_outputs.decoder_coords[-1],
+ decoder_class_logits=decoder_outputs.decoder_classes[-1],
+ init_reference_points=decoder_outputs.init_reference_points,
+ intermediate_reference_points=decoder_outputs.intermediate_reference_points,
+ encoder_coord_logits=decoder_outputs.encoder_coord_logits,
+ encoder_class_logits=decoder_outputs.encoder_class_logits,
+ encoder_extracted_states=encoder_outputs.extracted_states,
+ decoder_hidden_states=decoder_outputs.hidden_states,
+ decoder_attentions=decoder_outputs.attentions,
+ encoder_hidden_states=encoder_outputs.hidden_states,
+ encoder_attentions=encoder_outputs.attentions,
+ classes_structure=classes_structure,
+ )
+
+
+__all__ = ["OmDetTurboForObjectDetection", "OmDetTurboPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/processing_omdet_turbo.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/processing_omdet_turbo.py
new file mode 100644
index 0000000000000000000000000000000000000000..5185bee97c44ef2049f7cc86e580e6b4a932a409
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/omdet_turbo/processing_omdet_turbo.py
@@ -0,0 +1,412 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for OmDet-Turbo.
+"""
+
+import warnings
+from typing import TYPE_CHECKING, Optional, Union
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_transforms import center_to_corners_format
+from ...image_utils import ImageInput
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import (
+ TensorType,
+ is_torch_available,
+ is_torchvision_available,
+)
+from ...utils.import_utils import requires
+
+
+if TYPE_CHECKING:
+ from .modeling_omdet_turbo import OmDetTurboObjectDetectionOutput
+
+
+class OmDetTurboTextKwargs(TextKwargs, total=False):
+ task: Optional[Union[str, list[str], TextInput, PreTokenizedInput]]
+
+
+if is_torch_available():
+ import torch
+
+
+if is_torchvision_available():
+ from torchvision.ops.boxes import batched_nms
+
+
+class OmDetTurboProcessorKwargs(ProcessingKwargs, total=False):
+ text_kwargs: OmDetTurboTextKwargs
+ _defaults = {
+ "text_kwargs": {
+ "add_special_tokens": True,
+ "padding": "max_length",
+ "truncation": True,
+ "max_length": 77,
+ "stride": 0,
+ "return_overflowing_tokens": False,
+ "return_special_tokens_mask": False,
+ "return_offsets_mapping": False,
+ "return_token_type_ids": False,
+ "return_length": False,
+ "verbose": True,
+ "task": None,
+ },
+ "images_kwargs": {},
+ }
+
+
+class DictWithDeprecationWarning(dict):
+ message = (
+ "The `classes` key is deprecated for `OmDetTurboProcessor.post_process_grounded_object_detection` "
+ "output dict and will be removed in a 4.51.0 version. Please use `text_labels` instead."
+ )
+
+ def __getitem__(self, key):
+ if key == "classes":
+ warnings.warn(self.message, FutureWarning)
+ return super().__getitem__("text_labels")
+ return super().__getitem__(key)
+
+ def get(self, key, *args, **kwargs):
+ if key == "classes":
+ warnings.warn(self.message, FutureWarning)
+ return super().get("text_labels", *args, **kwargs)
+ return super().get(key, *args, **kwargs)
+
+
+def clip_boxes(box, box_size: tuple[int, int]):
+ """
+ Clip the boxes by limiting x coordinates to the range [0, width]
+ and y coordinates to the range [0, height].
+
+ Args:
+ box (Tensor): The box to be clipped.
+ box_size (height, width): The clipping box's size.
+ """
+ assert torch.isfinite(box).all(), "Box tensor contains infinite or NaN!"
+ height, width = box_size
+ x1 = box[:, 0].clamp(min=0, max=width)
+ y1 = box[:, 1].clamp(min=0, max=height)
+ x2 = box[:, 2].clamp(min=0, max=width)
+ y2 = box[:, 3].clamp(min=0, max=height)
+ box = torch.stack((x1, y1, x2, y2), dim=-1)
+
+ return box
+
+
+def compute_score(boxes):
+ """
+ Compute logit scores per class for each box (proposal) and an array of class indices
+ corresponding to each proposal, flattened across the proposal_num.
+ The indices in `classes` will later be used to filter and match the predicted classes
+ with the input class names.
+ """
+ num_classes = boxes.shape[2]
+ proposal_num = boxes.shape[1]
+ scores = torch.sigmoid(boxes)
+ classes = torch.arange(num_classes, device=boxes.device).unsqueeze(0).repeat(proposal_num, 1).flatten(0, 1)
+ return scores, classes
+
+
+def _post_process_boxes_for_image(
+ boxes: "torch.Tensor",
+ scores: "torch.Tensor",
+ labels: "torch.Tensor",
+ image_num_classes: int,
+ image_size: tuple[int, int],
+ threshold: float,
+ nms_threshold: float,
+ max_num_det: Optional[int] = None,
+) -> tuple["torch.Tensor", "torch.Tensor", "torch.Tensor"]:
+ """
+ Filter predicted results using given thresholds and NMS.
+
+ Args:
+ boxes (`torch.Tensor`):
+ A Tensor of predicted class-specific or class-agnostic boxes for the image.
+ Shape (num_queries, max_num_classes_in_batch * 4) if doing class-specific regression,
+ or (num_queries, 4) if doing class-agnostic regression.
+ scores (`torch.Tensor` of shape (num_queries, max_num_classes_in_batch + 1)):
+ A Tensor of predicted class scores for the image.
+ labels (`torch.Tensor` of shape (num_queries * (max_num_classes_in_batch + 1),)):
+ A Tensor of predicted labels for the image.
+ image_num_classes (`int`):
+ The number of classes queried for detection on the image.
+ image_size (`tuple[int, int]`):
+ A tuple of (height, width) for the image.
+ threshold (`float`):
+ Only return detections with a confidence score exceeding this threshold.
+ nms_threshold (`float`):
+ The threshold to use for box non-maximum suppression. Value in [0, 1].
+ max_num_det (`int`, *optional*):
+ The maximum number of detections to return. Default is None.
+
+ Returns:
+ Tuple: A tuple with the following:
+ "boxes" (Tensor): A tensor of shape (num_filtered_objects, 4), containing the predicted boxes in (x1, y1, x2, y2) format.
+ "scores" (Tensor): A tensor of shape (num_filtered_objects,), containing the predicted confidence scores for each detection.
+ "labels" (Tensor): A tensor of ids, where each id is the predicted class id for the corresponding detection
+ """
+
+ # Filter by max number of detections
+ proposal_num = len(boxes) if max_num_det is None else max_num_det
+ scores_per_image, topk_indices = scores.flatten(0, 1).topk(proposal_num, sorted=False)
+ labels_per_image = labels[topk_indices]
+ boxes_per_image = boxes.view(-1, 1, 4).repeat(1, scores.shape[1], 1).view(-1, 4)
+ boxes_per_image = boxes_per_image[topk_indices]
+
+ # Convert and scale boxes to original image size
+ boxes_per_image = center_to_corners_format(boxes_per_image)
+ boxes_per_image = boxes_per_image * torch.tensor(image_size[::-1]).repeat(2).to(boxes_per_image.device)
+
+ # Filtering by confidence score
+ filter_mask = scores_per_image > threshold # R x K
+ score_keep = filter_mask.nonzero(as_tuple=False).view(-1)
+ boxes_per_image = boxes_per_image[score_keep]
+ scores_per_image = scores_per_image[score_keep]
+ labels_per_image = labels_per_image[score_keep]
+
+ # Ensure we did not overflow to non existing classes
+ filter_classes_mask = labels_per_image < image_num_classes
+ classes_keep = filter_classes_mask.nonzero(as_tuple=False).view(-1)
+ boxes_per_image = boxes_per_image[classes_keep]
+ scores_per_image = scores_per_image[classes_keep]
+ labels_per_image = labels_per_image[classes_keep]
+
+ # NMS
+ keep = batched_nms(boxes_per_image, scores_per_image, labels_per_image, nms_threshold)
+ boxes_per_image = boxes_per_image[keep]
+ scores_per_image = scores_per_image[keep]
+ labels_per_image = labels_per_image[keep]
+
+ # Clip to image size
+ boxes_per_image = clip_boxes(boxes_per_image, image_size)
+
+ return boxes_per_image, scores_per_image, labels_per_image
+
+
+@requires(backends=("vision", "torchvision"))
+class OmDetTurboProcessor(ProcessorMixin):
+ r"""
+ Constructs a OmDet-Turbo processor which wraps a Deformable DETR image processor and an AutoTokenizer into a
+ single processor.
+
+ [`OmDetTurboProcessor`] offers all the functionalities of [`DetrImageProcessor`] and
+ [`AutoTokenizer`]. See the docstring of [`~OmDetTurboProcessor.__call__`] and [`~OmDetTurboProcessor.decode`]
+ for more information.
+
+ Args:
+ image_processor (`DetrImageProcessor`):
+ An instance of [`DetrImageProcessor`]. The image processor is a required input.
+ tokenizer (`AutoTokenizer`):
+ An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = ("DetrImageProcessor", "DetrImageProcessorFast")
+ tokenizer_class = "AutoTokenizer"
+
+ def __init__(self, image_processor, tokenizer):
+ super().__init__(image_processor, tokenizer)
+
+ def __call__(
+ self,
+ images: ImageInput = None,
+ text: Optional[Union[list[str], list[list[str]]]] = None,
+ audio=None,
+ videos=None,
+ **kwargs: Unpack[OmDetTurboProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ This method uses [*DetrImageProcessor.__call__] method to prepare image(s) for the model, and
+ [CLIPTokenizerFast.__call__] to prepare text for the model.
+
+ Please refer to the docstring of the above two methods for more information.
+
+ Args:
+ images (`ImageInput`):
+ Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255.
+ text (`Union[str, list[str], list[list[str]]]`):
+ The classes used to limit the scope of the open vocabulary detection. Expects a list of strings or a list
+ of list of strings. Batched classes can be of different lengths.
+ Examples: ["cat", "dog", "bird"], [["cat", "dog", "bird"], ["hat", "person"], ["car"]]
+ Kwargs:
+ task (`Union[str, list[str], TextInput, PreTokenizedInput]`):
+ The grounded text used to guide open vocabulary detection. Expects a single string or a list of strings.
+ Examples: "Detect a cat, a dog, and a bird.",[ "Detect everything.", "Detect trees and flowers."]
+ When not provided, the default task is "Detect [class1], [class2], [class3]" etc.
+ ...
+ """
+ if images is None or text is None:
+ raise ValueError("You have to specify both `images` and `text`")
+
+ output_kwargs = self._merge_kwargs(
+ OmDetTurboProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+
+ if isinstance(text, str):
+ text = text.strip(" ").split(",")
+
+ if not (len(text) and isinstance(text[0], (list, tuple))):
+ text = [text]
+
+ task = output_kwargs["text_kwargs"].pop("task", None)
+ if task is None:
+ task = ["Detect {}.".format(", ".join(text_single)) for text_single in text]
+ elif not isinstance(task, (list, tuple)):
+ task = [task]
+
+ encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"])
+ tasks_encoding = self.tokenizer(text=task, **output_kwargs["text_kwargs"])
+
+ classes = text
+
+ classes_structure = torch.tensor([len(class_single) for class_single in classes], dtype=torch.long)
+ classes_flattened = [class_single for class_batch in classes for class_single in class_batch]
+ classes_encoding = self.tokenizer(text=classes_flattened, **output_kwargs["text_kwargs"])
+
+ encoding = BatchFeature()
+ encoding.update({f"tasks_{key}": value for key, value in tasks_encoding.items()})
+ encoding.update({f"classes_{key}": value for key, value in classes_encoding.items()})
+ encoding.update({"classes_structure": classes_structure})
+ encoding.update(encoding_image_processor)
+
+ return encoding
+
+ # Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ # Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ def _get_default_image_size(self) -> tuple[int, int]:
+ height = (
+ self.image_processor.size["height"]
+ if "height" in self.image_processor.size
+ else self.image_processor.size["shortest_edge"]
+ )
+ width = (
+ self.image_processor.size["width"]
+ if "width" in self.image_processor.size
+ else self.image_processor.size["longest_edge"]
+ )
+ return height, width
+
+ def post_process_grounded_object_detection(
+ self,
+ outputs: "OmDetTurboObjectDetectionOutput",
+ text_labels: Optional[Union[list[str], list[list[str]]]] = None,
+ threshold: float = 0.3,
+ nms_threshold: float = 0.5,
+ target_sizes: Optional[Union[TensorType, list[tuple]]] = None,
+ max_num_det: Optional[int] = None,
+ ):
+ """
+ Converts the raw output of [`OmDetTurboForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
+ bottom_right_x, bottom_right_y) format and get the associated text class.
+
+ Args:
+ outputs ([`OmDetTurboObjectDetectionOutput`]):
+ Raw outputs of the model.
+ text_labels (Union[list[str], list[list[str]]], *optional*):
+ The input classes names. If not provided, `text_labels` will be set to `None` in `outputs`.
+ threshold (float, defaults to 0.3):
+ Only return detections with a confidence score exceeding this threshold.
+ nms_threshold (float, defaults to 0.5):
+ The threshold to use for box non-maximum suppression. Value in [0, 1].
+ target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*):
+ Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size
+ `(height, width)` of each image in the batch. If unset, predictions will not be resized.
+ max_num_det (`int`, *optional*):
+ The maximum number of detections to return.
+ Returns:
+ `list[Dict]`: A list of dictionaries, each dictionary containing the scores, classes and boxes for an image
+ in the batch as predicted by the model.
+ """
+
+ batch_size = len(outputs.decoder_coord_logits)
+
+ # Inputs consistency check for target sizes
+ if target_sizes is None:
+ height, width = self._get_default_image_size()
+ target_sizes = [(height, width)] * batch_size
+
+ if any(len(image_size) != 2 for image_size in target_sizes):
+ raise ValueError(
+ "Each element of target_sizes must contain the size (height, width) of each image of the batch"
+ )
+
+ if len(target_sizes) != batch_size:
+ raise ValueError("Make sure that you pass in as many target sizes as output sequences")
+
+ # Inputs consistency check for text labels
+ if text_labels is not None and isinstance(text_labels[0], str):
+ text_labels = [text_labels]
+
+ if text_labels is not None and len(text_labels) != batch_size:
+ raise ValueError("Make sure that you pass in as many classes group as output sequences")
+
+ # Convert target_sizes to list for easier handling
+ if isinstance(target_sizes, torch.Tensor):
+ target_sizes = target_sizes.tolist()
+
+ batch_boxes = outputs.decoder_coord_logits
+ batch_logits = outputs.decoder_class_logits
+ batch_num_classes = outputs.classes_structure
+
+ batch_scores, batch_labels = compute_score(batch_logits)
+
+ results = []
+ for boxes, scores, image_size, image_num_classes in zip(
+ batch_boxes, batch_scores, target_sizes, batch_num_classes
+ ):
+ boxes, scores, labels = _post_process_boxes_for_image(
+ boxes=boxes,
+ scores=scores,
+ labels=batch_labels,
+ image_num_classes=image_num_classes,
+ image_size=image_size,
+ threshold=threshold,
+ nms_threshold=nms_threshold,
+ max_num_det=max_num_det,
+ )
+ result = DictWithDeprecationWarning(
+ {"boxes": boxes, "scores": scores, "labels": labels, "text_labels": None}
+ )
+ results.append(result)
+
+ # Add text labels
+ if text_labels is not None:
+ for result, image_text_labels in zip(results, text_labels):
+ result["text_labels"] = [image_text_labels[idx] for idx in result["labels"]]
+
+ return results
+
+
+__all__ = ["OmDetTurboProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..df25ebac9d0b7a0f68f69c2dd012a8a0449ed14c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_oneformer import *
+ from .image_processing_oneformer import *
+ from .image_processing_oneformer_fast import *
+ from .modeling_oneformer import *
+ from .processing_oneformer import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/configuration_oneformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/configuration_oneformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b9229f040d11c2ca2ea1e18f0fdfd927e159e14
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/configuration_oneformer.py
@@ -0,0 +1,285 @@
+# coding=utf-8
+# Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""OneFormer model configuration"""
+
+from typing import Optional
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ...utils.backbone_utils import verify_backbone_config_arguments
+from ..auto import CONFIG_MAPPING
+
+
+logger = logging.get_logger(__name__)
+
+
+class OneFormerConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`OneFormerModel`]. It is used to instantiate a
+ OneFormer model according to the specified arguments, defining the model architecture. Instantiating a
+ configuration with the defaults will yield a similar configuration to that of the OneFormer
+ [shi-labs/oneformer_ade20k_swin_tiny](https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny) architecture
+ trained on [ADE20k-150](https://huggingface.co/datasets/scene_parse_150).
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ backbone_config (`PretrainedConfig`, *optional*, defaults to `SwinConfig`):
+ The configuration of the backbone model.
+ backbone (`str`, *optional*):
+ Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
+ will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
+ is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
+ use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
+ Whether to use pretrained weights for the backbone.
+ use_timm_backbone (`bool`, *optional*, defaults to `False`):
+ Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
+ library.
+ backbone_kwargs (`dict`, *optional*):
+ Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
+ e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
+ ignore_value (`int`, *optional*, defaults to 255):
+ Values to be ignored in GT label while calculating loss.
+ num_queries (`int`, *optional*, defaults to 150):
+ Number of object queries.
+ no_object_weight (`float`, *optional*, defaults to 0.1):
+ Weight for no-object class predictions.
+ class_weight (`float`, *optional*, defaults to 2.0):
+ Weight for Classification CE loss.
+ mask_weight (`float`, *optional*, defaults to 5.0):
+ Weight for binary CE loss.
+ dice_weight (`float`, *optional*, defaults to 5.0):
+ Weight for dice loss.
+ contrastive_weight (`float`, *optional*, defaults to 0.5):
+ Weight for contrastive loss.
+ contrastive_temperature (`float`, *optional*, defaults to 0.07):
+ Initial value for scaling the contrastive logits.
+ train_num_points (`int`, *optional*, defaults to 12544):
+ Number of points to sample while calculating losses on mask predictions.
+ oversample_ratio (`float`, *optional*, defaults to 3.0):
+ Ratio to decide how many points to oversample.
+ importance_sample_ratio (`float`, *optional*, defaults to 0.75):
+ Ratio of points that are sampled via importance sampling.
+ init_std (`float`, *optional*, defaults to 0.02):
+ Standard deviation for normal initialization.
+ init_xavier_std (`float`, *optional*, defaults to 1.0):
+ Standard deviation for xavier uniform initialization.
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+ Epsilon for layer normalization.
+ is_training (`bool`, *optional*, defaults to `False`):
+ Whether to run in training or inference mode.
+ use_auxiliary_loss (`bool`, *optional*, defaults to `True`):
+ Whether to calculate loss using intermediate predictions from transformer decoder.
+ output_auxiliary_logits (`bool`, *optional*, defaults to `True`):
+ Whether to return intermediate predictions from transformer decoder.
+ strides (`list`, *optional*, defaults to `[4, 8, 16, 32]`):
+ List containing the strides for feature maps in the encoder.
+ task_seq_len (`int`, *optional*, defaults to 77):
+ Sequence length for tokenizing text list input.
+ text_encoder_width (`int`, *optional*, defaults to 256):
+ Hidden size for text encoder.
+ text_encoder_context_length (`int`, *optional*, defaults to 77):
+ Input sequence length for text encoder.
+ text_encoder_num_layers (`int`, *optional*, defaults to 6):
+ Number of layers for transformer in text encoder.
+ text_encoder_vocab_size (`int`, *optional*, defaults to 49408):
+ Vocabulary size for tokenizer.
+ text_encoder_proj_layers (`int`, *optional*, defaults to 2):
+ Number of layers in MLP for project text queries.
+ text_encoder_n_ctx (`int`, *optional*, defaults to 16):
+ Number of learnable text context queries.
+ conv_dim (`int`, *optional*, defaults to 256):
+ Feature map dimension to map outputs from the backbone.
+ mask_dim (`int`, *optional*, defaults to 256):
+ Dimension for feature maps in pixel decoder.
+ hidden_dim (`int`, *optional*, defaults to 256):
+ Dimension for hidden states in transformer decoder.
+ encoder_feedforward_dim (`int`, *optional*, defaults to 1024):
+ Dimension for FFN layer in pixel decoder.
+ norm (`str`, *optional*, defaults to `"GN"`):
+ Type of normalization.
+ encoder_layers (`int`, *optional*, defaults to 6):
+ Number of layers in pixel decoder.
+ decoder_layers (`int`, *optional*, defaults to 10):
+ Number of layers in transformer decoder.
+ use_task_norm (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the task token.
+ num_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads in transformer layers in the pixel and transformer decoders.
+ dropout (`float`, *optional*, defaults to 0.1):
+ Dropout probability for pixel and transformer decoders.
+ dim_feedforward (`int`, *optional*, defaults to 2048):
+ Dimension for FFN layer in transformer decoder.
+ pre_norm (`bool`, *optional*, defaults to `False`):
+ Whether to normalize hidden states before attention layers in transformer decoder.
+ enforce_input_proj (`bool`, *optional*, defaults to `False`):
+ Whether to project hidden states in transformer decoder.
+ query_dec_layers (`int`, *optional*, defaults to 2):
+ Number of layers in query transformer.
+ common_stride (`int`, *optional*, defaults to 4):
+ Common stride used for features in pixel decoder.
+
+ Examples:
+ ```python
+ >>> from transformers import OneFormerConfig, OneFormerModel
+
+ >>> # Initializing a OneFormer shi-labs/oneformer_ade20k_swin_tiny configuration
+ >>> configuration = OneFormerConfig()
+ >>> # Initializing a model (with random weights) from the shi-labs/oneformer_ade20k_swin_tiny style configuration
+ >>> model = OneFormerModel(configuration)
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+ """
+
+ model_type = "oneformer"
+ attribute_map = {"hidden_size": "hidden_dim"}
+
+ def __init__(
+ self,
+ backbone_config: Optional[dict] = None,
+ backbone: Optional[str] = None,
+ use_pretrained_backbone: bool = False,
+ use_timm_backbone: bool = False,
+ backbone_kwargs: Optional[dict] = None,
+ ignore_value: int = 255,
+ num_queries: int = 150,
+ no_object_weight: int = 0.1,
+ class_weight: float = 2.0,
+ mask_weight: float = 5.0,
+ dice_weight: float = 5.0,
+ contrastive_weight: float = 0.5,
+ contrastive_temperature: float = 0.07,
+ train_num_points: int = 12544,
+ oversample_ratio: float = 3.0,
+ importance_sample_ratio: float = 0.75,
+ init_std: float = 0.02,
+ init_xavier_std: float = 1.0,
+ layer_norm_eps: float = 1e-05,
+ is_training: bool = False,
+ use_auxiliary_loss: bool = True,
+ output_auxiliary_logits: bool = True,
+ strides: Optional[list] = [4, 8, 16, 32],
+ task_seq_len: int = 77,
+ text_encoder_width: int = 256,
+ text_encoder_context_length: int = 77,
+ text_encoder_num_layers: int = 6,
+ text_encoder_vocab_size: int = 49408,
+ text_encoder_proj_layers: int = 2,
+ text_encoder_n_ctx: int = 16,
+ conv_dim: int = 256,
+ mask_dim: int = 256,
+ hidden_dim: int = 256,
+ encoder_feedforward_dim: int = 1024,
+ norm: str = "GN",
+ encoder_layers: int = 6,
+ decoder_layers: int = 10,
+ use_task_norm: bool = True,
+ num_attention_heads: int = 8,
+ dropout: float = 0.1,
+ dim_feedforward: int = 2048,
+ pre_norm: bool = False,
+ enforce_input_proj: bool = False,
+ query_dec_layers: int = 2,
+ common_stride: int = 4,
+ **kwargs,
+ ):
+ if backbone_config is None and backbone is None:
+ logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone.")
+ backbone_config = CONFIG_MAPPING["swin"](
+ image_size=224,
+ num_channels=3,
+ patch_size=4,
+ embed_dim=96,
+ depths=[2, 2, 6, 2],
+ num_heads=[3, 6, 12, 24],
+ window_size=7,
+ drop_path_rate=0.3,
+ use_absolute_embeddings=False,
+ out_features=["stage1", "stage2", "stage3", "stage4"],
+ )
+ elif isinstance(backbone_config, dict):
+ backbone_model_type = backbone_config.get("model_type")
+ config_class = CONFIG_MAPPING[backbone_model_type]
+ backbone_config = config_class.from_dict(backbone_config)
+
+ verify_backbone_config_arguments(
+ use_timm_backbone=use_timm_backbone,
+ use_pretrained_backbone=use_pretrained_backbone,
+ backbone=backbone,
+ backbone_config=backbone_config,
+ backbone_kwargs=backbone_kwargs,
+ )
+
+ self.backbone_config = backbone_config
+ self.backbone = backbone
+ self.use_pretrained_backbone = use_pretrained_backbone
+ self.use_timm_backbone = use_timm_backbone
+ self.backbone_kwargs = backbone_kwargs
+ self.ignore_value = ignore_value
+ self.num_queries = num_queries
+ self.no_object_weight = no_object_weight
+ self.class_weight = class_weight
+ self.mask_weight = mask_weight
+ self.dice_weight = dice_weight
+ self.contrastive_weight = contrastive_weight
+ self.contrastive_temperature = contrastive_temperature
+ self.train_num_points = train_num_points
+ self.oversample_ratio = oversample_ratio
+ self.importance_sample_ratio = importance_sample_ratio
+ self.init_std = init_std
+ self.init_xavier_std = init_xavier_std
+ self.layer_norm_eps = layer_norm_eps
+ self.is_training = is_training
+ self.use_auxiliary_loss = use_auxiliary_loss
+ self.output_auxiliary_logits = output_auxiliary_logits
+ self.strides = strides
+ self.task_seq_len = task_seq_len
+ self.text_encoder_width = text_encoder_width
+ self.text_encoder_context_length = text_encoder_context_length
+ self.text_encoder_num_layers = text_encoder_num_layers
+ self.text_encoder_vocab_size = text_encoder_vocab_size
+ self.text_encoder_proj_layers = text_encoder_proj_layers
+ self.text_encoder_n_ctx = text_encoder_n_ctx
+ self.conv_dim = conv_dim
+ self.mask_dim = mask_dim
+ self.hidden_dim = hidden_dim
+ self.encoder_feedforward_dim = encoder_feedforward_dim
+ self.norm = norm
+ self.encoder_layers = encoder_layers
+ self.decoder_layers = decoder_layers
+ self.use_task_norm = use_task_norm
+ self.num_attention_heads = num_attention_heads
+ self.dropout = dropout
+ self.dim_feedforward = dim_feedforward
+ self.pre_norm = pre_norm
+ self.enforce_input_proj = enforce_input_proj
+ self.query_dec_layers = query_dec_layers
+ self.common_stride = common_stride
+ self.num_hidden_layers = decoder_layers
+
+ super().__init__(**kwargs)
+
+ @property
+ def sub_configs(self):
+ return (
+ {"backbone_config": type(self.backbone_config)}
+ if getattr(self, "backbone_config", None) is not None
+ else {}
+ )
+
+
+__all__ = ["OneFormerConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/convert_to_hf_oneformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/convert_to_hf_oneformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bc7edfb74a4b9051d17b2ff84159e15b2c554df
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/convert_to_hf_oneformer.py
@@ -0,0 +1,1192 @@
+# coding=utf-8
+# Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Convert OneFormer checkpoints from the original repository. URL: https://github.com/SHI-Labs/OneFormer"""
+
+import os
+import sys
+from argparse import ArgumentParser
+from collections.abc import Iterator
+from dataclasses import dataclass
+from pathlib import Path
+from pprint import pformat
+from typing import Any
+
+import requests
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torch import Tensor, nn
+
+
+try:
+ from detectron2.checkpoint import DetectionCheckpointer
+ from detectron2.config import get_cfg
+ from detectron2.data import MetadataCatalog
+ from detectron2.projects.deeplab import add_deeplab_config
+except ImportError:
+ pass
+from transformers import CLIPTokenizer, DinatConfig, SwinConfig
+from transformers.models.oneformer.image_processing_oneformer import OneFormerImageProcessor
+from transformers.models.oneformer.modeling_oneformer import (
+ OneFormerConfig,
+ OneFormerForUniversalSegmentation,
+ OneFormerForUniversalSegmentationOutput,
+ OneFormerModel,
+ OneFormerModelOutput,
+)
+from transformers.models.oneformer.processing_oneformer import OneFormerProcessor
+from transformers.utils import logging
+
+
+StateDict = dict[str, Tensor]
+
+logging.set_verbosity_info()
+logger = logging.get_logger()
+
+torch.manual_seed(0)
+
+
+class TrackedStateDict:
+ def __init__(self, to_track: dict):
+ """This class "tracks" a python dictionary by keeping track of which item is accessed.
+
+ Args:
+ to_track (Dict): The dictionary we wish to track
+ """
+ self.to_track = to_track
+ self._seen: set[str] = set()
+
+ def __getitem__(self, key: str) -> Any:
+ return self.to_track[key]
+
+ def __setitem__(self, key: str, item: Any):
+ self._seen.add(key)
+ self.to_track[key] = item
+
+ def diff(self) -> list[str]:
+ """This method returns a set difference between the keys in the tracked state dict and the one we have access so far.
+ This is an effective method to check if we have update all the keys
+
+ Returns:
+ list[str]: List of keys not yet updated
+ """
+ return set(self.to_track.keys()) - self._seen
+
+ def copy(self) -> dict:
+ # proxy the call to the internal dictionary
+ return self.to_track.copy()
+
+
+# Image to verify the result
+def prepare_img():
+ url = "https://praeclarumjj3.github.io/files/coco.jpeg"
+ img_data = requests.get(url, stream=True).raw
+ im = Image.open(img_data)
+ return im
+
+
+@dataclass
+class Args:
+ """Fake command line arguments needed by oneformer/detectron2 implementation"""
+
+ config_file: str
+
+
+def setup_cfg(args: Args):
+ # load config from file and command-line arguments
+ cfg = get_cfg()
+ add_deeplab_config(cfg)
+ add_common_config(cfg)
+ add_oneformer_config(cfg)
+ add_swin_config(cfg)
+ add_dinat_config(cfg)
+ cfg.merge_from_file(args.config_file)
+ cfg.freeze()
+ return cfg
+
+
+class OriginalOneFormerConfigToOursConverter:
+ def __call__(self, original_config: object, is_swin: bool) -> OneFormerConfig:
+ model = original_config.MODEL
+
+ dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST_PANOPTIC[0])
+ id2label = dict(enumerate(dataset_catalog.stuff_classes))
+ label2id = {label: idx for idx, label in id2label.items()}
+
+ if is_swin:
+ if model.SWIN.EMBED_DIM == 96:
+ backbone_config = SwinConfig.from_pretrained(
+ "microsoft/swin-tiny-patch4-window7-224",
+ drop_path_rate=model.SWIN.DROP_PATH_RATE,
+ out_features=["stage1", "stage2", "stage3", "stage4"],
+ )
+ elif model.SWIN.EMBED_DIM == 192:
+ backbone_config = SwinConfig.from_pretrained(
+ "microsoft/swin-large-patch4-window12-384",
+ drop_path_rate=model.SWIN.DROP_PATH_RATE,
+ out_features=["stage1", "stage2", "stage3", "stage4"],
+ )
+ else:
+ raise ValueError(f"embed dim {model.SWIN.EMBED_DIM} not supported for Swin!")
+ else:
+ backbone_config = DinatConfig.from_pretrained(
+ "shi-labs/dinat-large-11x11-in22k-in1k-384",
+ dilations=model.DiNAT.DILATIONS,
+ kernel_size=model.DiNAT.KERNEL_SIZE,
+ out_features=["stage1", "stage2", "stage3", "stage4"],
+ )
+
+ config: OneFormerConfig = OneFormerConfig(
+ backbone_config=backbone_config,
+ output_attentions=True,
+ output_hidden_states=True,
+ return_dict=True,
+ ignore_value=model.SEM_SEG_HEAD.IGNORE_VALUE,
+ num_classes=model.SEM_SEG_HEAD.NUM_CLASSES,
+ num_queries=model.ONE_FORMER.NUM_OBJECT_QUERIES,
+ no_object_weight=model.ONE_FORMER.NO_OBJECT_WEIGHT,
+ class_weight=model.ONE_FORMER.CLASS_WEIGHT,
+ mask_weight=model.ONE_FORMER.MASK_WEIGHT,
+ dice_weight=model.ONE_FORMER.DICE_WEIGHT,
+ contrastive_weight=model.ONE_FORMER.CONTRASTIVE_WEIGHT,
+ contrastive_temperature=model.ONE_FORMER.CONTRASTIVE_TEMPERATURE,
+ train_num_points=model.ONE_FORMER.TRAIN_NUM_POINTS,
+ oversample_ratio=model.ONE_FORMER.OVERSAMPLE_RATIO,
+ importance_sample_ratio=model.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO,
+ init_std=0.02,
+ init_xavier_std=1.0,
+ layer_norm_eps=1e-05,
+ is_training=False,
+ use_auxiliary_loss=model.ONE_FORMER.DEEP_SUPERVISION,
+ output_auxiliary_logits=True,
+ strides=[4, 8, 16, 32],
+ task_seq_len=original_config.INPUT.TASK_SEQ_LEN,
+ max_seq_len=original_config.INPUT.MAX_SEQ_LEN,
+ text_encoder_width=model.TEXT_ENCODER.WIDTH,
+ text_encoder_context_length=model.TEXT_ENCODER.CONTEXT_LENGTH,
+ text_encoder_num_layers=model.TEXT_ENCODER.NUM_LAYERS,
+ text_encoder_vocab_size=model.TEXT_ENCODER.VOCAB_SIZE,
+ text_encoder_proj_layers=model.TEXT_ENCODER.PROJ_NUM_LAYERS,
+ text_encoder_n_ctx=model.TEXT_ENCODER.N_CTX,
+ conv_dim=model.SEM_SEG_HEAD.CONVS_DIM,
+ mask_dim=model.SEM_SEG_HEAD.MASK_DIM,
+ hidden_dim=model.ONE_FORMER.HIDDEN_DIM,
+ norm=model.SEM_SEG_HEAD.NORM,
+ encoder_layers=model.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS,
+ encoder_feedforward_dim=1024,
+ decoder_layers=model.ONE_FORMER.DEC_LAYERS,
+ use_task_norm=model.ONE_FORMER.USE_TASK_NORM,
+ num_attention_heads=model.ONE_FORMER.NHEADS,
+ dropout=model.ONE_FORMER.DROPOUT,
+ dim_feedforward=model.ONE_FORMER.DIM_FEEDFORWARD,
+ pre_norm=model.ONE_FORMER.PRE_NORM,
+ enforce_input_proj=model.ONE_FORMER.ENFORCE_INPUT_PROJ,
+ query_dec_layers=model.ONE_FORMER.CLASS_DEC_LAYERS,
+ common_stride=model.SEM_SEG_HEAD.COMMON_STRIDE,
+ id2label=id2label,
+ label2id=label2id,
+ )
+
+ return config
+
+
+class OriginalOneFormerConfigToProcessorConverter:
+ def __call__(self, original_config: object, model_repo: str) -> OneFormerProcessor:
+ model = original_config.MODEL
+ model_input = original_config.INPUT
+ dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST_PANOPTIC[0])
+
+ if "ade20k" in model_repo:
+ class_info_file = "ade20k_panoptic.json"
+ elif "coco" in model_repo:
+ class_info_file = "coco_panoptic.json"
+ elif "cityscapes" in model_repo:
+ class_info_file = "cityscapes_panoptic.json"
+ else:
+ raise ValueError("Invalid Dataset!")
+
+ image_processor = OneFormerImageProcessor(
+ image_mean=(torch.tensor(model.PIXEL_MEAN) / 255).tolist(),
+ image_std=(torch.tensor(model.PIXEL_STD) / 255).tolist(),
+ size=model_input.MIN_SIZE_TEST,
+ max_size=model_input.MAX_SIZE_TEST,
+ num_labels=model.SEM_SEG_HEAD.NUM_CLASSES,
+ ignore_index=dataset_catalog.ignore_label,
+ class_info_file=class_info_file,
+ )
+
+ tokenizer = CLIPTokenizer.from_pretrained(model_repo)
+
+ return OneFormerProcessor(
+ image_processor=image_processor,
+ tokenizer=tokenizer,
+ task_seq_length=original_config.INPUT.TASK_SEQ_LEN,
+ max_seq_length=original_config.INPUT.MAX_SEQ_LEN,
+ )
+
+
+class OriginalOneFormerCheckpointToOursConverter:
+ def __init__(self, original_model: nn.Module, config: OneFormerConfig):
+ self.original_model = original_model
+ self.config = config
+
+ def pop_all(self, renamed_keys: list[tuple[str, str]], dst_state_dict: StateDict, src_state_dict: StateDict):
+ for src_key, dst_key in renamed_keys:
+ dst_state_dict[dst_key] = src_state_dict.pop(src_key)
+
+ # Swin Backbone
+ def replace_swin_backbone(self, dst_state_dict: StateDict, src_state_dict: StateDict, config: OneFormerConfig):
+ dst_prefix: str = "pixel_level_module.encoder"
+ src_prefix: str = "backbone"
+
+ renamed_keys = [
+ (
+ f"{src_prefix}.patch_embed.proj.weight",
+ f"{dst_prefix}.embeddings.patch_embeddings.projection.weight",
+ ),
+ (f"{src_prefix}.patch_embed.proj.bias", f"{dst_prefix}.embeddings.patch_embeddings.projection.bias"),
+ (f"{src_prefix}.patch_embed.norm.weight", f"{dst_prefix}.embeddings.norm.weight"),
+ (f"{src_prefix}.patch_embed.norm.bias", f"{dst_prefix}.embeddings.norm.bias"),
+ ]
+ num_layers = len(config.backbone_config.depths)
+ for layer_idx in range(num_layers):
+ for block_idx in range(config.backbone_config.depths[layer_idx]):
+ renamed_keys.extend(
+ [ # src, dst
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.bias",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_bias_table",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_bias_table",
+ ),
+ ]
+ )
+ # now we need to handle the attentions
+ # read in weights + bias of input projection layer of cross-attention
+
+ src_att_weight = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight"]
+ src_att_bias = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias"]
+
+ size = src_att_weight.shape[0]
+ offset = size // 3
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.weight"
+ ] = src_att_weight[:offset, :]
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.bias"
+ ] = src_att_bias[:offset]
+
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.weight"
+ ] = src_att_weight[offset : offset * 2, :]
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.bias"
+ ] = src_att_bias[offset : offset * 2]
+
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.weight"
+ ] = src_att_weight[-offset:, :]
+ dst_state_dict[
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.bias"
+ ] = src_att_bias[-offset:]
+
+ # let's pop them
+ src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight")
+ src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias")
+ # proj
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.bias",
+ ),
+ ]
+ )
+
+ # second norm
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.bias",
+ ),
+ ]
+ )
+
+ # mlp
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.bias",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.bias",
+ ),
+ ]
+ )
+
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_index",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_index",
+ )
+ ]
+ )
+
+ if layer_idx < num_layers - 1:
+ # patch merging
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.reduction.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.downsample.reduction.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.norm.weight",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.downsample.norm.weight",
+ ),
+ (
+ f"{src_prefix}.layers.{layer_idx}.downsample.norm.bias",
+ f"{dst_prefix}.encoder.layers.{layer_idx}.downsample.norm.bias",
+ ),
+ ]
+ )
+
+ # hidden states norms
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.norm{layer_idx}.weight",
+ f"{dst_prefix}.hidden_states_norms.stage{layer_idx + 1}.weight",
+ ),
+ (
+ f"{src_prefix}.norm{layer_idx}.bias",
+ f"{dst_prefix}.hidden_states_norms.stage{layer_idx + 1}.bias",
+ ),
+ ]
+ )
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ # Dinat Backbone
+ def replace_dinat_backbone(self, dst_state_dict: StateDict, src_state_dict: StateDict, config: OneFormerConfig):
+ dst_prefix: str = "pixel_level_module.encoder"
+ src_prefix: str = "backbone"
+
+ def rename_keys_for_weight_bias(src_prefix: str, dst_prefix: str):
+ return [
+ (f"{src_prefix}.weight", f"{dst_prefix}.weight"),
+ (f"{src_prefix}.bias", f"{dst_prefix}.bias"),
+ ]
+
+ renamed_keys = rename_keys_for_weight_bias(f"{src_prefix}.patch_embed.norm", f"{dst_prefix}.embeddings.norm")
+
+ for i in range(2):
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(
+ f"{src_prefix}.patch_embed.proj.{i}",
+ f"{dst_prefix}.embeddings.patch_embeddings.projection.{i}",
+ )
+ )
+
+ num_layers = len(config.backbone_config.depths)
+ for layer_idx in range(num_layers):
+ for block_idx in range(config.backbone_config.depths[layer_idx]):
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(
+ f"{src_prefix}.levels.{layer_idx}.blocks.{block_idx}.norm1",
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.layernorm_before",
+ )
+ )
+
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(
+ f"{src_prefix}.levels.{layer_idx}.blocks.{block_idx}.norm2",
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.layernorm_after",
+ )
+ )
+
+ renamed_keys.extend(
+ [ # src, dst
+ (
+ f"{src_prefix}.levels.{layer_idx}.blocks.{block_idx}.attn.rpb",
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.attention.self.rpb",
+ ),
+ ]
+ )
+ # now we need to handle the attentions
+ # read in weights + bias of input projection layer of cross-attention
+
+ src_att_weight = src_state_dict[f"{src_prefix}.levels.{layer_idx}.blocks.{block_idx}.attn.qkv.weight"]
+ src_att_bias = src_state_dict[f"{src_prefix}.levels.{layer_idx}.blocks.{block_idx}.attn.qkv.bias"]
+
+ size = src_att_weight.shape[0]
+ offset = size // 3
+ dst_state_dict[
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.attention.self.query.weight"
+ ] = src_att_weight[:offset, :]
+ dst_state_dict[
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.attention.self.query.bias"
+ ] = src_att_bias[:offset]
+
+ dst_state_dict[
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.attention.self.key.weight"
+ ] = src_att_weight[offset : offset * 2, :]
+ dst_state_dict[
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.attention.self.key.bias"
+ ] = src_att_bias[offset : offset * 2]
+
+ dst_state_dict[
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.attention.self.value.weight"
+ ] = src_att_weight[-offset:, :]
+ dst_state_dict[
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.attention.self.value.bias"
+ ] = src_att_bias[-offset:]
+
+ # let's pop them
+ src_state_dict.pop(f"{src_prefix}.levels.{layer_idx}.blocks.{block_idx}.attn.qkv.weight")
+ src_state_dict.pop(f"{src_prefix}.levels.{layer_idx}.blocks.{block_idx}.attn.qkv.bias")
+ # proj
+
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(
+ f"{src_prefix}.levels.{layer_idx}.blocks.{block_idx}.attn.proj",
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.attention.output.dense",
+ )
+ )
+
+ # mlp
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(
+ f"{src_prefix}.levels.{layer_idx}.blocks.{block_idx}.mlp.fc1",
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.intermediate.dense",
+ )
+ )
+
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(
+ f"{src_prefix}.levels.{layer_idx}.blocks.{block_idx}.mlp.fc2",
+ f"{dst_prefix}.encoder.levels.{layer_idx}.layers.{block_idx}.output.dense",
+ )
+ )
+
+ if layer_idx < num_layers - 1:
+ # patch merging
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.levels.{layer_idx}.downsample.reduction.weight",
+ f"{dst_prefix}.encoder.levels.{layer_idx}.downsample.reduction.weight",
+ ),
+ (
+ f"{src_prefix}.levels.{layer_idx}.downsample.norm.weight",
+ f"{dst_prefix}.encoder.levels.{layer_idx}.downsample.norm.weight",
+ ),
+ (
+ f"{src_prefix}.levels.{layer_idx}.downsample.norm.bias",
+ f"{dst_prefix}.encoder.levels.{layer_idx}.downsample.norm.bias",
+ ),
+ ]
+ )
+
+ # hidden states norms
+ renamed_keys.extend(
+ [
+ (
+ f"{src_prefix}.norm{layer_idx}.weight",
+ f"{dst_prefix}.hidden_states_norms.stage{layer_idx + 1}.weight",
+ ),
+ (
+ f"{src_prefix}.norm{layer_idx}.bias",
+ f"{dst_prefix}.hidden_states_norms.stage{layer_idx + 1}.bias",
+ ),
+ ]
+ )
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ # Backbone + Pixel Decoder
+ def replace_pixel_module(self, dst_state_dict: StateDict, src_state_dict: StateDict, is_swin: bool):
+ dst_prefix: str = "pixel_level_module.decoder"
+ src_prefix: str = "sem_seg_head.pixel_decoder"
+
+ if is_swin:
+ self.replace_swin_backbone(dst_state_dict, src_state_dict, self.config)
+ else:
+ self.replace_dinat_backbone(dst_state_dict, src_state_dict, self.config)
+
+ def rename_keys_for_weight_bias(src_prefix: str, dst_prefix: str):
+ return [
+ (f"{src_prefix}.weight", f"{dst_prefix}.weight"),
+ (f"{src_prefix}.bias", f"{dst_prefix}.bias"),
+ ]
+
+ def rename_keys_for_self_attn(src_prefix: str, dst_prefix: str):
+ self_attn_keys = []
+ self_attn_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.attention_weights", f"{dst_prefix}.attention_weights")
+ )
+ self_attn_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.output_proj", f"{dst_prefix}.output_proj")
+ )
+ self_attn_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.sampling_offsets", f"{dst_prefix}.sampling_offsets")
+ )
+ self_attn_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.value_proj", f"{dst_prefix}.value_proj"))
+
+ return self_attn_keys
+
+ def rename_keys_for_encoder_layer(src_prefix: str, dst_prefix: str):
+ encoder_keys = []
+ encoder_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.linear1", f"{dst_prefix}.fc1"))
+ encoder_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.linear2", f"{dst_prefix}.fc2"))
+ encoder_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.norm1", f"{dst_prefix}.self_attn_layer_norm")
+ )
+ encoder_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.norm2", f"{dst_prefix}.final_layer_norm"))
+ encoder_keys.extend(rename_keys_for_self_attn(f"{src_prefix}.self_attn", f"{dst_prefix}.self_attn"))
+
+ return encoder_keys
+
+ # convolution layer for final features
+ renamed_keys = [
+ (f"{src_prefix}.adapter_1.weight", f"{dst_prefix}.adapter_1.0.weight"),
+ (f"{src_prefix}.adapter_1.norm.weight", f"{dst_prefix}.adapter_1.1.weight"),
+ (f"{src_prefix}.adapter_1.norm.bias", f"{dst_prefix}.adapter_1.1.bias"),
+ ]
+
+ renamed_keys.extend(
+ [
+ (f"{src_prefix}.layer_1.weight", f"{dst_prefix}.layer_1.0.weight"),
+ (f"{src_prefix}.layer_1.norm.weight", f"{dst_prefix}.layer_1.1.weight"),
+ (f"{src_prefix}.layer_1.norm.bias", f"{dst_prefix}.layer_1.1.bias"),
+ ]
+ )
+
+ # proj layers
+ for i in range(3):
+ for j in range(2):
+ renamed_keys.extend(
+ [
+ (f"{src_prefix}.input_proj.{i}.{j}.weight", f"{dst_prefix}.input_projections.{i}.{j}.weight"),
+ (f"{src_prefix}.input_proj.{i}.{j}.bias", f"{dst_prefix}.input_projections.{i}.{j}.bias"),
+ ]
+ )
+
+ renamed_keys.extend([(f"{src_prefix}.transformer.level_embed", f"{dst_prefix}.level_embed")])
+
+ # layers
+ for layer_idx in range(self.config.encoder_layers):
+ renamed_keys.extend(
+ rename_keys_for_encoder_layer(
+ f"{src_prefix}.transformer.encoder.layers.{layer_idx}", f"{dst_prefix}.encoder.layers.{layer_idx}"
+ )
+ )
+
+ # proj
+ renamed_keys.extend(
+ [
+ (f"{src_prefix}.mask_features.weight", f"{dst_prefix}.mask_projection.weight"),
+ (f"{src_prefix}.mask_features.bias", f"{dst_prefix}.mask_projection.bias"),
+ ]
+ )
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ # Transformer Decoder
+ def replace_keys_qkv_transformer_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "transformer_module.decoder.layers"
+ src_prefix: str = "sem_seg_head.predictor"
+ for i in range(self.config.decoder_layers - 1):
+ # read in weights + bias of input projection layer of self-attention
+ in_proj_weight = src_state_dict.pop(
+ f"{src_prefix}.transformer_self_attention_layers.{i}.self_attn.in_proj_weight"
+ )
+ in_proj_bias = src_state_dict.pop(
+ f"{src_prefix}.transformer_self_attention_layers.{i}.self_attn.in_proj_bias"
+ )
+ # next, add query, keys and values (in that order) to the state dict
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.self_attn.q_proj.bias"] = in_proj_bias[:256]
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.self_attn.k_proj.bias"] = in_proj_bias[256:512]
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
+ dst_state_dict[f"{dst_prefix}.{i}.self_attn.self_attn.v_proj.bias"] = in_proj_bias[-256:]
+
+ def replace_transformer_module(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "transformer_module"
+ src_prefix: str = "sem_seg_head.predictor"
+
+ def rename_keys_for_weight_bias(src_prefix: str, dst_prefix: str):
+ return [
+ (f"{src_prefix}.weight", f"{dst_prefix}.weight"),
+ (f"{src_prefix}.bias", f"{dst_prefix}.bias"),
+ ]
+
+ def rename_keys_for_attn(src_prefix: str, dst_prefix: str):
+ attn_keys = [
+ (f"{src_prefix}.in_proj_bias", f"{dst_prefix}.in_proj_bias"),
+ (f"{src_prefix}.in_proj_weight", f"{dst_prefix}.in_proj_weight"),
+ ]
+ attn_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.out_proj", f"{dst_prefix}.out_proj"))
+
+ return attn_keys
+
+ def rename_keys_for_self_attn(src_prefix: str, dst_prefix: str):
+ attn_keys = []
+ attn_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.out_proj", f"{dst_prefix}.out_proj"))
+
+ return attn_keys
+
+ def rename_keys_for_query_transformer_layer(src_prefix: str, dst_prefix: str):
+ query_transformer_layer_keys = []
+
+ query_transformer_layer_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.linear1", f"{dst_prefix}.linear1")
+ )
+ query_transformer_layer_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.linear2", f"{dst_prefix}.linear2")
+ )
+ query_transformer_layer_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.norm1", f"{dst_prefix}.norm1")
+ )
+ query_transformer_layer_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.norm2", f"{dst_prefix}.norm2")
+ )
+ query_transformer_layer_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.norm3", f"{dst_prefix}.norm3")
+ )
+
+ query_transformer_layer_keys.extend(
+ rename_keys_for_attn(f"{src_prefix}.self_attn", f"{dst_prefix}.self_attn")
+ )
+
+ query_transformer_layer_keys.extend(
+ rename_keys_for_attn(f"{src_prefix}.multihead_attn", f"{dst_prefix}.multihead_attn")
+ )
+
+ return query_transformer_layer_keys
+
+ def rename_keys_for_cross_attn_layer(src_prefix: str, dst_prefix: str):
+ cross_attn_layer_keys = []
+
+ cross_attn_layer_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.norm", f"{dst_prefix}.norm"))
+ cross_attn_layer_keys.extend(
+ rename_keys_for_attn(f"{src_prefix}.multihead_attn", f"{dst_prefix}.multihead_attn")
+ )
+
+ return cross_attn_layer_keys
+
+ def rename_keys_for_self_attn_layer(src_prefix: str, dst_prefix: str):
+ self_attn_layer_keys = []
+
+ self_attn_layer_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.norm", f"{dst_prefix}.norm"))
+ self_attn_layer_keys.extend(
+ rename_keys_for_self_attn(f"{src_prefix}.self_attn", f"{dst_prefix}.self_attn")
+ )
+
+ return self_attn_layer_keys
+
+ def rename_keys_for_ffn_layer(src_prefix: str, dst_prefix: str):
+ ffn_layer_keys = []
+
+ ffn_layer_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.linear1", f"{dst_prefix}.linear1"))
+ ffn_layer_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.linear2", f"{dst_prefix}.linear2"))
+ ffn_layer_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.norm", f"{dst_prefix}.norm"))
+
+ return ffn_layer_keys
+
+ def rename_keys_for_transformer_decoder_layer(src_prefix: str, dst_prefix: str, idx: int):
+ transformer_decoder_layer_keys = []
+
+ transformer_decoder_layer_keys.extend(
+ rename_keys_for_cross_attn_layer(
+ f"{src_prefix}.transformer_cross_attention_layers.{idx}", f"{dst_prefix}.{idx}.cross_attn"
+ )
+ )
+
+ transformer_decoder_layer_keys.extend(
+ rename_keys_for_self_attn_layer(
+ f"{src_prefix}.transformer_self_attention_layers.{idx}", f"{dst_prefix}.{idx}.self_attn"
+ )
+ )
+
+ transformer_decoder_layer_keys.extend(
+ rename_keys_for_ffn_layer(f"{src_prefix}.transformer_ffn_layers.{idx}", f"{dst_prefix}.{idx}.ffn")
+ )
+
+ return transformer_decoder_layer_keys
+
+ # positional embedding for object queries
+ renamed_keys = [
+ (f"{src_prefix}.query_embed.weight", f"{dst_prefix}.queries_embedder.weight"),
+ (f"{src_prefix}.level_embed.weight", f"{dst_prefix}.level_embed.weight"),
+ ]
+
+ # norm
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.decoder_norm", f"{dst_prefix}.decoder.decoder_norm")
+ )
+
+ # proj
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(
+ f"{src_prefix}.class_input_proj", f"{dst_prefix}.decoder.query_input_projection"
+ )
+ )
+
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.class_embed", f"{dst_prefix}.decoder.class_embed")
+ )
+
+ for i in range(3):
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(
+ f"{src_prefix}.mask_embed.layers.{i}", f"{dst_prefix}.decoder.mask_embed.layers.{i}.0"
+ )
+ )
+
+ # norm
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(
+ f"{src_prefix}.class_transformer.decoder.norm", f"{dst_prefix}.decoder.query_transformer.decoder.norm"
+ )
+ )
+
+ # transformer to update queries with task tokens
+ for i in range(self.config.query_dec_layers):
+ renamed_keys.extend(
+ rename_keys_for_query_transformer_layer(
+ f"{src_prefix}.class_transformer.decoder.layers.{i}",
+ f"{dst_prefix}.decoder.query_transformer.decoder.layers.{i}",
+ )
+ )
+
+ # decoder layers
+ for i in range(self.config.decoder_layers - 1):
+ renamed_keys.extend(
+ rename_keys_for_transformer_decoder_layer(
+ f"{src_prefix}",
+ f"{dst_prefix}.decoder.layers",
+ i,
+ )
+ )
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+ self.replace_keys_qkv_transformer_decoder(dst_state_dict, src_state_dict)
+
+ def replace_task_mlp(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "task_encoder"
+ src_prefix: str = "task_mlp"
+
+ def rename_keys_for_weight_bias(src_prefix: str, dst_prefix: str):
+ return [
+ (f"{src_prefix}.weight", f"{dst_prefix}.weight"),
+ (f"{src_prefix}.bias", f"{dst_prefix}.bias"),
+ ]
+
+ renamed_keys = []
+
+ for i in range(2):
+ renamed_keys.extend(
+ rename_keys_for_weight_bias(f"{src_prefix}.layers.{i}", f"{dst_prefix}.task_mlp.layers.{i}.0")
+ )
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ def replace_text_projector(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "text_mapper.text_projector"
+ src_prefix: str = "text_projector"
+
+ def rename_keys_for_weight_bias(src_prefix: str, dst_prefix: str):
+ return [
+ (f"{src_prefix}.weight", f"{dst_prefix}.weight"),
+ (f"{src_prefix}.bias", f"{dst_prefix}.bias"),
+ ]
+
+ renamed_keys = []
+
+ for i in range(self.config.text_encoder_config["text_encoder_proj_layers"]):
+ renamed_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.layers.{i}", f"{dst_prefix}.{i}.0"))
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ def replace_text_mapper(self, dst_state_dict: StateDict, src_state_dict: StateDict):
+ dst_prefix: str = "text_mapper.text_encoder"
+ src_prefix: str = "text_encoder"
+
+ self.replace_text_projector(dst_state_dict, src_state_dict)
+
+ def rename_keys_for_weight_bias(src_prefix: str, dst_prefix: str):
+ return [
+ (f"{src_prefix}.weight", f"{dst_prefix}.weight"),
+ (f"{src_prefix}.bias", f"{dst_prefix}.bias"),
+ ]
+
+ def rename_keys_for_attn(src_prefix: str, dst_prefix: str):
+ attn_keys = [
+ (f"{src_prefix}.in_proj_bias", f"{dst_prefix}.in_proj_bias"),
+ (f"{src_prefix}.in_proj_weight", f"{dst_prefix}.in_proj_weight"),
+ ]
+ attn_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.out_proj", f"{dst_prefix}.out_proj"))
+
+ return attn_keys
+
+ def rename_keys_for_layer(src_prefix: str, dst_prefix: str):
+ resblock_keys = []
+
+ resblock_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.mlp.c_fc", f"{dst_prefix}.mlp.fc1"))
+ resblock_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.mlp.c_proj", f"{dst_prefix}.mlp.fc2"))
+ resblock_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.ln_1", f"{dst_prefix}.layer_norm1"))
+ resblock_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.ln_2", f"{dst_prefix}.layer_norm2"))
+ resblock_keys.extend(rename_keys_for_attn(f"{src_prefix}.attn", f"{dst_prefix}.self_attn"))
+
+ return resblock_keys
+
+ renamed_keys = [
+ ("prompt_ctx.weight", "text_mapper.prompt_ctx.weight"),
+ ]
+
+ renamed_keys.extend(
+ [
+ (f"{src_prefix}.positional_embedding", f"{dst_prefix}.positional_embedding"),
+ (f"{src_prefix}.token_embedding.weight", f"{dst_prefix}.token_embedding.weight"),
+ ]
+ )
+
+ renamed_keys.extend(rename_keys_for_weight_bias(f"{src_prefix}.ln_final", f"{dst_prefix}.ln_final"))
+
+ for i in range(self.config.text_encoder_config["text_encoder_num_layers"]):
+ renamed_keys.extend(
+ rename_keys_for_layer(
+ f"{src_prefix}.transformer.resblocks.{i}", f"{dst_prefix}.transformer.layers.{i}"
+ )
+ )
+
+ self.pop_all(renamed_keys, dst_state_dict, src_state_dict)
+
+ def convert(self, oneformer: OneFormerModel, is_swin: bool) -> OneFormerModel:
+ dst_state_dict = TrackedStateDict(oneformer.state_dict())
+ src_state_dict = self.original_model.state_dict()
+
+ self.replace_pixel_module(dst_state_dict, src_state_dict, is_swin)
+ self.replace_transformer_module(dst_state_dict, src_state_dict)
+ self.replace_task_mlp(dst_state_dict, src_state_dict)
+ if self.config.is_training:
+ self.replace_text_mapper(dst_state_dict, src_state_dict)
+
+ logger.info(f"Missed keys are {pformat(dst_state_dict.diff())}")
+ logger.info(f"Not copied keys are {pformat(src_state_dict.keys())}")
+ logger.info("🙌 Done")
+
+ oneformer.load_state_dict(dst_state_dict)
+
+ return oneformer
+
+ @staticmethod
+ def using_dirs(checkpoints_dir: Path, config_dir: Path) -> Iterator[tuple[object, Path, Path]]:
+ checkpoints: list[Path] = checkpoints_dir.glob("**/*.pth")
+
+ for checkpoint in checkpoints:
+ logger.info(f"💪 Converting {checkpoint.stem}")
+ # find associated config file
+ config: Path = config_dir / f"{checkpoint.stem}.yaml"
+
+ yield config, checkpoint
+
+
+def post_process_sem_seg_output(outputs: OneFormerForUniversalSegmentationOutput, target_size: tuple[int, int]):
+ # class_queries_logits has shape [BATCH, QUERIES, CLASSES + 1]
+ class_queries_logits = outputs.class_queries_logits
+ # masks_queries_logits has shape [BATCH, QUERIES, HEIGHT, WIDTH]
+ masks_queries_logits = outputs.masks_queries_logits
+ if target_size is not None:
+ masks_queries_logits = torch.nn.functional.interpolate(
+ masks_queries_logits,
+ size=target_size,
+ mode="bilinear",
+ align_corners=False,
+ )
+ # remove the null class `[..., :-1]`
+ masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
+ # mask probs has shape [BATCH, QUERIES, HEIGHT, WIDTH]
+ masks_probs = masks_queries_logits.sigmoid()
+ # now we want to sum over the queries,
+ # $ out_{c,h,w} = \sum_q p_{q,c} * m_{q,h,w} $
+ # where $ softmax(p) \in R^{q, c} $ is the mask classes
+ # and $ sigmoid(m) \in R^{q, h, w}$ is the mask probabilities
+ # b(atch)q(uery)c(lasses), b(atch)q(uery)h(eight)w(idth)
+ segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
+
+ return segmentation
+
+
+def test(
+ original_model,
+ our_model: OneFormerForUniversalSegmentation,
+ processor: OneFormerProcessor,
+ model_repo: str,
+):
+ def _preprocess_text(text_list=None, max_length=77):
+ if text_list is None:
+ raise ValueError("tokens cannot be None.")
+
+ tokens = tokenizer(text_list, padding="max_length", max_length=max_length, truncation=True)
+
+ attention_masks, input_ids = tokens["attention_mask"], tokens["input_ids"]
+
+ token_inputs = []
+ for attn_mask, input_id in zip(attention_masks, input_ids):
+ token = torch.tensor(attn_mask) * torch.tensor(input_id)
+ token_inputs.append(token.unsqueeze(0))
+
+ token_inputs = torch.cat(token_inputs, dim=0)
+ return token_inputs
+
+ with torch.no_grad():
+ tokenizer = CLIPTokenizer.from_pretrained(model_repo)
+ original_model = original_model.eval()
+ our_model = our_model.eval()
+
+ im = prepare_img()
+
+ tr = T.Compose(
+ [
+ T.Resize((640, 640)),
+ T.ToTensor(),
+ T.Normalize(
+ mean=torch.tensor([123.675, 116.280, 103.530]) / 255.0,
+ std=torch.tensor([58.395, 57.120, 57.375]) / 255.0,
+ ),
+ ],
+ )
+
+ x = tr(im).unsqueeze(0)
+
+ task_input = ["the task is semantic"]
+ task_token = _preprocess_text(task_input, max_length=processor.task_seq_length)
+
+ original_model_backbone_features = original_model.backbone(x.clone())
+
+ our_model_output: OneFormerModelOutput = our_model.model(x.clone(), task_token, output_hidden_states=True)
+
+ for original_model_feature, our_model_feature in zip(
+ original_model_backbone_features.values(), our_model_output.encoder_hidden_states
+ ):
+ assert torch.allclose(original_model_feature, our_model_feature, atol=3e-3), (
+ "The backbone features are not the same."
+ )
+ mask_features, _, multi_scale_features, _, _ = original_model.sem_seg_head.pixel_decoder.forward_features(
+ original_model_backbone_features
+ )
+
+ original_pixel_decoder_features = []
+ original_pixel_decoder_features.append(mask_features)
+ for i in range(len(multi_scale_features)):
+ original_pixel_decoder_features.append(multi_scale_features[i])
+
+ for original_model_feature, our_model_feature in zip(
+ original_pixel_decoder_features, our_model_output.pixel_decoder_hidden_states
+ ):
+ assert torch.allclose(original_model_feature, our_model_feature, atol=3e-4), (
+ "The pixel decoder feature are not the same"
+ )
+
+ tr_complete = T.Compose(
+ [
+ T.Resize((640, 640)),
+ T.ToTensor(),
+ ],
+ )
+
+ y = (tr_complete(im) * 255.0).to(torch.int).float()
+
+ # let's test the full model
+ original_model_out = original_model([{"image": y.clone(), "task": "The task is semantic"}])
+
+ original_segmentation = original_model_out[0]["sem_seg"]
+
+ our_model_out: OneFormerForUniversalSegmentationOutput = our_model(
+ x.clone(), task_token, output_hidden_states=True
+ )
+
+ our_segmentation = post_process_sem_seg_output(our_model_out, target_size=(640, 640))[0]
+
+ assert torch.allclose(original_segmentation, our_segmentation, atol=1e-3), (
+ "The segmentation image is not the same."
+ )
+
+ logger.info("✅ Test passed!")
+
+
+def get_name(checkpoint_file: Path):
+ model_name_raw: str = checkpoint_file.stem
+
+ backbone = "swin" if "swin" in model_name_raw else "dinat"
+ dataset = ""
+ if "coco" in model_name_raw:
+ dataset = "coco"
+ elif "ade20k" in model_name_raw:
+ dataset = "ade20k"
+ elif "cityscapes" in model_name_raw:
+ dataset = "cityscapes"
+ else:
+ raise ValueError(
+ f"{model_name_raw} must be wrong since we didn't find 'coco' or 'ade20k' or 'cityscapes' in it "
+ )
+
+ backbone_types = ["tiny", "large"]
+
+ backbone_type = list(filter(lambda x: x in model_name_raw, backbone_types))[0]
+
+ model_name = f"oneformer_{dataset}_{backbone}_{backbone_type}"
+
+ return model_name
+
+
+if __name__ == "__main__":
+ parser = ArgumentParser(
+ description=(
+ "Command line to convert the original oneformer models (with swin backbone) to transformers"
+ " implementation."
+ )
+ )
+
+ parser.add_argument(
+ "--checkpoints_dir",
+ type=Path,
+ help=(
+ "A directory containing the model's checkpoints. The directory has to have the following structure:"
+ " structure: //.pth; where name must follow the"
+ " following nomenclature nomenclature: oneformer___"
+ ),
+ )
+ parser.add_argument(
+ "--configs_dir",
+ type=Path,
+ help=(
+ "A directory containing the model's configs, see detectron2 doc. The directory has to have the following"
+ " structure: //.yaml; where name must follow the"
+ " following nomenclature nomenclature: oneformer___"
+ ),
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path",
+ required=True,
+ type=Path,
+ help="Path to the folder to output PyTorch models.",
+ )
+ parser.add_argument(
+ "--oneformer_dir",
+ required=True,
+ type=Path,
+ help=(
+ "A path to OneFormer's original implementation directory. You can download from here: "
+ "https://github.com/SHI-Labs/OneFormer"
+ ),
+ )
+
+ args = parser.parse_args()
+
+ checkpoints_dir: Path = args.checkpoints_dir
+ config_dir: Path = args.configs_dir
+ save_directory: Path = args.pytorch_dump_folder_path
+ oneformer_dir: Path = args.oneformer_dir
+ # append the path to the parents to oneformer dir
+ sys.path.append(str(oneformer_dir.parent))
+ # and import what's needed
+ from OneFormer.oneformer import add_common_config, add_dinat_config, add_oneformer_config, add_swin_config
+ from OneFormer.oneformer.oneformer_model import OneFormer as OriginalOneFormer
+
+ if not save_directory.exists():
+ save_directory.mkdir(parents=True)
+
+ for config_file, checkpoint_file in OriginalOneFormerCheckpointToOursConverter.using_dirs(
+ checkpoints_dir, config_dir
+ ):
+ processor = OriginalOneFormerConfigToProcessorConverter()(
+ setup_cfg(Args(config_file=config_file)), os.path.join("shi-labs", config_file.stem)
+ )
+
+ original_config = setup_cfg(Args(config_file=config_file))
+ oneformer_kwargs = OriginalOneFormer.from_config(original_config)
+
+ original_model = OriginalOneFormer(**oneformer_kwargs).eval()
+
+ DetectionCheckpointer(original_model).load(str(checkpoint_file))
+
+ is_swin = "swin" in config_file.stem
+
+ config: OneFormerConfig = OriginalOneFormerConfigToOursConverter()(original_config, is_swin)
+
+ oneformer = OneFormerModel(config=config).eval()
+
+ converter = OriginalOneFormerCheckpointToOursConverter(original_model, config)
+
+ oneformer = converter.convert(oneformer, is_swin)
+
+ oneformer_for_universal_segmentation = OneFormerForUniversalSegmentation(config=config).eval()
+
+ oneformer_for_universal_segmentation.model = oneformer
+
+ test(
+ original_model,
+ oneformer_for_universal_segmentation,
+ processor,
+ os.path.join("shi-labs", config_file.stem),
+ )
+
+ model_name = get_name(checkpoint_file)
+ logger.info(f"🪄 Saving {model_name}")
+
+ processor.save_pretrained(save_directory / model_name)
+ oneformer_for_universal_segmentation.save_pretrained(save_directory / model_name)
+
+ processor.push_to_hub(
+ repo_id=os.path.join("shi-labs", config_file.stem),
+ commit_message="Add configs",
+ use_temp_dir=True,
+ )
+ oneformer_for_universal_segmentation.push_to_hub(
+ repo_id=os.path.join("shi-labs", config_file.stem),
+ commit_message="Add model",
+ use_temp_dir=True,
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/image_processing_oneformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/image_processing_oneformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a66918af17e31b635ed86212a94a81d9c608573
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/image_processing_oneformer.py
@@ -0,0 +1,1343 @@
+# coding=utf-8
+# Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for OneFormer."""
+
+import json
+import os
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import numpy as np
+from huggingface_hub import hf_hub_download
+from huggingface_hub.utils import RepositoryNotFoundError
+
+from ...image_processing_utils import INIT_SERVICE_KWARGS, BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import (
+ PaddingMode,
+ get_resize_output_image_size,
+ pad,
+ rescale,
+ resize,
+ to_channel_dimension_format,
+)
+from ...image_utils import (
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ get_image_size,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import (
+ IMAGENET_DEFAULT_MEAN,
+ IMAGENET_DEFAULT_STD,
+ TensorType,
+ filter_out_non_signature_kwargs,
+ is_torch_available,
+ is_torch_tensor,
+ logging,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+if is_torch_available():
+ import torch
+ from torch import nn
+
+
+# Copied from transformers.models.detr.image_processing_detr.max_across_indices
+def max_across_indices(values: Iterable[Any]) -> list[Any]:
+ """
+ Return the maximum value across all indices of an iterable of values.
+ """
+ return [max(values_i) for values_i in zip(*values)]
+
+
+# Copied from transformers.models.detr.image_processing_detr.get_max_height_width
+def get_max_height_width(
+ images: list[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None
+) -> list[int]:
+ """
+ Get the maximum height and width across all images in a batch.
+ """
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ if input_data_format == ChannelDimension.FIRST:
+ _, max_height, max_width = max_across_indices([img.shape for img in images])
+ elif input_data_format == ChannelDimension.LAST:
+ max_height, max_width, _ = max_across_indices([img.shape for img in images])
+ else:
+ raise ValueError(f"Invalid channel dimension format: {input_data_format}")
+ return (max_height, max_width)
+
+
+# Copied from transformers.models.detr.image_processing_detr.make_pixel_mask
+def make_pixel_mask(
+ image: np.ndarray, output_size: tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
+) -> np.ndarray:
+ """
+ Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
+
+ Args:
+ image (`np.ndarray`):
+ Image to make the pixel mask for.
+ output_size (`tuple[int, int]`):
+ Output size of the mask.
+ """
+ input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+ mask = np.zeros(output_size, dtype=np.int64)
+ mask[:input_height, :input_width] = 1
+ return mask
+
+
+# Copied from transformers.models.detr.image_processing_detr.binary_mask_to_rle
+def binary_mask_to_rle(mask):
+ """
+ Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format.
+
+ Args:
+ mask (`torch.Tensor` or `numpy.array`):
+ A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
+ segment_id or class_id.
+ Returns:
+ `List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
+ format.
+ """
+ if is_torch_tensor(mask):
+ mask = mask.numpy()
+
+ pixels = mask.flatten()
+ pixels = np.concatenate([[0], pixels, [0]])
+ runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
+ runs[1::2] -= runs[::2]
+ return list(runs)
+
+
+# Copied from transformers.models.detr.image_processing_detr.convert_segmentation_to_rle
+def convert_segmentation_to_rle(segmentation):
+ """
+ Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format.
+
+ Args:
+ segmentation (`torch.Tensor` or `numpy.array`):
+ A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
+ Returns:
+ `list[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
+ """
+ segment_ids = torch.unique(segmentation)
+
+ run_length_encodings = []
+ for idx in segment_ids:
+ mask = torch.where(segmentation == idx, 1, 0)
+ rle = binary_mask_to_rle(mask)
+ run_length_encodings.append(rle)
+
+ return run_length_encodings
+
+
+# Copied from transformers.models.detr.image_processing_detr.remove_low_and_no_objects
+def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
+ """
+ Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
+ `labels`.
+
+ Args:
+ masks (`torch.Tensor`):
+ A tensor of shape `(num_queries, height, width)`.
+ scores (`torch.Tensor`):
+ A tensor of shape `(num_queries)`.
+ labels (`torch.Tensor`):
+ A tensor of shape `(num_queries)`.
+ object_mask_threshold (`float`):
+ A number between 0 and 1 used to binarize the masks.
+ Raises:
+ `ValueError`: Raised when the first dimension doesn't match in all input tensors.
+ Returns:
+ `tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
+ < `object_mask_threshold`.
+ """
+ if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
+ raise ValueError("mask, scores and labels must have the same shape!")
+
+ to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
+
+ return masks[to_keep], scores[to_keep], labels[to_keep]
+
+
+# Copied from transformers.models.detr.image_processing_detr.check_segment_validity
+def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
+ # Get the mask associated with the k class
+ mask_k = mask_labels == k
+ mask_k_area = mask_k.sum()
+
+ # Compute the area of all the stuff in query k
+ original_area = (mask_probs[k] >= mask_threshold).sum()
+ mask_exists = mask_k_area > 0 and original_area > 0
+
+ # Eliminate disconnected tiny segments
+ if mask_exists:
+ area_ratio = mask_k_area / original_area
+ if not area_ratio.item() > overlap_mask_area_threshold:
+ mask_exists = False
+
+ return mask_exists, mask_k
+
+
+# Copied from transformers.models.detr.image_processing_detr.compute_segments
+def compute_segments(
+ mask_probs,
+ pred_scores,
+ pred_labels,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ label_ids_to_fuse: Optional[set[int]] = None,
+ target_size: Optional[tuple[int, int]] = None,
+):
+ height = mask_probs.shape[1] if target_size is None else target_size[0]
+ width = mask_probs.shape[2] if target_size is None else target_size[1]
+
+ segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
+ segments: list[dict] = []
+
+ if target_size is not None:
+ mask_probs = nn.functional.interpolate(
+ mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
+ )[0]
+
+ current_segment_id = 0
+
+ # Weigh each mask by its prediction score
+ mask_probs *= pred_scores.view(-1, 1, 1)
+ mask_labels = mask_probs.argmax(0) # [height, width]
+
+ # Keep track of instances of each class
+ stuff_memory_list: dict[str, int] = {}
+ for k in range(pred_labels.shape[0]):
+ pred_class = pred_labels[k].item()
+ should_fuse = pred_class in label_ids_to_fuse
+
+ # Check if mask exists and large enough to be a segment
+ mask_exists, mask_k = check_segment_validity(
+ mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
+ )
+
+ if mask_exists:
+ if pred_class in stuff_memory_list:
+ current_segment_id = stuff_memory_list[pred_class]
+ else:
+ current_segment_id += 1
+
+ # Add current object segment to final segmentation map
+ segmentation[mask_k] = current_segment_id
+ segment_score = round(pred_scores[k].item(), 6)
+ segments.append(
+ {
+ "id": current_segment_id,
+ "label_id": pred_class,
+ "was_fused": should_fuse,
+ "score": segment_score,
+ }
+ )
+ if should_fuse:
+ stuff_memory_list[pred_class] = current_segment_id
+
+ return segmentation, segments
+
+
+# Copied from transformers.models.maskformer.image_processing_maskformer.convert_segmentation_map_to_binary_masks
+def convert_segmentation_map_to_binary_masks(
+ segmentation_map: "np.ndarray",
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+):
+ if do_reduce_labels and ignore_index is None:
+ raise ValueError("If `do_reduce_labels` is True, `ignore_index` must be provided.")
+
+ if do_reduce_labels:
+ segmentation_map = np.where(segmentation_map == 0, ignore_index, segmentation_map - 1)
+
+ # Get unique ids (class or instance ids based on input)
+ all_labels = np.unique(segmentation_map)
+
+ # Drop background label if applicable
+ if ignore_index is not None:
+ all_labels = all_labels[all_labels != ignore_index]
+
+ # Generate a binary mask for each object instance
+ binary_masks = [(segmentation_map == i) for i in all_labels]
+
+ # Stack the binary masks
+ if binary_masks:
+ binary_masks = np.stack(binary_masks, axis=0)
+ else:
+ binary_masks = np.zeros((0, *segmentation_map.shape))
+
+ # Convert instance ids to class ids
+ if instance_id_to_semantic_id is not None:
+ labels = np.zeros(all_labels.shape[0])
+
+ for label in all_labels:
+ class_id = instance_id_to_semantic_id[label + 1 if do_reduce_labels else label]
+ labels[all_labels == label] = class_id - 1 if do_reduce_labels else class_id
+ else:
+ labels = all_labels
+
+ return binary_masks.astype(np.float32), labels.astype(np.int64)
+
+
+def get_oneformer_resize_output_image_size(
+ image: np.ndarray,
+ size: Union[int, tuple[int, int], list[int], tuple[int]],
+ max_size: Optional[int] = None,
+ default_to_square: bool = True,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+) -> tuple:
+ """
+ Computes the output size given the desired size.
+
+ Args:
+ image (`np.ndarray`):
+ The input image.
+ size (`int` or `tuple[int, int]` or `list[int]` or `tuple[int]`):
+ The size of the output image.
+ max_size (`int`, *optional*):
+ The maximum size of the output image.
+ default_to_square (`bool`, *optional*, defaults to `True`):
+ Whether to default to square if no size is provided.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If unset, will use the inferred format from the input.
+
+ Returns:
+ `tuple[int, int]`: The output size.
+ """
+ output_size = get_resize_output_image_size(
+ input_image=image,
+ size=size,
+ default_to_square=default_to_square,
+ max_size=max_size,
+ input_data_format=input_data_format,
+ )
+ return output_size
+
+
+def prepare_metadata(class_info):
+ metadata = {}
+ class_names = []
+ thing_ids = []
+ for key, info in class_info.items():
+ metadata[key] = info["name"]
+ class_names.append(info["name"])
+ if info["isthing"]:
+ thing_ids.append(int(key))
+ metadata["thing_ids"] = thing_ids
+ metadata["class_names"] = class_names
+ return metadata
+
+
+def load_metadata(repo_id, class_info_file):
+ fname = os.path.join("" if repo_id is None else repo_id, class_info_file)
+
+ if not os.path.exists(fname) or not os.path.isfile(fname):
+ if repo_id is None:
+ raise ValueError(f"Could not file {fname} locally. repo_id must be defined if loading from the hub")
+ # We try downloading from a dataset by default for backward compatibility
+ try:
+ fname = hf_hub_download(repo_id, class_info_file, repo_type="dataset")
+ except RepositoryNotFoundError:
+ fname = hf_hub_download(repo_id, class_info_file)
+
+ with open(fname, "r") as f:
+ class_info = json.load(f)
+
+ return class_info
+
+
+class OneFormerImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a OneFormer image processor. The image processor can be used to prepare image(s), task input(s) and
+ optional text inputs and targets for the model.
+
+ This image processor inherits from [`BaseImageProcessor`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the input to a certain `size`.
+ size (`int`, *optional*, defaults to 800):
+ Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
+ sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
+ the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
+ height / width, size)`.
+ resample (`int`, *optional*, defaults to `Resampling.BILINEAR`):
+ An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
+ `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
+ `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
+ to `True`.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the input to a certain `scale`.
+ rescale_factor (`float`, *optional*, defaults to `1/ 255`):
+ Rescale the input by the given factor. Only has an effect if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether or not to normalize the input with mean and standard deviation.
+ image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
+ The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
+ image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
+ The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
+ ImageNet std.
+ ignore_index (`int`, *optional*):
+ Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels
+ denoted with 0 (background) will be replaced with `ignore_index`.
+ do_reduce_labels (`bool`, *optional*, defaults to `False`):
+ Whether or not to decrement all label values of segmentation maps by 1. Usually used for datasets where 0
+ is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k).
+ The background label will be replaced by `ignore_index`.
+ repo_path (`str`, *optional*, defaults to `"shi-labs/oneformer_demo"`):
+ Path to hub repo or local directory containing the JSON file with class information for the dataset.
+ If unset, will look for `class_info_file` in the current working directory.
+ class_info_file (`str`, *optional*):
+ JSON file containing class information for the dataset. See `shi-labs/oneformer_demo/cityscapes_panoptic.json` for an example.
+ num_text (`int`, *optional*):
+ Number of text entries in the text input list.
+ num_labels (`int`, *optional*):
+ The number of labels in the segmentation map.
+ """
+
+ model_input_names = ["pixel_values", "pixel_mask", "task_inputs"]
+
+ @filter_out_non_signature_kwargs(extra=["max_size", "metadata", *INIT_SERVICE_KWARGS])
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ do_rescale: bool = True,
+ rescale_factor: float = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+ repo_path: Optional[str] = "shi-labs/oneformer_demo",
+ class_info_file: Optional[str] = None,
+ num_text: Optional[int] = None,
+ num_labels: Optional[int] = None,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ # Deprecated, backward compatibility
+ self._max_size = kwargs.pop("max_size", 1333)
+
+ size = size if size is not None else {"shortest_edge": 800, "longest_edge": self._max_size}
+ size = get_size_dict(size, max_size=self._max_size, default_to_square=False)
+
+ if class_info_file is None:
+ raise ValueError("You must provide a `class_info_file`")
+
+ self.do_resize = do_resize
+ self.size = size
+ self.resample = resample
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
+ self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
+ self.ignore_index = ignore_index
+ self.do_reduce_labels = do_reduce_labels
+ self.class_info_file = class_info_file
+ self.repo_path = repo_path
+ self.metadata = prepare_metadata(load_metadata(repo_path, class_info_file))
+ self.num_text = num_text
+ self.num_labels = num_labels
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.to_dict
+ def to_dict(self) -> dict[str, Any]:
+ """
+ Serializes this instance to a Python dictionary. This method calls the superclass method and then removes the
+ `_max_size` attribute from the dictionary.
+ """
+ image_processor_dict = super().to_dict()
+ image_processor_dict.pop("_max_size", None)
+ return image_processor_dict
+
+ @filter_out_non_signature_kwargs(extra=["max_size"])
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BILINEAR,
+ data_format=None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize the image to the given size. Size can be min_size (scalar) or `(height, width)` tuple. If size is an
+ int, smaller edge of the image will be matched to this number.
+ """
+
+ # Deprecated, backward compatibility
+ max_size = kwargs.pop("max_size", None)
+
+ size = get_size_dict(size, max_size=max_size, default_to_square=False)
+ if "shortest_edge" in size and "longest_edge" in size:
+ size, max_size = size["shortest_edge"], size["longest_edge"]
+ elif "height" in size and "width" in size:
+ size = (size["height"], size["width"])
+ max_size = None
+ else:
+ raise ValueError(
+ "Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
+ f" {size.keys()}."
+ )
+ size = get_oneformer_resize_output_image_size(
+ image=image, size=size, max_size=max_size, default_to_square=False, input_data_format=input_data_format
+ )
+ image = resize(
+ image, size=size, resample=resample, data_format=data_format, input_data_format=input_data_format
+ )
+ return image
+
+ # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.rescale
+ def rescale(
+ self,
+ image: np.ndarray,
+ rescale_factor: float,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Rescale the image by the given factor. image = image * rescale_factor.
+
+ Args:
+ image (`np.ndarray`):
+ Image to rescale.
+ rescale_factor (`float`):
+ The value to use for rescaling.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the output image. If unset, the channel dimension format of the input
+ image is used. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format for the input image. If unset, is inferred from the input image. Can be
+ one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ """
+ return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format)
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.convert_segmentation_map_to_binary_masks
+ def convert_segmentation_map_to_binary_masks(
+ self,
+ segmentation_map: "np.ndarray",
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+ ):
+ do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
+ ignore_index = ignore_index if ignore_index is not None else self.ignore_index
+ return convert_segmentation_map_to_binary_masks(
+ segmentation_map=segmentation_map,
+ instance_id_to_semantic_id=instance_id_to_semantic_id,
+ ignore_index=ignore_index,
+ do_reduce_labels=do_reduce_labels,
+ )
+
+ def __call__(self, images, task_inputs=None, segmentation_maps=None, **kwargs) -> BatchFeature:
+ return self.preprocess(images, task_inputs=task_inputs, segmentation_maps=segmentation_maps, **kwargs)
+
+ def _preprocess(
+ self,
+ image: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ if do_resize:
+ image = self.resize(image, size=size, resample=resample, input_data_format=input_data_format)
+ if do_rescale:
+ image = self.rescale(image, rescale_factor=rescale_factor, input_data_format=input_data_format)
+ if do_normalize:
+ image = self.normalize(image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+ return image
+
+ def _preprocess_image(
+ self,
+ image: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """Preprocesses a single image."""
+ # All transformations expect numpy arrays.
+ image = to_numpy_array(image)
+ if do_rescale and is_scaled_image(image):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(image)
+ image = self._preprocess(
+ image=image,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ input_data_format=input_data_format,
+ )
+ if data_format is not None:
+ image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+ return image
+
+ def _preprocess_mask(
+ self,
+ segmentation_map: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """Preprocesses a single mask."""
+ segmentation_map = to_numpy_array(segmentation_map)
+ # Add channel dimension if missing - needed for certain transformations
+ if segmentation_map.ndim == 2:
+ added_channel_dim = True
+ segmentation_map = segmentation_map[None, ...]
+ input_data_format = ChannelDimension.FIRST
+ else:
+ added_channel_dim = False
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(segmentation_map, num_channels=1)
+ # TODO: (Amy)
+ # Remork segmentation map processing to include reducing labels and resizing which doesn't
+ # drop segment IDs > 255.
+ segmentation_map = self._preprocess(
+ image=segmentation_map,
+ do_resize=do_resize,
+ resample=PILImageResampling.NEAREST,
+ size=size,
+ do_rescale=False,
+ do_normalize=False,
+ input_data_format=input_data_format,
+ )
+ # Remove extra channel dimension if added for processing
+ if added_channel_dim:
+ segmentation_map = segmentation_map.squeeze(0)
+ return segmentation_map
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ task_inputs: Optional[list[str]] = None,
+ segmentation_maps: Optional[ImageInput] = None,
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: Optional[bool] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> BatchFeature:
+ if task_inputs is None:
+ # Default value
+ task_inputs = ["panoptic"]
+
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ size = size if size is not None else self.size
+ size = get_size_dict(size, default_to_square=False, max_size=self._max_size)
+ resample = resample if resample is not None else self.resample
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ ignore_index = ignore_index if ignore_index is not None else self.ignore_index
+ do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+
+ if segmentation_maps is not None and not valid_images(segmentation_maps):
+ raise ValueError(
+ "Invalid segmentation map type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ images = make_list_of_images(images)
+ if segmentation_maps is not None:
+ segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2)
+
+ if segmentation_maps is not None and len(images) != len(segmentation_maps):
+ raise ValueError("Images and segmentation maps must have the same length.")
+
+ images = [
+ self._preprocess_image(
+ image,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ for image in images
+ ]
+
+ if segmentation_maps is not None:
+ segmentation_maps = [
+ self._preprocess_mask(segmentation_map, do_resize, size, input_data_format=input_data_format)
+ for segmentation_map in segmentation_maps
+ ]
+ encoded_inputs = self.encode_inputs(
+ images,
+ task_inputs,
+ segmentation_maps,
+ instance_id_to_semantic_id,
+ ignore_index,
+ do_reduce_labels,
+ return_tensors,
+ input_data_format=data_format,
+ )
+ return encoded_inputs
+
+ # Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor._pad_image
+ def _pad_image(
+ self,
+ image: np.ndarray,
+ output_size: tuple[int, int],
+ constant_values: Union[float, Iterable[float]] = 0,
+ data_format: Optional[ChannelDimension] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> np.ndarray:
+ """
+ Pad an image with zeros to the given size.
+ """
+ input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+ output_height, output_width = output_size
+
+ pad_bottom = output_height - input_height
+ pad_right = output_width - input_width
+ padding = ((0, pad_bottom), (0, pad_right))
+ padded_image = pad(
+ image,
+ padding,
+ mode=PaddingMode.CONSTANT,
+ constant_values=constant_values,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ return padded_image
+
+ # Copied from transformers.models.vilt.image_processing_vilt.ViltImageProcessor.pad
+ def pad(
+ self,
+ images: list[np.ndarray],
+ constant_values: Union[float, Iterable[float]] = 0,
+ return_pixel_mask: bool = True,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ data_format: Optional[ChannelDimension] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ) -> BatchFeature:
+ """
+ Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
+ in the batch and optionally returns their corresponding pixel mask.
+
+ Args:
+ image (`np.ndarray`):
+ Image to pad.
+ constant_values (`float` or `Iterable[float]`, *optional*):
+ The value to use for the padding if `mode` is `"constant"`.
+ return_pixel_mask (`bool`, *optional*, defaults to `True`):
+ Whether to return a pixel mask.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the image. If not provided, it will be the same as the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred.
+ """
+ pad_size = get_max_height_width(images, input_data_format=input_data_format)
+
+ padded_images = [
+ self._pad_image(
+ image,
+ pad_size,
+ constant_values=constant_values,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ )
+ for image in images
+ ]
+ data = {"pixel_values": padded_images}
+
+ if return_pixel_mask:
+ masks = [
+ make_pixel_mask(image=image, output_size=pad_size, input_data_format=input_data_format)
+ for image in images
+ ]
+ data["pixel_mask"] = masks
+
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+ def get_semantic_annotations(self, label, num_class_obj):
+ annotation_classes = label["classes"]
+ annotation_masks = label["masks"]
+
+ texts = ["a semantic photo"] * self.num_text
+ classes = []
+ masks = []
+
+ for idx in range(len(annotation_classes)):
+ class_id = annotation_classes[idx]
+ mask = annotation_masks[idx]
+ if not np.all(mask is False):
+ if class_id not in classes:
+ cls_name = self.metadata[str(class_id)]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+ else:
+ idx = classes.index(class_id)
+ masks[idx] += mask
+ masks[idx] = np.clip(masks[idx], 0, 1)
+
+ num = 0
+ for i, cls_name in enumerate(self.metadata["class_names"]):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = np.array(classes)
+ masks = np.array(masks)
+ return classes, masks, texts
+
+ def get_instance_annotations(self, label, num_class_obj):
+ annotation_classes = label["classes"]
+ annotation_masks = label["masks"]
+
+ texts = ["an instance photo"] * self.num_text
+ classes = []
+ masks = []
+
+ for idx in range(len(annotation_classes)):
+ class_id = annotation_classes[idx]
+ mask = annotation_masks[idx]
+
+ if class_id in self.metadata["thing_ids"]:
+ if not np.all(mask is False):
+ cls_name = self.metadata[str(class_id)]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+
+ num = 0
+ for i, cls_name in enumerate(self.metadata["class_names"]):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = np.array(classes)
+ masks = np.array(masks)
+ return classes, masks, texts
+
+ def get_panoptic_annotations(self, label, num_class_obj):
+ annotation_classes = label["classes"]
+ annotation_masks = label["masks"]
+
+ texts = ["an panoptic photo"] * self.num_text
+ classes = []
+ masks = []
+
+ for idx in range(len(annotation_classes)):
+ class_id = annotation_classes[idx]
+ mask = annotation_masks[idx].data
+ if not np.all(mask is False):
+ cls_name = self.metadata[str(class_id)]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+
+ num = 0
+ for i, cls_name in enumerate(self.metadata["class_names"]):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = np.array(classes)
+ masks = np.array(masks)
+ return classes, masks, texts
+
+ def encode_inputs(
+ self,
+ pixel_values_list: list[ImageInput],
+ task_inputs: list[str],
+ segmentation_maps: ImageInput = None,
+ instance_id_to_semantic_id: Optional[Union[list[dict[int, int]], dict[int, int]]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ ):
+ """
+ Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
+
+ OneFormer addresses semantic segmentation with a mask classification paradigm, thus input segmentation maps
+ will be converted to lists of binary masks and their respective labels. Let's see an example, assuming
+ `segmentation_maps = [[2,6,7,9]]`, the output will contain `mask_labels =
+ [[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]` (four binary masks) and `class_labels = [2,6,7,9]`, the labels for
+ each mask.
+
+ Args:
+ pixel_values_list (`list[ImageInput]`):
+ List of images (pixel values) to be padded. Each image should be a tensor of shape `(channels, height,
+ width)`.
+
+ task_inputs (`list[str]`):
+ List of task values.
+
+ segmentation_maps (`ImageInput`, *optional*):
+ The corresponding semantic segmentation maps with the pixel-wise annotations.
+
+ (`bool`, *optional*, defaults to `True`):
+ Whether or not to pad images up to the largest image in a batch and create a pixel mask.
+
+ If left to the default, will return a pixel mask that is:
+
+ - 1 for pixels that are real (i.e. **not masked**),
+ - 0 for pixels that are padding (i.e. **masked**).
+
+ instance_id_to_semantic_id (`list[dict[int, int]]` or `dict[int, int]`, *optional*):
+ A mapping between object instance ids and class ids. If passed, `segmentation_maps` is treated as an
+ instance segmentation map where each pixel represents an instance id. Can be provided as a single
+ dictionary with a global/dataset-level mapping or as a list of dictionaries (one per image), to map
+ instance ids in each image separately.
+
+ return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+ If set, will return tensors instead of NumPy arrays. If set to `'pt'`, return PyTorch `torch.Tensor`
+ objects.
+
+ input_data_format (`str` or `ChannelDimension`, *optional*):
+ The channel dimension format of the input image. If not provided, it will be inferred from the input
+ image.
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **pixel_values** -- Pixel values to be fed to a model.
+ - **pixel_mask** -- Pixel mask to be fed to a model (when `=True` or if `pixel_mask` is in
+ `self.model_input_names`).
+ - **mask_labels** -- Optional list of mask labels of shape `(labels, height, width)` to be fed to a model
+ (when `annotations` are provided).
+ - **class_labels** -- Optional list of class labels of shape `(labels)` to be fed to a model (when
+ `annotations` are provided). They identify the labels of `mask_labels`, e.g. the label of
+ `mask_labels[i][j]` if `class_labels[i][j]`.
+ - **text_inputs** -- Optional list of text string entries to be fed to a model (when `annotations` are
+ provided). They identify the binary masks present in the image.
+ """
+ ignore_index = self.ignore_index if ignore_index is None else ignore_index
+ do_reduce_labels = self.do_reduce_labels if do_reduce_labels is None else do_reduce_labels
+ pixel_values_list = [to_numpy_array(pixel_values) for pixel_values in pixel_values_list]
+
+ if input_data_format is None:
+ input_data_format = infer_channel_dimension_format(pixel_values_list[0])
+
+ pad_size = get_max_height_width(pixel_values_list, input_data_format=input_data_format)
+ encoded_inputs = self.pad(
+ pixel_values_list, return_tensors=return_tensors, input_data_format=input_data_format
+ )
+
+ annotations = None
+ if segmentation_maps is not None:
+ segmentation_maps = map(np.array, segmentation_maps)
+ annotations = []
+ for idx, segmentation_map in enumerate(segmentation_maps):
+ # Use instance2class_id mapping per image
+ if isinstance(instance_id_to_semantic_id, list):
+ instance_id = instance_id_to_semantic_id[idx]
+ else:
+ instance_id = instance_id_to_semantic_id
+ # Use instance2class_id mapping per image
+ masks, classes = self.convert_segmentation_map_to_binary_masks(
+ segmentation_map, instance_id, ignore_index=ignore_index, do_reduce_labels=do_reduce_labels
+ )
+ annotations.append({"masks": masks, "classes": classes})
+
+ if annotations is not None:
+ mask_labels = []
+ class_labels = []
+ text_inputs = []
+
+ num_class_obj = {}
+ for cls_name in self.metadata["class_names"]:
+ num_class_obj[cls_name] = 0
+
+ for i, label in enumerate(annotations):
+ task = task_inputs[i]
+ if task == "semantic":
+ classes, masks, texts = self.get_semantic_annotations(label, num_class_obj)
+ elif task == "instance":
+ classes, masks, texts = self.get_instance_annotations(label, num_class_obj)
+ elif task == "panoptic":
+ classes, masks, texts = self.get_panoptic_annotations(label, num_class_obj)
+ else:
+ raise ValueError(f"{task} was not expected, expected `semantic`, `instance` or `panoptic`")
+
+ # we cannot batch them since they don't share a common class size
+ masks = [mask[None, ...] for mask in masks]
+ masks = [
+ self._pad_image(image=mask, output_size=pad_size, constant_values=ignore_index) for mask in masks
+ ]
+ masks = np.concatenate(masks, axis=0)
+ mask_labels.append(torch.from_numpy(masks))
+ class_labels.append(torch.from_numpy(classes).long())
+ text_inputs.append(texts)
+
+ encoded_inputs["mask_labels"] = mask_labels
+ encoded_inputs["class_labels"] = class_labels
+ encoded_inputs["text_inputs"] = text_inputs
+
+ # This needs to be tokenized before sending to the model.
+ encoded_inputs["task_inputs"] = [f"the task is {task_input}" for task_input in task_inputs]
+
+ return encoded_inputs
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.post_process_semantic_segmentation
+ def post_process_semantic_segmentation(
+ self, outputs, target_sizes: Optional[list[tuple[int, int]]] = None
+ ) -> "torch.Tensor":
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentation`] into semantic segmentation maps. Only supports
+ PyTorch.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentation`]):
+ Raw outputs of the model.
+ target_sizes (`list[tuple[int, int]]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ Returns:
+ `list[torch.Tensor]`:
+ A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
+ corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
+ `torch.Tensor` correspond to a semantic class id.
+ """
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ # Remove the null class `[..., :-1]`
+ masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
+ masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Semantic segmentation logits of shape (batch_size, num_classes, height, width)
+ segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
+ batch_size = class_queries_logits.shape[0]
+
+ # Resize logits and compute semantic segmentation maps
+ if target_sizes is not None:
+ if batch_size != len(target_sizes):
+ raise ValueError(
+ "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+ )
+
+ semantic_segmentation = []
+ for idx in range(batch_size):
+ resized_logits = torch.nn.functional.interpolate(
+ segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+ )
+ semantic_map = resized_logits[0].argmax(dim=0)
+ semantic_segmentation.append(semantic_map)
+ else:
+ semantic_segmentation = segmentation.argmax(dim=1)
+ semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+ return semantic_segmentation
+
+ def post_process_instance_segmentation(
+ self,
+ outputs,
+ task_type: str = "instance",
+ is_demo: bool = True,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ return_coco_annotation: Optional[bool] = False,
+ ):
+ """
+ Converts the output of [`OneFormerForUniversalSegmentationOutput`] into image instance segmentation
+ predictions. Only supports PyTorch.
+
+ Args:
+ outputs ([`OneFormerForUniversalSegmentationOutput`]):
+ The outputs from [`OneFormerForUniversalSegmentationOutput`].
+ task_type (`str`, *optional*, defaults to "instance"):
+ The post processing depends on the task token input. If the `task_type` is "panoptic", we need to
+ ignore the stuff predictions.
+ is_demo (`bool`, *optional)*, defaults to `True`):
+ Whether the model is in demo mode. If true, use threshold to predict final masks.
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ target_sizes (`list[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction in batch. If left to None, predictions will not be
+ resized.
+ return_coco_annotation (`bool`, *optional)*, defaults to `False`):
+ Whether to return predictions in COCO format.
+
+ Returns:
+ `list[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set
+ to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized
+ to the corresponding `target_sizes` entry.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- an integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
+ Multiple instances of the same class / label were fused and assigned a single `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ device = masks_queries_logits.device
+ batch_size = class_queries_logits.shape[0]
+ num_queries = class_queries_logits.shape[1]
+ num_classes = class_queries_logits.shape[-1] - 1
+
+ # Loop over items in batch size
+ results: list[dict[str, torch.Tensor]] = []
+
+ for i in range(batch_size):
+ # [Q, K]
+ scores = torch.nn.functional.softmax(class_queries_logits[i], dim=-1)[:, :-1]
+ labels = torch.arange(num_classes, device=device).unsqueeze(0).repeat(num_queries, 1).flatten(0, 1)
+
+ # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False)
+ scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False)
+ labels_per_image = labels[topk_indices]
+
+ topk_indices = torch.div(topk_indices, num_classes, rounding_mode="floor")
+ # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1)
+ mask_pred = masks_queries_logits[i][topk_indices]
+
+ # Only consider scores with confidence over [threshold] for demo
+ if is_demo:
+ keep = scores_per_image > threshold
+ scores_per_image = scores_per_image[keep]
+ labels_per_image = labels_per_image[keep]
+ mask_pred = mask_pred[keep]
+
+ # if this is panoptic segmentation, we only keep the "thing" classes
+ if task_type == "panoptic":
+ keep = torch.zeros_like(scores_per_image).bool()
+ for j, lab in enumerate(labels_per_image):
+ keep[j] = lab in self.metadata["thing_ids"]
+
+ scores_per_image = scores_per_image[keep]
+ labels_per_image = labels_per_image[keep]
+ mask_pred = mask_pred[keep]
+
+ if mask_pred.shape[0] <= 0:
+ height, width = target_sizes[i] if target_sizes is not None else mask_pred.shape[1:]
+ segmentation = torch.zeros((height, width)) - 1
+ results.append({"segmentation": segmentation, "segments_info": []})
+ continue
+
+ if "ade20k" in self.class_info_file and not is_demo and "instance" in task_type:
+ for j in range(labels_per_image.shape[0]):
+ labels_per_image[j] = self.metadata["thing_ids"].index(labels_per_image[j].item())
+
+ # Get segmentation map and segment information of batch item
+ target_size = target_sizes[i] if target_sizes is not None else None
+ segmentation, segments = compute_segments(
+ mask_pred,
+ scores_per_image,
+ labels_per_image,
+ mask_threshold,
+ overlap_mask_area_threshold,
+ set(),
+ target_size,
+ )
+
+ # Return segmentation map in run-length encoding (RLE) format
+ if return_coco_annotation:
+ segmentation = convert_segmentation_to_rle(segmentation)
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.post_process_panoptic_segmentation
+ def post_process_panoptic_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ label_ids_to_fuse: Optional[set[int]] = None,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ ) -> list[dict]:
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image panoptic segmentation
+ predictions. Only supports PyTorch.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentationOutput`]):
+ The outputs from [`MaskFormerForInstanceSegmentation`].
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ label_ids_to_fuse (`Set[int]`, *optional*):
+ The labels in this state will have all their instances be fused together. For instance we could say
+ there can only be one sky in an image, but several persons, so the label ID for sky would be in that
+ set, but not the one for person.
+ target_sizes (`list[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction in batch. If left to None, predictions will not be
+ resized.
+
+ Returns:
+ `list[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set
+ to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized
+ to the corresponding `target_sizes` entry.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- an integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
+ Multiple instances of the same class / label were fused and assigned a single `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+
+ if label_ids_to_fuse is None:
+ logger.warning("`label_ids_to_fuse` unset. No instance will be fused.")
+ label_ids_to_fuse = set()
+
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ batch_size = class_queries_logits.shape[0]
+ num_labels = class_queries_logits.shape[-1] - 1
+
+ mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Predicted label and score of each query (batch_size, num_queries)
+ pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(batch_size):
+ mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
+ mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
+ )
+
+ # No mask found
+ if mask_probs_item.shape[0] <= 0:
+ height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
+ segmentation = torch.zeros((height, width)) - 1
+ results.append({"segmentation": segmentation, "segments_info": []})
+ continue
+
+ # Get segmentation map and segment information of batch item
+ target_size = target_sizes[i] if target_sizes is not None else None
+ segmentation, segments = compute_segments(
+ mask_probs=mask_probs_item,
+ pred_scores=pred_scores_item,
+ pred_labels=pred_labels_item,
+ mask_threshold=mask_threshold,
+ overlap_mask_area_threshold=overlap_mask_area_threshold,
+ label_ids_to_fuse=label_ids_to_fuse,
+ target_size=target_size,
+ )
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+
+__all__ = ["OneFormerImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/image_processing_oneformer_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/image_processing_oneformer_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..a61745e87e58609d92afc36d12100f23ec108047
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/image_processing_oneformer_fast.py
@@ -0,0 +1,1006 @@
+# coding=utf-8
+# Copyright 2025 SHI Labs and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Image processor class for OneFormer."""
+
+from typing import Optional, Union
+
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ BatchFeature,
+ DefaultFastImageProcessorKwargs,
+ get_max_height_width,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_utils import (
+ IMAGENET_DEFAULT_MEAN,
+ IMAGENET_DEFAULT_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ SizeDict,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+ logging,
+)
+from .image_processing_oneformer import load_metadata, prepare_metadata
+
+
+logger = logging.get_logger(__name__)
+
+if is_torch_available():
+ import torch
+ from torch import nn
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+def make_pixel_mask(image: "torch.Tensor", output_size: tuple[int, int]) -> "torch.Tensor":
+ """
+ Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
+
+ Args:
+ image (`torch.Tensor`):
+ Image to make the pixel mask for.
+ output_size (`Tuple[int, int]`):
+ Output size of the mask.
+ """
+
+ input_height, input_width = image.shape[-2], image.shape[-1]
+ mask = torch.zeros(output_size, dtype=torch.int64)
+ mask[:input_height, :input_width] = 1
+ return mask
+
+
+def binary_mask_to_rle(mask):
+ """
+ Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format.
+
+ Args:
+ mask (`torch.Tensor` or `numpy.array`):
+ A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
+ segment_id or class_id.
+ Returns:
+ `List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
+ format.
+ """
+ pixels = mask.flatten()
+ pixels = torch.concat([[0], pixels, [0]])
+ runs = torch.where(pixels[1:] != pixels[:-1])[0] + 1
+ runs[1::2] -= runs[::2]
+ return list(runs)
+
+
+def convert_segmentation_to_rle(segmentation):
+ """
+ Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format.
+
+ Args:
+ segmentation (`torch.Tensor` or `numpy.array`):
+ A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
+ Returns:
+ `List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
+ """
+ segment_ids = torch.unique(segmentation)
+
+ run_length_encodings = []
+ for idx in segment_ids:
+ mask = torch.where(segmentation == idx, 1, 0)
+ rle = binary_mask_to_rle(mask)
+ run_length_encodings.append(rle)
+
+ return run_length_encodings
+
+
+def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
+ """
+ Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
+ `labels`.
+
+ Args:
+ masks (`torch.Tensor`):
+ A tensor of shape `(num_queries, height, width)`.
+ scores (`torch.Tensor`):
+ A tensor of shape `(num_queries)`.
+ labels (`torch.Tensor`):
+ A tensor of shape `(num_queries)`.
+ object_mask_threshold (`float`):
+ A number between 0 and 1 used to binarize the masks.
+ Raises:
+ `ValueError`: Raised when the first dimension doesn't match in all input tensors.
+ Returns:
+ `Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
+ < `object_mask_threshold`.
+ """
+ if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
+ raise ValueError("mask, scores and labels must have the same shape!")
+
+ to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
+
+ return masks[to_keep], scores[to_keep], labels[to_keep]
+
+
+def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
+ # Get the mask associated with the k class
+ mask_k = mask_labels == k
+ mask_k_area = mask_k.sum()
+
+ # Compute the area of all the stuff in query k
+ original_area = (mask_probs[k] >= mask_threshold).sum()
+ mask_exists = mask_k_area > 0 and original_area > 0
+
+ # Eliminate disconnected tiny segments
+ if mask_exists:
+ area_ratio = mask_k_area / original_area
+ if not area_ratio.item() > overlap_mask_area_threshold:
+ mask_exists = False
+
+ return mask_exists, mask_k
+
+
+def compute_segments(
+ mask_probs,
+ pred_scores,
+ pred_labels,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ label_ids_to_fuse: Optional[set[int]] = None,
+ target_size: Optional[tuple[int, int]] = None,
+):
+ height = mask_probs.shape[1] if target_size is None else target_size[0]
+ width = mask_probs.shape[2] if target_size is None else target_size[1]
+
+ segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
+ segments: list[dict] = []
+
+ if target_size is not None:
+ mask_probs = F.resize(
+ mask_probs.unsqueeze(0),
+ size=target_size,
+ interpolation=F.InterpolationMode.BILINEAR,
+ )[0]
+
+ current_segment_id = 0
+
+ mask_probs *= pred_scores.view(-1, 1, 1)
+ mask_labels = mask_probs.argmax(0) # [height, width]
+
+ stuff_memory_list: dict[str, int] = {}
+ for k in range(pred_labels.shape[0]):
+ pred_class = pred_labels[k].item()
+ should_fuse = pred_class in label_ids_to_fuse
+
+ mask_exists, mask_k = check_segment_validity(
+ mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
+ )
+
+ if mask_exists:
+ if pred_class in stuff_memory_list:
+ current_segment_id = stuff_memory_list[pred_class]
+ else:
+ current_segment_id += 1
+
+ segmentation[mask_k] = current_segment_id
+ segment_score = round(pred_scores[k].item(), 6)
+ segments.append(
+ {
+ "id": current_segment_id,
+ "label_id": pred_class,
+ "was_fused": should_fuse,
+ "score": segment_score,
+ }
+ )
+ if should_fuse:
+ stuff_memory_list[pred_class] = current_segment_id
+
+ return segmentation, segments
+
+
+def convert_segmentation_map_to_binary_masks_fast(
+ segmentation_map: "torch.Tensor",
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+):
+ if do_reduce_labels and ignore_index is None:
+ raise ValueError("If `do_reduce_labels` is True, `ignore_index` must be provided.")
+
+ if do_reduce_labels:
+ segmentation_map = torch.where(segmentation_map == 0, ignore_index, segmentation_map - 1)
+
+ all_labels = torch.unique(segmentation_map)
+
+ if ignore_index is not None:
+ all_labels = all_labels[all_labels != ignore_index]
+
+ binary_masks = [(segmentation_map == i) for i in all_labels]
+
+ if binary_masks:
+ binary_masks = torch.stack(binary_masks, dim=0)
+ else:
+ binary_masks = torch.zeros((0, *segmentation_map.shape), device=segmentation_map.device)
+
+ # Convert instance ids to class ids
+ if instance_id_to_semantic_id is not None:
+ labels = torch.zeros(all_labels.shape[0], device=segmentation_map.device)
+
+ for i, label in enumerate(all_labels):
+ class_id = instance_id_to_semantic_id[(label.item() + 1 if do_reduce_labels else label.item())]
+ labels[i] = class_id - 1 if do_reduce_labels else class_id
+ else:
+ labels = all_labels
+
+ return (
+ binary_masks.float(),
+ labels.long(),
+ )
+
+
+def get_oneformer_resize_output_image_size(
+ image: "torch.Tensor",
+ size: Union[int, tuple[int, int], list[int], tuple[int]],
+ max_size: Optional[int] = None,
+ default_to_square: bool = True,
+) -> tuple:
+ """
+ Computes the output size given the desired size.
+
+ Args:
+ image (`torch.Tensor`):
+ The input image.
+ size (`int` or `Tuple[int, int]` or `List[int]` or `Tuple[int]`):
+ The size of the output image.
+ max_size (`int`, *optional*):
+ The maximum size of the output image.
+ default_to_square (`bool`, *optional*, defaults to `True`):
+ Whether to default to square if no size is provided.
+ Returns:
+ `Tuple[int, int]`: The output size.
+ """
+ if isinstance(size, (tuple, list)):
+ if len(size) == 2:
+ return tuple(size)
+ elif len(size) == 1:
+ # Perform same logic as if size was an int
+ size = size[0]
+ else:
+ raise ValueError("size must have 1 or 2 elements if it is a list or tuple")
+
+ if default_to_square:
+ return (size, size)
+
+ height, width = image.shape[-2], image.shape[-1]
+ short, long = (width, height) if width <= height else (height, width)
+ requested_new_short = size
+
+ new_short, new_long = requested_new_short, int(requested_new_short * long / short)
+
+ if max_size is not None:
+ if max_size <= requested_new_short:
+ raise ValueError(
+ f"max_size = {max_size} must be strictly greater than the requested "
+ f"size for the smaller edge size = {size}"
+ )
+ if new_long > max_size:
+ new_short, new_long = int(max_size * new_short / new_long), max_size
+
+ return (new_long, new_short) if width <= height else (new_short, new_long)
+
+
+class OneFormerFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ r"""
+ repo_path (`str`, *optional*, defaults to `shi-labs/oneformer_demo`):
+ Path to a local directory or Hugging Face Hub repository containing model metadata.
+ class_info_file (`str`, *optional*):
+ Path to the JSON file within the repository that contains class metadata.
+ num_text (`int`, *optional*):
+ Number of text queries for the text encoder, used as task-guiding prompts.
+ num_labels (`int`, *optional*):
+ Number of semantic classes for segmentation, determining the output layer's size.
+ ignore_index (`int`, *optional*):
+ Label to ignore in segmentation maps, often used for padding.
+ do_reduce_labels (`bool`, *optional*, defaults to `False`):
+ Whether to decrement all label values by 1, mapping the background class to `ignore_index`.
+ """
+
+ repo_path: Optional[str]
+ class_info_file: Optional[str]
+ num_text: Optional[int]
+ num_labels: Optional[int]
+ ignore_index: Optional[int]
+ do_reduce_labels: Optional[bool]
+
+
+@auto_docstring
+class OneFormerImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BILINEAR
+ image_mean = IMAGENET_DEFAULT_MEAN
+ image_std = IMAGENET_DEFAULT_STD
+ size = {"shortest_edge": 800, "longest_edge": 1333}
+ crop_size = None
+ do_resize = True
+ do_rescale = True
+ do_normalize = True
+ default_to_square = False
+ do_center_crop = False
+ do_convert_rgb = True
+ rescale_factor = 1 / 255
+ ignore_index = None
+ do_reduce_labels = False
+ repo_path = "shi-labs/oneformer_demo"
+ class_info_file = None
+ num_text = None
+ num_labels = None
+ valid_kwargs = OneFormerFastImageProcessorKwargs
+ model_input_names = ["pixel_values", "pixel_mask", "task_inputs"]
+
+ def __init__(self, **kwargs: Unpack[OneFormerFastImageProcessorKwargs]):
+ super().__init__(**kwargs)
+ if self.class_info_file:
+ self.metadata = prepare_metadata(load_metadata(self.repo_path, self.class_info_file))
+
+ @auto_docstring
+ def preprocess(
+ self,
+ images: ImageInput,
+ task_inputs: Optional[list[str]] = None,
+ segmentation_maps: Optional[ImageInput] = None,
+ instance_id_to_semantic_id: Optional[Union[list[dict[int, int]], dict[int, int]]] = None,
+ **kwargs: Unpack[OneFormerFastImageProcessorKwargs],
+ ) -> BatchFeature:
+ r"""
+ task_inputs (`list[str]`, *optional*):
+ List of tasks (`"panoptic"`, `"instance"`, `"semantic"`) for each image in the batch.
+ segmentation_maps (`ImageInput`, *optional*):
+ The segmentation maps.
+ instance_id_to_semantic_id (`Union[list[dict[int, int]], dict[int, int]]`, *optional*):
+ A mapping from instance IDs to semantic IDs.
+ """
+ return super().preprocess(
+ images,
+ task_inputs,
+ segmentation_maps,
+ instance_id_to_semantic_id,
+ **kwargs,
+ )
+
+ def _preprocess_image_like_inputs(
+ self,
+ images: ImageInput,
+ task_inputs: Optional[list[str]],
+ segmentation_maps: ImageInput,
+ instance_id_to_semantic_id: Optional[Union[list[dict[int, int]], dict[int, int]]],
+ do_convert_rgb: bool,
+ input_data_format: ChannelDimension,
+ device: Optional[Union[str, "torch.device"]] = None,
+ **kwargs: Unpack[OneFormerFastImageProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Preprocess image-like inputs.
+ To be overridden by subclasses when image-like inputs other than images should be processed.
+ It can be used for segmentation maps, depth maps, etc.
+ """
+ # Prepare input images
+ images = self._prepare_image_like_inputs(
+ images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
+ )
+ if segmentation_maps is not None:
+ segmentation_maps = self._prepare_image_like_inputs(
+ images=segmentation_maps,
+ expected_ndims=2,
+ do_convert_rgb=False,
+ input_data_format=ChannelDimension.FIRST,
+ )
+ return self._preprocess(images, task_inputs, segmentation_maps, instance_id_to_semantic_id, **kwargs)
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ task_inputs: Optional[list[str]],
+ segmentation_maps: list["torch.Tensor"],
+ instance_id_to_semantic_id: Optional[Union[list[dict[int, int]], dict[int, int]]],
+ do_resize: bool,
+ size: SizeDict,
+ interpolation: Optional["F.InterpolationMode"],
+ do_rescale: bool,
+ rescale_factor: float,
+ do_normalize: bool,
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ ignore_index: Optional[int],
+ do_reduce_labels: Optional[bool],
+ disable_grouping: Optional[bool],
+ return_tensors: Optional[Union[str, TensorType]],
+ **kwargs,
+ ) -> BatchFeature:
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+
+ processed_images_grouped = {}
+
+ for shape, stacked_images in grouped_images.items():
+ if do_resize:
+ stacked_images = self.resize(image=stacked_images, size=size, interpolation=interpolation)
+ stacked_images = self.rescale_and_normalize(
+ stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ processed_images_grouped[shape] = stacked_images
+ processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+
+ processed_segmentation_maps = None
+ if segmentation_maps is not None:
+ grouped_segmentation_maps, grouped_segmentation_maps_index = group_images_by_shape(
+ segmentation_maps, disable_grouping=disable_grouping
+ )
+ processed_segmentation_maps_grouped = {}
+ for shape, stacked_segmentation_maps in grouped_segmentation_maps.items():
+ if do_resize:
+ stacked_segmentation_maps = self.resize(
+ stacked_segmentation_maps,
+ size=size,
+ interpolation=F.InterpolationMode.NEAREST_EXACT
+ if is_torchvision_v2_available()
+ else F.InterpolationMode.NEAREST,
+ )
+ processed_segmentation_maps_grouped[shape] = stacked_segmentation_maps
+ processed_segmentation_maps = reorder_images(
+ processed_segmentation_maps_grouped, grouped_segmentation_maps_index
+ )
+
+ encoded_inputs = self._encode_inputs_fast(
+ processed_images,
+ task_inputs,
+ segmentation_maps=processed_segmentation_maps,
+ instance_id_to_semantic_id=instance_id_to_semantic_id,
+ ignore_index=ignore_index,
+ do_reduce_labels=do_reduce_labels,
+ return_tensors=return_tensors,
+ )
+
+ return encoded_inputs
+
+ def _pad_image_fast(
+ self,
+ image: "torch.Tensor",
+ output_size: tuple[int, int],
+ constant_values: float = 0,
+ ) -> "torch.Tensor":
+ """
+ Pad an image with zeros to the given size using torch operations.
+
+ Args:
+ image (`torch.Tensor`):
+ Image tensor in channel-first format (C, H, W).
+ output_size (`tuple[int, int]`):
+ Target output size (height, width).
+ constant_values (`float`, *optional*, defaults to 0):
+ The value to use for padding.
+
+ Returns:
+ `torch.Tensor`: The padded image.
+ """
+ input_height, input_width = image.shape[1], image.shape[2]
+ output_height, output_width = output_size
+
+ pad_bottom = output_height - input_height
+ pad_right = output_width - input_width
+
+ padded_image = F.pad(image, padding=[0, 0, pad_right, pad_bottom], fill=constant_values)
+
+ return padded_image
+
+ def pad(
+ self,
+ images: list["torch.Tensor"],
+ return_pixel_mask: bool = True,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ ) -> BatchFeature:
+ """
+ Pad a batch of images to the same size using torch operations.
+
+ Args:
+ images (`List[torch.Tensor]`):
+ List of image tensors in channel-first format.
+ return_pixel_mask (`bool`, *optional*, defaults to `True`):
+ Whether to return pixel masks.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return.
+
+ Returns:
+ `BatchFeature`: Padded images and optional pixel masks.
+ """
+ pad_size = get_max_height_width(images)
+
+ padded_images = []
+ pixel_masks = []
+
+ for image in images:
+ padded_image = self._pad_image_fast(
+ image=image,
+ output_size=pad_size,
+ constant_values=0,
+ )
+ padded_images.append(padded_image)
+
+ if return_pixel_mask:
+ input_height, input_width = image.shape[1], image.shape[2]
+ mask = torch.zeros(pad_size, dtype=torch.int64, device=image.device)
+ mask[:input_height, :input_width] = 1
+ pixel_masks.append(mask)
+
+ if return_tensors:
+ padded_images = torch.stack(padded_images, dim=0)
+ if return_pixel_mask:
+ pixel_masks = torch.stack(pixel_masks, dim=0)
+
+ data = {"pixel_values": padded_images}
+ if return_pixel_mask:
+ data["pixel_mask"] = pixel_masks
+
+ return BatchFeature(data=data, tensor_type=return_tensors)
+
+ def convert_segmentation_map_to_binary_masks(
+ self,
+ segmentation_map: "torch.Tensor",
+ instance_id_to_semantic_id: Optional[dict[int, int]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+ ):
+ return convert_segmentation_map_to_binary_masks_fast(
+ segmentation_map=segmentation_map,
+ instance_id_to_semantic_id=instance_id_to_semantic_id,
+ ignore_index=ignore_index,
+ do_reduce_labels=do_reduce_labels,
+ )
+
+ def get_semantic_annotations(self, label, num_class_obj):
+ annotation_classes = label["classes"]
+ annotation_masks = label["masks"]
+
+ texts = ["a semantic photo"] * self.num_text
+ classes = []
+ masks = []
+
+ for idx in range(len(annotation_classes)):
+ class_id = annotation_classes[idx]
+ mask = annotation_masks[idx]
+ if not torch.all(mask == 0):
+ if class_id not in classes:
+ cls_name = self.metadata[str(class_id.cpu().item())]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+ else:
+ idx = classes.index(class_id)
+ masks[idx] += mask
+ masks[idx] = torch.clamp(masks[idx], 0, 1)
+
+ num = 0
+ for i, cls_name in enumerate(self.metadata["class_names"]):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = torch.stack(classes)
+ masks = torch.stack(masks)
+ return classes, masks, texts
+
+ def get_instance_annotations(self, label, num_class_obj):
+ annotation_classes = label["classes"]
+ annotation_masks = label["masks"]
+
+ texts = ["an instance photo"] * self.num_text
+ classes = []
+ masks = []
+
+ for idx in range(len(annotation_classes)):
+ class_id = annotation_classes[idx]
+ mask = annotation_masks[idx]
+
+ if class_id in self.metadata["thing_ids"]:
+ if not torch.all(mask == 0):
+ cls_name = self.metadata[str(class_id.cpu().item())]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+
+ num = 0
+ for i, cls_name in enumerate(self.metadata["class_names"]):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = torch.stack(classes)
+ masks = torch.stack(masks)
+ return classes, masks, texts
+
+ def get_panoptic_annotations(self, label, num_class_obj):
+ annotation_classes = label["classes"]
+ annotation_masks = label["masks"]
+
+ texts = ["an panoptic photo"] * self.num_text
+ classes = []
+ masks = []
+ for idx in range(len(annotation_classes)):
+ class_id = annotation_classes[idx]
+ mask = annotation_masks[idx] if hasattr(annotation_masks[idx], "data") else annotation_masks[idx]
+ if not torch.all(mask == 0):
+ cls_name = self.metadata[str(class_id.cpu().item())]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+
+ num = 0
+ for i, cls_name in enumerate(self.metadata["class_names"]):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = torch.stack(classes)
+ masks = torch.stack(masks)
+ return classes, masks, texts
+
+ def _encode_inputs_fast(
+ self,
+ pixel_values_list: list["torch.Tensor"],
+ task_inputs: Optional[list[str]] = None,
+ segmentation_maps: Optional[list["torch.Tensor"]] = None,
+ instance_id_to_semantic_id: Optional[Union[list[dict[int, int]], dict[int, int]]] = None,
+ ignore_index: Optional[int] = None,
+ do_reduce_labels: bool = False,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ ) -> BatchFeature:
+ if task_inputs is None:
+ task_inputs = ["panoptic"]
+
+ pad_size = get_max_height_width(pixel_values_list)
+ encoded_inputs = self.pad(pixel_values_list, return_tensors=return_tensors)
+
+ annotations = None
+ if segmentation_maps is not None:
+ annotations = []
+ for idx, segmentation_map in enumerate(segmentation_maps):
+ # Use instance2class_id mapping per image
+ if isinstance(instance_id_to_semantic_id, list):
+ instance_id = instance_id_to_semantic_id[idx]
+ else:
+ instance_id = instance_id_to_semantic_id
+
+ # Convert segmentation map to binary masks using torch operations
+ masks, classes = self.convert_segmentation_map_to_binary_masks(
+ segmentation_map,
+ instance_id,
+ ignore_index=ignore_index,
+ do_reduce_labels=do_reduce_labels,
+ )
+
+ annotations.append({"masks": masks, "classes": classes})
+
+ if annotations is not None:
+ mask_labels = []
+ class_labels = []
+ text_inputs = []
+ num_class_obj = dict.fromkeys(self.metadata["class_names"], 0)
+
+ for i, label in enumerate(annotations):
+ task = task_inputs[i]
+
+ if task == "semantic":
+ classes, masks, texts = self.get_semantic_annotations(label, num_class_obj)
+ elif task == "instance":
+ classes, masks, texts = self.get_instance_annotations(label, num_class_obj)
+ elif task == "panoptic":
+ classes, masks, texts = self.get_panoptic_annotations(label, num_class_obj)
+ else:
+ raise ValueError(f"{task} was not expected, expected `semantic`, `instance` or `panoptic`")
+ # Pad masks to max size using torch operations
+ padded_masks = [
+ self._pad_image_fast(image=mask, output_size=pad_size, constant_values=ignore_index)
+ for mask in masks
+ ]
+ padded_masks = torch.cat(padded_masks, dim=0)
+ mask_labels.append(padded_masks)
+ class_labels.append(classes)
+ text_inputs.append(texts)
+
+ encoded_inputs["mask_labels"] = mask_labels
+ encoded_inputs["class_labels"] = class_labels
+ encoded_inputs["text_inputs"] = text_inputs
+
+ encoded_inputs["task_inputs"] = [f"the task is {task_input}" for task_input in task_inputs]
+ return encoded_inputs
+
+ def post_process_semantic_segmentation(
+ self, outputs, target_sizes: Optional[list[tuple[int, int]]] = None
+ ) -> "torch.Tensor":
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentation`] into semantic segmentation maps. Only supports
+ PyTorch.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentation`]):
+ Raw outputs of the model.
+ target_sizes (`List[Tuple[int, int]]`, *optional*):
+ List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction. If left to None, predictions will not be resized.
+ Returns:
+ `List[torch.Tensor]`:
+ A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width)
+ corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each
+ `torch.Tensor` correspond to a semantic class id.
+ """
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ # Remove the null class `[..., :-1]`
+ masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1]
+ masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Semantic segmentation logits of shape (batch_size, num_classes, height, width)
+ segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
+ batch_size = class_queries_logits.shape[0]
+
+ # Resize logits and compute semantic segmentation maps
+ if target_sizes is not None:
+ if batch_size != len(target_sizes):
+ raise ValueError(
+ "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+ )
+
+ semantic_segmentation = []
+ for idx in range(batch_size):
+ resized_logits = F.resize(
+ segmentation[idx].unsqueeze(dim=0),
+ size=target_sizes[idx],
+ interpolation=F.InterpolationMode.BILINEAR,
+ )
+ semantic_map = resized_logits[0].argmax(dim=0)
+ semantic_segmentation.append(semantic_map)
+ else:
+ semantic_segmentation = segmentation.argmax(dim=1)
+ semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+ return semantic_segmentation
+
+ def post_process_instance_segmentation(
+ self,
+ outputs,
+ task_type: str = "instance",
+ is_demo: bool = True,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ return_coco_annotation: Optional[bool] = False,
+ ):
+ """
+ Converts the output of [`OneFormerForUniversalSegmentationOutput`] into image instance segmentation
+ predictions. Only supports PyTorch.
+
+ Args:
+ outputs ([`OneFormerForUniversalSegmentationOutput`]):
+ The outputs from [`OneFormerForUniversalSegmentationOutput`].
+ task_type (`str`, *optional*, defaults to "instance"):
+ The post processing depends on the task token input. If the `task_type` is "panoptic", we need to
+ ignore the stuff predictions.
+ is_demo (`bool`, *optional)*, defaults to `True`):
+ Whether the model is in demo mode. If true, use threshold to predict final masks.
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ target_sizes (`List[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction in batch. If left to None, predictions will not be
+ resized.
+ return_coco_annotation (`bool`, *optional)*, defaults to `False`):
+ Whether to return predictions in COCO format.
+
+ Returns:
+ `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set
+ to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized
+ to the corresponding `target_sizes` entry.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- an integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
+ Multiple instances of the same class / label were fused and assigned a single `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ device = masks_queries_logits.device
+ batch_size = class_queries_logits.shape[0]
+ num_queries = class_queries_logits.shape[1]
+ num_classes = class_queries_logits.shape[-1] - 1
+
+ # Loop over items in batch size
+ results: list[dict[str, torch.Tensor]] = []
+
+ for i in range(batch_size):
+ # [Q, K]
+ scores = nn.functional.softmax(class_queries_logits[i], dim=-1)[:, :-1]
+ labels = torch.arange(num_classes, device=device).unsqueeze(0).repeat(num_queries, 1).flatten(0, 1)
+
+ # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False)
+ scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False)
+ labels_per_image = labels[topk_indices]
+
+ topk_indices = torch.div(topk_indices, num_classes, rounding_mode="floor")
+ # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1)
+ mask_pred = masks_queries_logits[i][topk_indices]
+
+ # Only consider scores with confidence over [threshold] for demo
+ if is_demo:
+ keep = scores_per_image > threshold
+ scores_per_image = scores_per_image[keep]
+ labels_per_image = labels_per_image[keep]
+ mask_pred = mask_pred[keep]
+
+ # if this is panoptic segmentation, we only keep the "thing" classes
+ if task_type == "panoptic":
+ keep = torch.zeros_like(scores_per_image).bool()
+ for j, lab in enumerate(labels_per_image):
+ keep[j] = lab in self.metadata["thing_ids"]
+
+ scores_per_image = scores_per_image[keep]
+ labels_per_image = labels_per_image[keep]
+ mask_pred = mask_pred[keep]
+
+ if mask_pred.shape[0] <= 0:
+ height, width = target_sizes[i] if target_sizes is not None else mask_pred.shape[1:]
+ segmentation = torch.zeros((height, width)) - 1
+ results.append({"segmentation": segmentation, "segments_info": []})
+ continue
+
+ if "ade20k" in self.class_info_file and not is_demo and "instance" in task_type:
+ for j in range(labels_per_image.shape[0]):
+ labels_per_image[j] = self.metadata["thing_ids"].index(labels_per_image[j].item())
+
+ # Get segmentation map and segment information of batch item
+ target_size = target_sizes[i] if target_sizes is not None else None
+ segmentation, segments = compute_segments(
+ mask_pred,
+ scores_per_image,
+ labels_per_image,
+ mask_threshold,
+ overlap_mask_area_threshold,
+ set(),
+ target_size,
+ )
+
+ # Return segmentation map in run-length encoding (RLE) format
+ if return_coco_annotation:
+ segmentation = convert_segmentation_to_rle(segmentation)
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+ # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.post_process_panoptic_segmentation
+ def post_process_panoptic_segmentation(
+ self,
+ outputs,
+ threshold: float = 0.5,
+ mask_threshold: float = 0.5,
+ overlap_mask_area_threshold: float = 0.8,
+ label_ids_to_fuse: Optional[set[int]] = None,
+ target_sizes: Optional[list[tuple[int, int]]] = None,
+ ) -> list[dict]:
+ """
+ Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image panoptic segmentation
+ predictions. Only supports PyTorch.
+
+ Args:
+ outputs ([`MaskFormerForInstanceSegmentationOutput`]):
+ The outputs from [`MaskFormerForInstanceSegmentation`].
+ threshold (`float`, *optional*, defaults to 0.5):
+ The probability score threshold to keep predicted instance masks.
+ mask_threshold (`float`, *optional*, defaults to 0.5):
+ Threshold to use when turning the predicted masks into binary values.
+ overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8):
+ The overlap mask area threshold to merge or discard small disconnected parts within each binary
+ instance mask.
+ label_ids_to_fuse (`Set[int]`, *optional*):
+ The labels in this state will have all their instances be fused together. For instance we could say
+ there can only be one sky in an image, but several persons, so the label ID for sky would be in that
+ set, but not the one for person.
+ target_sizes (`list[Tuple]`, *optional*):
+ List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested
+ final size (height, width) of each prediction in batch. If left to None, predictions will not be
+ resized.
+
+ Returns:
+ `list[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys:
+ - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set
+ to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized
+ to the corresponding `target_sizes` entry.
+ - **segments_info** -- A dictionary that contains additional information on each segment.
+ - **id** -- an integer representing the `segment_id`.
+ - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`.
+ - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise.
+ Multiple instances of the same class / label were fused and assigned a single `segment_id`.
+ - **score** -- Prediction score of segment with `segment_id`.
+ """
+
+ if label_ids_to_fuse is None:
+ logger.warning("`label_ids_to_fuse` unset. No instance will be fused.")
+ label_ids_to_fuse = set()
+
+ class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1]
+ masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width]
+
+ batch_size = class_queries_logits.shape[0]
+ num_labels = class_queries_logits.shape[-1] - 1
+
+ mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width]
+
+ # Predicted label and score of each query (batch_size, num_queries)
+ pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1)
+
+ # Loop over items in batch size
+ results: list[dict[str, TensorType]] = []
+
+ for i in range(batch_size):
+ mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects(
+ mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels
+ )
+
+ # No mask found
+ if mask_probs_item.shape[0] <= 0:
+ height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:]
+ segmentation = torch.zeros((height, width)) - 1
+ results.append({"segmentation": segmentation, "segments_info": []})
+ continue
+
+ # Get segmentation map and segment information of batch item
+ target_size = target_sizes[i] if target_sizes is not None else None
+ segmentation, segments = compute_segments(
+ mask_probs=mask_probs_item,
+ pred_scores=pred_scores_item,
+ pred_labels=pred_labels_item,
+ mask_threshold=mask_threshold,
+ overlap_mask_area_threshold=overlap_mask_area_threshold,
+ label_ids_to_fuse=label_ids_to_fuse,
+ target_size=target_size,
+ )
+
+ results.append({"segmentation": segmentation, "segments_info": segments})
+ return results
+
+
+__all__ = ["OneFormerImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/modeling_oneformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/modeling_oneformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c2e633e11bc052f46ec34053069e891da0df58b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/modeling_oneformer.py
@@ -0,0 +1,3210 @@
+# coding=utf-8
+# Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch OneFormer model."""
+
+import copy
+import math
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import numpy as np
+import torch
+from torch import Tensor, nn
+from torch.cuda.amp import autocast
+
+from ...activations import ACT2FN
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import compile_compatible_method_lru_cache
+from ...utils import (
+ ModelOutput,
+ auto_docstring,
+ is_accelerate_available,
+ is_scipy_available,
+ logging,
+ requires_backends,
+)
+from ...utils.backbone_utils import load_backbone
+from .configuration_oneformer import OneFormerConfig
+
+
+if is_accelerate_available():
+ from accelerate import PartialState
+ from accelerate.utils import reduce
+
+logger = logging.get_logger(__name__)
+
+
+if is_scipy_available():
+ from scipy.optimize import linear_sum_assignment
+
+
+def _get_clones(module, N):
+ return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def multi_scale_deformable_attention(
+ value: Tensor,
+ value_spatial_shapes: Union[Tensor, list[tuple]],
+ sampling_locations: Tensor,
+ attention_weights: Tensor,
+) -> Tensor:
+ batch_size, _, num_heads, hidden_dim = value.shape
+ _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
+ value_list = value.split([height * width for height, width in value_spatial_shapes], dim=1)
+ sampling_grids = 2 * sampling_locations - 1
+ sampling_value_list = []
+ for level_id, (height, width) in enumerate(value_spatial_shapes):
+ # batch_size, height*width, num_heads, hidden_dim
+ # -> batch_size, height*width, num_heads*hidden_dim
+ # -> batch_size, num_heads*hidden_dim, height*width
+ # -> batch_size*num_heads, hidden_dim, height, width
+ value_l_ = (
+ value_list[level_id].flatten(2).transpose(1, 2).reshape(batch_size * num_heads, hidden_dim, height, width)
+ )
+ # batch_size, num_queries, num_heads, num_points, 2
+ # -> batch_size, num_heads, num_queries, num_points, 2
+ # -> batch_size*num_heads, num_queries, num_points, 2
+ sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1)
+ # batch_size*num_heads, hidden_dim, num_queries, num_points
+ sampling_value_l_ = nn.functional.grid_sample(
+ value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
+ )
+ sampling_value_list.append(sampling_value_l_)
+ # (batch_size, num_queries, num_heads, num_levels, num_points)
+ # -> (batch_size, num_heads, num_queries, num_levels, num_points)
+ # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points)
+ attention_weights = attention_weights.transpose(1, 2).reshape(
+ batch_size * num_heads, 1, num_queries, num_levels * num_points
+ )
+ output = (
+ (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
+ .sum(-1)
+ .view(batch_size, num_heads * hidden_dim, num_queries)
+ )
+ return output.transpose(1, 2).contiguous()
+
+
+# Copied from transformers.models.maskformer.modeling_maskformer.dice_loss
+def dice_loss(inputs: Tensor, labels: Tensor, num_masks: int) -> Tensor:
+ r"""
+ Compute the DICE loss, similar to generalized IOU for masks as follows:
+
+ $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x \cap y }{x \cup y + 1}} $$
+
+ In practice, since `labels` is a binary mask, (only 0s and 1s), dice can be computed as follow
+
+ $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x * y }{x + y + 1}} $$
+
+ Args:
+ inputs (`torch.Tensor`):
+ A tensor representing a mask.
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ num_masks (`int`):
+ The number of masks present in the current batch, used for normalization.
+
+ Returns:
+ `torch.Tensor`: The computed loss.
+ """
+ probs = inputs.sigmoid().flatten(1)
+ numerator = 2 * (probs * labels).sum(-1)
+ denominator = probs.sum(-1) + labels.sum(-1)
+ loss = 1 - (numerator + 1) / (denominator + 1)
+ loss = loss.sum() / num_masks
+ return loss
+
+
+# Copied from transformers.models.mask2former.modeling_mask2former.sigmoid_cross_entropy_loss
+def sigmoid_cross_entropy_loss(inputs: torch.Tensor, labels: torch.Tensor, num_masks: int) -> torch.Tensor:
+ r"""
+ Args:
+ inputs (`torch.Tensor`):
+ A float tensor of arbitrary shape.
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+
+ Returns:
+ loss (`torch.Tensor`): The computed loss.
+ """
+ criterion = nn.BCEWithLogitsLoss(reduction="none")
+ cross_entropy_loss = criterion(inputs, labels)
+
+ loss = cross_entropy_loss.mean(1).sum() / num_masks
+ return loss
+
+
+# Copied from transformers.models.maskformer.modeling_maskformer.pair_wise_dice_loss
+def pair_wise_dice_loss(inputs: Tensor, labels: Tensor) -> Tensor:
+ """
+ A pair wise version of the dice loss, see `dice_loss` for usage.
+
+ Args:
+ inputs (`torch.Tensor`):
+ A tensor representing a mask
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+
+ Returns:
+ `torch.Tensor`: The computed loss between each pairs.
+ """
+ inputs = inputs.sigmoid().flatten(1)
+ numerator = 2 * torch.matmul(inputs, labels.T)
+ # using broadcasting to get a [num_queries, NUM_CLASSES] matrix
+ denominator = inputs.sum(-1)[:, None] + labels.sum(-1)[None, :]
+ loss = 1 - (numerator + 1) / (denominator + 1)
+ return loss
+
+
+# Copied from transformers.models.mask2former.modeling_mask2former.pair_wise_sigmoid_cross_entropy_loss
+def pair_wise_sigmoid_cross_entropy_loss(inputs: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
+ r"""
+ A pair wise version of the cross entropy loss, see `sigmoid_cross_entropy_loss` for usage.
+
+ Args:
+ inputs (`torch.Tensor`):
+ A tensor representing a mask.
+ labels (`torch.Tensor`):
+ A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+
+ Returns:
+ loss (`torch.Tensor`): The computed loss between each pairs.
+ """
+
+ height_and_width = inputs.shape[1]
+
+ criterion = nn.BCEWithLogitsLoss(reduction="none")
+ cross_entropy_loss_pos = criterion(inputs, torch.ones_like(inputs))
+ cross_entropy_loss_neg = criterion(inputs, torch.zeros_like(inputs))
+
+ loss_pos = torch.matmul(cross_entropy_loss_pos / height_and_width, labels.T)
+ loss_neg = torch.matmul(cross_entropy_loss_neg / height_and_width, (1 - labels).T)
+ loss = loss_pos + loss_neg
+ return loss
+
+
+# Copied from transformers.models.mask2former.modeling_mask2former.sample_point
+def sample_point(
+ input_features: torch.Tensor, point_coordinates: torch.Tensor, add_dim=False, **kwargs
+) -> torch.Tensor:
+ """
+ A wrapper around `torch.nn.functional.grid_sample` to support 3D point_coordinates tensors.
+
+ Args:
+ input_features (`torch.Tensor` of shape (batch_size, channels, height, width)):
+ A tensor that contains features map on a height * width grid
+ point_coordinates (`torch.Tensor` of shape (batch_size, num_points, 2) or (batch_size, grid_height, grid_width,:
+ 2)):
+ A tensor that contains [0, 1] * [0, 1] normalized point coordinates
+ add_dim (`bool`):
+ boolean value to keep track of added dimension
+
+ Returns:
+ point_features (`torch.Tensor` of shape (batch_size, channels, num_points) or (batch_size, channels,
+ height_grid, width_grid):
+ A tensor that contains features for points in `point_coordinates`.
+ """
+ if point_coordinates.dim() == 3:
+ add_dim = True
+ point_coordinates = point_coordinates.unsqueeze(2)
+
+ # use nn.function.grid_sample to get features for points in `point_coordinates` via bilinear interpolation
+ point_features = torch.nn.functional.grid_sample(input_features, 2.0 * point_coordinates - 1.0, **kwargs)
+ if add_dim:
+ point_features = point_features.squeeze(3)
+
+ return point_features
+
+
+# Refactored from https://github.com/SHI-Labs/OneFormer/blob/33ebb56ed34f970a30ae103e786c0cb64c653d9a/oneformer/modeling/matcher.py#L93
+class OneFormerHungarianMatcher(nn.Module):
+ def __init__(
+ self, cost_class: float = 1.0, cost_mask: float = 1.0, cost_dice: float = 1.0, num_points: int = 12544
+ ):
+ """This class computes an assignment between the labels and the predictions of the network.
+
+ For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more
+ predictions than labels. In this case, we do a 1-to-1 matching of the best predictions, while the others are
+ un-matched (and thus treated as non-objects).
+
+ Params:
+ cost_class (float, *optional*, defaults to 1.0):
+ This is the relative weight of the classification error in the matching cost.
+ cost_mask (float, *optional*, defaults to 1.0):
+ This is the relative weight of the sigmoid ce loss of the binary mask in the matching cost.
+ cost_dice (float, *optional*, defaults to 1.0):
+ This is the relative weight of the dice loss of the binary mask in the matching cost
+ num_points (int, *optional*, defaults to 12544):
+ Number of points to be sampled for dice and mask loss matching cost.
+ """
+ super().__init__()
+ if cost_class == 0 and cost_mask == 0 and cost_dice == 0:
+ raise ValueError("All costs can't be 0")
+ self.cost_class = cost_class
+ self.cost_mask = cost_mask
+ self.cost_dice = cost_dice
+ self.num_points = num_points
+
+ @torch.no_grad()
+ def forward(self, masks_queries_logits, class_queries_logits, mask_labels, class_labels) -> list[tuple[Tensor]]:
+ """Performs the matching
+
+ Params:
+ masks_queries_logits (`torch.Tensor`):
+ A tensor` of dim `batch_size, num_queries, num_labels` with the
+ classification logits.
+ class_queries_logits (`torch.Tensor`):
+ A tensor` of dim `batch_size, num_queries, height, width` with the
+ predicted masks.
+
+ class_labels (`torch.Tensor`):
+ A tensor` of dim `num_target_boxes` (where num_target_boxes is the number
+ of ground-truth objects in the target) containing the class labels.
+ mask_labels (`torch.Tensor`):
+ A tensor` of dim `num_target_boxes, height, width` containing the target
+ masks.
+
+ Returns:
+ `list[tuple[Tensor]]`: A list of size batch_size, containing tuples of (index_i, index_j) where:
+ - index_i is the indices of the selected predictions (in order)
+ - index_j is the indices of the corresponding selected labels (in order)
+ For each batch element, it holds:
+ len(index_i) = len(index_j) = min(num_queries, num_targets).
+ """
+ indices: list[tuple[np.array]] = []
+
+ num_queries = class_queries_logits.shape[1]
+
+ preds_masks = masks_queries_logits
+ preds_probs = class_queries_logits
+ # iterate through batch size
+ for pred_probs, pred_mask, target_mask, labels in zip(preds_probs, preds_masks, mask_labels, class_labels):
+ pred_probs = pred_probs.softmax(-1)
+ # Compute the classification cost. Contrary to the loss, we don't use the NLL,
+ # but approximate it in 1 - proba[target class].
+ # The 1 is a constant that doesn't change the matching, it can be omitted.
+ cost_class = -pred_probs[:, labels]
+
+ pred_mask = pred_mask[:, None]
+ target_mask = target_mask[:, None].to(pred_mask.device)
+
+ # all masks share the same set of points for efficient matching!
+ point_coords = torch.rand(1, self.num_points, 2, device=pred_mask.device)
+
+ # get ground truth labels
+ target_mask = sample_point(
+ target_mask,
+ point_coords.repeat(target_mask.shape[0], 1, 1),
+ align_corners=False,
+ ).squeeze(1)
+
+ pred_mask = sample_point(
+ pred_mask,
+ point_coords.repeat(pred_mask.shape[0], 1, 1),
+ align_corners=False,
+ ).squeeze(1)
+
+ with autocast(enabled=False):
+ pred_mask = pred_mask.float()
+ target_mask = target_mask.float()
+
+ # compute the sigmoid ce loss
+ cost_mask = pair_wise_sigmoid_cross_entropy_loss(pred_mask, target_mask)
+ # Compute the dice loss
+ cost_dice = pair_wise_dice_loss(pred_mask, target_mask)
+ # final cost matrix
+ cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice
+ cost_matrix = cost_matrix.reshape(num_queries, -1).cpu()
+ # do the assigmented using the hungarian algorithm in scipy
+ assigned_indices: tuple[np.array] = linear_sum_assignment(cost_matrix.cpu())
+ indices.append(assigned_indices)
+
+ # It could be stacked in one tensor
+ matched_indices = [
+ (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices
+ ]
+ return matched_indices
+
+
+class OneFormerLoss(nn.Module):
+ def __init__(
+ self,
+ num_classes: int,
+ matcher: OneFormerHungarianMatcher,
+ weight_dict: dict[str, float],
+ eos_coef: float,
+ num_points: int,
+ oversample_ratio: float,
+ importance_sample_ratio: float,
+ contrastive_temperature: Optional[float] = None,
+ ):
+ """
+ This class computes the losses using the class predictions, mask predictions and the contrastive queries.
+
+ Oneformer calculates the classification CE loss on the class predictions. Mask predictions are used for
+ calculating the binary CE loss and dice loss. The contrastive queries are used for calculating the contrastive
+ loss.
+
+ Args:
+ num_labels (`int`):
+ The number of classes.
+ matcher (`OneFormerHungarianMatcher`):
+ A torch module that computes the assignments between the predictions and labels.
+ weight_dict (`dict[str, float]`):
+ A dictionary of weights to be applied to the different losses.
+ eos_coef (`float`):
+ Weight to apply to the null class.
+ num_points (`int`):
+ Number of points to be sampled for dice and mask loss calculations.
+ oversample_ratio (`float`):
+ Required for pointwise loss calculation.
+ importance_sample_ratio (`float`):
+ Required for pointwise loss calculation.
+ contrastive_temperature (`float`):
+ Temperature for scaling the contrastive logits.
+ """
+ requires_backends(self, ["scipy"])
+ super().__init__()
+ self.num_classes = num_classes
+ self.matcher = matcher
+ self.weight_dict = weight_dict
+ self.eos_coef = eos_coef
+ empty_weight = torch.ones(self.num_classes + 1)
+ empty_weight[-1] = self.eos_coef
+ self.register_buffer("empty_weight", empty_weight)
+
+ # pointwise mask loss parameters
+ self.num_points = num_points
+ self.oversample_ratio = oversample_ratio
+ self.importance_sample_ratio = importance_sample_ratio
+ self.contrastive_temperature = contrastive_temperature
+ if self.contrastive_temperature is not None:
+ self.logit_scale = nn.Parameter(torch.tensor(np.log(1 / contrastive_temperature)))
+
+ def _max_by_axis(self, the_list: list[list[int]]) -> list[int]:
+ maxes = the_list[0]
+ for sublist in the_list[1:]:
+ for index, item in enumerate(sublist):
+ maxes[index] = max(maxes[index], item)
+ return maxes
+
+ def _pad_images_to_max_in_batch(self, tensors: list[Tensor]) -> tuple[Tensor, Tensor]:
+ # get the maximum size in the batch
+ max_size = self._max_by_axis([list(tensor.shape) for tensor in tensors])
+ batch_size = len(tensors)
+ # compute finel size
+ batch_shape = [batch_size] + max_size
+ b, _, h, w = batch_shape
+ # get metadata
+ dtype = tensors[0].dtype
+ device = tensors[0].device
+ padded_tensors = torch.zeros(batch_shape, dtype=dtype, device=device)
+ padding_masks = torch.ones((b, h, w), dtype=torch.bool, device=device)
+ # pad the tensors to the size of the biggest one
+ for tensor, padded_tensor, padding_mask in zip(tensors, padded_tensors, padding_masks):
+ padded_tensor[: tensor.shape[0], : tensor.shape[1], : tensor.shape[2]].copy_(tensor)
+ padding_mask[: tensor.shape[1], : tensor.shape[2]] = False
+
+ return padded_tensors, padding_masks
+
+ def loss_contrastive(self, contrastive_queries_logits: Tensor, text_queries: Tensor):
+ """Compute the query-text contrastive loss.
+
+ Args:
+ contrastive_queries_logits (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, hidden_dim`
+ text_queries (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, hidden_dim`
+ Returns:
+ `dict[str, Tensor]`: A dict of `torch.Tensor` containing the following key:
+ - **loss_contrastive** -- The query-text contrastive loss computed using task-guided queries
+ and text queries derived from input text list.
+ """
+
+ image_queries = contrastive_queries_logits.float()
+
+ # [batch_size, hidden_dim]
+ image_queries = nn.functional.normalize(image_queries.flatten(1), dim=-1)
+ text_queries = nn.functional.normalize(text_queries.flatten(1), dim=-1)
+
+ logit_scale = torch.clamp(self.logit_scale.exp(), max=100)
+
+ logits_per_text = torch.matmul(text_queries, image_queries.t()) * logit_scale
+ logits_per_img = logits_per_text.t()
+
+ loss_img = nn.functional.cross_entropy(
+ logits_per_img, torch.arange(len(logits_per_img), device=logits_per_text.device)
+ )
+ loss_text = nn.functional.cross_entropy(
+ logits_per_text, torch.arange(len(logits_per_text), device=logits_per_text.device)
+ )
+
+ loss_contrastive = loss_img + loss_text
+
+ losses = {"loss_contrastive": loss_contrastive}
+ return losses
+
+ def loss_labels(
+ self, class_queries_logits: Tensor, class_labels: list[Tensor], indices: tuple[np.array]
+ ) -> dict[str, Tensor]:
+ """Compute the losses related to the labels using cross entropy.
+
+ Args:
+ class_queries_logits (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, num_labels`
+ class_labels (`list[torch.Tensor]`):
+ List of class labels of shape `(labels)`.
+ indices (`tuple[np.array])`:
+ The indices computed by the Hungarian matcher.
+
+ Returns:
+ `dict[str, Tensor]`: A dict of `torch.Tensor` containing the following key:
+ - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels.
+ """
+ pred_logits = class_queries_logits
+ batch_size, num_queries, _ = pred_logits.shape
+ criterion = nn.CrossEntropyLoss(weight=self.empty_weight)
+ idx = self._get_predictions_permutation_indices(indices)
+
+ # shape = (batch_size, num_queries)
+ target_classes_o = torch.cat([target[j] for target, (_, j) in zip(class_labels, indices)])
+ # shape = (batch_size, num_queries)
+ target_classes = torch.full(
+ (batch_size, num_queries), fill_value=self.num_classes, dtype=torch.int64, device=pred_logits.device
+ )
+ target_classes[idx] = target_classes_o
+ # permute pred_logits (batch_size, num_queries, num_labels) -> (batch_size, num_labels, num_queries)
+ pred_logits_transposed = pred_logits.transpose(1, 2)
+ loss_ce = criterion(pred_logits_transposed, target_classes)
+ losses = {"loss_cross_entropy": loss_ce}
+ return losses
+
+ def loss_masks(
+ self, masks_queries_logits: Tensor, mask_labels: list[Tensor], indices: tuple[np.array], num_masks: int
+ ) -> dict[str, Tensor]:
+ """Compute the losses related to the masks using focal and dice loss.
+
+ Args:
+ masks_queries_logits (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, height, width`
+ mask_labels (`torch.Tensor`):
+ List of mask labels of shape `(labels, height, width)`.
+ indices (`tuple[np.array])`:
+ The indices computed by the Hungarian matcher.
+ num_masks (`int)`:
+ The number of masks, used for normalization.
+
+ Returns:
+ `dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys:
+ - **loss_mask** -- The loss computed using sigmoid ce loss on the predicted and ground truth masks.
+ - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth
+ masks.
+ """
+ src_idx = self._get_predictions_permutation_indices(indices)
+ tgt_idx = self._get_targets_permutation_indices(indices)
+ # shape (batch_size * num_queries, height, width)
+ pred_masks = masks_queries_logits[src_idx]
+ # shape (batch_size, num_queries, height, width)
+ # pad all and stack the targets to the num_labels dimension
+ # upsample predictions to the target size, we have to add one dim to use interpolate
+ target_masks, _ = self._pad_images_to_max_in_batch(mask_labels)
+ target_masks = target_masks[tgt_idx]
+
+ pred_masks = pred_masks[:, None]
+ target_masks = target_masks[:, None]
+
+ with torch.no_grad():
+ # sample point_coords
+ point_coords = self.sample_points_using_uncertainty(
+ pred_masks,
+ self.calculate_uncertainty,
+ self.num_points,
+ self.oversample_ratio,
+ self.importance_sample_ratio,
+ )
+ # get ground-truth labels
+ point_labels = sample_point(target_masks, point_coords, align_corners=False).squeeze(1)
+
+ point_logits = sample_point(pred_masks, point_coords, align_corners=False).squeeze(1)
+
+ losses = {
+ "loss_mask": sigmoid_cross_entropy_loss(point_logits, point_labels, num_masks),
+ "loss_dice": dice_loss(point_logits, point_labels, num_masks),
+ }
+
+ del pred_masks
+ del target_masks
+ return losses
+
+ # Copied from transformers.models.mask2former.modeling_mask2former.Mask2FormerLoss.calculate_uncertainty
+ def calculate_uncertainty(self, logits: torch.Tensor) -> torch.Tensor:
+ """
+ In Mask2Former paper, uncertainty is estimated as L1 distance between 0.0 and the logit prediction in 'logits'
+ for the foreground class in `classes`.
+
+ Args:
+ logits (`torch.Tensor`):
+ A tensor of shape (R, 1, ...) for class-specific or class-agnostic, where R is the total number of predicted masks in all images and C is:
+ the number of foreground classes. The values are logits.
+
+ Returns:
+ scores (`torch.Tensor`): A tensor of shape (R, 1, ...) that contains uncertainty scores with the most
+ uncertain locations having the highest uncertainty score.
+ """
+ uncertainty_scores = -(torch.abs(logits))
+ return uncertainty_scores
+
+ # Copied from transformers.models.mask2former.modeling_mask2former.Mask2FormerLoss.sample_points_using_uncertainty
+ def sample_points_using_uncertainty(
+ self,
+ logits: torch.Tensor,
+ uncertainty_function,
+ num_points: int,
+ oversample_ratio: int,
+ importance_sample_ratio: float,
+ ) -> torch.Tensor:
+ """
+ This function is meant for sampling points in [0, 1] * [0, 1] coordinate space based on their uncertainty. The
+ uncertainty is calculated for each point using the passed `uncertainty function` that takes points logit
+ prediction as input.
+
+ Args:
+ logits (`float`):
+ Logit predictions for P points.
+ uncertainty_function:
+ A function that takes logit predictions for P points and returns their uncertainties.
+ num_points (`int`):
+ The number of points P to sample.
+ oversample_ratio (`int`):
+ Oversampling parameter.
+ importance_sample_ratio (`float`):
+ Ratio of points that are sampled via importance sampling.
+
+ Returns:
+ point_coordinates (`torch.Tensor`):
+ Coordinates for P sampled points.
+ """
+
+ num_boxes = logits.shape[0]
+ num_points_sampled = int(num_points * oversample_ratio)
+
+ # Get random point coordinates
+ point_coordinates = torch.rand(num_boxes, num_points_sampled, 2, device=logits.device)
+ # Get sampled prediction value for the point coordinates
+ point_logits = sample_point(logits, point_coordinates, align_corners=False)
+ # Calculate the uncertainties based on the sampled prediction values of the points
+ point_uncertainties = uncertainty_function(point_logits)
+
+ num_uncertain_points = int(importance_sample_ratio * num_points)
+ num_random_points = num_points - num_uncertain_points
+
+ idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
+ shift = num_points_sampled * torch.arange(num_boxes, dtype=torch.long, device=logits.device)
+ idx += shift[:, None]
+ point_coordinates = point_coordinates.view(-1, 2)[idx.view(-1), :].view(num_boxes, num_uncertain_points, 2)
+
+ if num_random_points > 0:
+ point_coordinates = torch.cat(
+ [point_coordinates, torch.rand(num_boxes, num_random_points, 2, device=logits.device)],
+ dim=1,
+ )
+ return point_coordinates
+
+ def _get_predictions_permutation_indices(self, indices):
+ # permute predictions following indices
+ batch_indices = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
+ predictions_indices = torch.cat([src for (src, _) in indices])
+ return batch_indices, predictions_indices
+
+ def _get_targets_permutation_indices(self, indices):
+ # permute labels following indices
+ batch_indices = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
+ target_indices = torch.cat([tgt for (_, tgt) in indices])
+ return batch_indices, target_indices
+
+ def forward(
+ self,
+ masks_queries_logits: Tensor,
+ class_queries_logits: Tensor,
+ contrastive_queries_logits: Tensor,
+ mask_labels: list[Tensor],
+ class_labels: list[Tensor],
+ text_queries: Tensor,
+ auxiliary_predictions: Optional[dict[str, Tensor]] = None,
+ calculate_contrastive_loss: bool = True,
+ ) -> dict[str, Tensor]:
+ """
+ This performs the loss computation.
+
+ Args:
+ masks_queries_logits (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, height, width`
+ class_queries_logits (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, num_labels`
+ contrastive_queries_logits (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, hidden_dim`
+ mask_labels (`torch.Tensor`):
+ List of mask labels of shape `(labels, height, width)`.
+ class_labels (`list[torch.Tensor]`):
+ List of class labels of shape `(labels)`.
+ text_queries (`torch.Tensor`):
+ A tensor of shape `batch_size, num_queries, hidden_dim`
+ auxiliary_predictions (`dict[str, torch.Tensor]`, *optional*):
+ if `use_auxiliary_loss` was set to `true` in [`OneFormerConfig`], then it contains the logits from the
+ inner layers of the Detr's Decoder.
+ calculate_contrastive_loss (`bool`, *optional*, defaults to `True`):
+ Whether or not to calculate the contrastive loss.
+
+ Returns:
+ `dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys:
+ - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels.
+ - **loss_mask** -- The loss computed using sigmoid ce loss on the predicted and ground truth masks.
+ - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth
+ masks.
+ - **loss_contrastive** -- The query-text contrstive loss computed using object and text queries.
+ if `use_auxiliary_loss` was set to `true` in [`OneFormerConfig`], the dictionary contains additional losses
+ for each auxiliary predictions.
+ """
+
+ # retrieve the matching between the outputs of the last layer and the labels
+ indices = self.matcher(masks_queries_logits, class_queries_logits, mask_labels, class_labels)
+ # compute the average number of target masks for normalization purposes
+ num_masks = self.get_num_masks(class_labels, device=class_labels[0].device)
+ # get all the losses
+ losses: dict[str, Tensor] = {
+ **self.loss_masks(masks_queries_logits, mask_labels, indices, num_masks),
+ **self.loss_labels(class_queries_logits, class_labels, indices),
+ }
+ if calculate_contrastive_loss:
+ losses = {**losses, **self.loss_contrastive(contrastive_queries_logits, text_queries)}
+
+ # in case of auxiliary losses, we repeat this process with the output of each intermediate layer.
+ if auxiliary_predictions is not None:
+ for idx, aux_outputs in enumerate(auxiliary_predictions):
+ masks_queries_logits = aux_outputs["masks_queries_logits"]
+ class_queries_logits = aux_outputs["class_queries_logits"]
+ loss_dict = self.forward(
+ masks_queries_logits,
+ class_queries_logits,
+ None,
+ mask_labels,
+ class_labels,
+ None,
+ calculate_contrastive_loss=False,
+ )
+ loss_dict = {f"{key}_{idx}": value for key, value in loss_dict.items()}
+ losses.update(loss_dict)
+
+ return losses
+
+ def get_num_masks(self, class_labels: torch.Tensor, device: torch.device) -> torch.Tensor:
+ """
+ Computes the average number of target masks across the batch, for normalization purposes.
+ """
+ num_masks = sum([len(classes) for classes in class_labels])
+ num_masks = torch.as_tensor([num_masks], dtype=torch.float, device=device)
+ world_size = 1
+ if is_accelerate_available():
+ if PartialState._shared_state != {}:
+ num_masks = reduce(num_masks)
+ world_size = PartialState().num_processes
+
+ num_masks = torch.clamp(num_masks / world_size, min=1)
+ return num_masks
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of the Transformer decoder. This class adds attributes for class predictions, mask
+ predictions and contrastive logits to BaseModelOutputWithCrossAttentions.
+ """
+)
+class OneFormerTransformerDecoderOutput(BaseModelOutput):
+ r"""
+ object_queries (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`):
+ Queries representation for the region proposals.
+ contrastive_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`):
+ Queries representation for the contrastive loss.
+ prediction_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height, width)`):
+ Mask predictions from last layer of the transformer decoder.
+ prediction_class (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes+1)`):
+ Class predictions from last layer of the transformer decoder.
+ auxiliary_predictions (Tuple of Dict of `str, torch.FloatTensor`, *optional*):
+ Tuple of class and mask predictions from each layer of the transformer decoder.
+ """
+
+ object_queries: Optional[torch.FloatTensor] = None
+ contrastive_logits: Optional[torch.FloatTensor] = None
+ prediction_masks: Optional[torch.FloatTensor] = None
+ prediction_class: Optional[torch.FloatTensor] = None
+ auxiliary_predictions: Optional[tuple[dict[str, torch.FloatTensor]]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ OneFormer's pixel decoder module output, practically a Multi-Scale Deformable Attention based decoder. It returns
+ the mask features and the multiscale features.
+ """
+)
+# Copied from transformers.models.mask2former.modeling_mask2former.Mask2FormerPixelDecoderOutput with Mask2->One
+class OneFormerPixelDecoderOutput(ModelOutput):
+ r"""
+ multi_scale_features (`tuple(torch.FloatTensor)`):
+ Tuple of multi-scale features of scales [1/8, 1/16, 1/32] and shape `(batch_size, num_channels, height,
+ width)`from the Multi-Scale Deformable Attenntion based Pixel Decoder.
+ mask_features (`torch.FloatTensor`):
+ Tensor of shape `(batch_size, num_channels, height, width)`, 1/4 scale features from the last Pixel Decoder
+ Layer.
+ attentions (`tuple(torch.FloatTensor)`, *optional*):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Attentions weights from pixel decoder. Returned when `output_attentions=True` is passed
+ or when `config.output_attentions=True`
+ """
+
+ multi_scale_features: tuple[torch.FloatTensor] = None
+ mask_features: Optional[torch.FloatTensor] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ OneFormer's pixel level module output. It returns both the last and (optionally) the hidden states from the
+ `encoder` and `decoder`. By default, the `encoder` is a Swin/Dinat Backbone and the `decoder` is a Multi-Scale
+ Deformable Attention based decoder.
+ """
+)
+class OneFormerPixelLevelModuleOutput(ModelOutput):
+ r"""
+ encoder_features (List of `(torch.FloatTensor)`):
+ List of `torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`. Hidden-states (also
+ called feature maps) of the model at the output of each stage.
+ decoder_features (List of `(torch.FloatTensor)`):
+ List of `torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`. Hidden-states (also
+ called feature maps) of the model at the output of each stage.
+ decoder_last_feature (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)):
+ 1/4 scale features from the last Pixel Decoder Layer.
+ """
+
+ encoder_features: list[torch.FloatTensor] = None
+ decoder_features: list[torch.FloatTensor] = None
+ decoder_last_feature: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Class for outputs of [`OneFormerModel`]. This class returns all the needed hidden states to compute the logits.
+ """
+)
+class OneFormerModelOutput(ModelOutput):
+ r"""
+ encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
+ model at the output of each stage.
+ pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
+ decoder model at the output of each stage.
+ transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the
+ transformer decoder at the output of each stage.
+ transformer_decoder_object_queries (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`):
+ Output object queries from the last layer in the transformer decoder.
+ transformer_decoder_contrastive_queries (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`):
+ Contrastive queries from the transformer decoder.
+ transformer_decoder_mask_predictions (`torch.FloatTensor` of shape `(batch_size, num_queries, height, width)`):
+ Mask Predictions from the last layer in the transformer decoder.
+ transformer_decoder_class_predictions (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes+1)`):
+ Class Predictions from the last layer in the transformer decoder.
+ transformer_decoder_auxiliary_predictions (Tuple of Dict of `str, torch.FloatTensor`, *optional*):
+ Tuple of class and mask predictions from each layer of the transformer decoder.
+ text_queries (`torch.FloatTensor`, *optional* of shape `(batch_size, num_queries, hidden_dim)`):
+ Text queries derived from the input text list used for calculating contrastive loss during training.
+ task_token (`torch.FloatTensor` of shape `(batch_size, hidden_dim)`):
+ 1D task token to condition the queries.
+ attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tuple(torch.FloatTensor)` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Self and Cross Attentions weights from transformer decoder.
+ """
+
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ pixel_decoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ transformer_decoder_hidden_states: Optional[torch.FloatTensor] = None
+ transformer_decoder_object_queries: Optional[torch.FloatTensor] = None
+ transformer_decoder_contrastive_queries: Optional[torch.FloatTensor] = None
+ transformer_decoder_mask_predictions: Optional[torch.FloatTensor] = None
+ transformer_decoder_class_predictions: Optional[torch.FloatTensor] = None
+ transformer_decoder_auxiliary_predictions: Optional[tuple[dict[str, torch.FloatTensor]]] = None
+ text_queries: Optional[torch.FloatTensor] = None
+ task_token: Optional[torch.FloatTensor] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Class for outputs of [`OneFormerForUniversalSegmentationOutput`].
+
+ This output can be directly passed to [`~OneFormerImageProcessor.post_process_semantic_segmentation`] or
+ [`~OneFormerImageProcessor.post_process_instance_segmentation`] or
+ [`~OneFormerImageProcessor.post_process_panoptic_segmentation`] depending on the task. Please, see
+ [`~OneFormerImageProcessor] for details regarding usage.
+ """
+)
+class OneFormerForUniversalSegmentationOutput(ModelOutput):
+ r"""
+ loss (`torch.Tensor`, *optional*):
+ The computed loss, returned when labels are present.
+ class_queries_logits (`torch.FloatTensor`):
+ A tensor of shape `(batch_size, num_queries, num_labels + 1)` representing the proposed classes for each
+ query. Note the `+ 1` is needed because we incorporate the null class.
+ masks_queries_logits (`torch.FloatTensor`):
+ A tensor of shape `(batch_size, num_queries, height, width)` representing the proposed masks for each
+ query.
+ auxiliary_predictions (List of Dict of `str, torch.FloatTensor`, *optional*):
+ List of class and mask predictions from each layer of the transformer decoder.
+ encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder
+ model at the output of each stage.
+ pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel
+ decoder model at the output of each stage.
+ transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
+ shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the
+ transformer decoder at the output of each stage.
+ transformer_decoder_object_queries (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`):
+ Output object queries from the last layer in the transformer decoder.
+ transformer_decoder_contrastive_queries (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`):
+ Contrastive queries from the transformer decoder.
+ transformer_decoder_mask_predictions (`torch.FloatTensor` of shape `(batch_size, num_queries, height, width)`):
+ Mask Predictions from the last layer in the transformer decoder.
+ transformer_decoder_class_predictions (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes+1)`):
+ Class Predictions from the last layer in the transformer decoder.
+ transformer_decoder_auxiliary_predictions (List of Dict of `str, torch.FloatTensor`, *optional*):
+ List of class and mask predictions from each layer of the transformer decoder.
+ text_queries (`torch.FloatTensor`, *optional* of shape `(batch_size, num_queries, hidden_dim)`):
+ Text queries derived from the input text list used for calculating contrastive loss during training.
+ task_token (`torch.FloatTensor` of shape `(batch_size, hidden_dim)`):
+ 1D task token to condition the queries.
+ attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tuple(torch.FloatTensor)` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`. Self and Cross Attentions weights from transformer decoder.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ class_queries_logits: Optional[torch.FloatTensor] = None
+ masks_queries_logits: Optional[torch.FloatTensor] = None
+ auxiliary_predictions: list[dict[str, torch.FloatTensor]] = None
+ encoder_hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ pixel_decoder_hidden_states: Optional[list[torch.FloatTensor]] = None
+ transformer_decoder_hidden_states: Optional[torch.FloatTensor] = None
+ transformer_decoder_object_queries: Optional[torch.FloatTensor] = None
+ transformer_decoder_contrastive_queries: Optional[torch.FloatTensor] = None
+ transformer_decoder_mask_predictions: Optional[torch.FloatTensor] = None
+ transformer_decoder_class_predictions: Optional[torch.FloatTensor] = None
+ transformer_decoder_auxiliary_predictions: Optional[list[dict[str, torch.FloatTensor]]] = None
+ text_queries: Optional[torch.FloatTensor] = None
+ task_token: Optional[torch.FloatTensor] = None
+ attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
+
+
+# Modified from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrFrozenBatchNorm2d with DeformableDetr->OneFormerPixelDecoder
+class OneFormerPixelDecoderFrozenBatchNorm2d(nn.Module):
+ """
+ BatchNorm2d where the batch statistics and the affine parameters are fixed.
+
+ Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
+ torchvision.models.resnet[18,34,50,101] produce nans.
+ """
+
+ def __init__(self, n):
+ super().__init__()
+ self.register_buffer("weight", torch.ones(n))
+ self.register_buffer("bias", torch.zeros(n))
+ self.register_buffer("running_mean", torch.zeros(n))
+ self.register_buffer("running_var", torch.ones(n))
+
+ def _load_from_state_dict(
+ self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+ ):
+ num_batches_tracked_key = prefix + "num_batches_tracked"
+ if num_batches_tracked_key in state_dict:
+ del state_dict[num_batches_tracked_key]
+
+ super()._load_from_state_dict(
+ state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+ )
+
+ def forward(self, x):
+ weight = self.weight.reshape(1, -1, 1, 1)
+ bias = self.bias.reshape(1, -1, 1, 1)
+ running_var = self.running_var.reshape(1, -1, 1, 1)
+ running_mean = self.running_mean.reshape(1, -1, 1, 1)
+ epsilon = 1e-5
+ scale = weight * (running_var + epsilon).rsqrt()
+ bias = bias - running_mean * scale
+ return x * scale + bias
+
+
+# Modified from transformers.models.detr.modeling_deformable_detr.DeformableDetrMultiscaleDeformableAttention with DeformableDetr->OneFormerPixelDecoderEncoder
+class OneFormerPixelDecoderEncoderMultiscaleDeformableAttention(nn.Module):
+ """
+ Multiscale deformable attention as proposed in Deformable DETR.
+ """
+
+ def __init__(self, embed_dim: int, num_heads: int, n_levels: int, n_points: int):
+ super().__init__()
+ if embed_dim % num_heads != 0:
+ raise ValueError(
+ f"embed_dim (d_model) must be divisible by num_heads, but got {embed_dim} and {num_heads}"
+ )
+ dim_per_head = embed_dim // num_heads
+ # check if dim_per_head is power of 2
+ if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0):
+ warnings.warn(
+ "You'd better set embed_dim (d_model) in DeformableDetrMultiscaleDeformableAttention to make the"
+ " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA"
+ " implementation."
+ )
+
+ self.im2col_step = 128
+
+ self.d_model = embed_dim
+ self.n_levels = n_levels
+ self.n_heads = num_heads
+ self.n_points = n_points
+
+ self.sampling_offsets = nn.Linear(embed_dim, num_heads * n_levels * n_points * 2)
+ self.attention_weights = nn.Linear(embed_dim, num_heads * n_levels * n_points)
+ self.value_proj = nn.Linear(embed_dim, embed_dim)
+ self.output_proj = nn.Linear(embed_dim, embed_dim)
+
+ def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
+ return tensor if position_embeddings is None else tensor + position_embeddings
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ encoder_hidden_states=None,
+ encoder_attention_mask=None,
+ position_embeddings: Optional[torch.Tensor] = None,
+ reference_points=None,
+ spatial_shapes=None,
+ level_start_index=None,
+ output_attentions: bool = False,
+ ):
+ # add position embeddings to the hidden states before projecting to queries and keys
+ if position_embeddings is not None:
+ hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
+
+ batch_size, num_queries, _ = hidden_states.shape
+ batch_size, sequence_length, _ = encoder_hidden_states.shape
+ if (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() != sequence_length:
+ raise ValueError(
+ "Make sure to align the spatial shapes with the sequence length of the encoder hidden states"
+ )
+
+ value = self.value_proj(encoder_hidden_states)
+ if attention_mask is not None:
+ # we invert the attention_mask
+ value = value.masked_fill(attention_mask[..., None], float(0))
+ value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads)
+ sampling_offsets = self.sampling_offsets(hidden_states).view(
+ batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2
+ )
+ attention_weights = self.attention_weights(hidden_states).view(
+ batch_size, num_queries, self.n_heads, self.n_levels * self.n_points
+ )
+ attention_weights = nn.functional.softmax(attention_weights, -1).view(
+ batch_size, num_queries, self.n_heads, self.n_levels, self.n_points
+ )
+ # batch_size, num_queries, n_heads, n_levels, n_points, 2
+ if reference_points.shape[-1] == 2:
+ offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
+ sampling_locations = (
+ reference_points[:, :, None, :, None, :]
+ + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+ )
+ elif reference_points.shape[-1] == 4:
+ sampling_locations = (
+ reference_points[:, :, None, :, None, :2]
+ + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
+ )
+ else:
+ raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}")
+ # PyTorch implementation
+ output = multi_scale_deformable_attention(value, spatial_shapes, sampling_locations, attention_weights)
+ output = self.output_proj(output)
+
+ return output, attention_weights
+
+
+class OneFormerPixelDecoderEncoderLayer(nn.Module):
+ def __init__(self, config: OneFormerConfig):
+ super().__init__()
+ self.embed_dim = config.conv_dim
+ self.self_attn = OneFormerPixelDecoderEncoderMultiscaleDeformableAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.num_attention_heads,
+ n_levels=3,
+ n_points=4,
+ )
+
+ self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+ self.dropout = config.dropout
+ self.activation_fn = nn.functional.relu
+ self.activation_dropout = config.dropout
+ self.fc1 = nn.Linear(self.embed_dim, config.encoder_feedforward_dim)
+ self.fc2 = nn.Linear(config.encoder_feedforward_dim, self.embed_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+ self.is_training = config.is_training
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: torch.Tensor,
+ position_embeddings: Optional[torch.Tensor] = None,
+ reference_points=None,
+ spatial_shapes=None,
+ level_start_index=None,
+ output_attentions: bool = False,
+ ):
+ """
+ Args:
+ hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Input to the layer.
+ attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
+ Attention mask.
+ position_embeddings (`torch.FloatTensor`, *optional*):
+ Position embeddings, to be added to `hidden_states`.
+ reference_points (`torch.FloatTensor`, *optional*):
+ Reference points.
+ spatial_shapes (`torch.LongTensor`, *optional*):
+ Spatial shapes of the backbone feature maps.
+ level_start_index (`torch.LongTensor`, *optional*):
+ Level start index.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+ residual = hidden_states
+
+ # Apply Multi-scale Deformable Attention Module on the multi-scale feature maps.
+ hidden_states, attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ encoder_hidden_states=hidden_states,
+ encoder_attention_mask=attention_mask,
+ position_embeddings=position_embeddings,
+ reference_points=reference_points,
+ spatial_shapes=spatial_shapes,
+ level_start_index=level_start_index,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.is_training)
+ hidden_states = residual + hidden_states
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ residual = hidden_states
+ hidden_states = self.activation_fn(self.fc1(hidden_states))
+ hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.is_training)
+
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.is_training)
+
+ hidden_states = residual + hidden_states
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ if self.is_training:
+ if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any():
+ clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+ hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (attn_weights,)
+
+ return outputs
+
+
+# Modified from from transformers.models.detr.modeling_deformable_detr.DeformableDetrEncoder with DeformableDetrEncoder->OneFormerPixelDecoderEncoderOnly
+class OneFormerPixelDecoderEncoderOnly(nn.Module):
+ """
+ Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
+ [`OneFormerPixelDecoderEncoderLayer`].
+
+ The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers.
+
+ Args:
+ config: OneFormerConfig
+ """
+
+ def __init__(self, config: OneFormerConfig):
+ super().__init__()
+
+ self.config = config
+ self.dropout = config.dropout
+ self.layers = nn.ModuleList([OneFormerPixelDecoderEncoderLayer(config) for _ in range(config.encoder_layers)])
+
+ @staticmethod
+ def get_reference_points(spatial_shapes, valid_ratios, device):
+ """
+ Get reference points for each feature map. Used in decoder.
+
+ Args:
+ spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
+ Spatial shapes of each feature map.
+ valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
+ Valid ratios of each feature map.
+ device (`torch.device`):
+ Device on which to create the tensors.
+ Returns:
+ `torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)`
+ """
+ reference_points_list = []
+ for lvl, (height, width) in enumerate(spatial_shapes):
+ ref_y, ref_x = torch.meshgrid(
+ torch.linspace(0.5, height - 0.5, height, dtype=valid_ratios.dtype, device=device),
+ torch.linspace(0.5, width - 0.5, width, dtype=valid_ratios.dtype, device=device),
+ )
+ ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * height)
+ ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * width)
+ ref = torch.stack((ref_x, ref_y), -1)
+ reference_points_list.append(ref)
+ reference_points = torch.cat(reference_points_list, 1)
+ reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+ return reference_points
+
+ def forward(
+ self,
+ inputs_embeds=None,
+ attention_mask=None,
+ position_embeddings=None,
+ spatial_shapes=None,
+ level_start_index=None,
+ valid_ratios=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`:
+ - 1 for pixel features that are real (i.e. **not masked**),
+ - 0 for pixel features that are padding (i.e. **masked**).
+ [What are attention masks?](../glossary#attention-mask)
+ position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Position embeddings that are added to the queries and keys in each self-attention layer.
+ spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
+ Spatial shapes of each feature map.
+ level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`):
+ Starting index of each feature map.
+ valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
+ Ratio of valid area in each feature level.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ hidden_states = inputs_embeds
+ reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=inputs_embeds.device)
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+ for i, encoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ position_embeddings=position_embeddings,
+ reference_points=reference_points,
+ spatial_shapes=spatial_shapes,
+ level_start_index=level_start_index,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ return BaseModelOutput(
+ last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+ )
+
+
+# Modified from from transformers.models.mask2former.modeling_mask2former.Mask2FormerPixelDecoder with Mask2->One
+class OneFormerPixelDecoder(nn.Module):
+ def __init__(self, config: OneFormerConfig, feature_channels):
+ super().__init__()
+
+ self.config = config
+
+ # positional encoding
+ self.position_embedding = OneFormerSinePositionEmbedding(num_pos_feats=config.conv_dim // 2, normalize=True)
+ self.num_feature_levels = 3
+ transformer_in_channels = feature_channels[-self.num_feature_levels :]
+ self.transformer_feature_strides = config.strides[-self.num_feature_levels :]
+ self.feature_channels = feature_channels
+ self.level_embed = nn.Parameter(torch.Tensor(self.num_feature_levels, config.conv_dim))
+
+ # Create input projection layers
+ if self.num_feature_levels > 1:
+ input_projections_list = []
+ for in_channels in transformer_in_channels[::-1]:
+ input_projections_list.append(
+ nn.Sequential(
+ nn.Conv2d(in_channels, config.conv_dim, kernel_size=1),
+ nn.GroupNorm(32, config.conv_dim),
+ )
+ )
+ self.input_projections = nn.ModuleList(input_projections_list)
+ else:
+ self.input_projections = nn.ModuleList(
+ [
+ nn.Sequential(
+ nn.Conv2d(transformer_in_channels[-1], config.conv_dim, kernel_size=1),
+ nn.GroupNorm(32, config.conv_dim),
+ )
+ ]
+ )
+
+ self.encoder = OneFormerPixelDecoderEncoderOnly(config)
+
+ self.mask_projection = nn.Conv2d(
+ config.conv_dim,
+ config.mask_dim,
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ )
+
+ self.common_stride = config.common_stride
+
+ # extra fpn levels
+ stride = min(self.transformer_feature_strides)
+ self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))
+
+ lateral_convs = []
+ output_convs = []
+
+ for idx, in_channels in enumerate(self.feature_channels[: self.num_fpn_levels]):
+ lateral_conv = nn.Sequential(
+ nn.Conv2d(
+ in_channels,
+ config.conv_dim,
+ kernel_size=1,
+ bias=False,
+ ),
+ nn.GroupNorm(32, config.conv_dim),
+ )
+ output_conv = nn.Sequential(
+ nn.Conv2d(
+ config.conv_dim,
+ config.conv_dim,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=False,
+ ),
+ nn.GroupNorm(32, config.conv_dim),
+ nn.ReLU(),
+ )
+ self.add_module(f"adapter_{idx + 1}", lateral_conv)
+ self.add_module(f"layer_{idx + 1}", output_conv)
+
+ lateral_convs.append(lateral_conv)
+ output_convs.append(output_conv)
+ # Place convs into top-down order (from low to high resolution)
+ # to make the top-down computation in forward clearer.
+ self.lateral_convs = lateral_convs[::-1]
+ self.output_convs = output_convs[::-1]
+
+ def get_valid_ratio(self, mask, dtype=torch.float32):
+ """Get the valid ratio of all feature maps."""
+
+ _, height, width = mask.shape
+ valid_height = torch.sum(~mask[:, :, 0], 1)
+ valid_width = torch.sum(~mask[:, 0, :], 1)
+ valid_ratio_height = valid_height.to(dtype) / height
+ valid_ratio_width = valid_width.to(dtype) / width
+ valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1)
+ return valid_ratio
+
+ def forward(
+ self,
+ features,
+ encoder_outputs=None,
+ output_attentions=None,
+ output_hidden_states=None,
+ return_dict=None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ # Then, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default)
+ sources = []
+ position_embeddings_list = []
+ for level, source in enumerate(features[::-1][: self.num_feature_levels]):
+ sources.append(self.input_projections[level](source))
+ position_embeddings_list.append(self.position_embedding(source.shape, source.device, source.dtype))
+
+ masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in sources]
+
+ # Prepare encoder inputs (by flattening)
+ source_flatten = []
+ mask_flatten = []
+ lvl_pos_embed_flatten = []
+ spatial_shapes = []
+ for level, (source, mask, pos_embed) in enumerate(zip(sources, masks, position_embeddings_list)):
+ batch_size, num_channels, height, width = source.shape
+ spatial_shape = (height, width)
+ spatial_shapes.append(spatial_shape)
+ source = source.flatten(2).transpose(1, 2)
+ mask = mask.flatten(1)
+ pos_embed = pos_embed.flatten(2).transpose(1, 2)
+ lvl_pos_embed = pos_embed + self.level_embed[level].view(1, 1, -1)
+ lvl_pos_embed_flatten.append(lvl_pos_embed)
+ source_flatten.append(source)
+ mask_flatten.append(mask)
+ source_flatten = torch.cat(source_flatten, 1)
+ mask_flatten = torch.cat(mask_flatten, 1)
+ lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
+ spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=source_flatten.device)
+ level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
+ valid_ratios = torch.stack([self.get_valid_ratio(m, dtype=source_flatten.dtype) for m in masks], 1)
+
+ # Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder
+ # Also provide spatial_shapes, level_start_index and valid_ratios
+ if encoder_outputs is None:
+ encoder_outputs = self.encoder(
+ inputs_embeds=source_flatten,
+ attention_mask=mask_flatten,
+ position_embeddings=lvl_pos_embed_flatten,
+ spatial_shapes=spatial_shapes,
+ level_start_index=level_start_index,
+ valid_ratios=valid_ratios,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ y = encoder_outputs.last_hidden_state
+ bs = y.shape[0]
+
+ split_size_or_sections = [None] * self.num_feature_levels
+ for i in range(self.num_feature_levels):
+ if i < self.num_feature_levels - 1:
+ split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]
+ else:
+ split_size_or_sections[i] = y.shape[1] - level_start_index[i]
+ y = torch.split(y, split_size_or_sections, dim=1)
+
+ out = []
+ multi_scale_features = []
+ num_cur_levels = 0
+ for i, z in enumerate(y):
+ out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))
+
+ # append `out` with extra FPN levels
+ # Reverse feature maps into top-down order (from low to high resolution)
+ for idx, feats in enumerate(features[: self.num_fpn_levels][::-1]):
+ lateral_conv = self.lateral_convs[idx]
+ output_conv = self.output_convs[idx]
+ cur_fpn = lateral_conv(feats)
+ # Following FPN implementation, we use nearest upsampling here
+ y = cur_fpn + nn.functional.interpolate(
+ out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False
+ )
+ y = output_conv(y)
+ out.append(y)
+
+ for o in out:
+ if num_cur_levels < self.num_feature_levels:
+ multi_scale_features.append(o)
+ num_cur_levels += 1
+
+ return OneFormerPixelDecoderOutput(
+ mask_features=self.mask_projection(out[-1]),
+ multi_scale_features=multi_scale_features,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+# Modified from from transformers.models.mask2former.modeling_mask2former.Mask2FormerPixelLevelModule with Mask2->One
+class OneFormerPixelLevelModule(nn.Module):
+ def __init__(self, config: OneFormerConfig):
+ """
+ Pixel Level Module proposed in [Masked-attention Mask Transformer for Universal Image
+ Segmentation](https://huggingface.co/papers/2112.01527). It runs the input image through a backbone and a pixel
+ decoder, generating multi-scale feature maps and pixel embeddings.
+
+ Args:
+ config ([`OneFormerConfig`]):
+ The configuration used to instantiate this model.
+ """
+ super().__init__()
+ self.encoder = load_backbone(config)
+ self.decoder = OneFormerPixelDecoder(config, feature_channels=self.encoder.channels)
+
+ def forward(self, pixel_values: Tensor, output_hidden_states: bool = False) -> OneFormerPixelLevelModuleOutput:
+ features: list[Tensor] = self.encoder(pixel_values).feature_maps
+ decoder_output: OneFormerPixelDecoderOutput = self.decoder(features, output_hidden_states=output_hidden_states)
+ return OneFormerPixelLevelModuleOutput(
+ encoder_features=tuple(features),
+ decoder_features=decoder_output.multi_scale_features,
+ decoder_last_feature=decoder_output.mask_features,
+ )
+
+
+# Modified from transformers.models.detr.modeling_detr.DetrAttention with Detr->OneFormer
+class OneFormerAttention(nn.Module):
+ """
+ Multi-headed attention from 'Attention Is All You Need' paper. Here, we add position embeddings to the queries and
+ keys (as explained in the DETR paper).
+ """
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: float = 0.0,
+ is_decoder: bool = False,
+ bias: bool = True,
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.num_heads = num_heads
+ self.dropout = dropout
+ self.head_dim = embed_dim // num_heads
+ if self.head_dim * num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+ f" {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+
+ self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+ self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int):
+ return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Optional[Tensor]):
+ return tensor if position_embeddings is None else tensor + position_embeddings
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_embeddings: Optional[torch.Tensor] = None,
+ key_value_states: Optional[torch.Tensor] = None,
+ key_value_position_embeddings: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+ """Input shape: Batch x Time x Channel"""
+
+ hidden_states = hidden_states.permute(1, 0, 2) if hidden_states is not None else None
+ position_embeddings = position_embeddings.permute(1, 0, 2) if position_embeddings is not None else None
+ key_value_states = key_value_states.permute(1, 0, 2) if key_value_states is not None else None
+ key_value_position_embeddings = (
+ key_value_position_embeddings.permute(1, 0, 2) if key_value_position_embeddings is not None else None
+ )
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+ batch_size, target_len, embed_dim = hidden_states.size()
+
+ # add position embeddings to the hidden states before projecting to queries and keys
+ if position_embeddings is not None:
+ hidden_states_original = hidden_states
+ hidden_states = self.with_pos_embed(hidden_states, position_embeddings)
+
+ # add key-value position embeddings to the key value states
+ if key_value_position_embeddings is not None:
+ key_value_states_original = key_value_states
+ key_value_states = self.with_pos_embed(key_value_states, key_value_position_embeddings)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states) * self.scaling
+ # get key, value proj
+ if is_cross_attention:
+ # cross_attentions
+ key_states = self._shape(self.k_proj(key_value_states), -1, batch_size)
+ value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size)
+ else:
+ # self_attention
+ key_states = self._shape(self.k_proj(hidden_states), -1, batch_size)
+ value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size)
+
+ proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
+ query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape)
+ key_states = key_states.view(*proj_shape)
+ value_states = value_states.view(*proj_shape)
+
+ source_len = key_states.size(1)
+
+ attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
+
+ if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len):
+ raise ValueError(
+ f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (batch_size * self.num_heads, target_len, source_len):
+ raise ValueError(
+ f"Attention mask should be of size {(target_len, batch_size * self.num_heads, source_len)}, but is"
+ f" {attention_mask.size()}"
+ )
+ attn_weights += attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+ if output_attentions:
+ # this operation is a bit awkward, but it's required to
+ # make sure that attn_weights keeps its gradient.
+ # In order to do so, attn_weights have to reshaped
+ # twice and have to be reused in the following
+ attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
+ attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
+ else:
+ attn_weights_reshaped = None
+
+ attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+ attn_output = torch.bmm(attn_probs, value_states)
+
+ if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
+ attn_output = attn_output.transpose(1, 2)
+ attn_output = attn_output.reshape(batch_size, target_len, embed_dim)
+
+ attn_output = self.out_proj(attn_output).permute(1, 0, 2)
+
+ return attn_output, attn_weights_reshaped
+
+
+class OneFormerTransformerDecoderSelfAttentionLayer(nn.Module):
+ def __init__(
+ self, embed_dim, num_heads, dropout=0.0, activation="relu", normalize_before=False, layer_norm_eps=1e-05
+ ):
+ super().__init__()
+ self.self_attn = OneFormerAttention(embed_dim=embed_dim, num_heads=num_heads, dropout=dropout, is_decoder=True)
+
+ self.norm = nn.LayerNorm(embed_dim, eps=layer_norm_eps)
+ self.dropout = nn.Dropout(dropout)
+
+ self.activation = ACT2FN[activation]
+ self.normalize_before = normalize_before
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(
+ self,
+ output,
+ output_mask: Optional[Tensor] = None,
+ output_key_padding_mask: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ output2, attention_weights = self.self_attn(
+ hidden_states=output, position_embeddings=query_pos, attention_mask=output_mask, output_attentions=True
+ )
+ output = output + self.dropout(output2)
+ output = self.norm(output)
+
+ return output, attention_weights
+
+ def forward_pre(
+ self,
+ output,
+ output_mask: Optional[Tensor] = None,
+ output_key_padding_mask: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ output2 = self.norm(output)
+ output2, attention_weights = self.self_attn(
+ hidden_states=output2, position_embeddings=query_pos, attention_mask=output_mask, output_attentions=True
+ )
+ output = output + self.dropout(output2)
+
+ return output, attention_weights
+
+ def forward(
+ self,
+ output,
+ output_mask: Optional[Tensor] = None,
+ output_key_padding_mask: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ if self.normalize_before:
+ return self.forward_pre(output, output_mask, output_key_padding_mask, query_pos)
+ return self.forward_post(output, output_mask, output_key_padding_mask, query_pos)
+
+
+class OneFormerTransformerDecoderCrossAttentionLayer(nn.Module):
+ def __init__(
+ self, embed_dim, num_heads, dropout=0.0, activation="relu", normalize_before=False, layer_norm_eps=1e-05
+ ):
+ super().__init__()
+ self.multihead_attn = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)
+
+ self.norm = nn.LayerNorm(embed_dim, eps=layer_norm_eps)
+ self.dropout = nn.Dropout(dropout)
+
+ self.activation = ACT2FN[activation]
+ self.normalize_before = normalize_before
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(
+ self,
+ output,
+ memory,
+ memory_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ output2, attention_weights = self.multihead_attn(
+ query=self.with_pos_embed(output, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory,
+ attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask,
+ )
+ output = output + self.dropout(output2)
+ output = self.norm(output)
+
+ return output, attention_weights
+
+ def forward_pre(
+ self,
+ output,
+ memory,
+ memory_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ output2 = self.norm(output)
+ output2, attention_weights = self.multihead_attn(
+ query=self.with_pos_embed(output2, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory,
+ attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask,
+ )
+ output = output + self.dropout(output2)
+
+ return output, attention_weights
+
+ def forward(
+ self,
+ output,
+ memory,
+ memory_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ if self.normalize_before:
+ return self.forward_pre(output, memory, memory_mask, memory_key_padding_mask, pos, query_pos)
+ return self.forward_post(output, memory, memory_mask, memory_key_padding_mask, pos, query_pos)
+
+
+class OneFormerTransformerDecoderFFNLayer(nn.Module):
+ def __init__(
+ self,
+ d_model,
+ dim_feedforward=2048,
+ dropout=0.0,
+ activation="relu",
+ normalize_before=False,
+ layer_norm_eps=1e-05,
+ ):
+ super().__init__()
+ # Implementation of Feedforward model
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm = nn.LayerNorm(d_model, eps=layer_norm_eps)
+
+ self.activation = ACT2FN[activation]
+ self.normalize_before = normalize_before
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(self, output):
+ output2 = self.linear2(self.dropout(self.activation(self.linear1(output))))
+ output = output + self.dropout(output2)
+ output = self.norm(output)
+ return output
+
+ def forward_pre(self, output):
+ output2 = self.norm(output)
+ output2 = self.linear2(self.dropout(self.activation(self.linear1(output2))))
+ output = output + self.dropout(output2)
+ return output
+
+ def forward(self, output):
+ if self.normalize_before:
+ return self.forward_pre(output)
+ return self.forward_post(output)
+
+
+class OneFormerMLPPredictionHead(nn.Module):
+ def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3):
+ """
+ A classic Multi Layer Perceptron (MLP).
+
+ Args:
+ input_dim (`int`):
+ The input dimensions.
+ hidden_dim (`int`):
+ The hidden dimensions.
+ output_dim (`int`):
+ The output dimensions.
+ num_layers (int, *optional*, defaults to 3):
+ The number of layers.
+ """
+ super().__init__()
+ in_dims = [input_dim] + [hidden_dim] * (num_layers - 1)
+ out_dims = [hidden_dim] * (num_layers - 1) + [output_dim]
+
+ layers = []
+ for i, (in_dim, out_dim) in enumerate(zip(in_dims, out_dims)):
+ layers.append(
+ PredictionBlock(in_dim, out_dim, activation=nn.ReLU() if i < num_layers - 1 else nn.Identity())
+ )
+
+ self.layers = nn.Sequential(*layers)
+
+ def forward(self, input: Tensor) -> Tensor:
+ return self.layers(input)
+
+
+# refactored from original implementation
+class OneFormerTransformerDecoderLayer(nn.Module):
+ def __init__(self, config: OneFormerConfig):
+ super().__init__()
+ self.embed_dim = config.hidden_dim
+ self.num_feature_levels = 3
+
+ self.cross_attn = OneFormerTransformerDecoderCrossAttentionLayer(
+ embed_dim=self.embed_dim,
+ num_heads=config.num_attention_heads,
+ dropout=0.0,
+ normalize_before=config.pre_norm,
+ layer_norm_eps=config.layer_norm_eps,
+ )
+
+ self.self_attn = OneFormerTransformerDecoderSelfAttentionLayer(
+ embed_dim=self.embed_dim,
+ num_heads=config.num_attention_heads,
+ dropout=0.0,
+ normalize_before=config.pre_norm,
+ layer_norm_eps=config.layer_norm_eps,
+ )
+
+ self.ffn = OneFormerTransformerDecoderFFNLayer(
+ d_model=self.embed_dim,
+ dim_feedforward=config.dim_feedforward,
+ dropout=0.0,
+ normalize_before=config.pre_norm,
+ layer_norm_eps=config.layer_norm_eps,
+ )
+
+ def forward(
+ self,
+ index: int,
+ output: torch.Tensor,
+ multi_stage_features: list[torch.Tensor],
+ multi_stage_positional_embeddings: list[torch.Tensor],
+ attention_mask: Optional[torch.Tensor] = None,
+ query_embeddings: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ ):
+ """
+ Args:
+ index (`int`): index of the layer in the Transformer decoder.
+ output (`torch.FloatTensor`): the object queries of shape `(N, batch, hidden_dim)`
+ multi_stage_features (`list[torch.Tensor]`): the multi-scale features from the pixel decoder.
+ multi_stage_positional_embeddings (`list[torch.Tensor]`):
+ positional embeddings for the multi_stage_features
+ attention_mask (`torch.FloatTensor`): attention mask for the masked cross attention layer
+ query_embeddings (`torch.FloatTensor`, *optional*):
+ position embeddings that are added to the queries and keys in the self-attention layer.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ """
+
+ level_index = index % self.num_feature_levels
+ attention_mask[torch.where(attention_mask.sum(-1) == attention_mask.shape[-1])] = False
+
+ # Masked Cross Attention
+ output, cross_attn_weights = self.cross_attn(
+ output,
+ multi_stage_features[level_index],
+ memory_mask=attention_mask,
+ memory_key_padding_mask=None, # here we do not apply masking on padded region
+ pos=multi_stage_positional_embeddings[level_index],
+ query_pos=query_embeddings,
+ )
+
+ # Self Attention
+ output, self_attn_weights = self.self_attn(
+ output,
+ output_mask=None,
+ output_key_padding_mask=None,
+ query_pos=query_embeddings,
+ )
+
+ # Fully Connected
+ output = self.ffn(output)
+
+ outputs = (output,)
+
+ if output_attentions:
+ outputs += (self_attn_weights, cross_attn_weights)
+
+ return outputs
+
+
+class OneFormerTransformerDecoderQueryTransformerDecoder(nn.Module):
+ def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
+ super().__init__()
+ self.layers = _get_clones(decoder_layer, num_layers)
+ self.num_layers = num_layers
+ self.norm = norm
+ self.return_intermediate = return_intermediate
+
+ def forward(
+ self,
+ output,
+ memory,
+ output_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ output_key_padding_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ intermediate = []
+
+ for layer in self.layers:
+ output = layer(
+ output,
+ memory,
+ output_mask=output_mask,
+ memory_mask=memory_mask,
+ output_key_padding_mask=output_key_padding_mask,
+ memory_key_padding_mask=memory_key_padding_mask,
+ pos=pos,
+ query_pos=query_pos,
+ )
+ if self.return_intermediate:
+ intermediate.append(self.norm(output))
+
+ if self.norm is not None:
+ output = self.norm(output)
+ if self.return_intermediate:
+ intermediate.pop()
+ intermediate.append(output)
+
+ if self.return_intermediate:
+ return torch.stack(intermediate)
+
+ return output.unsqueeze(0)
+
+
+class OneFormerTransformerDecoderQueryTransformerDecoderLayer(nn.Module):
+ def __init__(
+ self,
+ d_model,
+ nhead,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation="relu",
+ normalize_before=False,
+ layer_norm_eps=1e-05,
+ ):
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+ self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+ # Implementation of Feedforward model
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+ self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+ self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
+ self.dropout3 = nn.Dropout(dropout)
+
+ self.activation = ACT2FN[activation]
+ self.normalize_before = normalize_before
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(
+ self,
+ output,
+ memory,
+ output_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ output_key_padding_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ q = k = self.with_pos_embed(output, query_pos)
+ output2 = self.self_attn(q, k, value=output, attn_mask=output_mask, key_padding_mask=output_key_padding_mask)
+ output2 = output2[0]
+ output = output + self.dropout1(output2)
+ output = self.norm1(output)
+ output2 = self.multihead_attn(
+ query=self.with_pos_embed(output, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory,
+ attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask,
+ )
+ output2 = output2[0]
+ output = output + self.dropout2(output2)
+ output = self.norm2(output)
+ output2 = self.linear2(self.dropout(self.activation(self.linear1(output))))
+ output = output + self.dropout3(output2)
+ output = self.norm3(output)
+ return output
+
+ def forward_pre(
+ self,
+ output,
+ memory,
+ output_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ output_key_padding_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ output2 = self.norm1(output)
+ q = k = self.with_pos_embed(output2, query_pos)
+ output2 = self.self_attn(q, k, value=output2, attn_mask=output_mask, key_padding_mask=output_key_padding_mask)
+ output2 = output2[0]
+ output = output + self.dropout1(output2)
+ output2 = self.norm2(output)
+ output2 = self.multihead_attn(
+ query=self.with_pos_embed(output2, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory,
+ attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask,
+ )
+ output2 = output2[0]
+ output = output + self.dropout2(output2)
+ output2 = self.norm3(output)
+ output2 = self.linear2(self.dropout(self.activation(self.linear1(output2))))
+ output = output + self.dropout3(output2)
+ return output
+
+ def forward(
+ self,
+ output,
+ memory,
+ output_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ output_key_padding_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ if self.normalize_before:
+ return self.forward_pre(
+ output,
+ memory,
+ output_mask,
+ memory_mask,
+ output_key_padding_mask,
+ memory_key_padding_mask,
+ pos,
+ query_pos,
+ )
+ return self.forward_post(
+ output,
+ memory,
+ output_mask,
+ memory_mask,
+ output_key_padding_mask,
+ memory_key_padding_mask,
+ pos,
+ query_pos,
+ )
+
+
+class OneFormerTransformerDecoderQueryTransformer(nn.Module):
+ def __init__(
+ self,
+ d_model=512,
+ nhead=8,
+ num_decoder_layers=6,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation="relu",
+ normalize_before=False,
+ return_intermediate_dec=False,
+ layer_norm_eps=1e-05,
+ ):
+ super().__init__()
+
+ decoder_layer = OneFormerTransformerDecoderQueryTransformerDecoderLayer(
+ d_model, nhead, dim_feedforward, dropout, activation, normalize_before, layer_norm_eps
+ )
+ decoder_norm = nn.LayerNorm(d_model, eps=layer_norm_eps)
+ self.decoder = OneFormerTransformerDecoderQueryTransformerDecoder(
+ decoder_layer,
+ num_decoder_layers,
+ decoder_norm,
+ return_intermediate=return_intermediate_dec,
+ )
+
+ self.d_model = d_model
+ self.nhead = nhead
+
+ def forward(self, src, mask, query_embed, pos_embed, task_token=None):
+ batch_size = src.shape[0]
+ src = src.flatten(2).permute(2, 0, 1)
+ pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
+ query_embed = query_embed.unsqueeze(1).repeat(1, batch_size, 1)
+ if mask is not None:
+ mask = mask.flatten(1)
+
+ if task_token is None:
+ queries = torch.zeros_like(query_embed)
+ else:
+ queries = task_token.repeat(query_embed.shape[0], 1, 1)
+
+ queries = self.decoder(queries, src, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed)
+ return queries.transpose(1, 2)
+
+
+class OneFormerTransformerDecoder(nn.Module):
+ """
+ Transformer decoder
+ """
+
+ def __init__(self, in_channels: int, config: OneFormerConfig):
+ super().__init__()
+ self.config = config
+
+ self.dropout = config.dropout
+ self.num_heads = config.num_attention_heads
+ self.is_training = config.is_training
+ self.use_task_norm = config.use_task_norm
+ self.use_auxiliary_loss = config.use_auxiliary_loss
+
+ self.query_transformer = OneFormerTransformerDecoderQueryTransformer(
+ d_model=config.hidden_dim,
+ dropout=config.dropout,
+ nhead=config.num_attention_heads,
+ dim_feedforward=config.dim_feedforward,
+ num_decoder_layers=config.query_dec_layers,
+ normalize_before=config.pre_norm,
+ return_intermediate_dec=False,
+ layer_norm_eps=config.layer_norm_eps,
+ )
+
+ self.decoder_norm = nn.LayerNorm(config.hidden_dim, eps=config.layer_norm_eps)
+
+ self.num_feature_levels = 3
+
+ self.layers = nn.ModuleList(
+ [OneFormerTransformerDecoderLayer(config) for _ in range(config.decoder_layers - 1)]
+ )
+
+ self.query_input_projection = nn.Conv2d(in_channels, config.hidden_dim, kernel_size=1)
+
+ self.class_embed = nn.Linear(config.hidden_dim, config.num_labels + 1)
+ self.mask_embed = OneFormerMLPPredictionHead(
+ config.hidden_dim,
+ config.hidden_dim,
+ config.mask_dim,
+ 3,
+ )
+
+ def forward(
+ self,
+ task_token=None,
+ multi_stage_features=None,
+ multi_stage_positional_embeddings=None,
+ mask_features=None,
+ query_features=None,
+ query_embeddings=None,
+ query_embedder=None,
+ size_list=None,
+ output_attentions=None,
+ ):
+ if self.use_task_norm:
+ task_token = self.decoder_norm(task_token)
+
+ object_queries = self.query_transformer(
+ query_features,
+ None,
+ query_embedder.weight[:-1],
+ self.query_input_projection(mask_features),
+ task_token if self.use_task_norm else None,
+ )
+
+ object_queries = object_queries[0].permute(1, 0, 2)
+
+ queries = torch.cat([object_queries, task_token], dim=0)
+
+ output = queries.clone()
+
+ intermediate_class_predictions = []
+ intermediate_mask_predictions = []
+
+ # prediction heads on learnable query features
+ outputs_class, outputs_mask, attention_mask = self.forward_prediction_heads(
+ output, mask_features, attention_mask_target_size=size_list[0]
+ )
+ intermediate_class_predictions.append(outputs_class)
+ intermediate_mask_predictions.append(outputs_mask)
+
+ attentions = ()
+
+ for index, layer in enumerate(self.layers):
+ layer_outputs = layer(
+ index=index,
+ output=output,
+ multi_stage_features=multi_stage_features,
+ multi_stage_positional_embeddings=multi_stage_positional_embeddings,
+ attention_mask=attention_mask,
+ query_embeddings=query_embeddings,
+ output_attentions=output_attentions,
+ )
+
+ output = layer_outputs[0]
+ attentions += (layer_outputs[1:],)
+
+ outputs_class, outputs_mask, attention_mask = self.forward_prediction_heads(
+ output, mask_features, attention_mask_target_size=size_list[(index + 1) % self.num_feature_levels]
+ )
+ intermediate_class_predictions.append(outputs_class)
+ intermediate_mask_predictions.append(outputs_mask)
+
+ if not len(intermediate_mask_predictions) == len(self.layers) + 1:
+ raise ValueError(
+ "Intermediate predictions in the transformer decoder must have the same number of elements as number"
+ " of layers"
+ )
+
+ object_queries = layer_outputs[0].permute(1, 0, 2)
+
+ contrastive_logits = queries.permute(1, 0, 2)
+
+ return OneFormerTransformerDecoderOutput(
+ object_queries=object_queries,
+ contrastive_logits=contrastive_logits,
+ prediction_masks=intermediate_mask_predictions[-1],
+ prediction_class=intermediate_class_predictions[-1],
+ auxiliary_predictions=self._get_aux_predictions(
+ intermediate_class_predictions, intermediate_mask_predictions
+ )
+ if self.use_auxiliary_loss
+ else None,
+ attentions=attentions,
+ )
+
+ def forward_prediction_heads(self, output, mask_features, attention_mask_target_size):
+ decoder_output = self.decoder_norm(output)
+ decoder_output = decoder_output.transpose(0, 1)
+ outputs_class = self.class_embed(decoder_output)
+ mask_embed = self.mask_embed(decoder_output)
+ outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features)
+
+ attention_mask = nn.functional.interpolate(
+ outputs_mask, size=attention_mask_target_size, mode="bilinear", align_corners=False
+ )
+
+ # must use bool type
+ # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged.
+ attention_mask = (
+ attention_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5
+ ).bool()
+ attention_mask = attention_mask.detach()
+
+ return outputs_class, outputs_mask, attention_mask
+
+ @torch.jit.unused
+ def _get_aux_predictions(self, outputs_class, outputs_seg_masks):
+ # this is a workaround to make torchscript happy, as torchscript
+ # doesn't support dictionary with non-homogeneous values, such
+ # as a dict having both a Tensor and a list.
+ aux_list = [
+ {"class_queries_logits": a, "masks_queries_logits": b}
+ for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1])
+ ]
+ return tuple(aux_list)
+
+
+class OneFormerTransformerModule(nn.Module):
+ """
+ The OneFormer's transformer module.
+ """
+
+ def __init__(self, in_features: int, config: OneFormerConfig):
+ super().__init__()
+ hidden_dim = config.hidden_dim
+ self.num_feature_levels = 3
+ self.position_embedder = OneFormerSinePositionEmbedding(num_pos_feats=hidden_dim // 2, normalize=True)
+ self.queries_embedder = nn.Embedding(config.num_queries, hidden_dim)
+ self.input_projections = []
+
+ for _ in range(self.num_feature_levels):
+ if in_features != hidden_dim or config.enforce_input_proj:
+ self.input_projections.append(nn.Conv2d(in_features, hidden_dim, kernel_size=1))
+ else:
+ self.input_projections.append(nn.Sequential())
+
+ self.decoder = OneFormerTransformerDecoder(in_channels=in_features, config=config)
+ self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)
+
+ def forward(
+ self,
+ multi_scale_features: list[Tensor],
+ mask_features: Tensor,
+ task_token: Tensor,
+ output_attentions: bool = False,
+ ) -> OneFormerTransformerDecoderOutput:
+ if not len(multi_scale_features) == self.num_feature_levels:
+ raise ValueError(
+ f"Number of elements in multi_scale_features ({len(multi_scale_features)}) and num_feature_levels"
+ f" ({self.num_feature_levels}) do not match!"
+ )
+ multi_stage_features = []
+ multi_stage_positional_embeddings = []
+ size_list = []
+
+ for i in range(self.num_feature_levels):
+ size_list.append(multi_scale_features[i].shape[-2:])
+ multi_stage_positional_embeddings.append(
+ self.position_embedder(
+ multi_scale_features[i].shape, multi_scale_features[i].device, multi_scale_features[i].dtype, None
+ ).flatten(2)
+ )
+ multi_stage_features.append(
+ self.input_projections[i](multi_scale_features[i]).flatten(2)
+ + self.level_embed.weight[i][None, :, None]
+ )
+
+ # flatten NxCxHxW to HWxNxC
+ multi_stage_positional_embeddings[-1] = multi_stage_positional_embeddings[-1].permute(2, 0, 1)
+ multi_stage_features[-1] = multi_stage_features[-1].permute(2, 0, 1)
+
+ _, batch_size, _ = multi_stage_features[0].shape
+
+ # QxNxC
+ query_embeddings = self.queries_embedder.weight.unsqueeze(1).repeat(1, batch_size, 1)
+ task_token = task_token.unsqueeze(0)
+
+ query_features = self.position_embedder(mask_features.shape, mask_features.device, mask_features.dtype, None)
+
+ return self.decoder(
+ task_token=task_token,
+ multi_stage_features=multi_stage_features,
+ multi_stage_positional_embeddings=multi_stage_positional_embeddings,
+ mask_features=mask_features,
+ query_features=query_features,
+ query_embeddings=query_embeddings,
+ query_embedder=self.queries_embedder,
+ size_list=size_list,
+ output_attentions=output_attentions,
+ )
+
+
+# Copied from transformers.models.maskformer.modeling_maskformer.MaskFormerSinePositionEmbedding with Mask->One
+class OneFormerSinePositionEmbedding(nn.Module):
+ """
+ This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
+ need paper, generalized to work on images.
+ """
+
+ def __init__(
+ self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: Optional[float] = None
+ ):
+ super().__init__()
+ if scale is not None and normalize is False:
+ raise ValueError("normalize should be True if scale is passed")
+ self.num_pos_feats = num_pos_feats
+ self.temperature = temperature
+ self.normalize = normalize
+ self.scale = 2 * math.pi if scale is None else scale
+
+ @compile_compatible_method_lru_cache(maxsize=1)
+ def forward(
+ self,
+ shape: torch.Size,
+ device: Union[torch.device, str],
+ dtype: torch.dtype,
+ mask: Optional[Tensor] = None,
+ ) -> Tensor:
+ if mask is None:
+ mask = torch.zeros((shape[0], shape[2], shape[3]), device=device, dtype=torch.bool)
+ not_mask = (~mask).to(dtype)
+ y_embed = not_mask.cumsum(1)
+ x_embed = not_mask.cumsum(2)
+ if self.normalize:
+ eps = 1e-6
+ y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+ x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+ dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=device).to(dtype)
+ dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats)
+
+ pos_x = x_embed[:, :, :, None] / dim_t
+ pos_y = y_embed[:, :, :, None] / dim_t
+ pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
+ pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
+ pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+ return pos
+
+
+# Copied from transformers.models.maskformer.modeling_maskformer.PredictionBlock
+class PredictionBlock(nn.Module):
+ def __init__(self, in_dim: int, out_dim: int, activation: nn.Module) -> None:
+ super().__init__()
+ self.layers = [nn.Linear(in_dim, out_dim), activation]
+ # Maintain submodule indexing as if part of a Sequential block
+ for i, layer in enumerate(self.layers):
+ self.add_module(str(i), layer)
+
+ def forward(self, input: Tensor) -> Tensor:
+ hidden_state = input
+ for layer in self.layers:
+ hidden_state = layer(hidden_state)
+ return hidden_state
+
+
+class OneFormerTextMapperAttention(nn.Module):
+ def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0):
+ super().__init__()
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+ self.scale = qk_scale or head_dim**-0.5
+
+ self.q_proj = nn.Linear(dim, dim, bias=qkv_bias)
+ self.k_proj = nn.Linear(dim, dim, bias=qkv_bias)
+ self.v_proj = nn.Linear(dim, dim, bias=qkv_bias)
+
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop)
+
+ def forward(self, q, k, v):
+ batch_size, q_sequence_length, num_channels = q.shape
+ if not k.shape == v.shape:
+ raise ValueError(f"keys ({list(k.shape)}) and values ({list(v.shape)}) have different shapes!")
+ batch_size, k_sequence_length, num_channels = k.shape
+ q = self.q_proj(q).reshape(batch_size, q_sequence_length, self.num_heads, num_channels // self.num_heads)
+ k = self.k_proj(k).reshape(batch_size, k_sequence_length, self.num_heads, num_channels // self.num_heads)
+ v = self.v_proj(v).reshape(batch_size, k_sequence_length, self.num_heads, num_channels // self.num_heads)
+
+ attn = torch.einsum("bnkc,bmkc->bknm", q, k) * self.scale
+
+ attn = attn.softmax(dim=-1)
+
+ output = torch.einsum("bknm,bmkc->bnkc", attn, v).reshape(batch_size, q_sequence_length, num_channels)
+
+ output = self.proj(output)
+ output = self.proj_drop(output)
+ return output
+
+
+class OneFormerTextTransformerDecoderLayer(nn.Module):
+ def __init__(
+ self,
+ d_model,
+ nhead,
+ dropout=0.1,
+ layer_norm_eps=1e-05,
+ ):
+ super().__init__()
+ self.self_attn = OneFormerTextMapperAttention(d_model, nhead, proj_drop=dropout)
+ self.cross_attn = OneFormerTextMapperAttention(d_model, nhead, proj_drop=dropout)
+
+ self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+ self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+ self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps)
+ self.dropout = nn.Dropout(dropout)
+
+ self.mlp = nn.Sequential(
+ nn.Linear(d_model, d_model * 4), nn.GELU(), nn.Dropout(dropout), nn.Linear(d_model * 4, d_model)
+ )
+
+ def forward(self, hidden_state, mem):
+ q = k = v = self.norm1(hidden_state)
+ hidden_state = hidden_state + self.self_attn(q, k, v)
+ q = self.norm2(hidden_state)
+ hidden_state = hidden_state + self.cross_attn(q, mem, mem)
+ hidden_state = hidden_state + self.dropout(self.mlp(self.norm3(hidden_state)))
+ return hidden_state
+
+
+class OneFormerTextContextDecoder(nn.Module):
+ def __init__(
+ self,
+ transformer_width=256,
+ transformer_heads=4,
+ transformer_layers=6,
+ visual_dim=1024,
+ dropout=0.1,
+ layer_norm_eps=1e-05,
+ **kwargs,
+ ):
+ super().__init__()
+
+ self.memory_proj = nn.Sequential(
+ nn.LayerNorm(visual_dim, eps=layer_norm_eps),
+ nn.Linear(visual_dim, transformer_width),
+ nn.LayerNorm(transformer_width, eps=layer_norm_eps),
+ )
+
+ self.text_proj = nn.Sequential(
+ nn.LayerNorm(visual_dim, eps=layer_norm_eps),
+ nn.Linear(visual_dim, transformer_width),
+ )
+
+ self.decoder = nn.ModuleList(
+ [
+ OneFormerTextTransformerDecoderLayer(transformer_width, transformer_heads, dropout, layer_norm_eps)
+ for _ in range(transformer_layers)
+ ]
+ )
+
+ self.out_proj = nn.Sequential(
+ nn.LayerNorm(transformer_width, eps=layer_norm_eps), nn.Linear(transformer_width, visual_dim)
+ )
+
+ def forward(self, text, visual):
+ visual = self.memory_proj(visual)
+ hidden_state = self.text_proj(text)
+
+ for layer in self.decoder:
+ hidden_state = layer(hidden_state, visual)
+
+ return self.out_proj(hidden_state)
+
+
+class OneFormerTextMLP(nn.Module):
+ def __init__(
+ self,
+ hidden_size: Optional[int] = None,
+ intermediate_size: Optional[int] = None,
+ output_size: Optional[int] = None,
+ ):
+ super().__init__()
+ self.activation_fn = ACT2FN["quick_gelu"]
+ hidden_size = hidden_size
+ intermediate_size = intermediate_size
+ output_size = output_size
+ self.fc1 = nn.Linear(hidden_size, intermediate_size)
+ self.fc2 = nn.Linear(intermediate_size, output_size)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+ hidden_states = self.fc2(hidden_states)
+ return hidden_states
+
+
+class OneFormerTextTransformerLayer(GradientCheckpointingLayer):
+ def __init__(self, width: int, heads: int, attn_mask: torch.Tensor, layer_norm_eps=1e-05):
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(width, heads)
+ self.layer_norm1 = nn.LayerNorm(width, eps=layer_norm_eps)
+ self.mlp = OneFormerTextMLP(width, width * 4, width)
+ self.layer_norm2 = nn.LayerNorm(width, eps=layer_norm_eps)
+ self.attn_mask = attn_mask
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ key_padding_mask: Optional[torch.Tensor] = None,
+ ) -> torch.FloatTensor:
+ residual = hidden_states
+
+ hidden_states = self.layer_norm1(hidden_states)
+ hidden_states = self.self_attn(
+ hidden_states,
+ hidden_states,
+ hidden_states,
+ need_weights=False,
+ key_padding_mask=key_padding_mask,
+ )[0]
+ hidden_states = residual + hidden_states
+
+ residual = hidden_states
+ hidden_states = self.layer_norm2(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ return hidden_states
+
+
+class OneFormerTextTransformer(nn.Module):
+ def __init__(
+ self,
+ width: int,
+ layers: int,
+ heads: int,
+ attn_mask: Optional[torch.Tensor] = None,
+ use_checkpoint=False,
+ layer_norm_eps=1e-05,
+ ):
+ super().__init__()
+ self.width = width
+ self.num_layers = layers
+ self.layers = nn.Sequential(
+ *[OneFormerTextTransformerLayer(width, heads, attn_mask, layer_norm_eps) for _ in range(layers)]
+ )
+ self.use_checkpoint = use_checkpoint
+
+ def forward(self, hidden_states: torch.Tensor):
+ for layer in self.layers:
+ hidden_states = layer(hidden_states)
+ return hidden_states
+
+
+class OneFormerTextEncoder(nn.Module):
+ def __init__(
+ self,
+ context_length: int,
+ width: int,
+ layers: int,
+ vocab_size,
+ use_checkpoint=False,
+ layer_norm_eps=1e-05,
+ ):
+ super().__init__()
+ heads = width // 64
+ self.context_length = context_length
+ self.width = width
+ self.transformer = OneFormerTextTransformer(
+ width=width,
+ layers=layers,
+ heads=heads,
+ attn_mask=self.build_attention_mask(),
+ use_checkpoint=use_checkpoint,
+ layer_norm_eps=layer_norm_eps,
+ )
+
+ self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width))
+ self.ln_final = nn.LayerNorm(width, eps=layer_norm_eps)
+ self.token_embedding = nn.Embedding(vocab_size, width)
+
+ def build_attention_mask(self):
+ # lazily create causal attention mask, with full attention between the vision tokens
+ # pytorch uses additive attention mask; fill with -inf
+ mask = torch.empty(self.context_length, self.context_length)
+ mask.fill_(float("-inf"))
+ mask.triu_(1) # zero out the lower diagonal
+ return mask
+
+ def forward(self, text):
+ hidden_state = self.token_embedding(text)
+ hidden_state = hidden_state + self.positional_embedding
+ hidden_state = hidden_state.permute(1, 0, 2)
+ hidden_state = self.transformer(hidden_state)
+ hidden_state = hidden_state.permute(1, 0, 2)
+ hidden_state = self.ln_final(hidden_state)
+ hidden_state = hidden_state[torch.arange(hidden_state.shape[0]), text.argmax(dim=-1)]
+
+ return hidden_state
+
+
+class OneFormerTextMapper(nn.Module):
+ def __init__(self, config: OneFormerConfig):
+ super().__init__()
+ self.text_encoder = OneFormerTextEncoder(
+ context_length=config.text_encoder_context_length,
+ width=config.text_encoder_width,
+ layers=config.text_encoder_num_layers,
+ vocab_size=config.text_encoder_vocab_size,
+ layer_norm_eps=config.layer_norm_eps,
+ )
+
+ self.text_projector = OneFormerMLPPredictionHead(
+ config.text_encoder_width,
+ config.hidden_dim,
+ config.hidden_dim,
+ config.text_encoder_proj_layers,
+ )
+ if config.text_encoder_n_ctx > 0:
+ self.prompt_ctx = nn.Embedding(
+ config.text_encoder_n_ctx,
+ config.text_encoder_width,
+ )
+ else:
+ self.prompt_ctx = None
+
+ def forward(
+ self,
+ inputs: Tensor,
+ ) -> Tensor:
+ text_queries = self.encode_text(inputs)
+
+ return text_queries
+
+ def encode_text(self, text):
+ if text.ndim is None:
+ raise ValueError("text must not be NoneType")
+ if text.ndim not in [2, 3]:
+ raise ValueError("Number of dimensions in text must be 2 or 3")
+ squeeze_dim = False
+ num_text = 1
+ if text.ndim == 3:
+ num_text = text.shape[1]
+ batch_size, num_text, hidden_dim = text.shape
+ text = text.reshape(batch_size * num_text, hidden_dim)
+ squeeze_dim = True
+
+ # [batch_size, num_channels]
+ encoded_text = self.text_encoder(text)
+
+ text_queries = self.text_projector(encoded_text)
+
+ if squeeze_dim:
+ _, hidden_dim = text_queries.shape
+ text_queries = text_queries.reshape(batch_size, num_text, hidden_dim)
+ if self.prompt_ctx is not None:
+ text_queries_ctx = self.prompt_ctx.weight.unsqueeze(0).repeat(text_queries.shape[0], 1, 1)
+ text_queries = torch.cat([text_queries, text_queries_ctx], dim=1)
+
+ return text_queries
+
+
+class OneFormerTaskModel(nn.Module):
+ def __init__(self, config: OneFormerConfig):
+ super().__init__()
+ self.task_mlp = OneFormerMLPPredictionHead(
+ config.task_seq_len,
+ config.hidden_dim,
+ config.hidden_dim,
+ 2,
+ )
+
+ def forward(self, inputs: Tensor) -> Tensor:
+ task_tokens = self.task_mlp(inputs)
+ return task_tokens
+
+
+@auto_docstring
+class OneFormerPreTrainedModel(PreTrainedModel):
+ config: OneFormerConfig
+ base_model_prefix = "model"
+ main_input_name = "pixel_values"
+
+ def _init_weights(self, module: nn.Module):
+ xavier_std = self.config.init_xavier_std
+ std = self.config.init_std
+ if isinstance(module, OneFormerTransformerModule):
+ if module.input_projections is not None:
+ for input_projection in module.input_projections:
+ if not isinstance(input_projection, nn.Sequential):
+ nn.init.xavier_uniform_(input_projection.weight, gain=xavier_std)
+ nn.init.constant_(input_projection.bias, 0)
+ elif isinstance(module, OneFormerTransformerDecoder):
+ nn.init.xavier_uniform_(module.query_input_projection.weight, gain=xavier_std)
+ nn.init.constant_(module.query_input_projection.bias, 0)
+ elif isinstance(module, OneFormerPixelDecoderEncoderMultiscaleDeformableAttention):
+ nn.init.constant_(module.sampling_offsets.weight.data, 0.0)
+ thetas = torch.arange(module.n_heads, dtype=torch.int64).float() * (2.0 * math.pi / module.n_heads)
+ grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
+ grid_init = (
+ (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
+ .view(module.n_heads, 1, 1, 2)
+ .repeat(1, module.n_levels, module.n_points, 1)
+ )
+ for i in range(module.n_points):
+ grid_init[:, :, i, :] *= i + 1
+ with torch.no_grad():
+ module.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
+ nn.init.constant_(module.attention_weights.weight.data, 0.0)
+ nn.init.constant_(module.attention_weights.bias.data, 0.0)
+ nn.init.xavier_uniform_(module.value_proj.weight.data)
+ nn.init.constant_(module.value_proj.bias.data, 0.0)
+ nn.init.xavier_uniform_(module.output_proj.weight.data)
+ nn.init.constant_(module.output_proj.bias.data, 0.0)
+ elif isinstance(module, OneFormerPixelDecoder):
+ nn.init.normal_(module.level_embed, std=0)
+ elif isinstance(module, (OneFormerTransformerDecoderLayer, OneFormerTransformerDecoderQueryTransformer)):
+ for p in module.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p, gain=xavier_std)
+ elif isinstance(module, OneFormerTextTransformer):
+ proj_std = (module.width**-0.5) * ((2 * module.num_layers) ** -0.5)
+ attn_std = module.width**-0.5
+ fc_std = (2 * module.width) ** -0.5
+ for layer in module.layers:
+ nn.init.normal_(layer.self_attn.in_proj_weight, std=attn_std)
+ nn.init.normal_(layer.self_attn.out_proj.weight, std=proj_std)
+ nn.init.normal_(layer.mlp.fc1.weight, std=fc_std)
+ nn.init.normal_(layer.mlp.fc2.weight, std=proj_std)
+ elif isinstance(module, OneFormerTextEncoder):
+ nn.init.normal_(module.token_embedding.weight, std=0.02)
+ nn.init.normal_(module.positional_embedding, std=0.01)
+ if hasattr(module, "reference_points"):
+ nn.init.xavier_uniform_(module.reference_points.weight.data, gain=1.0)
+ nn.init.constant_(module.reference_points.bias.data, 0.0)
+ elif isinstance(module, OneFormerMLPPredictionHead):
+ for submodule in module.modules():
+ if isinstance(submodule, nn.Linear):
+ nn.init.xavier_uniform_(submodule.weight, gain=xavier_std)
+ nn.init.constant_(submodule.bias, 0)
+ elif isinstance(module, nn.MultiheadAttention):
+ module.in_proj_weight.data.normal_(mean=0.0, std=std)
+ module.in_proj_bias.data.zero_()
+ elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, OneFormerLoss):
+ module.logit_scale.data.fill_(np.log(1 / self.config.contrastive_temperature))
+
+
+@auto_docstring
+class OneFormerModel(OneFormerPreTrainedModel):
+ main_input_name = ["pixel_values", "task_inputs"]
+
+ def __init__(self, config: OneFormerConfig):
+ super().__init__(config)
+ self.pixel_level_module = OneFormerPixelLevelModule(config)
+ self.transformer_module = OneFormerTransformerModule(in_features=config.conv_dim, config=config)
+ self.task_encoder = OneFormerTaskModel(config)
+ self.is_training = config.is_training
+
+ if self.is_training:
+ self.text_mapper = OneFormerTextMapper(config)
+ else:
+ self.text_mapper = None
+
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Tensor,
+ task_inputs: Tensor,
+ text_inputs: Optional[Tensor] = None,
+ pixel_mask: Optional[Tensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> OneFormerModelOutput:
+ r"""
+ task_inputs (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
+ Task inputs. Task inputs can be obtained using [`AutoImageProcessor`]. See [`OneFormerProcessor.__call__`]
+ for details.
+ text_inputs (`list[torch.Tensor]`, *optional*):
+ Tensor fof shape `(num_queries, sequence_length)` to be fed to a model
+
+ Example:
+
+ ```python
+ >>> import torch
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import OneFormerProcessor, OneFormerModel
+
+ >>> # download texting image
+ >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> # load processor for preprocessing the inputs
+ >>> processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")
+ >>> model = OneFormerModel.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")
+ >>> inputs = processor(image, ["semantic"], return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+
+ >>> mask_predictions = outputs.transformer_decoder_mask_predictions
+ >>> class_predictions = outputs.transformer_decoder_class_predictions
+
+ >>> f"👉 Mask Predictions Shape: {list(mask_predictions.shape)}, Class Predictions Shape: {list(class_predictions.shape)}"
+ '👉 Mask Predictions Shape: [1, 150, 128, 171], Class Predictions Shape: [1, 150, 151]'
+ ```"""
+
+ if pixel_values is None:
+ raise ValueError("You have to specify pixel_values")
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ batch_size, _, height, width = pixel_values.shape
+
+ if pixel_mask is None:
+ pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device)
+
+ pixel_level_module_output = self.pixel_level_module(pixel_values, output_hidden_states)
+
+ multi_scale_features = pixel_level_module_output.decoder_features
+ mask_features = pixel_level_module_output.decoder_last_feature
+
+ task_token = self.task_encoder(task_inputs.to(self.dtype))
+
+ if self.is_training:
+ text_queries = self.text_mapper(text_inputs)
+ else:
+ text_queries = None
+
+ transformer_module_output = self.transformer_module(
+ multi_scale_features=multi_scale_features,
+ mask_features=mask_features,
+ task_token=task_token,
+ output_attentions=output_attentions,
+ )
+
+ queries = transformer_module_output.object_queries
+
+ encoder_hidden_states = None
+ pixel_decoder_hidden_states = None
+ transformer_decoder_hidden_states = None
+
+ if output_hidden_states:
+ encoder_hidden_states = pixel_level_module_output.encoder_features
+ pixel_decoder_hidden_states = (pixel_level_module_output.decoder_last_feature,)
+ for f in pixel_level_module_output.decoder_features:
+ pixel_decoder_hidden_states += (f,)
+ transformer_decoder_hidden_states = transformer_module_output.auxiliary_predictions
+
+ output = OneFormerModelOutput(
+ encoder_hidden_states=encoder_hidden_states,
+ pixel_decoder_hidden_states=pixel_decoder_hidden_states,
+ transformer_decoder_hidden_states=transformer_decoder_hidden_states,
+ transformer_decoder_object_queries=queries,
+ transformer_decoder_contrastive_queries=transformer_module_output.contrastive_logits,
+ transformer_decoder_mask_predictions=transformer_module_output.prediction_masks,
+ transformer_decoder_class_predictions=transformer_module_output.prediction_class,
+ transformer_decoder_auxiliary_predictions=transformer_module_output.auxiliary_predictions,
+ text_queries=text_queries,
+ task_token=task_token,
+ attentions=transformer_module_output.attentions,
+ )
+
+ if not return_dict:
+ output = tuple(v for v in output.values())
+
+ return output
+
+
+@auto_docstring(
+ custom_intro="""
+ OneFormer Model for instance, semantic and panoptic image segmentation.
+ """
+)
+class OneFormerForUniversalSegmentation(OneFormerPreTrainedModel):
+ main_input_name = ["pixel_values", "task_inputs"]
+
+ def __init__(self, config: OneFormerConfig):
+ super().__init__(config)
+ self.model = OneFormerModel(config)
+
+ self.matcher = OneFormerHungarianMatcher(
+ cost_class=config.class_weight,
+ cost_dice=config.dice_weight,
+ cost_mask=config.mask_weight,
+ num_points=config.train_num_points,
+ )
+
+ self.weight_dict: dict[str, float] = {
+ "loss_cross_entropy": config.class_weight,
+ "loss_mask": config.mask_weight,
+ "loss_dice": config.dice_weight,
+ "loss_contrastive": config.contrastive_weight,
+ }
+
+ self.criterion = OneFormerLoss(
+ num_classes=config.num_labels,
+ matcher=self.matcher,
+ weight_dict=self.weight_dict,
+ eos_coef=config.no_object_weight,
+ num_points=config.train_num_points,
+ oversample_ratio=config.oversample_ratio,
+ importance_sample_ratio=config.importance_sample_ratio,
+ contrastive_temperature=config.contrastive_temperature,
+ )
+
+ self.post_init()
+
+ def get_loss_dict(
+ self,
+ masks_queries_logits: Tensor,
+ class_queries_logits: Tensor,
+ contrastive_queries_logits: Tensor,
+ mask_labels: Tensor,
+ class_labels: Tensor,
+ text_queries: Tensor,
+ auxiliary_predictions: dict[str, Tensor],
+ calculate_contrastive_loss: bool,
+ ) -> dict[str, Tensor]:
+ loss_dict: dict[str, Tensor] = self.criterion(
+ masks_queries_logits=masks_queries_logits,
+ class_queries_logits=class_queries_logits,
+ contrastive_queries_logits=contrastive_queries_logits,
+ mask_labels=mask_labels,
+ class_labels=class_labels,
+ text_queries=text_queries,
+ auxiliary_predictions=auxiliary_predictions,
+ calculate_contrastive_loss=calculate_contrastive_loss,
+ )
+
+ # weight each loss by `self.weight_dict[]` including auxiliary losses
+ for key, weight in self.weight_dict.items():
+ for loss_key, loss in loss_dict.items():
+ if key in loss_key:
+ loss *= weight
+
+ return loss_dict
+
+ def get_loss(self, loss_dict: dict[str, Tensor]) -> Tensor:
+ return sum(loss_dict.values())
+
+ @auto_docstring
+ def forward(
+ self,
+ pixel_values: Tensor,
+ task_inputs: Tensor,
+ text_inputs: Optional[Tensor] = None,
+ mask_labels: Optional[list[Tensor]] = None,
+ class_labels: Optional[list[Tensor]] = None,
+ pixel_mask: Optional[Tensor] = None,
+ output_auxiliary_logits: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> OneFormerForUniversalSegmentationOutput:
+ r"""
+ task_inputs (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
+ Task inputs. Task inputs can be obtained using [`AutoImageProcessor`]. See [`OneFormerProcessor.__call__`]
+ for details.
+ text_inputs (`list[torch.Tensor]`, *optional*):
+ Tensor fof shape `(num_queries, sequence_length)` to be fed to a model
+ mask_labels (`list[torch.Tensor]`, *optional*):
+ List of mask labels of shape `(num_labels, height, width)` to be fed to a model
+ class_labels (`list[torch.LongTensor]`, *optional*):
+ list of target class labels of shape `(num_labels, height, width)` to be fed to a model. They identify the
+ labels of `mask_labels`, e.g. the label of `mask_labels[i][j]` if `class_labels[i][j]`.
+ output_auxiliary_logits (`bool`, *optional*):
+ Whether or not to output auxiliary logits.
+
+ Example:
+
+ Universal segmentation example:
+
+ ```python
+ >>> from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation
+ >>> from PIL import Image
+ >>> import requests
+ >>> import torch
+
+ >>> # load OneFormer fine-tuned on ADE20k for universal segmentation
+ >>> processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")
+ >>> model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny")
+
+ >>> url = (
+ ... "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
+ ... )
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> # Semantic Segmentation
+ >>> inputs = processor(image, ["semantic"], return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+ >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)`
+ >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
+ >>> class_queries_logits = outputs.class_queries_logits
+ >>> masks_queries_logits = outputs.masks_queries_logits
+
+ >>> # you can pass them to processor for semantic postprocessing
+ >>> predicted_semantic_map = processor.post_process_semantic_segmentation(
+ ... outputs, target_sizes=[(image.height, image.width)]
+ ... )[0]
+ >>> f"👉 Semantic Predictions Shape: {list(predicted_semantic_map.shape)}"
+ '👉 Semantic Predictions Shape: [512, 683]'
+
+ >>> # Instance Segmentation
+ >>> inputs = processor(image, ["instance"], return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+ >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)`
+ >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
+ >>> class_queries_logits = outputs.class_queries_logits
+ >>> masks_queries_logits = outputs.masks_queries_logits
+
+ >>> # you can pass them to processor for instance postprocessing
+ >>> predicted_instance_map = processor.post_process_instance_segmentation(
+ ... outputs, target_sizes=[(image.height, image.width)]
+ ... )[0]["segmentation"]
+ >>> f"👉 Instance Predictions Shape: {list(predicted_instance_map.shape)}"
+ '👉 Instance Predictions Shape: [512, 683]'
+
+ >>> # Panoptic Segmentation
+ >>> inputs = processor(image, ["panoptic"], return_tensors="pt")
+
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+ >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)`
+ >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
+ >>> class_queries_logits = outputs.class_queries_logits
+ >>> masks_queries_logits = outputs.masks_queries_logits
+
+ >>> # you can pass them to processor for panoptic postprocessing
+ >>> predicted_panoptic_map = processor.post_process_panoptic_segmentation(
+ ... outputs, target_sizes=[(image.height, image.width)]
+ ... )[0]["segmentation"]
+ >>> f"👉 Panoptic Predictions Shape: {list(predicted_panoptic_map.shape)}"
+ '👉 Panoptic Predictions Shape: [512, 683]'
+ ```
+ """
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.model(
+ pixel_values=pixel_values,
+ task_inputs=task_inputs,
+ text_inputs=text_inputs,
+ pixel_mask=pixel_mask,
+ output_hidden_states=output_hidden_states or self.config.use_auxiliary_loss,
+ output_attentions=output_attentions,
+ return_dict=True,
+ )
+
+ loss, loss_dict, auxiliary_predictions = None, None, None
+
+ class_queries_logits = outputs.transformer_decoder_class_predictions
+ masks_queries_logits = outputs.transformer_decoder_mask_predictions
+ contrastive_queries_logits = outputs.transformer_decoder_contrastive_queries
+ auxiliary_predictions = outputs.transformer_decoder_auxiliary_predictions
+ text_queries = outputs.text_queries
+
+ if mask_labels is not None and class_labels is not None:
+ loss_dict: dict[str, Tensor] = self.get_loss_dict(
+ masks_queries_logits=masks_queries_logits,
+ class_queries_logits=class_queries_logits,
+ contrastive_queries_logits=contrastive_queries_logits,
+ mask_labels=mask_labels,
+ class_labels=class_labels,
+ text_queries=text_queries,
+ auxiliary_predictions=auxiliary_predictions,
+ calculate_contrastive_loss=self.config.contrastive_temperature is not None,
+ )
+ loss = self.get_loss(loss_dict)
+
+ output_auxiliary_logits = (
+ self.config.output_auxiliary_logits if output_auxiliary_logits is None else output_auxiliary_logits
+ )
+ if not output_auxiliary_logits:
+ auxiliary_predictions = None
+
+ output = OneFormerForUniversalSegmentationOutput(
+ class_queries_logits=class_queries_logits,
+ masks_queries_logits=masks_queries_logits,
+ auxiliary_predictions=auxiliary_predictions,
+ loss=loss,
+ **outputs,
+ )
+
+ if not return_dict:
+ output = tuple(v for v in output.values())
+ if loss is not None:
+ output = (loss) + output
+ return output
+
+
+__all__ = ["OneFormerForUniversalSegmentation", "OneFormerModel", "OneFormerPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/processing_oneformer.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/processing_oneformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcc0f9cbb61e73546537dbad7e9cd5a1d7727f0c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/oneformer/processing_oneformer.py
@@ -0,0 +1,205 @@
+# coding=utf-8
+# Copyright 2022 SHI Labs and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Image/Text processor class for OneFormer
+"""
+
+from ...processing_utils import ProcessorMixin
+from ...utils import is_torch_available
+
+
+if is_torch_available():
+ import torch
+
+
+class OneFormerProcessor(ProcessorMixin):
+ r"""
+ Constructs an OneFormer processor which wraps [`OneFormerImageProcessor`] and
+ [`CLIPTokenizer`]/[`CLIPTokenizerFast`] into a single processor that inherits both the image processor and
+ tokenizer functionalities.
+
+ Args:
+ image_processor ([`OneFormerImageProcessor`]):
+ The image processor is a required input.
+ tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`]):
+ The tokenizer is a required input.
+ max_seq_len (`int`, *optional*, defaults to 77)):
+ Sequence length for input text list.
+ task_seq_len (`int`, *optional*, defaults to 77):
+ Sequence length for input task token.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "OneFormerImageProcessor"
+ tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
+
+ def __init__(
+ self, image_processor=None, tokenizer=None, max_seq_length: int = 77, task_seq_length: int = 77, **kwargs
+ ):
+ if image_processor is None:
+ raise ValueError("You need to specify an `image_processor`.")
+ if tokenizer is None:
+ raise ValueError("You need to specify a `tokenizer`.")
+
+ self.max_seq_length = max_seq_length
+ self.task_seq_length = task_seq_length
+
+ super().__init__(image_processor, tokenizer)
+
+ def _preprocess_text(self, text_list=None, max_length=77):
+ if text_list is None:
+ raise ValueError("tokens cannot be None.")
+
+ tokens = self.tokenizer(text_list, padding="max_length", max_length=max_length, truncation=True)
+
+ attention_masks, input_ids = tokens["attention_mask"], tokens["input_ids"]
+
+ token_inputs = []
+ for attn_mask, input_id in zip(attention_masks, input_ids):
+ token = torch.tensor(attn_mask) * torch.tensor(input_id)
+ token_inputs.append(token.unsqueeze(0))
+
+ token_inputs = torch.cat(token_inputs, dim=0)
+ return token_inputs
+
+ def __call__(self, images=None, task_inputs=None, segmentation_maps=None, **kwargs):
+ """
+ Main method to prepare for the model one or several task input(s) and image(s). This method forwards the
+ `task_inputs` and `kwargs` arguments to CLIPTokenizer's [`~CLIPTokenizer.__call__`] if `task_inputs` is not
+ `None` to encode. To prepare the image(s), this method forwards the `images` and `kwargs` arguments to
+ OneFormerImageProcessor's [`~OneFormerImageProcessor.__call__`] if `images` is not `None`. Please refer to the
+ docstring of the above two methods for more information.
+
+ Args:
+ task_inputs (`str`, `list[str]`):
+ The sequence or batch of task_inputs sequences to be encoded. Each sequence can be a string or a list
+ of strings of the template "the task is {task}".
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`,
+ `list[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ segmentation_maps (`ImageInput`, *optional*):
+ The corresponding semantic segmentation maps with the pixel-wise annotations.
+
+ (`bool`, *optional*, defaults to `True`):
+ Whether or not to pad images up to the largest image in a batch and create a pixel mask.
+
+ If left to the default, will return a pixel mask that is:
+
+ - 1 for pixels that are real (i.e. **not masked**),
+ - 0 for pixels that are padding (i.e. **masked**).
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+ - **task_inputs** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+ """
+
+ if task_inputs is None:
+ raise ValueError("You have to specify the task_input. Found None.")
+ elif images is None:
+ raise ValueError("You have to specify the image. Found None.")
+
+ if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs):
+ raise ValueError("task_inputs must be semantic, instance, or panoptic.")
+
+ encoded_inputs = self.image_processor(images, task_inputs, segmentation_maps, **kwargs)
+
+ if isinstance(task_inputs, str):
+ task_inputs = [task_inputs]
+
+ if isinstance(task_inputs, list) and all(isinstance(task_input, str) for task_input in task_inputs):
+ task_token_inputs = []
+ for task in task_inputs:
+ task_input = f"the task is {task}"
+ task_token_inputs.append(task_input)
+ encoded_inputs["task_inputs"] = self._preprocess_text(task_token_inputs, max_length=self.task_seq_length)
+ else:
+ raise TypeError("Task Inputs should be a string or a list of strings.")
+
+ if hasattr(encoded_inputs, "text_inputs"):
+ texts_list = encoded_inputs.text_inputs
+
+ text_inputs = []
+ for texts in texts_list:
+ text_input_list = self._preprocess_text(texts, max_length=self.max_seq_length)
+ text_inputs.append(text_input_list.unsqueeze(0))
+
+ encoded_inputs["text_inputs"] = torch.cat(text_inputs, dim=0)
+
+ return encoded_inputs
+
+ def encode_inputs(self, images=None, task_inputs=None, segmentation_maps=None, **kwargs):
+ """
+ This method forwards all its arguments to [`OneFormerImageProcessor.encode_inputs`] and then tokenizes the
+ task_inputs. Please refer to the docstring of this method for more information.
+ """
+
+ if task_inputs is None:
+ raise ValueError("You have to specify the task_input. Found None.")
+ elif images is None:
+ raise ValueError("You have to specify the image. Found None.")
+
+ if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs):
+ raise ValueError("task_inputs must be semantic, instance, or panoptic.")
+
+ encoded_inputs = self.image_processor.encode_inputs(images, task_inputs, segmentation_maps, **kwargs)
+
+ if isinstance(task_inputs, str):
+ task_inputs = [task_inputs]
+
+ if isinstance(task_inputs, list) and all(isinstance(task_input, str) for task_input in task_inputs):
+ task_token_inputs = []
+ for task in task_inputs:
+ task_input = f"the task is {task}"
+ task_token_inputs.append(task_input)
+ encoded_inputs["task_inputs"] = self._preprocess_text(task_token_inputs, max_length=self.task_seq_length)
+ else:
+ raise TypeError("Task Inputs should be a string or a list of strings.")
+
+ if hasattr(encoded_inputs, "text_inputs"):
+ texts_list = encoded_inputs.text_inputs
+
+ text_inputs = []
+ for texts in texts_list:
+ text_input_list = self._preprocess_text(texts, max_length=self.max_seq_length)
+ text_inputs.append(text_input_list.unsqueeze(0))
+
+ encoded_inputs["text_inputs"] = torch.cat(text_inputs, dim=0)
+
+ return encoded_inputs
+
+ def post_process_semantic_segmentation(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to [`OneFormerImageProcessor.post_process_semantic_segmentation`].
+ Please refer to the docstring of this method for more information.
+ """
+ return self.image_processor.post_process_semantic_segmentation(*args, **kwargs)
+
+ def post_process_instance_segmentation(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to [`OneFormerImageProcessor.post_process_instance_segmentation`].
+ Please refer to the docstring of this method for more information.
+ """
+ return self.image_processor.post_process_instance_segmentation(*args, **kwargs)
+
+ def post_process_panoptic_segmentation(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to [`OneFormerImageProcessor.post_process_panoptic_segmentation`].
+ Please refer to the docstring of this method for more information.
+ """
+ return self.image_processor.post_process_panoptic_segmentation(*args, **kwargs)
+
+
+__all__ = ["OneFormerProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a07b0ab669f3a8b386e0fc99e99f5d5a780ef9c5
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_openai import *
+ from .modeling_openai import *
+ from .modeling_tf_openai import *
+ from .tokenization_openai import *
+ from .tokenization_openai_fast import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/configuration_openai.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/configuration_openai.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4f2fae9d304bc63b6e69a3e456141531a644bfd
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/configuration_openai.py
@@ -0,0 +1,156 @@
+# coding=utf-8
+# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""OpenAI GPT configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class OpenAIGPTConfig(PretrainedConfig):
+ """
+ This is the configuration class to store the configuration of a [`OpenAIGPTModel`] or a [`TFOpenAIGPTModel`]. It is
+ used to instantiate a GPT model according to the specified arguments, defining the model architecture.
+ Instantiating a configuration with the defaults will yield a similar configuration to that of the GPT
+ [openai-community/openai-gpt](https://huggingface.co/openai-community/openai-gpt) architecture from OpenAI.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 40478):
+ Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`OpenAIGPTModel`] or [`TFOpenAIGPTModel`].
+ n_positions (`int`, *optional*, defaults to 512):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ n_embd (`int`, *optional*, defaults to 768):
+ Dimensionality of the embeddings and hidden states.
+ n_layer (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ n_head (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ afn (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ resid_pdrop (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ embd_pdrop (`int`, *optional*, defaults to 0.1):
+ The dropout ratio for the embeddings.
+ attn_pdrop (`float`, *optional*, defaults to 0.1):
+ The dropout ratio for the attention.
+ layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
+ The epsilon to use in the layer normalization layers
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ summary_type (`str`, *optional*, defaults to `"cls_index"`):
+ Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
+ [`OpenAIGPTDoubleHeadsModel`].
+
+ Has to be one of the following options:
+
+ - `"last"`: Take the last token hidden state (like XLNet).
+ - `"first"`: Take the first token hidden state (like BERT).
+ - `"mean"`: Take the mean of all tokens hidden states.
+ - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
+ - `"attn"`: Not implemented now, use multi-head attention.
+ summary_use_proj (`bool`, *optional*, defaults to `True`):
+ Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
+ [`OpenAIGPTDoubleHeadsModel`].
+
+ Whether or not to add a projection after the vector extraction.
+ summary_activation (`str`, *optional*):
+ Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
+ [`OpenAIGPTDoubleHeadsModel`].
+
+ Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
+ summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
+ Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
+ [`OpenAIGPTDoubleHeadsModel`].
+
+ Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
+ summary_first_dropout (`float`, *optional*, defaults to 0.1):
+ Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
+ [`OpenAIGPTDoubleHeadsModel`].
+
+ The dropout ratio to be used after the projection and activation.
+
+
+ Examples:
+
+ ```python
+ >>> from transformers import OpenAIGPTConfig, OpenAIGPTModel
+
+ >>> # Initializing a GPT configuration
+ >>> configuration = OpenAIGPTConfig()
+
+ >>> # Initializing a model (with random weights) from the configuration
+ >>> model = OpenAIGPTModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "openai-gpt"
+ attribute_map = {
+ "max_position_embeddings": "n_positions",
+ "hidden_size": "n_embd",
+ "num_attention_heads": "n_head",
+ "num_hidden_layers": "n_layer",
+ }
+
+ def __init__(
+ self,
+ vocab_size=40478,
+ n_positions=512,
+ n_embd=768,
+ n_layer=12,
+ n_head=12,
+ afn="gelu",
+ resid_pdrop=0.1,
+ embd_pdrop=0.1,
+ attn_pdrop=0.1,
+ layer_norm_epsilon=1e-5,
+ initializer_range=0.02,
+ summary_type="cls_index",
+ summary_use_proj=True,
+ summary_activation=None,
+ summary_proj_to_labels=True,
+ summary_first_dropout=0.1,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.n_positions = n_positions
+ self.n_embd = n_embd
+ self.n_layer = n_layer
+ self.n_head = n_head
+ self.afn = afn
+ self.resid_pdrop = resid_pdrop
+ self.embd_pdrop = embd_pdrop
+ self.attn_pdrop = attn_pdrop
+ self.layer_norm_epsilon = layer_norm_epsilon
+ self.initializer_range = initializer_range
+ self.summary_type = summary_type
+ self.summary_use_proj = summary_use_proj
+ self.summary_activation = summary_activation
+ self.summary_first_dropout = summary_first_dropout
+ self.summary_proj_to_labels = summary_proj_to_labels
+ super().__init__(**kwargs)
+
+
+__all__ = ["OpenAIGPTConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/convert_openai_original_tf_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/convert_openai_original_tf_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d5218c204262f639a0b862c4106a3a04dc27d0b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/convert_openai_original_tf_checkpoint_to_pytorch.py
@@ -0,0 +1,74 @@
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert OpenAI GPT checkpoint."""
+
+import argparse
+
+import torch
+
+from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
+from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
+
+
+logging.set_verbosity_info()
+
+
+def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_config_file, pytorch_dump_folder_path):
+ # Construct model
+ if openai_config_file == "":
+ config = OpenAIGPTConfig()
+ else:
+ config = OpenAIGPTConfig.from_json_file(openai_config_file)
+ model = OpenAIGPTModel(config)
+
+ # Load weights from numpy
+ load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path)
+
+ # Save pytorch-model
+ pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
+ pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
+ print(f"Save PyTorch model to {pytorch_weights_dump_path}")
+ torch.save(model.state_dict(), pytorch_weights_dump_path)
+ print(f"Save configuration file to {pytorch_config_dump_path}")
+ with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
+ f.write(config.to_json_string())
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--openai_checkpoint_folder_path",
+ default=None,
+ type=str,
+ required=True,
+ help="Path to the TensorFlow checkpoint path.",
+ )
+ parser.add_argument(
+ "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
+ )
+ parser.add_argument(
+ "--openai_config_file",
+ default="",
+ type=str,
+ help=(
+ "An optional config json file corresponding to the pre-trained OpenAI model. \n"
+ "This specifies the model architecture."
+ ),
+ )
+ args = parser.parse_args()
+ convert_openai_checkpoint_to_pytorch(
+ args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
+ )
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/modeling_openai.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/modeling_openai.py
new file mode 100644
index 0000000000000000000000000000000000000000..27c84910cb43340b06e3b19a30ffbd793072ea3e
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/modeling_openai.py
@@ -0,0 +1,846 @@
+# coding=utf-8
+# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch OpenAI GPT model."""
+
+import json
+import math
+import os
+from dataclasses import dataclass
+from typing import Any, Callable, Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import gelu_new, get_activation, silu
+from ...generation import GenerationMixin
+from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer
+from ...utils import (
+ ModelOutput,
+ auto_docstring,
+ logging,
+)
+from .configuration_openai import OpenAIGPTConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
+ """Load tf pre-trained weights in a pytorch model (from NumPy arrays here)"""
+ import re
+
+ import numpy as np
+
+ if ".ckpt" in openai_checkpoint_folder_path:
+ openai_checkpoint_folder_path = os.path.dirname(openai_checkpoint_folder_path)
+
+ logger.info(f"Loading weights from {openai_checkpoint_folder_path}")
+
+ with open(openai_checkpoint_folder_path + "/parameters_names.json", "r", encoding="utf-8") as names_handle:
+ names = json.load(names_handle)
+ with open(openai_checkpoint_folder_path + "/params_shapes.json", "r", encoding="utf-8") as shapes_handle:
+ shapes = json.load(shapes_handle)
+ offsets = np.cumsum([np.prod(shape) for shape in shapes])
+ init_params = [np.load(openai_checkpoint_folder_path + f"/params_{n}.npy") for n in range(10)]
+ init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
+ init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]
+
+ # This was used when we had a single embedding matrix for positions and tokens
+ # init_params[0] = np.concatenate([init_params[1], init_params[0]], 0)
+ # del init_params[1]
+ init_params = [arr.squeeze() for arr in init_params]
+
+ # Check that the token and position embeddings weight dimensions map those of the init parameters.
+ if model.tokens_embed.weight.shape != init_params[1].shape:
+ raise ValueError(
+ f"tokens_embed.weight.shape: {model.tokens_embed.weight.shape} does not match init_param[1].shape:"
+ f" {init_params[1].shape}"
+ )
+
+ if model.positions_embed.weight.shape != init_params[0].shape:
+ raise ValueError(
+ f"positions_embed.weight.shape: {model.positions_embed.weight.shape} does not match init_param[0].shape:"
+ f" {init_params[0].shape}"
+ )
+
+ model.tokens_embed.weight.data = torch.from_numpy(init_params[1])
+ model.positions_embed.weight.data = torch.from_numpy(init_params[0])
+ names.pop(0)
+ # Pop position and token embedding arrays
+ init_params.pop(0)
+ init_params.pop(0)
+
+ for name, array in zip(names, init_params): # names[1:n_transfer], init_params[1:n_transfer]):
+ name = name[6:] # skip "model/"
+ if name[-2:] != ":0":
+ raise ValueError(f"Layer {name} does not end with :0")
+ name = name[:-2]
+ name = name.split("/")
+ pointer = model
+ for m_name in name:
+ if re.fullmatch(r"[A-Za-z]+\d+", m_name):
+ scope_names = re.split(r"(\d+)", m_name)
+ else:
+ scope_names = [m_name]
+ if scope_names[0] == "g":
+ pointer = getattr(pointer, "weight")
+ elif scope_names[0] == "b":
+ pointer = getattr(pointer, "bias")
+ elif scope_names[0] == "w":
+ pointer = getattr(pointer, "weight")
+ else:
+ pointer = getattr(pointer, scope_names[0])
+ if len(scope_names) >= 2:
+ num = int(scope_names[1])
+ pointer = pointer[num]
+
+ # Ensure that the pointer and array have compatible shapes.
+ if pointer.shape != array.shape:
+ raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
+
+ logger.info(f"Initialize PyTorch weight {name}")
+ pointer.data = torch.from_numpy(array)
+ return model
+
+
+ACT_FNS = {"relu": nn.ReLU(), "silu": silu, "gelu": gelu_new, "swish": silu}
+
+
+class Attention(nn.Module):
+ def __init__(self, nx, n_positions, config, scale=False):
+ super().__init__()
+ n_state = nx # in Attention: n_state=768 (nx=n_embd)
+ # [switch nx => n_state from Block to Attention to keep identical to TF implementation]
+ if n_state % config.n_head != 0:
+ raise ValueError(f"Attention n_state shape: {n_state} must be divisible by config.n_head {config.n_head}")
+ self.register_buffer(
+ "bias",
+ torch.tril(torch.ones(n_positions, n_positions)).view(1, 1, n_positions, n_positions),
+ persistent=False,
+ )
+ self.n_head = config.n_head
+ self.split_size = n_state
+ self.scale = scale
+
+ self.c_attn = Conv1D(n_state * 3, nx)
+ self.c_proj = Conv1D(n_state, nx)
+ self.attn_dropout = nn.Dropout(config.attn_pdrop)
+ self.resid_dropout = nn.Dropout(config.resid_pdrop)
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ if len(heads) == 0:
+ return
+ heads, index = find_pruneable_heads_and_indices(
+ heads, self.n_head, self.split_size // self.n_head, self.pruned_heads
+ )
+ index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
+ # Prune conv1d layers
+ self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1)
+ self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0)
+ # Update hyper params
+ self.split_size = (self.split_size // self.n_head) * (self.n_head - len(heads))
+ self.n_head = self.n_head - len(heads)
+ self.pruned_heads = self.pruned_heads.union(heads)
+
+ def _attn(self, q, k, v, attention_mask=None, head_mask=None, output_attentions=False):
+ w = torch.matmul(q, k)
+ if self.scale:
+ w = w / math.sqrt(v.size(-1))
+ # w = w * self.bias + -1e9 * (1 - self.bias) # TF implementation method: mask_attn_weights
+ # XD: self.b may be larger than w, so we need to crop it
+ b = self.bias[:, :, : w.size(-2), : w.size(-1)]
+ w = w * b + -1e4 * (1 - b)
+
+ if attention_mask is not None:
+ # Apply the attention mask
+ w = w + attention_mask
+
+ w = nn.functional.softmax(w, dim=-1)
+ w = self.attn_dropout(w)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ w = w * head_mask
+
+ outputs = [torch.matmul(w, v)]
+ if output_attentions:
+ outputs.append(w)
+ return outputs
+
+ def merge_heads(self, x):
+ x = x.permute(0, 2, 1, 3).contiguous()
+ new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
+ return x.view(*new_x_shape) # in Tensorflow implementation: fct merge_states
+
+ def split_heads(self, x, k=False):
+ new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
+ x = x.view(*new_x_shape) # in Tensorflow implementation: fct split_states
+ if k:
+ return x.permute(0, 2, 3, 1)
+ else:
+ return x.permute(0, 2, 1, 3)
+
+ def forward(self, x, attention_mask=None, head_mask=None, output_attentions=False):
+ x = self.c_attn(x)
+ query, key, value = x.split(self.split_size, dim=2)
+ query = self.split_heads(query)
+ key = self.split_heads(key, k=True)
+ value = self.split_heads(value)
+
+ attn_outputs = self._attn(query, key, value, attention_mask, head_mask, output_attentions)
+ a = attn_outputs[0]
+
+ a = self.merge_heads(a)
+ a = self.c_proj(a)
+ a = self.resid_dropout(a)
+
+ outputs = [a] + attn_outputs[1:]
+ return outputs # a, (attentions)
+
+
+class MLP(nn.Module):
+ def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd)
+ super().__init__()
+ nx = config.n_embd
+ self.c_fc = Conv1D(n_state, nx)
+ self.c_proj = Conv1D(nx, n_state)
+ self.act = ACT_FNS[config.afn]
+ self.dropout = nn.Dropout(config.resid_pdrop)
+
+ def forward(self, x):
+ h = self.act(self.c_fc(x))
+ h2 = self.c_proj(h)
+ return self.dropout(h2)
+
+
+class Block(nn.Module):
+ def __init__(self, n_positions, config, scale=False):
+ super().__init__()
+ nx = config.n_embd
+ self.attn = Attention(nx, n_positions, config, scale)
+ self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
+ self.mlp = MLP(4 * nx, config)
+ self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
+
+ def forward(self, x, attention_mask=None, head_mask=None, output_attentions=False):
+ attn_outputs = self.attn(
+ x,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ output_attentions=output_attentions,
+ )
+ a = attn_outputs[0]
+
+ n = self.ln_1(x + a)
+ m = self.mlp(n)
+ h = self.ln_2(n + m)
+
+ outputs = [h] + attn_outputs[1:]
+ return outputs
+
+
+# Copied from transformers.models.xlm.modeling_xlm.XLMSequenceSummary with XLM->OpenAIGPT
+class OpenAIGPTSequenceSummary(nn.Module):
+ r"""
+ Compute a single vector summary of a sequence hidden states.
+
+ Args:
+ config ([`OpenAIGPTConfig`]):
+ The config used by the model. Relevant arguments in the config class of the model are (refer to the actual
+ config class of your model for the default values it uses):
+
+ - **summary_type** (`str`) -- The method to use to make this summary. Accepted values are:
+
+ - `"last"` -- Take the last token hidden state (like XLNet)
+ - `"first"` -- Take the first token hidden state (like Bert)
+ - `"mean"` -- Take the mean of all tokens hidden states
+ - `"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2)
+ - `"attn"` -- Not implemented now, use multi-head attention
+
+ - **summary_use_proj** (`bool`) -- Add a projection after the vector extraction.
+ - **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes
+ (otherwise to `config.hidden_size`).
+ - **summary_activation** (`Optional[str]`) -- Set to `"tanh"` to add a tanh activation to the output,
+ another string or `None` will add no activation.
+ - **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation.
+ - **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation.
+ """
+
+ def __init__(self, config: OpenAIGPTConfig):
+ super().__init__()
+
+ self.summary_type = getattr(config, "summary_type", "last")
+ if self.summary_type == "attn":
+ # We should use a standard multi-head attention module with absolute positional embedding for that.
+ # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
+ # We can probably just use the multi-head attention module of PyTorch >=1.1.0
+ raise NotImplementedError
+
+ self.summary = nn.Identity()
+ if hasattr(config, "summary_use_proj") and config.summary_use_proj:
+ if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
+ num_classes = config.num_labels
+ else:
+ num_classes = config.hidden_size
+ self.summary = nn.Linear(config.hidden_size, num_classes)
+
+ activation_string = getattr(config, "summary_activation", None)
+ self.activation: Callable = get_activation(activation_string) if activation_string else nn.Identity()
+
+ self.first_dropout = nn.Identity()
+ if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
+ self.first_dropout = nn.Dropout(config.summary_first_dropout)
+
+ self.last_dropout = nn.Identity()
+ if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
+ self.last_dropout = nn.Dropout(config.summary_last_dropout)
+
+ def forward(
+ self, hidden_states: torch.FloatTensor, cls_index: Optional[torch.LongTensor] = None
+ ) -> torch.FloatTensor:
+ """
+ Compute a single vector summary of a sequence hidden states.
+
+ Args:
+ hidden_states (`torch.FloatTensor` of shape `[batch_size, seq_len, hidden_size]`):
+ The hidden states of the last layer.
+ cls_index (`torch.LongTensor` of shape `[batch_size]` or `[batch_size, ...]` where ... are optional leading dimensions of `hidden_states`, *optional*):
+ Used if `summary_type == "cls_index"` and takes the last token of the sequence as classification token.
+
+ Returns:
+ `torch.FloatTensor`: The summary of the sequence hidden states.
+ """
+ if self.summary_type == "last":
+ output = hidden_states[:, -1]
+ elif self.summary_type == "first":
+ output = hidden_states[:, 0]
+ elif self.summary_type == "mean":
+ output = hidden_states.mean(dim=1)
+ elif self.summary_type == "cls_index":
+ if cls_index is None:
+ cls_index = torch.full_like(
+ hidden_states[..., :1, :],
+ hidden_states.shape[-2] - 1,
+ dtype=torch.long,
+ )
+ else:
+ cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
+ cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
+ # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states
+ output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size)
+ elif self.summary_type == "attn":
+ raise NotImplementedError
+
+ output = self.first_dropout(output)
+ output = self.summary(output)
+ output = self.activation(output)
+ output = self.last_dropout(output)
+
+ return output
+
+
+@auto_docstring
+class OpenAIGPTPreTrainedModel(PreTrainedModel):
+ config: OpenAIGPTConfig
+ load_tf_weights = load_tf_weights_in_openai_gpt
+ base_model_prefix = "transformer"
+
+ def _init_weights(self, module):
+ """Initialize the weights."""
+ if isinstance(module, (nn.Linear, Conv1D)):
+ # Slightly different from the TF version which uses truncated_normal for initialization
+ # cf https://github.com/pytorch/pytorch/pull/5617
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for outputs of models predicting if two sentences are consecutive or not.
+ """
+)
+class OpenAIGPTDoubleHeadsModelOutput(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss.
+ mc_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mc_labels` is provided):
+ Multiple choice classification loss.
+ logits (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ mc_logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
+ Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ mc_loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ mc_logits: Optional[torch.FloatTensor] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@auto_docstring
+class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.tokens_embed = nn.Embedding(config.vocab_size, config.n_embd)
+ self.positions_embed = nn.Embedding(config.n_positions, config.n_embd)
+ self.drop = nn.Dropout(config.embd_pdrop)
+ self.h = nn.ModuleList([Block(config.n_positions, config, scale=True) for _ in range(config.n_layer)])
+
+ self.register_buffer("position_ids", torch.arange(config.n_positions), persistent=False)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.tokens_embed
+
+ def set_input_embeddings(self, new_embeddings):
+ self.tokens_embed = new_embeddings
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
+ """
+ for layer, heads in heads_to_prune.items():
+ self.h[layer].attn.prune_heads(heads)
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], BaseModelOutput]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ input_shape = input_ids.size()
+ input_ids = input_ids.view(-1, input_shape[-1])
+ elif inputs_embeds is not None:
+ input_shape = inputs_embeds.size()[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if position_ids is None:
+ # Code is different from when we had a single embedding matrix from position and token embeddings
+ position_ids = self.position_ids[None, : input_shape[-1]]
+
+ # Attention mask.
+ if attention_mask is not None:
+ # We create a 3D attention mask from a 2D tensor mask.
+ # Sizes are [batch_size, 1, 1, to_seq_length]
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+ # this attention mask is more simple than the triangular masking of causal attention
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+ attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and the dtype's smallest value for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+ attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
+ attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
+
+ # Prepare head mask if needed
+ head_mask = self.get_head_mask(head_mask, self.config.n_layer)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.tokens_embed(input_ids)
+ position_embeds = self.positions_embed(position_ids)
+ if token_type_ids is not None:
+ token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
+ token_type_embeds = self.tokens_embed(token_type_ids)
+ else:
+ token_type_embeds = 0
+ hidden_states = inputs_embeds + position_embeds + token_type_embeds
+ hidden_states = self.drop(hidden_states)
+
+ output_shape = input_shape + (hidden_states.size(-1),)
+
+ all_attentions = () if output_attentions else None
+ all_hidden_states = () if output_hidden_states else None
+ for i, block in enumerate(self.h):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ outputs = block(hidden_states, attention_mask, head_mask[i], output_attentions=output_attentions)
+ hidden_states = outputs[0]
+ if output_attentions:
+ all_attentions = all_attentions + (outputs[1],)
+
+ hidden_states = hidden_states.view(*output_shape)
+ # Add last layer
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
+
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ OpenAI GPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
+ embeddings).
+ """
+)
+class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.transformer = OpenAIGPTModel(config)
+ self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ **kwargs,
+ ) -> Union[tuple[torch.Tensor], CausalLMOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.transformer(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ lm_logits = self.lm_head(hidden_states)
+
+ loss = None
+ if labels is not None:
+ # Flatten the tokens
+ loss = self.loss_function(
+ lm_logits,
+ labels,
+ vocab_size=self.config.vocab_size,
+ **kwargs,
+ )
+
+ if not return_dict:
+ output = (lm_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return CausalLMOutput(
+ loss=loss,
+ logits=lm_logits,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(self, input_ids: torch.LongTensor, **kwargs) -> dict[str, Any]:
+ # Overwritten -- old model with reduced inputs
+ return {"input_ids": input_ids}
+
+
+@auto_docstring(
+ custom_intro="""
+ OpenAI GPT Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
+ RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
+ input embeddings, the classification head takes as input the input of a specified classification token index in the
+ input sequence).
+ """
+)
+class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ config.num_labels = 1
+ self.transformer = OpenAIGPTModel(config)
+ self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+ self.multiple_choice_head = OpenAIGPTSequenceSummary(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ mc_token_ids: Optional[torch.LongTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ mc_labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], OpenAIGPTDoubleHeadsModelOutput]:
+ r"""
+ mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
+ Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
+ 1]`.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+ `labels = input_ids` Indices are selected in `[-1, 0, ..., config.vocab_size]` All labels set to `-100` are
+ ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+ mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
+ where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above)
+
+ Examples:
+
+ ```python
+ >>> from transformers import AutoTokenizer, OpenAIGPTDoubleHeadsModel
+ >>> import torch
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/openai-gpt")
+ >>> model = OpenAIGPTDoubleHeadsModel.from_pretrained("openai-community/openai-gpt")
+ >>> tokenizer.add_special_tokens(
+ ... {"cls_token": "[CLS]"}
+ ... ) # Add a [CLS] to the vocabulary (we should train it also!)
+ >>> model.resize_token_embeddings(len(tokenizer))
+
+ >>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
+ >>> input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
+ >>> mc_token_ids = torch.tensor([input_ids.size(-1) - 1, input_ids.size(-1) - 1]).unsqueeze(0) # Batch size 1
+
+ >>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
+ >>> lm_logits = outputs.logits
+ >>> mc_logits = outputs.mc_logits
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.transformer(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+
+ lm_logits = self.lm_head(hidden_states)
+ mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)
+
+ lm_loss, mc_loss = None, None
+ if mc_labels is not None:
+ loss_fct = CrossEntropyLoss()
+ mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))
+ if labels is not None:
+ shift_logits = lm_logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ loss_fct = CrossEntropyLoss()
+ lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+ if not return_dict:
+ output = (lm_logits, mc_logits) + transformer_outputs[1:]
+ if mc_loss is not None:
+ output = (mc_loss,) + output
+ return ((lm_loss,) + output) if lm_loss is not None else output
+
+ return OpenAIGPTDoubleHeadsModelOutput(
+ loss=lm_loss,
+ mc_loss=mc_loss,
+ logits=lm_logits,
+ mc_logits=mc_logits,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The Original OpenAI GPT Model transformer with a sequence classification head on top (linear layer).
+ [`OpenAIGPTForSequenceClassification`] uses the last token in order to do the classification, as other causal
+ models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the
+ last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding
+ token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since
+ it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take
+ the last value in each row of the batch).
+ """
+)
+class OpenAIGPTForSequenceClassification(OpenAIGPTPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.transformer = OpenAIGPTModel(config)
+ self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ token_type_ids: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[tuple[torch.Tensor], SequenceClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.transformer(
+ input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size, sequence_length = input_ids.shape[:2]
+ else:
+ batch_size, sequence_length = inputs_embeds.shape[:2]
+
+ # Ensure the batch size is > 1 if there is no padding.
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ last_non_pad_token = -1
+ elif input_ids is not None:
+ # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+ non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+ token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
+ last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+ else:
+ last_non_pad_token = -1
+ logger.warning_once(
+ f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+ "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+ )
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=pooled_logits,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+
+__all__ = [
+ "OpenAIGPTDoubleHeadsModel",
+ "OpenAIGPTForSequenceClassification",
+ "OpenAIGPTLMHeadModel",
+ "OpenAIGPTModel",
+ "OpenAIGPTPreTrainedModel",
+ "load_tf_weights_in_openai_gpt",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/modeling_tf_openai.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/modeling_tf_openai.py
new file mode 100644
index 0000000000000000000000000000000000000000..0235159633b4fc6410f1e973dca90c29a1ddaa08
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/modeling_tf_openai.py
@@ -0,0 +1,936 @@
+# coding=utf-8
+# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TF 2.0 OpenAI GPT model."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput, TFSequenceClassifierOutput
+from ...modeling_tf_utils import (
+ TFCausalLanguageModelingLoss,
+ TFConv1D,
+ TFModelInputType,
+ TFPreTrainedModel,
+ TFSequenceClassificationLoss,
+ TFSequenceSummary,
+ TFSharedEmbeddings,
+ get_initializer,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
+from ...utils import (
+ ModelOutput,
+ add_code_sample_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+ replace_return_docstrings,
+)
+from .configuration_openai import OpenAIGPTConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "openai-community/openai-gpt"
+_CONFIG_FOR_DOC = "OpenAIGPTConfig"
+
+
+class TFAttention(keras.layers.Layer):
+ def __init__(self, nx, config, scale=False, **kwargs):
+ super().__init__(**kwargs)
+
+ n_state = nx # in Attention: n_state=768 (nx=n_embd)
+ # [switch nx => n_state from Block to Attention to keep identical to TF implementation]
+ assert n_state % config.n_head == 0, (
+ f"Hidden dimension {n_state} not dividable by number of heads {config.n_head}"
+ )
+ self.n_head = config.n_head
+ self.split_size = n_state
+ self.scale = scale
+ self.output_attentions = config.output_attentions
+
+ self.c_attn = TFConv1D(n_state * 3, nx, initializer_range=config.initializer_range, name="c_attn")
+ self.c_proj = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_proj")
+ self.attn_dropout = keras.layers.Dropout(config.attn_pdrop)
+ self.resid_dropout = keras.layers.Dropout(config.resid_pdrop)
+ self.n_state = n_state
+ self.pruned_heads = set()
+
+ def prune_heads(self, heads):
+ pass
+
+ @staticmethod
+ def causal_attention_mask(nd, ns):
+ """
+ 1's in the lower triangle, counting from the lower right corner. Same as tf.matrix_band_part(tf.ones([nd, ns]),
+ -1, ns-nd), but doesn't produce garbage on TPUs.
+ """
+ i = tf.range(nd)[:, None]
+ j = tf.range(ns)
+ m = i >= j - ns + nd
+ return m
+
+ def _attn(self, q, k, v, attention_mask, head_mask, output_attentions, training=False):
+ # q, k, v have shape [batch, heads, sequence, features]
+ w = tf.matmul(q, k, transpose_b=True)
+ if self.scale:
+ dk = tf.cast(shape_list(k)[-1], dtype=w.dtype) # scale attention_scores
+ w = w / tf.math.sqrt(dk)
+
+ # w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
+ _, _, nd, ns = shape_list(w)
+ b = tf.cast(self.causal_attention_mask(nd, ns), dtype=w.dtype)
+ b = tf.reshape(b, [1, 1, nd, ns])
+ w = w * b - 1e4 * (1 - b)
+
+ if attention_mask is not None:
+ # Apply the attention mask
+ attention_mask = tf.cast(attention_mask, dtype=w.dtype)
+ w = w + attention_mask
+
+ w = stable_softmax(w, axis=-1)
+ w = self.attn_dropout(w, training=training)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ w = w * head_mask
+
+ outputs = [tf.matmul(w, v)]
+ if output_attentions:
+ outputs.append(w)
+ return outputs
+
+ def merge_heads(self, x):
+ x = tf.transpose(x, [0, 2, 1, 3])
+ x_shape = shape_list(x)
+ new_x_shape = x_shape[:-2] + [x_shape[-2] * x_shape[-1]]
+ return tf.reshape(x, new_x_shape)
+
+ def split_heads(self, x):
+ x_shape = shape_list(x)
+ new_x_shape = x_shape[:-1] + [self.n_head, x_shape[-1] // self.n_head]
+ x = tf.reshape(x, new_x_shape)
+ return tf.transpose(x, (0, 2, 1, 3)) # (batch, head, seq_length, head_features)
+
+ def call(self, x, attention_mask, head_mask, output_attentions, training=False):
+ x = self.c_attn(x)
+ query, key, value = tf.split(x, 3, axis=2)
+ query = self.split_heads(query)
+ key = self.split_heads(key)
+ value = self.split_heads(value)
+
+ attn_outputs = self._attn(query, key, value, attention_mask, head_mask, output_attentions, training=training)
+ a = attn_outputs[0]
+
+ a = self.merge_heads(a)
+ a = self.c_proj(a)
+ a = self.resid_dropout(a, training=training)
+
+ outputs = [a] + attn_outputs[1:]
+ return outputs # a, (attentions)
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "c_attn", None) is not None:
+ with tf.name_scope(self.c_attn.name):
+ self.c_attn.build([None, None, self.n_state * 3])
+ if getattr(self, "c_proj", None) is not None:
+ with tf.name_scope(self.c_proj.name):
+ self.c_proj.build([None, None, self.n_state])
+
+
+class TFMLP(keras.layers.Layer):
+ def __init__(self, n_state, config, **kwargs):
+ super().__init__(**kwargs)
+ nx = config.n_embd
+ self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_fc")
+ self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initializer_range, name="c_proj")
+ self.act = get_tf_activation("gelu")
+ self.dropout = keras.layers.Dropout(config.resid_pdrop)
+ self.nx = nx
+ self.n_state = n_state
+
+ def call(self, x, training=False):
+ h = self.act(self.c_fc(x))
+ h2 = self.c_proj(h)
+ h2 = self.dropout(h2, training=training)
+ return h2
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "c_fc", None) is not None:
+ with tf.name_scope(self.c_fc.name):
+ self.c_fc.build([None, None, self.n_state])
+ if getattr(self, "c_proj", None) is not None:
+ with tf.name_scope(self.c_proj.name):
+ self.c_proj.build([None, None, self.nx])
+
+
+class TFBlock(keras.layers.Layer):
+ def __init__(self, config, scale=False, **kwargs):
+ super().__init__(**kwargs)
+ nx = config.n_embd
+ self.attn = TFAttention(nx, config, scale, name="attn")
+ self.ln_1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
+ self.mlp = TFMLP(4 * nx, config, name="mlp")
+ self.ln_2 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_2")
+ self.nx = nx
+
+ def call(self, x, attention_mask, head_mask, output_attentions, training=False):
+ output_attn = self.attn(x, attention_mask, head_mask, output_attentions, training=training)
+ a = output_attn[0] # output_attn: a, (attentions)
+
+ n = self.ln_1(x + a)
+ m = self.mlp(n, training=training)
+ h = self.ln_2(n + m)
+
+ outputs = [h] + output_attn[1:]
+ return outputs # x, (attentions)
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "attn", None) is not None:
+ with tf.name_scope(self.attn.name):
+ self.attn.build(None)
+ if getattr(self, "ln_1", None) is not None:
+ with tf.name_scope(self.ln_1.name):
+ self.ln_1.build([None, None, self.nx])
+ if getattr(self, "mlp", None) is not None:
+ with tf.name_scope(self.mlp.name):
+ self.mlp.build(None)
+ if getattr(self, "ln_2", None) is not None:
+ with tf.name_scope(self.ln_2.name):
+ self.ln_2.build([None, None, self.nx])
+
+
+@keras_serializable
+class TFOpenAIGPTMainLayer(keras.layers.Layer):
+ config_class = OpenAIGPTConfig
+
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(*inputs, **kwargs)
+
+ self.config = config
+ self.output_hidden_states = config.output_hidden_states
+ self.output_attentions = config.output_attentions
+ self.return_dict = config.use_return_dict
+ self.num_hidden_layers = config.n_layer
+ self.n_embd = config.n_embd
+ self.n_positions = config.n_positions
+ self.initializer_range = config.initializer_range
+
+ self.tokens_embed = TFSharedEmbeddings(
+ config.vocab_size, config.n_embd, initializer_range=config.initializer_range, name="tokens_embed"
+ )
+ self.drop = keras.layers.Dropout(config.embd_pdrop)
+ self.h = [TFBlock(config, scale=True, name=f"h_._{i}") for i in range(config.n_layer)]
+
+ def build(self, input_shape=None):
+ with tf.name_scope("positions_embed"):
+ self.positions_embed = self.add_weight(
+ name="embeddings",
+ shape=[self.n_positions, self.n_embd],
+ initializer=get_initializer(self.initializer_range),
+ )
+
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "tokens_embed", None) is not None:
+ with tf.name_scope(self.tokens_embed.name):
+ self.tokens_embed.build(None)
+ if getattr(self, "h", None) is not None:
+ for layer in self.h:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+ def get_input_embeddings(self):
+ return self.tokens_embed
+
+ def set_input_embeddings(self, value):
+ self.tokens_embed.weight = value
+ self.tokens_embed.vocab_size = shape_list(value)[0]
+
+ def _prune_heads(self, heads_to_prune):
+ """
+ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
+ """
+ raise NotImplementedError
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFBaseModelOutput:
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if position_ids is None:
+ position_ids = tf.expand_dims(tf.range(input_shape[-1]), axis=0)
+
+ if attention_mask is not None:
+ # We create a 3D attention mask from a 2D tensor mask.
+ # Sizes are [batch_size, 1, 1, to_seq_length]
+ # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+ # this attention mask is more simple than the triangular masking of causal attention
+ # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+ attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1]))
+
+ # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+ # masked positions, this operation will create a tensor which is 0.0 for
+ # positions we want to attend and -10000.0 for masked positions.
+ # Since we are adding it to the raw scores before the softmax, this is
+ # effectively the same as removing these entirely.
+
+ one_cst = tf.constant(1.0)
+ attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype)
+ attention_mask = tf.multiply(tf.subtract(one_cst, attention_mask), tf.constant(-10000.0))
+ else:
+ attention_mask = None
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ if head_mask is not None:
+ raise NotImplementedError
+ else:
+ head_mask = [None] * self.num_hidden_layers
+ # head_mask = tf.constant([0] * self.num_hidden_layers)
+
+ position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
+
+ if inputs_embeds is None:
+ check_embeddings_within_bounds(input_ids, self.config.vocab_size)
+ inputs_embeds = self.tokens_embed(input_ids, mode="embedding")
+ position_embeds = tf.gather(self.positions_embed, position_ids)
+ if token_type_ids is not None:
+ token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
+ check_embeddings_within_bounds(token_type_ids, self.config.vocab_size, "token_type_ids")
+ token_type_embeds = self.tokens_embed(token_type_ids, mode="embedding")
+ else:
+ token_type_embeds = 0
+ hidden_states = inputs_embeds + position_embeds + token_type_embeds
+ hidden_states = self.drop(hidden_states, training=training)
+
+ output_shape = input_shape + [shape_list(hidden_states)[-1]]
+
+ all_attentions = () if output_attentions else None
+ all_hidden_states = () if output_hidden_states else None
+ for i, block in enumerate(self.h):
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
+
+ outputs = block(
+ hidden_states,
+ attention_mask,
+ head_mask[i],
+ output_attentions,
+ training=training,
+ )
+ hidden_states = outputs[0]
+ if output_attentions:
+ all_attentions = all_attentions + (outputs[1],)
+
+ hidden_states = tf.reshape(hidden_states, output_shape)
+ # Add last hidden state
+ if output_hidden_states:
+ all_hidden_states = all_hidden_states + (hidden_states,)
+
+ if output_attentions:
+ # let the number of heads free (-1) so we can extract attention even after head pruning
+ attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:]
+ all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions)
+
+ if not return_dict:
+ return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
+
+ return TFBaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=all_hidden_states,
+ attentions=all_attentions,
+ )
+
+
+class TFOpenAIGPTPreTrainedModel(TFPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = OpenAIGPTConfig
+ base_model_prefix = "transformer"
+
+
+@dataclass
+class TFOpenAIGPTDoubleHeadsModelOutput(ModelOutput):
+ """
+ Base class for outputs of models predicting if two sentences are consecutive or not.
+
+ Args:
+ logits (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ mc_logits (`tf.Tensor` of shape `(batch_size, num_choices)`):
+ Prediction scores of the multiple choice classification head (scores for each choice before SoftMax).
+ hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
+ `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+ attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`.
+
+ Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+ heads.
+ """
+
+ logits: tf.Tensor | None = None
+ mc_logits: tf.Tensor | None = None
+ hidden_states: tuple[tf.Tensor] | None = None
+ attentions: tuple[tf.Tensor] | None = None
+
+
+OPENAI_GPT_START_DOCSTRING = r"""
+
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Parameters:
+ config ([`OpenAIGPTConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+OPENAI_GPT_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
+ [`PreTrainedTokenizer.encode`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ token_type_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ head_mask (`tf.Tensor` or `Numpy array` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+ config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+ used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+ eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+"""
+
+
+@add_start_docstrings(
+ "The bare OpenAI GPT transformer model outputting raw hidden-states without any specific head on top.",
+ OPENAI_GPT_START_DOCSTRING,
+)
+class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFBaseModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFBaseModelOutput:
+ outputs = self.transformer(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ return outputs
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "transformer", None) is not None:
+ with tf.name_scope(self.transformer.name):
+ self.transformer.build(None)
+
+
+@add_start_docstrings(
+ """
+ OpenAI GPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
+ embeddings).
+ """,
+ OPENAI_GPT_START_DOCSTRING,
+)
+class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel, TFCausalLanguageModelingLoss):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
+ # OpenAIGPT does not have past caching features
+ self.supports_xla_generation = False
+
+ def get_output_embeddings(self):
+ return self.get_input_embeddings()
+
+ def set_output_embeddings(self, value):
+ self.set_input_embeddings(value)
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFCausalLMOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFCausalLMOutput:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
+ config.vocab_size - 1]`.
+ """
+
+ transformer_outputs = self.transformer(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ hidden_states = transformer_outputs[0]
+
+ logits = self.transformer.tokens_embed(hidden_states, mode="linear")
+
+ loss = None
+ if labels is not None:
+ # shift labels to the left and cut last logit token
+ shifted_logits = logits[:, :-1]
+ labels = labels[:, 1:]
+ loss = self.hf_compute_loss(labels, shifted_logits)
+
+ if not return_dict:
+ output = (logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFCausalLMOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(self, inputs, **kwargs):
+ return {"input_ids": inputs}
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "transformer", None) is not None:
+ with tf.name_scope(self.transformer.name):
+ self.transformer.build(None)
+
+
+@add_start_docstrings(
+ """
+ OpenAI GPT Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
+ RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
+ input embeddings, the classification head takes as input the input of a specified classification token index in the
+ input sequence).
+ """,
+ OPENAI_GPT_START_DOCSTRING,
+)
+class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ config.num_labels = 1
+ self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
+ self.multiple_choice_head = TFSequenceSummary(
+ config, initializer_range=config.initializer_range, name="multiple_choice_head"
+ )
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=TFOpenAIGPTDoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC)
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ mc_token_ids: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFOpenAIGPTDoubleHeadsModelOutput:
+ r"""
+ mc_token_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
+ Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
+ 1]`.
+
+ Return:
+
+ Examples:
+
+ ```python
+ >>> import tensorflow as tf
+ >>> from transformers import AutoTokenizer, TFOpenAIGPTDoubleHeadsModel
+
+ >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/openai-gpt")
+ >>> model = TFOpenAIGPTDoubleHeadsModel.from_pretrained("openai-community/openai-gpt")
+
+ >>> # Add a [CLS] to the vocabulary (we should train it also!)
+ >>> tokenizer.add_special_tokens({"cls_token": "[CLS]"})
+ >>> model.resize_token_embeddings(len(tokenizer)) # Update the model embeddings with the new vocabulary size
+ >>> print(tokenizer.cls_token_id, len(tokenizer)) # The newly token the last token of the vocabulary
+
+ >>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"]
+ >>> encoding = tokenizer(choices, return_tensors="tf")
+ >>> inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
+ >>> inputs["mc_token_ids"] = tf.constant(
+ ... [inputs["input_ids"].shape[-1] - 1, inputs["input_ids"].shape[-1] - 1]
+ ... )[
+ ... None, :
+ ... ] # Batch size 1
+ >>> outputs = model(inputs)
+ >>> lm_prediction_scores, mc_prediction_scores = outputs[:2]
+ ```"""
+
+ if input_ids is not None:
+ input_shapes = shape_list(input_ids)
+ else:
+ input_shapes = shape_list(inputs_embeds)[:-1]
+
+ seq_length = input_shapes[-1]
+ flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
+ flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
+ flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
+ flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
+ transformer_outputs = self.transformer(
+ flat_input_ids,
+ flat_attention_mask,
+ flat_token_type_ids,
+ flat_position_ids,
+ head_mask,
+ inputs_embeds,
+ output_attentions,
+ output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ hidden_states = transformer_outputs[0]
+ hidden_states = tf.reshape(hidden_states, input_shapes + shape_list(hidden_states)[-1:])
+ if return_dict and output_hidden_states:
+ # We do this to match the slightly odd PT behaviour - the final hidden state is reshaped to rank 4 when the
+ # input is rank 3, but all other hidden states remain at rank-3 (with the first 2 dims merged)
+ all_hidden_states = transformer_outputs.hidden_states[:-1] + (hidden_states,)
+ else:
+ all_hidden_states = None
+ lm_logits = self.transformer.tokens_embed(hidden_states, mode="linear")
+ mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids, training=training)
+ mc_logits = tf.squeeze(mc_logits, axis=-1)
+
+ if not return_dict:
+ return (lm_logits, mc_logits) + transformer_outputs[1:]
+
+ return TFOpenAIGPTDoubleHeadsModelOutput(
+ logits=lm_logits,
+ mc_logits=mc_logits,
+ hidden_states=all_hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+ @property
+ def input_signature(self):
+ return {
+ "input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
+ "attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
+ "mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
+ }
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "transformer", None) is not None:
+ with tf.name_scope(self.transformer.name):
+ self.transformer.build(None)
+ if getattr(self, "multiple_choice_head", None) is not None:
+ with tf.name_scope(self.multiple_choice_head.name):
+ self.multiple_choice_head.build(None)
+
+
+@add_start_docstrings(
+ """
+ The OpenAI GPT Model transformer with a sequence classification head on top (linear layer).
+
+ [`TFOpenAIGPTForSequenceClassification`] uses the last token in order to do the classification, as other causal
+ models (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """,
+ OPENAI_GPT_START_DOCSTRING,
+)
+class TFOpenAIGPTForSequenceClassification(TFOpenAIGPTPreTrainedModel, TFSequenceClassificationLoss):
+ def __init__(self, config, *inputs, **kwargs):
+ super().__init__(config, *inputs, **kwargs)
+ self.num_labels = config.num_labels
+ self.score = keras.layers.Dense(
+ config.num_labels,
+ kernel_initializer=get_initializer(config.initializer_range),
+ name="score",
+ use_bias=False,
+ )
+ self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
+ self.config = config
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFSequenceClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ token_type_ids: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple | TFSequenceClassifierOutput:
+ r"""
+ labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
+ config.vocab_size - 1]`.
+ """
+ transformer_outputs = self.transformer(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+ logits_shape = shape_list(logits)
+ batch_size = logits_shape[0]
+
+ if self.config.pad_token_id is None:
+ last_non_pad_token = tf.fill((batch_size,), value=logits_shape[1] - 1)
+ else:
+ if input_ids is not None:
+ token_indices = tf.range(shape_list(input_ids)[-1])
+ non_pad_mask = tf.cast(input_ids != self.config.pad_token_id, token_indices.dtype)
+ last_non_pad_token = tf.reduce_max(token_indices * non_pad_mask, axis=-1)
+ else:
+ last_non_pad_token = tf.fill((batch_size,), value=logits_shape[1] - 1)
+ logger.warning_once(
+ f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+ "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+ )
+ loss = None
+
+ pooled_logits = tf.gather(logits, last_non_pad_token, batch_dims=1, axis=1)
+
+ if labels is not None:
+ if self.config.pad_token_id is None and logits_shape[0] != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+
+ loss = self.hf_compute_loss(tf.reshape(labels, [-1]), tf.reshape(pooled_logits, [-1, self.num_labels]))
+
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFSequenceClassifierOutput(
+ loss=loss,
+ logits=pooled_logits,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "score", None) is not None:
+ with tf.name_scope(self.score.name):
+ self.score.build([None, None, self.config.n_embd])
+ if getattr(self, "transformer", None) is not None:
+ with tf.name_scope(self.transformer.name):
+ self.transformer.build(None)
+
+
+__all__ = [
+ "TFOpenAIGPTDoubleHeadsModel",
+ "TFOpenAIGPTForSequenceClassification",
+ "TFOpenAIGPTLMHeadModel",
+ "TFOpenAIGPTMainLayer",
+ "TFOpenAIGPTModel",
+ "TFOpenAIGPTPreTrainedModel",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/tokenization_openai.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/tokenization_openai.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a9184cc395cab5beabfea30ef11eab928aca89c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/tokenization_openai.py
@@ -0,0 +1,396 @@
+# coding=utf-8
+# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for OpenAI GPT."""
+
+import json
+import os
+import re
+import unicodedata
+from typing import Optional
+
+from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {
+ "vocab_file": "vocab.json",
+ "merges_file": "merges.txt",
+}
+
+
+# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
+def whitespace_tokenize(text):
+ """Runs basic whitespace cleaning and splitting on a piece of text."""
+ text = text.strip()
+ if not text:
+ return []
+ tokens = text.split()
+ return tokens
+
+
+# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
+class BasicTokenizer:
+ """
+ Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
+
+ Args:
+ do_lower_case (`bool`, *optional*, defaults to `True`):
+ Whether or not to lowercase the input when tokenizing.
+ never_split (`Iterable`, *optional*):
+ Collection of tokens which will never be split during tokenization. Only has an effect when
+ `do_basic_tokenize=True`
+ tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+ Whether or not to tokenize Chinese characters.
+
+ This should likely be deactivated for Japanese (see this
+ [issue](https://github.com/huggingface/transformers/issues/328)).
+ strip_accents (`bool`, *optional*):
+ Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+ value for `lowercase` (as in the original BERT).
+ do_split_on_punc (`bool`, *optional*, defaults to `True`):
+ In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
+ the full context of the words, such as contractions.
+ """
+
+ def __init__(
+ self,
+ do_lower_case=True,
+ never_split=None,
+ tokenize_chinese_chars=True,
+ strip_accents=None,
+ do_split_on_punc=True,
+ ):
+ if never_split is None:
+ never_split = []
+ self.do_lower_case = do_lower_case
+ self.never_split = set(never_split)
+ self.tokenize_chinese_chars = tokenize_chinese_chars
+ self.strip_accents = strip_accents
+ self.do_split_on_punc = do_split_on_punc
+
+ def tokenize(self, text, never_split=None):
+ """
+ Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
+
+ Args:
+ never_split (`List[str]`, *optional*)
+ Kept for backward compatibility purposes. Now implemented directly at the base class level (see
+ [`PreTrainedTokenizer.tokenize`]) List of token not to split.
+ """
+ # union() returns a new set by concatenating the two sets.
+ never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
+ text = self._clean_text(text)
+
+ # This was added on November 1st, 2018 for the multilingual and Chinese
+ # models. This is also applied to the English models now, but it doesn't
+ # matter since the English models were not trained on any Chinese data
+ # and generally don't have any Chinese data in them (there are Chinese
+ # characters in the vocabulary because Wikipedia does have some Chinese
+ # words in the English Wikipedia.).
+ if self.tokenize_chinese_chars:
+ text = self._tokenize_chinese_chars(text)
+ # prevents treating the same character with different unicode codepoints as different characters
+ unicode_normalized_text = unicodedata.normalize("NFC", text)
+ orig_tokens = whitespace_tokenize(unicode_normalized_text)
+ split_tokens = []
+ for token in orig_tokens:
+ if token not in never_split:
+ if self.do_lower_case:
+ token = token.lower()
+ if self.strip_accents is not False:
+ token = self._run_strip_accents(token)
+ elif self.strip_accents:
+ token = self._run_strip_accents(token)
+ split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+ output_tokens = whitespace_tokenize(" ".join(split_tokens))
+ return output_tokens
+
+ def _run_strip_accents(self, text):
+ """Strips accents from a piece of text."""
+ text = unicodedata.normalize("NFD", text)
+ output = []
+ for char in text:
+ cat = unicodedata.category(char)
+ if cat == "Mn":
+ continue
+ output.append(char)
+ return "".join(output)
+
+ def _run_split_on_punc(self, text, never_split=None):
+ """Splits punctuation on a piece of text."""
+ if not self.do_split_on_punc or (never_split is not None and text in never_split):
+ return [text]
+ chars = list(text)
+ i = 0
+ start_new_word = True
+ output = []
+ while i < len(chars):
+ char = chars[i]
+ if _is_punctuation(char):
+ output.append([char])
+ start_new_word = True
+ else:
+ if start_new_word:
+ output.append([])
+ start_new_word = False
+ output[-1].append(char)
+ i += 1
+
+ return ["".join(x) for x in output]
+
+ def _tokenize_chinese_chars(self, text):
+ """Adds whitespace around any CJK character."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if self._is_chinese_char(cp):
+ output.append(" ")
+ output.append(char)
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+ def _is_chinese_char(self, cp):
+ """Checks whether CP is the codepoint of a CJK character."""
+ # This defines a "chinese character" as anything in the CJK Unicode block:
+ # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+ #
+ # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+ # despite its name. The modern Korean Hangul alphabet is a different block,
+ # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+ # space-separated words, so they are not treated specially and handled
+ # like the all of the other languages.
+ if (
+ (cp >= 0x4E00 and cp <= 0x9FFF)
+ or (cp >= 0x3400 and cp <= 0x4DBF)
+ or (cp >= 0x20000 and cp <= 0x2A6DF)
+ or (cp >= 0x2A700 and cp <= 0x2B73F)
+ or (cp >= 0x2B740 and cp <= 0x2B81F)
+ or (cp >= 0x2B820 and cp <= 0x2CEAF)
+ or (cp >= 0xF900 and cp <= 0xFAFF)
+ or (cp >= 0x2F800 and cp <= 0x2FA1F)
+ ):
+ return True
+
+ return False
+
+ def _clean_text(self, text):
+ """Performs invalid character removal and whitespace cleanup on text."""
+ output = []
+ for char in text:
+ cp = ord(char)
+ if cp == 0 or cp == 0xFFFD or _is_control(char):
+ continue
+ if _is_whitespace(char):
+ output.append(" ")
+ else:
+ output.append(char)
+ return "".join(output)
+
+
+def get_pairs(word):
+ """
+ Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length
+ strings)
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+def text_standardize(text):
+ """
+ fixes some issues the spacy tokenizer had on books corpus also does some whitespace standardization
+ """
+ text = text.replace("—", "-")
+ text = text.replace("–", "-")
+ text = text.replace("―", "-")
+ text = text.replace("…", "...")
+ text = text.replace("´", "'")
+ text = re.sub(r"""(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)""", r" \1 ", text)
+ text = re.sub(r"\s*\n\s*", " \n ", text)
+ text = re.sub(r"[^\S\n]+", " ", text)
+ return text.strip()
+
+
+class OpenAIGPTTokenizer(PreTrainedTokenizer):
+ """
+ Construct a GPT Tokenizer. Based on Byte-Pair-Encoding with the following peculiarities:
+
+ - lowercases all inputs,
+ - uses `SpaCy` tokenizer and `ftfy` for pre-BPE tokenization if they are installed, fallback to BERT's
+ `BasicTokenizer` if not.
+
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+ this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+
+ def __init__(self, vocab_file, merges_file, unk_token="", **kwargs):
+ try:
+ import ftfy
+ from spacy.lang.en import English
+
+ _nlp = English()
+ self.nlp = _nlp.tokenizer
+ self.fix_text = ftfy.fix_text
+ except ImportError:
+ logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.")
+ self.nlp = BasicTokenizer(do_lower_case=True)
+ self.fix_text = None
+
+ with open(vocab_file, encoding="utf-8") as vocab_handle:
+ self.encoder = json.load(vocab_handle)
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ with open(merges_file, encoding="utf-8") as merges_handle:
+ merges = merges_handle.read().split("\n")[1:-1]
+ merges = [tuple(merge.split()) for merge in merges]
+ self.bpe_ranks = dict(zip(merges, range(len(merges))))
+ self.cache = {}
+
+ super().__init__(unk_token=unk_token, **kwargs)
+
+ @property
+ def do_lower_case(self):
+ return True
+
+ @property
+ def vocab_size(self):
+ return len(self.encoder)
+
+ def get_vocab(self):
+ return dict(self.encoder, **self.added_tokens_encoder)
+
+ def bpe(self, token):
+ word = tuple(token[:-1]) + (token[-1] + "",)
+ if token in self.cache:
+ return self.cache[token]
+ pairs = get_pairs(word)
+
+ if not pairs:
+ return token + ""
+
+ while True:
+ bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+ if bigram not in self.bpe_ranks:
+ break
+ first, second = bigram
+ new_word = []
+ i = 0
+ while i < len(word):
+ try:
+ j = word.index(first, i)
+ except ValueError:
+ new_word.extend(word[i:])
+ break
+ else:
+ new_word.extend(word[i:j])
+ i = j
+
+ if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+ new_word.append(first + second)
+ i += 2
+ else:
+ new_word.append(word[i])
+ i += 1
+ new_word = tuple(new_word)
+ word = new_word
+ if len(word) == 1:
+ break
+ else:
+ pairs = get_pairs(word)
+ word = " ".join(word)
+ if word == "\n ":
+ word = "\n"
+ self.cache[token] = word
+ return word
+
+ def _tokenize(self, text):
+ """Tokenize a string."""
+ split_tokens = []
+ if self.fix_text is None:
+ # Using BERT's BasicTokenizer
+ text = self.nlp.tokenize(text)
+ for token in text:
+ split_tokens.extend(list(self.bpe(token).split(" ")))
+ else:
+ # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
+ text = self.nlp(text_standardize(self.fix_text(text)))
+ for token in text:
+ split_tokens.extend(list(self.bpe(token.text.lower()).split(" ")))
+ return split_tokens
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+ def _convert_id_to_token(self, index):
+ """Converts an id in a token (BPE) using the vocab."""
+ return self.decoder.get(index, self.unk_token)
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ out_string = "".join(tokens).replace("", " ").strip()
+ return out_string
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+ merge_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
+ )
+
+ with open(vocab_file, "w", encoding="utf-8") as f:
+ f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+ index = 0
+ with open(merge_file, "w", encoding="utf-8") as writer:
+ writer.write("#version: 0.2\n")
+ for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+ if index != token_index:
+ logger.warning(
+ f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
+ " Please check that the tokenizer is not corrupted!"
+ )
+ index = token_index
+ writer.write(" ".join(bpe_tokens) + "\n")
+ index += 1
+
+ return vocab_file, merge_file
+
+
+__all__ = ["OpenAIGPTTokenizer"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/tokenization_openai_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/tokenization_openai_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..83edf5eafa9468347c4b1c3b6bfab9d0e7759ec3
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/openai/tokenization_openai_fast.py
@@ -0,0 +1,66 @@
+# coding=utf-8
+# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Fast Tokenization classes for OpenAI GPT."""
+
+from typing import Optional
+
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import logging
+from .tokenization_openai import OpenAIGPTTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
+
+
+class OpenAIGPTTokenizerFast(PreTrainedTokenizerFast):
+ """
+ Construct a "fast" GPT Tokenizer (backed by HuggingFace's *tokenizers* library). Based on Byte-Pair-Encoding with
+ the following peculiarities:
+
+ - lower case all inputs
+ - uses BERT's BasicTokenizer for pre-BPE tokenization
+
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
+ refer to this superclass for more information regarding those methods.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ merges_file (`str`):
+ Path to the merges file.
+ unk_token (`str`, *optional*, defaults to `""`):
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+ token instead.
+ """
+
+ vocab_files_names = VOCAB_FILES_NAMES
+ model_input_names = ["input_ids", "attention_mask"]
+ slow_tokenizer_class = OpenAIGPTTokenizer
+
+ def __init__(self, vocab_file=None, merges_file=None, tokenizer_file=None, unk_token="", **kwargs):
+ super().__init__(vocab_file, merges_file, tokenizer_file=tokenizer_file, unk_token=unk_token, **kwargs)
+
+ @property
+ def do_lower_case(self):
+ return True
+
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
+ files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+ return tuple(files)
+
+
+__all__ = ["OpenAIGPTTokenizerFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d230de5ecadc752b4c0a1784a6295cebbb20ed55
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_opt import *
+ from .modeling_flax_opt import *
+ from .modeling_opt import *
+ from .modeling_tf_opt import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/configuration_opt.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/configuration_opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..58c2569d4ebbbd0f7d3f49519bf0a597f60e5104
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/configuration_opt.py
@@ -0,0 +1,146 @@
+# coding=utf-8
+# Copyright 2022 The Metaseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""OPT model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class OPTConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate a OPT model
+ according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+ defaults will yield a similar configuration to that of the OPT
+ [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) architecture.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 50272):
+ Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`OPTModel`]
+ hidden_size (`int`, *optional*, defaults to 768):
+ Dimensionality of the layers and the pooler layer.
+ num_hidden_layers (`int`, *optional*, defaults to 12):
+ Number of decoder layers.
+ ffn_dim (`int`, *optional*, defaults to 3072):
+ Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+ num_attention_heads (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"silu"` and `"gelu_new"` are supported.
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
+ just in case (e.g., 512 or 1024 or 2048).
+ do_layer_norm_before (`bool`, *optional*, defaults to `True`):
+ Whether to perform layer normalization before the attention block.
+ word_embed_proj_dim (`int`, *optional*):
+ `word_embed_proj_dim` can be set to down-project word embeddings, *e.g.* `opt-350m`. Defaults to
+ `hidden_size`.
+ dropout (`float`, *optional*, defaults to 0.1):
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ layerdrop (`float`, *optional*, defaults to 0.0):
+ The LayerDrop probability. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more
+ details.
+ init_std (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models).
+ enable_bias (`bool`, *optional*, defaults to `True`):
+ Whether or not if the linear layers in the attention blocks should use the bias term.
+ layer_norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+ Whether or not if the layer norms should have learnable parameters.
+
+ Example:
+
+ ```python
+ >>> from transformers import OPTConfig, OPTModel
+
+ >>> # Initializing a OPT facebook/opt-large style configuration
+ >>> configuration = OPTConfig()
+
+ >>> # Initializing a model (with random weights) from the facebook/opt-large style configuration
+ >>> model = OPTModel(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "opt"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=50272,
+ hidden_size=768,
+ num_hidden_layers=12,
+ ffn_dim=3072,
+ max_position_embeddings=2048,
+ do_layer_norm_before=True,
+ _remove_final_layer_norm=False,
+ word_embed_proj_dim=None,
+ dropout=0.1,
+ attention_dropout=0.0,
+ num_attention_heads=12,
+ activation_function="relu",
+ layerdrop=0.0,
+ init_std=0.02,
+ use_cache=True,
+ pad_token_id=1,
+ bos_token_id=2,
+ eos_token_id=2,
+ enable_bias=True,
+ layer_norm_elementwise_affine=True,
+ **kwargs,
+ ):
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ **kwargs,
+ )
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.num_attention_heads = num_attention_heads
+ self.word_embed_proj_dim = word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
+ self.ffn_dim = ffn_dim
+ self.hidden_size = hidden_size
+ self.num_hidden_layers = num_hidden_layers
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_function = activation_function
+ self.init_std = init_std
+ self.layerdrop = layerdrop
+ self.use_cache = use_cache
+ self.do_layer_norm_before = do_layer_norm_before
+ # We keep these variables at `True` for backward compatibility.
+ self.enable_bias = enable_bias
+ self.layer_norm_elementwise_affine = layer_norm_elementwise_affine
+
+ # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
+ # with checkpoints that have been fine-tuned before transformers v4.20.1
+ # see https://github.com/facebookresearch/metaseq/pull/164
+ self._remove_final_layer_norm = _remove_final_layer_norm
+
+
+__all__ = ["OPTConfig"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/convert_opt_original_pytorch_checkpoint_to_pytorch.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/convert_opt_original_pytorch_checkpoint_to_pytorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..98731ed2120391958b3aba2c9abcc1e226afaaf9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/convert_opt_original_pytorch_checkpoint_to_pytorch.py
@@ -0,0 +1,113 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert OPT checkpoint."""
+
+import argparse
+from pathlib import Path
+
+import torch
+
+from transformers import OPTConfig, OPTModel
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def load_checkpoint(checkpoint_path):
+ """Checkpoint path should end in model.pt"""
+ sd = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
+ if "model" in sd:
+ sd = torch.load(checkpoint_path, map_location="cpu", weights_only=True)["model"]
+
+ # pop unnecessary weights
+ keys_to_delete = [
+ "decoder.version",
+ "decoder.output_projection.weight",
+ ]
+ for key in keys_to_delete:
+ if key in sd:
+ sd.pop(key)
+
+ keys_to_rename = {
+ "decoder.project_in_dim.weight": "decoder.project_in.weight",
+ "decoder.project_out_dim.weight": "decoder.project_out.weight",
+ "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
+ "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
+ }
+ for old_key, new_key in keys_to_rename.items():
+ if old_key in sd:
+ sd[new_key] = sd.pop(old_key)
+
+ keys = list(sd.keys())
+ for key in keys:
+ if ".qkv_proj." in key:
+ value = sd[key]
+ # We split QKV in separate Q,K,V
+
+ q_name = key.replace(".qkv_proj.", ".q_proj.")
+ k_name = key.replace(".qkv_proj.", ".k_proj.")
+ v_name = key.replace(".qkv_proj.", ".v_proj.")
+
+ depth = value.shape[0]
+ assert depth % 3 == 0
+ # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
+ # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
+ k, v, q = torch.split(value, depth // 3, dim=0)
+
+ sd[q_name] = q
+ sd[k_name] = k
+ sd[v_name] = v
+ del sd[key]
+
+ return sd
+
+
+@torch.no_grad()
+def convert_opt_checkpoint(checkpoint_path, pytorch_dump_folder_path, config=None):
+ """
+ Copy/paste/tweak model's weights to our BERT structure.
+ """
+ state_dict = load_checkpoint(checkpoint_path)
+
+ if config is not None:
+ config = OPTConfig.from_pretrained(config)
+ else:
+ config = OPTConfig()
+
+ model = OPTModel(config).half().eval()
+ model.load_state_dict(state_dict)
+
+ # Check results
+ Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+ model.save_pretrained(pytorch_dump_folder_path)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ # Required parameters
+ parser.add_argument(
+ "--fairseq_path",
+ type=str,
+ help=(
+ "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
+ " https://huggingface.co/models?other=opt_metasq"
+ ),
+ )
+ parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
+ parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
+ args = parser.parse_args()
+ convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/modeling_flax_opt.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/modeling_flax_opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2f77ecbee264eb862c2d089a320206b1228990c
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/modeling_flax_opt.py
@@ -0,0 +1,802 @@
+# coding=utf-8
+# Copyright 2022 The Fairseq Authors and The Google Flax Team Authors And The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Flax OPT model."""
+
+from functools import partial
+from typing import Optional
+
+import flax.linen as nn
+import jax
+import jax.numpy as jnp
+from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
+from flax.linen import combine_masks, make_causal_mask
+from flax.linen.attention import dot_product_attention_weights
+from flax.traverse_util import flatten_dict, unflatten_dict
+from jax import lax
+from jax.random import PRNGKey
+
+from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxMaskedLMOutput
+from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring
+from ...utils import add_start_docstrings, logging
+from .configuration_opt import OPTConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
+_CONFIG_FOR_DOC = "OPTConfig"
+
+
+OPT_START_DOCSTRING = r"""
+ This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a Flax Linen
+ [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
+ regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
+
+ Finally, this model supports inherent JAX features such as:
+
+ - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
+ - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
+ - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
+ - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
+
+ Parameters:
+ config ([`OPTConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
+ dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
+ The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
+ `jax.numpy.bfloat16` (on TPUs).
+
+ This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
+ specified all the computation will be performed with the given `dtype`.
+
+ **Note that this only specifies the dtype of the computation and does not influence the dtype of model
+ parameters.**
+
+ If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
+ [`~FlaxPreTrainedModel.to_bf16`].
+"""
+
+OPT_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->OPT
+class FlaxOPTAttention(nn.Module):
+ config: OPTConfig
+ embed_dim: int
+ num_heads: int
+ dropout: float = 0.0
+ causal: bool = False
+ bias: bool = True
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self) -> None:
+ self.head_dim = self.embed_dim // self.num_heads
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ dense = partial(
+ nn.Dense,
+ self.embed_dim,
+ use_bias=self.bias,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+
+ self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
+ self.out_proj = dense()
+
+ self.dropout_layer = nn.Dropout(rate=self.dropout)
+
+ if self.causal:
+ self.causal_mask = make_causal_mask(
+ jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
+ )
+
+ def _split_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
+
+ def _merge_heads(self, hidden_states):
+ return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
+
+ @nn.compact
+ def _concatenate_to_cache(self, key, value, query, attention_mask):
+ """
+ This function takes projected key, value states from a single input token and concatenates the states to cached
+ states from previous steps. This function is slightly adapted from the official Flax repository:
+ https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
+ """
+ # detect if we're initializing by absence of existing cache data.
+ is_initialized = self.has_variable("cache", "cached_key")
+ cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
+ cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
+ cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
+
+ if is_initialized:
+ *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
+ # update key, value caches with our new 1d spatial slices
+ cur_index = cache_index.value
+ indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
+ key = lax.dynamic_update_slice(cached_key.value, key, indices)
+ value = lax.dynamic_update_slice(cached_value.value, value, indices)
+ cached_key.value = key
+ cached_value.value = value
+ num_updated_cache_vectors = query.shape[1]
+ cache_index.value = cache_index.value + num_updated_cache_vectors
+ # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
+ pad_mask = jnp.broadcast_to(
+ jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
+ tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
+ )
+ attention_mask = combine_masks(pad_mask, attention_mask)
+ return key, value, attention_mask
+
+ def __call__(
+ self,
+ hidden_states: jnp.ndarray,
+ key_value_states: Optional[jnp.ndarray] = None,
+ attention_mask: Optional[jnp.ndarray] = None,
+ init_cache: bool = False,
+ deterministic: bool = True,
+ ) -> tuple[jnp.ndarray]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+ batch_size = hidden_states.shape[0]
+
+ # get query proj
+ query_states = self.q_proj(hidden_states)
+ # get key, value proj
+ if is_cross_attention:
+ # cross_attentions
+ key_states = self.k_proj(key_value_states)
+ value_states = self.v_proj(key_value_states)
+ else:
+ # self_attention
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = self._split_heads(query_states)
+ key_states = self._split_heads(key_states)
+ value_states = self._split_heads(value_states)
+
+ # handle cache prepare causal attention mask
+ if self.causal:
+ query_length, key_length = query_states.shape[1], key_states.shape[1]
+ if self.has_variable("cache", "cached_key"):
+ mask_shift = self.variables["cache"]["cache_index"]
+ max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
+ causal_mask = lax.dynamic_slice(
+ self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
+ )
+ else:
+ causal_mask = self.causal_mask[:, :, :query_length, :key_length]
+ causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
+
+ # combine masks if needed
+ if attention_mask is not None and self.causal:
+ attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
+ attention_mask = combine_masks(attention_mask, causal_mask)
+ elif self.causal:
+ attention_mask = causal_mask
+ elif attention_mask is not None:
+ attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
+
+ # During fast autoregressive decoding, we feed one position at a time,
+ # and cache the keys and values step by step.
+ if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
+ key_states, value_states, attention_mask = self._concatenate_to_cache(
+ key_states, value_states, query_states, attention_mask
+ )
+
+ # Convert the boolean attention mask to an attention bias.
+ if attention_mask is not None:
+ # attention mask in the form of attention bias
+ attention_bias = lax.select(
+ attention_mask > 0,
+ jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
+ jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
+ )
+ else:
+ attention_bias = None
+
+ dropout_rng = None
+ if not deterministic and self.dropout > 0.0:
+ dropout_rng = self.make_rng("dropout")
+
+ attn_weights = dot_product_attention_weights(
+ query_states,
+ key_states,
+ bias=attention_bias,
+ dropout_rng=dropout_rng,
+ dropout_rate=self.dropout,
+ broadcast_dropout=True,
+ deterministic=deterministic,
+ dtype=self.dtype,
+ precision=None,
+ )
+
+ attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
+ attn_output = self._merge_heads(attn_output)
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+class FlaxOPTDecoderLayer(nn.Module):
+ config: OPTConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self) -> None:
+ self.embed_dim = self.config.hidden_size
+ self.self_attn = FlaxOPTAttention(
+ config=self.config,
+ embed_dim=self.embed_dim,
+ num_heads=self.config.num_attention_heads,
+ dropout=self.config.attention_dropout,
+ causal=True,
+ dtype=self.dtype,
+ )
+ self.do_layer_norm_before = self.config.do_layer_norm_before
+ self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+ self.activation_fn = ACT2FN[self.config.activation_function]
+
+ self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+ self.fc1 = nn.Dense(
+ self.config.ffn_dim,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+ self.fc2 = nn.Dense(
+ self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
+ )
+ self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+
+ def __call__(
+ self,
+ hidden_states: jnp.ndarray,
+ attention_mask: jnp.ndarray,
+ init_cache: bool = False,
+ output_attentions: bool = True,
+ deterministic: bool = True,
+ ) -> tuple[jnp.ndarray]:
+ residual = hidden_states
+
+ # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+ if self.do_layer_norm_before:
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ init_cache=init_cache,
+ deterministic=deterministic,
+ )
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+ hidden_states = residual + hidden_states
+ # 350m applies layer norm AFTER attention
+ if not self.do_layer_norm_before:
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Fully Connected
+ hidden_states_shape = hidden_states.shape
+ hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1])
+ residual = hidden_states
+
+ # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+ if self.do_layer_norm_before:
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+
+ hidden_states = (residual + hidden_states).reshape(hidden_states_shape)
+
+ # 350m applies layer norm AFTER attention
+ if not self.do_layer_norm_before:
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ return outputs
+
+
+class FlaxOPTDecoderLayerCollection(nn.Module):
+ config: OPTConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.layers = [
+ FlaxOPTDecoderLayer(self.config, name=str(i), dtype=self.dtype)
+ for i in range(self.config.num_hidden_layers)
+ ]
+ self.layerdrop = self.config.layerdrop
+
+ def __call__(
+ self,
+ hidden_states,
+ attention_mask,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ ):
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ deterministic=deterministic,
+ )
+
+ hidden_states = layer_outputs[0]
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ outputs = [hidden_states, all_hidden_states, all_self_attns]
+ return outputs
+
+
+class FlaxOPTLearnedPositionalEmbedding(nn.Embed):
+ """
+ This module learns positional embeddings up to a fixed maximum size.
+ """
+
+ def setup(self):
+ self.offset = 2
+ self.embedding = self.param(
+ "embedding", self.embedding_init, (self.num_embeddings + self.offset, self.features), self.param_dtype
+ )
+
+ def __call__(self, positions):
+ """`input_ids_shape` is expected to be [bsz x seqlen]."""
+
+ return super().__call__(positions + self.offset)
+
+
+class FlaxOPTDecoder(nn.Module):
+ config: OPTConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+ offset: int = 2
+
+ def setup(self):
+ self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+
+ embed_dim = self.config.hidden_size
+ self.padding_idx = self.config.pad_token_id
+ self.max_target_positions = self.config.max_position_embeddings
+
+ self.embed_tokens = nn.Embed(
+ self.config.vocab_size,
+ self.config.word_embed_proj_dim,
+ embedding_init=jax.nn.initializers.normal(self.config.init_std),
+ dtype=self.dtype,
+ )
+
+ self.embed_positions = FlaxOPTLearnedPositionalEmbedding(
+ self.config.max_position_embeddings,
+ embed_dim,
+ embedding_init=jax.nn.initializers.normal(self.config.init_std),
+ dtype=self.dtype,
+ )
+
+ if self.config.word_embed_proj_dim != self.config.hidden_size:
+ self.project_in = nn.Dense(self.config.hidden_size, use_bias=False)
+ self.project_out = nn.Dense(self.config.word_embed_proj_dim, use_bias=False)
+
+ else:
+ self.project_in = None
+ self.project_out = None
+
+ # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
+ # with checkpoints that have been fine-tuned before transformers v4.20.1
+ # see https://github.com/facebookresearch/metaseq/pull/164
+ if self.config.do_layer_norm_before and not self.config._remove_final_layer_norm:
+ self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
+ else:
+ self.final_layer_norm = None
+
+ self.layers = FlaxOPTDecoderLayerCollection(self.config, self.dtype)
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ position_ids,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ input_shape = input_ids.shape
+ input_ids = input_ids.reshape(-1, input_shape[-1])
+
+ inputs_embeds = self.embed_tokens(input_ids)
+ if self.project_in is not None:
+ inputs_embeds = self.project_in(inputs_embeds)
+
+ positions = self.embed_positions(position_ids)
+
+ hidden_states = inputs_embeds + positions
+
+ hidden_state, all_hidden_states, attentions = self.layers(
+ hidden_states,
+ attention_mask,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ )
+
+ if self.final_layer_norm is not None:
+ hidden_state = self.final_layer_norm(hidden_state)
+
+ if self.project_out is not None:
+ hidden_state = self.project_out(hidden_state)
+
+ if output_hidden_states:
+ all_hidden_states += (hidden_state,)
+
+ outputs = [hidden_state, all_hidden_states, attentions]
+
+ if not return_dict:
+ return tuple(v for v in outputs if v is not None)
+
+ return FlaxBaseModelOutput(
+ last_hidden_state=hidden_state,
+ hidden_states=all_hidden_states,
+ attentions=attentions,
+ )
+
+
+class FlaxOPTPreTrainedModel(FlaxPreTrainedModel):
+ config_class = OPTConfig
+ base_model_prefix: str = "model"
+ module_class: nn.Module = None
+
+ def __init__(
+ self,
+ config: OPTConfig,
+ input_shape: tuple[int] = (1, 1),
+ seed: int = 0,
+ dtype: jnp.dtype = jnp.float32,
+ _do_init: bool = True,
+ **kwargs,
+ ):
+ module = self.module_class(config=config, dtype=dtype, **kwargs)
+ super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
+
+ def init_weights(self, rng: jax.random.PRNGKey, input_shape: tuple, params: FrozenDict = None) -> FrozenDict:
+ # init input tensors
+ input_ids = jnp.zeros(input_shape, dtype="i4")
+ attention_mask = jnp.ones_like(input_ids)
+
+ batch_size, sequence_length = input_ids.shape
+ position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+
+ params_rng, dropout_rng = jax.random.split(rng)
+ rngs = {"params": params_rng, "dropout": dropout_rng}
+
+ module_init_outputs = self.module.init(
+ rngs,
+ input_ids,
+ attention_mask,
+ position_ids,
+ return_dict=False,
+ )
+
+ random_params = module_init_outputs["params"]
+ if params is not None:
+ random_params = flatten_dict(unfreeze(random_params))
+ params = flatten_dict(unfreeze(params))
+ for missing_key in self._missing_keys:
+ params[missing_key] = random_params[missing_key]
+ self._missing_keys = set()
+ return freeze(unflatten_dict(params))
+ else:
+ return random_params
+
+ def init_cache(self, batch_size, max_length):
+ r"""
+ Args:
+ batch_size (`int`):
+ batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
+ max_length (`int`):
+ maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
+ cache.
+ """
+ # init input variables to retrieve cache
+ input_ids = jnp.ones((batch_size, max_length), dtype="i4")
+ attention_mask = jnp.ones_like(input_ids, dtype="i4")
+ position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
+
+ init_variables = self.module.init(
+ jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
+ )
+ return unfreeze(init_variables["cache"])
+
+ def __call__(
+ self,
+ input_ids: jnp.ndarray,
+ attention_mask: Optional[jnp.ndarray] = None,
+ position_ids: Optional[jnp.ndarray] = None,
+ params: Optional[dict] = None,
+ past_key_values: Optional[dict] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ dropout_rng: PRNGKey = None,
+ deterministic: bool = True,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ if attention_mask is None:
+ attention_mask = jnp.ones_like(input_ids)
+
+ if position_ids is None:
+ position_ids = (attention_mask.cumsum(axis=1) * attention_mask) - 1
+
+ # Handle any PRNG if needed
+ rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
+
+ inputs = {"params": params or self.params}
+
+ # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed
+ # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be
+ # changed by FlaxOPTAttention module
+ if past_key_values:
+ inputs["cache"] = past_key_values
+ mutable = ["cache"]
+ else:
+ mutable = False
+
+ outputs = self.module.apply(
+ inputs,
+ input_ids=jnp.array(input_ids, dtype="i4"),
+ attention_mask=jnp.array(attention_mask, dtype="i4"),
+ position_ids=jnp.array(position_ids, dtype="i4"),
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ rngs=rngs,
+ mutable=mutable,
+ )
+
+ # add updated cache to model output
+ if past_key_values is not None and return_dict:
+ outputs, past_key_values = outputs
+ outputs["past_key_values"] = unfreeze(past_key_values["cache"])
+ return outputs
+ elif past_key_values is not None and not return_dict:
+ outputs, past_key_values = outputs
+ outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
+
+ return outputs
+
+
+class FlaxOPTModule(nn.Module):
+ config: OPTConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+
+ def setup(self):
+ self.decoder = FlaxOPTDecoder(self.config, dtype=self.dtype)
+
+ def _get_decoder_module(self):
+ return self.decoder
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ position_ids,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ init_cache=False,
+ ):
+ decoder_outputs = self.decoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ )
+
+ if not return_dict:
+ return decoder_outputs
+
+ return FlaxBaseModelOutput(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ hidden_states=decoder_outputs.hidden_states,
+ attentions=decoder_outputs.attentions,
+ )
+
+
+# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModel with Bart->OPT
+class FlaxOPTModel(FlaxOPTPreTrainedModel):
+ config: OPTConfig
+ dtype: jnp.dtype = jnp.float32 # the dtype of the computation
+ module_class = FlaxOPTModule
+
+
+append_call_sample_docstring(FlaxOPTModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC)
+
+
+@add_start_docstrings(
+ "The bare OPT Model transformer outputting raw hidden-states without any specific head on top.",
+ OPT_START_DOCSTRING,
+)
+class FlaxOPTForCausalLMModule(nn.Module):
+ config: OPTConfig
+ dtype: jnp.dtype = jnp.float32
+
+ def setup(self):
+ self.model = FlaxOPTModule(config=self.config, dtype=self.dtype)
+ self.lm_head = nn.Dense(
+ self.config.vocab_size,
+ use_bias=False,
+ dtype=self.dtype,
+ kernel_init=jax.nn.initializers.normal(self.config.init_std),
+ )
+
+ def __call__(
+ self,
+ input_ids,
+ attention_mask,
+ position_ids,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ deterministic: bool = True,
+ ):
+ outputs = self.model(
+ input_ids,
+ attention_mask,
+ position_ids,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ deterministic=deterministic,
+ )
+
+ hidden_states = outputs[0]
+
+ if self.config.tie_word_embeddings:
+ shared_embedding = self.model.variables["params"]["decoder"]["embed_tokens"]["embedding"]
+ lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
+ else:
+ lm_logits = self.lm_head(hidden_states)
+
+ if not return_dict:
+ return (lm_logits,) + outputs[1:]
+
+ return FlaxMaskedLMOutput(
+ logits=lm_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@add_start_docstrings(
+ """
+ OPT Model with a language modeling head on top (linear layer with weights tied to the input embeddings) e.g for
+ autoregressive tasks.
+ """,
+ OPT_START_DOCSTRING,
+)
+class FlaxOPTForCausalLM(FlaxOPTPreTrainedModel):
+ module_class = FlaxOPTForCausalLMModule
+
+ def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
+ # initializing the cache
+ batch_size, seq_length = input_ids.shape
+
+ past_key_values = self.init_cache(batch_size, max_length)
+ # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
+ # But since the decoder uses a causal mask, those positions are masked anyway.
+ # Thus, we can create a single static attention_mask here, which is more efficient for compilation
+ extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
+
+ if attention_mask is not None:
+ position_ids = attention_mask.cumsum(axis=1) - 1
+ extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
+ else:
+ position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
+
+ return {
+ "past_key_values": past_key_values,
+ "attention_mask": extended_attention_mask,
+ "position_ids": position_ids,
+ }
+
+ def update_inputs_for_generation(self, model_outputs, model_kwargs):
+ model_kwargs["past_key_values"] = model_outputs.past_key_values
+ model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
+ return model_kwargs
+
+
+append_call_sample_docstring(
+ FlaxOPTForCausalLM,
+ _CHECKPOINT_FOR_DOC,
+ FlaxBaseModelOutput,
+ _CONFIG_FOR_DOC,
+)
+
+
+__all__ = ["FlaxOPTForCausalLM", "FlaxOPTModel", "FlaxOPTPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/modeling_opt.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/modeling_opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..738c556ace7820655e78914e6f05128364fa9edc
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/modeling_opt.py
@@ -0,0 +1,1113 @@
+# coding=utf-8
+# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch OPT model."""
+
+from typing import Callable, Optional, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...modeling_attn_mask_utils import AttentionMaskConverter
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+ BaseModelOutputWithPast,
+ CausalLMOutputWithPast,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutputWithPast,
+)
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
+from ...utils.deprecation import deprecate_kwarg
+from .configuration_opt import OPTConfig
+
+
+if is_torch_flex_attn_available():
+ from torch.nn.attention.flex_attention import BlockMask
+
+ from ...integrations.flex_attention import make_flex_block_causal_mask
+
+
+logger = logging.get_logger(__name__)
+
+
+class OPTLearnedPositionalEmbedding(nn.Embedding):
+ """
+ This module learns positional embeddings up to a fixed maximum size.
+ """
+
+ def __init__(self, num_embeddings: int, embedding_dim: int):
+ # OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
+ # and adjust num_embeddings appropriately. Other models don't have this hack
+ self.offset = 2
+ super().__init__(num_embeddings + self.offset, embedding_dim)
+
+ def forward(
+ self,
+ attention_mask: torch.LongTensor,
+ past_key_values_length: int = 0,
+ position_ids: Optional[torch.LongTensor] = None,
+ ):
+ """`input_ids_shape` is expected to be [bsz x seqlen]."""
+
+ if position_ids is None:
+ position_ids = torch.cumsum(attention_mask, dim=1)
+ position_ids = (position_ids * attention_mask - 1).long()
+ # cut positions if `past_key_values_length` is > 0
+ position_ids = position_ids[:, past_key_values_length:]
+
+ return super().forward(position_ids + self.offset)
+
+
+# Copied from transformers.models.siglip.modeling_siglip.eager_attention_forward
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs,
+):
+ attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
+ if attention_mask is not None:
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class OPTAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(
+ self,
+ config: OPTConfig,
+ layer_idx: Optional[int] = None,
+ **kwargs,
+ ):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.dropout = config.attention_dropout
+ self.enable_bias = config.enable_bias
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.head_dim = self.embed_dim // self.num_heads
+ self.is_causal = True
+
+ if (self.head_dim * self.num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+
+ self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
+ self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
+ self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
+ self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ past_key_values: Optional[tuple[torch.Tensor]] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ output_attentions: bool = False,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
+ """Input shape: Batch x Time x Channel"""
+ bsz, tgt_len, _ = hidden_states.size()
+
+ # Scaling is susceptible to floating point arithmetics' inprecisions
+ # which can lead to different results (this is dependent from model
+ # to model, e.g. whisper is one such case). We therefore keep the
+ # original order of scaling to follow the original implementation
+ # and enforce no scaling (1.0) in the attention call below.
+ query_states = self.q_proj(hidden_states) * self.scaling
+ query_states = query_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+ key_states = key_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+ if past_key_values is not None:
+ # save all key/value_states to cache to be re-used for fast auto-regressive generation
+ key_states, value_states = past_key_values.update(
+ key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+ )
+
+ attention_interface: Callable = eager_attention_forward
+
+ if self.config._attn_implementation != "eager":
+ if self.config._attn_implementation == "sdpa" and output_attentions:
+ logger.warning_once(
+ "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
+ 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ else:
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ dropout=0.0 if not self.training else self.dropout,
+ scaling=1.0,
+ **kwargs,
+ )
+
+ attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
+ attn_output = self.out_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights
+
+
+class OPTDecoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: OPTConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.embed_dim = config.hidden_size
+
+ self.self_attn = OPTAttention(config=config, layer_idx=layer_idx)
+
+ self.do_layer_norm_before = config.do_layer_norm_before
+ self.dropout = config.dropout
+ self.activation_fn = ACT2FN[config.activation_function]
+
+ self.self_attn_layer_norm = nn.LayerNorm(
+ self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
+ )
+ self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=config.enable_bias)
+ self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=config.enable_bias)
+ self.final_layer_norm = nn.LayerNorm(self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)
+
+ @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ layer_head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ position_ids: Optional[torch.LongTensor] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size
+ `(encoder_attention_heads,)`.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_values (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence..
+ """
+
+ residual = hidden_states
+
+ # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+ if self.do_layer_norm_before:
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_values=past_key_values,
+ position_ids=position_ids,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ output_attentions=output_attentions,
+ cache_position=cache_position,
+ **kwargs,
+ )
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+ hidden_states = residual + hidden_states
+
+ # 350m applies layer norm AFTER attention
+ if not self.do_layer_norm_before:
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Fully Connected
+ hidden_states_shape = hidden_states.shape
+ hidden_states = hidden_states.reshape(-1, hidden_states.size(-1))
+ residual = hidden_states
+
+ # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+ if self.do_layer_norm_before:
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+ hidden_states = (residual + hidden_states).view(hidden_states_shape)
+
+ # 350m applies layer norm AFTER attention
+ if not self.do_layer_norm_before:
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ return outputs
+
+
+@auto_docstring
+class OPTPreTrainedModel(PreTrainedModel):
+ config: OPTConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["OPTDecoderLayer"]
+ _supports_attention_backend = True
+ _supports_flash_attn = True
+ _supports_sdpa = True
+ _supports_flex_attn = True
+
+ _can_compile_fullgraph = True
+
+ def _init_weights(self, module):
+ std = self.config.init_std
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.weight.data.fill_(1.0)
+ module.bias.data.zero_()
+
+
+class OPTDecoder(OPTPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`]
+
+ Args:
+ config: OPTConfig
+ """
+
+ def __init__(self, config: OPTConfig):
+ super().__init__(config)
+ self.dropout = config.dropout
+ self.layerdrop = config.layerdrop
+ self.padding_idx = config.pad_token_id
+ self.max_target_positions = config.max_position_embeddings
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.word_embed_proj_dim, self.padding_idx)
+ self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size)
+
+ if config.word_embed_proj_dim != config.hidden_size:
+ self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False)
+ else:
+ self.project_out = None
+
+ if config.word_embed_proj_dim != config.hidden_size:
+ self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False)
+ else:
+ self.project_in = None
+
+ # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
+ # with checkpoints that have been fine-tuned before transformers v4.20.1
+ # see https://github.com/facebookresearch/metaseq/pull/164
+ if config.do_layer_norm_before and not config._remove_final_layer_norm:
+ self.final_layer_norm = nn.LayerNorm(
+ config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine
+ )
+ else:
+ self.final_layer_norm = None
+
+ self.layers = nn.ModuleList([OPTDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
+ def _update_causal_mask(
+ self,
+ attention_mask: Union[torch.Tensor, "BlockMask"],
+ input_tensor: torch.Tensor,
+ cache_position: torch.Tensor,
+ past_key_values: Cache,
+ output_attentions: bool = False,
+ ):
+ if self.config._attn_implementation == "flash_attention_2":
+ if attention_mask is not None and (attention_mask == 0.0).any():
+ return attention_mask
+ return None
+ if self.config._attn_implementation == "flex_attention":
+ if isinstance(attention_mask, torch.Tensor):
+ attention_mask = make_flex_block_causal_mask(attention_mask)
+ return attention_mask
+
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+ # to infer the attention mask.
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
+
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+ if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
+ attention_mask,
+ inputs_embeds=input_tensor,
+ past_key_values_length=past_seen_tokens,
+ is_training=self.training,
+ ):
+ return None
+
+ dtype = input_tensor.dtype
+ sequence_length = input_tensor.shape[1]
+ if using_compilable_cache:
+ target_length = past_key_values.get_max_cache_shape()
+ else:
+ target_length = (
+ attention_mask.shape[-1]
+ if isinstance(attention_mask, torch.Tensor)
+ else past_seen_tokens + sequence_length + 1
+ )
+
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+ causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask,
+ sequence_length=sequence_length,
+ target_length=target_length,
+ dtype=dtype,
+ cache_position=cache_position,
+ batch_size=input_tensor.shape[0],
+ )
+
+ if (
+ self.config._attn_implementation == "sdpa"
+ and attention_mask is not None
+ and attention_mask.device.type in ["cuda", "xpu", "npu"]
+ and not output_attentions
+ ):
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+ # Details: https://github.com/pytorch/pytorch/issues/110213
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+ return causal_mask
+
+ @staticmethod
+ # Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
+ def _prepare_4d_causal_attention_mask_with_cache_position(
+ attention_mask: torch.Tensor,
+ sequence_length: int,
+ target_length: int,
+ dtype: torch.dtype,
+ cache_position: torch.Tensor,
+ batch_size: int,
+ **kwargs,
+ ):
+ """
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+ Args:
+ attention_mask (`torch.Tensor`):
+ A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+ `(batch_size, 1, query_length, key_value_length)`.
+ sequence_length (`int`):
+ The sequence length being processed.
+ target_length (`int`):
+ The target length: when generating with static cache, the mask should be as long as the static cache,
+ to account for the 0 padding, the part of the cache that is not filled yet.
+ dtype (`torch.dtype`):
+ The dtype to use for the 4D attention mask.
+ cache_position (`torch.Tensor`):
+ Indices depicting the position of the input sequence tokens in the sequence.
+ batch_size (`torch.Tensor`):
+ Batch size.
+ """
+ if attention_mask is not None and attention_mask.dim() == 4:
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+ causal_mask = attention_mask
+ else:
+ min_dtype = torch.finfo(dtype).min
+ causal_mask = torch.full(
+ (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
+ )
+ if sequence_length != 1:
+ causal_mask = torch.triu(causal_mask, diagonal=1)
+ causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+ if attention_mask is not None:
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
+ mask_length = attention_mask.shape[-1]
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
+ causal_mask.device
+ )
+ padding_mask = padding_mask == 0
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+ padding_mask, min_dtype
+ )
+
+ return causal_mask
+
+ @can_return_tuple
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Cache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+ cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+ that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+ all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`. for padding use -1.
+
+ [What are position IDs?](../glossary#position-ids)
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+ Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+ this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+ the complete sequence length.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training and use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+ )
+ use_cache = False
+
+ if input_ids is not None:
+ input_ids = input_ids.view(-1, input_ids.shape[-1])
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if use_cache and past_key_values is None:
+ past_key_values = DynamicCache()
+
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+ if cache_position is None:
+ cache_position = torch.arange(
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+ )
+
+ if attention_mask is None:
+ seq_length = past_seen_tokens + inputs_embeds.shape[1]
+ attention_mask = torch.ones(inputs_embeds.shape[0], seq_length, device=inputs_embeds.device)
+
+ causal_mask = self._update_causal_mask(
+ attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+ )
+
+ # embed positions
+ if position_ids is None:
+ # position_ids = cache_position.unsqueeze(0)
+ position_ids = torch.cumsum(attention_mask, dim=1)
+ position_ids = (position_ids * attention_mask - 1).long()
+ # cut positions if `past_seen_tokens` is > 0
+ position_ids = position_ids[:, past_seen_tokens:]
+
+ pos_embeds = self.embed_positions(attention_mask, past_seen_tokens, position_ids=position_ids)
+
+ if self.project_in is not None:
+ inputs_embeds = self.project_in(inputs_embeds)
+
+ hidden_states = inputs_embeds + pos_embeds.to(inputs_embeds.device)
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+
+ # check if head_mask has a correct number of layers specified if desired
+ for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
+ if attn_mask is not None:
+ if attn_mask.size()[0] != (len(self.layers)):
+ raise ValueError(
+ f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
+ f" {head_mask.size()[0]}."
+ )
+
+ for idx, decoder_layer in enumerate(self.layers):
+ # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if self.training:
+ dropout_probability = torch.rand([])
+ if dropout_probability < self.layerdrop:
+ continue
+
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=causal_mask,
+ position_ids=position_ids,
+ layer_head_mask=(head_mask[idx] if head_mask is not None else None),
+ past_key_values=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if self.final_layer_norm is not None:
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ if self.project_out is not None:
+ hidden_states = self.project_out(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=past_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+@auto_docstring
+class OPTModel(OPTPreTrainedModel):
+ def __init__(self, config: OPTConfig):
+ super().__init__(config)
+ self.decoder = OPTDecoder(config)
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.decoder.embed_tokens = value
+
+ def get_decoder(self):
+ return self.decoder
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[FlashAttentionKwargs],
+ ) -> Union[tuple, BaseModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
+ decoder_outputs = self.decoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ return BaseModelOutputWithPast(
+ last_hidden_state=decoder_outputs.last_hidden_state,
+ past_key_values=decoder_outputs.past_key_values,
+ hidden_states=decoder_outputs.hidden_states,
+ attentions=decoder_outputs.attentions,
+ )
+
+
+class OPTForCausalLM(OPTPreTrainedModel, GenerationMixin):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = OPTModel(config)
+
+ # the lm_head weight is automatically tied to the embed tokens weight
+ self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.decoder.embed_tokens = value
+
+ def set_decoder(self, decoder):
+ self.model.decoder = decoder
+
+ def get_decoder(self):
+ return self.model.decoder
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ cache_position: Optional[torch.Tensor] = None,
+ **kwargs: Unpack[TransformersKwargs],
+ ) -> Union[tuple, CausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, OPTForCausalLM
+
+ >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious. I'm just a little bit of a weirdo."
+ ```"""
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model.decoder(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ logits = self.lm_head(outputs[0]).contiguous()
+
+ loss = None
+ if labels is not None:
+ # move labels to correct device to enable model parallelism
+ labels = labels.to(logits.device)
+ loss = self.loss_function(
+ logits,
+ labels,
+ vocab_size=self.config.vocab_size,
+ **kwargs,
+ )
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@auto_docstring(
+ custom_intro="""
+ The OPT Model transformer with a sequence classification head on top (linear layer).
+
+ [`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """
+)
+class OPTForSequenceClassification(OPTPreTrainedModel):
+ def __init__(self, config: OPTConfig):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.model = OPTModel(config)
+ self.score = nn.Linear(config.word_embed_proj_dim, self.num_labels, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.model(
+ input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size, sequence_length = input_ids.shape[:2]
+ else:
+ batch_size, sequence_length = inputs_embeds.shape[:2]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ last_non_pad_token = -1
+ elif input_ids is not None:
+ # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+ non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+ token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
+ last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+ else:
+ last_non_pad_token = -1
+ logger.warning_once(
+ f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+ "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+ )
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+ loss = None
+ if labels is not None:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+ def get_input_embeddings(self):
+ return self.model.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.decoder.embed_tokens = value
+
+
+@auto_docstring
+class OPTForQuestionAnswering(OPTPreTrainedModel):
+ def __init__(self, config: OPTConfig):
+ super().__init__(config)
+ self.model = OPTModel(config)
+ self.qa_outputs = nn.Linear(config.word_embed_proj_dim, 2)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: Optional[torch.LongTensor] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ head_mask: Optional[torch.FloatTensor] = None,
+ past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ start_positions: Optional[torch.LongTensor] = None,
+ end_positions: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ ) -> Union[tuple, QuestionAnsweringModelOutput]:
+ r"""
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, OPTForQuestionAnswering
+ >>> import torch
+
+ >>> torch.manual_seed(4) # doctest: +IGNORE_RESULT
+ >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+
+ >>> # note: we are loading a OPTForQuestionAnswering from the hub here,
+ >>> # so the head will be randomly initialized, hence the predictions will be random
+ >>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")
+
+ >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
+
+ >>> inputs = tokenizer(question, text, return_tensors="pt")
+ >>> with torch.no_grad():
+ ... outputs = model(**inputs)
+
+ >>> answer_start_index = outputs.start_logits.argmax()
+ >>> answer_end_index = outputs.end_logits.argmax()
+
+ >>> answer_offset = len(tokenizer(question)[0])
+
+ >>> predict_answer_tokens = inputs.input_ids[
+ ... 0, answer_offset + answer_start_index : answer_offset + answer_end_index + 1
+ ... ]
+ >>> predicted = tokenizer.decode(predict_answer_tokens)
+ >>> predicted
+ ' a nice puppet'
+ ```"""
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.model(
+ input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+
+ logits = self.qa_outputs(hidden_states)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index).to(logits.device)
+ end_positions = end_positions.clamp(0, ignored_index).to(logits.device)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + transformer_outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
+
+ def get_input_embeddings(self):
+ return self.model.decoder.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.decoder.embed_tokens = value
+
+
+__all__ = [
+ "OPTForCausalLM",
+ "OPTModel",
+ "OPTPreTrainedModel",
+ "OPTForSequenceClassification",
+ "OPTForQuestionAnswering",
+]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/modeling_tf_opt.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/modeling_tf_opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..f996256063c073fcb03c48e8b00fc17a8f4a0047
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/opt/modeling_tf_opt.py
@@ -0,0 +1,1092 @@
+# coding=utf-8
+# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TF 2.0 OPT model."""
+
+from __future__ import annotations
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast
+
+# Public API
+from ...modeling_tf_utils import (
+ TFCausalLanguageModelingLoss,
+ TFModelInputType,
+ TFPreTrainedModel,
+ TFSharedEmbeddings,
+ keras,
+ keras_serializable,
+ unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
+from ...utils import (
+ add_code_sample_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+ replace_return_docstrings,
+)
+from .configuration_opt import OPTConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
+_CONFIG_FOR_DOC = "OPTConfig"
+
+# Base model docstring
+_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]
+
+# Causal LM output
+_CAUSAL_LM_EXPECTED_OUTPUT = (
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious. I'm just a little bit of a weirdo."
+)
+
+LARGE_NEGATIVE = -1e8
+
+
+def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
+ """
+ Make causal mask used for bi-directional self-attention.
+ """
+ bsz = input_ids_shape[0]
+ tgt_len = input_ids_shape[1]
+ # We need triu with k = 1 but TF expects known compile-time dims for that, so we hack around it
+ mask = tf.fill((tgt_len, tgt_len), tf.cast(LARGE_NEGATIVE, tf.float32))
+ mask = tf.linalg.band_part(mask, 0, -1) - tf.linalg.band_part(mask, 0, 0)
+
+ if past_key_values_length > 0:
+ mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
+
+ return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
+
+
+# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
+def _expand_mask(mask: tf.Tensor, tgt_len: int | None = None):
+ """
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+ """
+ src_len = shape_list(mask)[1]
+ tgt_len = tgt_len if tgt_len is not None else src_len
+ one_cst = tf.constant(1.0)
+ mask = tf.cast(mask, dtype=one_cst.dtype)
+ expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
+
+ return (one_cst - expanded_mask) * LARGE_NEGATIVE
+
+
+class TFOPTLearnedPositionalEmbedding(keras.layers.Embedding):
+ """
+ This module learns positional embeddings up to a fixed maximum size.
+ """
+
+ def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
+ # OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
+ # and adjust num_embeddings appropriately. Other models don't have this hack
+ self.offset = 2
+ super().__init__(num_embeddings + self.offset, embedding_dim, **kwargs)
+
+ def call(self, attention_mask, past_key_values_length: int = 0):
+ """`input_ids_shape` is expected to be [bsz x seqlen]."""
+ attention_mask = tf.cast(attention_mask, tf.int64)
+
+ # create positions depending on attention_mask
+ positions = tf.math.cumsum(attention_mask, axis=1) * attention_mask - 1
+
+ # cut positions if `past_key_values_length` is > 0
+ positions = positions[:, past_key_values_length:]
+
+ return super().call(positions + self.offset)
+
+
+# Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with Bart->OPT
+class TFOPTAttention(keras.layers.Layer):
+ """Multi-headed attention from "Attention Is All You Need"""
+
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ dropout: float = 0.0,
+ is_decoder: bool = False,
+ bias: bool = True,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+ self.embed_dim = embed_dim
+
+ self.num_heads = num_heads
+ self.dropout = keras.layers.Dropout(dropout)
+ self.head_dim = embed_dim // num_heads
+ if (self.head_dim * num_heads) != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+ f" and `num_heads`: {num_heads})."
+ )
+ self.scaling = self.head_dim**-0.5
+ self.is_decoder = is_decoder
+
+ self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
+ self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
+ self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
+ self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
+
+ def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
+ return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ key_value_states: tf.Tensor | None = None,
+ past_key_value: tuple[tuple[tf.Tensor]] | None = None,
+ attention_mask: tf.Tensor | None = None,
+ layer_head_mask: tf.Tensor | None = None,
+ training: bool | None = False,
+ ) -> tuple[tf.Tensor, tf.Tensor | None]:
+ """Input shape: Batch x Time x Channel"""
+
+ # if key_value_states are provided this layer is used as a cross-attention layer
+ # for the decoder
+ is_cross_attention = key_value_states is not None
+ bsz, tgt_len, embed_dim = shape_list(hidden_states)
+
+ # get query proj
+ query_states = self.q_proj(hidden_states) * self.scaling
+ # get key, value proj
+ if is_cross_attention and past_key_value is not None:
+ # reuse k,v, cross_attentions
+ key_states = past_key_value[0]
+ value_states = past_key_value[1]
+ elif is_cross_attention:
+ # cross_attentions
+ key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
+ value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
+ elif past_key_value is not None:
+ # reuse k, v, self_attention
+ key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+ value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+ key_states = tf.concat([past_key_value[0], key_states], axis=2)
+ value_states = tf.concat([past_key_value[1], value_states], axis=2)
+ else:
+ # self_attention
+ key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
+ value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
+
+ if self.is_decoder:
+ # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
+ # Further calls to cross_attention layer can then reuse all cross-attention
+ # key/value_states (first "if" case)
+ # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
+ # all previous decoder key/value_states. Further calls to uni-directional self-attention
+ # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+ # if encoder bi-directional self-attention `past_key_value` is always `None`
+ past_key_value = (key_states, value_states)
+
+ proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+ query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
+ key_states = tf.reshape(key_states, proj_shape)
+ value_states = tf.reshape(value_states, proj_shape)
+
+ src_len = shape_list(key_states)[1]
+ attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
+
+ tf.debugging.assert_equal(
+ shape_list(attn_weights),
+ [bsz * self.num_heads, tgt_len, src_len],
+ message=(
+ f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
+ f" {shape_list(attn_weights)}"
+ ),
+ )
+
+ if attention_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(attention_mask),
+ [bsz, 1, tgt_len, src_len],
+ message=(
+ f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
+ f" {shape_list(attention_mask)}"
+ ),
+ )
+
+ attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
+ attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
+ attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
+
+ attn_weights = stable_softmax(attn_weights, axis=-1)
+
+ if layer_head_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(layer_head_mask),
+ [self.num_heads],
+ message=(
+ f"Head mask for a single layer should be of size {(self.num_heads)}, but is"
+ f" {shape_list(layer_head_mask)}"
+ ),
+ )
+
+ attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(
+ attn_weights, (bsz, self.num_heads, tgt_len, src_len)
+ )
+ attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
+
+ attn_probs = self.dropout(attn_weights, training=training)
+ attn_output = tf.matmul(attn_probs, value_states)
+
+ tf.debugging.assert_equal(
+ shape_list(attn_output),
+ [bsz * self.num_heads, tgt_len, self.head_dim],
+ message=(
+ f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
+ f" {shape_list(attn_output)}"
+ ),
+ )
+
+ attn_output = tf.transpose(
+ tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3)
+ )
+ attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
+
+ attn_output = self.out_proj(attn_output)
+ attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
+
+ return attn_output, attn_weights, past_key_value
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "k_proj", None) is not None:
+ with tf.name_scope(self.k_proj.name):
+ self.k_proj.build([None, None, self.embed_dim])
+ if getattr(self, "q_proj", None) is not None:
+ with tf.name_scope(self.q_proj.name):
+ self.q_proj.build([None, None, self.embed_dim])
+ if getattr(self, "v_proj", None) is not None:
+ with tf.name_scope(self.v_proj.name):
+ self.v_proj.build([None, None, self.embed_dim])
+ if getattr(self, "out_proj", None) is not None:
+ with tf.name_scope(self.out_proj.name):
+ self.out_proj.build([None, None, self.embed_dim])
+
+
+class TFOPTDecoderLayer(keras.layers.Layer):
+ def __init__(self, config: OPTConfig, **kwargs):
+ super().__init__(**kwargs)
+ self.do_layer_norm_before = config.do_layer_norm_before
+ self.embed_dim = config.hidden_size
+ self.self_attn = TFOPTAttention(
+ embed_dim=self.embed_dim,
+ num_heads=config.num_attention_heads,
+ dropout=config.attention_dropout,
+ name="self_attn",
+ is_decoder=True,
+ )
+ self.dropout = keras.layers.Dropout(config.dropout)
+ self.activation_fn = get_tf_activation(config.activation_function)
+
+ self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
+ self.fc1 = keras.layers.Dense(config.ffn_dim, name="fc1")
+ self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2")
+ self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+ self.config = config
+
+ def call(
+ self,
+ hidden_states: tf.Tensor,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ layer_head_mask: tf.Tensor | None = None,
+ past_key_value: tuple[tuple[np.ndarray | tf.Tensor]] | None = None,
+ training: bool | None = False,
+ output_attentions: bool | None = False,
+ use_cache: bool | None = False,
+ ) -> tuple[tf.Tensor, tf.Tensor, tuple[tuple[tf.Tensor]]]:
+ """
+ Args:
+ hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`tf.Tensor`, *optional*): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ layer_head_mask (`tf.Tensor`, *optional*): mask for attention heads in a given layer of size
+ `(decoder_attention_heads,)`
+ past_key_value (`Tuple(tf.Tensor)`, *optional*): cached past key and value projection states
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+ """
+ residual = hidden_states
+
+ # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+ if self.do_layer_norm_before:
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Self Attention
+ # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+ self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+
+ # add present self-attn cache to positions 1,2 of present_key_value tuple
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ past_key_value=self_attn_past_key_value,
+ attention_mask=attention_mask,
+ layer_head_mask=layer_head_mask,
+ )
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+
+ # 350m applies layer norm AFTER attention
+ if not self.do_layer_norm_before:
+ hidden_states = self.self_attn_layer_norm(hidden_states)
+
+ # Fully Connected
+ residual = hidden_states
+ # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
+ if self.do_layer_norm_before:
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ hidden_states = self.fc1(hidden_states)
+ hidden_states = self.activation_fn(hidden_states)
+
+ hidden_states = self.fc2(hidden_states)
+ hidden_states = self.dropout(hidden_states, training=training)
+ hidden_states = residual + hidden_states
+
+ # 350m applies layer norm AFTER attention
+ if not self.do_layer_norm_before:
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ return (hidden_states, self_attn_weights, present_key_value)
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "self_attn", None) is not None:
+ with tf.name_scope(self.self_attn.name):
+ self.self_attn.build(None)
+ if getattr(self, "self_attn_layer_norm", None) is not None:
+ with tf.name_scope(self.self_attn_layer_norm.name):
+ self.self_attn_layer_norm.build([None, None, self.embed_dim])
+ if getattr(self, "fc1", None) is not None:
+ with tf.name_scope(self.fc1.name):
+ self.fc1.build([None, None, self.embed_dim])
+ if getattr(self, "fc2", None) is not None:
+ with tf.name_scope(self.fc2.name):
+ self.fc2.build([None, None, self.config.ffn_dim])
+ if getattr(self, "final_layer_norm", None) is not None:
+ with tf.name_scope(self.final_layer_norm.name):
+ self.final_layer_norm.build([None, None, self.embed_dim])
+
+
+OPT_START_DOCSTRING = r"""
+ This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+ as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+ behavior.
+
+
+
+ TensorFlow models and layers in `transformers` accept two formats as input:
+
+ - having all inputs as keyword arguments (like PyTorch models), or
+ - having all inputs as a list, tuple or dict in the first positional argument.
+
+ The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+ and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+ pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+ format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+ the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+ positional argument:
+
+ - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+ - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+ `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+ - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+ `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+ Note that when creating models and layers with
+ [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+ about any of this, as you can just pass inputs like you would to any other Python function!
+
+
+
+ Args:
+ config ([`OPTConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare OPT Model outputting raw hidden-states without any specific head on top.",
+ OPT_START_DOCSTRING,
+)
+class TFOPTPreTrainedModel(TFPreTrainedModel):
+ """
+ TFOPT Pretrained Model that inheritates from transformers.TFPreTrainedModel
+
+ Args:
+ config: OPTConfig
+ """
+
+ config_class = OPTConfig
+ base_model_prefix = "model"
+
+
+OPT_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`tf.Tensor` of shape `({0})`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `({0})`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`tuple[tuple[tf.Tensor]]` of length `config.n_layers`)
+ contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`). Set to `False` during training, `True` during generation
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+ config will be used instead.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+ used instead.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
+ eager mode, in graph mode the value will always be set to True.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+"""
+
+
+@keras_serializable
+class TFOPTDecoder(keras.layers.Layer):
+ config_class = OPTConfig
+
+ def __init__(self, config: OPTConfig, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.padding_idx = config.pad_token_id
+ self.layerdrop = config.layerdrop
+ num_embeddings = config.max_position_embeddings
+ self.embed_tokens = TFSharedEmbeddings(
+ config.vocab_size, config.word_embed_proj_dim, config.pad_token_id, name="embed_tokens"
+ )
+ self.embed_positions = TFOPTLearnedPositionalEmbedding(
+ num_embeddings,
+ config.hidden_size,
+ name="embed_positions",
+ )
+
+ # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
+ # with checkpoints that have been fine-tuned before transformers v4.20.1
+ # see https://github.com/facebookresearch/metaseq/pull/164
+ if config.do_layer_norm_before and not config._remove_final_layer_norm:
+ self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+ else:
+ self.final_layer_norm = None
+
+ if config.word_embed_proj_dim != config.hidden_size:
+ self.project_out = keras.layers.Dense(config.word_embed_proj_dim, name="project_out", use_bias=False)
+ self.project_in = keras.layers.Dense(config.hidden_size, name="project_in", use_bias=False)
+
+ else:
+ self.project_in = None
+ self.project_out = None
+
+ self.layers = [TFOPTDecoderLayer(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)]
+ self.dropout = keras.layers.Dropout(config.dropout)
+
+ def get_embed_tokens(self):
+ return self.embed_tokens
+
+ def set_embed_tokens(self, embed_tokens):
+ self.embed_tokens = embed_tokens
+
+ def set_input_embeddings(self, new_embeddings):
+ self.embed_tokens.vocab_size = new_embeddings.shape[0]
+ self.embed_tokens.weight = new_embeddings
+
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, past_key_values_length):
+ # create causal mask
+ # # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ _, seq_length = input_shape
+ tf.debugging.assert_equal(
+ seq_length + past_key_values_length,
+ shape_list(attention_mask)[1],
+ message="Attention mask shape should be (batch_size, seq_length + past_key_values_length)"
+ f" but is {shape_list(attention_mask)[1]} with input_ids shape {input_shape} and past length"
+ f" {past_key_values_length}.",
+ )
+
+ expanded_attn_mask = _expand_mask(attention_mask, tgt_len=input_shape[-1])
+ if seq_length > 1:
+ combined_attention_mask = (
+ _make_causal_mask(input_shape, past_key_values_length=past_key_values_length) + expanded_attn_mask
+ )
+ else:
+ combined_attention_mask = expanded_attn_mask
+
+ return combined_attention_mask
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ past_key_values: tuple[tuple[np.ndarray | tf.Tensor]] | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ ) -> TFBaseModelOutputWithPast | tuple[tf.Tensor]:
+ r"""
+ Args:
+ input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`tuple[tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+ Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
+ decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+ that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+ all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`tf.Tensor` of
+ shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing
+ `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more
+ control over how to convert `input_ids` indices into associated vectors than the model's internal
+ embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ training (`bool`, *optional*, defaults to `False`):
+ Whether or not to use the model in training mode (some modules like dropout modules have different
+ behaviors between training and evaluation).
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ input_shape = shape_list(input_ids)
+ elif inputs_embeds is not None:
+ input_shape = shape_list(inputs_embeds)[:-1]
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
+
+ if inputs_embeds is None:
+ check_embeddings_within_bounds(input_ids, self.embed_tokens.vocab_size)
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if attention_mask is None:
+ attention_mask = tf.ones((input_shape[0], input_shape[1] + past_key_values_length), dtype=tf.bool)
+ else:
+ tf.debugging.assert_equal(
+ shape_list(attention_mask)[1],
+ past_key_values_length + input_shape[1],
+ message=(
+ f"The provided attention mask has length {tf.shape(attention_mask)[1]}, but its length should be "
+ f"{past_key_values_length + input_shape[1]} (sum of the lengths of current and past inputs)"
+ ),
+ )
+ pos_embeds = self.embed_positions(attention_mask, past_key_values_length)
+
+ attention_mask = self._prepare_decoder_attention_mask(attention_mask, input_shape, past_key_values_length)
+
+ if self.project_in is not None:
+ inputs_embeds = self.project_in(inputs_embeds)
+
+ hidden_states = inputs_embeds + pos_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ present_key_values = () if use_cache else None
+
+ # check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired
+ for attn_mask_name, attn_mask in [("head_mask", head_mask)]:
+ if attn_mask is not None:
+ tf.debugging.assert_equal(
+ shape_list(attn_mask)[0],
+ len(self.layers),
+ message=(
+ f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for"
+ f" {shape_list(attn_mask)[0]}."
+ ),
+ )
+
+ for idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+ hidden_states, layer_self_attn, present_key_value = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ layer_head_mask=head_mask[idx] if head_mask is not None else None,
+ past_key_value=past_key_value,
+ )
+
+ if use_cache:
+ present_key_values += (present_key_value,)
+
+ if output_attentions:
+ all_self_attns += (layer_self_attn,)
+
+ if self.final_layer_norm is not None:
+ hidden_states = self.final_layer_norm(hidden_states)
+
+ if self.project_out is not None:
+ hidden_states = self.project_out(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if not return_dict:
+ return tuple(
+ v for v in [hidden_states, present_key_values, all_hidden_states, all_self_attns] if v is not None
+ )
+
+ else:
+ return TFBaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=present_key_values,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "embed_tokens", None) is not None:
+ with tf.name_scope(self.embed_tokens.name):
+ self.embed_tokens.build(None)
+ if getattr(self, "embed_positions", None) is not None:
+ with tf.name_scope(self.embed_positions.name):
+ self.embed_positions.build(None)
+ if getattr(self, "final_layer_norm", None) is not None:
+ with tf.name_scope(self.final_layer_norm.name):
+ self.final_layer_norm.build([None, None, self.config.hidden_size])
+ if getattr(self, "project_out", None) is not None:
+ with tf.name_scope(self.project_out.name):
+ self.project_out.build([None, None, self.config.hidden_size])
+ if getattr(self, "project_in", None) is not None:
+ with tf.name_scope(self.project_in.name):
+ self.project_in.build([None, None, self.config.word_embed_proj_dim])
+ if getattr(self, "layers", None) is not None:
+ for layer in self.layers:
+ with tf.name_scope(layer.name):
+ layer.build(None)
+
+
+@keras_serializable
+class TFOPTMainLayer(keras.layers.Layer):
+ config_class = OPTConfig
+
+ def __init__(self, config: OPTConfig, **kwargs):
+ super().__init__(**kwargs)
+ self.config = config
+ self.decoder = TFOPTDecoder(config, name="decoder")
+
+ def get_input_embeddings(self):
+ return self.decoder.embed_tokens
+
+ def set_input_embeddings(self, new_embeddings):
+ self.decoder.set_input_embeddings(new_embeddings)
+
+ @unpack_inputs
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ past_key_values: tuple[tuple[np.ndarray | tf.Tensor]] | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ **kwargs,
+ ) -> TFBaseModelOutputWithPast | tuple[tf.Tensor]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.decoder(
+ input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ if not return_dict:
+ return outputs
+
+ return TFBaseModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "decoder", None) is not None:
+ with tf.name_scope(self.decoder.name):
+ self.decoder.build(None)
+
+
+@add_start_docstrings(
+ "The bare TF OPT Model outputting raw hidden-states without any specific head on top.",
+ OPT_START_DOCSTRING,
+)
+@keras_serializable
+class TFOPTModel(TFOPTPreTrainedModel):
+ config_class = OPTConfig
+
+ def __init__(self, config: OPTConfig, **kwargs):
+ super().__init__(config, **kwargs)
+ self.config = config
+ self.model = TFOPTMainLayer(config, name="model")
+
+ def get_input_embeddings(self):
+ return self.model.decoder.embed_tokens
+
+ def set_input_embeddings(self, new_embeddings):
+ self.model.set_input_embeddings(new_embeddings)
+
+ @unpack_inputs
+ @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFBaseModelOutputWithPast,
+ config_class=_CONFIG_FOR_DOC,
+ expected_output=_EXPECTED_OUTPUT_SHAPE,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ past_key_values: tuple[tuple[np.ndarray | tf.Tensor]] | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ **kwargs,
+ ) -> TFBaseModelOutputWithPast | tuple[tf.Tensor]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ head_mask=head_mask,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ if not return_dict:
+ return outputs
+
+ return TFBaseModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def serving_output(self, output):
+ pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
+ hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+ attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+
+ return TFBaseModelOutputWithPast(
+ last_hidden_state=output.last_hidden_state,
+ past_key_values=pkv,
+ hidden_states=hs,
+ attentions=attns,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "model", None) is not None:
+ with tf.name_scope(self.model.name):
+ self.model.build(None)
+
+
+@add_start_docstrings(
+ """
+ The OPT Model transformer with a language modeling head on top.
+ """,
+ OPT_START_DOCSTRING,
+)
+@keras_serializable
+class TFOPTForCausalLM(TFOPTPreTrainedModel, TFCausalLanguageModelingLoss):
+ config_class = OPTConfig
+
+ def __init__(self, config: OPTConfig, **kwargs):
+ super().__init__(config, **kwargs)
+ self.config = config
+ self.model = TFOPTMainLayer(config, name="model")
+
+ def get_output_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def prepare_inputs_for_generation(self, inputs, past_key_values=None, use_cache=None, **kwargs):
+ attention_mask = kwargs.get("attention_mask")
+
+ # only last token for inputs_ids if past is defined in kwargs
+ if past_key_values:
+ inputs = tf.expand_dims(inputs[:, -1], -1)
+
+ return {
+ "input_ids": inputs,
+ "attention_mask": attention_mask,
+ "past_key_values": past_key_values,
+ "use_cache": use_cache,
+ }
+
+ @unpack_inputs
+ @replace_return_docstrings(output_type=TFCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=TFCausalLMOutputWithPast,
+ config_class=_CONFIG_FOR_DOC,
+ expected_output=_CAUSAL_LM_EXPECTED_OUTPUT,
+ )
+ def call(
+ self,
+ input_ids: TFModelInputType | None = None,
+ past_key_values: tuple[tuple[np.ndarray | tf.Tensor]] | None = None,
+ attention_mask: np.ndarray | tf.Tensor | None = None,
+ position_ids: np.ndarray | tf.Tensor | None = None,
+ head_mask: np.ndarray | tf.Tensor | None = None,
+ inputs_embeds: np.ndarray | tf.Tensor | None = None,
+ labels: np.ndarray | tf.Tensor | None = None,
+ use_cache: bool | None = None,
+ output_attentions: bool | None = None,
+ output_hidden_states: bool | None = None,
+ return_dict: bool | None = None,
+ training: bool | None = False,
+ **kwargs,
+ ) -> TFCausalLMOutputWithPast | tuple[tf.Tensor]:
+ r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+ provide it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
+ Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+ shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
+ tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+ cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ outputs = self.model(
+ input_ids=input_ids,
+ past_key_values=past_key_values,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ training=training,
+ )
+
+ logits = self.model.decoder.embed_tokens(outputs[0], mode="linear")
+ loss = None
+ if labels is not None:
+ # shift labels to the left and cut last logit token
+ shifted_logits = logits[:, :-1]
+ labels = labels[:, 1:]
+ loss = self.hf_compute_loss(labels, shifted_logits)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return TFCausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def serving_output(self, output):
+ pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
+ hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
+ attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
+
+ return TFCausalLMOutputWithPast(
+ past_key_values=pkv,
+ hidden_states=hs,
+ attentions=attns,
+ loss=output.loss,
+ logits=output.logits,
+ )
+
+ def build(self, input_shape=None):
+ if self.built:
+ return
+ self.built = True
+ if getattr(self, "model", None) is not None:
+ with tf.name_scope(self.model.name):
+ self.model.build(None)
+
+
+__all__ = ["TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/__init__.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..902e015b055f816e59bb2a69ccb0357d5b05cdb1
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/__init__.py
@@ -0,0 +1,32 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+ from .configuration_ovis2 import *
+ from .image_processing_ovis2 import *
+ from .image_processing_ovis2_fast import *
+ from .modeling_ovis2 import *
+ from .processing_ovis2 import *
+else:
+ import sys
+
+ _file = globals()["__file__"]
+ sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/configuration_ovis2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/configuration_ovis2.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd2a1c0af4d622f3aa878739be942d27e06e2785
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/configuration_ovis2.py
@@ -0,0 +1,182 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ...configuration_utils import PretrainedConfig
+from ..qwen2.configuration_qwen2 import Qwen2Config
+
+
+class Ovis2VisionConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Ovis2VisionModel`]. It is used to instantiate a
+ Ovis2VisionModel model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of Ovis2.
+
+ Args:
+ hidden_size (`int`, *optional*, defaults to 1024):
+ Dimensionality of the encoder layers and the pooler layer.
+ intermediate_size (`int`, *optional*, defaults to 2816):
+ Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+ num_hidden_layers (`int`, *optional*, defaults to 24):
+ Number of hidden layers in the Transformer encoder.
+ num_attention_heads (`int`, *optional*, defaults to 8):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ num_channels (`int`, *optional*, defaults to 3):
+ Number of channels in the input images.
+ image_size (`int`, *optional*, defaults to 224):
+ The size (resolution) of each image.
+ patch_size (`int`, *optional*, defaults to 14):
+ The size (resolution) of each patch.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+ The epsilon used by the RMSNorm layers.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ qkv_bias (`bool`, *optional*, defaults to `False`):
+ Whether to add a learnable bias to the query, key, and value sequences at each attention head.
+ mlp_bias (`bool`, *optional*, defaults to `False`):
+ Whether to add a learnable bias to the MLP layers.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+ `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
+ vocab_size (`int`, *optional*, defaults to 16384):
+ Vocabulary size of the Vision Transformer.
+ hidden_stride (`int`, *optional*, defaults to 1):
+ The stride of the hidden layer in the Vision Transformer.
+ num_visual_indicator_tokens (`int`, *optional*, defaults to 5):
+ Number of visual indicator tokens.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated normal initializer for initializing all weight matrices.
+ tokenize_function (`str`, *optional*, defaults to `"softmax"`):
+ The function used to tokenize the visual indicator tokens.
+ """
+
+ base_config_key = "vision_config"
+
+ def __init__(
+ self,
+ hidden_size: int = 1024,
+ intermediate_size: int = 2816,
+ num_hidden_layers: int = 24,
+ num_attention_heads: int = 8,
+ num_channels: int = 3,
+ image_size: int = 224,
+ patch_size: int = 14,
+ rms_norm_eps: float = 1e-5,
+ attention_dropout: float = 0.0,
+ qkv_bias: bool = False,
+ mlp_bias: bool = False,
+ hidden_act="silu",
+ vocab_size=16384,
+ hidden_stride=1,
+ num_visual_indicator_tokens=5,
+ initializer_range=0.02,
+ tokenize_function="softmax",
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.num_channels = num_channels
+ self.patch_size = patch_size
+ self.image_size = image_size
+
+ self.attention_dropout = attention_dropout
+ self.hidden_act = hidden_act
+ self.qkv_bias = qkv_bias
+ self.mlp_bias = mlp_bias
+ self.rms_norm_eps = rms_norm_eps
+ self.vocab_size = vocab_size
+ self.hidden_stride = hidden_stride
+ self.num_visual_indicator_tokens = num_visual_indicator_tokens
+ self.tokenize_function = tokenize_function
+ self.initializer_range = initializer_range
+
+
+class Ovis2Config(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`Ovis2ForConditionalGeneration`]. It is used to instantiate a
+ Ovis2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of Ovis2.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+ e.g. [thisisiron/Ovis2-1B-hf](https://huggingface.co/thisisiron/Ovis2-1B-hf)
+
+ Args:
+ vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Ovis2VisionConfig`):
+ The config object or dictionary of the vision backbone.
+ text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Qwen2Config`):
+ The config object or dictionary of the text backbone.
+ image_token_id (`int`, *optional*, defaults to 151665):
+ The image token id to encode the image prompt.
+ visual_indicator_token_ids (`List[int]`, *optional*, defaults to `[151666, 151667, 151668, 151669, 151670]`):
+ The visual indicator token ids to encode the image prompt.
+ vocab_size (`int`, *optional*, defaults to 151643):
+ Vocabulary size of the text model.
+ hidden_size (`int`, *optional*, defaults to 1536):
+ Dimensionality of the encoder layers and the pooler layer.
+
+ ```python
+ >>> from transformers import Ovis2ForConditionalGeneration, Ovis2Config
+
+ >>> # Initializing a Ovis2 style configuration
+ >>> configuration = Ovis2Config()
+
+ >>> # Initializing a model from the Ovis2-2B style configuration
+ >>> model = Ovis2ForConditionalGeneration(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```
+ """
+
+ model_type = "ovis2"
+ sub_configs = {"text_config": Qwen2Config, "vision_config": Ovis2VisionConfig}
+
+ def __init__(
+ self,
+ vision_config=None,
+ text_config=None,
+ image_token_id=151665,
+ visual_indicator_token_ids=[151666, 151667, 151668, 151669, 151670],
+ vocab_size=151643,
+ hidden_size=1536,
+ **kwargs,
+ ):
+ if isinstance(vision_config, dict):
+ self.vision_config = Ovis2VisionConfig(**vision_config)
+ elif isinstance(vision_config, Ovis2VisionConfig):
+ self.vision_config = vision_config
+ if vision_config is None:
+ self.vision_config = Ovis2VisionConfig(num_visual_indicator_tokens=len(visual_indicator_token_ids))
+
+ if isinstance(text_config, dict):
+ self.text_config = Qwen2Config(**text_config)
+ elif isinstance(text_config, Qwen2Config):
+ self.text_config = text_config
+ elif text_config is None:
+ self.text_config = Qwen2Config()
+
+ self.vocab_size = vocab_size
+ self.hidden_size = hidden_size
+ self.image_token_id = image_token_id
+ self.visual_indicator_token_ids = visual_indicator_token_ids
+ super().__init__(**kwargs)
+
+
+__all__ = ["Ovis2VisionConfig", "Ovis2Config"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/convert_ovis2_weights_to_hf.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/convert_ovis2_weights_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..d98bfbb5dc4ea0f62e82b7fdb3d66b84245bd149
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/convert_ovis2_weights_to_hf.py
@@ -0,0 +1,404 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import os
+import re
+
+import requests
+import torch
+from PIL import Image
+
+from transformers import (
+ AutoModelForCausalLM,
+ AutoModelForImageTextToText,
+ AutoProcessor,
+ AutoTokenizer,
+)
+from transformers.models.auto.configuration_auto import CONFIG_MAPPING_NAMES
+from transformers.models.ovis2.configuration_ovis2 import Ovis2Config, Ovis2VisionConfig
+from transformers.models.ovis2.image_processing_ovis2 import Ovis2ImageProcessor
+from transformers.models.ovis2.modeling_ovis2 import Ovis2ForConditionalGeneration
+from transformers.models.ovis2.processing_ovis2 import Ovis2Processor
+from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
+
+
+# Constants
+CONTEXT_LENGTH = 32768 # multimodal_max_length
+
+
+# fmt: off
+
+# Mapping from original model key patterns to HF key patterns
+ORIGINAL_TO_HF_MAPPING = {
+ r"trunk.blocks\.(\d+)\.norm_1": r"encoder.layers.\1.rms_norm1",
+ r"trunk.blocks\.(\d+)\.norm_2": r"encoder.layers.\1.rms_norm2",
+ r"trunk.blocks\.(\d+)\.attn.proj": r"encoder.layers.\1.attention.out_proj",
+ r"visual_tokenizer": r"model.vision_tower",
+ r"backbone": r"transformer",
+ r"preprocessor": r"embeddings",
+ r"patchifier.proj": r"patch_embedding",
+ r"patchifier.norm": r"rms_norm",
+ r"trunk.post_trunk_norm": r"rms_norm",
+ r"trunk.blocks": r"encoder.layers",
+ r"mlp.fc1": r"ffn.gate_proj",
+ r"mlp.fc2": r"ffn.down_proj",
+ r"mlp.fc3": r"ffn.up_proj",
+ r"head.0": r"head_linear",
+ r"head.1": r"head_norm",
+ r"vte.weight": r"model.visual_embeddings_table.weight",
+ r"llm.model": r"model.language_model",
+ r"llm.lm_head": r"lm_head",
+}
+# fmt: on
+
+# Special tokens for the tokenizer
+SPECIAL_TOKENS = [
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+]
+
+# Configuration keys to ignore when converting
+UNNECESSARY_CONFIG_KEYS = [
+ "_name_or_path",
+ "_attn_implementation_autoset",
+ "auto_map",
+ "use_bfloat16",
+ "use_flash_attn",
+ "qk_normalization",
+ "bias",
+ "norm_type",
+]
+
+# Chat template for the tokenizer
+CHAT_TEMPLATE = (
+ "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+ "{% for message in messages %}"
+ "{{'<|im_start|>' + message['role'] + '\n'}}"
+ "{% if message['content'] is string %}"
+ "{{ message['content'] }}"
+ "{% else %}"
+ "{% for content in message['content'] %}"
+ "{% if content['type'] == 'image' %}"
+ "{{ '\n' }}"
+ "{% elif content['type'] == 'text' %}"
+ "{{ content['text'] }}"
+ "{% endif %}"
+ "{% endfor %}"
+ "{% endif %}"
+ "{{'<|im_end|>\n'}}"
+ "{% endfor %}"
+ "{% if add_generation_prompt %}"
+ "{{'<|im_start|>assistant\n' }}"
+ "{% endif %}"
+)
+
+
+def create_tokenizer(model_name_or_path, save_dir):
+ """
+ Create and configure a tokenizer for the Ovis2 model.
+
+ Args:
+ model_name_or_path: Path to the source model or tokenizer
+ save_dir: Directory to save the tokenizer to
+
+ Returns:
+ The configured tokenizer
+ """
+ tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, return_token_type_ids=False)
+ tokenizer.model_max_length = CONTEXT_LENGTH
+ tokenizer.add_special_tokens({"additional_special_tokens": SPECIAL_TOKENS})
+ tokenizer.chat_template = CHAT_TEMPLATE
+ setattr(tokenizer, "image_token", "") # 151665
+ setattr(tokenizer, "image_token_id", tokenizer.convert_tokens_to_ids(tokenizer.image_token))
+
+ return tokenizer
+
+
+def create_image_processor(save_dir):
+ """
+ Create and save an image processor for the Ovis2 model.
+
+ Args:
+ save_dir: Directory to save the image processor to
+
+ Returns:
+ The configured image processor
+ """
+ image_processor = Ovis2ImageProcessor(
+ crop_to_patches=True,
+ size={"height": 448, "width": 448},
+ )
+ return image_processor
+
+
+def extract_vision_config_from_original(orig_config):
+ """
+ Extract and format vision configuration from the original model config.
+
+ Args:
+ orig_config: Original model configuration
+
+ Returns:
+ dict: Cleaned vision configuration dictionary
+ """
+ visual_tokenizer_config = orig_config.visual_tokenizer_config.to_dict()
+ # backbone_config = visual_tokenizer_config.pop("backbone_config")
+
+ # Copy required fields from backbone config
+ visual_tokenizer_config["hidden_size"] = orig_config.visual_tokenizer_config.backbone_config.hidden_size
+ visual_tokenizer_config["intermediate_size"] = (
+ orig_config.visual_tokenizer_config.backbone_config.intermediate_size
+ )
+ visual_tokenizer_config["num_attention_heads"] = (
+ orig_config.visual_tokenizer_config.backbone_config.num_attention_heads
+ )
+ visual_tokenizer_config["num_hidden_layers"] = (
+ orig_config.visual_tokenizer_config.backbone_config.num_hidden_layers
+ )
+ visual_tokenizer_config["rms_norm_eps"] = orig_config.visual_tokenizer_config.backbone_config.rms_norm_eps
+ visual_tokenizer_config["image_size"] = orig_config.visual_tokenizer_config.backbone_config.image_size
+ visual_tokenizer_config["num_channels"] = orig_config.visual_tokenizer_config.backbone_config.num_channels
+ visual_tokenizer_config["patch_size"] = orig_config.visual_tokenizer_config.backbone_config.patch_size
+ visual_tokenizer_config["qkv_bias"] = orig_config.visual_tokenizer_config.backbone_config.qkv_bias
+
+ # Remove unnecessary keys
+ return {k: v for k, v in visual_tokenizer_config.items() if k not in UNNECESSARY_CONFIG_KEYS}
+
+
+def get_ovis2_config(model_name_or_path):
+ """
+ Create an Ovis2 configuration from the original model.
+
+ Args:
+ model_name_or_path: Path to the original model
+
+ Returns:
+ Ovis2Config: Configuration for the HF implementation
+ """
+ orig_config = AutoModelForCausalLM.from_pretrained(
+ model_name_or_path,
+ trust_remote_code=True,
+ ).config
+
+ # Extract and clean LLM config
+ llm_config = orig_config.llm_config.to_dict()
+ llm_config = {k: v for k, v in llm_config.items() if k not in UNNECESSARY_CONFIG_KEYS}
+
+ # Extract and clean vision config
+ visual_tokenizer_config = extract_vision_config_from_original(orig_config)
+
+ return Ovis2Config(
+ text_config=Qwen2Config(**llm_config),
+ vision_config=Ovis2VisionConfig(**visual_tokenizer_config),
+ hidden_size=llm_config["hidden_size"],
+ vocab_size=llm_config["vocab_size"],
+ initializer_range=llm_config["initializer_range"],
+ )
+
+
+def load_orig_state_dict(model_name_or_path):
+ """
+ Load the state dictionary from the original model.
+
+ Args:
+ model_name_or_path: Path to the original model
+
+ Returns:
+ dict: Original model state dictionary
+ """
+ model = AutoModelForCausalLM.from_pretrained(
+ model_name_or_path,
+ torch_dtype=torch.bfloat16,
+ trust_remote_code=True,
+ ).eval()
+
+ return model.state_dict()
+
+
+def convert_orig2hf(state_dict, dim):
+ """
+ Convert original state dictionary keys to HF format.
+
+ Args:
+ state_dict: Original state dictionary
+ dim: Hidden dimension for splitting QKV weights
+
+ Returns:
+ dict: Converted state dictionary for HF model
+ """
+ new_state_dict = {}
+
+ for key, val in state_dict.items():
+ orig_key = key
+
+ # Apply regex pattern replacements
+ for pattern, replacement in ORIGINAL_TO_HF_MAPPING.items():
+ key = re.sub(pattern, replacement, key)
+
+ # Handle special cases
+ if "attn.qkv" in key:
+ # Split QKV into separate Q, K, V matrices
+ new_key_query = key.replace("attn.qkv", "attention.q_proj")
+ new_state_dict[new_key_query] = state_dict[orig_key][:dim]
+
+ new_key_key = key.replace("attn.qkv", "attention.k_proj")
+ new_state_dict[new_key_key] = state_dict[orig_key][dim : 2 * dim]
+
+ new_key_value = key.replace("attn.qkv", "attention.v_proj")
+ new_state_dict[new_key_value] = state_dict[orig_key][-dim:]
+
+ elif "pos_embed" in key:
+ new_key = key.replace("pos_embed", "position_embedding.weight")
+ new_state_dict[new_key] = state_dict[orig_key][0]
+
+ else:
+ new_state_dict[key] = val
+
+ return new_state_dict
+
+
+def convert_model(model_name_or_path):
+ """
+ Convert and save the model in HF format.
+
+ Args:
+ model_name_or_path: Path to the original model
+ save_dir: Directory to save the converted model
+
+ Returns:
+ The converted model
+ """
+
+ config = get_ovis2_config(model_name_or_path)
+ config.architectures = ["Ovis2ForConditionalGeneration"]
+
+ # Load and convert weights
+ orig_state_dict = load_orig_state_dict(model_name_or_path)
+ new_state_dict = convert_orig2hf(orig_state_dict, config.vision_config.hidden_size)
+
+ # Create model and load converted weights
+ model = Ovis2ForConditionalGeneration(config)
+ missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False)
+
+ # Report any issues with weight loading
+ if missing_keys:
+ print(f"Missing keys: {missing_keys}")
+ if unexpected_keys:
+ print(f"Unexpected keys: {unexpected_keys}")
+
+ return model
+
+
+def main():
+ """Process command line arguments and execute the conversion pipeline."""
+ parser = argparse.ArgumentParser(description="Convert Ovis2 model to HF format")
+ parser.add_argument(
+ "--model_name_or_path",
+ default="AIDC-AI/Ovis2-2B",
+ choices=[
+ "AIDC-AI/Ovis2-1B",
+ "AIDC-AI/Ovis2-2B",
+ "AIDC-AI/Ovis2-4B",
+ "AIDC-AI/Ovis2-8B",
+ "AIDC-AI/Ovis2-16B",
+ "AIDC-AI/Ovis2-34B",
+ ],
+ help="Location of original Ovis2 model",
+ )
+ parser.add_argument("--save_dir", default="Ovis2-2B-hf", help="Location to write HF model and processors")
+ parser.add_argument("--hub_dir", default="thisisiron/Ovis2-2B-hf", help="Hub repository name if pushing to hub")
+ parser.add_argument(
+ "--push_to_hub", action="store_true", help="Whether to push the converted model to the Hugging Face hub"
+ )
+
+ args = parser.parse_args()
+
+ # Execute conversion pipeline
+ print(f"Converting model from {args.model_name_or_path} to {args.save_dir}")
+
+ # If already included in the transformers library, remove to avoid duplication.
+ if "aimv2" in CONFIG_MAPPING_NAMES:
+ CONFIG_MAPPING_NAMES.pop("aimv2")
+
+ tokenizer = create_tokenizer(
+ model_name_or_path=args.model_name_or_path,
+ save_dir=args.save_dir,
+ )
+
+ image_processor = create_image_processor(
+ save_dir=args.save_dir,
+ )
+
+ os.makedirs(args.save_dir, exist_ok=True)
+
+ # Convert and save the model
+ model = convert_model(model_name_or_path=args.model_name_or_path)
+ model.save_pretrained(args.save_dir)
+
+ # Save the processor
+ processor = Ovis2Processor(tokenizer=tokenizer, image_processor=image_processor, chat_template=CHAT_TEMPLATE)
+ processor.save_pretrained(args.save_dir)
+
+ # Push to hub if requested
+ if args.push_to_hub:
+ processor.push_to_hub(args.hub_dir, use_temp_dir=True)
+ model.push_to_hub(args.hub_dir, use_temp_dir=True)
+
+ model = (
+ AutoModelForImageTextToText.from_pretrained(
+ args.save_dir,
+ torch_dtype=torch.bfloat16,
+ )
+ .eval()
+ .to("cuda:0")
+ )
+ processor = AutoProcessor.from_pretrained(args.save_dir)
+
+ messages = [
+ {
+ "role": "user",
+ "content": [
+ {"type": "image"},
+ {"type": "text", "text": "Describe the image."},
+ ],
+ },
+ ]
+ url = "http://images.cocodataset.org/val2014/COCO_val2014_000000537955.jpg"
+ image = Image.open(requests.get(url, stream=True).raw)
+ messages = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+ print(messages)
+
+ inputs = processor(
+ images=[image],
+ text=messages,
+ return_tensors="pt",
+ )
+ inputs = inputs.to("cuda:0")
+ inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
+
+ with torch.inference_mode():
+ output_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
+ generated_ids = [output_ids[len(input_ids) :] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
+ output_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
+ print(output_text)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/image_processing_ovis2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/image_processing_ovis2.py
new file mode 100644
index 0000000000000000000000000000000000000000..39f558f0334cdaeb6a07dfc14165469ab205ce89
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/image_processing_ovis2.py
@@ -0,0 +1,573 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from functools import lru_cache
+from typing import Optional, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import convert_to_rgb, resize, to_channel_dimension_format
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ChannelDimension,
+ ImageInput,
+ PILImageResampling,
+ infer_channel_dimension_format,
+ is_scaled_image,
+ make_flat_list_of_images,
+ to_numpy_array,
+ valid_images,
+ validate_preprocess_arguments,
+)
+from ...utils import TensorType, filter_out_non_signature_kwargs, is_vision_available, logging
+
+
+if is_vision_available():
+ import PIL
+
+
+logger = logging.get_logger(__name__)
+
+
+# Similar to image_processing_mllama.get_all_supported_aspect_ratios
+@lru_cache(maxsize=10)
+def get_all_supported_aspect_ratios(min_image_tiles: int, max_image_tiles: int) -> list[tuple[int, int]]:
+ """
+ Computes all allowed aspect ratios for a given minimum and maximum number of input tiles.
+
+ This function calculates all possible arrangements of tiles that can be formed
+ within the constraint of the minimum and maximum number of tiles. Each arrangement is
+ represented by its aspect ratio (width/height) and the corresponding tile configuration.
+
+ Args:
+ min_image_tiles (`int`):
+ The minimum number of tiles allowed.
+ max_image_tiles (`int`):
+ The maximum number of tiles allowed.
+
+ Returns:
+ `List[Tuple[int, int]]`: A list of tuples, each tuple representing a valid (width, height)
+ configuration in terms of number of tiles.
+
+ Example:
+ >>> get_all_supported_aspect_ratios(1, 4)
+ [(1, 1), (1, 2), (2, 1), (1, 3), (3, 1), (1, 4), (2, 2), (4, 1)]
+
+ """
+ aspect_ratios = []
+ for width in range(1, max_image_tiles + 1):
+ for height in range(1, max_image_tiles + 1):
+ if width * height <= max_image_tiles and width * height >= min_image_tiles:
+ aspect_ratios.append((width, height))
+
+ aspect_ratios = sorted(aspect_ratios, key=lambda x: x[0] * x[1])
+
+ return aspect_ratios
+
+
+@lru_cache(maxsize=100)
+def get_optimal_tiled_canvas(
+ original_image_size: tuple[int, int],
+ target_tile_size: tuple[int, int],
+ min_image_tiles: int,
+ max_image_tiles: int,
+) -> tuple[int, int]:
+ """
+ Given a minimum and maximum number of tiles, find the canvas with the closest aspect ratio to the
+ original image aspect ratio.
+ In case of tie-breaking condition when two canvases have the same aspect ratio difference, we favor the canvas with
+ more tiles, until the area covered by the tiles is more than twice the target area, in order to avoid unnecessarily
+ excessive tiling.
+ """
+ possible_tile_arrangements = get_all_supported_aspect_ratios(min_image_tiles, max_image_tiles)
+
+ original_height, original_width = original_image_size
+ target_tile_height, target_tile_width = target_tile_size
+ aspect_ratio = original_width / original_height
+ area = original_width * original_height
+
+ # find the grid with the best aspect ratio
+ best_ratio_diff = float("inf")
+ best_grid = (1, 1)
+ for grid in possible_tile_arrangements:
+ grid_aspect_ratio = grid[0] / grid[1]
+ ratio_diff = abs(aspect_ratio - grid_aspect_ratio)
+ if ratio_diff < best_ratio_diff:
+ best_ratio_diff = ratio_diff
+ best_grid = grid
+ elif ratio_diff == best_ratio_diff:
+ # if the aspect ratio difference is the same, we favor the grid with more patches
+ # until the area covered by the patches is more than twice the original image area
+ if area > 0.5 * target_tile_height * target_tile_width * grid[0] * grid[1]:
+ best_grid = grid
+
+ return best_grid
+
+
+def compute_patch_covering_area(left: int, upper: int, right: int, lower: int, side: int) -> float:
+ w = right - left
+ h = lower - upper
+ w, h = max(w, h), min(w, h)
+ if w > side:
+ h = h / w * side
+ w = side
+ return w * h
+
+
+def split_image_into_grid(h: int, w: int, grid: tuple[int, int]) -> list[tuple[int, int, int, int]]:
+ row_height = h // grid[0]
+ col_width = w // grid[1]
+ return [
+ (
+ col * col_width,
+ row * row_height,
+ w if col == grid[1] - 1 else (col + 1) * col_width,
+ h if row == grid[0] - 1 else (row + 1) * row_height,
+ )
+ for row in range(grid[0])
+ for col in range(grid[1])
+ ]
+
+
+@lru_cache(maxsize=100)
+def get_min_tile_covering_grid(
+ image_size: tuple[int, int],
+ target_patch_size: int,
+ max_image_tiles: int,
+ covering_threshold: float = 0.9,
+) -> tuple[int, int]:
+ image_height, image_width = image_size
+ image_area = image_width * image_height
+
+ candidate_tile_grids = get_all_supported_aspect_ratios(1, max_image_tiles)
+ evaluated_grids = []
+ sufficient_covering_grids = []
+
+ for tile_grid in candidate_tile_grids:
+ tile_regions = split_image_into_grid(image_height, image_width, tile_grid)
+ tile_covering_ratio = (
+ sum([compute_patch_covering_area(*region, target_patch_size) for region in tile_regions]) / image_area
+ )
+
+ evaluated_grids.append((tile_grid, tile_covering_ratio))
+ if tile_covering_ratio > covering_threshold:
+ sufficient_covering_grids.append((tile_grid, tile_covering_ratio))
+
+ if sufficient_covering_grids:
+ # Prefer fewer tiles and higher covering ratio
+ return sorted(sufficient_covering_grids, key=lambda x: (x[0][0] * x[0][1], -x[1]))[0][0]
+ else:
+ # Fallback: prefer higher covering even if below threshold
+ return sorted(evaluated_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0][0]
+
+
+class Ovis2ImageProcessor(BaseImageProcessor):
+ r"""
+ Constructs a Ovis2 image processor.
+
+ Args:
+ do_resize (`bool`, *optional*, defaults to `True`):
+ Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
+ `do_resize` parameter in the `preprocess` method.
+ size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`):
+ Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
+ method.
+ crop_to_patches (`bool`, *optional*, defaults to `False`):
+ Whether to crop the image to patches. Can be overridden by the `crop_to_patches` parameter in the
+ `preprocess` method.
+ min_patches (`int`, *optional*, defaults to 1):
+ The minimum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is
+ set to `True`. Can be overridden by the `min_patches` parameter in the `preprocess` method.
+ max_patches (`int`, *optional*, defaults to 12):
+ The maximum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is
+ set to `True`. Can be overridden by the `max_patches` parameter in the `preprocess` method.
+ resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+ Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
+ overridden by the `resample` parameter in the `preprocess` method.
+ do_rescale (`bool`, *optional*, defaults to `True`):
+ Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
+ `do_rescale` parameter in the `preprocess` method.
+ rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+ Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
+ overridden by the `rescale_factor` parameter in the `preprocess` method.
+ do_normalize (`bool`, *optional*, defaults to `True`):
+ Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+ method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
+ image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
+ Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+ channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
+ overridden by the `image_mean` parameter in the `preprocess` method.
+ image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
+ Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+ number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+ Can be overridden by the `image_std` parameter in the `preprocess` method.
+ do_convert_rgb (`bool`, *optional*, defaults to `True`):
+ Whether to convert the image to RGB.
+ use_covering_area_grid (`bool`, *optional*, defaults to `True`):
+ Whether to use the covering area grid to determine the number of patches. Only has an effect if
+ `crop_to_patches` is set to `True`. Can be overridden by the `use_covering_area_grid` parameter in the
+ `preprocess` method.
+ """
+
+ model_input_names = ["pixel_values"]
+
+ def __init__(
+ self,
+ do_resize: bool = True,
+ size: Optional[dict[str, int]] = None,
+ crop_to_patches: bool = False,
+ min_patches: int = 1,
+ max_patches: int = 12,
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ do_rescale: bool = True,
+ rescale_factor: Union[int, float] = 1 / 255,
+ do_normalize: bool = True,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ do_convert_rgb: bool = True,
+ use_covering_area_grid: bool = True,
+ **kwargs,
+ ) -> None:
+ super().__init__(**kwargs)
+ size = size if size is not None else {"height": 384, "width": 384}
+ size = get_size_dict(size, default_to_square=True)
+
+ self.do_resize = do_resize
+ self.size = size
+ self.crop_to_patches = crop_to_patches
+ self.min_patches = min_patches
+ self.max_patches = max_patches
+ self.resample = resample
+ self.do_rescale = do_rescale
+ self.rescale_factor = rescale_factor
+ self.do_normalize = do_normalize
+ self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
+ self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
+ self.do_convert_rgb = do_convert_rgb
+
+ def resize(
+ self,
+ image: np.ndarray,
+ size: dict[str, int],
+ resample: PILImageResampling = PILImageResampling.BICUBIC,
+ data_format: Optional[Union[str, ChannelDimension]] = None,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ **kwargs,
+ ) -> np.ndarray:
+ """
+ Resize an image to `(size["height"], size["width"])`.
+
+ Args:
+ image (`np.ndarray`):
+ Image to resize.
+ size (`Dict[str, int]`):
+ Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
+ resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+ `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BICUBIC`.
+ data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the output image. If unset, the channel dimension format of the input
+ image is used. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+
+ Returns:
+ `np.ndarray`: The resized image.
+ """
+ size = get_size_dict(size)
+ if "height" not in size or "width" not in size:
+ raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
+ output_size = (size["height"], size["width"])
+ return resize(
+ image,
+ size=output_size,
+ resample=resample,
+ data_format=data_format,
+ input_data_format=input_data_format,
+ **kwargs,
+ )
+
+ @filter_out_non_signature_kwargs()
+ def preprocess(
+ self,
+ images: ImageInput,
+ do_resize: Optional[bool] = None,
+ size: Optional[dict[str, int]] = None,
+ crop_to_patches: Optional[bool] = None,
+ min_patches: Optional[int] = None,
+ max_patches: Optional[int] = None,
+ resample: PILImageResampling = None,
+ do_rescale: Optional[bool] = None,
+ rescale_factor: Optional[float] = None,
+ do_normalize: Optional[bool] = None,
+ image_mean: Optional[Union[float, list[float]]] = None,
+ image_std: Optional[Union[float, list[float]]] = None,
+ return_tensors: Optional[Union[str, TensorType]] = None,
+ do_convert_rgb: Optional[bool] = None,
+ data_format: ChannelDimension = ChannelDimension.FIRST,
+ input_data_format: Optional[Union[str, ChannelDimension]] = None,
+ use_covering_area_grid: bool = True,
+ ) -> PIL.Image.Image:
+ """
+ Preprocess an image or batch of images.
+
+ Args:
+ images (`ImageInput`):
+ Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+ passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+ do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+ Whether to resize the image.
+ size (`Dict[str, int]`, *optional*, defaults to `self.size`):
+ Controls the size of the image after `resize`. The shortest edge of the image is resized to
+ `size["shortest_edge"]` whilst preserving the aspect ratio. If the longest edge of this resized image
+ is > `int(size["shortest_edge"] * (1333 / 800))`, then the image is resized again to make the longest
+ edge equal to `int(size["shortest_edge"] * (1333 / 800))`.
+ crop_to_patches (`bool`, *optional*, defaults to `self.crop_to_patches`):
+ Whether to crop the image to patches.
+ min_patches (`int`, *optional*, defaults to `self.min_patches`):
+ The minimum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is
+ set to `True`.
+ max_patches (`int`, *optional*, defaults to `self.max_patches`):
+ The maximum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is
+ set to `True`.
+ resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+ Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`.
+ do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+ Whether to rescale the image values between [0 - 1].
+ rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+ Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+ do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+ Whether to normalize the image.
+ image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+ Image mean to normalize the image by if `do_normalize` is set to `True`.
+ image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+ Image standard deviation to normalize the image by if `do_normalize` is set to `True`.
+ do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+ Whether to convert the image to RGB.
+ return_tensors (`str` or `TensorType`, *optional*):
+ The type of tensors to return. Can be one of:
+ - Unset: Return a list of `np.ndarray`.
+ - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+ - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+ - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+ - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+ data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+ The channel dimension format for the output image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - Unset: Use the channel dimension format of the input image.
+ input_data_format (`ChannelDimension` or `str`, *optional*):
+ The channel dimension format for the input image. If unset, the channel dimension format is inferred
+ from the input image. Can be one of:
+ - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+ - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+ - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+ use_covering_area_grid (`bool`, *optional*, defaults to `True`):
+ Whether to use the covering area grid to determine the number of patches. Only has an effect if
+ `crop_to_patches` is set to `True`.
+ """
+ do_resize = do_resize if do_resize is not None else self.do_resize
+ crop_to_patches = crop_to_patches if crop_to_patches is not None else self.crop_to_patches
+ min_patches = min_patches if min_patches is not None else self.min_patches
+ max_patches = max_patches if max_patches is not None else self.max_patches
+ resample = resample if resample is not None else self.resample
+ do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+ rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+ do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+ image_mean = image_mean if image_mean is not None else self.image_mean
+ image_std = image_std if image_std is not None else self.image_std
+ do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+ size = size if size is not None else self.size
+ size = get_size_dict(size, default_to_square=False)
+ images = make_flat_list_of_images(images)
+
+ if not valid_images(images):
+ raise ValueError(
+ "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+ "torch.Tensor, tf.Tensor or jax.ndarray."
+ )
+
+ validate_preprocess_arguments(
+ do_rescale=do_rescale,
+ rescale_factor=rescale_factor,
+ do_normalize=do_normalize,
+ image_mean=image_mean,
+ image_std=image_std,
+ do_resize=do_resize,
+ size=size,
+ resample=resample,
+ )
+ # PIL RGBA images are converted to RGB
+ if do_convert_rgb:
+ images = [convert_to_rgb(image) for image in images]
+
+ # All transformations expect numpy arrays.
+ images = [to_numpy_array(image) for image in images]
+
+ if do_rescale and is_scaled_image(images[0]):
+ logger.warning_once(
+ "It looks like you are trying to rescale already rescaled images. If the input"
+ " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+ )
+
+ if input_data_format is None:
+ # We assume that all images have the same channel dimension format.
+ input_data_format = infer_channel_dimension_format(images[0])
+
+ if crop_to_patches and max_patches > 1:
+ images = [
+ self.crop_image_to_patches(
+ image,
+ min_patches=min_patches,
+ max_patches=max_patches,
+ patch_size=size,
+ data_format=input_data_format,
+ use_covering_area_grid=use_covering_area_grid,
+ )
+ for image in images
+ ]
+ grids = [grid for _, grid in images]
+ images = [image for images_list, _ in images for image in images_list]
+ else:
+ grids = [(1, 1)] * len(images)
+
+ for i, image in enumerate(images):
+ if do_resize:
+ images[i] = self.resize(image, size=size, resample=resample, input_data_format=input_data_format)
+
+ if do_rescale:
+ images[i] = self.rescale(image=images[i], scale=rescale_factor, input_data_format=input_data_format)
+
+ if do_normalize:
+ images[i] = self.normalize(
+ image=images[i],
+ mean=image_mean,
+ std=image_std,
+ input_data_format=input_data_format,
+ )
+
+ images[i] = to_channel_dimension_format(images[i], data_format, input_channel_dim=input_data_format)
+
+ encoded_outputs = BatchFeature(data={"pixel_values": images, "grids": grids}, tensor_type=return_tensors)
+
+ return encoded_outputs
+
+ def crop_image_to_patches(
+ self,
+ images: np.ndarray,
+ min_patches: int,
+ max_patches: int,
+ use_covering_area_grid: bool = True,
+ patch_size: Optional[Union[tuple, int, dict]] = None,
+ data_format: ChannelDimension = None,
+ covering_threshold: float = 0.9,
+ ):
+ """
+ Crop the image to patches and return a list of cropped images.
+ The number of patches and their grid arrangement are determined by the original image size,
+ the target patch size and the minimum and maximum number of patches.
+ The aspect ratio of the patches grid is chosen to be the closest to the original image aspect ratio.
+
+ Args:
+ images (`np.ndarray`):
+ The image to be cropped.
+ min_patches (`int`):
+ The minimum number of patches to be extracted from the image.
+ max_patches (`int`):
+ The maximum number of patches to be extracted from the image.
+ use_covering_area_grid (`bool`, *optional*, defaults to `True`):
+ Whether to use the covering area grid to determine the number of patches.
+ patch_size (`int`, `Tuple[int, int]`, `dict`, *optional*):
+ The size of the output patches.
+ data_format (`ChannelDimension`, *optional*):
+ The format of the image data. If `None`, the format is inferred from the input image.
+ covering_threshold (`float`, *optional*, defaults to `0.9`):
+ The threshold for the covering area grid. If the covering area is less than this value, the grid is
+ considered invalid.
+
+ Returns:
+ List[`PIL.Image.Image`] or List[np.ndarray]: The list of cropped images.
+ """
+ if data_format is None:
+ data_format = infer_channel_dimension_format(images)
+ images = to_channel_dimension_format(images, ChannelDimension.FIRST, data_format)
+ patch_size_height, patch_size_width = patch_size["height"], patch_size["width"]
+ original_height, original_width = images.shape[-2:]
+
+ if use_covering_area_grid:
+ # Use the original OVIS2 approach: compute the minimal number of tiles that cover at least 90% of the image area
+ num_columns, num_rows = get_min_tile_covering_grid(
+ (original_height, original_width),
+ target_patch_size=patch_size_height, # square patch size
+ max_image_tiles=max_patches,
+ covering_threshold=covering_threshold,
+ )
+ else:
+ # find the closest aspect ratio to the target
+ num_columns, num_rows = get_optimal_tiled_canvas(
+ (original_height, original_width),
+ (patch_size_height, patch_size_width),
+ min_patches,
+ max_patches,
+ )
+
+ # calculate the target width and height
+ target_width = patch_size_width * num_columns
+ target_height = patch_size_height * num_rows
+ num_blocks = num_columns * num_rows
+
+ # resize the image so that each patch is of patch_size
+ resized_image = self.resize(
+ images,
+ {"height": target_height, "width": target_width},
+ data_format=ChannelDimension.FIRST,
+ input_data_format=ChannelDimension.FIRST,
+ )
+
+ # split the image into patches
+ processed_images = []
+ for i in range(num_blocks):
+ column = i % num_columns
+ row = i // num_columns
+ box = (
+ column * patch_size_width,
+ row * patch_size_height,
+ (column + 1) * patch_size_width,
+ (row + 1) * patch_size_height,
+ )
+ # split the image
+ patch_image = resized_image[..., box[1] : box[3], box[0] : box[2]]
+ patch_image = to_channel_dimension_format(patch_image, data_format, ChannelDimension.FIRST)
+ processed_images.append(patch_image)
+
+ if len(processed_images) != 1:
+ thumbnail_img = self.resize(
+ images, patch_size, data_format=data_format, input_data_format=ChannelDimension.FIRST
+ )
+ processed_images.insert(0, thumbnail_img)
+
+ return processed_images, (num_rows, num_columns)
+
+
+__all__ = ["Ovis2ImageProcessor"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/image_processing_ovis2_fast.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/image_processing_ovis2_fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5940421828dc633ae424e0f134ac5dddfbd4153
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/image_processing_ovis2_fast.py
@@ -0,0 +1,254 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Optional, Union
+
+from ...image_processing_utils import BatchFeature
+from ...image_processing_utils_fast import (
+ BaseImageProcessorFast,
+ DefaultFastImageProcessorKwargs,
+ group_images_by_shape,
+ reorder_images,
+)
+from ...image_utils import (
+ OPENAI_CLIP_MEAN,
+ OPENAI_CLIP_STD,
+ ImageInput,
+ PILImageResampling,
+ SizeDict,
+)
+from ...processing_utils import Unpack
+from ...utils import (
+ TensorType,
+ auto_docstring,
+ is_torch_available,
+ is_torchvision_available,
+ is_torchvision_v2_available,
+)
+from .image_processing_ovis2 import get_min_tile_covering_grid, get_optimal_tiled_canvas
+
+
+if is_torch_available():
+ import torch
+
+if is_torchvision_available():
+ if is_torchvision_v2_available():
+ from torchvision.transforms.v2 import functional as F
+ else:
+ from torchvision.transforms import functional as F
+
+
+class Ovis2ImageProcessorKwargs(DefaultFastImageProcessorKwargs):
+ """
+ Args:
+ crop_to_patches (`bool`, *optional*, defaults to `False`):
+ Whether to crop the image to patches. Can be overridden by the `crop_to_patches` parameter in the
+ `preprocess` method.
+ min_patches (`int`, *optional*, defaults to 1):
+ The minimum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is
+ set to `True`. Can be overridden by the `min_patches` parameter in the `preprocess` method.
+ max_patches (`int`, *optional*, defaults to 12):
+ The maximum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is
+ set to `True`. Can be overridden by the `max_patches` parameter in the `preprocess` method.
+ use_covering_area_grid (`bool`, *optional*, defaults to `True`):
+ Whether to use the covering area grid to determine the number of patches. Only has an effect if
+ `crop_to_patches` is set to `True`. Can be overridden by the `use_covering_area_grid` parameter in the
+ `preprocess` method.
+ """
+
+ crop_to_patches: Optional[bool]
+ min_patches: Optional[int]
+ max_patches: Optional[int]
+ use_covering_area_grid: Optional[bool]
+
+
+@auto_docstring
+class Ovis2ImageProcessorFast(BaseImageProcessorFast):
+ resample = PILImageResampling.BICUBIC
+ image_mean = OPENAI_CLIP_MEAN
+ image_std = OPENAI_CLIP_STD
+ size = {"height": 384, "width": 384}
+ default_to_square = None
+ do_resize = True
+ do_rescale = True
+ do_normalize = True
+ do_convert_rgb = True
+ crop_to_patches = False
+ min_patches = 1
+ max_patches = 12
+ use_covering_area_grid = True
+ valid_kwargs = Ovis2ImageProcessorKwargs
+
+ @auto_docstring
+ def preprocess(self, images: ImageInput, **kwargs: Unpack[Ovis2ImageProcessorKwargs]) -> BatchFeature:
+ return super().preprocess(images, **kwargs)
+
+ def crop_image_to_patches(
+ self,
+ images: "torch.Tensor",
+ min_patches: int,
+ max_patches: int,
+ use_covering_area_grid: bool = True,
+ covering_threshold: float = 0.9,
+ patch_size: Optional[Union[tuple, int, dict]] = None,
+ interpolation: Optional["F.InterpolationMode"] = None,
+ ):
+ """
+ Crop the images to patches and return a list of cropped images.
+ The number of patches and their grid arrangement are determined by the original image size,
+ the target patch size and the minimum and maximum number of patches.
+ The aspect ratio of the patches grid is chosen to be the closest to the original image aspect ratio.
+
+ Args:
+ images (`torch.Tensor`):
+ The images to be cropped.
+ min_patches (`int`):
+ The minimum number of patches to be extracted from the image.
+ max_patches (`int`):
+ The maximum number of patches to be extracted from the image.
+ use_covering_area_grid (`bool`, *optional*, defaults to `True`):
+ Whether to use the original OVIS2 approach: compute the minimal number of tiles that cover at least 90%
+ of the image area. If `False`, the closest aspect ratio to the target is used.
+ covering_threshold (`float`, *optional*, defaults to `0.9`):
+ The threshold for the covering area. Only has an effect if `use_covering_area_grid` is set to `True`.
+ patch_size (`int`, `Tuple[int, int]`, `dict`, *optional*):
+ The size of the output patches.
+ The format of the image data. If `None`, the format is inferred from the input image.
+ interpolation (`InterpolationMode`):
+ Resampling filter to use if resizing the image.
+
+ Returns:
+ List[`PIL.Image.Image`] or List[np.ndarray]: The list of cropped images.
+ """
+ num_image = images.shape[0]
+ patch_size_height, patch_size_width = patch_size.height, patch_size.width
+ original_height, original_width = images.shape[-2:]
+
+ if use_covering_area_grid:
+ # Use the original OVIS2 approach: compute the minimal number of tiles that cover at least 90% of the image area
+ num_columns, num_rows = get_min_tile_covering_grid(
+ (original_height, original_width),
+ target_patch_size=patch_size_height, # square patch size
+ max_image_tiles=max_patches,
+ covering_threshold=covering_threshold,
+ )
+ else:
+ # find the closest aspect ratio to the target
+ num_columns, num_rows = get_optimal_tiled_canvas(
+ (original_height, original_width), (patch_size_height, patch_size_width), min_patches, max_patches
+ )
+
+ # calculate the target width and height
+ target_width = patch_size_width * num_columns
+ target_height = patch_size_height * num_rows
+ num_blocks = num_columns * num_rows
+
+ # resize the image so that each patch is of patch_size
+ resized_image = self.resize(
+ images, SizeDict(height=target_height, width=target_width), interpolation=interpolation
+ )
+ # split the image into patches
+ processed_images = []
+ for i in range(num_blocks):
+ column = i % num_columns
+ row = i // num_columns
+ box = (
+ column * patch_size_width,
+ row * patch_size_height,
+ (column + 1) * patch_size_width,
+ (row + 1) * patch_size_height,
+ )
+ # split the image
+ patch_image = resized_image[..., box[1] : box[3], box[0] : box[2]]
+ processed_images.append(patch_image)
+
+ if len(processed_images) != 1:
+ thumbnail_img = self.resize(images, patch_size, interpolation=interpolation)
+ processed_images.insert(0, thumbnail_img)
+
+ processed_images = torch.stack(processed_images, dim=0).transpose(0, 1).contiguous()
+ grid = [[num_rows, num_columns] for _ in range(num_image)]
+
+ return processed_images, grid
+
+ def _preprocess(
+ self,
+ images: list["torch.Tensor"],
+ do_resize: bool,
+ size: SizeDict,
+ crop_to_patches: bool,
+ min_patches: int,
+ max_patches: int,
+ use_covering_area_grid: bool,
+ interpolation: Optional["F.InterpolationMode"],
+ do_center_crop: bool,
+ crop_size: SizeDict,
+ do_rescale: bool,
+ rescale_factor: float,
+ do_normalize: bool,
+ image_mean: Optional[Union[float, list[float]]],
+ image_std: Optional[Union[float, list[float]]],
+ disable_grouping: Optional[bool],
+ return_tensors: Optional[Union[str, TensorType]],
+ ) -> BatchFeature:
+ if crop_to_patches and max_patches > 1:
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ processed_images_grouped = {}
+ grids = {}
+ for shape, stacked_images in grouped_images.items():
+ stacked_images, grid = self.crop_image_to_patches(
+ stacked_images,
+ min_patches,
+ max_patches,
+ patch_size=size,
+ use_covering_area_grid=use_covering_area_grid,
+ interpolation=interpolation,
+ )
+ processed_images_grouped[shape] = stacked_images
+ grids[shape] = grid
+ images = reorder_images(processed_images_grouped, grouped_images_index)
+ images = [image for images_list in images for image in images_list]
+ grids = reorder_images(grids, grouped_images_index)
+ else:
+ grids = [[1, 1] for _ in range(len(images))]
+
+ # Group images by size for batched resizing
+ grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+ resized_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_resize:
+ stacked_images = self.resize(image=stacked_images, size=size, interpolation=interpolation)
+ resized_images_grouped[shape] = stacked_images
+ resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+ # Group images by size for further processing
+ # Needed in case do_resize is False, or resize returns images with different sizes
+ grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+ processed_images_grouped = {}
+ for shape, stacked_images in grouped_images.items():
+ if do_center_crop:
+ stacked_images = self.center_crop(stacked_images, crop_size)
+ # Fused rescale and normalize
+ stacked_images = self.rescale_and_normalize(
+ stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+ )
+ processed_images_grouped[shape] = stacked_images
+
+ processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+ processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
+ return BatchFeature(data={"pixel_values": processed_images, "grids": grids}, tensor_type=return_tensors)
+
+
+__all__ = ["Ovis2ImageProcessorFast"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/modeling_ovis2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/modeling_ovis2.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd4da633d710ea7407ec0841702d2d92684101dd
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+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/modeling_ovis2.py
@@ -0,0 +1,902 @@
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# This file was automatically generated from src/transformers/models/ovis2/modular_ovis2.py.
+# Do NOT edit this file manually as any edits will be overwritten by the generation of
+# the file from the modular. If any change should be done, please apply the change to the
+# modular_ovis2.py file directly. One of our CI enforces this.
+# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from dataclasses import dataclass
+from typing import Callable, Optional, Union
+
+import torch
+from torch import nn
+
+from ...activations import ACT2FN
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...utils import ModelOutput, auto_docstring, can_return_tuple, is_torchdynamo_compiling
+from ..auto import AutoModel
+from .configuration_ovis2 import Ovis2Config, Ovis2VisionConfig
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Llava outputs, with hidden states and attentions.
+ """
+)
+class Ovis2ModelOutputWithPast(BaseModelOutputWithPast):
+ r"""
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+
+@dataclass
+@auto_docstring(
+ custom_intro="""
+ Base class for Ovis2 causal language model (or autoregressive) outputs.
+ """
+)
+class Ovis2CausalLMOutputWithPast(ModelOutput):
+ r"""
+ loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+ Language modeling loss (for next-token prediction).
+ logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+ `past_key_values` input) to speed up sequential decoding.
+ image_hidden_states (`torch.FloatTensor`, *optional*):
+ A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`.
+ image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ logits: Optional[torch.FloatTensor] = None
+ past_key_values: Optional[list[torch.FloatTensor]] = None
+ hidden_states: Optional[tuple[torch.FloatTensor]] = None
+ attentions: Optional[tuple[torch.FloatTensor]] = None
+ image_hidden_states: Optional[torch.FloatTensor] = None
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class Ovis2RMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ Ovis2RMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+ def extra_repr(self):
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class Ovis2VisionMLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+ return down_proj
+
+
+class Ovis2VisionEmbeddings(nn.Module):
+ def __init__(self, config: Ovis2VisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.image_size = config.image_size
+ self.patch_size = config.patch_size
+
+ self.patch_embedding = nn.Conv2d(
+ in_channels=config.num_channels,
+ out_channels=self.embed_dim,
+ kernel_size=self.patch_size,
+ stride=self.patch_size,
+ padding="valid",
+ )
+
+ self.num_patches = (self.image_size // self.patch_size) ** 2
+ self.num_positions = self.num_patches
+ self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+ self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
+ self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
+
+ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+ target_dtype = self.patch_embedding.weight.dtype
+ patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
+ embeddings = patch_embeds.flatten(2).transpose(1, 2)
+ embeddings = self.rms_norm(embeddings)
+
+ embeddings = embeddings + self.position_embedding(self.position_ids)
+
+ return embeddings
+
+
+def eager_attention_forward(
+ module: nn.Module,
+ query: torch.Tensor,
+ key: torch.Tensor,
+ value: torch.Tensor,
+ attention_mask: Optional[torch.Tensor],
+ scaling: float,
+ dropout: float = 0.0,
+ **kwargs,
+):
+ attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
+ if attention_mask is not None:
+ attn_weights = attn_weights + attention_mask
+
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+
+ attn_output = torch.matmul(attn_weights, value)
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ return attn_output, attn_weights
+
+
+class Ovis2VisionAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.embed_dim // self.num_heads
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+ f" {self.num_heads})."
+ )
+ self.scale = self.head_dim**-0.5
+ self.dropout = config.attention_dropout
+ self.is_causal = False
+ self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
+ self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
+ self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
+ self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ """Input shape: Batch x Time x Channel"""
+
+ batch_size, seq_length, embed_dim = hidden_states.shape
+
+ queries = self.q_proj(hidden_states)
+ keys = self.k_proj(hidden_states)
+ values = self.v_proj(hidden_states)
+
+ queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+ keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+ values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ queries,
+ keys,
+ values,
+ attention_mask,
+ is_causal=self.is_causal,
+ scaling=self.scale,
+ dropout=0.0 if not self.training else self.dropout,
+ )
+
+ attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous()
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+class Ovis2MLP(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+ return down_proj
+
+
+class Ovis2Attention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.embed_dim // self.num_heads
+ if self.head_dim * self.num_heads != self.embed_dim:
+ raise ValueError(
+ f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+ f" {self.num_heads})."
+ )
+ self.scale = self.head_dim**-0.5
+ self.dropout = config.attention_dropout
+ self.is_causal = False
+ self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
+ self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
+ self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
+ self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+ """Input shape: Batch x Time x Channel"""
+
+ batch_size, seq_length, embed_dim = hidden_states.shape
+
+ queries = self.q_proj(hidden_states)
+ keys = self.k_proj(hidden_states)
+ values = self.v_proj(hidden_states)
+
+ queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+ keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+ values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+
+ attention_interface: Callable = eager_attention_forward
+ if self.config._attn_implementation != "eager":
+ attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+ attn_output, attn_weights = attention_interface(
+ self,
+ queries,
+ keys,
+ values,
+ attention_mask,
+ is_causal=self.is_causal,
+ scaling=self.scale,
+ dropout=0.0 if not self.training else self.dropout,
+ )
+
+ attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous()
+ attn_output = self.out_proj(attn_output)
+
+ return attn_output, attn_weights
+
+
+class Ovis2VisionEncoderLayer(GradientCheckpointingLayer):
+ def __init__(self, config: Ovis2VisionConfig):
+ super().__init__()
+ self.attention = Ovis2Attention(config)
+ self.ffn = Ovis2MLP(config)
+ self.rms_norm1 = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
+ self.rms_norm2 = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> tuple[torch.Tensor, torch.Tensor]:
+ norm_hidden_states = self.rms_norm1(hidden_states)
+ attn_output, attn_weights = self.attention(hidden_states=norm_hidden_states, attention_mask=attention_mask)
+
+ hidden_states = hidden_states + attn_output
+ norm_hidden_states = self.rms_norm2(hidden_states)
+ mlp_output = self.ffn(norm_hidden_states)
+
+ hidden_states = hidden_states + mlp_output
+ return (hidden_states, attn_weights) if output_attentions else (hidden_states, None)
+
+
+class Ovis2VisionEncoder(nn.Module):
+ """
+ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+ [`Ovis2VisionEncoderLayer`].
+
+ Args:
+ config: Ovis2VisionConfig
+ """
+
+ def __init__(self, config: Ovis2VisionConfig):
+ super().__init__()
+ self.config = config
+ self.layers = nn.ModuleList([Ovis2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+ self.gradient_checkpointing = False
+
+ # Ignore copy
+ @can_return_tuple
+ def forward(
+ self,
+ inputs_embeds,
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ ) -> BaseModelOutput:
+ r"""
+ Args:
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+ This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+ than the model's internal embedding lookup matrix.
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+ for more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+ """
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ encoder_states = () if output_hidden_states else None
+ all_attentions = () if output_attentions else None
+
+ hidden_states = inputs_embeds
+ for encoder_layer in self.layers:
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ layer_outputs = encoder_layer(
+ hidden_states,
+ attention_mask,
+ output_attentions=output_attentions,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if output_attentions:
+ all_attentions = all_attentions + (layer_outputs[1],)
+
+ if output_hidden_states:
+ encoder_states = encoder_states + (hidden_states,)
+
+ return BaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=encoder_states,
+ attentions=all_attentions,
+ )
+
+
+class Ovis2VisionTransformer(nn.Module):
+ def __init__(self, config: Ovis2VisionConfig):
+ super().__init__()
+ self.config = config
+ self.embeddings = Ovis2VisionEmbeddings(config)
+ self.encoder = Ovis2VisionEncoder(config)
+ self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
+ self.gradient_checkpointing = False
+
+ @can_return_tuple
+ def forward(
+ self,
+ pixel_values,
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ hidden_states = self.embeddings(pixel_values)
+
+ encoder_outputs = self.encoder(
+ inputs_embeds=hidden_states,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ last_hidden_state = encoder_outputs[0]
+ last_hidden_state = self.rms_norm(last_hidden_state)
+
+ return BaseModelOutput(
+ last_hidden_state=last_hidden_state,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+class Ovis2VisualEmbeddingTable(nn.Embedding):
+ def forward(self, visual_tokens: torch.Tensor) -> torch.Tensor:
+ if visual_tokens.dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.long]:
+ return super().forward(visual_tokens)
+ return torch.matmul(visual_tokens, self.weight)
+
+
+class Ovis2PreTrainedModel(PreTrainedModel):
+ config: Ovis2Config
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["Ovis2VisionAttention"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_cache_class = True
+ _supports_flash_attn = True
+ _supports_flex_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+ _supports_attention_backend = True
+
+
+def hard_softmax(logits: torch.Tensor, dim: int):
+ y_soft = logits.softmax(dim)
+ # Straight through.
+ index = y_soft.max(dim, keepdim=True)[1]
+ y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
+ ret = y_hard - y_soft.detach() + y_soft
+
+ return ret
+
+
+class Ovis2VisionModel(Ovis2PreTrainedModel):
+ config: Ovis2VisionConfig
+
+ def __init__(self, config: Ovis2VisionConfig):
+ super().__init__(config)
+ self.config = config
+ self.transformer = Ovis2VisionTransformer(config)
+ self.num_visual_indicator_tokens = config.num_visual_indicator_tokens
+ self.vocab_size = config.vocab_size
+ self.head_linear = nn.Linear(
+ config.hidden_size * config.hidden_stride * config.hidden_stride,
+ self.vocab_size - self.num_visual_indicator_tokens,
+ bias=False,
+ )
+ self.head_norm = nn.LayerNorm(self.vocab_size - self.num_visual_indicator_tokens)
+
+ def forward(self, pixel_values: torch.FloatTensor) -> tuple[torch.Tensor, torch.Tensor]:
+ outputs = self.transformer(pixel_values)
+ last_hidden_state = outputs.last_hidden_state
+
+ if self.config.hidden_stride > 1:
+ num_images, seq_len, hidden_dim = last_hidden_state.shape
+ hidden_stride = self.config.hidden_stride
+
+ sqrt_l = int(math.sqrt(seq_len))
+ if sqrt_l * sqrt_l != seq_len:
+ raise ValueError("Token sequence length must be a perfect square")
+
+ pad_size = (hidden_stride - (sqrt_l % hidden_stride)) % hidden_stride
+ last_hidden_state = nn.functional.pad(last_hidden_state, (0, 0, 0, pad_size, 0, pad_size), "constant", 0)
+ sqrt_l += pad_size
+
+ last_hidden_state = last_hidden_state.reshape(
+ num_images, sqrt_l // hidden_stride, hidden_stride, sqrt_l // hidden_stride, hidden_stride, hidden_dim
+ )
+ last_hidden_state = last_hidden_state.permute(0, 1, 3, 2, 4, 5)
+ last_hidden_state = last_hidden_state.reshape(
+ num_images, -1, hidden_stride * hidden_stride * hidden_dim
+ ) # (n, (sqrt_l//hs)^2, hs^2*d)
+
+ logits = self.head_linear(last_hidden_state)
+ logits = self.head_norm(logits)
+
+ if self.config.tokenize_function == "gumbel_argmax":
+ prob_token = nn.functional.gumbel_softmax(logits, dim=-1, hard=True)
+ elif self.config.tokenize_function == "st_argmax":
+ prob_token = hard_softmax(logits, dim=-1)
+ elif self.config.tokenize_function == "softmax":
+ prob_token = nn.functional.softmax(logits, dim=-1)
+
+ return prob_token
+
+
+@auto_docstring(
+ custom_intro="""
+ The Ovis2 model which consists of a vision backbone and a language model, without a language modeling head.
+ """
+)
+class Ovis2Model(Ovis2PreTrainedModel):
+ _checkpoint_conversion_mapping = {}
+
+ def __init__(self, config: Ovis2Config):
+ super().__init__(config)
+ self.vision_tower = Ovis2VisionModel(config.vision_config)
+ self.language_model = AutoModel.from_config(config.text_config)
+ self.visual_embeddings_table = Ovis2VisualEmbeddingTable(config.vision_config.vocab_size, config.hidden_size)
+
+ self.visual_vocab_size = config.vision_config.vocab_size
+ self.vocab_size = config.vocab_size
+ self.visual_indicator_token_ids = config.visual_indicator_token_ids
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.language_model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.language_model.set_input_embeddings(value)
+
+ def set_decoder(self, decoder):
+ self.language_model = decoder
+
+ def get_decoder(self):
+ return self.language_model
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ ) -> torch.FloatTensor:
+ """
+ Obtains image last hidden states from the vision tower and apply multimodal projection.
+
+ Args:
+ pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
+ The tensors corresponding to the input images.
+ vision_feature_layer (`Union[int, list[int]]`, *optional*):
+ The index of the layer to select the vision feature. If multiple indices are provided,
+ the vision feature of the corresponding indices will be concatenated to form the
+ vision features.
+ vision_feature_select_strategy (`str`, *optional*):
+ The feature selection strategy used to select the vision feature from the vision backbone.
+ Can be one of `"default"` or `"full"`
+ Returns:
+ image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
+ """
+ image_features = self.vision_tower(pixel_values)
+ batch_size, img_seq_len, _ = image_features.shape
+ padding_tensor = torch.zeros(
+ (batch_size, img_seq_len, self.vision_tower.num_visual_indicator_tokens),
+ dtype=image_features.dtype,
+ device=image_features.device,
+ requires_grad=False,
+ layout=image_features.layout,
+ )
+ image_features = torch.cat([image_features, padding_tensor], dim=2)
+ image_features = self.visual_embeddings_table(image_features)
+
+ visual_indicator = torch.arange(
+ self.visual_vocab_size - self.vision_tower.num_visual_indicator_tokens,
+ self.visual_vocab_size,
+ dtype=torch.long,
+ ).to(image_features.device)
+ visual_indicator_features = self.visual_embeddings_table(visual_indicator)
+
+ return image_features, visual_indicator_features
+
+ def get_placeholder_mask(
+ self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
+ ):
+ """
+ Obtains multimodal placeholdr mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+ equal to the length of multimodal features. If the lengths are different, an error is raised.
+ """
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ n_image_features = image_features.shape[0] * image_features.shape[1]
+ if inputs_embeds[special_image_mask].numel() != image_features.numel():
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+ )
+ return special_image_mask
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs,
+ ) -> Union[tuple, Ovis2ModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ if pixel_values is not None:
+ image_features, visual_indicator_features = self.get_image_features(pixel_values=pixel_values)
+
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+ image_features = image_features.reshape(-1, image_features.shape[-1])
+ n_image_features = image_features.shape[0]
+ if not is_torchdynamo_compiling() and n_image_tokens != n_image_features:
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+ for i, visual_indicator_id in enumerate(self.visual_indicator_token_ids):
+ if input_ids is None:
+ mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(visual_indicator_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ mask = mask.all(-1)
+ else:
+ mask = (input_ids == visual_indicator_id).to(inputs_embeds.device)
+
+ if mask.any():
+ inputs_embeds[mask] = (
+ visual_indicator_features[i]
+ .expand_as(inputs_embeds[mask])
+ .to(inputs_embeds.device, inputs_embeds.dtype)
+ )
+
+ outputs = self.language_model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ return Ovis2ModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=image_features if pixel_values is not None else None,
+ )
+
+
+@auto_docstring
+class Ovis2ForConditionalGeneration(Ovis2PreTrainedModel, GenerationMixin):
+ _checkpoint_conversion_mapping = {}
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: Ovis2Config):
+ super().__init__(config)
+ self.model = Ovis2Model(config)
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.get_input_embeddings()
+
+ def set_input_embeddings(self, value):
+ self.model.set_input_embeddings(value)
+
+ def get_output_embeddings(self) -> nn.Module:
+ return self.lm_head
+
+ def set_decoder(self, decoder):
+ self.model.set_decoder(decoder)
+
+ def get_decoder(self):
+ return self.model.get_decoder()
+
+ def get_image_features(self, pixel_values: torch.FloatTensor):
+ return self.model.get_image_features(pixel_values=pixel_values)
+
+ # Make modules available through conditional class for BC
+ @property
+ def language_model(self):
+ return self.model.language_model
+
+ @property
+ def vision_tower(self):
+ return self.model.vision_tower
+
+ @property
+ def multi_modal_projector(self):
+ raise AttributeError("Not needed for Ovis2")
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs,
+ ) -> Union[tuple, Ovis2CausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import AutoProcessor, Ovis2ForConditionalGeneration
+
+ >>> model = Ovis2ForConditionalGeneration.from_pretrained("thisisiron/Ovis2-2B-hf")
+ >>> processor = AutoProcessor.from_pretrained("thisisiron/Ovis2-2B-hf")
+
+ >>> prompt = "<|im_start|>user\n\nDescribe the image.<|im_end|>\n<|im_start|>assistant\n"
+ >>> url = "http://images.cocodataset.org/val2014/COCO_val2014_000000537955.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(**inputs, max_new_tokens=15)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0]
+ "user\n\nDescribe the image.\nassistant\nThe image features a brown dog standing on a wooden floor, looking up with"
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return Ovis2CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ inputs_embeds=None,
+ pixel_values=None,
+ attention_mask=None,
+ cache_position=None,
+ logits_to_keep=None,
+ **kwargs,
+ ):
+ # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+
+ model_inputs = super().prepare_inputs_for_generation(
+ input_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ attention_mask=attention_mask,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ if cache_position[0] == 0:
+ # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
+ # Otherwise we need pixel values to be passed to model
+ model_inputs["pixel_values"] = pixel_values
+
+ return model_inputs
+
+
+__all__ = ["Ovis2PreTrainedModel", "Ovis2Model", "Ovis2ForConditionalGeneration"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/modular_ovis2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/modular_ovis2.py
new file mode 100644
index 0000000000000000000000000000000000000000..63d946eebb9095e9ae13ce2a87766e21ad48be6f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/modular_ovis2.py
@@ -0,0 +1,443 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import Optional, Union
+
+import torch
+from torch import nn
+
+from ...generation import GenerationMixin
+from ...modeling_outputs import BaseModelOutput
+from ...modeling_utils import PreTrainedModel
+from ...utils import auto_docstring, can_return_tuple, is_torchdynamo_compiling
+from ..aimv2.modeling_aimv2 import Aimv2Attention, Aimv2EncoderLayer
+from ..auto import AutoModel
+from ..llama.modeling_llama import LlamaMLP, LlamaRMSNorm
+from ..llava.modeling_llava import LlavaForConditionalGeneration, LlavaModel
+from ..llava_next.modeling_llava_next import LlavaNextCausalLMOutputWithPast, LlavaNextModelOutputWithPast
+from ..siglip.modeling_siglip import SiglipEncoder, SiglipVisionEmbeddings
+from .configuration_ovis2 import Ovis2Config, Ovis2VisionConfig
+
+
+def hard_softmax(logits: torch.Tensor, dim: int):
+ y_soft = logits.softmax(dim)
+ # Straight through.
+ index = y_soft.max(dim, keepdim=True)[1]
+ y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
+ ret = y_hard - y_soft.detach() + y_soft
+
+ return ret
+
+
+class Ovis2ModelOutputWithPast(LlavaNextModelOutputWithPast):
+ pass
+
+
+class Ovis2CausalLMOutputWithPast(LlavaNextCausalLMOutputWithPast):
+ pass
+
+
+class Ovis2RMSNorm(LlamaRMSNorm):
+ pass
+
+
+class Ovis2VisionMLP(LlamaMLP):
+ pass
+
+
+class Ovis2VisionEmbeddings(SiglipVisionEmbeddings):
+ def __init__(self, config: Ovis2VisionConfig):
+ super().__init__()
+ self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
+
+ def interpolate_pos_encoding(self):
+ raise NotImplementedError("Not needed for Ovis2")
+
+ def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+ target_dtype = self.patch_embedding.weight.dtype
+ patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
+ embeddings = patch_embeds.flatten(2).transpose(1, 2)
+ embeddings = self.rms_norm(embeddings)
+
+ embeddings = embeddings + self.position_embedding(self.position_ids)
+
+ return embeddings
+
+
+class Ovis2VisionAttention(Aimv2Attention):
+ pass
+
+
+class Ovis2VisionEncoderLayer(Aimv2EncoderLayer):
+ pass
+
+
+class Ovis2VisionEncoder(SiglipEncoder):
+ def __init__(self, config: Ovis2VisionConfig):
+ super().__init__()
+ self.layers = nn.ModuleList([Ovis2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+
+
+class Ovis2VisionTransformer(nn.Module):
+ def __init__(self, config: Ovis2VisionConfig):
+ super().__init__()
+ self.config = config
+ self.embeddings = Ovis2VisionEmbeddings(config)
+ self.encoder = Ovis2VisionEncoder(config)
+ self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps)
+ self.gradient_checkpointing = False
+
+ @can_return_tuple
+ def forward(
+ self,
+ pixel_values,
+ attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ hidden_states = self.embeddings(pixel_values)
+
+ encoder_outputs = self.encoder(
+ inputs_embeds=hidden_states,
+ attention_mask=attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ )
+
+ last_hidden_state = encoder_outputs[0]
+ last_hidden_state = self.rms_norm(last_hidden_state)
+
+ return BaseModelOutput(
+ last_hidden_state=last_hidden_state,
+ hidden_states=encoder_outputs.hidden_states,
+ attentions=encoder_outputs.attentions,
+ )
+
+
+class Ovis2VisualEmbeddingTable(nn.Embedding):
+ def forward(self, visual_tokens: torch.Tensor) -> torch.Tensor:
+ if visual_tokens.dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.long]:
+ return super().forward(visual_tokens)
+ return torch.matmul(visual_tokens, self.weight)
+
+
+class Ovis2PreTrainedModel(PreTrainedModel):
+ config: Ovis2Config
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["Ovis2VisionAttention"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_cache_class = True
+ _supports_flash_attn = True
+ _supports_flex_attn = True
+ _supports_sdpa = True
+
+ _can_compile_fullgraph = True
+ _supports_attention_backend = True
+
+
+class Ovis2VisionModel(Ovis2PreTrainedModel):
+ config: Ovis2VisionConfig
+
+ def __init__(self, config: Ovis2VisionConfig):
+ super().__init__(config)
+ self.config = config
+ self.transformer = Ovis2VisionTransformer(config)
+ self.num_visual_indicator_tokens = config.num_visual_indicator_tokens
+ self.vocab_size = config.vocab_size
+ self.head_linear = nn.Linear(
+ config.hidden_size * config.hidden_stride * config.hidden_stride,
+ self.vocab_size - self.num_visual_indicator_tokens,
+ bias=False,
+ )
+ self.head_norm = nn.LayerNorm(self.vocab_size - self.num_visual_indicator_tokens)
+
+ def forward(self, pixel_values: torch.FloatTensor) -> tuple[torch.Tensor, torch.Tensor]:
+ outputs = self.transformer(pixel_values)
+ last_hidden_state = outputs.last_hidden_state
+
+ if self.config.hidden_stride > 1:
+ num_images, seq_len, hidden_dim = last_hidden_state.shape
+ hidden_stride = self.config.hidden_stride
+
+ sqrt_l = int(math.sqrt(seq_len))
+ if sqrt_l * sqrt_l != seq_len:
+ raise ValueError("Token sequence length must be a perfect square")
+
+ pad_size = (hidden_stride - (sqrt_l % hidden_stride)) % hidden_stride
+ last_hidden_state = nn.functional.pad(last_hidden_state, (0, 0, 0, pad_size, 0, pad_size), "constant", 0)
+ sqrt_l += pad_size
+
+ last_hidden_state = last_hidden_state.reshape(
+ num_images, sqrt_l // hidden_stride, hidden_stride, sqrt_l // hidden_stride, hidden_stride, hidden_dim
+ )
+ last_hidden_state = last_hidden_state.permute(0, 1, 3, 2, 4, 5)
+ last_hidden_state = last_hidden_state.reshape(
+ num_images, -1, hidden_stride * hidden_stride * hidden_dim
+ ) # (n, (sqrt_l//hs)^2, hs^2*d)
+
+ logits = self.head_linear(last_hidden_state)
+ logits = self.head_norm(logits)
+
+ if self.config.tokenize_function == "gumbel_argmax":
+ prob_token = nn.functional.gumbel_softmax(logits, dim=-1, hard=True)
+ elif self.config.tokenize_function == "st_argmax":
+ prob_token = hard_softmax(logits, dim=-1)
+ elif self.config.tokenize_function == "softmax":
+ prob_token = nn.functional.softmax(logits, dim=-1)
+
+ return prob_token
+
+
+class Ovis2Model(LlavaModel):
+ _checkpoint_conversion_mapping = {}
+
+ def __init__(self, config: Ovis2Config):
+ super().__init__(config)
+ self.vision_tower = Ovis2VisionModel(config.vision_config)
+ self.visual_embeddings_table = Ovis2VisualEmbeddingTable(config.vision_config.vocab_size, config.hidden_size)
+
+ self.visual_vocab_size = config.vision_config.vocab_size
+ self.vocab_size = config.vocab_size
+ self.visual_indicator_token_ids = config.visual_indicator_token_ids
+ self.language_model = AutoModel.from_config(config.text_config)
+ del self.multi_modal_projector
+
+ def get_image_features(
+ self,
+ pixel_values: torch.FloatTensor,
+ ) -> torch.FloatTensor:
+ image_features = self.vision_tower(pixel_values)
+ batch_size, img_seq_len, _ = image_features.shape
+ padding_tensor = torch.zeros(
+ (batch_size, img_seq_len, self.vision_tower.num_visual_indicator_tokens),
+ dtype=image_features.dtype,
+ device=image_features.device,
+ requires_grad=False,
+ layout=image_features.layout,
+ )
+ image_features = torch.cat([image_features, padding_tensor], dim=2)
+ image_features = self.visual_embeddings_table(image_features)
+
+ visual_indicator = torch.arange(
+ self.visual_vocab_size - self.vision_tower.num_visual_indicator_tokens,
+ self.visual_vocab_size,
+ dtype=torch.long,
+ ).to(image_features.device)
+ visual_indicator_features = self.visual_embeddings_table(visual_indicator)
+
+ return image_features, visual_indicator_features
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs,
+ ) -> Union[tuple, Ovis2ModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ if (input_ids is None) ^ (inputs_embeds is not None):
+ raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+ if inputs_embeds is None:
+ inputs_embeds = self.get_input_embeddings()(input_ids)
+
+ if pixel_values is not None:
+ image_features, visual_indicator_features = self.get_image_features(pixel_values=pixel_values)
+
+ if input_ids is None:
+ special_image_mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ special_image_mask = special_image_mask.all(-1)
+ else:
+ special_image_mask = input_ids == self.config.image_token_id
+
+ n_image_tokens = special_image_mask.sum()
+ special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+ image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+ image_features = image_features.reshape(-1, image_features.shape[-1])
+ n_image_features = image_features.shape[0]
+ if not is_torchdynamo_compiling() and n_image_tokens != n_image_features:
+ raise ValueError(
+ f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+ )
+ inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+ for i, visual_indicator_id in enumerate(self.visual_indicator_token_ids):
+ if input_ids is None:
+ mask = inputs_embeds == self.get_input_embeddings()(
+ torch.tensor(visual_indicator_id, dtype=torch.long, device=inputs_embeds.device)
+ )
+ mask = mask.all(-1)
+ else:
+ mask = (input_ids == visual_indicator_id).to(inputs_embeds.device)
+
+ if mask.any():
+ inputs_embeds[mask] = (
+ visual_indicator_features[i]
+ .expand_as(inputs_embeds[mask])
+ .to(inputs_embeds.device, inputs_embeds.dtype)
+ )
+
+ outputs = self.language_model(
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ logits_to_keep=logits_to_keep,
+ **kwargs,
+ )
+
+ return Ovis2ModelOutputWithPast(
+ last_hidden_state=outputs.last_hidden_state,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=image_features if pixel_values is not None else None,
+ )
+
+
+@auto_docstring
+class Ovis2ForConditionalGeneration(LlavaForConditionalGeneration, GenerationMixin):
+ _checkpoint_conversion_mapping = {}
+
+ def __init__(self, config: Ovis2Config):
+ super().__init__(config)
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ @property
+ def multi_modal_projector(self):
+ raise AttributeError("Not needed for Ovis2")
+
+ def get_image_features(self, pixel_values: torch.FloatTensor):
+ return self.model.get_image_features(pixel_values=pixel_values)
+
+ @can_return_tuple
+ @auto_docstring
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ pixel_values: torch.FloatTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[list[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ logits_to_keep: Union[int, torch.Tensor] = 0,
+ **kwargs,
+ ) -> Union[tuple, Ovis2CausalLMOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Example:
+
+ ```python
+ >>> from PIL import Image
+ >>> import requests
+ >>> from transformers import AutoProcessor, Ovis2ForConditionalGeneration
+
+ >>> model = Ovis2ForConditionalGeneration.from_pretrained("thisisiron/Ovis2-2B-hf")
+ >>> processor = AutoProcessor.from_pretrained("thisisiron/Ovis2-2B-hf")
+
+ >>> prompt = "<|im_start|>user\n\nDescribe the image.<|im_end|>\n<|im_start|>assistant\n"
+ >>> url = "http://images.cocodataset.org/val2014/COCO_val2014_000000537955.jpg"
+ >>> image = Image.open(requests.get(url, stream=True).raw)
+
+ >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(**inputs, max_new_tokens=15)
+ >>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0]
+ "user\n\nDescribe the image.\nassistant\nThe image features a brown dog standing on a wooden floor, looking up with"
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+
+ outputs = self.model(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=True,
+ cache_position=cache_position,
+ **kwargs,
+ )
+
+ hidden_states = outputs[0]
+ # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+ slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+ logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+ loss = None
+ if labels is not None:
+ loss = self.loss_function(
+ logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+ )
+
+ return Ovis2CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ image_hidden_states=outputs.image_hidden_states,
+ )
+
+
+__all__ = ["Ovis2PreTrainedModel", "Ovis2Model", "Ovis2ForConditionalGeneration"]
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/processing_ovis2.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/processing_ovis2.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcc9b25d7b34d18cd7856ec97804ba2eab5c1a51
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/src/transformers/models/ovis2/processing_ovis2.py
@@ -0,0 +1,181 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Union
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_utils import ImageInput
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Ovis2ProcessorKwargs(ProcessingKwargs, total=False):
+ _defaults = {
+ "text_kwargs": {
+ "padding": False,
+ },
+ "image_kwargs": {},
+ }
+
+
+class Ovis2Processor(ProcessorMixin):
+ r"""
+ Constructs a Ovis2 processor which wraps Ovis2 image processor and a Qwen2 tokenizer into a single processor.
+
+ [`Ovis2Processor`] offers all the functionalities of [`Ovis2VideoProcessor`], [`Ovis2ImageProcessor`] and [`Qwen2TokenizerFast`]. See the
+ [`~Ovis2Processor.__call__`] and [`~Ovis2Processor.decode`] for more information.
+
+ Args:
+ image_processor ([`Ovis2ImageProcessor`], *optional*):
+ The image processor is a required input.
+ tokenizer ([`Qwen2TokenizerFast`], *optional*):
+ The tokenizer is a required input.
+ chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+ in a chat into a tokenizable string.
+ image_token (`str`, *optional*, defaults to `""`):
+ Special token used to denote image location.
+ image_seq_length (`int`, *optional*, defaults to 256):
+ The number of image tokens to be used for each image in the input.
+ """
+
+ attributes = ["image_processor", "tokenizer"]
+ image_processor_class = "AutoImageProcessor"
+ tokenizer_class = "AutoTokenizer"
+
+ def __init__(
+ self,
+ image_processor=None,
+ tokenizer=None,
+ chat_template=None,
+ image_token="",
+ image_seq_length=256,
+ **kwargs,
+ ):
+ self.image_seq_length = image_seq_length
+ self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
+ self.image_token_id = (
+ tokenizer.image_token_id
+ if getattr(tokenizer, "image_token_id", None)
+ else tokenizer.convert_tokens_to_ids(self.image_token)
+ )
+ super().__init__(image_processor, tokenizer, chat_template=chat_template, **kwargs)
+
+ def __call__(
+ self,
+ images: ImageInput = None,
+ text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+ **kwargs: Unpack[Ovis2ProcessorKwargs],
+ ) -> BatchFeature:
+ """
+ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+ and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
+ the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+ Ovis2ImageProcessor's [`~Ovis2ImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
+ of the above two methods for more information.
+
+ Args:
+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+ tensor. Both channels-first and channels-last formats are supported.
+ text (`str`, `List[str]`, `List[List[str]]`):
+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+
+ Returns:
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+ `None`).
+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+ - **image_sizes** -- Size of each image that will be used to unpad an image. Returned when `images` is not `None`.
+ """
+
+ output_kwargs = self._merge_kwargs(
+ Ovis2ProcessorKwargs,
+ tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+ **kwargs,
+ )
+
+ if isinstance(text, str):
+ text = [text]
+ elif not isinstance(text, list) and not isinstance(text[0], str):
+ raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+
+ image_inputs = {}
+
+ if images is not None:
+ image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+ image_grids = image_inputs.pop("grids").tolist()
+ text = self._expand_image_tokens(text, image_grids)
+
+ text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+ return BatchFeature(data={**text_inputs, **image_inputs})
+
+ def _expand_image_tokens(
+ self,
+ text: list[TextInput],
+ grids: list[list[int]],
+ ):
+ processed_text = []
+ grid_index = 0
+ for sample in text:
+ while "" in sample:
+ grid = grids[grid_index]
+ row, col = grid[0], grid[1]
+ placeholder = f"{'' * self.image_seq_length}"
+ if row * col > 1:
+ for r in range(row):
+ for c in range(col):
+ placeholder += f"{'' * self.image_seq_length}"
+ if c < col - 1:
+ placeholder += ""
+ if r < row - 1:
+ placeholder += ""
+ placeholder += ""
+
+ sample = sample.replace("", placeholder, 1)
+ grid_index += 1
+ processed_text.append(sample)
+ return processed_text
+
+ def batch_decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+ refer to the docstring of this method for more information.
+ """
+ return self.tokenizer.batch_decode(*args, **kwargs)
+
+ def decode(self, *args, **kwargs):
+ """
+ This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+ the docstring of this method for more information.
+ """
+ return self.tokenizer.decode(*args, **kwargs)
+
+ @property
+ def model_input_names(self):
+ tokenizer_input_names = self.tokenizer.model_input_names
+ image_processor_input_names = self.image_processor.model_input_names
+ return list(tokenizer_input_names) + list(image_processor_input_names)
+
+
+__all__ = ["Ovis2Processor"]